A quality evaluation method and system for a vehicle networking cloud service

Abstract

The application relates to the technical field of vehicle network cloud service, in particular to a quality evaluation method and system for vehicle network cloud service; the method mainly comprises the following steps: constructing an index matrix based on unified measurement, defining index names, constructing an index model, extracting cloud service data, and analyzing and calculating output results according to the constructed index model; in the scheme, the measurement is unified, demanders can use the same measurement index to evaluate the quality of different cloud services, and the quality of different cloud services is evaluated from multiple dimensions, systems and business dimensions, so that the evaluation accuracy is improved; in the scheme, a universally applicable index matrix is constructed, the cloud service is measured according to a unified standard from multiple angles, cloud service log data is processed by using a data warehouse technology, the accuracy of index calculation is improved, and the index calculation process is simplified.

Description

Technical Field

[0001] This invention relates to the field of vehicle network cloud service technology, and specifically to a method and system for quality assessment of vehicle network cloud services. Background Technology

[0002] Currently, the working principle of the common cloud service deployment architecture based on the Internet of Vehicles in the industry is that any external business initiates a request to the cloud service platform. Each request first accesses the network management system, and is then routed by the gateway to the corresponding service. Service A may directly handle the request of the external business, or Service A may continue to call Service B to handle the request. Similarly, Service B may continue to call Service C to handle the request. Figure 1 This is a schematic diagram of the overall architecture of the existing cloud service platform.

[0003] Furthermore, the collaboration and response methods between cloud services are very complex, making the single request chain of external businesses difficult to understand. If the quality of cloud services can be effectively evaluated, it can greatly help to identify the shortcomings of the cloud service platform and improve overall efficiency. Figure 2 This is a diagram illustrating the call relationships between cloud services. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a quality assessment method and system for vehicle network cloud services that can combine the quality compliance standards customized by the demand side to realize the monitoring and horizontal comparison of cloud service quality.

[0005] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: the quality assessment method for the vehicle-to-everything (V2X) cloud service, which includes the following steps:

[0006] (1) Construct an index matrix based on a unified measurement standard;

[0007] (2) Define the indicator name;

[0008] (3) Construct the indicator model. The calculation model for the indicator is as follows:

[0009]

[0010] In the above formula, a is the tuning parameter, and θ j Let be the data slope, and i be the i-th cloud service;

[0011] (4) Extract cloud service data and analyze and calculate it according to the indicator model constructed in step (3), and output the results.

[0012] Furthermore, in step (1), the number of requests, the number of exceptions, the response time, and availability are used as metrics to measure the quality of cloud services.

[0013] Furthermore, the client can set their own quality standards for both the system and business dimensions.

[0014] Furthermore, the anomaly count metric includes the number of system anomalies and the number of service anomalies; the response time metric includes the average response time, the maximum response time, and the minimum response time; and the availability metric includes system availability and service availability.

[0015] Furthermore, in the average response time measure, the average response time of the i-th cloud service is... Where a is the average response time of the i-th cloud service over the past 30 days, a0; if each t j If the impact on the calculation result is equal in weight, then θ j =1, at this time If each t j Data slope and t j If the order of appearance is inversely proportional, then θ j =1 / t, at this time

[0016] Furthermore, in the availability metric, the availability of the i-th cloud service... Where 'a' is the minimum availability of the i-th cloud service over the past 30 days, a0.

[0017]

[0018] Furthermore, in step (2), the request count metric is defined as the number of times the cloud service is requested by external services; the exception count metric is defined as the number of times the cloud service returns a failure code; the response time metric is defined as the time it takes for the cloud service to respond to a call request, in milliseconds; the availability metric is defined as the proportion of non-failed requests to the total number of requests; and the compliance metric is defined as the result of judging the availability metric based on the quality compliance standard set by the demand side. If the availability is ≥ the compliance standard, it is judged as compliant; otherwise, it is not compliant.

[0019] Furthermore, step (4) includes the following specific steps:

[0020] (4.1) Extract cloud service data into the storage system;

[0021] (4.2) Calculate relevant quality indicators based on cloud service data;

[0022] (4.3) Based on the indicators obtained in step (4.2), perform drill-down analysis on the data;

[0023] (4.4) Generate a visualization report based on the results of the drill-down analysis in step (4.3).

[0024] Further, in step (4.2), a data warehouse model is used to calculate the cloud service data. The model consists of the source layer ODS, the detail layer DWD, the business layer DWS, and the application layer ADS from bottom to top. Step (4.1) is completed in the source layer ODS, steps (4.2) and (4.3) are completed in the detail layer DWD and the business layer DSW, respectively, and step (4.4) is completed in the application layer ADS.

[0025] The aforementioned quality assessment system for vehicle-to-everything (V2X) cloud services includes a standard measurement module, a model building module, and an analysis and processing module.

[0026] The standard measurement module constructs an indicator matrix based on a unified metric from both business and system dimensions. It defines indicator names, the number of requests (the number of times the cloud service is requested externally), the number of anomalies (the number of times the cloud service returns a failure code), the response time (the time it takes for the cloud service to respond to a request, in milliseconds), the availability (the ratio of non-failed requests to total requests), and the compliance indicator (based on the quality compliance standards set by the requester, judging the availability indicator; if availability is greater than or equal to the compliance standard, it is considered compliant; otherwise, it is considered non-compliant).

[0027] The model building module is used to construct the indicator model, and the calculation model of the indicator is as follows:

[0028]

[0029] In the above formula, a is the tuning parameter, and θ j Let be the data slope, and i be the i-th cloud service;

[0030] The analysis and processing module is used to extract cloud service data and perform analysis, calculation and output results based on the indicator model established by the model building module.

[0031] Compared with existing technologies, the significant advantages of this solution are:

[0032] 1. This solution standardizes the metrics, allowing customers to use the same metrics to evaluate the quality of different cloud services.

[0033] 2. This solution is designed to evaluate the quality of different cloud services from multiple dimensions, including system and business dimensions, thereby improving the accuracy of the evaluation;

[0034] 3. This solution constructs a universally applicable indicator matrix to measure cloud services in a unified standard from multiple perspectives;

[0035] 4. This solution utilizes data warehouse technology to process cloud service log data, which improves the accuracy of indicator calculation and simplifies the indicator calculation process. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0037] Figure 1 This is a schematic diagram of the overall architecture of a cloud service platform in existing technologies;

[0038] Figure 2 This is a diagram illustrating the inter-service call relationships in existing technologies.

[0039] Figure 3 This is a schematic diagram of the steps of the evaluation method in an embodiment of the present invention;

[0040] Figure 4 This is a data modeling result table in an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram showing the calculation results of the index matrix in an embodiment of the present invention;

[0042] Figure 6 This is a schematic diagram illustrating the compliance standards in an embodiment of the present invention. Detailed Implementation

[0043] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0044] Based on the relevant descriptions in the background section, some terms will be explained below.

[0045] External business: Other services in the vehicle networking system that are not cloud services.

[0046] Gateway: Also known as an internetwork connector or protocol converter. A gateway is a facility used to provide network compatibility functions such as protocol conversion, routing, and data exchange when networks using different architectures or protocols need to communicate with each other.

[0047] IOT Hub: Internet of Things Hub, aims to provide a secure, stable, and efficient connectivity platform to help developers achieve reliable, high-concurrency data communication between devices, users, and applications at low cost and quickly.

[0048] Kafka: A high-throughput distributed publish-subscribe messaging system that can handle all action streams of data from consumers on a website.

[0049] like Figure 3 As shown, the quality assessment method for vehicle-to-everything (V2X) cloud services of the present invention includes the following steps:

[0050] (1) Using the same metrics (the same metrics), construct an indicator matrix based on the unified metrics from both business and system dimensions, and combine it with the quality compliance standards customized by the demand side to achieve monitoring and horizontal comparison of cloud service quality.

[0051] Specifically, request count, exception count, response time, and availability are used as metrics to measure cloud service quality. An indicator matrix is ​​constructed from both system and business dimensions, enabling unified standard measurement of all cloud services from multiple perspectives. Requesters can set their own quality standards for both system and business dimensions, such as setting system availability at 99.99% and business availability at 99.5% as the cloud service quality compliance standard. In this embodiment, the indicator matrix is ​​shown in the table below:

[0052]

[0053]

[0054] Table 1 Indicator Matrix

[0055] The indicator matrix in Table 1 can be further refined into a detailed indicator matrix in Table 2.

[0056]

[0057] Table 2 Detailed Indicator Matrix

[0058] (2) Define the indicator name:

[0059] The number of requests is defined as the number of times a cloud service is requested by an external service. In a single request, a single cloud service may be called multiple times.

[0060] Define the anomaly count metric as the number of times the cloud service returns a failure code;

[0061] The response time metric is defined as the time it takes for a cloud service to respond to a request, usually in milliseconds.

[0062] The availability metric is defined as the percentage of non-failed requests out of the total number of requests.

[0063] The criteria for meeting the standards are defined as the quality standards set by the demand side. The availability index is judged based on the results. If the availability is greater than or equal to the standard, it is judged as meeting the standards; otherwise, it is not meeting the standards.

[0064] (3) Constructing the indicator model. In this embodiment, the calculation model for the indicator is as follows:

[0065]

[0066] In the above formula, 'a' is a tuning parameter. The reason for introducing 'a' as a tuning parameter is that the actual data reporting situation in cloud services is quite complex, and data reports may occur that do not conform to normal logic. If there is no network signal or the network signal is poor, the data will be temporarily stored locally and reported to the cloud server after the network signal is restored. To improve the accuracy of the data calculation results, a tuning parameter is introduced.

[0067] θ j The slope of the data may vary for different indicators. This indicator is used to adjust the degree of influence of input variables on the results.

[0068] The calculation model used in this embodiment can improve the accuracy of indicator calculation and simplify the indicator calculation process.

[0069] (4) Extract cloud service data and analyze and calculate it according to the indicator model constructed in step (3), and output the results;

[0070] Specifically, this step includes the following steps:

[0071] (4.1) Extract cloud service data into the storage system;

[0072] (4.2) Calculate relevant quality indicators based on cloud service data;

[0073] (4.3) Based on the indicators obtained in step (4.2), perform drill-down analysis on the data;

[0074] (4.4) Generate a visualization report based on the results of the drill-down analysis in step (4.3).

[0075] In addition, in step (4.2), a data warehouse model is used to calculate cloud service data. The model consists of source layer ODS, detail layer DWD, business layer DWS, and application layer ADS from bottom to top. Step (4.1) is completed in the source layer ODS, steps (4.2) and (4.3) are completed in the detail layer DWD and the business layer DSW, respectively, and step (4.4) is completed in the application layer ADS.

[0076] The following is a description using specific embodiments:

[0077] The raw log offline data is obtained from the vehicle network cloud service platform and stored as HDFS files on the Hadoop big data platform. Data cleaning is then performed on Hive. Since the calculation methods for cloud service quality indicators are all in the ADS model construction stage, this embodiment will begin the explanation from the ADS part.

[0078] Figure 4 This is the data modeling result table in this embodiment.

[0079] Based on the results table, an index matrix is ​​constructed, and the results are obtained through the calculation model described above. The calculation results are as follows: Figure 5 As shown.

[0080] Based on the constructed indicator matrix and the compliance standards specified by the demand side, determine whether the cloud service meets the standards. The compliance standards are as follows: Figure 6 As shown.

[0081] Example of indicator calculation model:

[0082] Average response time and availability metrics are used to monitor the response speed and stability of cloud services, respectively. In this embodiment, these two metrics are used as examples to illustrate the calculation process of the calculation model.

[0083] (1) Average response time indicator: A single cloud service may appear in multiple request links on the vehicle terminal, or even multiple times in a single request link.

[0084] The average response time of the i-th cloud service is Where a is the average response time of the i-th cloud service over the past 30 days, a0; if each t j If the impact on the calculation result is equal in weight, then θ j =1, at this time If each t j Data slope and t j If the order of appearance is inversely proportional, then θ j =1 / t, at this time

[0085] Where i represents the i-th cloud service, j represents the j-th invocation of the i-th cloud service, and t j This represents the response time for the j-th call to the i-th cloud service, and n represents the total number of calls.

[0086] (2) Availability metric: Availability of the i-th cloud service Where 'a' is the minimum availability of the i-th cloud service over the past 30 days, a0.

[0087]

[0088] Where i represents the i-th cloud service, j represents the j-th invocation of the i-th cloud service, and t j This represents the stability metric when the i-th cloud service is invoked for the j-th time, 0 ≤ t j ≤1, where n represents the total number of calls.

[0089] The quality assessment system for vehicle-to-everything (V2X) cloud services described in this invention includes a standard measurement module, a model building module, and an analysis and processing module.

[0090] The standard metrics module constructs a metric matrix based on unified metrics from both business and system dimensions, and defines metric names. The request count metric is defined as the number of times the cloud service is requested externally; the exception count metric is defined as the number of times the cloud service returns a failure code; the response time metric is defined as the time it takes for the cloud service to respond to a request, in milliseconds; the availability metric is defined as the ratio of non-failed requests to the total number of requests; and the compliance metric is defined as the result of judging the availability metric based on the quality compliance standards set by the requester. If availability is greater than or equal to the compliance standard, it is judged as compliant; otherwise, it is not compliant.

[0091] The model building module is used to construct indicator models. The calculation model for the indicators is as follows:

[0092]

[0093] In the above formula, a is the tuning parameter, and θ j Let be the data slope, and i be the i-th cloud service;

[0094] The analysis and processing module is used to extract cloud service data and perform analysis, calculation and output results based on the indicator model established by the model building module.

[0095] In summary, compared with existing technologies, the significant advantages of this solution are:

[0096] 1. This solution standardizes the metrics, allowing customers to use the same metrics to evaluate the quality of different cloud services.

[0097] 2. This solution is designed to evaluate the quality of different cloud services from multiple dimensions, including system and business dimensions, thereby improving the accuracy of the evaluation;

[0098] 3. This solution constructs a universally applicable indicator matrix to measure cloud services in a unified standard from multiple perspectives;

[0099] 4. This solution utilizes data warehouse technology to process cloud service log data, which improves the accuracy of indicator calculation and simplifies the indicator calculation process.

[0100] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quality assessment method for vehicle-to-everything (V2X) cloud services, characterized in that, The method includes the following steps: (1) Construct an indicator matrix based on a unified measurement standard, so that the demand side can set the quality standards for both the system and business dimensions. (2) Define the indicator name; (3) Constructing an indicator calculation model: Average response time and availability metrics are used to monitor the response speed and stability of cloud services, respectively. Regarding the average response time metric: a single cloud service may appear in multiple request chains on the vehicle's infotainment system, or even multiple times in a single request chain; The average response time of the i-th cloud service is ,in, The average response time of the i-th cloud service over the past 30 days. If each If the impact on the calculation result is equal in weight, then ,at this time If each Data slope and If the order of appearance is inversely proportional, then ,at this time ; Where i represents the i-th cloud service, and j represents the j-th time the i-th cloud service is invoked. This represents the response time for the j-th call to the i-th cloud service, and n represents the total number of calls. Regarding availability metrics: the availability of the i-th cloud service ,in, The minimum availability of the i-th cloud service over the past 30 days. , Where i represents the i-th cloud service, and j represents the j-th time the i-th cloud service is invoked. This represents the stability metric when the i-th cloud service is invoked for the j-th time, 0 ≤ ≤1, where n represents the total number of calls; (4) Extract cloud service data and analyze and calculate it according to the indicator model constructed in step (3), and output the results.

2. The quality assessment method for vehicle-to-everything (V2X) cloud services according to claim 1, characterized in that, In step (2), the request count metric is defined as the number of times the cloud service is requested by external services; the exception count metric is defined as the number of times the cloud service returns a failure code; the response time metric is defined as the time it takes for the cloud service to respond to a request, in milliseconds; the availability metric is defined as the ratio of non-failed requests to the total number of requests; and the compliance metric is defined as the result of judging the availability metric based on the quality compliance standards set by the demand side. If the standard is met, it is judged as meeting the standard; otherwise, it is not meeting the standard.

3. The quality assessment method for vehicle-to-everything (V2X) cloud services according to claim 1, characterized in that, Step (4) includes the following specific steps: (4.1) Extract cloud service data into the storage system; (4.2) Calculate relevant quality indicators based on cloud service data; (4.3) Based on the indicators obtained in step (4.2), perform drill-down analysis on the data; (4.4) Generate a visualization report based on the results of the drill-down analysis in step (4.3).

4. The quality assessment method for vehicle-to-everything (V2X) cloud services according to claim 3, characterized in that, In step (4.2), a data warehouse model is used to calculate cloud service data. The model consists of source layer ODS, detail layer DWD, business layer DWS, and application layer ADS from bottom to top. Step (4.1) is completed in the source layer ODS, steps (4.2) and (4.3) are completed in the detail layer DWD and the business layer DSW, respectively, and step (4.4) is completed in the application layer ADS.

5. A quality assessment system for vehicle-to-everything (V2X) cloud services, characterized in that, The system includes a standard measurement module, a model building module, and an analysis and processing module; The standard measurement module constructs an indicator matrix based on unified metrics from both business and system dimensions, defining indicator names, the number of requests (the number of times the cloud service is requested externally), the number of anomalies (the number of times the cloud service returns failure codes), the response time (the time it takes for the cloud service to respond to a request, in milliseconds), the availability (the ratio of non-failed requests to total requests), and the compliance indicator (based on the quality compliance standards set by the requester, the result of judging the availability indicator; if availability is...). If the standard is met, it is judged as meeting the standard; otherwise, it is not meeting the standard. The model building module is used to construct the indicator calculation model: Average response time and availability metrics are used to monitor the response speed and stability of cloud services, respectively. Regarding the average response time metric: a single cloud service may appear in multiple request chains on the vehicle's infotainment system, or even multiple times in a single request chain; The average response time of the i-th cloud service is ,in, The average response time of the i-th cloud service over the past 30 days. If each If the impact on the calculation result is equal in weight, then ,at this time If each Data slope and If the order of appearance is inversely proportional, then ,at this time ; Where i represents the i-th cloud service, and j represents the j-th time the i-th cloud service is invoked. This represents the response time for the j-th call to the i-th cloud service, and n represents the total number of calls. Regarding availability metrics: the availability of the i-th cloud service ,in, The minimum availability of the i-th cloud service over the past 30 days. , Where i represents the i-th cloud service, and j represents the j-th time the i-th cloud service is invoked. This represents the stability metric when the i-th cloud service is invoked for the j-th time, 0 ≤ ≤1, where n represents the total number of calls; The analysis and processing module is used to extract cloud service data and perform analysis, calculation and output results based on the indicator model established by the model building module.

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