Platform delivery quality control method and device and computer equipment

Abstract

The invention relates to a platform delivery quality control method and device and computer equipment, and the method comprises the steps: obtaining platform delivery data of a hybrid cloud service management platform, inputting the platform delivery data into a target decision model for decision processing, and obtaining key index data; and finally, carrying out quality detection on the hybrid cloud service management platform by utilizing a quality rule engine according to the key index data to obtain a quality detection result for representing whether the hybrid cloud service management platform reaches a delivery standard or not. According to the method and the device, the platform delivery data is acquired and processed through the target decision model to obtain the key index data, so that core elements playing a key role in the platform delivery quality can be analyzed, and the pertinence of quality evaluation is improved. Quality detection is carried out by means of the quality rule engine according to the key index data, automation of the detection process is achieved, labor cost and time cost are greatly saved, and stability of delivery quality of the hybrid cloud service management platform is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a method, device and computer equipment for controlling platform delivery quality. Background Art

[0002] With the rapid development of cloud computing technology, hybrid cloud business management platforms play a vital role in the digital transformation of enterprises. Hybrid cloud architecture combines the advantages of private cloud and public cloud, providing enterprises with a more flexible, scalable and secure infrastructure, which strongly supports the business operations and innovative development of enterprises.

[0003] However, during the delivery of the hybrid cloud business management platform, the technical architecture and system characteristics of each platform are different, resulting in compatibility issues such as interface mismatch and data interaction anomalies during the continuous delivery process, which seriously affects the stability of the delivery process.

[0004] Due to the relatively fixed processes and models of traditional delivery methods, quality control of hybrid cloud business management platforms has become a technical issue that needs to be solved urgently. Summary of the invention

[0005] Based on this, it is necessary to provide a platform delivery quality control method, device and computer equipment that can solve the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for controlling platform delivery quality, the method comprising:

[0007] Obtain platform delivery data of the hybrid cloud business management platform;

[0008] Input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0009] The quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0010] In one embodiment, the quality rule engine is used to perform quality inspection on the hybrid cloud service management platform according to key indicator data to obtain quality inspection results, including:

[0011] Use the quality rule engine to perform quality inspection on the hybrid cloud business management platform based on key indicator data to obtain performance evaluation indicator scores, availability scores, and security data scores;

[0012] The quality inspection results are determined based on the performance evaluation index scores, availability scores and security data scores.

[0013] In one embodiment, the key indicator data includes performance evaluation indicator data, availability data and security data; the quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to the key indicator data to obtain performance evaluation indicator scores, availability scores and security data scores, including:

[0014] Using the quality rule engine, the performance evaluation index data is input into the first detection model for performance evaluation processing to obtain a performance evaluation index score; the first detection model includes a corresponding relationship between the performance evaluation index score and at least one of a response time score, a throughput score, a resource utilization score, a system stability score, and a concurrent processing capability score;

[0015] The availability data is input into a second detection model for availability evaluation processing to obtain an availability score; the second detection model includes a corresponding relationship between the availability score and at least one of a normal operation time score, a fault recovery time score, a maintenance window management score, and a disaster recovery backup score;

[0016] The security data is input into a third detection model for security evaluation processing to obtain a security data score; the third detection model includes a corresponding relationship between the security data score and at least one of a data encryption score, an access control score, a vulnerability management score and a network security score.

[0017] In one embodiment, determining the quality inspection result according to the performance evaluation index score, the availability score and the security data score includes:

[0018] Obtaining pre-specified risk thresholds; risk thresholds include performance evaluation index thresholds, availability thresholds, and security thresholds;

[0019] When the performance evaluation index score is lower than the performance evaluation index threshold, the performance evaluation index threshold is lower than the availability threshold, and the security data score is lower than the security threshold, the quality inspection result that the hybrid cloud business management platform does not meet the delivery standard is obtained;

[0020] When the performance evaluation index score is higher than the performance evaluation index threshold, the performance evaluation index threshold is higher than the availability threshold, and the security data score is higher than the security threshold, the quality inspection result that the hybrid cloud business management platform meets the delivery standard is obtained.

[0021] In one embodiment, the process of constructing the target decision model includes:

[0022] Input the pre-acquired training data into the initial decision model for training decision processing to obtain the training decision result; the training data is determined based on the platform delivery data and the pre-established test cases;

[0023] Determine the target loss value based on the training decision results and the target loss function;

[0024] When the target loss value meets the preset value, the target decision model is determined.

[0025] In one embodiment, the training decision result includes a training performance evaluation result, a training usability result, and training safety data; the target loss function includes a first loss function, a second loss function, and a third loss function; the target loss value includes a first loss value, a second loss value, and a third loss value; and determining the target loss value according to the training decision result and the target loss function includes:

[0026] According to the training performance evaluation result, the first loss function is subjected to parameter adjustment processing to determine the first loss value; the first loss function includes the corresponding relationship between the first loss value and the paradigm, the first cross entropy term and the first relative error parameter, the first correlation parameter and the first weight;

[0027] According to the training availability result, a parameter adjustment process is performed on the second loss function to determine a second loss value; the second loss function includes a corresponding relationship between the second loss value and the second cross entropy term and the second relative error parameter, the second correlation parameter and the second weight;

[0028] The third loss function is subjected to parameter adjustment processing according to the training security data to determine a third loss value; the third loss function includes a correspondence between the third loss value and a third cross entropy term and a third relative error parameter, a third correlation parameter and a third weight.

[0029] In one embodiment, the method further comprises:

[0030] Obtaining test cases; the test cases are determined based on a pre-established test strategy; the test cases include at least one of a system availability test case, a mean time between failures test case, a resource utilization test case, a security vulnerability number test case, a cross-cloud performance fluctuation range test case, and a storage capacity accuracy test case;

[0031] According to the test cases, the hybrid cloud business management platform is tested and processed to obtain test results; the test results are used to train the target decision model.

[0032] In one embodiment, the method further comprises:

[0033] The test strategy is optimized according to the test results to obtain optimized test cases.

[0034] In a second aspect, the present application also provides a device for controlling the quality of platform delivery. The device includes:

[0035] A data acquisition module is used to acquire platform delivery data of the hybrid cloud business management platform;

[0036] The decision module is used to input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0037] The quality inspection module is used to use the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0038] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0039] Obtain platform delivery data of the hybrid cloud business management platform;

[0040] Input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0041] The quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain platform delivery data of the hybrid cloud business management platform;

[0044] Input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0045] The quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0046] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0047] Obtain platform delivery data of the hybrid cloud business management platform;

[0048] Input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0049] The quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0050] The above-mentioned platform delivery quality control method, device and computer equipment first obtain the platform delivery data of the hybrid cloud business management platform, then input the platform delivery data into the target decision model for decision processing, obtain key indicator data, and finally use the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to the key indicator data, and obtain the quality inspection results used to characterize whether the hybrid cloud business management platform meets the delivery standards. The present application obtains the platform delivery data and obtains the key indicator data through the target decision model, so that it can focus on the core elements that play a key role in the platform delivery quality for analysis, thereby improving the pertinence of the quality assessment. With the help of the quality rule engine to perform quality inspection based on the key indicator data, the automation of the inspection process is realized, which not only greatly saves labor costs and time costs, but also ensures the stability of the delivery quality of the hybrid cloud business management platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is an application environment diagram of a method for controlling platform delivery quality in one embodiment;

[0052] Figure 2 It is a flowchart of a method for controlling platform delivery quality in one embodiment;

[0053] Figure 3 A schematic diagram of a quality inspection process in one embodiment;

[0054] Figure 4 A schematic diagram of a process for determining a quality inspection result in one embodiment;

[0055] Figure 5 A schematic diagram of a process of constructing a target decision model in an embodiment;

[0056] Figure 6 A schematic diagram of a process for determining a test result in one embodiment;

[0057] Figure 7 It is a structural block diagram of a device for controlling platform delivery quality in one embodiment;

[0058] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] First of all, before specifically introducing the technical solution of the embodiments of the present application, the technical background based on the embodiments of the present application is introduced.

[0061] With the rapid development of cloud computing technology, hybrid cloud business management platform has become an important tool for enterprise digital transformation. Hybrid cloud architecture combines the advantages of private cloud and public cloud, and can provide enterprises with a more flexible, scalable and secure infrastructure.

[0062] Common delivery methods for hybrid cloud management platforms include automated build and deployment, automated testing, continuous monitoring and feedback, and standardization and consistency management. These methods ensure the reliability and effectiveness of hybrid cloud continuous delivery through automated testing, data collection, strategy monitoring, and the use of standardized interfaces and protocols. However, due to technical differences and data format differences between different platforms and systems in a hybrid cloud environment, the continuous delivery process becomes complex and difficult to manage. The main challenges include: stability and compatibility issues of different management platforms, potential problem handling issues, time-consuming and high error rates in manual delivery, and insufficient flexibility in responding to changes in enterprise needs.

[0063] Based on this, the present application provides a method, device and computer equipment for controlling platform delivery quality, aiming to solve the above technical problems.

[0064] The platform delivery quality control method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the hybrid cloud business management platform 104 through the network. The data storage system can store the data that the hybrid cloud business management platform 104 needs to process. The data storage system can be integrated on the hybrid cloud business management platform 104, or it can be placed on the cloud or other network servers. The terminal obtains the platform delivery data of the hybrid cloud business management platform, and then inputs the platform delivery data into the target decision model for decision processing to obtain key indicator data, and finally uses the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to the key indicator data to obtain a quality inspection result used to characterize whether the hybrid cloud business management platform meets the delivery standard. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, and tablet computers. The hybrid cloud business management platform 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0065] In one embodiment, Figure 2As shown, a method for controlling the quality of platform delivery is provided, and the method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0066] S201, obtaining platform delivery data of the hybrid cloud business management platform.

[0067] Among them, the hybrid cloud business management platform is a comprehensive platform that integrates multiple cloud computing resources (including public and private clouds) and provides enterprises with unified business management, resource allocation, data processing and other functions. It can help enterprises achieve flexible deployment, efficient operation and effective management of their businesses in a hybrid cloud environment.

[0068] Platform delivery data refers to all kinds of data and information related to platform performance, functions, configuration, etc. involved in the process of hybrid cloud business management platform from development completion to delivery to users. These data may include but are not limited to system architecture parameters, test data of software function modules, hardware resource configuration information, network connection status data, etc. They are important bases for evaluating the quality of platform delivery.

[0069] The embodiment of the present application deploys a monitoring agent on each key node of the hybrid cloud environment to collect performance indicators, business requirements, resource usage, historical delivery data, etc. in real time. The historical delivery data includes key evaluation indicators such as code delivery quality, overall delivery time, and user satisfaction after interaction. Integrate with external systems (such as CI / CD tools, version control systems, etc.) through API interfaces to obtain real-time data during the delivery process. Perform pre-processing operations such as cleaning, deduplication, and formatting on the collected raw data to ensure data quality. Store the processed data in a high-performance database or data warehouse for subsequent analysis.

[0070] Another implementation method: In addition to the above-mentioned direct docking and collection method, data crawling technology can also be used. After obtaining authorization from the relevant parties of the platform, use a special web crawler tool to perform targeted crawling operations on various data repositories, log files, etc. in the development environment, test environment, and pre-deployment environment where the hybrid cloud business management platform is located, and extract data content related to platform delivery. At the same time, combined with the method of manual assisted entry, for some special key data that cannot be obtained through automatic crawling (such as some manual configuration parameters involving the core business logic of the enterprise, etc.), professionals will manually enter it, so as to further improve the acquisition of platform delivery data.

[0071] S202, input the platform delivery data into the target decision model for decision processing to obtain key indicator data.

[0072] Among them, the target decision model is a pre-trained and optimized mathematical model, usually built based on machine learning or deep learning algorithms. It can analyze, process and make decisions on the input platform delivery data, and extract the most critical indicator data for evaluating the platform delivery quality. For example, it can be a model built based on a neural network algorithm, which is trained with a large amount of historical delivery data to enable it to accurately identify key quality indicators.

[0073] Key indicator data is obtained by processing platform delivery data through the target decision model. It is a data set that can centrally reflect the key characteristics of the delivery quality of the hybrid cloud business management platform.

[0074] The embodiment of the present application firstly standardizes the acquired platform delivery data according to a pre-set data format to ensure the consistency and processability of the data. Then, the standardized platform delivery data is input into the trained target decision model, which is built based on the convolutional neural network (CNN) in deep learning. Through the operation and feature extraction of multiple layers of neurons, the input data is deeply analyzed, and finally the key indicator data closely related to the platform delivery quality is output.

[0075] Another implementation method: A target decision model built based on a decision tree algorithm can be used for decision processing. Before the platform delivery data is input, the data is classified and preprocessed, and classified and sorted according to the source of the data (such as from the software testing link, hardware configuration link, etc.) and the type of data (such as numerical type, text type, etc.). Then, the classified and sorted different types of data are input into the corresponding decision tree branches for processing. Each decision tree branch analyzes the input data according to its preset judgment rules, and gradually summarizes the key indicator data. This decision tree-based processing method can more clearly carry out targeted processing according to the characteristics of different types of data, thereby improving the accuracy of decision-making.

[0076] S203, using the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0077] The quality rule engine is a software component that performs logical judgment and processing based on predefined rules. It analyzes and compares the input key indicator data based on a series of pre-set rules related to platform delivery quality to determine whether the hybrid cloud business management platform meets the delivery standards.

[0078] In an embodiment of the present application, a complete set of quality rule systems covering platform performance, functions, security and other aspects can be pre-set in the quality rule engine. The key indicator data obtained from the target decision model are input into the quality rule engine one by one, and the quality rule engine compares and judges each key indicator data one by one according to the established rule order. For example, for the rules on platform performance, it will check whether the response time in the key indicator data meets the set threshold requirements; for the functional rules, it will verify whether all the business functions reflected by the key indicator data are all implemented normally. Through comprehensive testing of all key indicator data, the quality test results are finally obtained, which are used to characterize whether the hybrid cloud business management platform meets the delivery standards.

[0079] Another implementation method: Before using the quality rule engine for detection, the key indicator data is weighted. According to the different focuses of the platform delivery quality (for example, for certain business scenarios with extremely high performance requirements, performance-related key indicator data can be given a higher weight; for scenarios involving core business security, security-related key indicator data can be given a higher weight, etc.), set a corresponding weight value for each key indicator data. Then, the weighted key indicator data is input into the quality rule engine. When the quality rule engine compares and judges the rules, it not only considers whether the indicator data itself meets the rule requirements, but also comprehensively considers its weight factors. The quality detection results obtained in this way can better meet actual business needs and more accurately reflect the delivery quality of the platform in different business scenarios.

[0080] The above-mentioned platform delivery quality control method, device and computer equipment first obtain the platform delivery data of the hybrid cloud business management platform, then input the platform delivery data into the target decision model for decision processing, obtain key indicator data, and finally use the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to the key indicator data, and obtain the quality inspection results used to characterize whether the hybrid cloud business management platform meets the delivery standards. The present application obtains the platform delivery data and obtains the key indicator data through the target decision model, so that it can focus on the core elements that play a key role in the platform delivery quality for analysis, thereby improving the pertinence of the quality assessment. With the help of the quality rule engine to perform quality inspection based on the key indicator data, the automation of the inspection process is realized, which not only greatly saves labor costs and time costs, but also ensures the stability of the delivery quality of the hybrid cloud business management platform.

[0081] In an exemplary embodiment, based on the above embodiment, see Figure 3 The embodiment of the present application relates to a process of using a quality rule engine to perform quality inspection on a hybrid cloud business management platform according to key indicator data to obtain a quality inspection result, including the following steps:

[0082] S301, using the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to key indicator data, and obtaining performance evaluation indicator scores, availability scores and security data scores.

[0083] In the embodiment of the present application, the quality rule engine is pre-configured with detailed rule sets for the three key dimensions of performance, availability and security. For performance evaluation indicators, the rules cover specific thresholds and judgment conditions such as response time, throughput, and resource utilization. For example, it is stipulated that the average response time of the platform under normal load should not exceed 500 milliseconds. If the response time in the key indicator data meets this condition, a certain score can be obtained on the corresponding sub-item of the performance evaluation indicator; if it exceeds, the corresponding score will be deducted according to the degree of excess.

[0084] For availability, the rules involve the platform's fault recovery time, the system's normal operating time ratio, etc. For example, if the system is required to resume normal operation within 10 minutes after a fault occurs, if the key indicator data shows that this condition is met, the relevant sub-items of the availability score can be scored, otherwise points will be deducted.

[0085] In terms of security, the rules include data encryption strength, access control policy effectiveness, vulnerability detection, etc. For example, if it is stipulated that a specific encryption algorithm must be used during data transmission and the encryption strength must reach a certain level, if the key indicator data shows that this requirement is met, the corresponding sub-item of the security data score can be scored, otherwise no score will be given.

[0086] By comparing and calculating the key indicator data with these preset rules one by one, the performance evaluation index score, availability score and security data score are finally obtained.

[0087] S302, determining the quality inspection result according to the performance evaluation index score, the availability score and the security data score.

[0088] In an embodiment of the present application, weight coefficients are set for scores in different dimensions. For example, the weight of the performance evaluation index score is 0.4, the weight of the availability score is 0.3, and the weight of the security data score is 0.3. Then, each score is multiplied by its corresponding weight coefficient and added together to obtain a comprehensive score. This comprehensive score is compared with the pre-set delivery standard threshold. If the comprehensive score is greater than or equal to the threshold, the hybrid cloud business management platform is judged to have met the delivery standard, and the quality inspection result is "qualified"; if the comprehensive score is less than the threshold, it is judged that the delivery standard has not been met, and the quality inspection result is "unqualified".

[0089] The embodiments of the present application construct various detection models based on a large amount of historical data and industry standards, and set clear scoring calculation rules and corresponding relationships, so that the quality detection process has clear standards to follow, and can maintain relatively stable detection results in different hybrid cloud business management platform delivery projects, so that enterprises can accurately evaluate the quality status of the platform in terms of performance, availability and security.

[0090] In an exemplary embodiment, based on the above embodiment, the key indicator data of the embodiment of the present application includes performance evaluation indicator data, availability data and security data; the present application relates to a process of using a quality rule engine to perform quality inspection on a hybrid cloud business management platform according to key indicator data to obtain a performance evaluation indicator score, an availability score and a security data score, including the following steps:

[0091] The quality rule engine is used to input the performance evaluation index data into the first detection model for performance evaluation processing to obtain a performance evaluation index score.

[0092] Among them, the first detection model includes a corresponding relationship between a performance evaluation index score and at least one of a response time score, a throughput score, a resource utilization score, a system stability score and a concurrent processing capability score.

[0093] In order to reflect the relative importance of different quality standards in the overall evaluation, a scientific weight allocation method is adopted. The weight allocation follows a specific calculation formula (as shown in Formula 1), which takes into account the importance coefficient and criticality coefficient of the standard. represents the weight of the i-th quality standard, is the importance coefficient of the criterion, is its corresponding criticality coefficient, and n is the total number of quality standards. Each quality standard is assigned a value according to its importance and criticality in the delivery requirements, and weighted using formula (1). For different delivery scenarios, considering the actual technical difficulty and resource constraints, different allocation coefficients are obtained, and unique quality rules for different delivery scenarios are established.

[0094] (1)

[0095] One of the more specific scoring criteria is given below, and the coefficients can be adjusted according to actual business needs.

[0096] The performance evaluation index P score formula is as described in (2), where RT represents the response time score, TH represents the throughput score, RU represents the resource utilization score, SS represents the system stability score, and CP represents the concurrent processing capability.

[0097] (2)

[0098] The formulas for each parameter are as follows:

[0099] (3)

[0100] (4)

[0101] (5)

[0102] (6)

[0103] (7)

[0104] In the embodiment of the present application, the usability data is input into the second detection model for usability evaluation processing to obtain a usability score.

[0105] The second detection model includes a corresponding relationship between the availability score and at least one of a normal operation time score, a fault recovery time score, a maintenance window management score, and a disaster recovery backup score.

[0106] The availability score A and the formulas for each parameter are as follows, where UT represents the normal operating time score, FR represents the fault recovery time score, MW represents the maintenance window management score, and DB represents the disaster recovery backup score.

[0107] (8)

[0108] (9)

[0109] (10)

[0110] (11)

[0111] (12)

[0112] In the embodiment of the present application, the safety data is input into the third detection model for safety evaluation processing to obtain a safety data score.

[0113] Among them, the third detection model includes a corresponding relationship between the security data score and at least one of the data encryption score, the access control score, the vulnerability management score and the network security score.

[0114] The security score formula S and the parameter formulas are as follows, where DE represents the data encryption score, AC represents the access control score, VM represents the vulnerability management score, and NS represents the network security score.

[0115] (13)

[0116] (14)

[0117] (15)

[0118] (16)

[0119] (17)

[0120] In an exemplary embodiment, based on the above embodiment, see Figure 4 The embodiment of the present application relates to determining the quality inspection result according to the performance evaluation index score, the availability score and the security data score, including the following steps:

[0121] S401, obtaining a pre-specified risk threshold; the risk threshold includes a performance evaluation index threshold, an availability threshold, and a security threshold.

[0122] In the embodiments of the present application, the threshold of the performance evaluation index is determined based on industry standards, the performance of similar platforms in the past, and the specific requirements of the enterprise itself for platform performance. For example, for response time, refer to the efficient response time standard generally recognized in the industry, and combine the sensitivity of the enterprise business to the response speed to set a threshold of the average response time, such as 500 milliseconds. For throughput, resource utilization, system stability, and concurrent processing capabilities, the respective thresholds are also determined according to industry specifications and the actual needs of the enterprise.

[0123] The setting of the availability threshold takes into account the continuity requirements of the enterprise's business and the acceptable range of platform maintenance and fault recovery. For example, based on the enterprise's business expectations for uninterrupted service, the threshold for normal operation time is set at 95%, that is, the platform's normal operation time should reach more than 95% of the total time within a month; for the fault recovery time, combined with the acceptable degree of impact of platform failures on the business, the fault recovery time threshold is set at 10 minutes, that is, the platform should resume normal operation within 10 minutes after a failure.

[0124] The security threshold is determined based on cybersecurity regulations, industry security standards, and the importance that enterprises place on data security. For example, in terms of data encryption, according to relevant regulations and industry standards, the data encryption strength should reach a certain level of a specific encryption algorithm as the threshold of the data encryption score; for access control, it is required to be able to effectively prevent unauthorized access in order to meet the threshold conditions of the access control score; in terms of vulnerability management and cybersecurity, specific thresholds are also set according to the corresponding standards and enterprise requirements.

[0125] S402, when the performance evaluation index score is lower than the performance evaluation index threshold, the performance evaluation index threshold is lower than the availability threshold, and the security data score is lower than the security threshold, the quality inspection result of the hybrid cloud business management platform not meeting the delivery standard is obtained; when the performance evaluation index score is higher than the performance evaluation index threshold, the performance evaluation index threshold is higher than the availability threshold, and the security data score is higher than the security threshold, the quality inspection result of the hybrid cloud business management platform meeting the delivery standard is obtained.

[0126] In an embodiment of the present application, it is assumed that a hybrid cloud business management platform is tested and obtains a performance evaluation index score, an availability score, and a security data score, respectively: performance evaluation index score = 40 (the full score is assumed to be 100), availability score = 50, and security data score = 30.

[0127] It is known that the pre-set risk thresholds are: performance evaluation index threshold = 60, availability threshold = 70, and security threshold = 50.

[0128] Since the performance evaluation index score (40) is lower than the performance evaluation index threshold (60), the performance evaluation index threshold (60) is lower than the availability threshold (70), and the security data score (30) is lower than the security threshold (50), the conditions for not meeting the delivery standard are met. Therefore, it is determined that the hybrid cloud business management platform does not meet the delivery standard and the quality inspection result is "unqualified".

[0129] On the contrary, if the performance evaluation index score of a platform = 70, the availability score = 80, the security data score = 60, and the risk threshold remains unchanged, then the performance evaluation index score (70) is higher than the performance evaluation index threshold (60), the performance evaluation index threshold (60) is higher than the availability threshold (70), and the security data score (60) is higher than the security threshold (50), the conditions for meeting the delivery standards are met, and the hybrid cloud business management platform is judged to meet the delivery standards, and the quality inspection result is "qualified".

[0130] In the embodiments of the present application, fixed risk thresholds are set based on industry standards, fixed enterprise requirements, etc., so that the criteria for judging quality inspection results in different hybrid cloud business management platform delivery projects remain unified and clear. Enterprises can clearly know what standards the platform needs to meet in terms of performance, availability, and security to be considered as meeting the delivery standards, which facilitates quality assessment and comparison of the quality status of different platforms.

[0131] In an exemplary embodiment, based on the above embodiment, see Figure 5 The process of constructing the target decision model of the embodiment of the present application includes the following steps:

[0132] S501, inputting the pre-acquired training data into the initial decision model for training decision processing to obtain a training decision result.

[0133] Among them, the training data is determined based on the platform delivery data and pre-established test cases.

[0134] In the embodiment of the present application, firstly, the platform delivery data generated by the hybrid cloud business management platform during the actual development, testing and trial operation stages are collected. These data cover various performance indicators of the platform (such as response time, throughput, etc.), availability indicators (such as normal operation time, fault recovery time, etc.), security indicators (such as data encryption, access control, etc.) and other relevant configuration information.

[0135] At the same time, relevant data is collected and organized according to pre-defined test cases. For example, for system availability test cases, the actual response time of the platform, page loading status and other data will be recorded as part of the training data under the simulation of different business loads and the number of concurrent user accesses; for security vulnerability test cases, the number of detected security vulnerabilities, vulnerability severity and other information will be included in the training data. These various types of data obtained based on platform delivery data and test cases are integrated and preprocessed (such as data cleaning, normalization and other operations) to form the final training data set.

[0136] S502, determining a target loss value according to the training decision result and the target loss function.

[0137] In the embodiment of the present application, the target loss function is set based on the demand for delivery quality assessment of the hybrid cloud business management platform and the characteristics of the training decision results. For example, in terms of performance evaluation, the target loss function may include items related to indicators such as response time and throughput, and the training effect of the model in performance evaluation is measured by calculating the difference between the actual output value of these indicators and the expected target value.

[0138] S503: When the target loss value meets the preset value, determine the target decision model.

[0139] In the embodiment of the present application, the preset value is set according to actual needs and experience, and is used to determine whether the training effect of the initial decision model reaches an acceptable level. For example, the preset value can be set to a target loss value less than a certain threshold, such as 0.1.

[0140] When the target loss value calculated by the above embodiment is less than or equal to the preset threshold, it means that the initial decision model deviates less from the expected result during the training process, and its training effect has reached an acceptable level. At this time, the currently trained initial decision model can be determined as the target decision model, which can be used to analyze and process the delivery data of the hybrid cloud business management platform in the future to obtain key indicator data and evaluate the platform delivery quality.

[0141] The embodiment of the present application clearly stipulates the method of obtaining training data (based on platform delivery data and pre-established test cases), the setting form of the target loss function, and the preset value conditions determined by the model, so that the construction process of the target decision model has clear steps and standards to follow, which is easy to implement in actual projects and can effectively ensure the consistency and accuracy of model construction.

[0142] In an exemplary embodiment, based on the above embodiment, the training decision result of the embodiment of the present application includes a training performance evaluation result, a training usability result and training safety data; the target loss function includes a first loss function, a second loss function and a third loss function; the target loss value includes a first loss value, a second loss value and a third loss value; the embodiment of the present application involves a process of determining a target loss value according to the training decision result and the target loss function, including the following steps:

[0143] In the embodiment of the present application, the terminal performs parameter adjustment processing on the first loss function according to the training performance evaluation result to determine the first loss value. The first loss function includes the corresponding relationship between the first loss value and the paradigm, the first cross entropy term and the first relative error parameter, the first correlation parameter and the first weight.

[0144] For performance evaluation related indicators, such as throughput, resource utilization, etc., since these indicators are often affected by multiple complex factors, and there may be certain correlations and interactions between data, it is necessary to introduce the L2 paradigm to prevent the model from over-relying on certain parameters and causing overfitting.

[0145] Specifically, in the evaluation of throughput, it is constrained by multiple factors such as network bandwidth, processing speed, and the number of concurrent requests. In terms of resource utilization, such as CPU utilization and memory utilization, they are not only related to the current task load, but also closely related to multiple factors such as system configuration and application characteristics. If the model is overly dependent on one or two parameters, it may lead to the neglect of other important factors, resulting in deviations in predictions on new data.

[0146] The L2 paradigm constrains parameters so that the model does not focus too much on certain specific parameters during the learning process, but is able to comprehensively consider the influence of multiple factors, thereby improving the generalization ability of the model and making more balanced use of various related parameters to capture the changing patterns of resource utilization under different circumstances.

[0147] The specific formula (18) is as follows, where represents the L2 norm of the relevant model parameters, n represents the parameter, Representing the cross entropy term is shown in (19), represents the true value, represents the predicted value, is the relative error, as shown in formula (20):

[0148] (18)

[0149] (19)

[0150] (20)

[0151] In the embodiment of the present application, the terminal performs parameter adjustment processing on the second loss function according to the training availability result to determine the second loss value. The second loss function includes the corresponding relationship between the second loss value and the second cross entropy term and the second relative error parameter, the second correlation parameter and the second weight.

[0152] Availability-related indicators include disaster recovery capability and fault recovery. Among them, the performance of a single disaster recovery may be greatly affected by accidental factors. By calculating the average value, these accidental fluctuations can be smoothed out, and the overall disaster recovery level of the system can be more accurately reflected. Therefore, applying the average value helps to enhance the stability of the model. When the indicator values ​​in certain specific situations have large deviations, the average value can play a certain correction role to prevent the model from being excessively disturbed by these extreme values, thereby ensuring that the model's evaluation of availability will not fluctuate significantly due to individual abnormal situations, enhancing stability and robustness, and preventing a large deviation from causing a decline in model quality. The specific formula is shown in (21).

[0153] (twenty one)

[0154] In an embodiment of the present application, the terminal performs parameter adjustment processing on the third loss function according to the training security data to determine the third loss value.

[0155] The third loss function includes a correspondence between a third loss value, a third cross entropy term, a third relative error parameter, a third correlation parameter, and a third weight.

[0156] For security-related indicators including encryption, access control and other loss value calculations, due to the existence of abnormal environments and outliers in the business, a loss function with a certain degree of robustness to outliers is used for implementation. It can maintain a high degree of accuracy within the normal range and maintain a certain degree of stability when facing abnormal data. The specific formula is shown in (22).

[0157] (twenty two)

[0158] The trained model will be deployed to the production environment to predict and classify real-time data, and output key data such as throughput, disaster recovery capability, and encrypted access control. The quality rule engine compares and evaluates the model's prediction results with the actual data based on a series of pre-set quality rules and thresholds. If the delivery time predicted by the model deviates significantly from the actual progress and exceeds the set allowable threshold, it is identified as a possible delivery risk. When resource utilization continues to be below a certain level, it may mean that there are optimization points where resource allocation is unreasonable. For the identified delivery risks, their potential influencing factors and possible development trends will be further analyzed in depth. At the same time, for the optimization points found, specific improvement measures and suggestions will be proposed in combination with business needs and actual conditions, such as adjusting resource allocation strategies and optimizing business processes. For prediction values ​​with large deviations, the loss function parameter values ​​are adjusted to improve model performance and generalization capabilities.

[0159] In the embodiment of the present application, by clearly constructing the first, second, and third loss functions and specifying their corresponding relationships with various related parameters, there are clear calculation rules and methods to follow when determining the target loss value. Enterprises or researchers can substitute the training decision results into the corresponding functions for calculation according to these established rules. The operation is relatively simple and clear, which is convenient for evaluating the model training effect during the actual training process.

[0160] In an exemplary embodiment, based on the above embodiment, see Figure 6 , the method of the embodiment of the present application further includes the following steps:

[0161] S601, obtaining test cases; the test cases are determined based on a pre-established test strategy.

[0162] Among them, the test cases include at least one of system availability test cases, mean time between failures test cases, resource utilization test cases, security vulnerability number test cases, cross-cloud performance fluctuation range test cases and storage capacity accuracy test cases.

[0163] In the embodiment of the present application, first, a detailed test strategy is formulated based on the functional requirements, business application scenarios, quality requirements and other factors of the hybrid cloud business management platform. For example, for a hybrid cloud business management platform mainly used for enterprise e-commerce business, its test strategy may focus on aspects such as system availability, resource utilization and data security in high-concurrency shopping scenarios. Based on this test strategy, specific test cases are determined.

[0164] S602, testing the hybrid cloud business management platform according to the test case to obtain a test result; the test result is used to train the target decision model.

[0165] In the embodiment of the present application, when testing the hybrid cloud business management platform according to the test case, a weight allocation mechanism is introduced to allocate different weights to different test cases according to the key factors affecting the platform delivery quality and the focus of business requirements.

[0166] The automated testing of this application mainly includes the following steps: (1) Requirements analysis and test goal determination. To ensure the stability and reliability of the hybrid cloud platform delivery, the main goals of automated testing include long-term trouble-free operation of the system, stable operation under high load, disaster recovery and recovery capabilities, stability of dynamic resource adjustment, and stability across cloud environments. (2) Test environment construction. Prepare the required servers and cloud resources, build a hybrid cloud environment similar to production, configure the network and basic software, and ensure that the environment is consistent and repeatable. (3) Test case design. According to business requirements and functional specifications, design detailed test cases covering various modules and functions of the hybrid cloud platform, such as computing resource management, storage resource management, network configuration, application deployment and migration, etc. Define expected results, input data, preconditions and postconditions for each test case. Specifically, this method sets the following representative test cases. (4) Select automated testing tools and frameworks. Evaluate and select appropriate tools and frameworks, such as Selenium, JMeter, etc., determine test frameworks such as TestNG, etc., and configure them to make them compatible. (5) Test script development Write scripts based on test cases, set performance test parameters, define security test rules, and ensure good script characteristics. (6) Test result analysis and strategy optimization. Conduct in-depth analysis of automated test results, combine real-time monitoring data of the hybrid cloud platform, and feed back key information to the intelligent decision-making system. If the delivery standard is not met, feedback will be provided on the areas where the deficiencies are found. Based on the feedback results, adjust the test strategy, optimize the test scripts, and generate additional effective test cases to repeat the automated test process, ensure the test effect, and continuously improve the test process.

[0167] Test cases such as:

[0168] Test case 1: System availability test; Case number: TC01; Test name: System availability test; Business requirement: The hybrid cloud business management platform should maintain high availability within one month.

[0169] Functional Specification: The platform operates normally for a given period of time without major failures causing service interruptions. Input: One month of system operation time.

[0170] Prerequisites: 1. The system has been deployed and is running normally. 2. The monitoring tool has been configured and is running normally.

[0171] Execution steps: 1. Start the system and start timing. 2. Simulate normal business operations and loads within one month. 3. Record the time when the system fails and the time it recovers.

[0172] Expected result: The number of minutes of normal system operation divided by the total number of minutes reaches more than 99.9%. Precondition: Generate a system availability report.

[0173] Test case 2: Mean time between failures (MTBF) test; Case number: TC02; Test name: Mean time between failures test; Business requirement: The system should have a longer failure interval to ensure business continuity.

[0174] Functional specification: The average time interval between two failures should be greater than 30 days; Input: System failure records.

[0175] Prerequisites: 1. The system has been running stably for a period of time. 2. The fault records are accurate and complete.

[0176] Execution steps: 1. Collect the time records of two adjacent faults. 2. Calculate the time interval.

[0177] Expected result: Mean time between failures is greater than 30 days. Precondition: Generate MTBF report.

[0178] Test case 3: Resource utilization test; Case number: TC04; Test name: Resource utilization test; Business requirement: System resources should be used reasonably to avoid excessive consumption or idleness.

[0179] Functional specifications: The average CPU utilization does not exceed 80%, the average memory utilization does not exceed 75%, the storage utilization generally does not exceed 85%, and the average network bandwidth utilization does not exceed 70%.

[0180] Input: Hourly resource usage data.

[0181] Prerequisites: 1. The system is in normal operation. 2. The monitoring tool collects data normally.

[0182] Execution steps: 1. Collect hourly CPU utilization, memory utilization, storage utilization, and network bandwidth utilization data through monitoring tools. 2. Calculate the average resource utilization within a day.

[0183] Expected results: The average CPU utilization does not exceed 80%. The average memory utilization does not exceed 75%. The average storage utilization does not exceed 85%. The average network bandwidth utilization does not exceed 70%.

[0184] The embodiments of the present application can conduct comprehensive and systematic testing of the hybrid cloud business management platform by determining test cases covering multiple aspects such as system availability, mean time between failures, resource utilization, number of security vulnerabilities, cross-cloud performance fluctuations, and storage capacity accuracy based on pre-established test strategies, thereby ensuring that accurate information about the performance, functions, and quality status of the platform in different aspects is obtained, providing a solid foundation for subsequent model training and platform delivery quality assessment.

[0185] In an exemplary embodiment, based on the above embodiment, the method of the embodiment of the present application further includes:

[0186] In the embodiment of the present application, the test strategy is optimized according to the test results to obtain an optimized test case.

[0187] Specifically, first analyze the test results to determine the points to be optimized in the test strategy. After completing the test of the hybrid cloud business management platform and obtaining the test results, first conduct a comprehensive and in-depth analysis of the test results. Similarly, for the mean time between failures test results, if the statistical mean time between failures is significantly shorter than the expected target, it means that the reliability of the platform needs to be improved, which also points to the need to optimize the fault simulation and analysis part of the test strategy. For the resource utilization test results, if it is found that certain computing resources (such as CPU) are in a high-load state for a long time in a specific business scenario but the utilization rate is not ideal (that is, the resource performance is not fully utilized), this indicates that the utilization test link for the resource in the corresponding scenario in the test strategy needs to be adjusted.

[0188] Then, the test strategy is optimized based on the points to be optimized. Once the points to be optimized in the test strategy are determined, specific optimization operations are performed on these points. For example, for the optimization of the system availability test strategy, if there are problems with the response time during business peak hours, the test conditions that simulate a higher number of concurrent users during this period can be added, and the expected output results can be refined, such as clarifying a more accurate response time range under different concurrent user volumes, so as to strengthen the testing of system availability in key business scenarios.

[0189] Finally, the optimized test cases are generated according to the optimized test strategy. After completing the optimization of the test strategy, the test cases are regenerated according to the optimized test strategy. Taking the optimized system availability test strategy as an example, the system availability test cases are rebuilt according to the newly added high concurrent user access simulation conditions and the refined expected output results, making them more specific and targeted in content, and more effective in detecting the system availability of the platform in key business scenarios.

[0190] The embodiment of our application can accurately identify the problem points in the test strategy by conducting a detailed analysis of the test results, and then perform specific optimization operations on these points to be optimized, so that the optimized test strategy and the generated optimized test cases can more accurately detect problems in different aspects of the platform, thereby improving the targeted nature of quality inspection.

[0191] In an exemplary embodiment, based on the above embodiment, the method of the embodiment of the present application includes the following steps:

[0192] Step 1: Obtain test cases; perform test processing on the hybrid cloud business management platform according to the test cases to obtain test results for training the target decision model; optimize the test strategy according to the test results to obtain optimized test cases; input the training data containing the test results into the initial decision model for training decision processing to obtain training decision results;

[0193] Step 2: According to the training performance evaluation result in the training decision result, the first loss function in the target loss function is adjusted to determine the first loss value in the target loss value; according to the training availability result in the training decision result, the second loss function in the target loss function is adjusted to determine the second loss value in the target loss value; according to the training security data in the training decision result, the third loss function in the target loss function is adjusted to determine the third loss value in the target loss value; when the target loss value meets the preset value, the target decision model is determined;

[0194] Step 3: Obtain the platform delivery data of the hybrid cloud business management platform; use the quality rule engine to input the performance evaluation indicator data in the key indicator data into the first detection model for performance evaluation processing to obtain the performance evaluation indicator score; input the availability data in the key indicator data into the second detection model for availability evaluation processing to obtain the availability score; input the security data in the key indicator data into the third detection model for security evaluation processing to obtain the security data score;

[0195] Step 4: Obtain a pre-specified risk threshold; when the performance evaluation index score is lower than the performance evaluation index threshold, the performance evaluation index threshold is lower than the availability threshold, and the security data score is lower than the security threshold, a quality inspection result is obtained that the hybrid cloud business management platform does not meet the delivery standard; when the performance evaluation index score is higher than the performance evaluation index threshold, the performance evaluation index threshold is higher than the availability threshold, and the security data score is higher than the security threshold, a quality inspection result is obtained that the hybrid cloud business management platform meets the delivery standard.

[0196] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0197] Based on the same inventive concept, the embodiment of the present application also provides a platform delivery quality control device for implementing the platform delivery quality control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more platform delivery quality control devices provided below can refer to the limitations of the platform delivery quality control method above, and will not be repeated here.

[0198] In one embodiment, Figure 7 As shown, a control device 700 for platform delivery quality is provided, comprising:

[0199] The data acquisition module 701 is used to acquire the platform delivery data of the hybrid cloud business management platform;

[0200] Decision module 702, used to input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0201] The quality inspection module 703 is used to use the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0202] In one embodiment, the quality detection module 703 includes:

[0203] The quality inspection unit is used to use the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain performance evaluation indicator scores, availability scores, and security data scores;

[0204] The test result determination unit is used to determine the quality test result according to the performance evaluation index score, the availability score and the security data score.

[0205] In one embodiment, the key indicator data includes performance evaluation indicator data, availability data and security data; the quality detection unit includes:

[0206] The performance detection subunit is used to input the performance evaluation index data into the first detection model for performance evaluation processing by using the quality rule engine to obtain a performance evaluation index score; the first detection model includes a corresponding relationship between the performance evaluation index score and at least one of a response time score, a throughput score, a resource utilization score, a system stability score, and a concurrent processing capability score;

[0207] The availability detection subunit is used to input the availability data into a second detection model for availability evaluation processing to obtain an availability score; the second detection model includes a corresponding relationship between the availability score and at least one of a normal operation time score, a fault recovery time score, a maintenance window management score, and a disaster recovery backup score;

[0208] The security detection subunit is used to input the security data into the third detection model for security evaluation processing to obtain a security data score; the third detection model includes a corresponding relationship between the security data score and at least one of the data encryption score, access control score, vulnerability management score and network security score.

[0209] In one embodiment, the detection result determination unit includes:

[0210] A threshold acquisition subunit is used to acquire a pre-specified risk threshold; the risk threshold includes a performance evaluation index threshold, an availability threshold, and a security threshold;

[0211] A first result determination subunit is used to obtain a quality inspection result that the hybrid cloud business management platform does not meet the delivery standard when the performance evaluation index score is lower than the performance evaluation index threshold, the performance evaluation index threshold is lower than the availability threshold, and the security data score is lower than the security threshold;

[0212] The second result determination subunit is used to obtain a quality inspection result that the hybrid cloud business management platform meets the delivery standards when the performance evaluation index score is higher than the performance evaluation index threshold, the performance evaluation index threshold is higher than the availability threshold, and the security data score is higher than the security threshold.

[0213] In one embodiment, the above device comprises:

[0214] The training unit is used to input the pre-acquired training data into the initial decision model for training decision processing to obtain the training decision result; the training data is determined based on the platform delivery data and the pre-established test cases;

[0215] A loss value determination unit, used to determine a target loss value according to a training decision result and a target loss function;

[0216] The model determination unit is used to determine the target decision model when the target loss value meets the preset value.

[0217] In one embodiment, the training decision result includes a training performance evaluation result, a training usability result, and training safety data; the target loss function includes a first loss function, a second loss function, and a third loss function; the target loss value includes a first loss value, a second loss value, and a third loss value; and the loss value determination unit includes:

[0218] A first parameter adjustment subunit is used to perform parameter adjustment processing on the first loss function according to the training performance evaluation result to determine the first loss value; the first loss function includes a correspondence between the first loss value and the paradigm, the first cross entropy term and the first relative error parameter, the first correlation parameter and the first weight;

[0219] A second parameter adjustment subunit is used to perform parameter adjustment processing on the second loss function according to the training availability result to determine a second loss value; the second loss function includes a corresponding relationship between the second loss value and the second cross entropy term and the second relative error parameter, the second correlation parameter and the second weight;

[0220] The third parameter adjustment subunit is used to perform parameter adjustment processing on the third loss function according to the training security data to determine the third loss value; the third loss function includes the correspondence between the third loss value and the third cross entropy term and the third relative error parameter, the third related parameter and the third weight.

[0221] In one embodiment, the above device further comprises:

[0222] A case acquisition unit, used to acquire test cases; the test cases are determined based on a pre-established test strategy; the test cases include at least one of a system availability test case, a mean time between failures test case, a resource utilization test case, a security vulnerability number test case, a cross-cloud performance fluctuation range test case, and a storage capacity accuracy test case;

[0223] The test result determination unit is used to test the hybrid cloud business management platform according to the test case to obtain the test result; the test result is used to train the target decision model.

[0224] In one embodiment, the above device further comprises:

[0225] The optimization module is used to optimize the test strategy according to the test results to obtain optimized test cases.

[0226] Each module in the above-mentioned platform delivery quality control device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0227] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for controlling the quality of platform delivery is implemented.

[0228] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0229] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0230] Obtain platform delivery data of the hybrid cloud business management platform;

[0231] Input the platform delivery data into the target decision model for decision processing to obtain key indicator data;

[0232] The quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

[0233] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0234] Use the quality rule engine to perform quality inspection on the hybrid cloud business management platform based on key indicator data to obtain performance evaluation indicator scores, availability scores, and security data scores;

[0235] The quality inspection results are determined based on the performance evaluation index scores, availability scores and security data scores.

[0236] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0237] Using the quality rule engine, the performance evaluation index data is input into the first detection model for performance evaluation processing to obtain a performance evaluation index score; the first detection model includes a corresponding relationship between the performance evaluation index score and at least one of a response time score, a throughput score, a resource utilization score, a system stability score, and a concurrent processing capability score;

[0238] The availability data is input into a second detection model for availability evaluation processing to obtain an availability score; the second detection model includes a corresponding relationship between the availability score and at least one of a normal operation time score, a fault recovery time score, a maintenance window management score, and a disaster recovery backup score;

[0239] The security data is input into a third detection model for security evaluation processing to obtain a security data score; the third detection model includes a corresponding relationship between the security data score and at least one of a data encryption score, an access control score, a vulnerability management score and a network security score.

[0240] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0241] Obtaining pre-specified risk thresholds; risk thresholds include performance evaluation index thresholds, availability thresholds, and security thresholds;

[0242] When the performance evaluation index score is lower than the performance evaluation index threshold, the performance evaluation index threshold is lower than the availability threshold, and the security data score is lower than the security threshold, the quality inspection result that the hybrid cloud business management platform does not meet the delivery standard is obtained;

[0243] When the performance evaluation index score is higher than the performance evaluation index threshold, the performance evaluation index threshold is higher than the availability threshold, and the security data score is higher than the security threshold, the quality inspection result that the hybrid cloud business management platform meets the delivery standard is obtained.

[0244] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0245] Input the pre-acquired training data into the initial decision model for training decision processing to obtain the training decision result; the training data is determined based on the platform delivery data and the pre-established test cases;

[0246] Determine the target loss value based on the training decision results and the target loss function;

[0247] When the target loss value meets the preset value, the target decision model is determined.

[0248] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0249] According to the training performance evaluation result, the first loss function is subjected to parameter adjustment processing to determine the first loss value; the first loss function includes the corresponding relationship between the first loss value and the paradigm, the first cross entropy term and the first relative error parameter, the first correlation parameter and the first weight;

[0250] According to the training availability result, a parameter adjustment process is performed on the second loss function to determine a second loss value; the second loss function includes a corresponding relationship between the second loss value and the second cross entropy term and the second relative error parameter, the second correlation parameter and the second weight;

[0251] The third loss function is subjected to parameter adjustment processing according to the training security data to determine a third loss value; the third loss function includes a correspondence between the third loss value and a third cross entropy term and a third relative error parameter, a third correlation parameter and a third weight.

[0252] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0253] Obtaining test cases; the test cases are determined based on a pre-established test strategy; the test cases include at least one of a system availability test case, a mean time between failures test case, a resource utilization test case, a security vulnerability number test case, a cross-cloud performance fluctuation range test case, and a storage capacity accuracy test case;

[0254] According to the test cases, the hybrid cloud business management platform is tested and processed to obtain test results; the test results are used to train the target decision model.

[0255] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0256] The test strategy is optimized according to the test results to obtain optimized test cases.

[0257] According to some embodiments of the present application, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory including instructions, and the above instructions can be executed by a processor of an electronic device to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0258] According to some embodiments of the present application, a computer program product is also provided, and when the computer program is executed by a processor, the above method can be implemented. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, part or all of the above method can be implemented in whole or in part according to the process or function described in the embodiment of the present application.

[0259] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0260] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0261] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0262] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for controlling platform delivery quality, characterized in that: The method comprises: Obtain platform delivery data of the hybrid cloud business management platform; Inputting the platform delivery data into the target decision model for decision processing to obtain key indicator data; A quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to the key indicator data to obtain a quality inspection result; the quality inspection result is used to indicate whether the hybrid cloud business management platform meets the delivery standards.

2. The method according to claim 1, characterized in that The using the quality rule engine to perform quality inspection on the hybrid cloud service management platform according to the key indicator data to obtain a quality inspection result includes: Using a quality rule engine to perform quality inspection on the hybrid cloud business management platform according to the key indicator data, to obtain a performance evaluation indicator score, an availability score, and a security data score; The quality inspection result is determined according to the performance evaluation index score, the availability score and the security data score.

3. The method according to claim 2, characterized in that The key indicator data includes performance evaluation indicator data, availability data and security data; the quality rule engine is used to perform quality inspection on the hybrid cloud business management platform according to the key indicator data to obtain a performance evaluation indicator score, an availability score and a security data score, including: Using a quality rule engine, the performance evaluation indicator data is input into a first detection model for performance evaluation processing to obtain a performance evaluation indicator score; the first detection model includes a corresponding relationship between the performance evaluation indicator score and at least one of a response time score, a throughput score, a resource utilization score, a system stability score, and a concurrent processing capability score; Inputting the availability data into a second detection model for availability evaluation processing to obtain an availability score; the second detection model includes a corresponding relationship between the availability score and at least one of a normal operation time score, a fault recovery time score, a maintenance window management score, and a disaster recovery backup score; The security data is input into a third detection model for security evaluation processing to obtain a security data score; the third detection model includes a corresponding relationship between the security data score and at least one of a data encryption score, an access control score, a vulnerability management score and a network security score.

4. The method according to claim 3, characterized in that Determining the quality inspection result according to the performance evaluation index score, the availability score and the security data score includes: Obtaining a pre-specified risk threshold; the risk threshold includes a performance evaluation index threshold, an availability threshold, and a security threshold; When the performance evaluation index score is lower than the performance evaluation index threshold, the performance evaluation index threshold is lower than the availability threshold, and the security data score is lower than the security threshold, a quality inspection result that the hybrid cloud business management platform does not meet the delivery standard is obtained; When the performance evaluation index score is higher than the performance evaluation index threshold, the performance evaluation index threshold is higher than the availability threshold, and the security data score is higher than the security threshold, a quality inspection result is obtained that the hybrid cloud business management platform meets the delivery standard.

5. The method according to any one of claims 1 to 4, characterized in that: The process of constructing the target decision model includes: Inputting the pre-acquired training data into the initial decision model for training decision processing to obtain a training decision result; the training data is determined based on the platform delivery data and the pre-established test cases; Determining a target loss value according to the training decision result and the target loss function; When the target loss value meets the preset value, the target decision model is determined.

6. The method according to claim 5, characterized in that The training decision result includes a training performance evaluation result, a training usability result and training safety data; the target loss function includes a first loss function, a second loss function and a third loss function; the target loss value includes a first loss value, a second loss value and a third loss value; and determining the target loss value according to the training decision result and the target loss function includes: According to the training performance evaluation result, the first loss function is subjected to parameter adjustment processing to determine a first loss value; the first loss function includes a correspondence between the first loss value and a paradigm, a first cross entropy term and a first relative error parameter, a first correlation parameter and a first weight; Perform parameter adjustment processing on the second loss function according to the training availability result to determine a second loss value; the second loss function includes a corresponding relationship between the second loss value and a second cross entropy term and a second relative error parameter, a second correlation parameter and a second weight; The third loss function is subjected to parameter adjustment processing according to the training safety data to determine a third loss value; the third loss function includes a correspondence between the third loss value and a third cross entropy term and a third relative error parameter, a third correlation parameter and a third weight.

7. The method according to claim 5, characterized in that The method further comprises: Obtaining test cases; the test cases are determined based on a pre-established test strategy; the test cases include at least one of a system availability test case, a mean time between failures test case, a resource utilization test case, a security vulnerability number test case, a cross-cloud performance fluctuation range test case, and a storage capacity accuracy test case; According to the test case, the hybrid cloud business management platform is tested to obtain a test result; the test result is used to train the target decision model.

8. The method according to claim 7, characterized in that The method further comprises: The test strategy is optimized according to the test results to obtain an optimized test case.

9. A device for controlling the quality of platform delivery, characterized in that: The device comprises: A data acquisition module is used to acquire platform delivery data of the hybrid cloud business management platform; A decision module, used for inputting the platform delivery data into a target decision model for decision processing to obtain key indicator data; The quality inspection module is used to use the quality rule engine to perform quality inspection on the hybrid cloud business management platform according to the key indicator data to obtain quality inspection results; the quality inspection results are used to indicate whether the hybrid cloud business management platform meets the delivery standards.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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