Digital logistics freight platform test risk management system and method

Abstract

This invention discloses a test risk management system and method based on a digital logistics freight platform. The system includes: a data acquisition module for collecting multi-dimensional data; a risk identification module for identifying risks in the testing process based on multi-dimensional data and the characteristics of digital logistics business, combined with a pre-built risk identification rule base; an intelligent assessment module for quantifying risk levels and impact ranges through an intelligent assessment model that integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural networks; a dynamic monitoring and early warning module for real-time monitoring of test process data and risk status, triggering tiered early warnings when warning conditions are met; a risk handling module for generating and executing targeted handling plans based on risk levels, impact ranges, and business scenario characteristics; and a closed-loop iteration module for analyzing risk handling results and full-process data, using the handling results to optimize rules and models. The effect is that this invention can meet the requirements for comprehensive risk identification, accurate assessment, and targeted handling.

Description

Technical Field

[0001] This invention relates to the field of software testing and risk management technology, specifically to a test risk management system and method based on a digital logistics freight platform. Background Technology

[0002] With the rapid development of digital logistics, digital logistics freight platforms integrate complex business modules such as order management, intelligent scheduling, transportation monitoring, and settlement management. This involves multi-terminal adaptation, integration with multiple third-party platforms, and high-concurrency data processing, necessitating risk testing. However, existing risk management testing solutions have the following technical shortcomings:

[0003] (1) One-sided risk identification: focusing on a single test type without combining the characteristics of digital logistics business, resulting in a high risk omission rate;

[0004] (2) Low assessment accuracy: It relies on human experience or a single algorithm to assess the risk level, and does not quantify the scope and spread rate of the risk impact, resulting in low assessment accuracy.

[0005] (3) Monitoring lag: The static monitoring mode of post-event review is mostly adopted, which cannot capture the dynamic changes of risks during the test in real time, and is prone to untimely risk handling.

[0006] (4) Lack of targeted handling: Most of the solutions adopted were generalized solutions, without developing specific solutions for digital logistics business scenarios.

[0007] Therefore, it can be seen that some existing software testing risk management systems are not adapted to the business complexity and scenario specificity of digital logistics freight platforms, and are unable to meet their needs for comprehensive risk identification, accurate assessment and targeted handling. Summary of the Invention

[0008] In view of the technical defects mentioned in the background art, the purpose of this invention is to provide a test risk management system and method based on a digital logistics freight platform, which aims to at least partially solve one of the technical problems in the related art.

[0009] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a test risk management system based on a digital logistics freight platform, the system comprising:

[0010] The data acquisition module is used to collect multi-dimensional data during the testing process of the digital logistics freight platform;

[0011] The risk identification module is used to identify risks in the testing process based on the collected multi-dimensional data and the characteristics of digital logistics business, combined with a pre-built, dynamically updatable risk identification rule base; wherein, the risk identification includes functional risk, performance risk, security risk and compatibility risk identification.

[0012] The intelligent assessment module is used to quantify the risk level and scope of impact through an intelligent assessment model that integrates the analytic hierarchy process (AHP) and a backpropagation neural network.

[0013] The dynamic monitoring and early warning module is used to monitor test process data and risk status in real time, and trigger tiered early warnings when the early warning conditions are met.

[0014] The risk management module is used to generate and execute targeted management plans based on the risk level, scope of impact, and characteristics of the business scenario, and to record the management process.

[0015] The closed-loop iteration module is used to analyze the risk handling results and full-process data. The handling results are used to feed back into the rule and model optimization. After iteratively optimizing the rules and models, the results are synchronized to the risk identification module and intelligent assessment module.

[0016] As a specific implementation of this application, the system also includes a storage and collaboration module, which provides data query and retrieval support and enables hierarchical access across testing teams.

[0017] As a specific implementation of this application, the dynamic monitoring and early warning module includes a real-time monitoring unit, a threshold configuration unit, and an early warning push unit;

[0018] The threshold configuration unit supports user-defined risk level thresholds, indicator exceedance thresholds, and warning methods; the warning push unit is used to connect to various instant messaging platforms to achieve multi-channel warning synchronization.

[0019] As one specific implementation of this application, the construction and solution of the intelligent evaluation model includes:

[0020] The weights of risk assessment indicators are determined using the analytic hierarchy process (AHP), and a three-level assessment system consisting of an objective layer, a criterion layer, and an indicator layer is constructed. The weights in the criterion layer include functional risk weights, performance risk weights, safety risk weights, and compatibility risk weights. The indicator layer includes specific risk factors.

[0021] The data from the indicator layer is standardized and then input into the BP neural network.

[0022] The BP neural network is trained using historical risk data, and the model parameters are optimized using gradient descent to make the model prediction error less than a set threshold.

[0023] As one specific implementation of this application, the targeted solution includes:

[0024] When functional risks occur, return test case modification suggestions, business logic code debugging guidelines, and regression testing scope for related modules;

[0025] When performance risks occur, suggestions include expanding server resources, optimizing code, and adjusting concurrent stress test parameters.

[0026] In the event of security risks, data encryption algorithms will be upgraded, vulnerability fix code templates will be provided, and security test cases will be supplemented.

[0027] When compatibility risks arise, suggestions for developing multi-terminal adaptation patches and solutions for adjusting third-party interface parameters are provided.

[0028] As one specific implementation of this application, the closed-loop iterative optimization includes:

[0029] Update the risk identification rule base based on the effectiveness of risk management;

[0030] Adjust the indicator weights and BP neural network parameters of the intelligent evaluation model;

[0031] Optimize the testing process.

[0032] Secondly, embodiments of the present invention also provide a test risk management method based on a digital logistics freight platform, applied to the test risk management system based on a digital logistics freight platform described in the first aspect, the method comprising:

[0033] Collect multi-dimensional data during the testing process of the digital logistics freight platform;

[0034] Based on the collected multi-dimensional data and the characteristics of digital logistics business, and combined with a pre-built, dynamically updatable risk identification rule base, risks are identified in the testing phase; wherein, the risk identification includes functional risks, performance risks, security risks, and compatibility risks.

[0035] An intelligent assessment model that integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural network is used to quantify the risk level and the scope of impact.

[0036] Real-time monitoring of test process data and risk status; triggering tiered early warnings when warning conditions are met.

[0037] Based on the risk level, scope of impact, and characteristics of the business scenario, generate and execute targeted response plans, and record the response process;

[0038] Analyze the risk management results and full-process data, use the management results to feed back into the rule and model optimization, iterate and optimize the rules and models, and then synchronize them to the risk identification module and intelligent assessment module.

[0039] As a preferred implementation of this application, the method further includes: providing data query and retrieval support to enable hierarchical access across testing teams.

[0040] As one specific implementation of this application, the construction and solution of the intelligent evaluation model includes:

[0041] The weights of risk assessment indicators are determined using the analytic hierarchy process (AHP), and a three-level assessment system consisting of an objective layer, a criterion layer, and an indicator layer is constructed. The weights in the criterion layer include functional risk weights, performance risk weights, safety risk weights, and compatibility risk weights. The indicator layer includes specific risk factors.

[0042] The data from the indicator layer is standardized and then input into the BP neural network.

[0043] The BP neural network is trained using historical risk data, and the model parameters are optimized using gradient descent to make the model prediction error less than a set threshold.

[0044] The technical solutions provided in the embodiments of the present invention have the following beneficial effects:

[0045] 1. Improved comprehensiveness of risk identification: The identification coverage of four types of risks covering core business scenarios of digital logistics has been improved, reducing risk omissions;

[0046] 2. Improved accuracy of risk assessment: The intelligent assessment model, which integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural network, improves the accuracy of risk assessment compared to traditional manual assessment or single-algorithm methods.

[0047] 3. Enhanced real-time monitoring and early warning: Through real-time monitoring and tiered early warning mechanisms, risk response time has been effectively shortened, preventing the spread of risks;

[0048] 4. Improved targeting and effectiveness of handling: Customized handling solutions tailored to the characteristics of digital logistics business make handling more targeted, improve the success rate, and reduce testing and rework costs;

[0049] 5. Outstanding closed-loop iteration capability: After risk disposal, data iteration is generated, which feeds back into the optimization of risk identification rules and testing processes, thereby forming a complete closed loop of identification, assessment, monitoring, disposal and iteration. Attached Figure Description

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0051] Figure 1 This is a principle block diagram of a test risk management system based on a digital logistics freight platform provided in an embodiment of the present invention;

[0052] Figure 2 This is a flowchart of a risk management method for testing a digital logistics freight platform provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0055] It should be noted that, unless otherwise stated, the technical terms used in this embodiment have the common meaning as understood in the relevant technical field.

[0056] Please refer to Figure 1 This invention provides a test risk management system based on a digital logistics freight platform, the system comprising:

[0057] The data acquisition module is used to collect multi-dimensional data during the testing process of the digital logistics freight platform;

[0058] The risk identification module is used to identify risks in the testing phase based on the collected multi-dimensional data and the characteristics of digital logistics business, combined with a pre-built, dynamically updatable risk identification rule base. The risk identification includes functional risk, performance risk, security risk, and compatibility risk identification. The digital logistics business characteristics include order processing, intelligent scheduling, transportation monitoring, and settlement management.

[0059] The intelligent assessment module is used to quantify the risk level and scope of impact through an intelligent assessment model that integrates the analytic hierarchy process (AHP) and a backpropagation neural network.

[0060] The dynamic monitoring and early warning module is used to monitor test process data and risk status in real time, and trigger tiered early warnings when the early warning conditions are met.

[0061] The risk management module is used to generate and execute targeted management plans based on the risk level, scope of impact, and characteristics of the business scenario, and to record the management process.

[0062] The closed-loop iteration module is used to analyze the risk handling results and full-process data. The handling results are used to feed back into the rule and model optimization. After iteratively optimizing the rules and models, the results are synchronized to the risk identification module and intelligent assessment module.

[0063] The multi-dimensional data includes test environment data, business scenario test data, historical risk data, and real-time operational status data.

[0064] The test environment data includes server configuration, network bandwidth, operating system version, database type, and third-party interface adaptation parameters; the business scenario test data includes order concurrency, scheduling algorithm execution efficiency, transportation trajectory simulation data, settlement logic verification data, and multi-terminal adaptation test data.

[0065] The historical risk data includes historical test failure records, risk handling plans and their effects, and risk cases from similar platforms; the real-time operational status data includes test case execution pass rate, interface response latency, data transmission error rate, and server resource utilization rate.

[0066] The functional risks include errors in order entry logic, scheduling algorithm matching deviations, failure of transportation trajectory tracking, errors in settlement amount calculation, and access control vulnerabilities; the performance risks include timeouts in high-concurrency order processing, lag in large-scale data querying, and excessive server load; the security risks include data encryption failures, SQL injection vulnerabilities, and unauthorized access risks; and the compatibility risks include abnormal adaptation to different browsers and operating systems and failure to connect to third-party platform interfaces.

[0067] In another embodiment, based on the above technical solution, the system further includes a storage and collaboration module, which is used to provide data query and retrieval support and realize hierarchical access permissions across testing teams.

[0068] This system enables unified storage and refined management of multi-dimensional, multi-type, and multi-format data throughout the entire process of risk management for digital logistics freight platform testing. It solves the problems of scattered data storage, inconsistent formats, and difficulty in retrieval in traditional testing. It achieves end-to-end storage of data throughout the entire process, including raw test data, risk identification lists, risk level assessment reports, early warning records, handling process and effect data, rule and model optimization parameters, etc., forming a complete test risk data chain to ensure that every risk link has data that can be checked and traced.

[0069] At the same time, blockchain evidence storage technology is used to store key data such as risk handling records and test compliance reports in an immutable manner, providing traceable evidence for subsequent test acceptance and platform launch compliance review;

[0070] This also improves collaboration efficiency, enables cross-testing team data sharing and access control, reduces collaboration and communication costs, and shortens the testing cycle.

[0071] In this embodiment, the construction and solution of the intelligent evaluation model includes:

[0072] The weights of risk assessment indicators are determined using the Analytic Hierarchy Process (AHP), constructing a three-tiered assessment system consisting of a target layer, a criterion layer, and an indicator layer. The criterion layer weights include functional risk weights, performance risk weights, safety risk weights, and compatibility risk weights. The indicator layer includes specific risk factors. The target layer involves risk levels. The indicator layer data is standardized and then input into a backpropagation (BP) neural network. The BP neural network is trained using historical risk data, and the model parameters are optimized using gradient descent to ensure that the model's prediction error is less than a set threshold.

[0073] Specifically, the specific risk factors in the indicator layer include:

[0074] Functional risk factors, such as use case failure rate and frequency of logical errors;

[0075] Performance risk factors, such as concurrent processing latency and peak resource usage;

[0076] Security risk factors, such as the number of vulnerabilities and the risk of data breach;

[0077] Compatibility risk factors, such as the number of terminal types that failed to adapt and the interface connection error rate.

[0078] The dynamic monitoring and early warning module includes a real-time monitoring unit, a threshold configuration unit, and an early warning push unit;

[0079] The threshold configuration unit supports user-defined risk level thresholds, indicator exceedance thresholds, and warning methods; the warning push unit is used to connect to various instant messaging platforms to achieve multi-channel warning synchronization.

[0080] Risk levels are categorized as low, medium, high, and extremely high. Warning conditions include: a high risk level, key indicators exceeding limits in real-time operational data, a data transmission error rate reaching a threshold, and a test case execution pass rate falling below a threshold. These tiered warnings include: low-risk triggering system notifications, medium-risk triggering email warnings, high-risk triggering SMS and telephone warnings, and extremely high-risk triggering emergency response procedures.

[0081] For example, risk level output: For the risk of SQL injection vulnerability, after inputting indicator data, the model outputs a quantitative value of 8.5, which corresponds to high risk, and the scope of impact is: the security of all user data and the platform compliance;

[0082] Regarding the risk of abnormal map loading in iOS 16.0 apps, the output quantification value is 5.8, corresponding to medium risk, and the affected scope is: the location viewing function for iOS 16.0 users.

[0083] The targeted treatment plan includes:

[0084] When functional risks occur, return test case modification suggestions, business logic code debugging guidelines, and regression testing scope for related modules;

[0085] When performance risks occur, suggestions include expanding server resources, optimizing code, and adjusting concurrent stress test parameters.

[0086] In the event of security risks, data encryption algorithms will be upgraded, vulnerability fix code templates will be provided, and security test cases will be supplemented.

[0087] When compatibility risks arise, suggestions for developing multi-terminal adaptation patches and solutions for adjusting third-party interface parameters are provided.

[0088] Furthermore, the closed-loop iterative optimization includes:

[0089] Update the risk identification rule base based on the effectiveness of risk management;

[0090] Adjust the indicator weights and BP neural network parameters of the intelligent evaluation model;

[0091] Optimize the testing process.

[0092] Specifically, risk identification rules have been optimized: based on experience in handling SQL injection vulnerabilities, a new identification condition has been added to the identification rule base for user input interfaces that have not performed parameter validation.

[0093] Evaluation of model optimization: Based on historical data and new risk data, the BP neural network was retrained, the number of nodes in the first hidden layer was adjusted to 20, and the learning rate was adjusted to 0.008, which further reduced the model's prediction error; the weights of the criterion layer were adjusted, the weights of safety risks were increased, and the weights of compatibility risks were maintained, etc.

[0094] Test process optimization: Increase the timing of security testing intervention, moving it from the system testing phase to the requirements analysis phase; increase the test case density of high-risk modules, such as payment and user privacy-related modules; and establish a risk handling effectiveness review mechanism.

[0095] The above solution has the following beneficial effects:

[0096] 1. Improved comprehensiveness of risk identification: The identification coverage of four types of risks covering core business scenarios of digital logistics has been improved, reducing risk omissions;

[0097] 2. Improved accuracy of risk assessment: The intelligent assessment model, which integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural network, improves the accuracy of risk assessment compared to traditional manual assessment or single-algorithm methods.

[0098] 3. Enhanced real-time monitoring and early warning: Through real-time monitoring and tiered early warning mechanisms, risk response time has been effectively shortened, preventing the spread of risks;

[0099] 4. Improved targeting and effectiveness of handling: Customized handling solutions tailored to the characteristics of digital logistics business make handling more targeted, improve the success rate, and reduce testing and rework costs;

[0100] 5. Outstanding closed-loop iteration capability: After risk disposal, data iteration is generated, which feeds back into the optimization of risk identification rules and testing processes, thereby forming a complete closed loop of identification, assessment, monitoring, disposal and iteration.

[0101] Reference Figure 2 Based on the same inventive concept, this invention also provides a method for risk management of testing based on a digital logistics freight platform, applied to the aforementioned risk management system for testing based on a digital logistics freight platform. The method includes:

[0102] S101 collects multi-dimensional data during the testing process of the digital logistics freight platform;

[0103] S102, Based on the collected multi-dimensional data and the characteristics of digital logistics business, and combined with a pre-built, dynamically updatable risk identification rule base, risk identification is performed on the testing phase; wherein, the risk identification includes functional risk, performance risk, security risk, and compatibility risk identification.

[0104] S103 is an intelligent assessment model that integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural network to quantify the risk level and scope of impact.

[0105] S104, real-time monitoring of test process data and risk status, triggering graded warnings when warning conditions are met;

[0106] S105, Based on the risk level, scope of impact, and characteristics of the business scenario, generate and execute a targeted handling plan, and record the handling process;

[0107] S106 analyzes the risk handling results and full-process data, uses the handling results to feed back into the rule and model optimization, iterates and optimizes the rules and models, and then synchronizes them to the risk identification module and intelligent assessment module.

[0108] The construction and solution of the intelligent evaluation model include:

[0109] The weights of risk assessment indicators are determined using the analytic hierarchy process (AHP), and a three-level assessment system consisting of an objective layer, a criterion layer, and an indicator layer is constructed. The weights in the criterion layer include functional risk weights, performance risk weights, safety risk weights, and compatibility risk weights. The indicator layer includes specific risk factors.

[0110] The data from the indicator layer is standardized and then input into the BP neural network.

[0111] The BP neural network is trained using historical risk data, and the model parameters are optimized using gradient descent to make the model prediction error less than a set threshold.

[0112] When implementing dynamic monitoring and early warning, the threshold configurations involved include: setting risk level thresholds, key indicator thresholds, and risk spread rate thresholds (e.g., more than 2 related modules / hour).

[0113] In another embodiment, based on the above technical solution, the method further includes: providing data query and retrieval support to enable hierarchical access across testing teams.

[0114] It should be noted that for a more specific application description of the method embodiments, please refer to the aforementioned system embodiments section, which will not be repeated here.

[0115] This technical solution has the following beneficial effects:

[0116] 1. Improved comprehensiveness of risk identification: The identification coverage of four types of risks covering core business scenarios of digital logistics has been improved, reducing risk omissions;

[0117] 2. Improved accuracy of risk assessment: The intelligent assessment model, which integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural network, improves the accuracy of risk assessment compared to traditional manual assessment or single-algorithm methods.

[0118] 3. Enhanced real-time monitoring and early warning: Through real-time monitoring and tiered early warning mechanisms, risk response time has been effectively shortened, preventing the spread of risks;

[0119] 4. Improved targeting and effectiveness of handling: Customized handling solutions tailored to the characteristics of digital logistics business make handling more targeted, improve the success rate, and reduce testing and rework costs;

[0120] 5. Outstanding closed-loop iteration capability: After risk disposal, data iteration is generated, which feeds back into the optimization of risk identification rules and testing processes, thereby forming a complete closed loop of identification, assessment, monitoring, disposal and iteration.

[0121] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can also be implemented in other ways. The embodiments described above are merely illustrative. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or using a combination of dedicated hardware and computer instructions.

[0122] Furthermore, the functional modules in the various embodiments of this invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. The integrated modules can be implemented in hardware or as software functional modules. When in use, each module will only collect and store user information with the user's full authorization and in compliance with relevant laws and regulations, protecting the security and privacy of user data and strictly prohibiting unauthorized access; data processing will be conducted within the scope stipulated by law and will not exceed the purpose and scope authorized by the user; at the same time, users have the rights to access, correct, delete, restrict, and refuse processing of their personal data; and must strictly comply with applicable laws and regulations and conduct compliance reviews.

[0123] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A digital logistics freight platform based test risk management system, characterized in that, The system includes: The data acquisition module is used to collect multi-dimensional data during the testing process of the digital logistics freight platform; The risk identification module is used to identify risks in the testing process based on the collected multi-dimensional data and the characteristics of digital logistics business, combined with a pre-built, dynamically updatable risk identification rule base; wherein, the risk identification includes functional risk, performance risk, security risk and compatibility risk identification. The intelligent assessment module is used to quantify the risk level and scope of impact through an intelligent assessment model that integrates the analytic hierarchy process (AHP) and a backpropagation neural network. The dynamic monitoring and early warning module is used to monitor test process data and risk status in real time, and trigger tiered early warnings when the early warning conditions are met. The risk management module is used to generate and execute targeted management plans based on the risk level, scope of impact, and characteristics of the business scenario, and to record the management process. The closed-loop iteration module is used to analyze the risk handling results and full-process data. The handling results are used to feed back into the rule and model optimization. After iteratively optimizing the rules and models, the results are synchronized to the risk identification module and intelligent assessment module.

2. The system of claim 1, wherein, The system also includes a storage and collaboration module, which provides data query and retrieval support and enables hierarchical access across testing teams.

3. The system of claim 2, wherein, The dynamic monitoring and early warning module includes a real-time monitoring unit, a threshold configuration unit, and an early warning push unit; The threshold configuration unit supports user-defined risk level thresholds, indicator exceedance thresholds, and warning methods; the warning push unit is used to connect to various instant messaging platforms to achieve multi-channel warning synchronization.

4. The system of claim 3, wherein, The construction and solution of the intelligent evaluation model include: The weights of risk assessment indicators are determined using the analytic hierarchy process (AHP), and a three-level assessment system consisting of an objective layer, a criterion layer, and an indicator layer is constructed. The weights in the criterion layer include functional risk weights, performance risk weights, safety risk weights, and compatibility risk weights. The indicator layer includes specific risk factors. The data from the indicator layer is standardized and then input into the BP neural network. The BP neural network is trained using historical risk data, and the model parameters are optimized using gradient descent to make the model prediction error less than a set threshold.

5. The system of claim 4, wherein, The targeted treatment plan includes: When functional risks occur, return test case modification suggestions, business logic code debugging guidelines, and regression testing scope for related modules; When performance risks occur, suggestions include expanding server resources, optimizing code, and adjusting concurrent stress test parameters. In the event of security risks, data encryption algorithms will be upgraded, vulnerability fix code templates will be provided, and security test cases will be supplemented. When compatibility risks arise, suggestions for developing multi-terminal adaptation patches and solutions for adjusting third-party interface parameters are provided.

6. The system of any one of claims 1 to 5, wherein, The closed-loop iterative optimization includes: Update the risk identification rule base based on the effectiveness of risk management; Adjust the indicator weights and BP neural network parameters of the intelligent evaluation model; Optimize the testing process.

7. A method for testing risk management based on a digital logistics freight platform, characterized in that, The method applied to the test risk management system based on a digital logistics freight platform as described in claim 1 includes: Collect multi-dimensional data during the testing process of the digital logistics freight platform; Based on the collected multi-dimensional data and the characteristics of digital logistics business, and combined with a pre-built, dynamically updatable risk identification rule base, risks are identified in the testing phase; wherein, the risk identification includes functional risks, performance risks, security risks, and compatibility risks. An intelligent assessment model that integrates the analytic hierarchy process (AHP) and backpropagation (BP) neural network is used to quantify the risk level and the scope of impact. Real-time monitoring of test process data and risk status; triggering tiered early warnings when warning conditions are met. Based on the risk level, scope of impact, and characteristics of the business scenario, generate and execute targeted response plans, and record the response process; Analyze the risk management results and full-process data, use the management results to feed back into the rule and model optimization, iterate and optimize the rules and models, and then synchronize them to the risk identification module and intelligent assessment module.

8. The method of claim 7, wherein, The method also includes providing data query and retrieval support to enable hierarchical access across testing teams.

9. The method of claim 8, wherein, The construction and solution of the intelligent evaluation model include: The weights of risk assessment indicators are determined using the analytic hierarchy process (AHP), and a three-level assessment system consisting of an objective layer, a criterion layer, and an indicator layer is constructed. The weights in the criterion layer include functional risk weights, performance risk weights, safety risk weights, and compatibility risk weights. The indicator layer includes specific risk factors. The data from the indicator layer is standardized and then input into the BP neural network. The BP neural network is trained using historical risk data, and the model parameters are optimized using gradient descent to make the model prediction error less than a set threshold.

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