Remote debugging system and method for controller

Abstract

The present invention relates to the technical field related to remote control and adjustment, and specifically includes a remote debugging system and method for a controller. The system includes: an integrated remote communication module, which interacts with a remote debugging platform in real time for data; an integrated processing unit that analyzes historical parameters and builds an associated index; a bidirectional retrieval engine that traverses the index, establishes a parameter vector and verifies it; a remote control module receives the verification result and uploads it to the remote debugging platform through the remote communication module for remote control operations. This solves the technical problem of relying on physical detection and local adjustment, lacking effective integration with real-time operating data, resulting in the inability to timely discover potential faults and limited control operation accuracy. It achieves accurate prediction of controller performance and faults based on historical data, and through the combination of a bidirectional retrieval engine and a machine learning model, accurately diagnoses and adjusts the operating status of an electric tricycle, thereby improving the efficiency and accuracy of remote debugging.

Description

Technical Field

[0001] The present invention relates to the technical field related to remote control and regulation, and in particular to a remote debugging system and method for a controller. Background Art

[0002] With the widespread use of electric tricycles in urban transportation, especially in short-distance transportation and logistics, the performance and safety of electric tricycles have become increasingly important. In order to improve the operating efficiency of electric tricycles, ensure their safety and optimize the user experience, more and more electric tricycles are equipped with advanced controllers and electronic systems. However, the debugging and maintenance of controllers rely on physical inspection and local adjustment methods, which causes many inconveniences and potential risks during use. In addition, physical access to the controller for repair or adjustment is time-consuming and costly, especially when the vehicle is far away from the service point, and it is impossible to respond quickly to faults or performance anomalies. More importantly, the lack of real-time data support and intelligent analysis mechanisms makes it impossible to accurately predict and optimize the operating status of electric tricycles, and the risk of secondary failures still exists.

[0003] In summary, the existing technology has technical problems such as reliance on physical detection and local adjustments, lack of effective integration with real-time operation data, resulting in the inability to detect potential faults in a timely manner and limited control operation accuracy. Summary of the Invention

[0004] This application provides a remote debugging system and method for a controller, aiming to solve the technical problems in the prior art that rely on physical detection and local adjustment, lack effective integration with real-time operation data, resulting in the inability to detect potential faults in a timely manner and limited control operation accuracy.

[0005] In view of the above problems, the technical solution to implement this application is:

[0006] On the one hand, the present application provides a remote debugging system for a controller, wherein the system includes: a remote communication module, the remote communication module is integrated in the electric tricycle controller, and is used to establish a communication connection between the electric tricycle controller and a remote debugging platform, the remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform, and receives debugging instructions from the remote debugging platform; an integrated processing unit, the integrated processing unit is set in the electric tricycle controller, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the communication transmission instance, and sets a first directional correlation index structure; an index matching library, the index matching library cooperates with the integrated processing unit, and is used to perform a correlation analysis on the historical operating parameters and the communication transmission instance, and sets a first directional correlation index structure; an index matching library The integrated processing unit performs a correlation analysis between historical operating parameters and debugging response patterns, and sets a second directional association index structure. The index matching library stores a first directional association index structure and a second directional association index structure; a bidirectional retrieval engine performs a bidirectional traversal between the first directional association index structure and the first index item pointer, the second directional association index structure and the second index item pointer in the index matching library, establishes a parameter value vector for remote debugging, and uses a machine learning model to perform feedback verification on the parameter value vector; a remote control module is used to receive feedback verification results and upload them to the remote debugging platform through the remote communication module for remote control operations.

[0007] On the other hand, the present application provides a remote debugging method for a controller, wherein the method includes: a remote communication module is integrated in the electric tricycle controller, and is used to establish a communication connection between the electric tricycle controller and a remote debugging platform, the remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform, and receives debugging instructions from the remote debugging platform; an integrated processing unit is set in the electric tricycle controller, and the integrated processing unit performs a correlation analysis on historical operating parameters and communication transmission instances, and sets a first directional association index structure; an index matching library cooperates with the integrated processing unit, and the integrated processing unit performs a correlation analysis on historical operating parameters and debugging response patterns, and sets a second directional association index structure, and the index matching library stores a first directional association index structure and a second directional association index structure; bidirectional traversal is performed between the first directional association index structure and the first index item pointer, and the second directional association index structure and the second index item pointer in the index matching library to establish a parameter value vector for remote debugging, and feedback verification is performed on the parameter value vector using a machine learning model; the feedback verification result is received and uploaded to the remote debugging platform through the remote communication module for remote control operation.

[0008] In summary, the one or more technical solutions provided in this application solve the technical problems of relying on physical detection and local adjustment, lacking effective integration with real-time operation data, resulting in the inability to timely detect potential faults and limited control operation accuracy. It realizes accurate prediction of controller performance and faults based on historical data, and through the combination of a bidirectional retrieval engine and a machine learning model, accurately diagnoses and adjusts the operating status of the electric tricycle, thereby improving the efficiency and accuracy of remote debugging. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of the structure of a remote debugging system for a controller is provided for this application.

[0010] Figure 2 A flowchart of a remote debugging method for a controller is provided for this application.

[0011] Description of reference numerals: remote communication module M100, integrated processing unit M200, index matching library M300, bidirectional search engine M400, remote control module M500. DETAILED DESCRIPTION

[0012] Example 1

[0013] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, an embodiment of the present application provides a remote debugging system for a controller, wherein the system includes:

[0014] Remote communication module M100, which is integrated in the electric tricycle controller and is used to establish a communication connection between the electric tricycle controller and the remote debugging platform. The remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform and receives debugging instructions from the remote debugging platform.

[0015] Specifically, the remote communication module is used to realize data communication between the controller and the remote debugging platform. Furthermore, the remote communication module is responsible for transmitting the real-time operating parameters of the electric tricycle controller to the remote debugging platform, and at the same time receiving the debugging instructions sent from the remote debugging platform, providing support for remote monitoring and debugging of the electric tricycle controller.

[0016] The remote communication module needs to establish a stable communication connection with the remote debugging platform, usually through a wireless network (such as 4G / 5G, Wi-Fi, etc.); once the communication connection is established, the real-time operating parameters of the electric tricycle controller (such as speed, acceleration, battery status, etc.) will be sent to the remote debugging platform regularly or as needed; at the same time, the remote communication module must also be able to receive debugging instructions from the remote debugging platform, including adjusting parameters, performing diagnostic tests, etc.

[0017] The remote communication module is the basis for realizing the remote debugging function. Through this module, the operating status of the electric tricycle can be monitored in real time and remote adjustments can be made as needed, which improves the efficiency of debugging, especially when the electric tricycle is far away from the service center or working in remote areas. In addition, the transmission of real-time data also provides a basis for subsequent data analysis and fault prediction.

[0018] The integrated processing unit M200 is arranged in the electric tricycle controller, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the communication transmission instances to set a first directional correlation index structure.

[0019] Specifically, the integrated processing unit is responsible for processing and analyzing the historical operating parameters and communication transmission instances of the controller, and conducting in-depth analysis of the historical data through advanced algorithms and data processing technologies to identify and establish the correlation between historical operating parameters and communication transmissions; the first directed association index structure refers to a data structure created by the integrated processing unit based on the analysis results. This structure can help the system quickly retrieve and locate information associated with specific historical operating parameters and communication transmission instances, which is crucial to improving system response speed and processing efficiency.

[0020] The integrated processing unit collects historical operating parameters of the electric tricycle controller, including speed, acceleration, battery voltage, motor temperature, etc. At the same time, the integrated processing unit analyzes instances related to controller communication transmission, involving data exchange records between the controller and the remote debugging platform; through the mutual correlation analysis of historical operating parameters and communication transmission instances, the integrated processing unit can identify which parameter changes are associated with specific communication transmission behaviors; based on the above analysis, the integrated processing unit sets a first directional association index structure, which can help the system quickly access and process relevant data when needed.

[0021] By creating the first directed associative index structure, queries and processing requests can be responded to more quickly, thereby improving data processing efficiency. In addition, the integrated processing unit enables the system to intelligently process historical data, providing a basis for subsequent fault prediction and performance optimization, and providing data-driven decision support for the remote debugging platform, enabling more accurate debugging and optimization operations based on actual data.

[0022] The index matching library M300 cooperates with the integrated processing unit, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the debugging response mode, and sets a second directional correlation index structure. The index matching library stores the first directional correlation index structure and the second directional correlation index structure.

[0023] Specifically, the index matching library works closely with the integrated processing unit to store and manage two directional association index structures: a first directional association index structure and a second directional association index structure. These index structures are established by the system by analyzing the relationship between historical operating parameters and debugging response patterns, aiming to optimize and accelerate the data retrieval process and improve the system's understanding and response capabilities to the status of the electric tricycle controller; the second directional association index structure is created based on the integrated processing unit's analysis of the relationship between historical operating parameters and debugging response patterns. Together with the first directional association index structure, it provides the system with a comprehensive index framework for rapid access and processing of related data.

[0024] The index matching library is responsible for storing the first directional association index structure and the second directional association index structure created by the integrated processing unit; the integrated processing unit conducts an in-depth analysis of historical operating parameters and debugging response patterns, and based on the analysis results, sets the second directional association index structure and stores it in the index matching library; by maintaining these index structures, the index matching library enables the system to retrieve data related to specific problems or debugging requirements more quickly, thereby improving response speed and processing efficiency.

[0025] By maintaining two directional association index structures, the index matching library greatly improves the efficiency of the system in retrieving relevant data, providing support for rapid diagnosis and response to problems with electric tricycle controllers. In addition, the index matching library enables the system to more deeply analyze historical data and debugging response patterns, providing strong data support for the remote debugging platform. In-depth data analysis allows for intelligent debugging operations, thereby better understanding the controller's behavior and potential problems, improving the accuracy and efficiency of debugging. At the same time, the index matching library supports complex query operations, enabling the system to handle multi-dimensional data association analysis, providing technical conditions for accurate fault prediction and performance optimization.

[0026] The bidirectional search engine M400 performs bidirectional traversal between the first directional association index structure and the first index item pointer, the second directional association index structure and the second index item pointer in the index matching library, establishes a parameter value vector for remote debugging, and uses a machine learning model to perform feedback verification on the parameter value vector.

[0027] Specifically, the bidirectional retrieval engine is responsible for efficient data retrieval between the first directional associative index structure and the second directional associative index structure stored in the index matching library. The first index item pointer and the second index item pointer point to specific data items in the two index structures respectively, which are used to guide the retrieval engine to navigate in the data structure; the parameter value vector refers to a series of parameter values ​​extracted from the index structure, which are organized into a vector for subsequent data processing and analysis; feedback verification refers to the use of machine learning models to analyze the parameter value vector to verify and optimize the accuracy and effectiveness of these parameter values.

[0028] The bidirectional retrieval engine starts from the first directional associative index structure and traverses step by step along the first index item pointer. At the same time, it starts from the second directional associative index structure and traces back along the second index item pointer until the node where the two index structures intersect. During the bidirectional traversal process, key parameter values ​​are extracted and organized into a vector, which contains all the parameters required for remote debugging. The parameter value vector is analyzed and verified using a machine learning model to ensure the accuracy and effectiveness of these parameter values. At the same time, the parameter values ​​can also be optimized to adapt to different debugging scenarios.

[0029] Through bidirectional traversal, the required parameter values ​​can be quickly extracted from the two index structures, greatly improving the efficiency of data retrieval. In addition, through the application of machine learning models, the accuracy of parameter values ​​is verified, and the parameter values ​​are optimized based on historical data and pattern recognition, which improves the depth and breadth of data analysis. Furthermore, the establishment of parameter value vectors and feedback verification provide accurate data support for remote debugging, making the debugging process more intelligent and automated. At the same time, accurate parameter value vectors and timely feedback verification enable the system to respond to the debugging needs of the electric tricycle controller more quickly, improving the system's responsiveness and debugging effect.

[0030] The remote control module M500 is used to receive the feedback verification result and upload it to the remote debugging platform through the remote communication module to perform remote control operations.

[0031] Specifically, the remote control module is a key component responsible for executing remote control operations and receiving feedback verification results from the bidirectional retrieval engine. These results are usually parameter value vectors analyzed by the machine learning model, which contain the optimization parameters and adjustment suggestions of the electric tricycle controller; feedback verification results refer to the conclusions drawn by the machine learning model after analyzing the parameter value vectors, and these conclusions are used to guide remote control operations; remote control operations refer to adjusting and optimizing the electric tricycle controller based on the feedback verification results to improve its performance or resolve faults.

[0032] The remote control module receives feedback verification results from the bidirectional retrieval engine, which are obtained based on the analysis of the parameter value vector by the machine learning model; the feedback verification results are processed to determine which remote control operations need to be performed, such as adjusting controller parameters, performing specific diagnostic tests, etc.; the processed results are uploaded to the remote debugging platform through the remote communication module so that technicians can view and perform these operations; on the remote debugging platform, technicians perform specific remote control operations based on the uploaded feedback verification results and adjust the electric tricycle controller.

[0033] The electric tricycle controller can be controlled at a remote location without having to visit the site, which improves the efficiency of maintenance and troubleshooting. When potential faults or performance issues are discovered, a quick response is provided, which reduces the downtime of the electric tricycle and improves operational efficiency. This in turn improves the intelligence level of the entire system, making the debugging and maintenance of the electric tricycle controller more automated and intelligent.

[0034] Furthermore, the integrated processing unit further includes:

[0035] An indicator introduction module, the indicator introduction module is used to introduce safety control indicators, the safety control indicators include braking distance; a safety performance analysis module, the safety performance analysis module is used to perform safety performance analysis based on the electric tricycle controller in combination with the safety control indicators to determine the braking abnormality indicator; a mechanism access module, the mechanism access module is used to access the emergency response mechanism based on the first directional association index structure and under the direction of the first index item pointer, the emergency response mechanism is in a low power consumption state.

[0036] Specifically, the indicator introduction module is responsible for introducing indicators related to the safety control of electric tricycles, such as braking distance, to evaluate the safety of electric tricycles; the safety performance analysis module uses safety control indicators, combined with data from the electric tricycle controller, to conduct in-depth analysis to identify and determine braking abnormality indicators, that is, those parameters that may indicate problems with the braking system; the mechanism access module refers to a module that activates the emergency response mechanism in an emergency based on the guidance of the first directed association index structure and the first index item pointer. The emergency response mechanism maintains a low power consumption mode when inactive to prepare for emergencies.

[0037] According to the safety requirements of electric tricycles, key safety control indicators such as braking distance are determined; these indicators are integrated into the system for subsequent analysis and monitoring; relevant data of the electric tricycle controller and the safety control indicators provided by the indicator introduction module are collected, and the safety of the electric tricycle is improved by real-time monitoring and analysis of the safety control indicators; a comprehensive analysis is conducted by combining the controller data and the safety control indicators to identify possible braking anomalies, identify potential braking problems, implement preventive maintenance, and reduce accidents; based on the analysis results, determine which indicators are abnormal and may indicate problems with the braking system; under the guidance of the first directed association index structure, prepare to access the emergency response mechanism; in non-emergency situations, keep the emergency response mechanism in a low power state, which is both energy-saving and efficient; once the safety performance analysis module determines the braking abnormality indicator, the mechanism access module will activate the emergency response mechanism to deal with potential safety problems. When an anomaly is detected, the emergency response mechanism can be quickly activated to improve the ability to handle emergency situations.

[0038] Furthermore, the mechanism access module includes:

[0039] An indicator preset module, the indicator preset module is used for the emergency response mechanism to include a preset braking risk; a mechanism awakening module, the mechanism awakening module is used to compare the braking safety risk corresponding to the braking abnormality indicator with the preset braking risk and awaken the emergency response mechanism; an event response module, the event response module is used to quickly respond to the braking safety event corresponding to the braking abnormality indicator according to the first directional association index structure when the emergency response mechanism is in an awakened state.

[0040] Specifically, the indicator preset module is responsible for presetting braking risk indicators for the emergency response mechanism. These indicators are predefined and used to assess the risk level that the braking system may face; the mechanism awakening module means that when the braking abnormality indicator matches the preset braking risk, the emergency response mechanism will be triggered. The event response module will quickly respond to specific braking safety events according to the first directional association index structure after the emergency response mechanism is awakened.

[0041] Based on the characteristics and historical data of the electric tricycle's braking system, a series of preset braking risk indicators are defined and stored in the system. The indicator preset module allows the system to predefine and manage braking risks, enhancing its ability to control potential risks. The system monitors braking abnormality indicators from the safety performance analysis module and compares the monitored braking abnormality indicators with the preset braking risks in the indicator preset module. If the braking abnormality indicators match the preset braking risks, the mechanism wake-up module triggers the emergency response mechanism. The mechanism wake-up module ensures that the system can monitor the braking system status in real time and respond quickly when risks are detected. It also identifies the state in which the emergency response mechanism has been awakened. Based on the first directed association index structure, it quickly identifies and responds to braking safety events corresponding to the braking abnormality indicators. It then performs necessary actions, such as adjusting controller parameters or sending warnings, to handle braking safety events. The event response module improves the system's ability to handle emergency braking events, helping to reduce the occurrence of accidents and failures. Through the collaboration between the indicator preset module, the mechanism wake-up module, and the event response module, the safety and reliability of the electric tricycle's braking system are improved, protecting the safety of the driver and vehicle.

[0042] Furthermore, the event response module includes:

[0043] An index bit determination module, the index bit determination module is used to determine the key index bit of the first directional association index structure under the pointing of the first index item pointer when the emergency response mechanism is in an awake state; an index branch splitting module, the index branch splitting module is used to split the first index branch based on the first key index bit of the first directional association index structure, the first key index bit of the first directional association index structure supports parallel multipath processing; an immediate response processing module, the immediate response processing module is used to perform immediate response processing based on the emergency response mechanism according to the first key index bit and the first index branch.

[0044] Specifically, the index bit determination module is used to determine the key index bits in the first directional association index structure according to the direction of the first index item pointer when the emergency response mechanism is awakened. These key index bits are particularly important nodes in the index structure and are used to quickly locate and handle emergency situations; the index branch splitting module refers to further splitting the first index branch into finer branches according to these positions after determining the key index bits, so as to support parallel multipath processing and improve data processing efficiency and response speed; the immediate response processing module is a module that responds and handles emergencies immediately based on the emergency response mechanism and key index bits and branches.

[0045] The emergency response mechanism monitors the status of the emergency response mechanism and confirms that it has been awakened. Under the guidance of the first index item pointer, the module identifies the key index bit in the first directional association index structure. The index bit determination module can accurately locate the key data in the emergency situation and provide support for rapid response. The module uses the key index bit identified by the index bit determination module to split the first index branch. The module ensures that the split branches can support parallel multipath processing to improve data processing capabilities. The index branch splitting module supports parallel processing by splitting the index branches, thereby improving the efficiency of the system in handling emergencies. The module operates when the emergency response mechanism is awakened. The module operates according to the first key index bit and the first index branch. The module executes necessary response measures, such as adjusting controller parameters or executing specific security protocols. The immediate response processing module ensures that the system can quickly respond based on the key index bit and branch, reducing potential risks and damage. Through the collaboration between the index bit determination module, the index branch splitting module, and the immediate response processing module, the electric tricycle controller's ability to handle emergencies is improved, enhancing overall safety and reliability.

[0046] Furthermore, the instant response processing module includes:

[0047] A thread setting module, the thread setting module is used to set parallel multipath threads based on the first index branch, and the parallel multipath threads are used to simultaneously process multipath data streams; a thread configuration module, the thread configuration module is used to configure synchronization locks and thread workloads through the parallel multipath threads; and a balancing optimization module, the balancing optimization module is used to perform dynamic resource balancing optimization based on the first index branch through the synchronization locks and thread workloads.

[0048] Specifically, the thread setting module is responsible for creating and managing components of parallel multi-path threads, which can simultaneously process data streams from different paths (multipaths) to improve the parallelism and efficiency of data processing; parallel multi-path threads refer to threads that can execute multiple tasks simultaneously and can run independently of each other to speed up the overall processing speed; the thread configuration module refers to the module responsible for configuring and managing the synchronization locks and workloads of these parallel multi-path threads to ensure thread safety and efficient operation; the synchronization lock is a programming tool used to control the access of multiple threads to shared resources to prevent data competition and inconsistency; the thread workload refers to the amount of tasks assigned to each thread, and the balancing optimization module refers to a module that dynamically adjusts resource allocation based on the first index branch and thread configuration to optimize system performance.

[0049] Based on the first index branch, multiple parallel multipath threads are created to simultaneously process data streams from different paths; the life cycles of multiple threads are managed to ensure that each thread can work together efficiently. The thread setting module improves the system's ability to process multipath data streams by creating parallel multipath threads; synchronization locks are configured for parallel multipath threads to ensure thread safety and data consistency; based on the thread capabilities and other factors, thread workloads are reasonably allocated to ensure balanced resource utilization. The thread configuration module ensures thread safety and efficiency by configuring synchronization locks and reasonably allocating workloads; resource usage and workloads in parallel multipath threads are monitored; based on monitoring data, resource allocation is dynamically adjusted and thread workloads are optimized to achieve balanced resource utilization. The balanced optimization module optimizes the overall performance of the system by dynamically adjusting resource allocation and improves resource utilization efficiency. Through the mutual cooperation between the thread setting module, the thread configuration module, and the balanced optimization module, emergency responses can be faster, thereby improving the remote debugging and emergency response capabilities of the electric tricycle controller.

[0050] Furthermore, the balance optimization module includes:

[0051] A load monitoring module, which is used to fine-grainedly monitor the workload and resource usage of each thread in the parallel multipath thread; a resource pool establishment module, which is used to set up an elastic resource pool through the parallel multipath thread corresponding to the first index branch of the first directional association index structure to the parallel multipath thread corresponding to the Mth index branch; a resource allocation module, which is used to deploy edge computing nodes and set up a microservice architecture through the elastic resource pool and fine-grained monitoring data, wherein the microservice architecture includes multiple independent microservices and dynamically allocates and recycles computing resources.

[0052] Specifically, the load monitoring module is a component used to monitor the workload and resource usage of each parallel multi-path thread in real time. It can provide fine-grained monitoring data to understand the running status of each thread; the elastic resource pool refers to a collection of resources that can be dynamically resized according to system requirements, allowing the system to flexibly allocate resources under different workloads. The resource pool establishment module is responsible for setting up such resource pools in multiple index branches of the system; the edge computing node refers to a computing node that processes data near the data source, which can reduce data transmission delays and improve response speed; the microservice architecture is used to decompose an application into a set of small services, each of which runs in its own independent process and is usually built around specific business capabilities. These services can be independently deployed, upgraded and expanded.

[0053] The load monitoring module monitors the workload and resource usage of each thread in the parallel multipath thread in real time, collecting fine-grained data on thread performance and resource consumption. This fine-grained monitoring helps the system utilize resources more efficiently. Elastic resource pools are created for the parallel multipath threads corresponding to the first through the Mth index branches, and these resource pools are managed to ensure they can be dynamically adjusted based on system needs. The resource pool establishment module creates elastic resource pools, enabling the system to flexibly adjust resource allocation based on workload changes. Edge computing nodes are deployed based on the elastic resource pools, and a microservices architecture is established, consisting of multiple independent microservices. Computing resources are dynamically allocated and reclaimed based on the fine-grained monitoring data provided by the load monitoring module. The resource allocation module reduces data processing latency and improves system responsiveness by deploying edge computing nodes and establishing a microservices architecture. Furthermore, the microservices architecture makes the system easier to scale and maintain, as each microservice can be independently upgraded and expanded without affecting the operation of the entire system. Through collaboration among the load monitoring module, the resource pool establishment module, and the resource allocation module, resources are managed automatically and intelligently, improving system stability and reliability.

[0054] Furthermore, the bidirectional search engine includes:

[0055] A step-by-step traversal module, which is used to start from the first directional association index structure and step-by-step traverse along the first index item pointer; a cross-validation module, which is used to simultaneously start from the second directional association index structure and trace back along the second index item pointer to the node intersecting with the first index branch, and perform cross-validation; a numerical conversion module, which is used to extract feature values ​​from the matching index items after cross-validation, and perform numerical conversion to establish a parameter value vector for remote debugging.

[0056] Specifically, the step-by-step traversal module is used to start from the starting point of the first directed associative index structure and sequentially access each node along the pointer of the first index item; the cross-validation module refers to a component that starts from the starting point of the second directed associative index structure, traces back to the node intersecting with the first index branch, and performs cross-validation there, thereby verifying the consistency between different data sets or models through cross-validation; the numerical conversion module refers to a component that extracts eigenvalues ​​from the index items matched by cross-validation and converts them into parameter value vectors suitable for remote debugging. Eigenvalues ​​usually refer to numerical values ​​in the data that can represent a certain characteristic or pattern.

[0057] Starting from the starting node of the first directional associative index structure, each node is accessed in the order indicated by the first index item pointer, and each accessed node is processed, which may be a data extraction or marking operation. The step-by-step traversal module improves data processing efficiency by sequentially accessing and processing nodes. Starting from the starting point of the second directional associative index structure, the second index item pointer is traced back to the node that intersects with the first index branch. Cross-validation is performed at the intersecting node to verify the consistency and accuracy of the data in the two index structures. Furthermore, the step-by-step traversal module and the cross-validation module jointly ensure the consistency of data extracted from the two different index structures, enhancing data reliability. Key eigenvalues ​​are extracted from the cross-validation matched index items, and the extracted eigenvalues ​​are subjected to necessary numerical conversion to establish a parameter value vector suitable for remote debugging. The numerical conversion module extracts and converts the eigenvalues ​​to provide an accurate parameter value vector for data analysis and remote debugging. At the same time, the accurate eigenvalues ​​and parameter value vectors optimize the remote debugging process, making debugging more accurate and efficient. Through the collaboration between the step-by-step traversal module, the cross-validation module, and the numerical conversion module, the system's ability to process complex data and perform advanced data analysis is improved.

[0058] In summary, the beneficial effects of the embodiments of the present application are:

[0059] The remote communication module is integrated in the electric tricycle controller and is used to establish a communication connection between the electric tricycle controller and the remote debugging platform. The remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform and receives debugging instructions from the remote debugging platform; the integrated processing unit is set in the electric tricycle controller, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the communication transmission instance, and sets a first directional correlation index structure; the index matching library cooperates with the integrated processing unit, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the debugging response mode, and sets a second directional correlation index structure, and the index matching library stores the first directional correlation index structure. structure, and a second directional association index structure; the bidirectional retrieval engine performs bidirectional traversal between the first directional association index structure and the first index item pointer, the second directional association index structure and the second index item pointer in the index matching library, establishes a parameter value vector for remote debugging, and uses a machine learning model to perform feedback verification on the parameter value vector; the remote control module is used to receive feedback verification results, and upload them to the remote debugging platform through the remote communication module for remote control operations, thereby realizing accurate prediction of the performance and faults of the controller based on historical data, and through the combination of the bidirectional retrieval engine and the machine learning model, accurately diagnoses and adjusts the operating status of the electric tricycle, thereby improving the efficiency and accuracy of remote debugging.

[0060] Example 2

[0061] Based on the same inventive concept as the remote debugging system of the controller in the aforementioned embodiment, Figure 2 As shown, the present application provides a remote debugging method for a controller, wherein the method includes:

[0062] A remote communication module is integrated in the electric tricycle controller and is used to establish a communication connection between the electric tricycle controller and a remote debugging platform. The remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform and receives debugging instructions from the remote debugging platform. An integrated processing unit is set in the electric tricycle controller, and the integrated processing unit performs a correlation analysis on historical operating parameters and communication transmission instances, and sets a first directional association index structure. An index matching library cooperates with the integrated processing unit, and the integrated processing unit performs a correlation analysis on historical operating parameters and debugging response modes, and sets a second directional association index structure. The index matching library stores a first directional association index structure and a second directional association index structure. A bidirectional traversal is performed between the first directional association index structure and the first index item pointer, and the second directional association index structure and the second index item pointer in the index matching library to establish a parameter value vector for remote debugging, and the parameter value vector is feedback verified using a machine learning model. The feedback verification result is received and uploaded to the remote debugging platform through the remote communication module for remote control operation.

[0063] Furthermore, the present application method includes:

[0064] Safety control indicators are introduced, and the safety control indicators include braking distance; based on the electric tricycle controller, safety performance analysis is performed in combination with the safety control indicators to determine braking abnormality indicators; based on the first directional association index structure, under the direction of the first index item pointer, an emergency response mechanism is accessed, and the emergency response mechanism is in a low power consumption state.

[0065] Furthermore, the present application method includes:

[0066] The emergency response mechanism includes a preset braking risk; the braking safety risk corresponding to the braking abnormality indicator is compared with the preset braking risk and the emergency response mechanism is awakened; when the emergency response mechanism is in the awakened state, according to the first directional association index structure, a rapid response is given to the braking safety event corresponding to the braking abnormality indicator.

[0067] Furthermore, the present application method includes:

[0068] When the emergency response mechanism is in an awake state, under the direction of the first index item pointer, the key index bit of the first directional association index structure is determined; based on the first key index bit of the first directional association index structure, the first index branch is split, and the first key index bit of the first directional association index structure supports parallel multipath processing; based on the emergency response mechanism, immediate response processing is performed according to the first key index bit and the first index branch.

[0069] Furthermore, the present application method includes:

[0070] Based on the first index branch, a parallel multipath thread is set, and the parallel multipath thread is used to simultaneously process multipath data streams; through the parallel multipath thread, a synchronization lock and a thread workload are configured; based on the first index branch, dynamic resource balancing optimization is performed through the synchronization lock and the thread workload.

[0071] Furthermore, the present application method includes:

[0072] Fine-grained monitoring of the workload and resource usage of each thread in the parallel multipath thread; setting an elastic resource pool through the parallel multipath thread corresponding to the first index branch of the first directional association index structure to the parallel multipath thread corresponding to the Mth index branch; deploying edge computing nodes and setting a microservice architecture through the elastic resource pool and fine-grained monitoring data, wherein the microservice architecture includes multiple independent microservices and dynamically allocates and recycles computing resources.

[0073] Furthermore, the present application method also includes:

[0074] Starting from the first directional association index structure, traverse step by step along the first index item pointer; at the same time, starting from the second directional association index structure, trace back along the second index item pointer to the node intersecting with the first index branch, and perform cross-validation; extract feature values ​​from the matching index items after cross-validation, perform numerical conversion to establish a parameter value vector for remote debugging.

[0075] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory and can be called and recognized by an unlimited computer processor, without any unnecessary restrictions.

[0076] Furthermore, the above technical solution only reflects the preferred technical solution of the technical solution of the embodiment of the present application. Some changes that may be made to certain parts thereof by technical personnel in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technical personnel in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. The remote debugging system of the controller is characterized by: The system comprises: A remote communication module, which is integrated in the electric tricycle controller and is used to establish a communication connection between the electric tricycle controller and the remote debugging platform. The remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform and receives debugging instructions from the remote debugging platform; An integrated processing unit, the integrated processing unit being disposed in the electric tricycle controller, and performing correlation analysis on historical operating parameters and communication transmission instances by the integrated processing unit to set a first directional correlation index structure; An index matching library, the index matching library cooperates with the integrated processing unit, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the debugging response mode to set a second directional correlation index structure, and the index matching library is used to store the first directional correlation index structure and the second directional correlation index structure; A bidirectional search engine, which performs bidirectional traversal between a first directional association index structure and a first index item pointer, and a second directional association index structure and a second index item pointer in the index matching library to establish a parameter value vector for remote debugging, and uses a machine learning model to provide feedback verification on the parameter value vector, wherein the first index item pointer and the second index item pointer point to specific data items in the first directional association index structure and the second directional association index structure, respectively, to guide the search engine to navigate in the data structure; the parameter value vector refers to a series of parameter values ​​extracted from the index structure, which are organized into a vector for subsequent data processing and analysis; The remote control module is used to receive feedback verification results and upload them to the remote debugging platform through the remote communication module to perform remote control operations.

2. The remote debugging system for a controller according to claim 1, wherein: The integrated processing unit further includes: An indicator introduction module, the indicator introduction module is used to introduce safety control indicators, the safety control indicators including braking distance; A safety performance analysis module, configured to perform a safety performance analysis based on the electric tricycle controller and in combination with the safety control index to determine a braking abnormality index; A mechanism access module is used to access an emergency response mechanism based on the first directional association index structure and under the direction of the first index item pointer, and the emergency response mechanism is in a low power consumption state.

3. The remote debugging system for a controller according to claim 2, wherein: The mechanism access module includes: An indicator preset module, the indicator preset module is used for the emergency response mechanism to preset a braking risk indicator; a mechanism awakening module, configured to compare the braking safety risk corresponding to the braking abnormality indicator with a preset braking risk and awaken the emergency response mechanism; An event response module is configured to quickly respond to a braking safety event corresponding to the braking abnormality indicator according to the first directional association index structure when the emergency response mechanism is in an awakened state.

4. The remote debugging system for a controller according to claim 3, wherein: The event response module includes: An index bit determination module, configured to determine, when the emergency response mechanism is in an awake state, a key index bit of the first directional association index structure under the direction of the first index item pointer; an index branch splitting module, the index branch splitting module being configured to split the first index branch based on a first key index bit of the first directional associative index structure, the first key index bit of the first directional associative index structure supporting parallel multipath processing; An immediate response processing module is configured to perform immediate response processing based on the emergency response mechanism and according to the first key index bit and the first index branch.

5. The remote debugging system for a controller according to claim 4, wherein: The immediate response processing module includes: a thread setting module, the thread setting module being configured to set a parallel multipath thread based on the first index branch, the parallel multipath thread being configured to simultaneously process multipath data streams; A thread configuration module, configured to configure synchronization locks and thread workloads through the parallel multi-path threads; A balancing optimization module is used to perform dynamic balancing optimization of resources based on the first index branch through the synchronization lock and thread workload.

6. The remote debugging system for a controller according to claim 5, wherein: The balance optimization module includes: A load monitoring module, configured to monitor the workload and resource usage of each thread in the parallel multipath thread in a fine-grained manner; a resource pool establishment module, the resource pool establishment module being configured to set up an elastic resource pool through the parallel multipath threads corresponding to the first index branch of the first directional association index structure to the parallel multipath threads corresponding to the Mth index branch; A resource allocation module is used to deploy edge computing nodes and set up a microservice architecture through the elastic resource pool and fine-grained monitoring data. The microservice architecture includes multiple independent microservices and dynamically allocates and reclaims computing resources.

7. The remote debugging system for a controller according to claim 1, wherein: The bidirectional search engine includes: a step-by-step traversal module, configured to start from the first directional associative index structure and step-by-step traverse along the first index item pointer; a cross-validation module, configured to simultaneously start from the second directional association index structure, trace back along the second index item pointer to a node intersecting with the first index branch, and perform cross-validation; The numerical conversion module is used to extract characteristic values ​​from the matching index items after cross-validation, perform numerical conversion and establish a parameter value vector for remote debugging.

8. A remote debugging method for a controller, characterized in that: A remote debugging system for a controller according to any one of claims 1 to 7, the method comprising: The remote communication module is integrated in the electric tricycle controller and is used to establish a communication connection between the electric tricycle controller and the remote debugging platform. The remote communication module sends the real-time operating parameters of the electric tricycle controller to the remote debugging platform and receives debugging instructions from the remote debugging platform. An integrated processing unit is provided in the electric tricycle controller, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the communication transmission instances to set a first directional correlation index structure; The index matching library cooperates with the integrated processing unit, and the integrated processing unit performs a correlation analysis on the historical operating parameters and the debugging response mode to set a second directional correlation index structure, wherein the index matching library stores the first directional correlation index structure and the second directional correlation index structure; Perform bidirectional traversal between the first directional association index structure and the first index item pointer, and the second directional association index structure and the second index item pointer in the index matching library to establish a parameter value vector for remote debugging, and use a machine learning model to perform feedback verification on the parameter value vector; Receive feedback verification results and upload them to the remote debugging platform through the remote communication module for remote control operations.

Images