Network quality detection method and device and related equipment

Abstract

The invention provides a network quality detection method and device and related equipment, and relates to the technical field of network detection. The method comprises the following steps: receiving a network request, and determining a target historical request corresponding to the network request; obtaining performance data of the target historical request under the plurality of detection indexes; determining a detection result of each detection index according to the performance data of the target historical request under each detection index; and determining a network quality detection result of the network request according to the detection results of the plurality of detection indexes. According to the method, the target historical request information in the context is introduced, and a plurality of detection indexes are combined for comprehensive evaluation, so that the accuracy and stability of network quality evaluation are improved, a more accurate and reliable network quality judgment basis can be provided for client application, and the user experience is effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of network detection, and in particular to a network quality detection method, a network quality detection device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the widespread adoption of mobile internet, client applications are increasingly dependent on network quality, especially in video applications, where network quality directly impacts the user experience. Related technologies use the response time of network requests to determine network quality. For example, if the response time exceeds a certain threshold, the network quality is considered poor, while if it exceeds a certain threshold, the network quality is considered good. This approach uses a single request metric to determine network quality, fails to consider contextual influences, and is susceptible to server-side interference, resulting in inaccurate network quality results. Summary of the Invention

[0003] The present disclosure provides a network quality detection method, a network quality detection device, an electronic device, a computer-readable storage medium, and a computer program product, so as to overcome the above problems or at least partially solve the above problems.

[0004] An embodiment of the present disclosure provides a network quality detection method, the method comprising: receiving a network request, determining a target historical request corresponding to the network request; obtaining performance data of the target historical request under multiple detection indicators; determining a detection result for each detection indicator based on the performance data of the target historical request under each detection indicator; and determining a network quality detection result of the network request based on the detection results of the multiple detection indicators.

[0005] In an exemplary embodiment of the present disclosure, determining the target historical request corresponding to the network request includes: obtaining historical requests within a preset time period before the request time of the network request; classifying the obtained historical requests based on the identification information of the network request to obtain a first category of historical requests and a second category of historical requests; the identification information of the first category of historical requests matches the identification information of the network request, and the identification information of the second category of historical requests does not match the identification information of the network request; and determining the target historical request based on the access success rate of the first category of historical requests and the access success rate of the second category of historical requests.

[0006] In an exemplary embodiment of the present disclosure, the target historical request is determined based on the access success rate of the first category of historical requests and the access success rate of the second category of historical requests, including: in response to the first access success rate of the first category of historical requests and the second access success rate of the second category of historical requests being greater than a first success rate threshold, or the first access success rate and the second access success rate being less than a second success rate threshold, determining that the acquired historical request is the target historical request; the first success rate threshold is greater than the second success rate threshold; in response to the first access success rate being less than the second success rate threshold, and the second access success rate being greater than the first success rate threshold, determining that the second category of historical request is the target historical request.

[0007] In an exemplary embodiment of the present disclosure, the identification information of the network request includes: address information of the network request and / or domain name information of the network request.

[0008] In an exemplary embodiment of the present disclosure, the multiple detection indicators include: a first packet response time indicator and a download speed indicator; the detection result of each detection indicator is determined based on the performance data of the target historical request under each detection indicator, including: determining the detection result corresponding to the first packet response time indicator based on the first packet response time data of the target historical request; determining the detection result corresponding to the download speed indicator based on the download speed data of the target historical request.

[0009] In an exemplary embodiment of the present disclosure, determining the detection result corresponding to the first packet response time indicator based on the first packet response time data of the target historical request includes: performing statistical analysis on the first packet response time data of the target historical request to obtain a first packet response time statistical value; determining the detection score corresponding to the first packet response time statistical value based on a pre-generated first packet response time and score first mapping relationship; the first mapping relationship is obtained by performing statistical analysis on the first packet response time data of historical network requests.

[0010] In an exemplary embodiment of the present disclosure, determining the detection result corresponding to the download speed indicator based on the download speed data of the target historical requests includes: performing statistical analysis on the download speed data of the target historical requests to obtain a download speed statistical value; determining the detection score corresponding to the download speed statistical value based on a pre-generated second mapping relationship between download speed and score; the second mapping relationship is obtained by performing statistical analysis on the download speed data of historical network requests.

[0011] In an exemplary embodiment of the present disclosure, determining the network quality detection result of the network request based on the detection results of multiple detection indicators includes: determining the target application to which the network request belongs; in response to the target application being a non-video application, weighting the detection results of the multiple detection indicators based on the default weight ratios of the detection indicators to obtain the network quality detection result; in response to the target application being a video application, weighting the detection results of the multiple detection indicators according to the application scenario of the network request to obtain the network quality detection result.

[0012] In an exemplary embodiment of the present disclosure, the detection results of the multiple detection indicators are weighted according to the application scenario of the network request to obtain the network quality detection result, including: in response to the application scenario of the network request being a video download scenario, the detection results of the multiple detection indicators are weighted based on the weight ratio of the detection indicators corresponding to the video download scenario to obtain the network quality detection result; in response to the request type of the network request being a non-video download scenario, the detection results of the multiple detection indicators are weighted based on the weight ratio of the detection indicators corresponding to the non-video download scenario to obtain the network quality detection result.

[0013] In an exemplary embodiment of the present disclosure, the method further includes: obtaining performance data of the network request under multiple detection indicators; determining an actual quality detection result of the network request based on the performance data of the network request under multiple detection indicators; and adjusting a content transmission method of the network request in response to an error between the network quality detection result and the actual quality detection result being greater than a preset error threshold.

[0014] An embodiment of the present disclosure provides a network quality detection device, comprising: a request receiving module configured to receive a network request and determine a target historical request corresponding to the network request; a performance data acquisition module configured to obtain performance data of the target historical request under multiple detection indicators; a detection result determination module configured to determine a detection result for each detection indicator based on the performance data of the target historical request under each detection indicator; and determining a network quality detection result of the network request based on the detection results of the multiple detection indicators.

[0015] In an exemplary embodiment of the present disclosure, the request receiving module is further configured to: obtain historical requests within a preset time period before the request time of the network request; classify the obtained historical requests based on the identification information of the network request to obtain a first category of historical requests and a second category of historical requests; the identification information of the first category of historical requests matches the identification information of the network request, and the identification information of the second category of historical requests does not match the identification information of the network request; determine the target historical request based on the access success rate of the first category of historical requests and the access success rate of the second category of historical requests.

[0016] In an exemplary embodiment of the present disclosure, the request receiving module is further configured to: in response to the first access success rate of the first type of historical requests and the second access success rate of the second type of historical requests being greater than the first success rate threshold, or the first access success rate and the second access success rate being less than the second success rate threshold, determine that the acquired historical request is the target historical request; the first success rate threshold is greater than the second success rate threshold; in response to the first access success rate being less than the second success rate threshold, and the second access success rate being greater than the first success rate threshold, determine that the second type of historical request is the target historical request.

[0017] In an exemplary embodiment of the present disclosure, the identification information of the network request includes: address information of the network request and / or domain name information of the network request.

[0018] In an exemplary embodiment of the present disclosure, the multiple detection indicators include: a first packet response time indicator and a download speed indicator; the detection result determination module is also configured to: determine the detection result corresponding to the first packet response time indicator based on the first packet response time data of the target historical request; determine the detection result corresponding to the download speed indicator based on the download speed data of the target historical request.

[0019] In an exemplary embodiment of the present disclosure, the detection result determination module is further configured to: perform statistical analysis on the first packet response time data of the target historical request to obtain a first packet response time statistical value; determine the detection score corresponding to the first packet response time statistical value based on a pre-generated first mapping relationship between the first packet response time and the score; the first mapping relationship is obtained by performing statistical analysis on the first packet response time data of historical network requests.

[0020] In an exemplary embodiment of the present disclosure, the detection result determination module is further configured to: perform statistical analysis on the download speed data of the target historical requests to obtain a download speed statistical value; determine the detection score corresponding to the download speed statistical value based on a pre-generated second mapping relationship between download speed and score; the second mapping relationship is obtained by performing statistical analysis on the download speed data of historical network requests.

[0021] In an exemplary embodiment of the present disclosure, the detection result determination module is further configured to: determine the target application to which the network request belongs; in response to the target application being a non-video application, perform weighted processing on the detection results of the multiple detection indicators based on the default weight ratio of the detection indicators to obtain the network quality detection result; in response to the target application being a video application, perform weighted processing on the detection results of the multiple detection indicators according to the application scenario of the network request to obtain the network quality detection result.

[0022] In an exemplary embodiment of the present disclosure, the detection result determination module is further configured to: in response to the application scenario of the network request being a video download scenario, perform weighted processing on the detection results of the multiple detection indicators based on the weight ratio of the detection indicators corresponding to the video download scenario to obtain the network quality detection result; in response to the request type of the network request being a non-video download scenario, perform weighted processing on the detection results of the multiple detection indicators based on the weight ratio of the detection indicators corresponding to the non-video download scenario to obtain the network quality detection result.

[0023] In an exemplary embodiment of the present disclosure, the device also includes a content adjustment module, which is configured to: obtain performance data of the network request under multiple detection indicators; determine an actual quality detection result of the network request based on the performance data of the network request under multiple detection indicators; and adjust the content transmission method of the network request in response to an error between the network quality detection result and the actual quality detection result being greater than a preset error threshold.

[0024] An embodiment of the present disclosure provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the above-mentioned network quality detection method.

[0025] An embodiment of the present disclosure provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can perform the above-mentioned network quality detection method.

[0026] An embodiment of the present disclosure provides a computer program product, including a computer program, which implements the above-mentioned network quality detection method when executed by a processor.

[0027] The network quality detection method provided by the embodiment of the present disclosure receives a network request and determines its corresponding target historical request, obtains the performance data of the target historical request under multiple detection indicators, and based on these multi-dimensional performance data, determines the detection results of each detection indicator respectively, and then determines the network quality detection result of the network request. It can be seen that this method effectively avoids the problem of inaccurate network quality detection caused by abnormal interference on the server side by introducing the target historical request information in the context and combining multiple detection indicators for comprehensive evaluation, thereby improving the accuracy and stability of network quality detection, and by analyzing the performance data of the target historical request in the context under different detection indicators, it can more comprehensively reflect the overall state of the network environment, making the network quality detection results closer to the actual network conditions. This method can provide a more accurate and reliable basis for judging network quality for client applications, especially applications such as video that have a high dependence on network quality, thereby effectively improving the user experience.

[0028] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0030] Figure 1 is a flow chart showing a method for detecting network quality according to an exemplary embodiment;

[0031] Figure 2 is a flowchart illustrating a method for determining a target historical request corresponding to a network request according to an exemplary embodiment;

[0032] Figure 3 is a processing logic diagram illustrating selecting a target historical request from acquired historical requests according to an exemplary embodiment;

[0033] Figure 4 is a flowchart illustrating a method for determining a detection result corresponding to a first packet response time indicator based on first packet response time data of a target historical request according to an exemplary embodiment;

[0034] Figure 5is a flow chart illustrating determining a detection result corresponding to a download speed indicator based on download speed data of a target historical request according to an exemplary embodiment;

[0035] Figure 6 is a flowchart illustrating determining a network quality detection result of a network request based on detection results of multiple detection indicators according to an exemplary embodiment;

[0036] Figure 7 is a block diagram of a network quality detection device according to an exemplary embodiment;

[0037] Figure 8 A schematic structural diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0038] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the drawings represent like or similar parts, and thus repetitive description thereof will be omitted.

[0039] The features, structures or characteristics described in the present disclosure may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0040] The collection, collection, updating, analysis, processing, use, transmission, and storage of user personal information covered in this disclosure comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures will be taken with respect to user personal information to prevent unauthorized access to user personal information data and safeguard the security of user personal information, network security, and national security.

[0041] The accompanying drawings are merely schematic illustrations of the present disclosure. Identical reference numerals in the drawings denote identical or similar components, and thus their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in at least one hardware module or integrated circuit, or in different networks and / or processor devices and / or microcontroller devices.

[0042] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all content and steps, nor must they be executed in the order described. For example, some steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0043] In this specification, the terms "a", "an", "the", "said" and "at least one" are used to indicate the presence of at least one element / component / etc.; the term "plurality" refers to two or more; the terms "comprising", "including" and "having" are used to express open-ended inclusion and mean that additional elements / components / etc. may exist in addition to the listed elements / components / etc.; the terms "first", "second" and "third" etc. are used only as labels and are not intended to limit the quantity of their objects.

[0044] With the widespread adoption of mobile internet, client applications are increasingly dependent on network quality, which directly impacts the user experience. Taking video applications as an example, client-side optimization strategies can be tailored to client network quality. For example, when network quality is poor, strategies such as playing cached videos, reducing image resolution, and lowering live broadcast clarity can be employed to enhance the user experience. Therefore, accurately detecting current network quality is crucial. Related technologies use the response time of network requests to determine network quality. For example, if the response time exceeds a certain threshold, the network quality is considered poor, while if it exceeds a certain threshold, the network quality is considered good. However, this approach only considers the response time of a single request and fails to consider the context of other requests. If the total request time increases due to excessive server-side logic processing or local node link anomalies, this can easily lead to an inaccurate assessment of the client network quality, resulting in inaccurate network quality results.

[0045] In order to solve the above problems, embodiments of the present disclosure provide a network quality detection method, a network quality detection device, an electronic device, a computer-readable storage medium, and a computer program product.

[0046] Figure 1 FIG. 1 is a flow chart showing a method for detecting network quality according to an exemplary embodiment. Figure 1As shown, the network quality detection method can be used in a computer device, wherein the computer device described in this disclosure may include mobile terminal devices such as mobile phones, tablet computers, laptops, PDAs, and personal digital assistants (PDAs), as well as fixed terminal devices such as desktop computers. This exemplary embodiment uses the method applied to a computer device as an example. It is understandable that the method can also be applied to a server, and can also be applied to a system including a computer device and a server, and implemented through the interaction between the computer device and the server. Specifically, the following steps are included.

[0047] Step S110: receiving a network request and determining a target historical request corresponding to the network request.

[0048] Step S120: Obtain performance data of the target historical request under multiple detection indicators.

[0049] Step S130 : determining a detection result of each detection indicator according to the performance data of the target historical request under each detection indicator.

[0050] Step S140: Determine the network quality detection result of the network request according to the detection results of the multiple detection indicators.

[0051] According to the network quality detection method in the embodiment of the present disclosure, a network request is received and its corresponding target historical request is determined, and the performance data of the target historical request under multiple detection indicators is obtained. Based on these multi-dimensional performance data, the detection results of each detection indicator are determined respectively, and then the network quality detection result of the network request is determined. It can be seen that this method effectively avoids the problem of inaccurate network quality detection caused by abnormal interference on the server side by introducing the target historical request information in the context and combining multiple detection indicators for comprehensive evaluation, thereby improving the accuracy and stability of network quality detection. Moreover, by analyzing the performance data of the target historical request in the context under different detection indicators, it can more comprehensively reflect the overall state of the network environment, making the network quality detection results closer to the actual network conditions. This method can provide a more accurate and reliable basis for judging network quality for client applications, especially applications such as video that have a high dependence on network quality, thereby effectively improving the user experience.

[0052] The network quality detection method in this exemplary embodiment will be further described below.

[0053] In step S110 , a network request is received, and a target historical request corresponding to the network request is determined.

[0054] In an exemplary embodiment of the present disclosure, a network request refers to a network request initiated by a client application, such as obtaining a video playlist or downloading video data. This request is triggered based on a user operation or application logic.

[0055] In an exemplary embodiment of the present disclosure, the target historical request corresponding to the network request refers to a request for detecting network quality before the request time of the network request.

[0056] Figure 2 FIG. 1 is a flow chart showing a method for determining a target historical request corresponding to a network request according to an exemplary embodiment. Figure 2 As shown, follow the steps below to determine the target historical request corresponding to the network request.

[0057] Step S210: Obtain historical requests within a preset time period before the request time of the network request.

[0058] The request time of a network request refers to the time when the network request was initiated. The preset time period refers to a pre-set time range, such as 5 minutes or 10 minutes. Historical requests refer to requests initiated and recorded by the client application within a certain period of time in the past.

[0059] In this step, all historical requests within a preset time period before the request initiation time of the network request are screened out from the locally stored historical request records, and these historical requests are subsequently analyzed to obtain the target historical request corresponding to the network request.

[0060] Step S220 , classify the acquired historical requests based on the identification information of the network request to obtain first-category historical requests and second-category historical requests; the identification information of the first-category historical requests matches the identification information of the network request, and the identification information of the second-category historical requests does not match the identification information of the network request.

[0061] Based on the identification information of the network request, historical requests that match the identification information of the network request are screened out from the acquired historical requests, and these historical requests are classified into the first category of historical requests, and other unmatched historical requests are classified into the second category of historical requests.

[0062] In an exemplary embodiment of the present disclosure, the identification information of the network request includes: address information of the network request and / or domain name information of the network request.

[0063] Specifically, the identification information of the network request can be address information, such as IP address information, or domain name information. It is worth noting that, based on actual needs, only address information, only domain name information, or a combination of address information and domain name information can be selected to classify the historical requests obtained.

[0064] In the disclosed embodiment, the identification information of the network request is used to determine whether the historical request has the same access target or service source as the network request.

[0065] Step S230 : determining a target historical request according to the access success rate of the first type of historical requests and the access success rate of the second type of historical requests.

[0066] The first type of historical requests refers to those that match the identification information of a network request. When the identification information is an IP address or domain name, these requests are directed to the same server or service as the network request, and their performance data better reflects the operating status of the corresponding server or service.

[0067] The second category of historical requests refers to other historical requests that do not match the identification information of the network request. These requests share the same client network environment as the network request. Therefore, the performance data of the second category of historical requests can provide useful information about the stability of the client network environment and is not affected by specific server anomalies.

[0068] In the embodiment of the present disclosure, the first type of historical requests can reflect the operating status of the server to which the network request points, and the second type of historical requests can reflect the overall network environment of the client. Combining the access success rates of these two types of historical requests, historical requests that can evaluate the current network status are screened out, namely, target historical requests, so as to use the target historical requests as the basis for subsequent analysis.

[0069] In an exemplary embodiment of the present disclosure, a target historical request is determined based on an access success rate of a first type of historical request and an access success rate of a second type of historical request, including: in response to the first access success rate of the first type of historical request and the second access success rate of the second type of historical request being greater than a first success rate threshold, or the first access success rate and the second access success rate being less than the second success rate threshold, determining that the acquired historical request is a target historical request; the first success rate threshold is greater than the second success rate threshold; in response to the first access success rate being less than the second success rate threshold, and the second access success rate being greater than the first success rate threshold, determining that the second type of historical request is a target historical request.

[0070] By analyzing the access success rate of the first category of historical requests and the access success rate of the second category of historical requests, it is determined which historical requests can be the target historical requests, which are used as the basis for subsequent network quality assessment. Among them, the access success rate refers to the ratio of the number of successfully completed requests to the total number of requests, which is a key indicator for measuring network stability and service availability. The first access success rate refers to the ratio of the number of successfully completed requests in the first category of historical requests to the number of first category historical requests, and the second access success rate refers to the ratio of the number of successfully completed requests in the second category of historical requests to the number of second category historical requests. The first success rate threshold is a pre-set higher success rate threshold used to indicate a good network status, which can be set to 95%. The second success rate threshold is a pre-set lower success rate threshold used to indicate a poor network status, which can be set to 20%.

[0071] When the success rates of both types of requests are higher than the first success rate threshold, it means that the server to which the network request points and other servers perform well. In this case, all obtained historical requests (including the first and second types) can be used as target historical requests to make full use of data samples and improve the comprehensiveness and accuracy of the detection results.

[0072] When the success rates of both types of requests are lower than the second success rate threshold, it indicates that there may be a serious abnormality in the client network. In this case, all acquired historical requests (including the first and second types) are used as target historical requests to analyze whether there is a client network problem.

[0073] If the success rate of the first-category request is lower than the second success rate threshold, while the success rate of the second-category historical request is higher than the first success rate threshold, this indicates that the client's network environment is good, but there is a problem with the server to which the network request is directed. In this case, the second-category historical request is selected as the target historical request to avoid server-side anomalies affecting the client's network quality assessment, thereby more accurately assessing the client's actual network status.

[0074] In an exemplary embodiment of the present disclosure, in response to the first access success rate being greater than a first success rate threshold and the second access success rate being less than a second success rate threshold, the second type of historical request is determined to be a target historical request.

[0075] If the second access success rate is less than the second success rate threshold, it indicates poor client network quality. If the first access success rate is greater than the first success rate threshold, it indicates good service access to the server targeted by the network request. In this case, the client network itself is unstable or faulty, but the server targeted by the network request may have optimization measures. Based on this, the performance data of the second type of historical requests more accurately reflects the actual status of the client's network. Therefore, the second type of historical requests are used as the target historical requests.

[0076] In an exemplary embodiment of the present disclosure, in response to the absence of the first type of historical request in the acquired historical requests, the acquired historical request is used as the target historical request corresponding to the network request.

[0077] That is to say, if no historical request matching the identification information of the network request is found within the preset time period before the network request is initiated, all the obtained historical requests can be directly used as the target historical requests for this network quality assessment, which is suitable for the situation where the user accesses a service, interface or domain name for the first time.

[0078] Figure 3 This is a processing logic diagram for selecting a target historical request from acquired historical requests according to an exemplary embodiment. When analyzing the acquired historical requests, attention is paid to historical requests having the same feature dimensions as the network request. Figure 3 In this article, we use "IP address" as a dimension to divide historical requests into the following three situations, and explain their corresponding processing strategies:

[0079] Case a: All historical requests from the same IP address as the network request were successfully accessed, and all historical requests from other IP addresses were successfully accessed. In this case, the client's network quality is good, and the server corresponding to the network request IP is responding normally. The obtained historical requests can be used as the target historical requests.

[0080] Case b: All historical requests from the same IP address as the network request failed, while all historical requests from other IP addresses succeeded. In this case, the server corresponding to the network request IP address may be experiencing an anomaly. Therefore, when testing client network quality, exclude interference data from the network request IP address and retain historical requests from other IP addresses to prevent server-side anomalies from affecting the client's network status.

[0081] Case c. All historical requests with the same IP address as the network request failed, and all historical requests with other IP addresses failed. In this case, all historical requests failed, indicating that there may be a problem with the client network environment. The obtained historical requests are used as the target historical requests.

[0082] Figure 3 Taking IP addresses as an example, this paper demonstrates the processing logic for selecting target historical requests from acquired historical requests. By analyzing historical requests that share the same characteristic dimensions as network requests (such as IP addresses), the acquired historical requests are divided into three categories, and corresponding processing strategies are provided for each.

[0083] By comprehensively comparing the access success rates of the first and second types of historical requests, and combining them with the preset first and second success rate thresholds, the target historical requests for network quality testing are dynamically determined, which can filter out the historical data that best reflects the current network quality under different network environments. When the success rates of both types of requests are higher than the high threshold or lower than the low threshold, it indicates that the client network status is consistent. Selecting all historical requests as target historical requests can improve the comprehensiveness of the assessment. When the access success rate of the first type of historical requests is lower than the low threshold, and the access success rate of the second type of historical requests is higher than the high threshold, it indicates that the current access target may have a server-side anomaly. Selecting the second type of historical requests as the target historical requests can accurately reflect the client network status.

[0084] In step S120 , performance data of the target historical request under multiple detection indicators is obtained.

[0085] In step S130 , the detection result of each detection indicator is determined according to the performance data of the target historical request under each detection indicator.

[0086] Test metrics are pre-set indicators used to measure network quality. After obtaining the target historical requests, for each test metric, the performance data for the target historical request under the test metric is obtained. This refers to the network-related data recorded during the execution of the target historical request, such as first packet response time and download speed.

[0087] For each of the multiple detection indicators, performance data of the target historical request under the detection indicator is obtained, the performance data under the detection indicator is analyzed, and a detection result of the detection indicator is obtained to reflect the performance of the network under the detection indicator.

[0088] In an exemplary embodiment of the present disclosure, multiple detection indicators include: a first packet response time indicator and a download speed indicator; based on the performance data of the target historical request under each detection indicator, the detection result of each detection indicator is determined, including: based on the first packet response time data of the target historical request, determining the detection result corresponding to the first packet response time indicator; based on the download speed data of the target historical request, determining the detection result corresponding to the download speed indicator.

[0089] The First Packet Response Time metric measures the time from when a client initiates a request to when the first data packet is received from the server, and is used to assess network latency. A shorter First Packet Response Time indicates a faster network response. The Download Speed metric measures how quickly a client receives data from the server, and is used to assess network bandwidth and transmission efficiency. A faster download speed indicates a stronger network transmission capacity.

[0090] Statistical analysis is performed on the first packet response time data and download speed data of the target historical requests to obtain the detection results corresponding to the first packet response data indicator and the detection results corresponding to the download speed indicator.

[0091] In the disclosed embodiment, independent statistical analysis is performed on multi-dimensional indicators such as the first packet response time and download speed to obtain corresponding test results, thereby improving the accuracy of the evaluation of each indicator, providing a basis for the subsequent comprehensive judgment of network quality, and making the final network quality test results more comprehensive and precise, and suitable for diverse network environments and business scenarios.

[0092] Figure 4 FIG. 1 is a flow chart showing a method for determining a detection result corresponding to a first packet response time indicator based on first packet response time data of a target historical request according to an exemplary embodiment. Figure 4 As shown, the following steps may be included.

[0093] Step S410 , performing statistical analysis on the first packet response time data of the target historical request to obtain a first packet response time statistical value.

[0094] The first packet response time statistic is used to reflect the overall response performance in the current network environment. For example, the average, median, maximum, or minimum first packet response time data of the target historical requests can be calculated and the calculated value is used as the first packet response time statistic.

[0095] Step S420: Determine the detection score corresponding to the first packet response time statistical value based on a pre-generated first mapping relationship between the first packet response time and the score; the first mapping relationship is obtained by statistically analyzing the first packet response time data of historical network requests.

[0096] In an exemplary embodiment of the present disclosure, the first mapping relationship can be pre-generated by the following steps: collecting and reporting data on the client to obtain first packet response time data for a large number of historical requests to form a first packet response time data set; performing statistical analysis on the data set to calculate the first packet response time for different percentile values, for example, p10 (10th percentile value) corresponds to 20ms; associating each percentile value with its corresponding network quality score to establish a correspondence between the percentile value and the score, thereby obtaining a first mapping relationship between the first packet response time and the score. As shown in Table 1, a table of correspondences between percentile values, first packet response times, and scores is given, and the first mapping relationship between the first packet response time and the score is obtained based on the correspondence.

[0097] Table 1 shows the corresponding relationship between percentile value, first packet response time and score

[0098] Percentile First packet response time Fraction p0 0ms 100 p10 20ms 90 p20 50ms 80 p30 100ms 70 p40 200ms 60 p50 300ms 50 p60 500ms 40 p70 800ms 30 p80 1500ms 20 p90 2500ms 10 p100 5000ms 0

[0099] Furthermore, the relationship between percentile values and response time values is not fixed but can be dynamically adjusted based on the actual network environment and user behavior. For example, network load and user access patterns may change during different time periods, business cycles, or special time periods. The relationship between percentile values and first-packet response time can be regularly recalculated based on the latest collected data, and the corresponding mapping can be updated to ensure that the detection results more accurately reflect the current network status.

[0100] After obtaining the first packet response time statistics, linear interpolation is used to find or calculate the detection score corresponding to the first packet response time statistics based on the pre-generated first packet response time and score mapping relationship. For example, if the first packet response time statistics are 75ms, which is between p20 (50ms) and p30 (100ms), linear interpolation is used to obtain a corresponding detection score of 75.

[0101] In this disclosed embodiment, by statistically analyzing the first packet response time data of historical target requests and combining it with a first mapping relationship between first packet response time and scores constructed based on historical big data, the first packet response time statistics are quantified, improving the accuracy and interpretability of the detection results. Furthermore, this first mapping relationship can be dynamically updated based on the actual network environment, ensuring the score system has good adaptability and timeliness.

[0102] Figure 5 FIG. 1 is a flow chart showing a method for determining a detection result corresponding to a download speed indicator based on download speed data of a target historical request according to an exemplary embodiment. Figure 5 As shown, the following steps may be included.

[0103] Step S510: Statistically analyze the download speed data of the target historical requests to obtain a download speed statistical value.

[0104] The download speed statistics are used to reflect the data transmission performance under the current network environment. For example, the average, median, maximum, or minimum value of the download speed data of the target historical requests can be calculated and the calculated value is used as the download speed statistics.

[0105] Step S520 , determining a detection score corresponding to the download speed statistic based on a pre-generated second mapping relationship between download speed and score; the second mapping relationship is obtained by statistically analyzing the download speed data of historical network requests.

[0106] In an exemplary embodiment of the present disclosure, the second mapping relationship can be pre-generated by the following steps: collecting and reporting data on the client to obtain a large amount of download speed data for historical requests to form a download speed data set; performing statistical analysis on the data set to calculate the download speeds of different percentile values, for example, p10 (10th percentile value) corresponds to 50KB / s; associating each percentile value with its corresponding network quality score to establish a correspondence between the percentile value and the score, thereby obtaining a second mapping relationship between the download speed and the score. As shown in Table 2, a table of correspondences between percentile values, download speeds, and scores is provided, and the first mapping relationship between the download speed and the score is obtained based on this correspondence.

[0107] Table 2 shows the corresponding relationship between percentile value, download speed and score

[0108]

[0109]

[0110] Furthermore, the relationship between percentiles and download speeds isn't fixed; it can be dynamically adjusted based on the actual network environment and user behavior. For example, network load and user access patterns may change during different time periods, business cycles, or specific time periods. The relationship between percentiles and download speeds can be regularly recalculated based on the latest collected data, and the corresponding mapping can be updated to ensure that detection results more accurately reflect current network conditions.

[0111] After obtaining the download speed statistics, the detection score corresponding to the download speed statistics is found or calculated using linear interpolation based on the pre-generated second mapping relationship between download speed and score. For example, if the download speed statistics are 150KB / s, which is between p20 (100KB / s) and p30 (200KB / s), the corresponding detection score is 75 using linear interpolation.

[0112] In this disclosed embodiment, by statistically analyzing the download speed data of the target's historical requests and combining it with a secondary mapping relationship between download speed and score constructed based on historical big data, the download speed statistics are quantified, improving the accuracy and interpretability of the detection results. Furthermore, the second mapping relationship can be dynamically updated based on the actual network environment, ensuring the score system has good adaptability and timeliness.

[0113] In step S140 , a network quality detection result of the network request is determined based on the detection results of the multiple detection indicators.

[0114] After obtaining the test results for each test indicator, the test results of all test indicators are combined to finally obtain the network quality test result of the network request. This result integrates the test structure of all test indicators and can reflect the current network quality of the client, which can be used to guide subsequent operations.

[0115] Figure 6 FIG. 1 is a flow chart showing a method for determining a network quality detection result of a network request based on the detection results of multiple detection indicators according to an exemplary embodiment. Figure 6 As shown, the following steps may be included.

[0116] Step S610: Determine the target application to which the network request belongs.

[0117] Step S620 , in response to the target application being a non-video application, weighted processing is performed on the detection results of the multiple detection indicators based on the default weight ratios of the detection indicators to obtain a network quality detection result.

[0118] Step S630 , in response to the target application being a video application, weighted processing is performed on the detection results of the plurality of detection indicators according to the application scenario of the network request to obtain a network quality detection result.

[0119] The target application refers to the application that initiates the network request. If the target application is a non-video application, that is, the network request is initiated by a non-video application, the test results of each test indicator can be weighted based on the default weight ratio of the test indicator to finally obtain the network quality test result. For example, multiple test indicators include the first packet response time indicator and the download speed indicator, with a default ratio of 0.5:0.5. After determining that the target application is a non-video application such as social, payment, or map, the test results corresponding to the first packet response time indicator and the test results corresponding to the download speed indicator are weighted based on the default ratio of 0.5:0.5 to obtain the network quality test result.

[0120] If the target application is a video application, that is, the network request is initiated by the video application, the detection results of multiple detection indicators can be weighted according to the application scenario of the network request to obtain the network quality detection result.

[0121] In an exemplary embodiment of the present disclosure, the detection results of multiple detection indicators are weighted according to the application scenario of the network request to obtain a network quality detection result, including: in response to the application scenario of the network request being a video download scenario, the detection results of multiple detection indicators are weighted based on the weight ratio of the detection indicators corresponding to the video download scenario to obtain a network quality detection result; in response to the request type of the network request being a non-video download scenario, the detection results of multiple detection indicators are weighted based on the weight ratio of the detection indicators corresponding to the non-video download scenario to obtain a network quality detection result.

[0122] If the application scenario of the network request is a video download scenario, such as a user downloading a movie from a video platform, the detection indicator weight ratio set for the video download scenario is used to perform weighted calculation on the detection results of each detection indicator to obtain the final network quality detection result.

[0123] Among these, multiple detection indicators include first packet response time and download speed. For video downloads, which primarily involve large data transfers, the network experience in this scenario is significantly impacted by download speed. Therefore, the download speed indicator is weighted more heavily than the first packet response time indicator. For example, the download speed indicator is weighted to 0.7, while the first packet response time indicator is weighted to 0.3.

[0124] If the application scenario of the network request is a non-video download scenario, such as the user opens the personal settings, the detection indicator weight ratio set for the non-video download scenario is used to perform weighted calculation on the detection results of each detection indicator to obtain the final network quality detection result.

[0125] Among these, multiple detection indicators include first packet response time and download speed. For non-video download scenarios, which primarily involve requests with small data transfer volumes, the network experience in these scenarios is significantly impacted by first packet response time. Therefore, the weight of first packet response time is set higher than that of download speed. For example, set the weight of download speed to 0.3 and the weight of first packet response time to 0.7.

[0126] For ease of understanding, the processing logic of network quality detection is explained with a specific embodiment. The user initiates a network request through an application and obtains the target historical request corresponding to the network request. The first packet response time data of the target historical request is statistically analyzed to obtain the detection result corresponding to the first packet response time indicator; the download speed data of the target historical request is statistically analyzed to obtain the detection result corresponding to the download speed indicator. Among them, the default weight ratio of the first packet response time indicator and the download speed indicator is 0.5:0.5. If it is judged that the application that initiates the network request is a non-video application, the detection result corresponding to the first packet response time indicator and the detection result corresponding to the download speed indicator are directly weighted based on the default weight ratio to obtain the network quality detection result. If it is judged that the application that initiates the network request is a video application, it is further determined whether the application scenario of the network request is a video download scenario.

[0127] For video download scenarios, the weight ratio of the first packet response time indicator and the download speed indicator is 0.3:0.7. For non-video download scenarios, the weight ratio of the first packet response time indicator and the download speed indicator is 0.7:0.3. If the application scenario of the network request is a video download scenario, based on the weight ratio of 0.3:0.7, the test results corresponding to the first packet response time indicator and the test results corresponding to the download speed indicator are weighted and calculated to obtain the network quality test results. If it is a non-video download scenario, based on the weight ratio of 0.7:0.3, the test results corresponding to the first packet response time indicator and the test results corresponding to the download speed indicator are weighted and calculated to obtain the network quality test results.

[0128] In the disclosed embodiment, the weight ratio of the detection indicators is dynamically adjusted according to the target application to which the network request belongs and the application scenario of the network request, thereby realizing the refinement and scenario-based network quality detection. For non-video applications, the default weight is used for unified evaluation; for video applications, the indicator weight is further adjusted in combination with specific application scenarios, such as video download or non-video download, so that the detection results are more in line with actual business needs. This differentiated weighting strategy improves the accuracy and flexibility of network quality detection, and can better support the client's network optimization and decision-making in different scenarios, thereby improving the user experience.

[0129] In an exemplary embodiment of the present disclosure, the network quality detection method further includes: obtaining performance data of a network request under multiple detection indicators; determining an actual quality detection result of the network request based on the performance data of the network request under the multiple detection indicators; and adjusting a content transmission method of the network request in response to an error between the network quality detection result and the actual quality detection result being greater than a preset error threshold.

[0130] This network quality detection method further introduces a feedback mechanism. After a network request is initiated, the corresponding target historical request is obtained. The performance data of the target historical request under multiple detection indicators is used to determine the detection results of multiple detection indicators, thereby obtaining the network quality detection result of the network request. This network quality detection result is an estimated value. After the network request is processed, the performance data of the network request under multiple detection indicators is obtained. Based on the performance data of the network request under multiple detection indicators, the detection time of multiple detection indicators is determined, thereby obtaining the actual quality detection result of the network request, reflecting the actual network performance of this network request.

[0131] The network quality test result of the network request is compared with the actual quality test result. If the difference between the two is greater than the preset error threshold (for example, if the network quality test result is 50 points and the actual quality test result is 20 points, the difference between the two is greater than the preset error threshold of 15 points), then the network quality test result estimate is inaccurate. In this case, the content delivery method adjustment mechanism is triggered to adjust the content delivery method of the network request. For example, if the network quality assessment is poor, it will automatically switch to cached video playback to avoid lag issues caused by network fluctuations and improve the user viewing experience.

[0132] In this disclosed embodiment, performance data for network requests under multiple test indicators is collected and used to determine the actual quality test results of the network requests. When the error between the predicted network quality test results and the actual quality test results exceeds a preset error threshold, the system automatically adjusts the content delivery method, such as switching to cached video playback to cope with poor network conditions. This method can effectively optimize the user experience, especially ensuring service smoothness in unstable network environments, helping to achieve business goals, such as improving key indicators such as smooth playback rate by 0.5%.

[0133] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can be referred to each other, and each embodiment focuses on the differences from other embodiments. For related parts, please refer to the description of other method embodiments.

[0134] Figure 7 FIG. 1 is a block diagram of a network quality detection device according to an exemplary embodiment. Figure 7 As shown, the device 700 includes a request receiving module 710 , a performance data acquiring module 720 and a detection result determining module 730 .

[0135] The request receiving module 710 is configured to receive a network request and determine the target historical request corresponding to the network request. The performance data acquisition module 720 is configured to obtain performance data of the target historical request under multiple test indicators. The test result determination module 730 is configured to determine the test result of each test indicator based on the performance data of the target historical request under each test indicator; and determine the network quality test result of the network request based on the test results of the multiple test indicators.

[0136] In an exemplary embodiment of the present disclosure, the request receiving module 710 is further configured to: obtain historical requests within a preset time period before the request time of the network request; classify the obtained historical requests based on the identification information of the network request to obtain a first category of historical requests and a second category of historical requests; the identification information of the first category of historical requests matches the identification information of the network request, and the identification information of the second category of historical requests does not match the identification information of the network request; determine the target historical request based on the access success rate of the first category of historical requests and the access success rate of the second category of historical requests.

[0137] In an exemplary embodiment of the present disclosure, the request receiving module 710 is further configured to: in response to the first access success rate of the first type of historical requests and the second access success rate of the second type of historical requests being both greater than the first success rate threshold, or the first access success rate and the second access success rate being both less than the second success rate threshold, determine that the acquired historical request is the target historical request; the first success rate threshold is greater than the second success rate threshold; in response to the first access success rate being less than the second success rate threshold, and the second access success rate being greater than the first success rate threshold, determine that the second type of historical request is the target historical request.

[0138] In an exemplary embodiment of the present disclosure, the identification information of the network request includes: address information of the network request and / or domain name information of the network request.

[0139] In an exemplary embodiment of the present disclosure, multiple detection indicators include: a first packet response time indicator and a download speed indicator; the detection result determination module 730 is also configured to: determine the detection result corresponding to the first packet response time indicator based on the first packet response time data of the target historical request; determine the detection result corresponding to the download speed indicator based on the download speed data of the target historical request.

[0140] In an exemplary embodiment of the present disclosure, the detection result determination module 730 is further configured to: perform statistical analysis on the first packet response time data of the target historical request to obtain a first packet response time statistical value; determine the detection score corresponding to the first packet response time statistical value based on a pre-generated first mapping relationship between the first packet response time and the score; the first mapping relationship is obtained by performing statistical analysis on the first packet response time data of the historical network request.

[0141] In an exemplary embodiment of the present disclosure, the detection result determination module 730 is further configured to: perform statistical analysis on the download speed data of the target historical requests to obtain a download speed statistical value; determine the detection score corresponding to the download speed statistical value based on a pre-generated second mapping relationship between the download speed and the score; the second mapping relationship is obtained by performing statistical analysis on the download speed data of historical network requests.

[0142] In an exemplary embodiment of the present disclosure, the detection result determination module 730 is further configured to: determine the target application to which the network request belongs; in response to the target application being a non-video application, perform weighted processing on the detection results of multiple detection indicators based on the default weight ratio of the detection indicators to obtain a network quality detection result; in response to the target application being a video application, perform weighted processing on the detection results of multiple detection indicators according to the application scenario of the network request to obtain a network quality detection result.

[0143] In an exemplary embodiment of the present disclosure, the detection result determination module 730 is further configured to: in response to the application scenario of the network request being a video download scenario, perform weighted processing on the detection results of multiple detection indicators based on the weight ratio of the detection indicators corresponding to the video download scenario to obtain a network quality detection result; in response to the request type of the network request being a non-video download scenario, perform weighted processing on the detection results of multiple detection indicators based on the weight ratio of the detection indicators corresponding to the non-video download scenario to obtain a network quality detection result.

[0144] In an exemplary embodiment of the present disclosure, Figure 7 As shown, the device 700 also includes a content adjustment module 740, which is configured to: obtain performance data of the network request under multiple detection indicators; determine the actual quality detection result of the network request based on the performance data of the network request under multiple detection indicators; and adjust the content transmission method of the network request in response to the error between the network quality detection result and the actual quality detection result being greater than a preset error threshold.

[0145] Regarding the apparatus in the above embodiment, the specific manner in which each unit performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0146] Figure 8 FIG1 shows a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. It should be noted that: Figure 8 The electronic device 800 shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0147] like Figure 8 As shown, electronic device 800 is implemented as a general-purpose computing device. Components of electronic device 800 may include, but are not limited to, the aforementioned at least one processing unit 810, the aforementioned at least one storage unit 820, and a bus 830 connecting various system components (including storage unit 820 and processing unit 810).

[0148] The storage unit stores program codes that can be executed by the processing unit 810, so that the processing unit 810 performs the steps according to various exemplary embodiments of the present invention described in the "Exemplary Method" section above. For example, the processing unit 810 may perform the following steps: Figure 1 Follow the steps shown in .

[0149] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 8201 and / or a cache memory unit 8202 , and may further include a read-only memory unit (ROM) 8203 .

[0150] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0151] Bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0152] The electronic device 800 can also communicate with one or more external devices 900 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 800, and / or any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 850. Furthermore, the electronic device 800 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 840. As shown, the network adapter 840 communicates with other modules of the electronic device 800 via a bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 800, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0153] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0154] The program product for implementing the above-described method according to an embodiment of the present invention may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0155] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0156] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0157] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0158] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, and the like, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0159] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0160] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0161] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0162] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

[0163] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A network quality detection method, characterized in that: The method comprises: receiving a network request, and determining a target historical request corresponding to the network request; Obtaining performance data of the target historical request under multiple detection indicators; Determine a test result for each test indicator based on the performance data of the target historical request under each test indicator; A network quality detection result of the network request is determined based on the detection results of multiple detection indicators.

2. The method according to claim 1, characterized in that Determining the target historical request corresponding to the network request includes: Obtain historical requests within a preset time period before the request time of the network request; Classifying the acquired historical requests based on the identification information of the network request to obtain a first category of historical requests and a second category of historical requests; the identification information of the first category of historical requests matches the identification information of the network request, and the identification information of the second category of historical requests does not match the identification information of the network request; The target historical request is determined according to the access success rate of the first type of historical requests and the access success rate of the second type of historical requests.

3. The method according to claim 2, characterized in that The determining the target historical request according to the access success rate of the first type of historical requests and the access success rate of the second type of historical requests includes: In response to a first access success rate of the first type of historical requests and a second access success rate of the second type of historical requests both being greater than a first success rate threshold, or a first access success rate and a second access success rate both being less than a second success rate threshold, determining that the acquired historical request is the target historical request; and the first success rate threshold being greater than the second success rate threshold; In response to the first access success rate being less than the second success rate threshold, and the second access success rate being greater than the first success rate threshold, the second-type historical request is determined to be the target historical request.

4. The method according to claim 2, characterized in that The identification information of the network request includes: address information of the network request and / or domain name information of the network request.

5. The method according to claim 1, wherein The multiple detection indicators include: a first packet response time indicator and a download speed indicator; and determining a detection result of each detection indicator based on the performance data of the target historical request under each detection indicator includes: Determine the detection result corresponding to the first packet response time indicator according to the first packet response time data of the target historical request; A detection result corresponding to the download speed indicator is determined according to the download speed data of the target historical request.

6. The method according to claim 5, characterized in that The determining, based on the first packet response time data of the target historical request, a detection result corresponding to the first packet response time indicator includes: Performing statistical analysis on the first packet response time data of the target historical requests to obtain a first packet response time statistical value; Based on a pre-generated first mapping relationship between the first packet response time and the score, the detection score corresponding to the first packet response time statistical value is determined; the first mapping relationship is obtained by statistically analyzing the first packet response time data of historical network requests.

7. The method according to claim 5, characterized in that Determining the detection result corresponding to the download speed indicator according to the download speed data of the target historical request includes: Performing statistical analysis on the download speed data of the target historical requests to obtain a download speed statistical value; The detection score corresponding to the download speed statistical value is determined based on a pre-generated second mapping relationship between download speed and score; the second mapping relationship is obtained by statistically analyzing download speed data of historical network requests.

8. The method according to claim 1, characterized in that Determining the network quality detection result of the network request based on the detection results of the multiple detection indicators includes: Determining a target application to which the network request belongs; In response to the target application being a non-video application, weighting the detection results of the multiple detection indicators based on the default weight ratios of the detection indicators to obtain the network quality detection result; In response to the target application being a video application, the detection results of the multiple detection indicators are weighted according to the application scenario of the network request to obtain the network quality detection result.

9. The method according to claim 8, characterized in that The step of performing weighted processing on the detection results of the plurality of detection indicators according to the application scenario of the network request to obtain the network quality detection result includes: In response to the application scenario of the network request being a video download scenario, weighted processing is performed on the detection results of the multiple detection indicators based on the weight ratios of the detection indicators corresponding to the video download scenario to obtain the network quality detection result; In response to the request type of the network request being a non-video download scenario, the detection results of the multiple detection indicators are weighted based on the weight ratio of the detection indicators corresponding to the non-video download scenario to obtain the network quality detection result.

10. The method according to claim 1, characterized in that The method further comprises: Obtaining performance data of the network request under multiple detection indicators; Determining an actual quality test result of the network request based on performance data of the network request under multiple test indicators; In response to an error between the network quality detection result and the actual quality detection result being greater than a preset error threshold, adjusting a content transmission mode of the network request.

11. A network quality detection device, characterized in that: The device comprises: a request receiving module, configured to receive a network request and determine a target historical request corresponding to the network request; A performance data acquisition module is configured to acquire performance data of the target historical request under multiple detection indicators; The detection result determination module is configured to determine the detection result of each detection indicator based on the performance data of the target historical request under each detection indicator; and determine the network quality detection result of the network request based on the detection results of multiple detection indicators.

12. An electronic device, characterized in that: include: processor; A memory for storing the processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the network quality detection method according to any one of claims 1 to 10. 13 . A computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the network quality detection method according to claim 1 .

14. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the network quality detection method according to any one of claims 1 to 10 is implemented.

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