/*! * Chart.js * http://chartjs.org/ * * Copyright 2017 Nick Downie * Released under the MIT license * https://github.com/chartjs/Chart.js/blob/master/LICENSE.md */ !function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Chart=t()}}(function(){return function t(e,n,i){function a(r,l){if(!n[r]){if(!e[r]){var s="function"==typeof require&&require;if(!l&&s)return s(r,!0);if(o)return o(r,!0);var u=new Error("Cannot find module '"+r+"'");throw u.code="MODULE_NOT_FOUND",u}var d=n[r]={exports:{}};e[r][0].call(d.exports,function(t){var n=e[r][1][t];return a(n||t)},d,d.exports,t,e,n,i)}return n[r].exports}for(var o="function"==typeof require&&require,r=0;rn?(e+.05)/(n+.05):(n+.05)/(e+.05)},level:function(t){var e=this.contrast(t);return e>=7.1?"AAA":e>=4.5?"AA":""},dark:function(){var 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e=Math.atan2(r,o),(n=360*e/2/Math.PI)<0&&(n+=360),i=Math.sqrt(o*o+r*r),[a,i,n]}function k(t){return v(x(t))}function w(t){var e,n,i,a=t[0],o=t[1];return i=t[2]/360*2*Math.PI,e=o*Math.cos(i),n=o*Math.sin(i),[a,e,n]}function M(t){return S[t]}e.exports={rgb2hsl:i,rgb2hsv:a,rgb2hwb:o,rgb2cmyk:l,rgb2keyword:s,rgb2xyz:u,rgb2lab:d,rgb2lch:function(t){return y(d(t))},hsl2rgb:c,hsl2hsv:function(t){var e,n,i=t[0],a=t[1]/100,o=t[2]/100;return 0===o?[0,0,0]:(o*=2,a*=o<=1?o:2-o,n=(o+a)/2,e=2*a/(o+a),[i,100*e,100*n])},hsl2hwb:function(t){return o(c(t))},hsl2cmyk:function(t){return l(c(t))},hsl2keyword:function(t){return s(c(t))},hsv2rgb:h,hsv2hsl:function(t){var e,n,i=t[0],a=t[1]/100,o=t[2]/100;return n=(2-a)*o,e=a*o,e/=n<=1?n:2-n,e=e||0,n/=2,[i,100*e,100*n]},hsv2hwb:function(t){return o(h(t))},hsv2cmyk:function(t){return l(h(t))},hsv2keyword:function(t){return s(h(t))},hwb2rgb:f,hwb2hsl:function(t){return i(f(t))},hwb2hsv:function(t){return a(f(t))},hwb2cmyk:function(t){return 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o.each(n.data,function(n,a){t=e.data[a],isNaN(t)||n.hidden||(i+=Math.abs(t))}),i},calculateCircumference:function(t){var e=this.getMeta().total;return e>0&&!isNaN(t)?2*Math.PI*(t/e):0},getMaxBorderWidth:function(t){for(var e,n,i=0,a=this.index,o=t.length,r=0;r(i=e>i?e:i)?n:i;return i}})}},{25:25,40:40,45:45}],18:[function(t,e,n){"use strict";var i=t(25),a=t(40),o=t(45);i._set("line",{showLines:!0,spanGaps:!1,hover:{mode:"label"},scales:{xAxes:[{type:"category",id:"x-axis-0"}],yAxes:[{type:"linear",id:"y-axis-0"}]}}),e.exports=function(t){function e(t,e){return o.valueOrDefault(t.showLine,e.showLines)}t.controllers.line=t.DatasetController.extend({datasetElementType:a.Line,dataElementType:a.Point,update:function(t){var n,i,a,r=this,l=r.getMeta(),s=l.dataset,u=l.data||[],d=r.chart.options,c=d.elements.line,h=r.getScaleForId(l.yAxisID),f=r.getDataset(),g=e(f,d);for(g&&(a=s.custom||{},void 0!==f.tension&&void 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0!==f.fill?f.fill:c.fill,steppedLine:a.steppedLine?a.steppedLine:o.valueOrDefault(f.steppedLine,c.stepped),cubicInterpolationMode:a.cubicInterpolationMode?a.cubicInterpolationMode:o.valueOrDefault(f.cubicInterpolationMode,c.cubicInterpolationMode)},s.pivot()),n=0,i=u.length;n');var n=t.data,i=n.datasets,a=n.labels;if(i.length)for(var o=0;o'),a[o]&&e.push(a[o]),e.push("");return e.push(""),e.join("")},legend:{labels:{generateLabels:function(t){var e=t.data;return e.labels.length&&e.datasets.length?e.labels.map(function(n,i){var a=t.getDatasetMeta(0),r=e.datasets[0],l=a.data[i].custom||{},s=o.valueAtIndexOrDefault,u=t.options.elements.arc;return{text:n,fillStyle:l.backgroundColor?l.backgroundColor:s(r.backgroundColor,i,u.backgroundColor),strokeStyle:l.borderColor?l.borderColor:s(r.borderColor,i,u.borderColor),lineWidth:l.borderWidth?l.borderWidth:s(r.borderWidth,i,u.borderWidth),hidden:isNaN(r.data[i])||a.data[i].hidden,index:i}}):[]}},onClick:function(t,e){var n,i,a,o=e.index,r=this.chart;for(n=0,i=(r.data.datasets||[]).length;n0&&!isNaN(t)?2*Math.PI/e:0}})}},{25:25,40:40,45:45}],20:[function(t,e,n){"use strict";var i=t(25),a=t(40),o=t(45);i._set("radar",{scale:{type:"radialLinear"},elements:{line:{tension:0}}}),e.exports=function(t){t.controllers.radar=t.DatasetController.extend({datasetElementType:a.Line,dataElementType:a.Point,linkScales:o.noop,update:function(t){var e=this,n=e.getMeta(),i=n.dataset,a=n.data,r=i.custom||{},l=e.getDataset(),s=e.chart.options.elements.line,u=e.chart.scale;void 0!==l.tension&&void 0===l.lineTension&&(l.lineTension=l.tension),o.extend(n.dataset,{_datasetIndex:e.index,_scale:u,_children:a,_loop:!0,_model:{tension:r.tension?r.tension:o.valueOrDefault(l.lineTension,s.tension),backgroundColor:r.backgroundColor?r.backgroundColor:l.backgroundColor||s.backgroundColor,borderWidth:r.borderWidth?r.borderWidth:l.borderWidth||s.borderWidth,borderColor:r.borderColor?r.borderColor:l.borderColor||s.borderColor,fill:r.fill?r.fill:void 0!==l.fill?l.fill:s.fill,borderCapStyle:r.borderCapStyle?r.borderCapStyle:l.borderCapStyle||s.borderCapStyle,borderDash:r.borderDash?r.borderDash:l.borderDash||s.borderDash,borderDashOffset:r.borderDashOffset?r.borderDashOffset:l.borderDashOffset||s.borderDashOffset,borderJoinStyle:r.borderJoinStyle?r.borderJoinStyle:l.borderJoinStyle||s.borderJoinStyle}}),n.dataset.pivot(),o.each(a,function(n,i){e.updateElement(n,i,t)},e),e.updateBezierControlPoints()},updateElement:function(t,e,n){var i=this,a=t.custom||{},r=i.getDataset(),l=i.chart.scale,s=i.chart.options.elements.point,u=l.getPointPositionForValue(e,r.data[e]);void 0!==r.radius&&void 0===r.pointRadius&&(r.pointRadius=r.radius),void 0!==r.hitRadius&&void 0===r.pointHitRadius&&(r.pointHitRadius=r.hitRadius),o.extend(t,{_datasetIndex:i.index,_index:e,_scale:l,_model:{x:n?l.xCenter:u.x,y:n?l.yCenter:u.y,tension:a.tension?a.tension:o.valueOrDefault(r.lineTension,i.chart.options.elements.line.tension),radius:a.radius?a.radius:o.valueAtIndexOrDefault(r.pointRadius,e,s.radius),backgroundColor:a.backgroundColor?a.backgroundColor:o.valueAtIndexOrDefault(r.pointBackgroundColor,e,s.backgroundColor),borderColor:a.borderColor?a.borderColor:o.valueAtIndexOrDefault(r.pointBorderColor,e,s.borderColor),borderWidth:a.borderWidth?a.borderWidth:o.valueAtIndexOrDefault(r.pointBorderWidth,e,s.borderWidth),pointStyle:a.pointStyle?a.pointStyle:o.valueAtIndexOrDefault(r.pointStyle,e,s.pointStyle),hitRadius:a.hitRadius?a.hitRadius:o.valueAtIndexOrDefault(r.pointHitRadius,e,s.hitRadius)}}),t._model.skip=a.skip?a.skip:isNaN(t._model.x)||isNaN(t._model.y)},updateBezierControlPoints:function(){var t=this.chart.chartArea,e=this.getMeta();o.each(e.data,function(n,i){var a=n._model,r=o.splineCurve(o.previousItem(e.data,i,!0)._model,a,o.nextItem(e.data,i,!0)._model,a.tension);a.controlPointPreviousX=Math.max(Math.min(r.previous.x,t.right),t.left),a.controlPointPreviousY=Math.max(Math.min(r.previous.y,t.bottom),t.top),a.controlPointNextX=Math.max(Math.min(r.next.x,t.right),t.left),a.controlPointNextY=Math.max(Math.min(r.next.y,t.bottom),t.top),n.pivot()})},setHoverStyle:function(t){var e=this.chart.data.datasets[t._datasetIndex],n=t.custom||{},i=t._index,a=t._model;a.radius=n.hoverRadius?n.hoverRadius:o.valueAtIndexOrDefault(e.pointHoverRadius,i,this.chart.options.elements.point.hoverRadius),a.backgroundColor=n.hoverBackgroundColor?n.hoverBackgroundColor:o.valueAtIndexOrDefault(e.pointHoverBackgroundColor,i,o.getHoverColor(a.backgroundColor)),a.borderColor=n.hoverBorderColor?n.hoverBorderColor:o.valueAtIndexOrDefault(e.pointHoverBorderColor,i,o.getHoverColor(a.borderColor)),a.borderWidth=n.hoverBorderWidth?n.hoverBorderWidth:o.valueAtIndexOrDefault(e.pointHoverBorderWidth,i,a.borderWidth)},removeHoverStyle:function(t){var e=this.chart.data.datasets[t._datasetIndex],n=t.custom||{},i=t._index,a=t._model,r=this.chart.options.elements.point;a.radius=n.radius?n.radius:o.valueAtIndexOrDefault(e.pointRadius,i,r.radius),a.backgroundColor=n.backgroundColor?n.backgroundColor:o.valueAtIndexOrDefault(e.pointBackgroundColor,i,r.backgroundColor),a.borderColor=n.borderColor?n.borderColor:o.valueAtIndexOrDefault(e.pointBorderColor,i,r.borderColor),a.borderWidth=n.borderWidth?n.borderWidth:o.valueAtIndexOrDefault(e.pointBorderWidth,i,r.borderWidth)}})}},{25:25,40:40,45:45}],21:[function(t,e,n){"use strict";t(25)._set("scatter",{hover:{mode:"single"},scales:{xAxes:[{id:"x-axis-1",type:"linear",position:"bottom"}],yAxes:[{id:"y-axis-1",type:"linear",position:"left"}]},showLines:!1,tooltips:{callbacks:{title:function(){return""},label:function(t){return"("+t.xLabel+", "+t.yLabel+")"}}}}),e.exports=function(t){t.controllers.scatter=t.controllers.line}},{25:25}],22:[function(t,e,n){"use strict";var i=t(25),a=t(26),o=t(45);i._set("global",{animation:{duration:1e3,easing:"easeOutQuart",onProgress:o.noop,onComplete:o.noop}}),e.exports=function(t){t.Animation=a.extend({chart:null,currentStep:0,numSteps:60,easing:"",render:null,onAnimationProgress:null,onAnimationComplete:null}),t.animationService={frameDuration:17,animations:[],dropFrames:0,request:null,addAnimation:function(t,e,n,i){var a,o,r=this.animations;for(e.chart=t,i||(t.animating=!0),a=0,o=r.length;a1&&(n=Math.floor(t.dropFrames),t.dropFrames=t.dropFrames%1),t.advance(1+n);var i=Date.now();t.dropFrames+=(i-e)/t.frameDuration,t.animations.length>0&&t.requestAnimationFrame()},advance:function(t){for(var e,n,i=this.animations,a=0;a=e.numSteps?(o.callback(e.onAnimationComplete,[e],n),n.animating=!1,i.splice(a,1)):++a}},Object.defineProperty(t.Animation.prototype,"animationObject",{get:function(){return this}}),Object.defineProperty(t.Animation.prototype,"chartInstance",{get:function(){return this.chart},set:function(t){this.chart=t}})}},{25:25,26:26,45:45}],23:[function(t,e,n){"use strict";var i=t(25),a=t(45),o=t(28),r=t(48);e.exports=function(t){function e(t){var e=(t=t||{}).data=t.data||{};return e.datasets=e.datasets||[],e.labels=e.labels||[],t.options=a.configMerge(i.global,i[t.type],t.options||{}),t}function n(t){var e=t.options;e.scale?t.scale.options=e.scale:e.scales&&e.scales.xAxes.concat(e.scales.yAxes).forEach(function(e){t.scales[e.id].options=e}),t.tooltip._options=e.tooltips}function l(t){return"top"===t||"bottom"===t}var s=t.plugins;t.types={},t.instances={},t.controllers={},a.extend(t.prototype,{construct:function(n,i){var o=this;i=e(i);var l=r.acquireContext(n,i),s=l&&l.canvas,u=s&&s.height,d=s&&s.width;o.id=a.uid(),o.ctx=l,o.canvas=s,o.config=i,o.width=d,o.height=u,o.aspectRatio=u?d/u:null,o.options=i.options,o._bufferedRender=!1,o.chart=o,o.controller=o,t.instances[o.id]=o,Object.defineProperty(o,"data",{get:function(){return o.config.data},set:function(t){o.config.data=t}}),l&&s?(o.initialize(),o.update()):console.error("Failed to create chart: can't acquire context from the given item")},initialize:function(){var t=this;return s.notify(t,"beforeInit"),a.retinaScale(t,t.options.devicePixelRatio),t.bindEvents(),t.options.responsive&&t.resize(!0),t.ensureScalesHaveIDs(),t.buildScales(),t.initToolTip(),s.notify(t,"afterInit"),t},clear:function(){return a.canvas.clear(this),this},stop:function(){return t.animationService.cancelAnimation(this),this},resize:function(t){var e=this,n=e.options,i=e.canvas,o=n.maintainAspectRatio&&e.aspectRatio||null,r=Math.max(0,Math.floor(a.getMaximumWidth(i))),l=Math.max(0,Math.floor(o?r/o:a.getMaximumHeight(i)));if((e.width!==r||e.height!==l)&&(i.width=e.width=r,i.height=e.height=l,i.style.width=r+"px",i.style.height=l+"px",a.retinaScale(e,n.devicePixelRatio),!t)){var u={width:r,height:l};s.notify(e,"resize",[u]),e.options.onResize&&e.options.onResize(e,u),e.stop(),e.update(e.options.responsiveAnimationDuration)}},ensureScalesHaveIDs:function(){var t=this.options,e=t.scales||{},n=t.scale;a.each(e.xAxes,function(t,e){t.id=t.id||"x-axis-"+e}),a.each(e.yAxes,function(t,e){t.id=t.id||"y-axis-"+e}),n&&(n.id=n.id||"scale")},buildScales:function(){var e=this,n=e.options,i=e.scales={},o=[];n.scales&&(o=o.concat((n.scales.xAxes||[]).map(function(t){return{options:t,dtype:"category",dposition:"bottom"}}),(n.scales.yAxes||[]).map(function(t){return{options:t,dtype:"linear",dposition:"left"}}))),n.scale&&o.push({options:n.scale,dtype:"radialLinear",isDefault:!0,dposition:"chartArea"}),a.each(o,function(n){var o=n.options,r=a.valueOrDefault(o.type,n.dtype),s=t.scaleService.getScaleConstructor(r);if(s){l(o.position)!==l(n.dposition)&&(o.position=n.dposition);var u=new s({id:o.id,options:o,ctx:e.ctx,chart:e});i[u.id]=u,u.mergeTicksOptions(),n.isDefault&&(e.scale=u)}}),t.scaleService.addScalesToLayout(this)},buildOrUpdateControllers:function(){var e=this,n=[],i=[];return a.each(e.data.datasets,function(a,o){var r=e.getDatasetMeta(o),l=a.type||e.config.type;if(r.type&&r.type!==l&&(e.destroyDatasetMeta(o),r=e.getDatasetMeta(o)),r.type=l,n.push(r.type),r.controller)r.controller.updateIndex(o);else{var s=t.controllers[r.type];if(void 0===s)throw new Error('"'+r.type+'" is not a chart type.');r.controller=new s(e,o),i.push(r.controller)}},e),i},resetElements:function(){var t=this;a.each(t.data.datasets,function(e,n){t.getDatasetMeta(n).controller.reset()},t)},reset:function(){this.resetElements(),this.tooltip.initialize()},update:function(t){var e=this;if(t&&"object"==typeof t||(t={duration:t,lazy:arguments[1]}),n(e),!1!==s.notify(e,"beforeUpdate")){e.tooltip._data=e.data;var i=e.buildOrUpdateControllers();a.each(e.data.datasets,function(t,n){e.getDatasetMeta(n).controller.buildOrUpdateElements()},e),e.updateLayout(),a.each(i,function(t){t.reset()}),e.updateDatasets(),e.tooltip.initialize(),e.lastActive=[],s.notify(e,"afterUpdate"),e._bufferedRender?e._bufferedRequest={duration:t.duration,easing:t.easing,lazy:t.lazy}:e.render(t)}},updateLayout:function(){var e=this;!1!==s.notify(e,"beforeLayout")&&(t.layoutService.update(this,this.width,this.height),s.notify(e,"afterScaleUpdate"),s.notify(e,"afterLayout"))},updateDatasets:function(){var t=this;if(!1!==s.notify(t,"beforeDatasetsUpdate")){for(var e=0,n=t.data.datasets.length;e=0;--n)e.isDatasetVisible(n)&&e.drawDataset(n,t);s.notify(e,"afterDatasetsDraw",[t])}},drawDataset:function(t,e){var n=this,i=n.getDatasetMeta(t),a={meta:i,index:t,easingValue:e};!1!==s.notify(n,"beforeDatasetDraw",[a])&&(i.controller.draw(e),s.notify(n,"afterDatasetDraw",[a]))},_drawTooltip:function(t){var e=this,n=e.tooltip,i={tooltip:n,easingValue:t};!1!==s.notify(e,"beforeTooltipDraw",[i])&&(n.draw(),s.notify(e,"afterTooltipDraw",[i]))},getElementAtEvent:function(t){return o.modes.single(this,t)},getElementsAtEvent:function(t){return o.modes.label(this,t,{intersect:!0})},getElementsAtXAxis:function(t){return o.modes["x-axis"](this,t,{intersect:!0})},getElementsAtEventForMode:function(t,e,n){var i=o.modes[e];return"function"==typeof i?i(this,t,n):[]},getDatasetAtEvent:function(t){return o.modes.dataset(this,t,{intersect:!0})},getDatasetMeta:function(t){var e=this,n=e.data.datasets[t];n._meta||(n._meta={});var i=n._meta[e.id];return i||(i=n._meta[e.id]={type:null,data:[],dataset:null,controller:null,hidden:null,xAxisID:null,yAxisID:null}),i},getVisibleDatasetCount:function(){for(var t=0,e=0,n=this.data.datasets.length;e0||(a.forEach(function(e){delete t[e]}),delete t._chartjs)}}var a=["push","pop","shift","splice","unshift"];t.DatasetController=function(t,e){this.initialize(t,e)},i.extend(t.DatasetController.prototype,{datasetElementType:null,dataElementType:null,initialize:function(t,e){var n=this;n.chart=t,n.index=e,n.linkScales(),n.addElements()},updateIndex:function(t){this.index=t},linkScales:function(){var t=this,e=t.getMeta(),n=t.getDataset();null===e.xAxisID&&(e.xAxisID=n.xAxisID||t.chart.options.scales.xAxes[0].id),null===e.yAxisID&&(e.yAxisID=n.yAxisID||t.chart.options.scales.yAxes[0].id)},getDataset:function(){return this.chart.data.datasets[this.index]},getMeta:function(){return this.chart.getDatasetMeta(this.index)},getScaleForId:function(t){return this.chart.scales[t]},reset:function(){this.update(!0)},destroy:function(){this._data&&n(this._data,this)},createMetaDataset:function(){var t=this,e=t.datasetElementType;return e&&new e({_chart:t.chart,_datasetIndex:t.index})},createMetaData:function(t){var e=this,n=e.dataElementType;return n&&new n({_chart:e.chart,_datasetIndex:e.index,_index:t})},addElements:function(){var t,e,n=this,i=n.getMeta(),a=n.getDataset().data||[],o=i.data;for(t=0,e=a.length;ti&&t.insertElements(i,a-i)},insertElements:function(t,e){for(var n=0;n=n[e].length&&n[e].push({}),!n[e][r].type||s.type&&s.type!==n[e][r].type?o.merge(n[e][r],[t.scaleService.getScaleDefaults(l),s]):o.merge(n[e][r],s)}else o._merger(e,n,i,a)}})},o.where=function(t,e){if(o.isArray(t)&&Array.prototype.filter)return t.filter(e);var n=[];return o.each(t,function(t){e(t)&&n.push(t)}),n},o.findIndex=Array.prototype.findIndex?function(t,e,n){return t.findIndex(e,n)}:function(t,e,n){n=void 0===n?t:n;for(var i=0,a=t.length;i=0;i--){var a=t[i];if(e(a))return a}},o.isNumber=function(t){return!isNaN(parseFloat(t))&&isFinite(t)},o.almostEquals=function(t,e,n){return Math.abs(t-e)t},o.max=function(t){return t.reduce(function(t,e){return isNaN(e)?t:Math.max(t,e)},Number.NEGATIVE_INFINITY)},o.min=function(t){return t.reduce(function(t,e){return isNaN(e)?t:Math.min(t,e)},Number.POSITIVE_INFINITY)},o.sign=Math.sign?function(t){return Math.sign(t)}:function(t){return 0==(t=+t)||isNaN(t)?t:t>0?1:-1},o.log10=Math.log10?function(t){return Math.log10(t)}:function(t){return Math.log(t)/Math.LN10},o.toRadians=function(t){return t*(Math.PI/180)},o.toDegrees=function(t){return t*(180/Math.PI)},o.getAngleFromPoint=function(t,e){var n=e.x-t.x,i=e.y-t.y,a=Math.sqrt(n*n+i*i),o=Math.atan2(i,n);return o<-.5*Math.PI&&(o+=2*Math.PI),{angle:o,distance:a}},o.distanceBetweenPoints=function(t,e){return Math.sqrt(Math.pow(e.x-t.x,2)+Math.pow(e.y-t.y,2))},o.aliasPixel=function(t){return t%2==0?0:.5},o.splineCurve=function(t,e,n,i){var a=t.skip?e:t,o=e,r=n.skip?e:n,l=Math.sqrt(Math.pow(o.x-a.x,2)+Math.pow(o.y-a.y,2)),s=Math.sqrt(Math.pow(r.x-o.x,2)+Math.pow(r.y-o.y,2)),u=l/(l+s),d=s/(l+s),c=i*(u=isNaN(u)?0:u),h=i*(d=isNaN(d)?0:d);return{previous:{x:o.x-c*(r.x-a.x),y:o.y-c*(r.y-a.y)},next:{x:o.x+h*(r.x-a.x),y:o.y+h*(r.y-a.y)}}},o.EPSILON=Number.EPSILON||1e-14,o.splineCurveMonotone=function(t){var e,n,i,a,r=(t||[]).map(function(t){return{model:t._model,deltaK:0,mK:0}}),l=r.length;for(e=0;e0?r[e-1]:null,(a=e0?r[e-1]:null,a=e=t.length-1?t[0]:t[e+1]:e>=t.length-1?t[t.length-1]:t[e+1]},o.previousItem=function(t,e,n){return n?e<=0?t[t.length-1]:t[e-1]:e<=0?t[0]:t[e-1]},o.niceNum=function(t,e){var n=Math.floor(o.log10(t)),i=t/Math.pow(10,n);return(e?i<1.5?1:i<3?2:i<7?5:10:i<=1?1:i<=2?2:i<=5?5:10)*Math.pow(10,n)},o.requestAnimFrame="undefined"==typeof 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i(this)?(t=n.x,e=(n.y+n.base)/2):(t=(n.x+n.base)/2,e=n.y),{x:t,y:e}},getArea:function(){var t=this._view;return t.width*Math.abs(t.y-t.base)},tooltipPosition:function(){var t=this._view;return{x:t.x,y:t.y}}})},{25:25,26:26}],40:[function(t,e,n){"use strict";e.exports={},e.exports.Arc=t(36),e.exports.Line=t(37),e.exports.Point=t(38),e.exports.Rectangle=t(39)},{36:36,37:37,38:38,39:39}],41:[function(t,e,n){"use strict";var i=t(42),n=e.exports={clear:function(t){t.ctx.clearRect(0,0,t.width,t.height)},roundedRect:function(t,e,n,i,a,o){if(o){var r=Math.min(o,i/2),l=Math.min(o,a/2);t.moveTo(e+r,n),t.lineTo(e+i-r,n),t.quadraticCurveTo(e+i,n,e+i,n+l),t.lineTo(e+i,n+a-l),t.quadraticCurveTo(e+i,n+a,e+i-r,n+a),t.lineTo(e+r,n+a),t.quadraticCurveTo(e,n+a,e,n+a-l),t.lineTo(e,n+l),t.quadraticCurveTo(e,n,e+r,n)}else t.rect(e,n,i,a)},drawPoint:function(t,e,n,i,a){var o,r,l,s,u,d;if(!e||"object"!=typeof e||"[object HTMLImageElement]"!==(o=e.toString())&&"[object 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Casino Possono Prosperare con un Supporto Ibrido AI + Umano – Guida Tecnica per Operatori e Giocatori – Dr. Steven Kniffley Jr. Skip to main content
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Come i Tornei Live Casino Possono Prosperare con un Supporto Ibrido AI + Umano – Guida Tecnica per Operatori e Giocatori

By January 17, 2026May 1st, 2026No Comments

Come i Tornei Live Casino Possono Prosperare con un Supporto Ibrido AI + Umano – Guida Tecnica per Operatori e Giocatori

Nel mondo del gioco d’azzardo online i tornei live rappresentano il culmine dell’adrenalina: centinaia di giocatori si sfidano in tempo reale per jackpot da decine di migliaia di euro, mentre lo stream deve restare stabile e le regole chiare come il vetro. In questo scenario l’assistenza continua è più di un “nice‑to‑have”; è la spina dorsale che impedisce che un piccolo intoppo diventi una fuga di massa dal tavolo virtuale.

Per capire meglio quali soluzioni stanno emergendo, è utile dare un’occhiata a siti non AAMS(siti non AAMS). Questi portali di recensione mostrano come gli operatori più innovativi combinino intelligenza artificiale e operatori umani per offrire un servizio che sembra quasi “telepatico”.

Il risultato è una duplice promessa: per gli operatori una riduzione dei costi operativi grazie alla scalabilità dell’IA, e per i giocatori risposte più rapide e personalizzate che mantengono alta la concentrazione sul gioco. La presente guida svela passo passo come costruire questo modello ibrido nei tornei live, partendo dall’analisi dei problemi tipici fino al monitoraggio delle metriche più avanzate.

Sezione 1 – Perché i Tornei Live Richiedono Un Supporto Sempre Attivo

I tornei live sono eventi a ritmo serrato: lo streaming avviene in diretta su piattaforme dedicate, le quote cambiano ogni secondo e i premi possono includere cashback del 10 % o bonus fino a €500 su giochi ad alta volatilità come il nuovo slot “Volcano Rush”. Questa combinazione genera tre fattori critici per l’assistenza: volume elevato di utenti simultanei, complessità delle regole (ad esempio le varianti “double‑up” di AdmiralBet) e la pressione psicologica sui partecipanti quando il jackpot si avvicina al picco finale.

Tra i problemi più segnalati troviamo bug di latenza nello stream (ritardi che causano disconnessioni), dubbi sulle regole del torneo (quali sono i requisiti di wagering per il premio) e difficoltà nei payout quando un giocatore vince una somma superiore ai limiti predefiniti dal provider Lottomatica. Quando queste richieste rimangono inevase per più di trenta secondi, il tasso di abbandono sale rapidamente: studi interni indicano una perdita del 15 % dei partecipanti entro i primi cinque minuti se il supporto non risponde entro 20 secondi.

L’impatto sulla reputazione del brand è altrettanto grave. Un singolo episodio negativo può generare recensioni sfavorevoli su piattaforme come Oneplanetfood, dove gli utenti valutano la qualità dell’assistenza come uno dei criteri principali nella classifica dei casinò online. In sintesi, senza un supporto sempre attivo i tornei rischiano di trasformarsi da spettacolo entusiasmante a fonte di frustrazione diffusa.

Sezione 2 – IA al Servizio dei Tornei: Cosa Può Fare Oggi

Le tecnologie AI più diffuse nel settore casino includono chatbot basati su NLP (Natural Language Processing), motori di analisi predittiva delle performance dello stream e assistenti vocali integrati con API RTP‑aware. Un chatbot ben configurato può gestire fino all’80 % delle richieste standard – ad esempio fornire la lista delle FAQ sui criteri di qualificazione o calcolare il valore esatto del cashback per una puntata specifica – senza intervento umano.

Funzionalità chiave per i tornei live comprendono:
– Monitoraggio continuo della latenza dello stream con alert automatici quando il ping supera i 150 ms;
– Suggerimenti contestuali su regole specifiche (esempio: “Se hai accumulato più di €2000 nelle qualificazioni puoi accedere alla fase finale con bonus extra”).
Queste capacità permettono agli operatori di intervenire solo quando l’IA rileva anomalie fuori dalla norma, riducendo drasticamente il carico sul team umano.

Tuttavia l’IA ha ancora limiti importanti. Le richieste complesse che coinvolgono verifiche d’identità KYC o dispute su payout irregolari richiedono empatia e giudizio critico che le macchine faticano a riprodurre. Inoltre situazioni non standardizzate – ad esempio un glitch nella visualizzazione dei simboli Wild durante una mano decisiva – possono confondere anche gli algoritmi più avanzati, portando a risposte generiche o errate che aggravano ulteriormente la frustrazione dell’utente. In questi casi è fondamentale avere un passaggio fluido verso un operatore umano esperto nel contesto del torneo live.

Caratteristica IA Operatore Umano
Velocità risposta media ≤ 5 s ≤ 15 s
Gestione query standard ✅ ✅
Risoluzione dispute payout ❌ ✅
Empatia & tono personalizzato Limitata Elevata
Aggiornamento normativa (es.: nuove regole Lottomatica) Richiede retraining Immediato

Sezione 3 – Il Valore Insostituibile Dell’Assistenza Umana

Nei momenti critici dei tornei live l’intervento umano resta insostituibile perché combina competenze tecniche a capacità relazionali avanzate. Quando due giocatori contestano una scommessa sulla mano finale o quando l’identità KYC deve essere verificata sotto pressione temporale stretta, solo un operatore “tournament‑ready” può valutare rapidamente le prove video, confrontare log server e comunicare decisioni chiare al tavolo virtuale senza creare ambiguità.

Formare specialisti pronti al torneo richiede tre pilastri fondamentali: conoscenza approfondita delle regole specifiche del gioco (es.: modalità “Turbo” su slot AdmiralBet), abilità nell’utilizzo degli strumenti di ticketing avanzati e capacità comunicative orientate al cliente premium del segmento high‑roller. Programmi intensivi della durata di due settimane includono simulazioni live dove gli operatori gestiscono scenari con latency elevata e richieste simultanee da più canali (chat web, Discord e telefono).

Un esempio reale proviene da un casinò europeo che ha dovuto affrontare una crisi durante la finale del suo torneo mensile “Jackpot Blitz”. Un bug ha bloccato temporaneamente l’erogazione del premio principale da €12 000; grazie all’intervento rapido dell’agente senior, la questione è stata risolta in meno di otto minuti con comunicazione trasparente ai partecipanti via chat broadcast, evitando così polemiche sui forum dedicati a Oneplanetfood dove erano già emerse lamentele sul supporto automatizzato precedente. Questo caso dimostra come l’esperienza umana possa salvare non solo il flusso operativo ma anche la reputazione digitale dell’intero brand casino‑online.

Sezione 4 – Architettura Ibrida: Come Integrare IA e Operatori Umani

Una soluzione ibrida efficace si basa su quattro livelli distinti ma interconnessi: front‑end chatbot intelligente, motore di routing basato su KPI tournament‑specifici, stack operativo per agenti umani e layer di supervisione analytics in tempo reale. Ecco lo schema passo‑passo da implementare:

1️⃣ Front‑end chatbot – Il player accede alla barra laterale della pagina torneo dove il bot accoglie la richiesta usando NLP multilingua (italiano/inglese/spagnolo).
2️⃣ Routing intelligente – Il sistema analizza intent ed urgenza; se la richiesta rientra nelle FAQ o riguarda monitoraggio latenza viene gestita interamente dall’IA; se supera soglie predefinite (es.: tempo medio risposta > 12 s o livello VIP) viene inoltrata automaticamente a un agente umano disponibile tramite API RESTful integrata con il software ticketing Zendesk o Freshdesk.
3️⃣ Interfaccia agente – L’operatore visualizza tutti i dati contestuali raccolti dall’IA (log streaming, cronologia chat) su una dashboard customizzata; può rispondere direttamente nella stessa finestra chat garantendo continuità all’utente finale senza dover chiedere ulteriori informazioni ridondanti (“Hai già provato a ricaricare la pagina?”).
4️⃣ Supervisione analytics – Un modulo PowerBI aggiorna ogni minuto KPI chiave quali tempo medio risposta (< 15 s), percentuale First Contact Resolution (> 70 %) e tasso errore stream (< 2%). Quando uno dei parametri supera soglia critica scatta un alert al team tecnico per intervento immediato sia sull’infrastruttura cloud sia sull’algoritmo AI tramite retraining automatico basato sui feedback degli agenti umani più efficienti della settimana precedente.

Gli strumenti consigliati includono Dialogflow CX per la gestione avanzata dei flussi conversazionali, Twilio Flex come contact center omnicanale ed Elastic Stack per log aggregazione in tempo reale; tutti offrono SDK prontamente integrabili nelle architetture micro‑service tipiche degli ambienti casino cloud‑native moderni. Implementando questa architettura gli operatori ottengono tempi medi inferiori a dieci secondi nella fase preliminare del torneo ed elevano la qualità percepita dal cliente sopra il benchmark settoriale evidenziato dalle analisi condotte da Oneplanetfood nel suo report annuale sui casinò online europei.

Sezione 5 – Workflow Ottimizzato Durante le Fasi Chiave del Torneo

Dividere il ciclo vitale del torneo in blocchi funzionali permette al supporto ibirdo di allocare risorse con precisione chirurgica:

Registrazione & Qualificazioni

Durante queste fasi si registrano volumi massimi di domande relative alla procedura d’iscrizione (“Come inserisco il codice promozionale?”), ai requisiti minimi di deposito (€20) e alle prime prove sullo stream low‑latency testbed fornito dal provider tecnico interno all’evento LivePlay Suite™ . Qui l’automazione domina: FAQ dinamiche vengono aggiornate istantaneamente via bot con suggerimenti contestuali sulle soglie RTP degli slot coinvolti (“Questo slot ha un RTP del 96 %”) e messaggi push ricordano scadenze importanti come la chiusura delle qualificazioni entro le ore 22:00 CET.

Fase Intermedia & Semi‑finale

Il traffico diminuisce leggermente ma aumentano le richieste complesse legate alle strategie vincenti (“Qual è la migliore combinazione paylines su ‘Gold Rush Deluxe’? ”). Un mix tra IA predittiva — capace di proporre consigli basati sugli storico puntate degli utenti — ed intervento umano specializzato garantisce risposte puntuali senza far sentire l’utente sotto osservazione robotica costante.

Finale ad Alto Valore

Nell’ultimo round ogni secondo conta perché premi colossali — ad esempio €25 000 cashback + jackpot progressivo — sono in palio ed è probabile incorrere in dispute sui payout o sulla verifica KYC post‑vincita veloce richiesta dal regulator Lottomatica . Qui si attiva subito lo switch verso agenti umani dedicati “Live Final Desk”, dotati anche della possibilità audio/video call integrata nella piattaforma tournament hub per confermare identità attraverso facial recognition certificato ISO 27001 .

Check‑list Operativa per Ogni Fase

  • Verificare uptime streaming > 99 % prima dell’avvio fase registrazione
  • Aggiornare script bot con nuove promo “cashback extra 5 %” entro mezzanotte pre‐torneo
  • Attivare canale voce dedicato agli agenti durante semi‑finale
  • Confermare disponibilità squadre compliance KYC prima della finale

Seguendo questa suddivisione operativa si ottimizza l’impiego della forza lavoro umana nei momenti ad alto impatto economico mantenendo alta l’efficienza automatizzata nelle fasi meno critiche.

Sezione 6 – Monitoraggio della Qualità del Supporto con Metriche Avanzate

Per valutare concretamente l’efficacia dell’approccio ibirdo occorre tracciare indicatori specifici ai tornei live:

  • CSAT Live Tournament – punteggio medio post‐interazione raccolto via survey pop‑up (“Quanto sei soddisfatto della risposta ricevuta?”). Obiettivo > 4,5/5.
  • First Contact Resolution (FCR) – percentuale delle richieste chiuse nella prima interazione senza escalation verso altri dipartimenti; target > 75 %.
  • Error Rate dello Stream – numero incident video/latency segnalati rispetto al totale minuti trasmessi; soglia < 2 %.
  • Tempo Medio Risposta (ART) – media secondI dalla ricezione della domanda alla risposta iniziale; KPI < 15 s.
  • Ticket Volume Peak – picco massimo tickets/minuto durante fase finale; serve dimensionare correttamente pool agentistico.

Una dashboard realizzata con Grafana visualizza questi KPI affacciandosi sia sulla console IA sia sul pannello operatore umano mediante widget sincronizzati via WebSocket garantendo visibilità simultanea delle performance comparative tra bot ed esseri umani.

Feedback Loop Per Addestramento IA

Ogni volta che un agente umano chiude una conversazione classificandola “Eccellente”, viene estratto automaticamente lo snippet testuale completo insieme ai metadati d’intervento (tempo risoluzione =9 s). Questi dati alimentano pipeline ML basate su TensorFlow Lite che riaddestrano quotidianamente il modello NLP migliorandone precisione nel riconoscimento intent complessi (“dispute payout > €5000”). Parallelamente si conservano esempi negativi etichettati “Insoddisfacente” per affinare capacità discriminatoria dell’IA nell’individuare segnali emotivi delicati tramite sentiment analysis BERT fine-tuned su dataset casino-specific.

In sintesi questo ecosistema metric­he permette non solo monitoraggio realtime ma anche evoluzione continua dell’intelligenza artificiale guidata dall’expertise umana — principio cardine sottolineato da numerose recensione positive pubblicate da Oneplanetfood negli ultimi mesi sull’affidabilità tecnica dei casinò top Europe​​.​

Sezione 7 – Case Study: Un Casinò Online Che Ha Rivoluzionato I Suoi Tornei Live

Contesto

Un operatore medio‑large europeo specializzato in slot high volatility ha deciso nel Q2‑2024 di trasformare i propri tornei mensili “Mega Spin Challenge” passando da supporto puramente manuale ad architettura ibirda descritta nei capitoli precedenti.

Implementazione

1️⃣ Scelta piattaforma AI Dialogflow CX + integrazione SDK Zendesk Flex
2️⃣ Creazione team interno “Tournament Ops” composto da cinquanta agenti formati sulle policy AdmiralBet & Lottomatica
3️⃣ Deploy graduale fase beta durante torneo pilota con premio cash back €200 + jackpot progressivo €8k
4️⃣ Configurazione dashboard Grafana condivisa tra IT & Customer Care

Durante le prime due settimane sono stati impostati trigger automatichi per latency >120 ms ed error rate >1 % ; tutte le segnalazioni sono state instradate verso squadre operative dedicate entro <12 s.

Risultati

  • Riduzione abbandoni dal momento iscrizione alla finale dal 22 % al 7 %, grazie a tempi risposta medi passati da 28 s a 9 s grazie all’intervento sinergico IA+Umano
  • Incremento partecipanti totali (+35 %) attribuito alla maggiore fiducia percepita dagli utenti dopo aver letto recensione positiva su Oneplanetfood che evidenziava “support impeccabile”
  • Diminuzione costante dei costi operativi mensili del supporto live (~€18k vs €27k) derivante dalla maggior automazione delle FAQ
  • Aumento NPS generale da +38 a +52 punti entro tre mesi dalla messa in opera completa

Questo case study dimostra concretamente come una strategia ibirda possa tradursi sia in vantaggi economici sia in miglioramento tangibile dell’esperienza giocatore nei tornei live ad alto valore aggiunto.

Sezione 8 – Best Practices & Checklist Finale per Implementare il Supporto Ibrido nei Propri Tornei

✔️ Azione Scadenza Responsabile
Mappare tutti i punti di contatto del torneo (chat web, Discord, telefono) Settimana 1 Team prodotto
Scegliere piattaforma AI con integrazione SDK compatibile Zendesk/Freshdesk Settimana 2 IT
Definire SLA KPI (ART <15 s, CSAT ≥4,5) Settimana 2 Operations
Formare squadre “tournament‑ready” con simulazioni live realistiche Settimana 3–4 HR & Training
Configurare routing intelligente basato su intent + priorità VIP Settimana 4 DevOps
Implementare dashboard Grafana/PowerBI per monitoraggio realtime Settimana 5 Data Analytics
Avviare programma feedback loop IA → training settimanale Continuativo Machine Learning Team
Eseguire test load stress sul bot prima della fase qualificazioni Prima lancio evento QA

Elenco puntato delle raccomandazioni pratiche

  • Budget iniziale: prevedere almeno €45k per licenze AI + integrazioni SaaS; ROI stimato entro sei mesi grazie alla riduzione ticket cost ×30 %.
  • Error handling: programmare fallback automatico verso agente umano entro tre tentativi falliti del bot; evita dead‑end conversazionali frustranti .
  • Personalizzazione: utilizza dati storico giocatore (volatilità preferita , importo average bet) per offrire consigli mirati durante le qualificazioni .
  • Compliance: mantieni registro audit trail conforme alle linee guida anti‐money laundering richieste da Lottomatica .
  • Comunicazione cross‑channel: sincronizza messaggi bot tra web chat e app mobile mediante webhook push notification .

Errori comuni da evitare

1️⃣ Sovraccaricare il bot con troppi intent diversi prima della fase beta → degradazione accuracy NLP .
2️⃣ Ignorare metriche qualitative come sentiment analysis → rischio escalation frequente .
3️⃣ Non aggiornare periodicamente knowledge base dopo modifiche regolamentari → informazioni obsolete verso gli utenti .

Seguendo questa checklist gli operatorI potranno passare rapidamente dalla teoria all’esecuzione pratica mantenendo controllo totale sulla qualità dell’assistenza offerta nei loro tornei live.

Conclusione

La sinergia fra intelligenza artificiale ultra veloce ed esperti umani capacissimi rappresenta oggi il vero motore dietro tornei live casino affidabili ed emozionanti. Grazie all’automazione intelligente si tagliano tempi morta inutilizzati mentre gli operator​​⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠​​​​‍‍‍‍‍‍‌​​​​‌​​‌​​​‌​​​‌​​‌​​​‌​⁢⁡⁢⁢⁣⁣⁣⁣⁣⁣⁣‏‏‏‏‏‏‏‎‎‎‎‎‎‎‎ ‎ ‎ ⁣⁣⁣⁣ ‌ ‌ ‌ ‌ ‌ ‌ ‍‍ ‍ ‍ ‍ ‍ ‍ ⁤ ⁦ ⁨ ⁩ ​​​​​​​​​​​​​​​​​​​​​​​​‌‌‌‌‌‌‌‌‌‌‌‌‌‌‪‪‪‪‪‪‪‮‮‮‮‮‮‮‬‬‬‬‬‬——il loro approccio consente ai casinò online—di ridurre drasticamente abbandoni , aument­Are engagement ‑ ed offrire esperienze premium riconosciute dalle guide indipendenti come Oneplanetfood​. Invitiamo dunque gli stakeholder—dal product manager allo staff tecnico—a utilizzare immediatamente la checklist qui proposta , avviare piccoli progetti pilota e misurarne progressivamente KPI chiave . Solo così sarà possibile trasformare ogni torneo live in uno spettacolo fluido dove tecnologia ed empatia convivono armoniosamente senza interruzioni né frustrazioni.”

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