Hierarchical decoupling parameter solving method and system based on feedback optimization

Abstract

The invention relates to the technical field of vehicle machine system decoupling, and discloses a hierarchical decoupling parameter solving method and system based on feedback optimization, and the method comprises the steps: determining a decoupling standard parameter based on decoupling demand data and basic function data, carrying out the decoupling operation according to the decoupling standard parameter, and generating a first decoupling configuration result, testing the first decoupling configuration result, obtaining first performance data in the testing process, determining a parameter disturbance factor according to the decoupling feedback data and the first performance data, generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameters, and on the basis of an intelligent optimization algorithm, the second performance data and the M decoupling scheme parameters, an optimal scheme parameter is generated, so that through evaluation and optimization of the decoupling scheme parameters, the adaptability of decoupling configuration and the overall performance of the system are improved, and the problem that the decoupling effect is unstable in the field of the vehicle-mounted information entertainment system in the prior art is better solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-machine system decoupling, and more specifically, to a method and system for solving hierarchical decoupling parameters based on feedback optimization. Background Art

[0002] Hierarchical decoupling refers to isolating the functional modules of the vehicle system from each other by dividing the system into different layers (such as signal layer, application layer, and middleware layer). In this way, the business logic can be decoupled from the underlying hardware signal, ensuring that the differences in different vehicle models or underlying protocols will not affect the development and iteration of application functions. This decoupling method makes each layer of the system independent of each other, facilitating the optimization of signal management, function expansion, and system adaptation. Although relevant content about hierarchical decoupling has appeared in the existing technology, there are still certain problems in directly applying it to the field of vehicle systems.

[0003] For example, the Chinese patent application with publication number CN111258786A discloses a decoupling method, device, terminal and storage medium in a layered architecture. The patent defines a target interface and marks its implementation in the upper layer implementation. Through a mapping relationship, the target layer can call the services provided by the upper layer. This method reduces the coupling between layers, solves the dependence of the bottom layer on the upper layer and improves the execution speed.

[0004] Although the above-mentioned existing technologies can achieve hierarchical decoupling, in the field of vehicle-mounted systems, due to differences in signal standards of different vehicle models and models of mobile devices, the parameters required in the hierarchical decoupling process are different, such as interface adaptation parameters and communication protocol parameters. In addition, the existing technologies rarely involve means of evaluating the effects of hierarchical decoupling, making it difficult to determine the optimal decoupling parameter configuration, which to a certain extent affects the optimization of the overall system performance.

[0005] In view of this, the present invention proposes a method and system for solving hierarchical decoupling parameters based on feedback optimization to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for solving hierarchical decoupling parameters based on feedback optimization.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] First, a method for solving hierarchical decoupling parameters based on feedback optimization is provided, including:

[0009] Obtaining decoupling requirement data and basic function data corresponding to the middleware, determining decoupling standard parameters based on the decoupling requirement data and the basic function data, performing a decoupling operation according to the decoupling standard parameters, and generating a first decoupling configuration result;

[0010] Acquire decoupling feedback data during the decoupling operation, test the first decoupling configuration result, acquire first performance data during the test, and determine a parameter disturbance factor according to the decoupling feedback data and the first performance data;

[0011] Generate M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter, perform a decoupling operation according to the M decoupling scheme parameters, and generate a corresponding second decoupling configuration result;

[0012] Obtain second performance data corresponding to the second decoupling configuration result, and generate optimal solution parameters based on the intelligent optimization algorithm, the second performance data, and the M decoupling solution parameters.

[0013] Furthermore, the method for determining the decoupling standard parameters includes:

[0014] Determine the relationship between the first element in the decoupling requirement data and the second element in the basic function data, take the associated first and second elements as nodes, and the relationship between the first and second elements as edges, so as to construct a decoupling requirement graph, and retrieve the corresponding decoupling standard parameters from the pre-built database according to the decoupling requirement graph.

[0015] Furthermore, the method for determining the parameter disturbance factor according to the decoupling feedback data and the first performance data includes:

[0016] The decoupled feedback data is divided into H groups of sub-feedback data, and the feedback deviation distance is calculated based on the H groups of sub-feedback data. The first performance data is divided into Q groups of sub-performance data, and the performance deviation distance is calculated based on the Q groups of sub-performance data. The feedback deviation distance and the performance deviation distance are fused to generate a parameter disturbance factor, where H and Q are integers greater than 1, and H=Q.

[0017] Furthermore, the method for calculating the feedback deviation distance based on the H group of sub-feedback data includes:

[0018] The corresponding element indicators in the H group of sub-feedback data are combined to construct S fuzzy sets. The optimal element indicators and the worst element indicators in the S fuzzy sets are determined based on the preset feedback range value. The optimal element indicators in the S fuzzy sets are combined to construct a positive fuzzy ideal solution. The worst element indicators in the S fuzzy sets are combined to construct a negative fuzzy ideal solution. The positive deviation distance between the H group of sub-feedback data and the positive fuzzy ideal solution is calculated. The negative deviation distance between the H group of sub-feedback data and the negative fuzzy ideal solution is calculated. The feedback deviation distance is determined by geometrically averaging the positive deviation distance and the negative deviation distance.

[0019] Furthermore, the calculation method of the positive deviation distance includes:

[0020] ;

[0021] In the formula, For the The positive deviation distance between the group feedback data and the positive fuzzy ideal solution, For the The first element index, is the optimal element index corresponding to the positive fuzzy ideal solution, G is The total number of element indicators in the group feedback data.

[0022] Further, the method for calculating the negative deviation distance includes:

[0023] ;

[0024] In the formula, For the The negative deviation distance between the group feedback data and the negative fuzzy ideal solution, For the The first element index, is the worst element index corresponding to the negative fuzzy ideal solution.

[0025] Furthermore, the method for generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter includes:

[0026] The parameter disturbance factors and the decoupling standard parameters are input into the pre-built decoupling scheme model to obtain M decoupling scheme parameters.

[0027] The construction method of the decoupling solution model includes:

[0028] Acquire a sample data set, wherein the sample data set includes a historical parameter disturbance factor, a historical decoupling standard parameter, and a historical decoupling scheme parameter;

[0029] Divide the sample data set into a sample training set and a sample test set, and build a regression network;

[0030] The historical parameter disturbance factors and historical decoupling standard parameters in the sample training set are used as the input data of the regression network, and the historical decoupling scheme parameters in the sample training set are used as the output data of the regression network. The regression network is trained to obtain an initial regression network for predicting the decoupling scheme parameters.

[0031] The initial regression network is tested using a sample test set, and the initial regression network with an output smaller than the preset error value is used as the decoupling solution model.

[0032] Furthermore, a method for generating optimal solution parameters based on the intelligent optimization algorithm, the second performance data, and the M decoupling solution parameters includes:

[0033] S401: taking M decoupling scheme parameters as an initial population, calculating a performance influence value according to the second performance data, and taking the performance influence value as a fitness function to calculate a fitness value for each individual in the initial population;

[0034] S402: Using the tournament selection strategy, randomly select 4 individuals from the initial population, compare the fitness values ​​of the 4 individuals, and select the individual with the smallest fitness value as the parent individual;

[0035] S403: Repeat the selection process of S402 until N parent individuals are selected, and then proceed to S404;

[0036] S404: Select a crossover point on the path of each pair of parent individuals, use the single-point crossover method to operate on all parent individuals, exchange the parts of the parent individuals after the crossover point, and generate two new child individuals;

[0037] S405: Randomly select two nodes for each child individual and exchange the positions of the two nodes, form a new population with all the child individuals, and replace the original population, ensuring that the size of the new population is still N;

[0038] S406: The convergence threshold of the preset fitness value is CT, and the above S404-S405 are repeated. If the fitness value change of the optimal individual in the population of consecutive K generations is less than CT, the algorithm terminates;

[0039] S407: When the algorithm terminates, the sub-individual with the smallest fitness value in the population is the optimal solution parameter.

[0040] Furthermore, the second performance data includes data transmission delay, packet loss rate, fault occurrence frequency and application response time, and the performance impact value is calculated based on the data transmission delay, packet loss rate, fault occurrence frequency and application response time.

[0041] In a second aspect, a system for solving hierarchical decoupling parameters based on feedback optimization is provided, which is used to implement the above-mentioned method for solving hierarchical decoupling parameters based on feedback optimization, including:

[0042] Preliminary decoupling module: used to obtain decoupling requirement data and basic function data corresponding to the middleware, determine decoupling standard parameters based on the decoupling requirement data and the basic function data, perform decoupling operations according to the decoupling standard parameters, and generate a first decoupling configuration result;

[0043] Data processing module: used to obtain decoupling feedback data during the decoupling operation, test the first decoupling configuration result, obtain first performance data during the test, and determine the parameter disturbance factor according to the decoupling feedback data and the first performance data;

[0044] Parameter modification module: generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter, performing decoupling operation according to the M decoupling scheme parameters, and generating a corresponding second decoupling configuration result;

[0045] Parameter optimization module: used to obtain the second performance data corresponding to the second decoupling configuration result, and generate optimal solution parameters based on the intelligent optimization algorithm, the second performance data and M decoupling solution parameters.

[0046] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for solving hierarchical decoupling parameters based on feedback optimization when executing the computer program.

[0047] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the method for solving the hierarchical decoupling parameters based on feedback optimization is implemented.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention first determines the decoupling standard parameters based on the decoupling requirement data and the basic function data, performs a decoupling operation according to the decoupling standard parameters, generates a first decoupling configuration result, tests the first decoupling configuration result, obtains the first performance data in the test process, determines the parameter disturbance factor according to the decoupling feedback data and the first performance data, generates M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameters, and generates the optimal scheme parameters based on the intelligent optimization algorithm, the second performance data and the M decoupling scheme parameters. In this way, the adaptability of the decoupling configuration and the overall system performance are improved by evaluating and optimizing the decoupling scheme parameters, thereby better solving the problem of unstable decoupling effect in the field of vehicle-mounted system in the prior art, and ensuring that the decoupled signal layer and business layer can work efficiently and stably. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a method for solving hierarchical decoupling parameters based on feedback optimization in the present invention;

[0051] Figure 2 It is a structural schematic diagram of a system for solving hierarchical decoupling parameters based on feedback optimization in the present invention;

[0052] Figure 3 It is a schematic diagram of a vehicle system that is not decoupled in the prior art of the present invention;

[0053] Figure 4 The diagram is a schematic diagram of a vehicle computer system after decoupling using a method for solving hierarchical decoupling parameters based on feedback optimization in the prior art. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] See also Figure 1 As shown, this embodiment discloses a method for solving hierarchical decoupling parameters based on feedback optimization, including:

[0057] S10: Obtain decoupling requirement data and basic function data corresponding to the middleware, determine decoupling standard parameters based on the decoupling requirement data and the basic function data, perform a decoupling operation according to the decoupling standard parameters, and generate a first decoupling configuration result;

[0058] like Figure 3 As shown, Figure 3 It shows vehicle model signal 1, vehicle model signal 2, android9, android10, setting app, air conditioning app, car setting app and voice app, etc. Figure 3 It can be seen that each vehicle model signal needs to be connected with multiple apps. For example, vehicle model signal 1 is connected with the setting app, air conditioning app, vehicle setting app and voice app respectively. If the vehicle system in the prior art is not decoupled, the vehicle model is changed or the CAN signal interface is changed, resulting in the need for all re-connection. In this embodiment, Figure 4 As shown, Figure 4It also includes vehicle model signal 1, vehicle model signal 2, android9, android10, setting app, air conditioning app, vehicle setting app and voice app, etc., but after applying the method for solving the hierarchical decoupling parameters based on feedback optimization disclosed in this application, decoupling is performed through the middleware to generate a signal layer and a business layer. The signal layer includes vehicle model signal 1, vehicle model signal 2, android9, android10, etc., and the business layer includes setting app, air conditioning app, vehicle setting app and voice app, etc. The business layer and the signal layer are completely separated through the idea of ​​hierarchical decoupling. The business layer only needs to focus on the business logic, and the method of interacting with the signal layer is uniformly encapsulated by the middleware. The middleware refers to an intermediate software layer used to provide communication, data transmission and functional coordination between different levels of the vehicle system (such as signal layer, business layer, etc.). The middleware plays a bridge role in the vehicle system, responsible for managing the interaction between the underlying hardware (such as sensors, communication modules) and the upper-level application functions, so that the business logic is decoupled from the underlying hardware, thereby enhancing the scalability and adaptability of the system.

[0059] In this embodiment, the decoupling requirement data includes but is not limited to vehicle model signal protocol parameters, software interface adaptation, business logic and application function dependencies. Since the CAN bus and other communication protocols of different vehicle models are different, the differences in vehicle model signal protocol parameters need to be considered during the decoupling process to ensure that the signal layer can accurately transmit data. For the adaptation of different operating system versions (such as Android 9, 10) and vehicle-mounted applications, the decoupling requirement data needs to define interface adaptation parameters, and the dependency relationship between business logic and application functions refers to the specific requirements of each application function in the business layer (such as setting app, air conditioning app, voice app, etc.) for the underlying data and device functions provided by the signal layer. This dependency determines what information and services the business layer needs to obtain from the signal layer in actual operation to ensure that each application function can work normally.

[0060] Among them, basic functional data includes but is not limited to communication protocol support information, hardware interface type, application protocol stack configuration, etc. Communication protocol support information refers to the basic information of communication protocols such as CAN bus, LIN bus or Ethernet supported by the vehicle system, so as to effectively connect with the signal layer. Application protocol stack configuration refers to the configuration information of the protocol stack (such as TCP / IP, HTTP) supporting vehicle applications to ensure that various business applications can communicate and exchange data normally. Hardware interface type refers to the physical and wireless connection interface types supported in the vehicle system, which are used for data exchange and control connection with external devices or networks, such as USB, Bluetooth, Wi-Fi, etc.

[0061] Methods for determining decoupling standard parameters include:

[0062] Determine the relationship between the first element in the decoupling requirement data and the second element in the basic function data, take the associated first and second elements as nodes, and the relationship between the first and second elements as edges, so as to construct a decoupling requirement graph, and retrieve the corresponding decoupling standard parameters from the pre-built database according to the decoupling requirement graph.

[0063] It should be noted that the first element mentioned above refers to the specific requirement item in the decoupling requirement data, and the second element refers to the specific function item in the basic function data. For example, the first element can be the vehicle model signal protocol parameter, and the second element can be the communication protocol support information. Then the relationship between the first element and the second element is that the vehicle model signal protocol parameter (first element) depends on the communication protocol support information (second element), because the business layer needs to confirm whether the signal layer can support the specific communication protocol of the vehicle model (such as the CAN bus protocol) to ensure the accurate transmission of the data. Based on this association, the CAN protocol parameters adapted to the vehicle model signal can be retrieved from the pre-built database and defined as decoupling standard parameters. For example: set the baud rate of the CAN bus to 500 kbps to match the data transmission requirements of the vehicle model, specify the message format (such as 11-bit or 29-bit ID format) and specific signal mapping parameters to ensure the consistency of data transmission.

[0064] In this embodiment, performing a decoupling operation according to decoupling standard parameters refers to configuring and separating the signal layer and the business layer of the vehicle system according to the determined decoupling standard parameters to ensure that the business layer can be independent of changes in specific vehicle models or protocols and successfully implement functions. Then, generating the first decoupling configuration result represents the specific configuration state after completing the configuration and separation of the signal layer and the business layer according to the decoupling standard parameters.

[0065] S20: obtaining decoupling feedback data during the decoupling operation, testing the first decoupling configuration result, obtaining first performance data during the test, and determining a parameter disturbance factor according to the decoupling feedback data and the first performance data;

[0066] Among them, the decoupling feedback data includes but is not limited to the number of interface calls, the number of decoupling errors and the number of incompatibilities. The number of interface calls refers to the number of successful interface calls during the decoupling operation, the number of decoupling errors refers to the number of errors that occurred during the decoupling operation, and the number of incompatibilities refers to the number of interface incompatibilities that occurred during the decoupling operation.

[0067] It can be understood that the first decoupling configuration result represents the specific configuration state after the configuration and separation of the signal layer and the business layer are completed according to the decoupling standard parameters. Then testing the first decoupling configuration result refers to verifying the performance and stability of the signal layer and the business layer after the decoupling configuration. Specifically, this includes simulating the routine operation of the vehicle system in the actual operating environment, and checking whether the first decoupling configuration result meets the expected independence and compatibility requirements to ensure that the business layer can operate normally without being affected by changes in the signal layer or vehicle model. Then the first performance data includes but is not limited to data transmission delay, packet loss rate, fault frequency and application response time. In this embodiment, application response time refers to the average response time of the business layer application after receiving the signal layer data, reflecting the impact of decoupling on application performance.

[0068] The method for determining the parameter disturbance factor according to the decoupling feedback data and the first performance data includes:

[0069] The decoupled feedback data is divided into H groups of sub-feedback data, and the feedback deviation distance is calculated based on the H groups of sub-feedback data. The first performance data is divided into Q groups of sub-performance data, and the performance deviation distance is calculated based on the Q groups of sub-performance data. The feedback deviation distance and the performance deviation distance are fused to generate a parameter disturbance factor, where H and Q are integers greater than 1, and H=Q.

[0070] It should be noted that, taking decoupling feedback data as an example, technical personnel in this field will obtain a set of sub-feedback data each time a decoupling operation is performed by performing a decoupling operation. Similarly, the sub-feedback data includes the number of interface calls, the number of decoupling errors, and the number of incompatibilities. Therefore, a decoupling configuration result will be obtained after each decoupling operation. After each decoupling configuration result is tested, a set of corresponding sub-performance data will be obtained. Similarly, the sub-performance data includes data transmission delay, packet loss rate, fault occurrence frequency, and application response time.

[0071] The method for calculating the feedback deviation distance based on the H group of sub-feedback data includes:

[0072] The corresponding element indicators in the H group of sub-feedback data are combined to construct S fuzzy sets. The optimal element indicators and the worst element indicators in the S fuzzy sets are determined based on the preset feedback range value. The optimal element indicators in the S fuzzy sets are combined to construct a positive fuzzy ideal solution. The worst element indicators in the S fuzzy sets are combined to construct a negative fuzzy ideal solution. The positive deviation distance between the H group of sub-feedback data and the positive fuzzy ideal solution is calculated. The negative deviation distance between the H group of sub-feedback data and the negative fuzzy ideal solution is calculated. The feedback deviation distance is determined by geometrically averaging the positive deviation distance and the negative deviation distance.

[0073] It is not difficult to understand that the combination of corresponding element indicators in the above refers to summarizing the element indicators of the same type in H groups of sub-feedback data. For example, the sub-feedback data includes the number of interface calls, the number of decoupling errors and the number of incompatibilities. Then the number of interface calls in the H sub-feedback data is summarized to construct three fuzzy sets. The above preset feedback range value refers to the performance range used to determine each element indicator under ideal conditions. Taking the number of interface calls as an example, the feedback range value can be [80, 110]. 80 represents the minimum expected value of the number of interface calls. A value lower than this may indicate poor system performance or unsatisfactory decoupling effect. 110 represents the highest expected value of the number of interface calls. A value exceeding this may mean excessive use of system resources or redundant calls.

[0074] Among them, the positive fuzzy ideal solution represents the best value combination of each element index, and the negative fuzzy ideal solution represents the worst value combination of each element index.

[0075] Methods for calculating positive deviation distance include:

[0076] ;

[0077] In the formula, For the The positive deviation distance between the group feedback data and the positive fuzzy ideal solution, For the The first element index, is the optimal element index corresponding to the positive fuzzy ideal solution, G is The total number of element indicators in the group feedback data.

[0078] Methods for calculating negative deviation distance include:

[0079] ;

[0080] In the formula, For the The negative deviation distance between the group feedback data and the negative fuzzy ideal solution, For the The first element index, is the worst element index corresponding to the negative fuzzy ideal solution.

[0081] Similarly, the method for calculating the performance deviation distance is the same as the method for calculating the feedback deviation distance mentioned above, except that the sub-feedback data is replaced by the sub-performance data, and the feedback range value is replaced by the performance range value. As for the performance range value, it is also pre-set based on expert experience and prior knowledge, and this embodiment does not go into details about this.

[0082] In this embodiment, the fusion of the feedback deviation distance and the performance deviation distance can be a cascade fusion. Cascade fusion refers to connecting the feedback deviation distance and the performance deviation distance in a certain order to form a comprehensive vector or matrix so as to retain the characteristic information of both at the same time. This fusion method does not directly perform calculations on the feedback deviation distance and the performance deviation distance, but cascades them together as independent parts, so that the impact of the two deviations can be considered in the final analysis.

[0083] The decoupling configuration result first generated in this embodiment takes into account the independent requirements of the business logic and signal layers, and then is verified through actual testing, which can ensure that the configuration result adapts to the changing environment between the signal layer and the business layer, so that the system has good compatibility. By analyzing the decoupling feedback data and the first performance data of the first decoupling configuration result, the deficiencies in the current decoupling operation can be identified, providing a quantitative basis for the subsequent adjustment of the decoupling scheme parameters. This process can form a closed-loop optimization system, which is conducive to the gradual improvement of the decoupling scheme.

[0084] S30: generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter, performing a decoupling operation according to the M decoupling scheme parameters, and generating a corresponding second decoupling configuration result;

[0085] In one embodiment of the present invention, a method for generating M decoupling scheme parameters based on a parameter disturbance factor and a decoupling standard parameter includes:

[0086] The parameter disturbance factors and the decoupling standard parameters are input into the pre-built decoupling scheme model to obtain M decoupling scheme parameters.

[0087] The construction method of the decoupling solution model includes:

[0088] Acquire a sample data set, wherein the sample data set includes a historical parameter disturbance factor, a historical decoupling standard parameter, and a historical decoupling scheme parameter;

[0089] Divide the sample data set into a sample training set and a sample test set, and build a regression network;

[0090] The historical parameter disturbance factors and historical decoupling standard parameters in the sample training set are used as the input data of the regression network, and the historical decoupling scheme parameters in the sample training set are used as the output data of the regression network. The regression network is trained to obtain an initial regression network for predicting the decoupling scheme parameters.

[0091] The initial regression network is tested using a sample test set, and the output of the initial regression network that is less than the preset error value is used as the decoupling solution model. The initial regression network is a deep neural network model.

[0092] It should be added that, in this embodiment, the decoupling standard parameters are modified based on the parameter disturbance factor, so as to generate M decoupling scheme parameters. In the process of modifying the decoupling standard parameters based on the parameter disturbance factor, the parameter disturbance factor will serve as a "feedback adjustment mechanism" to help adjust the decoupling standard parameters to optimize the adaptability and performance of the system. The logic of this process is to use the feedback data obtained in the actual decoupling process (such as interface call success rate, number of decoupling errors, etc.), combined with the first performance data (such as data transmission delay, fault occurrence frequency, etc.), to calculate the feedback and performance deviation, and then generate a disturbance factor. This disturbance factor is used to fine-tune the decoupling standard parameters to better adapt to the differences and demand changes observed in the actual operating environment.

[0093] Exemplarily, the decoupling standard parameters include the baud rate of the CAN bus, which is initially set to 500 kbps. During the decoupling process, it is found that the decoupling feedback data shows that the "number of interface call errors" is high, and the "data transmission delay" is also high in the first performance data. These problems may mean that the current baud rate cannot fully meet the transmission requirements. By calculating the deviation between the decoupling feedback data and the first performance data, a larger positive parameter disturbance factor is generated. Based on this parameter disturbance factor, the system adjusts the baud rate to a higher value (such as 550 kbps) to reduce the number of errors and delays, thereby optimizing transmission performance.

[0094] S40: Obtain second performance data corresponding to the second decoupling configuration result, and generate optimal solution parameters based on the intelligent optimization algorithm, the second performance data, and the M decoupling solution parameters.

[0095] It can be understood that the second performance data has the same meaning as the first performance data, but is expressed differently. The second performance data includes but is not limited to data transmission delay, packet loss rate, fault frequency and application response time. Therefore, the way to obtain the second performance data is to test the second decoupling configuration result.

[0096] The intelligent optimization algorithm may be a genetic algorithm. Then, the method for generating optimal solution parameters based on the intelligent optimization algorithm, the second performance data, and the M decoupling solution parameters includes:

[0097] S401: taking M decoupling scheme parameters as an initial population, calculating a performance influence value according to the second performance data, and taking the performance influence value as a fitness function to calculate a fitness value for each individual in the initial population;

[0098] It can be understood that by calculating the fitness value of the decoupling scheme parameters, the actual performance of each scheme under the current configuration is measured. The performance impact calculation method enables the fitness value to quantify the pros and cons of each scheme, identify schemes with better and worse performance, and provide a basis for subsequent selection and optimization. The smaller the fitness value of an individual, the better its performance.

[0099] S402: Using the tournament selection strategy, randomly select 4 individuals from the initial population, compare the fitness values ​​of the 4 individuals, and select the individual with the smallest fitness value as the parent individual;

[0100] Among them, the tournament selection strategy is to randomly select several individuals from the population each time, and determine the better parent individuals by comparing the fitness values. This process ensures that each round of selection can gradually select more promising individuals based on performance indicators.

[0101] S403: Repeat the selection process of S402 until N parent individuals are selected, and then proceed to S404;

[0102] S404: Select a crossover point on the path of each pair of parent individuals, use the single-point crossover method to operate on all parent individuals, exchange the parts of the parent individuals after the crossover point, and generate two new child individuals;

[0103] Among them, single-point crossover is a commonly used genetic algorithm operation, which generates new individuals by exchanging part of the genetic information of parent individuals. This operation can produce offspring with different parameter combinations and enhance the diversity of the population.

[0104] S405: Randomly select two nodes for each child individual and exchange the positions of the two nodes, form a new population with all the child individuals, and replace the original population, ensuring that the size of the new population is still N;

[0105] S406: The convergence threshold of the preset fitness value is CT, and S404-S405 are repeated. If the fitness value change of the optimal individual in the population of consecutive K generations is less than CT, the algorithm terminates;

[0106] S407: When the algorithm terminates, the sub-individual with the smallest fitness value in the population is the optimal solution parameter.

[0107] This embodiment generates M decoupling scheme parameters and performs decoupling operations one by one to form corresponding second decoupling configuration results. This step ensures that the actual performance of the system under different parameter combinations is fully tested. Then, according to the second performance data generated by each second decoupling configuration result, the optimal decoupling scheme parameters are screened out through an intelligent optimization algorithm. This combination method ensures that not only a variety of decoupling scheme parameters can be obtained, but also the optimal scheme parameters can be selected after actual performance verification.

[0108] The second performance data includes data transmission delay, packet loss rate, fault occurrence frequency, and application response time. The method for calculating the performance impact value according to the second performance data includes:

[0109] PID= ;

[0110] In the formula, PID is the performance impact value, For data transmission delay, is the packet loss rate, is the frequency of failure, is the application response time, is the inverse cotangent function, is the hyperbolic cosine function, is the hyperbolic sine function, is the inverse tangent function, is the logarithmic function with base 3.

[0111] Among them, with respect to data transmission delay and fault occurrence frequency, when the data transmission delay and fault occurrence frequency are larger, it indicates that the performance of the second decoupling configuration result is poor, which means that the configuration result does not effectively optimize the response and stability of the system under these conditions. Therefore, from the above content, it can be seen that when the performance impact value is larger, the corresponding decoupling effect is worse.

[0112] This embodiment first determines the decoupling standard parameters based on the decoupling requirement data and the basic function data, performs a decoupling operation according to the decoupling standard parameters, generates a first decoupling configuration result, tests the first decoupling configuration result, obtains the first performance data during the test, determines the parameter disturbance factor according to the decoupling feedback data and the first performance data, generates M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameters, and generates the optimal scheme parameters based on the intelligent optimization algorithm, the second performance data and the M decoupling scheme parameters. In this way, the adaptability of the decoupling configuration and the overall system performance are improved by evaluating and optimizing the decoupling scheme parameters, thereby better solving the problem of unstable decoupling effect of the prior art in the field of vehicle-machine systems, and ensuring that the decoupled signal layer and business layer can work efficiently and stably.

[0113] Example 2

[0114] See also Figure 2 As shown, based on the same inventive concept, this embodiment discloses a system for solving hierarchical decoupling parameters based on feedback optimization. For details not provided in this embodiment, please refer to the description of the relevant parts in Embodiment 1. The system includes:

[0115] Preliminary decoupling module: used to obtain decoupling requirement data and basic function data corresponding to the middleware, determine decoupling standard parameters based on the decoupling requirement data and the basic function data, perform decoupling operations according to the decoupling standard parameters, and generate a first decoupling configuration result;

[0116] Methods for determining decoupling standard parameters include:

[0117] Determine the relationship between the first element in the decoupling requirement data and the second element in the basic function data, take the associated first and second elements as nodes, and the relationship between the first and second elements as edges, so as to construct a decoupling requirement graph, and retrieve the corresponding decoupling standard parameters from the pre-built database according to the decoupling requirement graph.

[0118] Data processing module: used to obtain decoupling feedback data during the decoupling operation, test the first decoupling configuration result, obtain first performance data during the test, and determine the parameter disturbance factor according to the decoupling feedback data and the first performance data;

[0119] The method for determining the parameter disturbance factor according to the decoupling feedback data and the first performance data includes:

[0120] The decoupled feedback data is divided into H groups of sub-feedback data, and the feedback deviation distance is calculated based on the H groups of sub-feedback data. The first performance data is divided into Q groups of sub-performance data, and the performance deviation distance is calculated based on the Q groups of sub-performance data. The feedback deviation distance and the performance deviation distance are fused to generate a parameter disturbance factor, where H and Q are integers greater than 1, and H=Q.

[0121] The method for calculating the feedback deviation distance based on the H group of sub-feedback data includes:

[0122] The corresponding element indicators in the H group of sub-feedback data are combined to construct S fuzzy sets. The optimal element indicators and the worst element indicators in the S fuzzy sets are determined based on the preset feedback range value. The optimal element indicators in the S fuzzy sets are combined to construct a positive fuzzy ideal solution. The worst element indicators in the S fuzzy sets are combined to construct a negative fuzzy ideal solution. The positive deviation distance between the H group of sub-feedback data and the positive fuzzy ideal solution is calculated. The negative deviation distance between the H group of sub-feedback data and the negative fuzzy ideal solution is calculated. The feedback deviation distance is determined by geometrically averaging the positive deviation distance and the negative deviation distance.

[0123] Methods for calculating positive deviation distance include:

[0124] ;

[0125] In the formula, For the The positive deviation distance between the group feedback data and the positive fuzzy ideal solution, For the The first element index, is the optimal element index corresponding to the positive fuzzy ideal solution, G is The total number of element indicators in the group feedback data.

[0126] Methods for calculating negative deviation distance include:

[0127] ;

[0128] In the formula, For the The negative deviation distance between the group feedback data and the negative fuzzy ideal solution, For the The first element index, is the worst element index corresponding to the negative fuzzy ideal solution.

[0129] Parameter modification module: generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter, performing decoupling operation according to the M decoupling scheme parameters, and generating a corresponding second decoupling configuration result;

[0130] In one embodiment of the present invention, a method for generating M decoupling scheme parameters based on a parameter disturbance factor and a decoupling standard parameter includes:

[0131] The parameter disturbance factors and the decoupling standard parameters are input into the pre-built decoupling scheme model to obtain M decoupling scheme parameters.

[0132] The construction method of the decoupling solution model includes:

[0133] Acquire a sample data set, wherein the sample data set includes a historical parameter disturbance factor, a historical decoupling standard parameter, and a historical decoupling scheme parameter;

[0134] Divide the sample data set into a sample training set and a sample test set, and build a regression network;

[0135] The historical parameter disturbance factors and historical decoupling standard parameters in the sample training set are used as the input data of the regression network, and the historical decoupling scheme parameters in the sample training set are used as the output data of the regression network. The regression network is trained to obtain an initial regression network for predicting the decoupling scheme parameters.

[0136] The initial regression network is tested using a sample test set, and the output of the initial regression network that is less than the preset error value is used as the decoupling solution model. The initial regression network is a deep neural network model.

[0137] Parameter optimization module: used to obtain the second performance data corresponding to the second decoupling configuration result, and generate optimal solution parameters based on the intelligent optimization algorithm, the second performance data and M decoupling solution parameters.

[0138] Example 3

[0139] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for solving hierarchical decoupling parameters based on feedback optimization when executing the computer program.

[0140] Since the electronic device introduced in this embodiment is an electronic device used to implement the method for solving the hierarchical decoupling parameters based on feedback optimization in the embodiment of the present application, based on the method for solving the hierarchical decoupling parameters based on feedback optimization introduced in the embodiment of the present application, technical personnel in the field can understand the specific implementation mode of the electronic device of the present embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the electronic device used by technical personnel in the field to implement the method for solving the hierarchical decoupling parameters based on feedback optimization in the embodiment of the present application falls within the scope of protection of the present application.

[0141] Example 4

[0142] The present embodiment discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the method for solving the hierarchical decoupling parameters based on feedback optimization is implemented.

[0143] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters, weights and thresholds in the formula are set by technicians in this field according to actual conditions.

[0144] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0145] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0146] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0147] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0150] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0151] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for solving hierarchical decoupling parameters based on feedback optimization, characterized in that: include: Obtaining decoupling requirement data and basic function data corresponding to the middleware, determining decoupling standard parameters based on the decoupling requirement data and the basic function data, performing a decoupling operation according to the decoupling standard parameters, and generating a first decoupling configuration result; Acquire decoupling feedback data during the decoupling operation, test the first decoupling configuration result, acquire first performance data during the test, and determine a parameter disturbance factor according to the decoupling feedback data and the first performance data; Generate M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter, perform a decoupling operation according to the M decoupling scheme parameters, and generate a corresponding second decoupling configuration result; Obtain second performance data corresponding to the second decoupling configuration result, and generate optimal solution parameters based on the intelligent optimization algorithm, the second performance data, and the M decoupling solution parameters.

2. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 1 is characterized in that: The method for determining the decoupling standard parameters comprises: Determine the relationship between the first element in the decoupling requirement data and the second element in the basic function data, take the associated first and second elements as nodes, and the relationship between the first and second elements as edges, so as to construct a decoupling requirement graph, and retrieve the corresponding decoupling standard parameters from the pre-built database according to the decoupling requirement graph.

3. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 1 is characterized in that: The method for determining the parameter disturbance factor according to the decoupling feedback data and the first performance data includes: The decoupled feedback data is divided into H groups of sub-feedback data, and the feedback deviation distance is calculated based on the H groups of sub-feedback data. The first performance data is divided into Q groups of sub-performance data, and the performance deviation distance is calculated based on the Q groups of sub-performance data. The feedback deviation distance and the performance deviation distance are fused to generate a parameter disturbance factor, where H and Q are integers greater than 1, and H=Q.

4. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 3 is characterized in that: The method for calculating the feedback deviation distance based on the H group of sub-feedback data includes: The corresponding element indicators in the H group of sub-feedback data are combined to construct S fuzzy sets. The optimal element indicators and the worst element indicators in the S fuzzy sets are determined based on the preset feedback range value. The optimal element indicators in the S fuzzy sets are combined to construct a positive fuzzy ideal solution. The worst element indicators in the S fuzzy sets are combined to construct a negative fuzzy ideal solution. The positive deviation distance between the H group of sub-feedback data and the positive fuzzy ideal solution is calculated. The negative deviation distance between the H group of sub-feedback data and the negative fuzzy ideal solution is calculated. The feedback deviation distance is determined by geometrically averaging the positive deviation distance and the negative deviation distance.

5. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 4 is characterized in that: The calculation method of the positive deviation distance includes: ； In the formula, For the The positive deviation distance between the group feedback data and the positive fuzzy ideal solution, For the The first element index, is the optimal element index corresponding to the positive fuzzy ideal solution, G is The total number of element indicators in the group feedback data.

6. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 4 is characterized in that: The method for calculating the negative deviation distance comprises: ； In the formula, For the The negative deviation distance between the group feedback data and the negative fuzzy ideal solution, For the The first element index, is the worst element index corresponding to the negative fuzzy ideal solution.

7. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 6 is characterized in that: The method for generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter includes: Input the parameter disturbance factor and the decoupling standard parameter into the pre-built decoupling scheme model to obtain M decoupling scheme parameters; The construction method of the decoupling solution model includes: Acquire a sample data set, wherein the sample data set includes a historical parameter disturbance factor, a historical decoupling standard parameter, and a historical decoupling scheme parameter; Divide the sample data set into a sample training set and a sample test set, and build a regression network; The historical parameter disturbance factors and historical decoupling standard parameters in the sample training set are used as the input data of the regression network, and the historical decoupling scheme parameters in the sample training set are used as the output data of the regression network. The regression network is trained to obtain an initial regression network for predicting the decoupling scheme parameters. The initial regression network is tested using a sample test set, and the initial regression network with an output smaller than the preset error value is used as the decoupling solution model.

8. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 1, characterized in that: The method for generating optimal solution parameters based on the intelligent optimization algorithm, the second performance data and M decoupling solution parameters includes: S401: taking M decoupling scheme parameters as an initial population, calculating a performance influence value according to the second performance data, and taking the performance influence value as a fitness function to calculate a fitness value for each individual in the initial population; S402: Using the tournament selection strategy, randomly select 4 individuals from the initial population, compare the fitness values ​​of the 4 individuals, and select the individual with the smallest fitness value as the parent individual; S403: Repeat the selection process of S402 until N parent individuals are selected, and then proceed to S404; S404: Select a crossover point on the path of each pair of parent individuals, use the single-point crossover method to operate on all parent individuals, exchange the parts of the parent individuals after the crossover point, and generate two new child individuals; S405: Randomly select two nodes for each child individual and exchange the positions of the two nodes, form a new population with all the child individuals, and replace the original population, ensuring that the size of the new population is still N; S406: The convergence threshold of the preset fitness value is CT, and S404-S405 are repeated. If the fitness value change of the optimal individual in the population of consecutive K generations is less than CT, the algorithm terminates; S407: When the algorithm terminates, the sub-individual with the smallest fitness value in the population is the optimal solution parameter.

9. The method for solving hierarchical decoupling parameters based on feedback optimization according to claim 8, characterized in that: The second performance data includes data transmission delay, packet loss rate, fault occurrence frequency and application response time, and the performance impact value is calculated based on the data transmission delay, packet loss rate, fault occurrence frequency and application response time.

10. A system for solving hierarchical decoupling parameters based on feedback optimization, which is used to implement the method for solving hierarchical decoupling parameters based on feedback optimization as claimed in any one of claims 1 to 9, characterized in that: include: Preliminary decoupling module: used to obtain decoupling requirement data and basic function data corresponding to the middleware, determine decoupling standard parameters based on the decoupling requirement data and the basic function data, perform decoupling operations according to the decoupling standard parameters, and generate a first decoupling configuration result; Data processing module: used to obtain decoupling feedback data during the decoupling operation, test the first decoupling configuration result, obtain first performance data during the test, and determine the parameter disturbance factor according to the decoupling feedback data and the first performance data; Parameter modification module: generating M decoupling scheme parameters based on the parameter disturbance factor and the decoupling standard parameter, performing decoupling operation according to the M decoupling scheme parameters, and generating a corresponding second decoupling configuration result; Parameter optimization module: used to obtain the second performance data corresponding to the second decoupling configuration result, and generate optimal solution parameters based on the intelligent optimization algorithm, the second performance data and M decoupling solution parameters.

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