Integrated control method and system for electronic control software of automobile

Abstract

The invention discloses an integrated control method and system for electric control software of an automobile, and relates to the field of automobile electric control, and the system comprises a data acquisition end, a dynamic distribution end, an early warning obstacle avoidance end and an execution monitoring end. According to the invention, the optimization unit constructs a priority quantification model, fuses the function criticality, the response real-time demand and the basic consumption of computing resources, accurately distinguishes the ECU software priority, ensures the core control function preferentially, avoids the response delay of key tasks due to resource competition, and adopts an autoregressive moving average model, so that the prediction efficiency is improved. The ECU resource demand change trend under the complex working condition of an automobile is accurately captured, resources are reserved for the ECU in advance, dynamic adaptation of the resource demand and allocation is achieved, the risk unit establishes a resource allocation proportion adjustment model based on the priority and the prediction demand, resources are allocated fairly and efficiently, key ECU resource supply is guaranteed, resource waste is avoided, and the resource allocation efficiency is improved. And the overall operation efficiency and stability of the electric control system are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive electronic control, and in particular to an integrated control method and system for automotive electronic control software. Background Technology

[0002] As automotive functions become increasingly sophisticated, the number of electronic control units (ECUs) in electronic control systems continues to grow, and the software functional characteristics of each ECU differ significantly. For example, the autonomous driving domain controller needs to process massive amounts of sensor data in real time, which places extremely high demands on computing resources, while the computing tasks of the door control system ECU are relatively simple, but it has stringent requirements for timely response.

[0003] In existing technologies, the integrated control system of electronic control software lacks a unified coordination mechanism in terms of resource requirements and running priorities among different ECU software during use. This leads to resource contention issues when multiple ECU software run simultaneously on a hardware platform with limited resources, causing some key functions to fail to respond in a timely manner and seriously affecting the overall performance of the vehicle and the driving experience.

[0004] Therefore, a software-integrated control method and system for automobile electronic control is proposed to solve the above problems. Summary of the Invention

[0005] The main objective of this invention is to provide an integrated control method and system for automotive electronic control software to solve the problems mentioned in the background.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: an electronic control software integrated control system for automobiles, the system comprising a data acquisition terminal, a dynamic allocation terminal, an early warning and obstacle avoidance terminal, and an execution monitoring terminal; The data acquisition terminal is used to collect the working parameters of each ECU chip in the vehicle in real time and perform normalization processing. The dynamic allocation terminal is used to distinguish the priority of automotive ECU software and predict the future resource needs of the ECU. Based on the priority and predicted needs, a resource allocation ratio adjustment model is established. The early warning and obstacle avoidance terminal quantifies the degree of abnormality of each hardware parameter of each automotive ECU chip based on the normalized data from the data acquisition terminal, weights and sums the abnormality coefficients of each hardware parameter, calculates the comprehensive fault risk value of each automotive ECU chip, and takes corresponding processing measures based on the comprehensive fault risk value. The execution monitoring terminal is used to collect the core parameters of each automotive ECU chip after the dynamic allocation terminal and the early warning and obstacle avoidance terminal adjust the resource strategy, calculate the task response latency and fault recurrence rate, as well as the comprehensive effect score of the ECU handling strategy, and determine whether to trigger a correction command based on the comprehensive effect score.

[0007] Preferably, the data acquisition terminal includes a first acquisition unit and a normalization unit; The first acquisition unit is used to acquire the operating parameters of each ECU chip in the vehicle in real time. The operating parameters include temperature information acquired by the ECU's built-in temperature sensor, voltage information acquired by the voltage sensor, current information acquired by the current sensor, and data transmission error rate detected by the communication protocol logic. The normalization unit is used to normalize the information collected in real time by the first acquisition unit.

[0008] Preferably, the dynamic allocation terminal includes an optimization unit, a prediction unit, and a risk unit.

[0009] Preferably, the selection unit distinguishes the priorities of automotive ECU software by constructing a priority quantization model, as follows: ; in, Represents the overall priority of the ECU software. Represents the criticality of a function. Represents the need for real-time response. Represents the basic consumption of computing resources. and This represents the corresponding weighting coefficient.

[0010] Preferably, the prediction unit predicts the future resource requirements of the ECU using an autoregressive moving average model. ; The risk unit is based on future resource needs. Overall software priority A resource allocation ratio adjustment model is established to calculate the actual resource allocation of the ECU software at time t. The calculation formula is as follows: ; in, This represents the actual resource allocation at time i of ECUt. M represents the resource requirements predicted by the prediction unit at time t in the future, and M represents the total number of ECU software programs. This represents the total resources currently available for allocation in the system.

[0011] Preferably, the early warning and obstacle avoidance terminal includes a quantization unit, an anomaly unit, and a processing unit; The quantization unit quantifies the degree of abnormality C of each hardware parameter of each automotive ECU chip based on the normalized data from the data acquisition terminal. The anomaly unit is used to weighted sum the anomaly coefficients and calculate the overall fault risk value of the ECU, as shown in the following formula: ; in, Represents the overall fault risk value of the ECU. Let i be the weight of the i-th hardware parameter. Let be the anomaly coefficient of the i-th hardware parameter, and n represent the number of types of hardware parameters collected.

[0012] Preferably, the processing unit sets a high-risk threshold T1 and a low-risk threshold T2; when When the value is less than the low-risk threshold T2, it means that the ECU hardware status is within the normal or safe range, and the current resource allocation and task execution status will be maintained by default. when When the risk level is greater than or equal to the low-risk threshold T2 and less than the high-risk threshold T1, it means that the ECU hardware status is low-risk. Reduce the allocation of non-critical task resources related to the ECU, transfer resources to other normal ECUs, and record the fault risk data. when If the value exceeds the high-risk threshold T1, it indicates that the ECU hardware is in a high-risk state. Immediately migrate the critical tasks of the ECU to the backup ECU and stop the operation of the faulty ECU.

[0013] Preferably, the execution monitoring terminal includes a second acquisition unit, an execution effect unit, and a correction unit; The second acquisition unit is used to acquire the core parameters of each automotive ECU chip after the dynamic allocation end and the early warning and obstacle avoidance end adjust the resource strategy. The core parameters include the timestamps of "task trigger signal" and "task completion signal" recorded by the ECU through software timer and task scheduler, the calculation of task response delay parameters, the ECU built-in fault diagnosis module to continuously monitor the ECU status, count the "fault code trigger number" and "task execution number" to calculate the fault recurrence rate, and the ECU resource occupancy rate acquired by the ECU built-in memory management unit. The execution effect unit calculates a comprehensive performance score for the ECU fault handling strategy based on task response delay parameters, fault recurrence rate, collected ECU resource occupancy rate, and theoretical resource value.

[0014] Preferably, the correction unit sets an effect threshold T3. When the overall effect score is greater than the effect threshold T3, the current resource allocation is maintained. When the overall effect score is less than or equal to the effect threshold T3, the system returns to the dynamic allocation terminal to regenerate.

[0015] A method for integrating electronic control software in automobiles includes the following steps: Step 1: Real-time parameter acquisition and data normalization processing; Step 2: Prioritize ECUs, predict resource requirements, and dynamically allocate resources; Step 3: Calculate the anomaly coefficient, conduct a comprehensive fault risk assessment, and develop a graded handling strategy for the ECU; Step 4: Collect core ECU parameters, score overall performance, and make strategy adjustment decisions.

[0016] The present invention has the following beneficial effects: 1. In this invention, the optimization unit constructs a priority quantification model, integrating functional criticality, real-time response requirements, and basic computational resource consumption to accurately distinguish ECU software priorities, ensuring that core control functions are given priority and avoiding response delays in critical tasks due to resource contention. The prediction unit adopts an autoregressive moving average model to accurately capture the changing trends of ECU resource requirements under complex automotive operating conditions, reserving resources for ECUs in advance and achieving dynamic adaptation between resource requirements and allocation. The risk unit establishes a resource allocation ratio adjustment model based on priorities and predicted requirements, allocating resources fairly and efficiently, ensuring the supply of resources to critical ECUs while avoiding resource waste, improving the overall operating efficiency and stability of the electronic control system, and laying a solid foundation for underlying resource management for the collaborative control execution of multiple ECUs in intelligent vehicles.

[0017] 2. In this invention, the quantization unit, based on normalized data, accurately quantifies the degree of abnormality of ECU hardware parameters through a piecewise function. The anomaly unit uses a weighted summation algorithm to integrate the anomaly coefficients of multiple hardware parameters, calculates the comprehensive fault risk value, and comprehensively assesses the health status of the ECU, avoiding misjudgment based on a single parameter anomaly. The processing unit responds in stages according to the risk value: maintaining the status quo when the risk is low to ensure system stability; adjusting non-critical resources and transferring data when the risk is medium to prevent problems in advance; and mitigating critical tasks and shutting down faulty ECUs when the risk is high to prevent the spread of faults. This approach accurately identifies potential faults and implements differentiated obstacle avoidance strategies, balancing system stability and response timeliness, and reducing driving risks caused by ECU hardware failures.

[0018] 3. In this invention, the second acquisition unit focuses on the core parameters of the ECU after resource adjustment, comprehensively capturing the actual performance after strategy execution. The execution effect unit uses a multi-dimensional formula to integrate actual resource occupation, planned value, fault recurrence rate, and task response delay to calculate a comprehensive effect score, quantifying the impact of the strategy on ECU operation. The correction unit compares the score with the threshold T3 to decide whether to maintain or regenerate the strategy. This can promptly detect resource allocation and fault avoidance strategy adaptation issues, avoid the long-term operation of ineffective strategies, dynamically optimize the resource management of the electronic control system, ensure the stable and reliable operation of the ECU, and enhance the system's adaptability. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the architecture of an electronic control software integrated control system for automobiles according to the present invention; Figure 2 This is a flowchart of an electronic control software integration control method for automobiles according to the present invention. Detailed Implementation

[0020] 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 embodiments of the present invention, and not all embodiments. 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.

[0021] Please see Figure 1 and Figure 2 The present invention provides a technical solution: an electronic control software integrated control system for automobiles, the system including a data acquisition terminal, a dynamic allocation terminal, an early warning and obstacle avoidance terminal, and an execution monitoring terminal; The data acquisition terminal is used to collect the operating parameters of various ECU chips in the car in real time and perform normalization processing; The dynamic allocation terminal is used to distinguish the priority of automotive ECU software and predict the future resource needs of the ECU. Based on the priority and predicted needs, a resource allocation ratio adjustment model is established. The early warning and obstacle avoidance terminal quantifies the degree of abnormality of each hardware parameter of each automotive ECU chip based on the normalized data from the data acquisition terminal, calculates the comprehensive fault risk value of each automotive ECU chip by weighted summation of the abnormality coefficients of each hardware parameter, and takes corresponding processing measures based on the comprehensive fault risk value. The execution monitoring end is used to collect the core parameters of each automotive ECU chip after the dynamic allocation end and the early warning and obstacle avoidance end adjust the resource strategy, calculate the task response latency and fault recurrence rate, and the comprehensive effect score of the ECU handling strategy. Based on the comprehensive effect score, it is determined whether to trigger a correction command.

[0022] The data acquisition terminal includes a first acquisition unit and a normalization unit; The first acquisition unit is used to acquire the operating parameters of each ECU chip in the car in real time. The operating parameters include temperature information acquired by the ECU's built-in temperature sensor, voltage information acquired by the voltage sensor, current information acquired by the current sensor, and data transmission error rate detected by the communication protocol logic. Specifically, the various ECU chips in a car include the engine control ECU, brake control ECU, steering control ECU, suspension control ECU, body control module, infotainment ECU, and instrument display ECU, etc. The normalization unit is used to normalize the information collected in real time by the first acquisition unit.

[0023] The dynamic allocation unit includes an optimization unit, a prediction unit, and a risk unit.

[0024] The optimization unit distinguishes the priorities of automotive ECU software by constructing a priority quantization model, as shown in the model below: ; in, Represents the overall priority of the ECU software. Represents the criticality of a function. Represents the need for real-time response. Represents the basic consumption of computing resources. and This represents the corresponding weighting coefficient.

[0025] The forecasting unit predicts the future resource requirements of the ECU by employing an autoregressive moving average model. ; Specific model: ; in, This represents the CPU resource requirement of the i-th ECU at time t. The coefficients represent the autoregressive component. This represents the CPU resource requirement of the i-th ECU at time tp. The coefficient representing the moving average component. This represents the prediction residual of the i-th ECU at time tq. The constant term representing the model, Represents the order of autoregression. Represents the order of the moving average. This represents the prediction residual of the i-th ECU at time t; Execution steps: By collecting historical CPU resource data from the ECU, performing median filtering and ADF testing, and using the processed data, the solution is obtained based on the least squares method or maximum likelihood estimation. , and The parameters determine the optimal one. , Combine d with d to construct an ARIMA model; input the current and historical CPU data for P time points, and substitute them into the model to calculate the resource requirements at the future time T.

[0026] Risk units are based on future resource needs. Overall software priority A resource allocation ratio adjustment model is established to calculate the actual resource allocation of the ECU software at time t. The calculation formula is as follows: ; in, This represents the actual resource allocation at time i of ECUt. M represents the resource requirements predicted by the prediction unit at time t in the future, and M represents the total number of ECU software programs. This represents the total resources currently available for allocation in the system.

[0027] The early warning and obstacle avoidance system includes a quantification unit, an anomaly unit, and a processing unit; The quantization unit quantifies the degree of anomaly C of each hardware parameter of each automotive ECU chip based on the normalized data from the data acquisition end. Each automotive ECU chip consists of hardware including a microcontroller, sensors, and actuators; Specifically, quantify the degree of abnormality C of each hardware parameter: ; in, Anomaly coefficient representing hardware parameters, Represents the normalized hardware parameter values. The lower limit threshold representing the normal range of the parameter. This represents the upper limit threshold of the normal range of the parameter.

[0028] The anomaly unit is used to calculate the weighted sum of anomaly coefficients and the overall fault risk value of the ECU, as shown in the following formula: ; in, Represents the overall fault risk value of the ECU. Let i be the weight of the i-th hardware parameter. Let be the anomaly coefficient of the i-th hardware parameter, and n represent the number of types of hardware parameters collected.

[0029] The processing unit sets a high-risk threshold T1 and a low-risk threshold T2; when When the value is less than the low-risk threshold T2, it means that the ECU hardware status is within the normal or safe range, and the current resource allocation and task execution status will be maintained by default. when When the risk level is greater than or equal to the low-risk threshold T2 and less than the high-risk threshold T1, it means that the ECU hardware status is low-risk. Reduce the allocation of non-critical task resources related to the ECU, transfer resources to other normal ECUs, and record the fault risk data. when If the value exceeds the high-risk threshold T1, it indicates that the ECU hardware is in a high-risk state. Immediately migrate the critical tasks of the ECU to the backup ECU and stop the operation of the faulty ECU.

[0030] Specifically, the high-risk threshold T1 and low-risk threshold T2 are determined by collecting historical data from the long-term operation of the ECU, including records of abnormal hardware parameters, fault risk events and corresponding R values. The historical data is classified and R data points for the "normal-low-risk transition" and "low-risk-high-risk transition" stages are selected. The distribution characteristics of R in the transition stage are calculated using a Python data analysis library, and the corresponding critical values ​​are determined as the high-risk threshold T1 and low-risk threshold T2.

[0031] The monitoring unit includes a second data acquisition unit, an execution effect unit, and a correction unit. The second acquisition unit is used to acquire the core parameters of each automotive ECU chip after the dynamic allocation end and the early warning and obstacle avoidance end adjust the resource strategy. The core parameters include the timestamps of the "task trigger signal" and "task completion signal" recorded by the ECU through the software timer and task scheduler, the calculation of task response delay parameters, the ECU's built-in fault diagnosis module, which continuously monitors the ECU status, counts the "number of fault code triggers" and "number of task executions", calculates the fault recurrence rate, and the ECU resource occupancy rate acquired by the ECU's built-in memory management unit. The performance unit calculates a comprehensive performance score for the ECU fault handling strategy based on task response delay parameters, fault recurrence rate, collected ECU resource utilization rate, and theoretical resource value. The formula for calculating the overall performance score is: ; in, The overall performance score at time t represents the total performance. This represents the actual ECU resource utilization rate at time t. The theoretical value of resources representing the plan, This represents the ECU fault recurrence rate at time t. This represents the ECU task response delay parameter at time t. Represents the weighting coefficient (calibrated from historical data).

[0032] The correction unit sets an effect threshold T3. If the overall effect score is greater than the effect threshold T3, the current resource allocation is maintained. If the overall effect score is less than or equal to the effect threshold T3, the system returns to the dynamic allocation terminal to regenerate.

[0033] Specifically, the effect threshold T3 is based on a large amount of historical ECU fault handling case data. It is a critical value that can ensure the stable and efficient operation of the system by statistically analyzing the comprehensive effect score. T3 is determined after expert experience screening and verification. For example, the effect score under different resource allocation schemes is monitored over a long period of time to find the score boundary value that keeps the system in a good state.

[0034] In this invention, an integrated control method and system for automotive electronic control software is provided. The optimization unit constructs a priority quantification model, integrating functional criticality, real-time response requirements, and basic computational resource consumption to accurately distinguish ECU software priorities. This ensures that core control functions receive priority protection, avoiding response delays in critical tasks due to resource contention. The prediction unit employs an autoregressive moving average model to accurately capture the changing trends of ECU resource demands under complex automotive operating conditions, reserving resources for ECUs in advance and achieving dynamic adaptation between resource demand and allocation. The risk unit establishes a resource allocation ratio adjustment model based on priority and predicted demand, allocating resources fairly and efficiently. This ensures the supply of resources to critical ECUs while avoiding resource waste, improving the overall operating efficiency and stability of the electronic control system and laying a solid foundation for underlying resource management for multi-ECU collaborative control execution in intelligent vehicles. The quantization unit, based on normalized data, accurately quantifies the degree of abnormality of ECU hardware parameters through a piecewise function. The anomaly unit uses a weighted summation algorithm to integrate the anomaly coefficients of multiple hardware parameters, calculates the comprehensive fault risk value, and comprehensively assesses the health status of the ECU, avoiding misjudgment based on a single parameter anomaly. The processing unit responds in stages according to the risk value: maintaining the status quo and ensuring system stability when the risk is low; adjusting non-critical resources and transferring data to prevent problems in advance when the risk is medium; and migrating critical tasks and shutting down the faulty ECU when the risk is high to prevent the fault from spreading. This approach not only accurately identifies potential faults but also implements differentiated obstacle avoidance strategies, balancing system stability and response timeliness, and reducing driving risks caused by ECU hardware failures. The second acquisition unit focuses on the core parameters of the ECU after resource adjustment, and comprehensively captures the real performance after strategy execution. The execution effect unit uses a multi-dimensional formula to integrate actual resource occupation, planned value, fault recurrence rate, and task response latency to calculate a comprehensive effect score, quantify the impact of the strategy on ECU operation, and the correction unit decides to maintain or regenerate the strategy based on the score and the threshold T3. This can promptly detect the adaptation problems of resource allocation and fault avoidance strategies, avoid the long-term operation of ineffective strategies, dynamically optimize the resource management of the electronic control system, ensure the stable and reliable operation of the ECU, and enhance the system's adaptability. A method for integrating electronic control software in automobiles. Step 1: Real-time parameter acquisition and data normalization processing; Step 2: Prioritize ECUs, predict resource requirements, and dynamically allocate resources; Step 3: Calculate the anomaly coefficient, conduct a comprehensive fault risk assessment, and develop a graded handling strategy for the ECU; Step 4: Collect core ECU parameters, score overall performance, and make strategy adjustment decisions.

[0035] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An integrated control system of electric control software of an automobile, characterized by, The system comprises a data acquisition end, a dynamic allocation end, a pre-warning obstacle avoidance end and an execution monitoring end; The data acquisition end is used for collecting working parameters of each ECU chip of the automobile in real time and performing normalization processing; The dynamic allocation end is used for distinguishing the priority of the ECU software of the automobile and predicting the future resource demand of the ECU, and based on the priority and the predicted demand, a resource allocation proportion adjustment model is established; The pre-warning obstacle avoidance end quantifies the abnormality degree of each hardware parameter of each ECU chip of the automobile based on the normalized data of the data acquisition end, and the abnormality coefficients of each hardware parameter are weighted and summed to calculate the comprehensive failure risk value of each ECU chip of the automobile, and based on the comprehensive failure risk value, corresponding processing measures are taken; The execution monitoring end is used for collecting the core parameters of each ECU chip after the resource strategy is adjusted by the dynamic allocation end and the pre-warning obstacle avoidance end, calculating the task response time delay and the failure recurrence rate, and the comprehensive effect score of the ECU disposal strategy, and based on the comprehensive effect score, it is judged whether the correction instruction is triggered.

2. The integrated control system for the electronic control software of an automobile according to claim 1, wherein The data acquisition end comprises a first acquisition unit and a normalization unit. The first acquisition unit is used for collecting working parameters of each ECU chip of the automobile in real time, and the working parameters include temperature information collected by the temperature sensor built in the ECU, voltage information collected by the voltage sensor, current information collected by the current sensor and data transmission error rate detected by the communication protocol logic; The normalization unit is used for performing normalization processing on the information collected by the first acquisition unit in real time.

3. The integrated control system for the electronic control software of an automobile according to claim 1, wherein The dynamic allocation end comprises a priority unit, a prediction unit and a risk unit.

4. The integrated control system for the electronic control software of an automobile according to claim 3, wherein The priority unit distinguishes the priority of the ECU software of the automobile by constructing a priority quantization model, and the model is as follows: ； wherein, represents the ECU software comprehensive priority, represents the function criticality, represents the response real-time requirement, represents the computing resource base consumption, and represents the corresponding weight coefficient.

5. The integrated control system for the electronic control software of an automobile according to claim 3, wherein The prediction unit predicts the future resource demand of the ECU by employing an autoregressive moving average model ; The risk unit is based on future resource requirements and software integration priority A resource allocation proportion adjustment model is established, and the actual allocated resources of the ECU software at time t are calculated. The calculation formula is as follows: ； wherein, represents the actual allocated resources at the ith ECUt time, represents the predicted future t time resource demand by the prediction unit, M represents the total number of ECUs, represents the total resources currently allocatable by the system.

6. The integrated control system for the electronic control software of an automobile according to claim 1, wherein The pre-warning obstacle avoidance end comprises a quantization unit, an abnormality unit and a processing unit; The quantization unit quantifies the abnormality degree C of each hardware parameter of each ECU chip of the automobile based on the normalized data of the data acquisition end; The abnormality unit is used for weighting and summing the abnormality coefficients to calculate the comprehensive failure risk value of the ECU, and the formula is as follows: ； wherein, represents an ECU comprehensive failure risk value, is the weight of the i-th hardware parameter, is the i-th hardware parameter anomaly coefficient, and n represents the number of types of collected hardware parameters.

7. The integrated control system for the electronic control software of an automobile according to claim 6, wherein The processing unit sets a high risk threshold T1 and a low risk threshold T2; When If less than the low risk threshold T2, then it represents that the ECU hardware state is in normal or safe range, and the current resource allocation and task execution state are kept by default; When If greater than or equal to the low risk threshold T2 and less than the high risk threshold T1, it means that the ECU hardware state is in low risk, the allocation of the ECU related non-critical task resources is reduced, the resources are transferred to other normal ECUs, and the fault risk data is recorded. When If greater than the high risk threshold T1, then it represents that the ECU hardware state is in high risk, immediately migrate the ECU key task to the standby ECU, stop the operation of the faulty ECU.

8. The integrated control system for software of an automobile according to claim 1, wherein The execution monitoring end comprises a second acquisition unit, an execution effect unit and a correction unit; The second acquisition unit is used for collecting the core parameters of each ECU chip after the resource strategy is adjusted by the dynamic allocation end and the pre-warning obstacle avoidance end, and the core parameters include the time stamp of the "task trigger signal" and the "task completion signal" recorded by the software timer and the task scheduler inside the ECU, the task response time delay parameter is calculated, the ECU built-in fault diagnosis module continuously monitors the ECU state, the "fault code trigger times" and the "task execution times" are counted to calculate the failure recurrence rate, and the ECU resource occupancy rate collected by the ECU built-in memory management unit; The execution effect unit calculates the comprehensive effect score of the ECU failure disposal strategy based on the task response time delay parameter, the failure recurrence rate, the collected ECU resource occupancy rate and the resource theoretical value.

9. The integrated control system for the electronically controlled software of an automobile according to claim 8, wherein The correction unit sets an effect threshold T3, when the comprehensive effect score is greater than the effect threshold T3, the current resource allocation is maintained, when the comprehensive effect score is less than or equal to the effect threshold T3, the dynamic allocation end is returned to regenerate.

10. The method for integrated control of the software of the electronic control of a motor vehicle, according to any one of the claims from 1 to 9, characterized in that, The method comprises the following steps: Step one: real-time parameter acquisition and data normalization processing; Step two: ECU priority quantization, resource demand prediction and dynamic resource allocation; Step three: ECU abnormal coefficient calculation, comprehensive fault risk assessment and hierarchical disposal strategy; Step four: ECU core parameter acquisition, comprehensive effect score and strategy correction decision.

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