Method, device and equipment for switching cloud service providers based on live broadcast flow, medium and program product

Abstract

The invention discloses a method, device and equipment for switching cloud service providers based on live broadcast flow, a medium and a program product, and relates to the technical field of cloud computing, and the method comprises the steps: accessing at least one cloud service provider, and obtaining the business index data of each cloud service provider through test flow calculation; inputting the business index data into a cloud service provider quality evaluation model, and predicting to obtain an experience score of the live broadcast audience for the live broadcast service provided by each cloud service provider; on the basis of the experience score, distributing live broadcast initial traffic to each cloud service provider; the live broadcast instant success rate of each cloud service provider is dynamically calculated, and after the live broadcast real-time traffic of each cloud service provider is redistributed, the cloud service provider bearing the current live broadcast traffic is switched to the target cloud service provider with the highest proportion of the live broadcast real-time traffic. The technical problem of low efficiency of cloud service provider switching based on live broadcast flow can be solved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a method for switching cloud service providers based on live broadcast traffic, an apparatus for switching cloud service providers based on live broadcast traffic, a device for switching cloud service providers based on live broadcast traffic, a storage medium, and a computer program product. Background Art

[0002] Currently, when live streaming fails, it relies heavily on manual intervention to adjust the live streaming traffic allocated to cloud service providers, and switching cloud service providers based on live streaming traffic is inefficient.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of this application is to provide a method for switching cloud service providers based on live broadcast traffic, an apparatus for switching cloud service providers based on live broadcast traffic, a device, a storage medium and a computer program product for switching cloud service providers based on live broadcast traffic, aiming to solve the technical problem of low efficiency in switching cloud service providers based on live broadcast traffic.

[0005] To achieve the above object, the present application proposes a method for switching cloud service providers based on live broadcast traffic, the method comprising: accessing at least one cloud service provider, and obtaining business indicator data of each cloud service provider by testing traffic calculation;

[0006] Input the business indicator data into the cloud service provider quality evaluation model to predict the experience score of the live broadcast audience for the live broadcast service provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast audience for the viewing experience of the historical live broadcast;

[0007] Based on the experience score, allocating live broadcast start traffic to each cloud service provider;

[0008] By dynamically calculating the instant success rate of live broadcast of each cloud service provider, the real-time traffic of live broadcast of each cloud service provider can be redistributed;

[0009] According to the live real-time traffic allocated to each cloud service provider, the target cloud service provider with the highest proportion of the live real-time traffic is determined, and the cloud service provider carrying the current live traffic is switched to the target cloud service provider.

[0010] In one embodiment, the step of constructing a cloud service provider quality evaluation model based on historical business indicator data and subjective comprehensive scores of historical live broadcast viewers on viewing experience of historical live broadcasts includes:

[0011] Generate at least one live streaming sample according to the live streaming requirements of the live streaming service;

[0012] Quantifying the historical subjective feedback of the live stream sample to obtain a subjective comprehensive score of the viewing experience of the historical live broadcast by the historical live broadcast audience;

[0013] Calculate historical business indicator data based on the test traffic of the live stream sample;

[0014] The historical business indicator data is used as input, and the subjective comprehensive score is used as output to train a cloud service provider quality evaluation model.

[0015] In one embodiment, after the step of training to obtain the cloud service provider quality evaluation model, the following steps are included:

[0016] Measuring the correlation between historical subjective feedback and predicted historical experience scores, where the historical experience scores are predicted by the cloud service provider quality evaluation model based on historical business indicator data;

[0017] If the correlation between the historical subjective feedback and the historical experience score does not reach a preset correlation, the model parameters of the cloud service provider quality evaluation model are optimized.

[0018] In one embodiment, the step of obtaining the business indicator data of each cloud service provider by testing traffic calculation includes:

[0019] Use test traffic for live broadcasting, monitor the viewing behavior of live viewers and the live broadcast services provided by various cloud service providers, and generate tracking logs;

[0020] Based on the tracking logs, the business indicator data of each cloud service provider is calculated.

[0021] In one embodiment, the step of dynamically calculating the instant live broadcast success rate of each cloud service provider and reallocating the live broadcast real-time traffic of each cloud service provider includes:

[0022] Allocate the live streaming real-time traffic to each cloud service provider based on the ranking of their live streaming success rates;

[0023] If the live broadcast instant success rate is lower than the preset success rate threshold, the live broadcast real-time traffic of the cloud service provider whose live broadcast instant success rate is lower than the preset success rate threshold is cut off, and the live broadcast real-time traffic is allocated to other cloud service providers.

[0024] In one embodiment, the method comprises:

[0025] Identify and separate the live streaming functions provided by cloud service providers, and define each live streaming function as a cloud service policy;

[0026] Recording registration information of the cloud service policy through a registration table;

[0027] During the live broadcast, an instance of the cloud service policy is searched and obtained from the registry.

[0028] In addition, to achieve the above purpose, the present application also proposes a device for switching cloud service providers based on live broadcast traffic, and the device for switching cloud service providers based on live broadcast traffic includes:

[0029] A calculation module is used to access at least one cloud service provider and obtain business indicator data of each cloud service provider by testing traffic calculation;

[0030] A prediction module is used to input the business indicator data into a cloud service provider quality evaluation model to predict the experience score of the live broadcast audience on the live broadcast service provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast audience on the viewing experience of the historical live broadcast;

[0031] A first allocation module, configured to allocate live broadcast start traffic to each cloud service provider based on the experience score;

[0032] The second allocation module is used to reallocate the live broadcast real-time traffic of each cloud service provider by dynamically calculating the live broadcast instant success rate of each cloud service provider;

[0033] The switching module is used to determine the target cloud service provider with the highest proportion of the live real-time traffic according to the live real-time traffic allocated to each cloud service provider, and switch the cloud service provider carrying the current live traffic to the target cloud service provider.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for switching cloud service providers based on live broadcast traffic, wherein the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for switching cloud service providers based on live broadcast traffic as described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for switching cloud service providers based on live broadcast traffic as described above are implemented.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for switching cloud service providers based on live broadcast traffic as described above.

[0037] This application predicts the experience ratings of live broadcast viewers for the live broadcast services provided by each cloud service provider by constructing a cloud service provider quality evaluation model, and allocates live broadcast start traffic to each cloud service provider based on the experience ratings, and then dynamically calculates the instant live broadcast success rate of each cloud service provider, readjusts the live broadcast real-time traffic of each cloud service provider, and switches the cloud service provider carrying the current live broadcast traffic to the target cloud service provider with the highest proportion of live broadcast real-time traffic. Compared with the current reliance on manual intervention to adjust the traffic allocated to each cloud service provider, through real-time monitoring and dynamic adjustment of the traffic allocated to each cloud service provider, it is ensured that when the current cloud service provider fails, it can be quickly and smoothly switched to the target cloud service provider, ensuring uninterrupted live broadcast and improving the efficiency and accuracy of switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 A flowchart diagram of the first embodiment of the method for switching cloud service providers based on live streaming traffic provided by this application;

[0041] Figure 2 A flowchart diagram of the second embodiment of the method for switching cloud service providers based on live broadcast traffic provided by the present application;

[0042] Figure 3 A schematic diagram of a cloud service provider quality evaluation model construction process provided in the second embodiment of the method for switching cloud service providers based on live traffic of the present application;

[0043] Figure 4 A flowchart diagram of the third embodiment of the method for switching cloud service providers based on live streaming traffic provided by the present application;

[0044] Figure 5 A schematic diagram of an alarm process provided in the third embodiment of the method for switching cloud service providers based on live traffic of this application;

[0045] Figure 6 This is a schematic diagram of the module structure of the device for switching cloud service providers based on live broadcast traffic in this application;

[0046] Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the method of switching cloud service providers based on live broadcast traffic in this application.

[0047] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] Currently, there are two main streaming architectures that live streaming relies on:

[0051] The first method is to integrate SDK (Software Development Kit) for streaming, which mainly relies on mobile interactive live streaming SDK provided by manufacturers such as Tencent Cloud. By integrating APP (Application) on both the host and user sides, both the host and user sides have the function of streaming. This method requires simple live streaming equipment, and only requires integrating SDK on the client side. Both the host and user sides can be the initiators of audio and video uploads; however, it only supports mobile phone push and pull streaming, and does not meet the needs of watching live broadcasts in other scenarios, such as H5 (mobile interactive pages developed based on HTML5 technology) and applets.

[0052] The second method is to push the stream to the remote server through the RTMP protocol (Real Time Messaging Protocol), which supports multiple video encoding formats and has good compatibility, and can be seamlessly connected with a variety of client and server software. By using professional streaming software such as OBS (Open Broadcaster Software, a free and open source video recording and live streaming software), you can integrate multiple high-definition video sources and push the audio and video streams to the preset server address in real time.

[0053] First, you need to obtain the URL (Uniform Resource Locator) and key of the RTMP streaming from the cloud live streaming service provider, and then configure this information in OBS. Next, you can adjust the streaming settings, such as encoding parameters, frame rate, and bit rate, to ensure the best transmission effect. Once you click "Start Streaming", OBS will use the RTMP protocol to push the content seamlessly to the cloud. In the cloud, these audio and video streams will undergo transcoding, storage, and other processing, and will be distributed to global audiences through the CDN network. The advantage of RTMP streaming lies in its professionalism and stability, although there may be slightly higher latency than SDK streaming.

[0054] Both methods have service dependency, latency risk and availability issues. Specifically, if the cloud service provider's RTMP service (streaming data transmission) fails, the entire live stream may be interrupted, affecting the user experience. Although current streaming technologies generally provide lower latency, latency may increase significantly when the network is unstable or the server is overloaded. If the network environment on the host side is not good, freezes, frame drops or even interruptions may occur.

[0055] Therefore, it is proposed to integrate multiple cloud service providers for integration and traffic allocation scheduling, but in the integration process:

[0056] Problem 1: The original live broadcast architecture involved more than 50 components and hundreds of deployment instances, resulting in high costs for adjusting the existing framework every time a new cloud service provider was introduced. Each time a new cloud provider was connected, multiple components and storage structures needed to be modified on a large scale, which was both time-consuming and labor-intensive.

[0057] In addition, there is problem 2: Only when large-scale failures occur in production or customers report fault information can we discover that the cloud service provider's CDN node in the live broadcast business has a slow downstream speed, the CDN transcoding service has an error, or the live broadcast stream stored in the CDN node has a problem. There is no complete monitoring and alarm function, and it is impossible to predict possible problems in advance.

[0058] To improve the viewing experience of end users, we need to rely on a comprehensive, objective and reliable evaluation system to achieve refined traffic allocation and scheduling; Problem 3: Currently, due to the lack of an objective and unified quality evaluation system for real-time monitoring and evaluation of CDN service performance, it is difficult to dynamically adjust traffic based on real-time data and balance the load to CDN nodes with better performance to optimize the user viewing experience.

[0059] The main solution of the embodiment of the present application is: based on access to multiple cloud service providers, it proposes to establish a dynamic cloud service quality evaluation and live broadcast traffic adjustment mechanism, thereby realizing intelligent and smooth switching of traffic during live broadcast.

[0060] It should be noted that the execution subject of this embodiment can be a device (referred to as a device) that switches cloud service providers based on live broadcast traffic, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a processor, etc. that can realize the above functions. The following takes the device as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, the embodiment of the present application provides a method for switching cloud service providers based on live broadcast traffic, referring to Figure 1 , Figure 1This is a flow chart of the first embodiment of the method for switching cloud service providers based on live broadcast traffic in this application.

[0062] In this embodiment, the method for switching cloud service providers based on live broadcast traffic includes steps S10 to S50:

[0063] Step S10, access at least one cloud service provider, and obtain business indicator data of each cloud service provider by testing traffic calculation;

[0064] It should be noted that during live broadcast, multiple cloud service providers are connected. By simulating actual usage scenarios, a certain amount of test data packets are sent to the services of cloud service providers. During the test, data related to cloud service performance is collected, including but not limited to source encoding bit rate, streaming time, Fen film market, transcoding bit rate, transcoding strategy, filling rate, fragment size, fragment receiving time, buffer status, playback rate and adaptive strategy. Based on the collected data, business indicator data is calculated. Business indicator data includes real-time (audio and video synchronization, delay), smoothness (number of freezes in the first second, duration of freezes in the first second), stability (bit rate switching) and picture quality (resolution, bit rate) that reflect the quality of experience of live audiences.

[0065] Among them, cloud service providers refer to companies that provide cloud computing services, such as Alibaba Cloud, Tencent Cloud, Huawei Cloud, etc. The test traffic simulates live audience access, data transmission, request response, etc., to evaluate the cloud service provider's carrying capacity and response speed in actual live broadcasts.

[0066] In one embodiment, step S10, the step of obtaining the business indicator data of each cloud service provider by testing traffic calculation includes steps A10 to A20:

[0067] Step A10, using the test traffic for live broadcast, monitoring the viewing behavior of the live audience and the live broadcast services provided by each cloud service provider, and generating a tracking log;

[0068] It should be noted that in the live broadcast scenario, test traffic refers to data traffic generated by simulating the viewing behavior of real viewers, including but not limited to video stream requests, playback, pause, buffering, switching clarity and other operations. Viewer viewing behavior refers to various operations performed by viewers when watching live broadcasts, including but not limited to: start viewing time, viewing duration, exit time; playback fluency, such as frequent buffering; selection of viewing clarity; interactive behaviors, such as likes, comments, gifts, etc. The live broadcast service provided by the cloud service provider refers to the live streaming media service provided by the cloud service provider, including but not limited to video encoding, distribution, storage, transcoding and other technologies to support high-quality live broadcast experience. Tracking point monitoring refers to the monitoring points pre-set in the live broadcast, which are used to capture user behavior data, record the audience's viewing behavior, and the performance data of the live broadcast service. Tracking point logs are used to record all data collected by tracking point monitoring, including audience behavior, live broadcast service performance indicators and other information.

[0069] Step A20: Calculate the business indicator data of each cloud service provider based on the tracking log.

[0070] It should be noted that the detailed point-of-sale logs of the client SDK are consumed in real time through Spark (Apache Spark is an open source cluster computing framework for large-scale data processing) tasks, stored in the ES (Elasticsearch) cluster, and then the business indicator data of each cloud service provider is calculated through scheduled tasks.

[0071] Among them, Spark tasks refer to data processing or analysis tasks running on Spark clusters, which can efficiently process large amounts of data; real-time consumption refers to processing data immediately when it is generated, rather than waiting for the data to accumulate to a certain amount before processing. Real-time consumption can obtain the latest information in a timely manner and perform instant analysis of data; scheduled tasks refer to automated tasks that are executed at fixed time points or periodically.

[0072] In response to the above-mentioned problem 2: the lack of complete monitoring functions, in this implementation, test traffic is used for live broadcast and the viewing behavior of live viewers and the performance of the live broadcast service of the cloud service provider are monitored, and a tracking log is generated to further calculate the business indicator data of each cloud service provider; through real-time tracking log monitoring, the quality and stability of the live broadcast service can be understood in real time, and through monitoring and optimization, it helps to quickly locate problems, such as network congestion, server failures, etc., so that timely measures can be taken to reduce service interruption time and improve the quality of live broadcast services.

[0073] Step S20, inputting the business indicator data into the cloud service provider quality evaluation model, and predicting the experience score of the live broadcast viewers on the live broadcast services provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast viewers on the viewing experience of the historical live broadcast;

[0074] It should be noted that the cloud service provider quality evaluation model is a prediction model that calculates the quantitative score of the cloud service provider's service quality based on business indicator data. This model can predict the audience's experience score of the live broadcast service. The historical business indicator data is calculated based on the live broadcast services provided by the cloud service provider in the historical period. The subjective comprehensive score of the historical live broadcast audience's viewing experience is the audience's subjective evaluation of the live broadcast viewing experience in the historical period.

[0075] Step S30, allocating live broadcast start traffic to each cloud service provider based on the experience score;

[0076] It should be noted that the experience score refers to a quantitative score of the live broadcast audience's experience of the live broadcast service provided by the cloud service provider, which is predicted based on the cloud service provider's quality evaluation model. In the initial stage of the live broadcast service, the audience traffic or bandwidth resources given to each cloud service provider are determined based on the cloud service provider's experience score. The "starting traffic" here usually refers to the number of viewers introduced in the early stage of the live broadcast, or the initial bandwidth resources allocated to the cloud service provider for the live broadcast service.

[0077] Step S40, dynamically calculating the instant live broadcast success rate of each cloud service provider, and reallocating the live broadcast real-time traffic of each cloud service provider;

[0078] It should be noted that dynamic calculation refers to the device continuously monitoring and analyzing the current service status of each cloud service provider to calculate the live broadcast instant success rate; the live broadcast instant success rate refers to the proportion of live broadcast services that are successfully running at a given moment. Based on the dynamic changes in the instant success rate, the system will adjust the live broadcast real-time traffic allocated to each cloud service provider. When a cloud service provider has a high live broadcast instant success rate, it will receive more live broadcast real-time traffic, and vice versa.

[0079] Step S50, according to the live real-time traffic allocated to each cloud service provider, determine the target cloud service provider with the highest proportion of live real-time traffic, and switch the cloud service provider carrying the current live traffic to the target cloud service provider.

[0080] It should be noted that the target cloud service provider with the highest proportion of live broadcast real-time traffic refers to the cloud service provider with the largest proportion of live broadcast real-time traffic allocated to it among all cloud service providers participating in the live broadcast service. According to the traffic distribution, the current live broadcast cloud service provider, that is, the cloud service provider carrying most of the live broadcast traffic, will be switched to the target cloud service provider with the highest traffic proportion, because the target cloud service provider provides better services (such as higher instant success rate, lower latency, etc.).

[0081] In this embodiment, a cloud service provider quality evaluation model is constructed to predict the experience score of the live broadcast audience for the live broadcast services provided by each cloud service provider, and the live broadcast starting traffic is allocated to each cloud service provider based on the experience score. Subsequently, the instant live broadcast success rate of each cloud service provider is dynamically calculated, the live broadcast real-time traffic of each cloud service provider is readjusted, and the cloud service provider carrying the current live broadcast traffic is switched to the target cloud service provider with the highest proportion of live broadcast real-time traffic. By real-time monitoring and dynamic adjustment of the traffic allocated to each cloud service provider, it is ensured that when the current cloud service provider fails, it can be quickly and smoothly switched to the target cloud service provider, ensuring uninterrupted live broadcast and improving the efficiency and accuracy of switching.

[0082] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can refer to the above introduction, and will not be repeated in the following. Figure 2 , step S20, the method for switching cloud service providers based on live broadcast traffic includes steps D10 to D40:

[0083] Step D10, generating at least one live stream sample according to the live broadcast demand of the live broadcast service;

[0084] It should be noted that the actual needs of the live broadcast service (such as real-time, acceptable delay, interactivity, smoothness, audience interaction function, etc.) should be analyzed, and one or more live data stream samples for testing and evaluation should be created according to the live broadcast needs, where the live stream sample can be a simulated video stream.

[0085] Step D20, quantifying the historical subjective feedback of the live stream samples to obtain the subjective comprehensive score of the viewing experience of the historical live broadcast by the historical live broadcast viewers;

[0086] It should be noted that historical subjective feedback refers to the subjective evaluation of live stream samples by past viewers, which comes from user surveys, comments, ratings, etc., and reflects the audience's personal feelings about the quality, content, interaction, etc. of the live broadcast. The qualitative subjective feedback is converted into quantitative values. The quantification process involves converting text feedback into ratings, and then performing weighted calculations on the subjective feedback data to obtain the subjective comprehensive rating of the live broadcast audience's viewing experience of the historical live broadcast.

[0087] Step D30, calculating historical business indicator data according to the test flow of the live stream sample;

[0088] It should be noted that during the test, the traffic data of the live stream sample when it is transmitted through the network is analyzed, and quantitative data reflecting the performance and quality of the live service in the past is calculated. By analyzing the test traffic of the live stream sample, quantitative data reflecting the performance and quality of the live service in the past is calculated.

[0089] Step D40, taking the historical business indicator data as input and the subjective comprehensive score as output, to train a cloud service provider quality evaluation model.

[0090] It should be noted that the cloud service provider quality evaluation model is a model established through machine learning, with historical business indicator data as input and subjective comprehensive scores as output. The model can predict the subjective satisfaction of live viewers based on business indicator data and is used to evaluate the live service quality of cloud service providers. Training refers to using historical business indicator data and corresponding subjective comprehensive scores as training sets, adjusting model parameters through algorithms, so that the model can learn and fit the relationship between input (business indicator data) and output (subjective comprehensive score).

[0091] In one embodiment, after step D40, steps E10 to E20 are included:

[0092] Step E10, measuring the correlation between historical subjective feedback and predicted historical experience scores, wherein the historical experience scores are predicted by the cloud service provider quality evaluation model based on historical business indicator data;

[0093] It should be noted that the Spearman correlation coefficient (SROCC) can be used to measure the correlation between historical subjective feedback and predicted historical experience scores, specifically:

[0094]

[0095] Where N represents the number of samples, d i It represents the difference between the subjective rating ranking of the i-th live audience and the predicted objective calculation score ranking.

[0096] Step E20: If the correlation between the historical subjective feedback and the historical experience score does not reach the preset correlation, the parameters of the training cloud service provider quality evaluation model are optimized.

[0097] It should be noted that the preset correlation level is: a threshold set to measure the correlation between historical subjective feedback and historical experience scores. When the correlation between historical subjective feedback and historical experience scores does not reach the preset threshold after model training, the model parameters need to be adjusted to improve the prediction accuracy and correlation of the model. Because the greater the correlation between historical subjective feedback and historical experience scores, the closer the results predicted by the cloud service provider's quality evaluation model are to the real feelings of live broadcast users.

[0098] For example, a cloud service provider quality evaluation model was trained using historical business indicator data and historical experience scores, but it was found that the correlation between the historical experience scores predicted by the model and historical subjective feedback was only 0.5 (the preset correlation was 0.7), indicating that there was a large deviation between the satisfaction predicted by the model and the actual feedback from users. By adjusting the model parameters, such as optimizing the algorithm and increasing the data dimension, the correlation was increased to 0.8. The experience scores predicted by the model were inconsistent with the actual feedback from users, resulting in inaccurate service quality evaluation.

[0099] For example, the cloud service provider quality evaluation model construction process is as follows: Figure 3 As shown in the figure, first analyze the live broadcast service demand, determine the factors affecting the live broadcast service, build a test environment to generate live broadcast stream samples, invite live broadcast viewers to watch these samples, and give scores on the picture quality, fluency and other experiences. After quantifying the scores, perform weighted calculations, and use historical business indicator data and historical subjective comprehensive scores as training data. The training process involves optimizing the structure, screening the influencing factors, and reasonably allocating the weights of each factor. The goal is to minimize the deviation between the predicted QoE (Quality of Experience) and the subjective QoE as much as possible. Then, according to the gap between the subjective QoE and the predicted QoE, adjust the parameters of the model to obtain a cloud service provider quality evaluation model with more accurate predictions. The predicted QoE refers to the data predicted by the cloud service provider quality evaluation model, and the subjective QoE refers to the score of the live broadcast by the live broadcast audience.

[0100] In this embodiment, parameter optimization is a continuous iterative process. With the accumulation of data and the improvement of algorithms, the model prediction capability continues to improve. Through parameter optimization, the model can more accurately predict user satisfaction and improve the consistency between the predicted value and the actual experience, which helps cloud service providers to promptly identify service quality issues, optimize resource allocation, and improve service quality.

[0101] In response to the above-mentioned problem 3: the lack of an objective and unified quality evaluation system, in this embodiment, historical data (historical business indicator data and historical subjective comprehensive scores) are used as training data for the cloud service provider quality evaluation model, so that the trained cloud service provider quality evaluation model can predict the business indicator data calculated in real time, and predict the subjective comprehensive score of the live broadcast audience for the live broadcast services provided by each cloud service provider, which is used instead of directly inviting users to evaluate the live broadcast, which helps to more efficiently allocate the live broadcast traffic to the cloud service provider and improve the stability of the live broadcast.

[0102] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can refer to the above introduction, and will not be repeated later. Figure 4 , step S40, the method for switching cloud service providers based on live broadcast traffic includes steps T10 to T20:

[0103] Step T10, allocating the live streaming real-time traffic of each cloud service provider according to the ranking of the live streaming instant success rate of each cloud service provider;

[0104] It should be noted that the live broadcast instant success rate of each cloud service provider is calculated based on the number of successful pulls from the user end / (the number of successful pulls from the user end + the number of failed pulls from the user end), and the cloud service providers are ranked according to the calculated live broadcast instant success rate, and real-time live broadcast traffic is allocated preferentially to cloud service providers with high rankings.

[0105] Step T20: If the live broadcast instant success rate is lower than the preset success rate threshold, the live broadcast real-time traffic of the cloud service provider whose live broadcast instant success rate is lower than the preset success rate threshold is cut off, and the live broadcast real-time traffic is allocated to other cloud service providers.

[0106] It should be noted that the preset success rate threshold is used to determine whether the live broadcast service meets the expected quality standards. If the live broadcast instant success rate is lower than this threshold, it indicates that there is a problem with the live broadcast service quality. When it is found that the live broadcast instant success rate of a cloud service provider is lower than the preset threshold, in order to ensure the quality of live broadcast service and user experience, the system will automatically cut off the live broadcast real-time traffic of the cloud service provider, and will immediately reallocate the live broadcast real-time traffic originally allocated to the cloud service provider to other cloud service providers with higher service quality.

[0107] For example: three cloud service providers A, B and C are connected, and the success rates of cloud service providers A, B and C are 99%, 97% and 96% respectively. According to the success rate ranking, their traffic share distribution is 90%, 5% and 5%. After a period of time, it is detected that the instant live broadcast success rate of cloud service provider A has dropped to 96.5%. The success rate ranking is updated from "ABC Cloud" to "BAC". According to the new ranking of success rate, the real-time live broadcast traffic distribution of cloud service providers A, cloud service providers B and cloud service providers C becomes 5%, 90% and 5%.

[0108] After a period of time, the live broadcast success rate of cloud service provider A dropped to 94%, which was lower than the threshold of 95%, triggering the traffic isolation mechanism, and all its traffic was transferred to cloud service B. At this time, the live broadcast real-time traffic distribution of cloud service providers A, B, and C was 0%, 95%, and 5%. When cloud service provider A successfully recovered, its success rate rose back to 96%, which was higher than the threshold of 95%, and was re-included in the traffic distribution. Based on the current success rate ranking, cloud service provider A regained 5% of the traffic distribution, and the live broadcast real-time traffic distribution of cloud service providers A, B, and C was 5%, 90%, and 5%.

[0109] In one embodiment, after step T20, Figure 5 When the success rate of cloud service provider A is lower than the preset success rate threshold, an early warning reminder will be sent and cloud service provider B will be switched. If it is found that the switching of cloud service providers is too slow, manual intervention in traffic allocation is supported. Manual intervention mainly includes manual traffic allocation and direct elimination of an abnormal vendor. After manual traffic allocation, there is still a small amount of traffic from the abnormal vendor. After direct elimination, the traffic of the abnormal vendor will be reduced to zero. When the success rate of the abnormal vendor rises above the threshold, the traffic return strategy will be enabled to determine whether cloud service provider A is lower than the preset success rate threshold of the alarm. If the instant live broadcast success rate of cloud service provider A is restored, it needs to be maintained for 10 clocks before reallocation can be performed; if the instant live broadcast success rate of cloud service provider A is higher than the instant live broadcast success rate of cloud service provider B, 90% of the traffic will be allocated to cloud service provider A.

[0110] In view of the above-mentioned problem 2: no alarm mechanism, in this embodiment, by implementing this mechanism, the impact of the decline in service quality of individual cloud service providers on the overall live broadcast can be effectively avoided, ensuring the stability of the live broadcast service and the optimization of user experience. At the same time, this dynamic traffic allocation method also improves the utilization efficiency of resources, avoids resource waste, and achieves cost optimization.

[0111] In one embodiment, the method for switching cloud service providers based on live broadcast traffic includes steps W10 to W30:

[0112] Step W10, identifying and separating the live broadcast functions provided by the cloud service provider, and defining each live broadcast function as a cloud service policy;

[0113] It should be noted that, first of all, the live broadcast function provided by the cloud service provider is identified, and each identified live broadcast function is separated and defined as an independent "cloud service strategy". This means breaking down the complex live broadcast service into a series of specific and manageable service units. For example, video encoding strategy, streaming media transmission strategy, etc. Specifically, the functions of cloud vendors are abstracted into separate services through the strategy pattern and the adapter pattern, so that the main logic of the live broadcast is decoupled from the live broadcast. Among them, the strategy pattern allows the system to select algorithms or behaviors at runtime, which enables the live broadcast system to dynamically select and switch different cloud service providers without modifying the core code. The adapter pattern is used to convert the interface of a class into another interface that the customer wants. Through the adapter, we can unify the API calls of different cloud vendors, so that cloud services can be easily integrated or replaced without changing the logic of the live broadcast system.

[0114] Step W20, recording the registration information of the cloud service policy through a registration table;

[0115] It should be noted that by adapting multi-cloud, a registry is used to record the registration information of each policy class, and an interface is provided for the policy class to call to complete the self-registration function.

[0116] Step W30, during the live broadcast process, search and obtain an instance of the cloud service policy from the registry.

[0117] It should be noted that the singleton pattern is used in combination with the policy environment registry. The singleton pattern ensures that there is only one instance of the policy class in the system and provides a global access point. In this way, whenever the function of the policy class needs to be accessed in the system, it can be ensured that the same instance is used, avoiding duplicate creation and waste of resources. In the policy environment class, we maintain a registry to record the registration information of all policy classes. When new policy classes (representing different cloud service policies) need to join the system, they add their own information (such as the policy name and the corresponding implementation class) to the registry by implementing a self-registration method.

[0118] In response to the above-mentioned problem 1: large-scale modifications are required to multiple components and storage structures, which is a time-consuming and labor-intensive process. This embodiment identifies and separates the live broadcast function provided by the cloud service provider and defines it as an independent cloud service strategy. This can achieve refined management of the live broadcast service and ensure efficient use of resources and reasonable control of costs.

[0119] This application also provides a device for switching cloud service providers based on live broadcast traffic, please refer to Figure 6 , the device for switching cloud service providers based on live broadcast traffic includes:

[0120] The calculation module 10 is used to access at least one cloud service provider and obtain the business indicator data of each cloud service provider by testing traffic calculation;

[0121] The prediction module 20 is used to input the business indicator data into the cloud service provider quality evaluation model to predict the experience score of the live broadcast audience on the live broadcast service provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast audience on the viewing experience of the historical live broadcast;

[0122] A first allocation module 30, configured to allocate live broadcast start traffic to each cloud service provider based on the experience score;

[0123] The second allocation module 40 is used to reallocate the live broadcast real-time traffic of each cloud service provider by dynamically calculating the live broadcast instant success rate of each cloud service provider;

[0124] The switching module 50 is used to determine the target cloud service provider with the highest proportion of the live real-time traffic according to the live real-time traffic allocated to each cloud service provider, and switch the cloud service provider carrying the current live traffic to the target cloud service provider.

[0125] Optionally, the computing module 10 is further used to use the test traffic for live broadcasting, to monitor the viewing behavior of the live broadcast audience and the live broadcast services provided by each cloud service provider, and to generate a tracking log;

[0126] Based on the tracking logs, the business indicator data of each cloud service provider is calculated.

[0127] Optionally, the prediction module 20 is further configured to generate at least one live streaming sample according to the live streaming demand of the live streaming service;

[0128] Quantifying the historical subjective feedback of the live stream sample to obtain a subjective comprehensive score of the viewing experience of the historical live broadcast by the historical live broadcast audience;

[0129] Calculate historical business indicator data based on the test traffic of the live stream sample;

[0130] The historical business indicator data is used as input, and the subjective comprehensive score is used as output to train a cloud service provider quality evaluation model.

[0131] Optionally, the prediction module 20 is further used to measure the correlation between the historical subjective feedback and the predicted historical experience score, wherein the historical experience score is predicted by the cloud service provider quality evaluation model based on historical business indicator data;

[0132] If the correlation between the historical subjective feedback and the historical experience score does not reach a preset correlation, the model parameters of the cloud service provider quality evaluation model are optimized.

[0133] Optionally, the second allocation module 40 is further used to allocate the live broadcast real-time traffic of each cloud service provider according to the ranking of the live broadcast instant success rate of each cloud service provider;

[0134] If the live broadcast instant success rate is lower than the preset success rate threshold, the live broadcast real-time traffic of the cloud service provider whose live broadcast instant success rate is lower than the preset success rate threshold is cut off, and the live broadcast real-time traffic is allocated to other cloud service providers.

[0135] The device for switching cloud service providers based on live traffic is also used for:

[0136] Identify and separate the live streaming functions provided by cloud service providers, and define each live streaming function as a cloud service policy;

[0137] Recording registration information of the cloud service policy through a registration table;

[0138] During the live broadcast, an instance of the cloud service policy is searched and obtained from the registry.

[0139] The device for switching cloud service providers based on live traffic provided by the present application adopts the method for switching cloud service providers based on live traffic in the above embodiment, which can solve the technical problem of low efficiency of switching cloud service providers based on live traffic. Compared with the prior art, the beneficial effects of the device for switching cloud service providers based on live traffic provided by the present application are the same as the beneficial effects of the method for switching cloud service providers based on live traffic provided by the above embodiment, and other technical features in the device for switching cloud service providers based on live traffic are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0140] The present application provides a device for switching cloud service providers based on live broadcast traffic, and the device for switching cloud service providers based on live broadcast traffic includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for switching cloud service providers based on live broadcast traffic in the first embodiment described above.

[0141] Reference below Figure 7, which shows a schematic diagram of the structure of a device based on live traffic switching cloud service provider suitable for implementing the embodiment of the present application. The device based on live traffic switching cloud service provider in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The device shown for switching cloud service providers based on live traffic is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0142] like Figure 7 As shown, the device for switching cloud service providers based on live traffic may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the device for switching cloud service providers based on live traffic are also stored in RAM1004. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the device based on the live traffic switching cloud service provider to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a device based on the live traffic switching cloud service provider with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0143] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0144] The device for switching cloud service providers based on live traffic provided by the present application adopts the method for switching cloud service providers based on live traffic in the above embodiment, which can solve the technical problem of low efficiency of switching cloud service providers based on live traffic. Compared with the prior art, the beneficial effects of the device for switching cloud service providers based on live traffic provided by the present application are the same as the beneficial effects of the method for switching cloud service providers based on live traffic provided by the above embodiment, and other technical features in the device for switching cloud service providers based on live traffic are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0145] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0146] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0147] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for switching cloud service providers based on live broadcast traffic in the above-mentioned embodiment.

[0148] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0149] The above-mentioned computer-readable storage medium may be included in the device for switching cloud service providers based on live broadcast traffic; or it may exist independently without being assembled into the device for switching cloud service providers based on live broadcast traffic.

[0150] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by a device that switches cloud service providers based on live traffic, the device that switches cloud service providers based on live traffic: accesses at least one cloud service provider, and obtains business indicator data of each cloud service provider by testing traffic calculation;

[0151] Input the business indicator data into the cloud service provider quality evaluation model to predict the experience score of the live broadcast audience for the live broadcast service provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast audience for the viewing experience of the historical live broadcast;

[0152] Based on the experience score, allocating live broadcast start traffic to each cloud service provider;

[0153] By dynamically calculating the instant success rate of live broadcast of each cloud service provider, the real-time traffic of live broadcast of each cloud service provider can be redistributed;

[0154] According to the live real-time traffic allocated to each cloud service provider, the target cloud service provider with the highest proportion of the live real-time traffic is determined, and the cloud service provider carrying the current live traffic is switched to the target cloud service provider.

[0155] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0156] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0157] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0158] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for switching cloud service providers based on live traffic, and can solve the technical problem of low efficiency of switching cloud service providers based on live traffic. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method for switching cloud service providers based on live traffic provided in the above-mentioned embodiment, and will not be repeated here.

[0159] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for switching cloud service providers based on live broadcast traffic.

[0160] The computer program product provided by this application can solve the technical problem of low efficiency in switching cloud service providers based on live traffic. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the method for switching cloud service providers based on live traffic provided by the above embodiment, and will not be repeated here.

[0161] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for switching cloud service providers based on live broadcast traffic, characterized in that: The method comprises: Access at least one cloud service provider and obtain the business indicator data of each cloud service provider through test traffic calculation; Input the business indicator data into the cloud service provider quality evaluation model to predict the experience score of the live broadcast audience for the live broadcast service provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast audience for the viewing experience of the historical live broadcast; Based on the experience score, allocating live broadcast start traffic to each cloud service provider; By dynamically calculating the instant success rate of live broadcast of each cloud service provider, the real-time traffic of live broadcast of each cloud service provider can be redistributed; According to the live real-time traffic allocated to each cloud service provider, the target cloud service provider with the highest proportion of the live real-time traffic is determined, and the cloud service provider carrying the current live traffic is switched to the target cloud service provider.

2. The method according to claim 1, characterized in that The step of constructing a cloud service provider quality evaluation model based on historical business indicator data and subjective comprehensive scores of historical live broadcast viewers on viewing experience of historical live broadcasts includes: Generate at least one live streaming sample according to the live streaming requirements of the live streaming service; Quantifying the historical subjective feedback of the live stream sample to obtain a subjective comprehensive score of the viewing experience of the historical live broadcast by the historical live broadcast audience; Calculate historical business indicator data based on the test traffic of the live stream sample; The historical business indicator data is used as input, and the subjective comprehensive score is used as output to train a cloud service provider quality evaluation model.

3. The method according to claim 2, characterized in that After the step of training to obtain the cloud service provider quality evaluation model, the following steps are included: Measuring the correlation between historical subjective feedback and predicted historical experience scores, where the historical experience scores are predicted by the cloud service provider quality evaluation model based on historical business indicator data; If the correlation between the historical subjective feedback and the historical experience score does not reach a preset correlation, the model parameters of the cloud service provider quality evaluation model are optimized.

4. The method according to claim 1, characterized in that The step of obtaining the business indicator data of each cloud service provider by testing traffic calculation includes: Use test traffic for live broadcasting, monitor the viewing behavior of live viewers and the live broadcast services provided by various cloud service providers, and generate tracking logs; Based on the tracking logs, the business indicator data of each cloud service provider is calculated.

5. The method according to claim 1, characterized in that The step of dynamically calculating the instant live broadcast success rate of each cloud service provider and reallocating the live broadcast real-time traffic of each cloud service provider comprises: Allocate the live streaming real-time traffic to each cloud service provider based on the ranking of their live streaming success rates; If the live broadcast instant success rate is lower than the preset success rate threshold, the live broadcast real-time traffic of the cloud service provider whose live broadcast instant success rate is lower than the preset success rate threshold is cut off, and the live broadcast real-time traffic is allocated to other cloud service providers.

6. The method according to claim 1, characterized in that The method comprises: Identify and separate the live streaming functions provided by cloud service providers, and define each live streaming function as a cloud service policy; Recording registration information of the cloud service policy through a registration table; During the live broadcast, an instance of the cloud service policy is searched and obtained from the registry.

7. A device for switching cloud service providers based on live broadcast traffic, characterized in that: The device comprises: A calculation module is used to access at least one cloud service provider and obtain business indicator data of each cloud service provider by testing traffic calculation; A prediction module is used to input the business indicator data into a cloud service provider quality evaluation model to predict the experience score of the live broadcast audience on the live broadcast service provided by each cloud service provider; wherein the cloud service provider quality evaluation model is constructed based on the historical business indicator data and the subjective comprehensive score of the historical live broadcast audience on the viewing experience of the historical live broadcast; A first allocation module, configured to allocate live broadcast start traffic to each cloud service provider based on the experience score; The second allocation module is used to reallocate the live broadcast real-time traffic of each cloud service provider by dynamically calculating the live broadcast instant success rate of each cloud service provider; The switching module is used to determine the target cloud service provider with the highest proportion of the live real-time traffic according to the live real-time traffic allocated to each cloud service provider, and switch the cloud service provider carrying the current live traffic to the target cloud service provider.

8. A device for switching cloud service providers based on live broadcast traffic, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, which implements the steps of the method according to any one of claims 1 to 6 when executed by a processor.

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