An automatic equipment chassis data acquisition and analysis system based on CAN bus

Abstract

The present invention relates to the technical field of engineering equipment, and specifically discloses an automatic equipment chassis data collection and analysis system based on a CAN bus. The system is used to solve the real-time optimization problem of chassis control and equipment operation of two types of engineering equipment vehicles in a complex environment, and ensure the flexibility, dynamic adaptability, and high efficiency and safety of operations in multi-task scheduling. The system comprises a bridge-building engineering module, chassis of two types of engineering equipment vehicles, a mine-sweeping engineering module, and a vehicle CPU. The chassis of the two types of engineering equipment vehicles are connected to a data analysis module, which is connected to the bridge-building engineering module, the chassis of the two types of engineering equipment vehicles, and the mine-sweeping engineering module. The bridge-building engineering module, the chassis of the two types of engineering equipment vehicles, and the mine-sweeping engineering module are all connected to the vehicle CPU. The present invention optimizes chassis and equipment operation strategies by separating chassis data and combining a deep learning model and a knowledge graph, thereby ensuring the flexibility and dynamic adaptability of multi-task scheduling and improving the efficiency of bridge-building and mine-sweeping tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and more particularly to an automatic acquisition and analysis system for equipment chassis data based on a CAN bus. Background Art

[0002] When two types of engineering equipment vehicles carry out bridge-building and mine-sweeping missions, the stability of the vehicle chassis and the accuracy of equipment operation are key to successfully completing the mission. However, during the mission, they will face complex environments such as rugged terrain, high and low temperatures, and obstacles, which pose huge challenges to the operation of vehicles and equipment. It is necessary to monitor the chassis status in real time through various chassis data, and at the same time, perceive external conditions through external environmental sensors. It is also necessary to coordinate the real-time data collected by the two types of engineering equipment and the data collected by the on-board system itself to ensure that the bridge-building and mine-sweeping engineering tasks can be carried out smoothly and coordinated with the chassis data, external perception data, two types of engineering equipment working data, and on-board information data. However, this data has high latitude and high frequency. How to extract key features that affect chassis control and equipment operation without sacrificing real-time performance, analyze key features to improve the flexibility and dynamic adaptability of these two types of engineering equipment vehicles, and respond quickly to changes in the external environment and optimize multi-task scheduling in the system is a major problem that needs to be solved urgently. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides an automatic equipment chassis data collection and analysis system based on the CAN bus. By separating the chassis data and combining it with a deep learning model and a knowledge graph, the chassis and equipment operation strategies are optimized in real time, ensuring the flexibility and dynamic adaptability of multi-task scheduling, and improving the efficiency and safety of bridge-building and mine-sweeping tasks.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A CAN bus-based equipment chassis data automatic collection and analysis system includes a bridge engineering module, two types of engineering equipment vehicle chassis, a mine-sweeping engineering module and a vehicle CPU. The two types of engineering equipment vehicle chassis are connected to a data analysis module, and the data analysis module is connected to the bridge engineering module, the two types of engineering equipment vehicle chassis and the mine-sweeping engineering module. The bridge engineering module, the two types of engineering equipment vehicle chassis and the mine-sweeping engineering module are connected to the vehicle CPU. The two types of engineering equipment vehicle chassis identify chassis data as chassis sensor direct data and CAN bus data, use a first deep learning model to obtain a first target feature from the CAN bus data, use a second deep learning module to divide the chassis sensor direct data into a first type of feature and a second type of feature according to a trigger condition, and use a first knowledge graph and a second knowledge graph to obtain pre-selected features and auxiliary screening features therefrom, respectively. Through the third knowledge graph, auxiliary screening features are used to obtain the second target features from the pre-selected features, and then the chassis target features are obtained through feature fusion, and the chassis target features are transmitted to the data analysis module. The data analysis module extracts peripheral features from peripheral conditions and vehicle-borne information through the peripheral feature extraction module, and combines the peripheral features and chassis target features for data analysis to obtain the coordinated optimization strategy of the chassis, bridge-building project, and mine-sweeping project, and transmits it to the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles to coordinately optimize the relevant parameters of the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles. At the same time, the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles feed back their respective status data to the vehicle CPU, and feedback control is performed on the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles.

[0006] As a further solution of the present invention, the chassis of two types of engineering equipment vehicles are connected to a chassis data acquisition module for collecting chassis data. The chassis data acquisition module classifies the data into chassis sensor direct data and CAN bus data through the embedded source information in the metadata of the collected data. The chassis sensor direct data is connected to ADC1 / DAC1 through APB1. ADC1 / DAC1 is connected to a data source label generation module to generate data with data source label codes of "chassis, vehicle related" and "external environment, accessory equipment related", and transmit the labeled data to the second deep learning model. The features acquired by the second deep learning model trigger the classification of the first type of features and the second type of features by judging the numerical value of the data source label code and the preset threshold. The first type of features are chassis and vehicle related features, and the second type of features are external environment and accessory equipment related features. The first type of features are input into the first knowledge graph, and pre-selected features are retrieved. The second type of features are input into the second knowledge graph to obtain auxiliary screening features. The pre-selected features are input into the third knowledge graph, and the second target features are obtained under the constraints of the auxiliary screening features.

[0007] As a further solution of the present invention, the data source label generation module and the chassis data acquisition module are both connected to the data analysis module through a feature fusion module. The feature fusion module fuses the first target feature and the second target feature through feature engineering to obtain the chassis target feature.

[0008] As a further solution of the present invention, in the chassis data acquisition module, the first target features obtained by the collected CAN bus data through the first deep learning model include data 1 and data 2. The length of data 1 and data 2 are both 8. Data 1 includes but is not limited to brake light status, left turn signal status, right turn signal status, left turn switch, right turn switch, oil pressure, power assist pressure, engine water temperature and engine oil temperature. Data 2 includes but is not limited to engine speed, equipment speed, mileage of this trip, and motorcycle hours. The data directly collected by the chassis sensor includes but is not limited to vehicle acceleration, vehicle angular velocity, chassis height, hydraulic cylinder working status, chassis position, tire pressure, and suspension system status.

[0009] As a further solution of the present invention, the first knowledge graph is constructed based on chassis status and vehicle-related data, and is used to identify and retrieve a knowledge base of vehicle chassis status and control preselected features. The preselected features are chassis height, bridge pusher position and pressure, hydraulic cylinder status, vehicle acceleration, and angular velocity. The second knowledge graph is constructed based on the external environment and auxiliary equipment status, and is used to identify and retrieve a knowledge base of auxiliary features that affect task operations. The auxiliary screening features are the degree of influence of external temperature on minesweeping and bridge erection working conditions, the degree of correlation between obstacle distance and path planning, and the degree of influence of wind speed on working stability. The third knowledge graph combines the preselected features and auxiliary features to generate a knowledge base of second target features for chassis control and two-type equipment operation, which can adapt to the actual working environment and task requirements to the greatest extent. The second target features are the adjustment amount for bridge pusher pressure and environmental adaptability, the path optimization adjustment amount, and the chassis posture and vehicle speed that maintain chassis stability and the optimal bridge erection and minesweeping working conditions. The chassis target features are the chassis posture adjustment amount, the bridge pusher and hydraulic system optimization amount, and the chassis speed and stability control amount.

[0010] As a further solution of the present invention, the peripheral feature extraction module is connected to the DWA controller memory, network adapter, and hard disk controller through AHB. The peripheral feature extraction module communicates information with the bus matrix through AHB to obtain vehicle CPU data. The peripheral feature extraction module is connected to APB2 through ADC2 / DAC2. APB2 is connected to USRT, UART, GPIO, PMW output, timer, and keyboard.

[0011] As a further solution of the present invention, the features extracted by the peripheral feature extraction module include but are not limited to environmental perception data, obstacle detection data, task priority data, and vehicle-mounted information data. The environmental data includes ambient temperature, ambient humidity, wind speed, wind direction, rainfall, lighting, and ground conditions. The obstacle detection data includes the position, size, number, and shape of obstacles. The vehicle-mounted information data is vehicle-related performance data obtained by the on-board CPU.

[0012] As a further solution of the present invention, the bridge-building engineering module and the mine-sweeping engineering module are connected to two types of engineering work data collection modules for collecting data on the working status of the bridge-building engineering and the working status of the mine-sweeping engineering. The data collected by the two types of engineering work data collection modules include bridge-building work data and mine-sweeping work data. The bridge-building work data includes bridge-opening cylinder data, pin-threading cylinder data, fixer data, front cantilever data, support shovel data, rear swing frame data, C and D data, security data, auxiliary arm data, continuous dismantling position data, bridge-retracting motor data, and operation mode. The mine-sweeping work data includes small flip box status, launcher status, rocket status, one-rope status, two-rope status, three-rope status, return oil blockage information, insurance status, delivery distance, standard operation mode, cover-opening cylinder status, standard oil cylinder status, emergency power status, mine-sweeping plow status, and auxiliary plow status.

[0013] As a further solution of the present invention, the chassis data acquisition module and the two-type engineering work data acquisition module are connected to the vehicle CPU through a feedback module.

[0014] As a further solution of the present invention, the first deep learning model and the second deep learning model are both incremental conditional variational autoencoder models. The first deep learning model uses directly generated data instead of historical data for training. The replay strategy focuses on the combination of new tasks and historical tasks, emphasizing classification accuracy. The second machine learning model combines different incremental learning strategies to maintain the utilization of historical knowledge. The replay strategy performs more detailed comparison and adjustment of new tasks and historical tasks to maintain the effectiveness of knowledge transfer.

[0015] In order to solve the technical problems existing in the prior art, the technical effect of the system proposed in the present invention is: the system proposed in the present invention divides vehicle chassis data into sensor direct acquisition data and CAN bus data, and extracts key features from them through deep learning models and knowledge graphs to generate target features for chassis control and equipment operation. Through the data analysis module, the system combines chassis data, external perception data and on-board system information, extracts peripheral features from environmental conditions and task requirements, and performs real-time optimization based on chassis features. It can cope with the challenges of complex environments, and automatically coordinates and optimizes the operating strategies of vehicle chassis and auxiliary equipment while ensuring real-time and high efficiency, realizing the flexibility and dynamic adaptability of multi-task scheduling, and significantly improving the operating efficiency and safety of two types of engineering equipment vehicles in bridge-building and mine-sweeping tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of data analysis of the system proposed in the present invention;

[0017] Figure 2 This is a schematic diagram of the model of the physical prototype (instrument testing mechanism);

[0018] Figure 3 This is the file information page for importing data collected from the bridging working status of the system proposed in the present invention;

[0019] Figure 4 This is the data collection and display attribute page of the minesweeping working status of the system proposed in the present invention;

[0020] Figure 5 This is the bridging working status collection page of the system proposed in the present invention;

[0021] Figure 6 This is the minesweeping working status collection page of the system proposed by the present invention;

[0022] Figure 7 This is the display platform interface for the minesweeping operation data of the system proposed in the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] like Figures 1 to 7As shown, the present invention proposes an automatic equipment chassis data collection and analysis system based on CAN bus, including a bridge engineering module, two types of engineering equipment vehicle chassis, a mine-sweeping engineering module and a vehicle CPU. The two types of engineering equipment vehicle chassis are connected to a data analysis module, and the data analysis module is connected to the bridge engineering module, the two types of engineering equipment vehicle chassis and the mine-sweeping engineering module. The bridge engineering module, the two types of engineering equipment vehicle chassis and the mine-sweeping engineering module are connected to the vehicle CPU. The two types of engineering equipment vehicle chassis identify the chassis data as chassis sensor direct acquisition data and CAN bus data, use the first deep learning model to obtain the first target feature from the CAN bus data, use the second deep learning module to divide the chassis sensor direct acquisition data into the first category of features and the second category of features according to the trigger condition, and use the first knowledge graph and the second knowledge graph to obtain pre-selected features and auxiliary features therefrom respectively. Filter features, through the third knowledge graph, use auxiliary filtering features to obtain the second target features from the pre-selected features, and then obtain the chassis target features through feature fusion, and transmit the chassis target features to the data analysis module. The data analysis module extracts peripheral features from peripheral conditions and vehicle-borne information through the peripheral feature extraction module, and combines peripheral features and chassis target features for data analysis to obtain the coordinated optimization strategy of the chassis, bridge-building project, and mine-sweeping project, and transmits it to the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles, and collaboratively optimizes the relevant parameters of the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles. At the same time, the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles feed back their respective status data to the vehicle CPU, and feedback and control the chassis, bridge-building project module, and mine-sweeping project module of the two types of engineering equipment vehicles.

[0025] The system proposed in the present invention divides vehicle chassis data into sensor direct acquisition data and CAN bus data, and extracts key features from them through deep learning models and knowledge graphs to generate target features for chassis control and equipment operation. Through the data analysis module, the system combines chassis data, external perception data and on-board system information, extracts peripheral features from environmental conditions and task requirements, and performs real-time optimization based on chassis features. It can cope with the challenges of complex environments, and automatically coordinates and optimizes the operating strategies of vehicle chassis and auxiliary equipment while ensuring real-time and high efficiency, realizing the flexibility and dynamic adaptability of multi-task scheduling, and significantly improving the operating efficiency and safety of two types of engineering equipment vehicles in bridge-building and mine-sweeping tasks.

[0026] It should be noted that the chassis of the two types of engineering equipment vehicles are connected to a chassis data acquisition module for collecting chassis data. The chassis data acquisition module classifies the data into chassis sensor direct data and CAN bus data through the embedded source information in the metadata of the collected data. The chassis sensor direct data is connected to ADC1 / DAC1 through APB1. ADC1 / DAC1 is connected to a data source label generation module to generate data with data source label codes of "chassis, vehicle related" and "external environment, auxiliary equipment related", and transmit the labeled data to the second deep learning model. The features acquired by the second deep learning model trigger the classification of the first and second features by judging the numerical value of the data source label code and the preset threshold. The first type of features are chassis and vehicle related features, and the second type of features are external environment and auxiliary equipment related features. The first type of features are input into the first knowledge graph, and pre-selected features are retrieved. The second type of features are input into the second knowledge graph to obtain auxiliary screening features. The pre-selected features are input into the third knowledge graph, and the second target features are obtained under the constraints of the auxiliary screening features.

[0027] By introducing a data source label generation module into the chassis data acquisition module, the system can clearly divide chassis data into direct chassis sensor data and CAN bus data, and further generate label codes for "chassis, vehicle-related" and "external environment, accessory equipment-related." This classification mechanism enables rapid identification of data sources and uses during transmission and processing, reducing unnecessary data confusion and ensuring a clear distinction between first-category features (chassis, vehicle-related) and second-category features (external environment, accessory equipment-related), thereby improving the accuracy and efficiency of feature extraction and classification.

[0028] It should be noted that the data source label generation module and the chassis data acquisition module are both connected to the data analysis module through the feature fusion module. The feature fusion module fuses the first target feature and the second target feature through feature engineering to obtain the chassis target feature.

[0029] It should be noted that, as shown in Table 1, in the chassis data acquisition module, the first target features of the collected CAN bus data obtained through the first deep learning model include data 1 and data 2. The length of data 1 and data 2 are both 8. Data 1 includes but is not limited to brake light status, left turn signal status, right turn signal status, left turn switch, right turn switch, oil pressure, power assist pressure, engine water temperature and engine oil temperature. Data 2 includes but is not limited to engine speed, equipment speed, mileage of this trip, and motorcycle hours. The data directly collected by the chassis sensor includes but is not limited to vehicle acceleration, vehicle angular velocity, chassis height, hydraulic cylinder working status, chassis position, tire pressure, and suspension system status.

[0030] Table 1

[0031]

[0032]

[0033] It should be noted that the first knowledge graph is constructed based on chassis status and vehicle-related data, and is used to identify and retrieve a knowledge base of vehicle chassis status and control preselected features. The preselected features are chassis height, bridge pusher position and pressure, hydraulic cylinder status, vehicle acceleration, and angular velocity. The second knowledge graph is constructed based on the external environment and auxiliary equipment status, and is used to identify and retrieve a knowledge base of auxiliary features that affect task operations. The auxiliary screening features are the degree of influence of external temperature on minesweeping and bridge erection working conditions, the degree of correlation between obstacle distance and path planning, and the degree of influence of wind speed on working stability. The third knowledge graph combines preselected features and auxiliary features to generate a knowledge base of second target features for chassis control and two-type equipment operation, which can adapt to the actual working environment and task requirements to the greatest extent. The second target features are the adjustment amount for bridge pusher pressure and environmental adaptability, the path optimization adjustment amount, and the chassis posture and vehicle speed that maintain chassis stability and the best working conditions for bridge erection and minesweeping. The chassis target features are the chassis posture adjustment amount, bridge pusher and hydraulic system optimization amount, and chassis speed and stability control amount.

[0034] Through a three-layer knowledge graph and feature fusion, the system accurately processes and optimizes multi-dimensional data, including chassis status, external environment, and equipment operation. First, the first knowledge graph focuses on chassis status and vehicle control. Pre-selected features (such as chassis height, bridge pusher position, and hydraulic cylinder status) provide precise chassis operation references, ensuring the system can quickly respond to changes in chassis status. Second, the second knowledge graph focuses on the external environment and auxiliary equipment. Using auxiliary screening features (such as external temperature, obstacle distance, and wind speed), it helps the system assess the impact of the external environment on task operations, improving its adaptability in complex operating environments. The introduction of the third knowledge graph organically combines pre-selected and auxiliary features. The resulting secondary target features (such as bridge pusher pressure adjustment, path optimization adjustment, chassis posture, and vehicle speed control) maximize chassis control and equipment operation. This knowledge graph fusion mechanism enables the system to not only accurately adjust chassis status but also adaptively adjust the operation of bridge-building and mine-clearing equipment based on dynamic changes in the external environment, ensuring efficient operation in various complex mission scenarios. Through this system, multi-dimensional data can be integrated and the chassis and equipment can be adjusted in real time to ensure the stability and flexibility of the vehicle in complex environments. At the same time, the efficiency of multi-task coordination is improved, and the dynamic adaptability and operating accuracy of the two types of engineering equipment vehicles are significantly enhanced, thereby improving the overall success rate of mission execution.

[0035] It should be noted that the peripheral feature extraction module is connected to the DWA controller memory, network adapter, and hard disk controller through AHB. The peripheral feature extraction module communicates with the bus matrix through AHB to obtain vehicle CPU data. The peripheral feature extraction module is connected to APB2 through ADC2 / DAC2. APB2 is connected to USRT, UART, GPIO, PMW output, timer, and keyboard.

[0036] The benefit of this design is that the peripheral feature extraction module is connected to key devices such as the DWA controller memory, network adapter, and hard disk controller via the AHB bus, enabling efficient and rapid data extraction from multiple system components. At the same time, the module communicates information with the bus matrix via the AHB, enabling real-time acquisition of the vehicle CPU's computational and status data, ensuring the system has sufficient real-time and responsiveness. Through the connection between ADC2 / DAC2 and APB2, the peripheral feature extraction module can directly interact with the underlying hardware (such as USRT, UART, GPIO, PWM output, timer, keyboard, etc.) to obtain external input, control signals, and device feedback. This multi-level data acquisition and interoperability design enables the system to integrate multi-source data, optimize the extraction and analysis of peripheral features, improve the data processing efficiency and dynamic response capabilities of the entire system, and ensure real-time adjustment and multi-dimensional data fusion of the vehicle chassis and equipment in complex tasks.

[0037] It should be noted that the features extracted by the peripheral feature extraction module include but are not limited to environmental perception data, obstacle detection data, task priority data, and vehicle information data. Environmental data includes ambient temperature, ambient humidity, wind speed, wind direction, rainfall, lighting, and ground conditions. Obstacle detection data includes the location, size, number, and shape of obstacles. Vehicle information data is vehicle-related performance data obtained through the on-board CPU.

[0038] The system comprehensively monitors and analyzes the external environment and vehicle performance in real time by extracting environmental perception data, obstacle detection data, task priority data, and onboard information data through the peripheral feature extraction module. Environmental data (such as temperature, humidity, wind speed, direction, and rainfall) enables the system to adaptively adjust vehicle and equipment operations based on external climatic conditions, ensuring operational stability. Obstacle detection data (such as location, size, number, and shape) helps the system plan routes in real time, avoid collisions, and optimize equipment efficiency and safety. Task priority data is used to dynamically adjust task scheduling, ensuring that important tasks are prioritized. Onboard information data (such as vehicle performance and operating status) is provided by the onboard CPU to ensure optimal vehicle operation and performance. This multi-dimensional data fusion and extraction enables the system to rapidly perceive external changes in complex mission environments and flexibly respond to mission requirements, improving real-time and dynamic adaptability. This addresses technical challenges such as adaptability to complex environments, vehicle stability, and task scheduling optimization during multi-tasking execution.

[0039] It should be noted that the bridge engineering module and the mine-clearing engineering module are connected to two types of engineering work data acquisition modules for collecting the working status data of the bridge engineering and the mine-clearing engineering. Figures 3 to 7 As shown, the data collected by the two types of engineering work data acquisition module include bridge-building work data and mine-sweeping work data. The bridge-building work data include bridge-opening cylinder data, pin-threading cylinder data, fixer data, front cantilever data, support shovel data, rear swing frame data, C and D data, security data, auxiliary arm data, continuous dismantling position data, bridge retraction motor data, and operation mode. The mine-sweeping work data include small flip box status, launcher status, rocket status, first rope status, second rope status, third rope status, return oil blockage information, insurance status, delivery distance, standard operation mode, cover opening cylinder status, standard oil cylinder status, emergency power status, mine-sweeping plow status, and auxiliary plow status.

[0040] Both bridge erection and minesweeping tasks are highly complex engineering operations involving multiple mechanical components and processes. Data related to the bridge opening cylinder, anchor, and support shovel during bridge erection directly impacts bridge stability and installation accuracy. Failure to monitor and adjust the status of these components in real time can lead to operational errors, bridge instability, or installation failure. Similarly, during minesweeping, the status of the launcher, rockets, and launch distance are crucial; any operational errors can result in mission failure or safety risks. Therefore, real-time monitoring of this data ensures that all operations meet mission requirements, ensuring the safety and success of the operation. The coordination of multiple components is crucial in bridge erection and minesweeping tasks. The front boom and support shovel during bridge erection require precise coordination to ensure the safe placement of the bridge. During minesweeping, the status of the minesweeping plow, the calibration method, and the launcher status must be closely linked. Monitoring the status of these components ensures that each part operates according to the intended operating mode, improving the efficiency of equipment collaboration. Complex operating environments and mission requirements require the system to be capable of real-time adjustments. By monitoring the specific operating status of bridge-building and mine-clearing equipment (such as bridge retraction motor data and mine-clearing plow status), the system can dynamically adjust the equipment's operating mode based on environmental changes or mission requirements, ensuring rapid response and task optimization in missions with high real-time requirements. Monitoring this data helps the system identify potential problems in advance. When abnormal conditions in hydraulic cylinders or robotic arms are detected, the system can quickly take countermeasures to avoid equipment damage or mission failure. This preventative monitoring can significantly improve operational reliability and equipment lifespan.

[0041] It should be noted that the chassis data acquisition module and the two-type engineering work data acquisition module are connected to the vehicle CPU through the feedback module.

[0042] It should be noted that both the first deep learning model and the second deep learning model are incremental conditional variational autoencoder models. The first deep learning model uses directly generated data instead of historical data for training. The replay strategy focuses on the combination of new tasks and historical tasks, emphasizing classification accuracy. The second machine learning model combines different incremental learning strategies to maintain the use of historical knowledge. The replay strategy makes more detailed comparisons and adjustments to new tasks and historical tasks to maintain the effectiveness of knowledge transfer.

[0043] The first deep learning model is trained using directly generated data instead of historical data, employing a replay strategy to combine new and past tasks. This approach enables the model to quickly adapt to new tasks while retaining knowledge from past tasks. Because the model prioritizes classification accuracy, it can handle new tasks while ensuring efficient classification and accurate judgment in ever-changing scenarios. This is particularly useful for real-time feature extraction in vehicle chassis control and equipment operation, enabling the system to rapidly adjust and optimize operations when processing new chassis data. The second deep learning model utilizes a replay mechanism that combines different incremental learning strategies to effectively retain knowledge from past tasks and meticulously compare and adjust new and past tasks. This approach allows the model to transfer and reuse valid knowledge from past tasks while tackling new tasks, avoiding "catastrophic forgetting." This mechanism helps ensure the system's long-term learning and optimization capabilities in complex environments, improving its overall operational efficiency and intelligence. Through the incremental learning strategy, the second model's replay strategy enables more detailed comparison and adjustment of new and past tasks, enabling the system to adapt to changing multi-task scenarios. Especially when handling complex bridge-building and mine-clearing tasks, the model can dynamically adjust the knowledge of historical tasks according to the requirements of the new task, making knowledge transfer more effective, thereby improving the system's flexibility and dynamic adaptability in multi-task environments. Because both models adopt incremental learning and knowledge transfer strategies, the system has long-term learning capabilities and can continuously improve its understanding and optimization capabilities of tasks through multiple task iterations. This approach ensures that the system can maintain efficient operational performance and task execution stability even in the face of constantly changing tasks and environments, significantly improving the system's intelligence level and adaptability to application scenarios.

[0044] In order to clearly explain the difference between the first deep learning model and the second deep learning model, a detailed description is given through embodiments.

[0045] Example

[0046] The first deep learning model uses an incremental conditional variational autoencoder. The replay strategy is to directly generate new data to replace historical data. It focuses on combining new tasks with historical data. The goal is to improve classification accuracy, with an emphasis on optimizing the processing of new task data. The specific operation process is as follows:

[0047] Step 1: The model obtains the bridge pushing position and hydraulic cylinder pressure data of the current bridge erection task, and generates corresponding simulated historical data for combined training;

[0048] Step 2: Generate new data to replace the historical data stored in the system and directly apply it to the classification and judgment of the current task;

[0049] Step 3: By optimizing the classification algorithm, the model improves the classification accuracy of the new mission data and determines in real time whether the bridge pushing status, position and chassis are suitable for the current terrain.

[0050] The second deep learning model adopts an incremental conditional variational autoencoder model, combined with multiple incremental learning strategies, to maintain the use of historical knowledge, perform more detailed comparison and adjustment of new task and historical task data, maintain the effectiveness of knowledge transfer, and optimize the stability of long-term learning.

[0051] When performing minesweeping tasks, the status data of the vehicle chassis and minesweeping equipment needs to be compared and adjusted with the relevant data in historical figures. The specific operation process is as follows:

[0052] Step 1: The model extracts data from the current minesweeping mission (plow depth, obstacle distance, etc.) and compares it with historical data accumulated from previous minesweeping missions.

[0053] Step 2: Based on the task-based knowledge transfer strategy, the model retains and reuses valid data from historical tasks to avoid forgetting historical experience;

[0054] Step 3: Fine-tune the new and historical tasks through the replay strategy to ensure that the current task is important and the stability of the minesweeping equipment operation can be optimized based on historical experience.

[0055] The task-based knowledge transfer strategy adopts a strategy based on knowledge distillation, selective knowledge transfer and cumulative learning. The replay strategy selects a forgetting-free generation replay strategy and a storage-free generation replay strategy. By refining the knowledge in the complex model and migrating it to a lighter model, it can reduce the computational burden while maintaining efficient task execution. For this technical solution, when processing bridge-building or mine-clearing tasks, by distilling the complex knowledge of historical tasks, the system can quickly adapt to new tasks without the need for a complete historical model, thereby reducing training time and computing resource consumption. By screening and migrating historical task knowledge, "negative transfer" (i.e., interference from irrelevant or erroneous knowledge) is avoided. In this embodiment, in two types of engineering tasks, in different environments of mine-clearing or bridge-building tasks, the system can selectively migrate effective feature data (such as bridge pushing position, obstacle distance) in historical tasks to optimize the accuracy and efficiency of current task execution. By generating simulated data for historical tasks, it is ensured that the model will not forget previously learned knowledge when training for new tasks. For this technical solution, in two types of engineering equipment missions, this strategy can generate key data from past missions (such as hydraulic cylinder pressure and chassis posture) for replay, maintaining memory of important historical knowledge even in new and complex mission environments. In the cumulative learning strategy, as the mission continues to execute, the model can continuously learn from past missions and accumulate experience, thereby better transferring knowledge to future missions. This helps this solution improve the system's adaptability and long-term performance in complex, multi-task scenarios through cumulative learning, making the system more intelligent and flexible in the face of changing operating conditions. The storage-free generation and replay strategy reduces data storage requirements by generating historical data instead of storing all historical mission data. In this solution, when handling bridge-building and mine-clearing missions, the system does not need to save large amounts of historical data; it only needs to reproduce key data from past missions through a generated model. This not only saves storage space but also reduces computing resource overhead. The combination of selective knowledge transfer and the forgetfulness-free generation and replay strategy ensures that the system can flexibly respond to complex and changing mission environments. When handling minesweeping tasks, the system can prioritize replaying historical knowledge related to the current task (such as obstacle detection and path planning) based on environmental changes, while using selective knowledge transfer to extract previous experience and further optimize the execution of new tasks. The approach adopted in this embodiment can achieve efficient learning, knowledge accumulation, and task adaptation for two types of engineering tasks. This combined strategy improves the system's learning speed for new tasks and its memory retention of historical tasks, reducing its reliance on historical data storage, ensuring the system's dynamic adaptability, long-term stability, and efficient resource utilization in complex task environments.

[0056] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0057] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A CAN bus-based equipment chassis data automatic collection and analysis system, including a bridge engineering module, two types of engineering equipment vehicle chassis, a minesweeping engineering module and a vehicle CPU, characterized in that: The chassis of the two types of engineering equipment vehicles are connected to a data analysis module, which is connected to the bridge engineering module, the chassis of the two types of engineering equipment vehicles and the mine-sweeping engineering module. The bridge engineering module, the chassis of the two types of engineering equipment vehicles and the mine-sweeping engineering module are connected to the vehicle CPU. The chassis of the two types of engineering equipment vehicles recognize the chassis data as chassis sensor direct-acquisition data and CAN bus data, and use the first deep learning model to obtain the first target feature from the CAN bus data. The second deep learning module is used to divide the chassis sensor direct-acquisition data into the first category of features and the second category of features according to the trigger conditions, and use the first knowledge graph and the second knowledge graph to obtain pre-selected features and auxiliary screening features therefrom respectively. Through the third knowledge graph, the auxiliary screening features are used to obtain the second target from the pre-selected features. Features are obtained, and then chassis target features are obtained through feature fusion, and the chassis target features are transmitted to the data analysis module. The data analysis module extracts peripheral features from peripheral conditions and vehicle-borne information through the peripheral feature extraction module, and performs data analysis on the peripheral features and chassis target features to obtain the coordinated optimization strategy of chassis, bridge-building project and mine-sweeping project, and transmits it to the chassis, bridge-building project module and mine-sweeping project module of the two types of engineering equipment vehicles, and coordinately optimizes the relevant parameters of the chassis, bridge-building project module and mine-sweeping project module of the two types of engineering equipment vehicles. At the same time, the chassis, bridge-building project module and mine-sweeping project module of the two types of engineering equipment vehicles feed back their respective status data to the vehicle CPU, and feedback control is performed on the chassis, bridge-building project module and mine-sweeping project module of the two types of engineering equipment vehicles.

2. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 1 is characterized in that: The chassis of the two types of engineering equipment vehicles are connected to a chassis data acquisition module for collecting chassis data. The chassis data acquisition module classifies the data into chassis sensor direct data and CAN bus data through the embedded source information in the metadata of the collected data. The chassis sensor direct data is connected to ADC1 / DAC1 through APB1. ADC1 / DAC1 is connected to a data source label generation module to generate data with data source label codes of "chassis, vehicle related" and "external environment, auxiliary equipment related", and transmit the labeled data to the second deep learning model. The features obtained by the second deep learning model trigger the classification of the first and second features by judging the numerical value of the data source label code and the preset threshold. The first type of features are chassis and vehicle related features, and the second type of features are external environment and auxiliary equipment related features. The first type of features are input into the first knowledge graph, and pre-selected features are retrieved. The second type of features are input into the second knowledge graph to obtain auxiliary screening features. The pre-selected features are input into the third knowledge graph, and the second target features are obtained under the constraints of the auxiliary screening features.

3. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 2 is characterized in that: The data source label generation module and the chassis data acquisition module are both connected to the data analysis module through the feature fusion module. The feature fusion module fuses the first target feature and the second target feature through feature engineering to obtain the chassis target feature.

4. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 1 is characterized in that: In the chassis data acquisition module, the first target features of the collected CAN bus data obtained through the first deep learning model include data 1 and data 2. The length of data 1 and data 2 are both 8. Data 1 includes but is not limited to brake light status, left turn signal status, right turn signal status, left turn switch, right turn switch, oil pressure, power assist pressure, engine water temperature and engine oil temperature. Data 2 includes but is not limited to engine speed, equipment speed, mileage of this trip, and motorcycle hours. The data directly collected by the chassis sensor includes but is not limited to vehicle acceleration, vehicle angular velocity, chassis height, hydraulic cylinder working status, chassis position, tire pressure, and suspension system status.

5. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 4 is characterized in that: The first knowledge graph is constructed based on chassis status and vehicle-related data. It is used to identify and retrieve a knowledge base of vehicle chassis status and control preselected features. The preselected features are chassis height, bridge pusher position and pressure, hydraulic cylinder status, vehicle acceleration, and angular velocity. The second knowledge graph is constructed based on the external environment and auxiliary equipment status. It is used to identify and retrieve a knowledge base of auxiliary features that affect task operations. Auxiliary screening features include the impact of external temperature on minesweeping and bridge erection operations, the correlation between obstacle distance and path planning, and the impact of wind speed on operational stability. The third knowledge graph combines preselected and auxiliary features to generate a knowledge base of second target features for chassis control and two-type equipment operation, which can maximize adaptability to the actual operating environment and task requirements. The second target features are the adjustment amount for bridge pusher pressure and environmental adaptability, the adjustment amount for path optimization, and the chassis posture and vehicle speed that maintain chassis stability and optimal bridge erection and minesweeping operations. The chassis target features are the chassis posture adjustment amount, the optimization amount for bridge pusher and hydraulic system, and the chassis speed and stability control amount.

6. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 1 is characterized in that: The peripheral feature extraction module is connected to the DWA controller memory, network adapter, and hard disk controller through AHB. The peripheral feature extraction module communicates with the bus matrix through AHB to obtain vehicle CPU data. The peripheral feature extraction module is connected to APB2 through ADC2 / DAC2. APB2 is connected to USRT, UART, GPIO, PMW output, timer, and keyboard.

7. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 6 is characterized in that: The features extracted by the peripheral feature extraction module include but are not limited to environmental perception data, obstacle detection data, task priority data, and vehicle information data. Environmental data includes ambient temperature, ambient humidity, wind speed, wind direction, rainfall, lighting, and ground conditions. Obstacle detection data includes the location, size, number, and shape of obstacles. Vehicle information data is vehicle-related performance data obtained through the on-board CPU.

8. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 1 is characterized in that: The bridge-building engineering module and the mine-sweeping engineering module are connected to two types of engineering work data collection modules for collecting the working status data of the bridge-building engineering and the mine-sweeping engineering. The data collected by the two types of engineering work data collection modules include bridge-building work data and mine-sweeping work data. The bridge-building work data includes bridge-opening cylinder data, pin-threading cylinder data, fixer data, front cantilever data, support shovel data, rear swing frame data, C and D data, security data, auxiliary arm data, continuous dismantling position data, bridge-retracting motor data, and operation mode. The mine-sweeping work data includes small flip box status, launcher status, rocket status, first rope status, second rope status, third rope status, return oil blockage information, insurance status, delivery distance, standard operation mode, cover opening cylinder status, standard oil cylinder status, emergency power status, mine-sweeping plow status, and auxiliary plow status.

9. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 8, characterized in that: The chassis data acquisition module and the two-type engineering work data acquisition module are connected to the vehicle CPU through the feedback module.

10. The automatic equipment chassis data acquisition and analysis system based on CAN bus according to claim 1, characterized in that: Both the first deep learning model and the second deep learning model are incremental conditional variational autoencoder models. The first deep learning model uses directly generated data instead of historical data for training. The replay strategy focuses on the combination of new tasks and historical tasks, emphasizing classification accuracy. The second machine learning model combines different incremental learning strategies to maintain the use of historical knowledge. The replay strategy makes more detailed comparisons and adjustments to new tasks and historical tasks to maintain the effectiveness of knowledge transfer.

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