A method, device, equipment and medium for monitoring the structural reliability of a heavy truck battery swapping cabinet

Abstract

The present invention provides a method, device, equipment and medium for monitoring the structural reliability of a heavy truck battery swapping cabinet, which relates to the technical field of battery swapping cabinet structure monitoring. This method uses digital twin technology to fuse physical entity data and virtual entity data. Physical data such as stress and displacement of the battery swapping cabinet are collected in real time through sensors, and virtual data is generated using the Trucksim dynamics model and the finite element model. These data together constitute a twin data set, which is analyzed by a deep neural network (DNN) model to achieve real-time monitoring of the structural reliability of the battery swapping cabinet. When the monitored stress or fatigue cumulative damage value exceeds the safety threshold, the system will automatically trigger an alarm to remind the operator to handle it in time. This method not only improves the monitoring accuracy and real-time performance, but also realizes more comprehensive monitoring and warning functions through virtual entity modeling and deep learning methods, effectively ensuring the use safety of battery swapping vehicles and having important practical application value.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring the structure of battery swapping cabinets, and particularly to a method, device, equipment and medium for monitoring the structural reliability of heavy truck battery swapping cabinets. Background Art

[0002] In the modern transportation industry, heavy trucks play an indispensable role. With the increasing awareness of environmental protection and the continuous progress of new energy technologies, the battery swapping mode, as an efficient and convenient energy supply method, has gradually attracted attention. As a key device for realizing the battery swapping function, the reliability of the structure of the battery swapping cabinet is crucial for ensuring the safety and stability of the battery swapping process. In actual applications, however, the battery swapping cabinet faces complex working condition challenges. It needs to be installed on different vehicles and adapt to various driving scenarios, including different loads, speeds, road conditions, etc. Being in such a complex and changeable alternating load environment for a long time, the structure of the battery swapping cabinet may gradually develop fatigue damage, and may even lead to structural failure, thus affecting the normal operation of battery swapping vehicles and posing safety hazards.

[0003] Currently, the means for monitoring the structural reliability of battery swapping cabinets are relatively limited. Traditional monitoring methods mainly rely on mechanical sensors, which can only provide information about the force state of the structure at a certain moment, and it is difficult to accurately predict potential safety risks of the structure, such as fatigue cumulative damage. In addition, although there are some early warning systems in the existing technologies that can monitor and transmit some basic information of the battery swapping cabinet, there are still gaps in the comprehensive monitoring and early warning functions for structural reliability.

[0004] Against this technical background, it is particularly urgent to develop a system that can monitor the structural reliability of the battery swapping cabinet in real time and accurately, and can give early warnings of potential risks. This not only helps to improve the operation safety of battery swapping vehicles, but also can optimize the maintenance strategy of the battery swapping cabinet, extend its service life, and reduce operating costs.

[0005] In view of this, the present application is proposed. Summary of the Invention

[0006] The present invention provides a method, device, equipment and medium for monitoring the structural reliability of heavy truck battery swapping cabinets, which can at least partially improve the above problems.

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

[0008] A method for monitoring the structural reliability of a heavy truck battery swapping cabinet, which includes:

[0009] Obtain a physical data set collected by a sensor assembly configured on a heavy truck and its battery swapping cabinet, and establish a virtual entity corresponding to the heavy truck based on the physical data set;

[0010] Obtain the virtual data set of the virtual entity, and based on the digital twin technology, fuse the virtual data set and the physical data set to generate a simulation data set;

[0011] Use a preset DNN model to calculate the simulation data set to generate a predicted value of the maximum stress at the risk point and a predicted value of the fatigue characteristics of the battery swapping cabinet;

[0012] Perform pre-warning processing on the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet, and give a warning prompt or output the predicted value.

[0013] The present invention also provides a device for monitoring the structural reliability of a heavy truck battery swapping cabinet, which includes:

[0014] A virtual entity creation unit, configured to obtain a physical data set collected by a sensor assembly configured on a heavy truck and its battery swapping cabinet, and based on the physical data set, create a virtual entity corresponding to the heavy truck;

[0015] A fusion unit, configured to obtain the virtual data set of the virtual entity, and based on the digital twin technology, fuse the virtual data set and the physical data set to generate a simulation data set;

[0016] A DNN unit, configured to use a preset DNN model to calculate the simulation data set to generate a predicted value of the maximum stress at the risk point and a predicted value of the fatigue characteristics of the battery swapping cabinet;

[0017] A warning unit, configured to perform pre-warning processing on the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet, and give a warning prompt or output the predicted value.

[0018] The present invention also provides a device for monitoring the structural reliability of a heavy truck battery swapping cabinet, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for monitoring the structural reliability of a heavy truck battery swapping cabinet as described in any one of the above.

[0019] The present invention also provides a readable storage medium, which stores a computer program that can be executed by a processor of a device where the storage medium is located to implement the method for monitoring the structural reliability of a heavy truck battery swapping cabinet as described in any one of the above.

[0020] In summary, the method for monitoring the structural reliability of heavy truck battery swapping cabinets uses digital twin technology to construct a comprehensive mapping between the physical entity and the virtual entity of the battery swapping cabinet. By installing various sensors on heavy trucks and battery swapping cabinets, physical data such as stress, mass, speed, acceleration, and displacement are collected in real time. At the same time, virtual data is generated using the Trucksim dynamics model and the finite element model. These physical data and virtual data are fused to form a twin dataset, and data training and learning are carried out through a deep neural network (DNN) model to achieve real-time monitoring of the structural reliability of the battery swapping cabinet. When it is detected that the stress of the battery swapping cabinet exceeds the safety threshold or the fatigue cumulative damage value is higher than the set standard, the system will trigger an early warning mechanism and prompt the operator through voice alarm so that measures can be taken in a timely manner. In addition, the system also predicts the fatigue characteristics of the battery swapping cabinet through the rain flow counting method and the Miner cumulative damage theory, further improving the accuracy and reliability of the monitoring.

[0021] The method for monitoring the structural reliability of heavy truck battery swapping cabinets not only improves the accuracy and real-time performance of the structural reliability monitoring of battery swapping cabinets, but also realizes more comprehensive monitoring and early warning functions through virtual entity modeling and deep learning methods, effectively ensuring the use safety of battery swapping vehicles. Brief Description of the Drawings

[0022] Figure 1 is a schematic flow chart of the method for monitoring the structural reliability of heavy truck battery swapping cabinets provided by the first embodiment of the present invention;

[0023] Figure 2 is a schematic framework diagram of the method for monitoring the structural reliability of heavy truck battery swapping cabinets provided by the first embodiment of the present invention;

[0024] Figure 3 is a schematic basic process diagram of the DNN model construction provided by the embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of the DNN model principle provided by the embodiment of the present invention;

[0026] Figure 5 is a schematic flow chart of the stress monitoring and early warning of the risk points of the battery swapping cabinet structure provided by the embodiment of the present invention;

[0027] Figure 6 is a schematic flow chart of the fatigue characteristic monitoring and early warning of the battery swapping cabinet provided by the embodiment of the present invention;

[0028] Figure 7 is a schematic module diagram of the device for monitoring the structural reliability of heavy truck battery swapping cabinets provided by the second embodiment of the present invention. Detailed Description of the Embodiments

[0029] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0030] Referring to Figure 1 and Figure 2 as shown, the first embodiment of the present invention discloses a method for monitoring the structural reliability of a heavy-duty truck battery swapping cabinet, which can be executed by a structural reliability monitoring device for heavy-duty truck battery swapping cabinets (hereinafter referred to as the monitoring device). In particular, it is executed by one or more processors in the monitoring device to implement the following method:

[0031] S1. Obtain a physical data set collected by a sensor assembly configured on a heavy-duty truck and its battery swapping cabinet, and establish a virtual entity corresponding to the heavy-duty truck based on the physical data set;

[0032] Specifically, step S1 includes: obtaining a physical data set collected by a sensor assembly configured on the areas of concern of a heavy-duty truck and its battery swapping cabinet, where the physical data set includes stress, mass, speed, acceleration, and displacement;

[0033] According to the physical data set, use Trucksim dynamics software to build different heavy-duty truck models, and select different road surface conditions to solve vehicle dynamics parameters to generate a heavy-duty truck Trucksim dynamics model;

[0034] Based on the geometric model of the battery swapping cabinet, use finite element software Altair HyperWorks to establish a finite element model of the battery swapping cabinet;

[0035] Perform a fusion process on the heavy-duty truck Trucksim dynamics model and the finite element model of the battery swapping cabinet to generate a virtual entity corresponding to the heavy-duty truck, where the output of the heavy-duty truck Trucksim dynamics model is introduced into the finite element model of the battery swapping cabinet to combine the dynamic equation of the battery swapping cabinet with the physical characteristics of the internal structure.

[0036] Preferably, the sensor assembly includes a resistance strain gauge sensor, a mass sensor, a speed sensor, an acceleration sensor, and a displacement sensor.

[0037] In this embodiment, a physical dataset collected by sensor components configured on a heavy truck and its battery swapping cabinet is obtained. These sensor components include resistance strain sensors, mass sensors, speed sensors, acceleration sensors, and displacement sensors, which are carefully arranged in the areas of concern of the heavy truck and its battery swapping cabinet to monitor key parameters such as the stress, mass, speed, acceleration, and displacement of the battery swapping cabinet in real time. The collection of these physical data provides a basis for the subsequent establishment of virtual entities, ensuring the comprehensiveness and accuracy of the monitoring data.

[0038] In principle, the stress data sample points collected by the simulation model include but are not limited to the sample points collected from the actual vehicle (including the battery swapping cabinet). Therefore, the stress data collected from the actual vehicle can be used to verify the simulation model, ensuring the mapping of mechanical characteristics between the simulation model and the actual vehicle model. In addition, mechanical characteristics that cannot be covered by the physically collected data can be presented through virtual simulation means. The categories of sensor components are shown in Table 1.

[0039] Table 1 Sensor Categories and Their Functions

[0040]

[0041] Since the driving conditions in the working environment of heavy trucks are relatively complex, different condition changes will affect the operating state of the battery swapping cabinet. First, various sensors installed on the heavy truck and the battery swapping cabinet are used to obtain data in real time, and then the data collected under different conditions are associated with the operating state. The possible conditions are shown in Table 2.

[0042] Table 2 Driving Conditions of Heavy Trucks and Battery Swapping Cabinets

[0043]

[0044] Secondly, based on the collected physical dataset, different heavy truck models are built using Trucksim dynamics software. When building the models, different road conditions are selected, such as flat roads, rough roads, etc., to simulate the dynamic behaviors of heavy trucks in various actual driving scenarios. By solving the vehicle dynamics parameters, a heavy truck Trucksim dynamics model is generated. This model can accurately reflect the dynamic characteristics of heavy trucks under different conditions, providing an important reference basis for the structural reliability monitoring of the battery swapping cabinet. Using this as the input condition for the simulation and testing of the battery swapping cabinet, the output of the dynamics model is subjected to time-frequency domain conversion to construct the driving condition power spectrum as the input for the finite element simulation of the battery swapping cabinet and the input for the structural entity of the battery swapping cabinet.

[0045] Meanwhile, based on the geometric model of the battery swapping cabinet, the finite element software Altair HyperWorks is used to establish a finite element model of the battery swapping cabinet. When establishing the finite element model, the material properties, boundary conditions, and excitation conditions are set in detail to ensure that the model can accurately simulate the mechanical properties of the internal structure of the battery swapping cabinet. Through finite element simulation analysis, key information such as the stress distribution of the battery swapping cabinet under different working conditions can be obtained, providing strong support for evaluating the structural reliability of the battery swapping cabinet.

[0046] To achieve a comprehensive mapping between physical entities and virtual entities, the dynamic model of the heavy truck Trucksim and the finite element model of the battery swapping cabinet are fused. Briefly, by combining the Trucksim simulation model and the finite element model, Trucksim is used to establish the dynamic equation, control system, and sensor simulation module of the battery swapping cabinet, and the finite element model is used to simulate the mechanical properties of the internal structure of the battery swapping cabinet; to realize the multi-domain and multi-scale fusion modeling of the overall system of the battery swapping cabinet, combining actual operation with theoretical simulation, and improving the accuracy of the digital twin model. The output of the heavy truck Trucksim dynamic model is introduced into the finite element model of the battery swapping cabinet, combining the dynamic equation of the battery swapping cabinet with the physical properties of the internal structure to generate a virtual entity corresponding to the heavy truck. This fusion process not only improves the accuracy of the virtual entity but also enables the virtual entity to more comprehensively reflect the state of the heavy truck and its battery swapping cabinet during actual operation, providing a solid foundation for subsequent structural reliability monitoring and early warning.

[0047] S2. Obtain the virtual data set of the virtual entity, and based on digital twin technology, fuse the virtual data set and the physical data set to generate a simulation data set;

[0048] Specifically, step S2 includes: obtaining the virtual data set of the virtual entity, and the virtual data set includes amplitude virtual data, frequency virtual data, and stress virtual data;

[0049] Combine the physical data sets under different working conditions with the virtual data set and input them into a preset simulation model to generate the stress values of the risk monitoring points;

[0050] Extract the actual stress values of the key areas, compare and verify the stress values of the risk monitoring points and the actual stress values of the key areas to generate a verification result;

[0051] When the verification result is not passed, recalculate using the simulation model;

[0052] When the verification result is passed, construct a stress data set of the risk points to generate a simulation data set.

[0053] Please refer to Figure 3, in this embodiment, the accuracy and depth of the reliability monitoring of the structure of the battery swapping cabinet are further enhanced. First, obtain the virtual data set generated by the virtual entities generated in step S1. This virtual data set covers key parameters such as amplitude virtual data, frequency virtual data, and stress virtual data, which are obtained through simulation calculations of the Trucksim dynamics model and the finite element model of the battery swapping cabinet, and can reflect the theoretical mechanical behavior of the battery swapping cabinet under different working conditions. Next, organically combine the physical data sets under different working conditions with the virtual data set. This process involves matching and integrating the physical data collected by sensors during actual operation, such as stress, mass, speed, acceleration, and displacement, with the virtual data such as amplitude, frequency, and stress in the virtual data set. The integrated data set is input into a preset simulation model, which is based on digital twin technology, can simulate the operating state of the battery swapping cabinet under various actual working conditions, and generate the stress values of the risk monitoring points.

[0054] To ensure the accuracy and reliability of the simulation data, extract the actual stress values of the key areas and compare them with the stress values of the risk monitoring points generated by the simulation model. This verification process is a key step to ensure the mapping accuracy between the digital twin model and the actual physical entity. If the result of the comparison and verification shows non-passing, that is, there are significant differences between the simulation data and the actual data, then the simulation model will be recalculated, the model parameters will be adjusted, or the simulation conditions will be reset to improve the accuracy of the simulation data. On the contrary, if the verification result is passing, that is, the simulation data and the actual data are within an acceptable error range, then a stress data set of risk points will be constructed and a final simulation data set will be generated. This simulation data set not only contains the real-time physical data from the sensors but also integrates the verified virtual data, providing rich information for the comprehensive evaluation of the reliability of the battery swapping cabinet structure.

[0055] Among them, the real-time operating state of the heavy truck and the battery swapping cabinet can be represented by stress , the total vehicle load m, speed v, acceleration ɑ, displacement x, these five parameters. After obtaining the physical data collected by the sensors and the virtual data obtained by the simulation model, conduct data comparison and verification on them. If the verification passes, a stress data set of risk points will be constructed.

[0056] S3. Use the preset DNN model to calculate the simulation data set to generate the maximum stress prediction value of the risk point and the fatigue characteristic prediction value of the battery swapping cabinet;

[0057] Please refer to Figure 4, Specifically, in this embodiment, the DNN algorithm is used to train the data, so as to predict the operation state of the battery swapping cabinet of the heavy truck, which is the key link to realize the accurate monitoring of the structural reliability of the battery swapping cabinet. Specifically, the pre-constructed and trained deep neural network (DNN) model is used to deeply calculate the simulation data set. The DNN model is trained based on a large amount of historical data and professional knowledge, and can learn and identify the stress change patterns and fatigue accumulation characteristics of the battery swapping cabinet under different working conditions.

[0058] When the simulation data set is input into the DNN model, the model performs multi-layer abstraction and feature extraction on the data through its complex neural network structure. This process involves the calculation of multiple hidden layers, and each layer uses a non-linear activation function to process the input data, so as to capture the complex non-linear relationships in the data. In this way, the DNN model can extract key feature information from the simulation data set, and then generate the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet.

[0059] The generation of the predicted value of the maximum stress at the risk point enables the monitoring system to identify in advance the parts of the battery swapping cabinet where the stress may be too high, which is crucial for preventing structural damage and failures. The calculation of the predicted value of the fatigue characteristics of the battery swapping cabinet helps to evaluate the fatigue accumulation of the battery swapping cabinet during long-term operation, providing a scientific basis for predicting the service life of the battery swapping cabinet and formulating a reasonable maintenance plan.

[0060] S4. Perform early warning preprocessing on the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet, and give an early warning prompt or output the predicted value.

[0061] Specifically, step S4 includes: judging whether the predicted value of the maximum stress at the risk point exceeds a preset safety threshold;

[0062] If not, output the maximum stress value of the current working condition;

[0063] If so, use a classification neural network to judge the position of the maximum stress value of the current working condition, output the position of the maximum stress value of the current working condition and its corresponding maximum stress value, and give a voice alarm prompt.

[0064] Judge whether the predicted value of the fatigue characteristics of the battery swapping cabinet exceeds a preset safety threshold;

[0065] If not, output the fatigue cumulative damage value of the current working condition;

[0066] If so, use a classification neural network to judge the fatigue characteristic category of the current working condition, output the fatigue cumulative damage value corresponding to the fatigue characteristic category of the current working condition, and give a voice alarm prompt.

[0067] Please refer toFigure 5 , Figure 6 , in this embodiment, pre-warning preprocessing is performed on the predicted maximum stress value of the risk point and the predicted fatigue characteristic value of the battery swapping cabinet, and corresponding pre-warning prompts or predicted values are output according to the processing results. This process is the core link to realize the intelligence and automation of the structural reliability monitoring and pre-warning system of the battery swapping cabinet, aiming to discover potential safety risks in advance through an effective pre-warning mechanism and ensure the safe operation of the battery swapping vehicle.

[0068] First, evaluate the predicted maximum stress value of the risk point to determine whether it exceeds the preset safety threshold. This safety threshold is preset based on the design standards and material properties of the battery swapping cabinet to ensure the structural safety of the battery swapping cabinet under normal operating conditions. If the predicted maximum stress value of the risk point does not exceed the safety threshold, the system will normally output the maximum stress value under the current working condition, providing real-time operating status information for the operator so that they can understand the current stress situation of the battery swapping cabinet, thereby making reasonable operation management and maintenance plans. Specifically, the stress monitoring of the risk point of the battery swapping cabinet obtains the stress data of the risk point of the battery swapping cabinet through a simulation model, predicts the maximum stress at the risk point position through a DNN neural network, and thus determines the position of the maximum stress risk point. When the maximum stress exceeds the safety threshold, the reliability category is judged by a classification neural network and the current state data and voice alarm prompt are output.

[0069] That is, if the predicted maximum stress value of the risk point exceeds the safety threshold, this indicates that there may be a risk of structural damage to the battery swapping cabinet. At this time, a classification neural network will be used to accurately judge the position of the maximum stress value under the current working condition. By learning a large amount of historical data and stress distribution patterns, the classification neural network can accurately identify the specific position where the stress is too high. It will output the position of the maximum stress value under the current working condition and its corresponding maximum stress value, and immediately start a voice alarm prompt to remind the operator of the potential structural risk, so that they can take timely measures, such as reducing the load, adjusting the operating parameters, or performing necessary inspections and repairs, thereby effectively avoiding the occurrence of structural failures and ensuring the safe operation of the battery swapping vehicle.

[0070] Secondly, evaluate the predicted value of the fatigue characteristics of the battery swapping cabinet to determine whether it exceeds the preset safety threshold. If the predicted value of the fatigue characteristics of the battery swapping cabinet does not exceed the safety threshold, the system will output the fatigue cumulative damage value under the current working condition, providing real-time information on the fatigue state of the battery swapping cabinet to help operators evaluate the service life and maintenance requirements of the battery swapping cabinet. Conversely, if the predicted value of the fatigue characteristics of the battery swapping cabinet exceeds the safety threshold, it indicates that the battery swapping cabinet may already be in a high-risk stage of fatigue cumulative damage. At this time, a classification neural network will be used to judge the fatigue characteristic category under the current working condition. The classification neural network can accurately identify the fatigue stage of the battery swapping cabinet, such as initial fatigue, intermediate fatigue, or late fatigue, according to the characteristics of the fatigue cumulative damage value. The fatigue cumulative damage value corresponding to the fatigue characteristic category of the current working condition will be output, and a voice alarm prompt will be activated to remind the operator that the battery swapping cabinet may need to be repaired or replaced to prevent serious accidents such as fatigue fracture from occurring.

[0071] Specifically, the stress data collected through the battery swapping cabinet structure, after being processed by the DNN model data, uses the rain flow counting method to draw the time-stress curve. Combining the linear cumulative damage theory - Miner theory and the S-N curve of the material's resistance index, according to the Miner cumulative damage theory, assume there are X levels of stress in one cycle , , ..., , the number of cycles of the X-level stress is , ..., . The number of cycles until fatigue failure of the component under the action of a single stress is , ..., , and this value can be found in the actual S-N curve of the material. The cycle ratio of fatigue damage is / , / , / ..., / . When the cumulative damage of the component reaches 1 under the continuous cyclic action of each level of stress, cracks will appear in the component, and then component failure will occur. The sum H of the critical damage degrees is: H = = 1. To further ensure the authenticity and reliability of this data, the above formula is repeatedly corrected through a large number of engineering tests. The test results show that when the value of H is smaller than 1, taking the value of H as 0.7 makes the data more authentic and reliable.

[0072] The linear cumulative damage theory - Miner theory and the S-N curve of the material's resistance index can well reflect its fatigue characteristics, thereby converting the remaining life of the heavy truck battery swapping cabinet. If the fatigue cumulative damage value of the battery swapping cabinet is lower than the safety threshold, the fatigue category is judged by the classification neural network, and the current state data and voice alarm prompt are output.

[0073] In this embodiment, the method for monitoring the structural reliability of the heavy truck battery swapping cabinet involves several criteria for judging the structural reliability of the battery swapping cabinet:

[0074] First, stress monitoring and early warning of risk points in the structure of the battery swapping cabinet. If it is predicted that the maximum stress of the battery swapping cabinet is within the normal working range (85% of the allowable stress of the structure), the current state information of the predicted battery swapping cabinet is normally output without triggering an early warning; if it is predicted that the maximum stress of the battery swapping cabinet is higher than the safety threshold (85% of the allowable stress of the structure), the state information of the battery swapping cabinet is output and an early warning is issued to remind the operator to check whether there are abnormal sounds, vibrations or frictions in the battery swapping cabinet, etc.

[0075] Second, fatigue characteristic monitoring and early warning of the battery swapping cabinet. If it is predicted that the fatigue cumulative damage value of the battery swapping cabinet is within the normal working range (H ≤ 0.7), the current state information of the predicted battery swapping cabinet is normally output without triggering an early warning; if it is predicted that the fatigue cumulative damage value is higher than the safety threshold (H > 0.7), the state information of the battery swapping cabinet is output and an early warning is issued to remind the operator to check whether the battery swapping cabinet needs to be repaired or replaced.

[0076] In summary, the method for monitoring the structural reliability of the heavy truck battery swapping cabinet, through the organic combination of digital twin technology, deep learning algorithm and intelligent early warning mechanism, provides strong support for the whole life cycle management of the battery swapping cabinet. It can not only monitor the structural state of the battery swapping cabinet in real time and accurately, but also issue an early warning in time when potential risks appear, effectively ensuring the use safety of the battery swapping vehicle, and has important practical application value and broad application prospects.

[0077] Please refer to Figure 7 , the second embodiment of the present invention provides a device for monitoring the structural reliability of a heavy truck battery swapping cabinet, which includes:

[0078] A virtual entity establishment unit 201, configured to obtain a physical data set collected by a sensor assembly configured on a heavy truck and its battery swapping cabinet, and establish a virtual entity corresponding to the heavy truck based on the physical data set;

[0079] A fusion unit 202, configured to obtain a virtual data set of the virtual entity, and fuse the virtual data set and the physical data set based on digital twin technology to generate a simulation data set;

[0080] The DNN unit 203 is used to calculate the simulation data set using a preset DNN model to generate a predicted value of the maximum stress at the risk point and a predicted value of the fatigue characteristics of the battery swapping cabinet.

[0081] The warning unit 204 is used to perform warning preprocessing on the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet, and give a warning prompt or output the predicted value.

[0082] The third embodiment of the present invention provides a heavy truck battery swapping cabinet structure reliability monitoring device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the heavy truck battery swapping cabinet structure reliability monitoring method described in any one of the above is implemented.

[0083] The fourth embodiment of the present invention provides a readable storage medium, which stores a computer program that can be executed by the processor of the device where the storage medium is located to implement the heavy truck battery swapping cabinet structure reliability monitoring method described in any one of the above.

[0084] Exemplarily, the above-mentioned various devices and various process steps can be implemented by a computer program. The computer program can be divided into one or more units, and the one or more units are stored in the memory and executed by the processor to complete the present invention.

[0085] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0086] The memory can be used to store the computer program and / or modules. By running or executing the computer program and / or modules stored in the memory, and by invoking the data stored in the memory, the processor can implement various functions of the present invention. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card (FlashCard), at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0087] Among them, if the unit integrated in the electronic device or printer is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0088] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0089] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A reliability monitoring method for the structure of a heavy - duty truck battery swapping cabinet, characterized in that, Including: Obtain a physical data set collected by a sensor assembly configured on a heavy truck and its battery swapping cabinet, and establish a virtual entity corresponding to the heavy truck based on the physical data set; Obtain the virtual data set of the virtual entity, and fuse the virtual data set and the physical data set based on digital twin technology to generate a simulation data set; Use a preset DNN model to calculate the simulation data set to generate a predicted value of the maximum stress at the risk point and a predicted value of the fatigue characteristics of the battery swapping cabinet; Perform early warning preprocessing on the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet, and perform early warning prompts or output the predicted values; Obtain a physical data set collected by a sensor assembly configured on a heavy truck and its battery swapping cabinet. Specifically: Obtain a physical data set collected by a sensor assembly configured on the areas of concern of a heavy truck and its battery swapping cabinet, where the physical data set includes stress, mass, speed, acceleration, and displacement; According to the physical data set, use Trucksim dynamics software to build different heavy truck models, and select different road conditions to solve vehicle dynamics parameters to generate a heavy truck Trucksim dynamics model; Based on the geometric model of the battery swapping cabinet, use finite element software Altair HyperWorks to establish a finite element model of the battery swapping cabinet; Perform a fusion process on the heavy truck Trucksim dynamics model and the finite element model of the battery swapping cabinet to generate a virtual entity corresponding to the heavy truck. Among them, the output of the heavy truck Trucksim dynamics model is introduced into the finite element model of the battery swapping cabinet to combine the dynamic equation of the battery swapping cabinet with the physical characteristics of the internal structure; Obtain the virtual data set of the virtual entity, and fuse the virtual data set and the physical data set based on digital twin technology to generate a simulation data set. Specifically: Obtain the virtual data set of the virtual entity, where the virtual data set includes amplitude virtual data, frequency virtual data, and stress virtual data; Combine the physical data sets under different working conditions with the virtual data set and input them into a preset simulation model to generate the stress value at the risk monitoring point; Extract the actual stress value of the key area, compare and verify the stress value at the risk monitoring point and the actual stress value of the key area to generate a verification result; When the verification result is not passed, recalculate using the simulation model; When the verification result is passed, construct a stress data set at the risk point to generate a simulation data set.

2. The reliability monitoring method for the heavy truck battery swapping cabinet structure according to claim 1, wherein The sensor assembly includes a resistance strain type sensor, a mass sensor, a speed sensor, an acceleration sensor, and a displacement sensor.

3. The reliability monitoring method for the heavy truck battery swapping cabinet structure according to claim 1, characterized in that, Perform early warning preprocessing on the predicted value of the maximum stress at the risk point, and perform early warning prompts or output the predicted value. Specifically: Judge whether the predicted value of the maximum stress at the risk point exceeds a preset safety threshold; If not, output the maximum stress value under the current working condition; If so, use a classification neural network to determine the position of the maximum stress value under the current working condition, output the position of the maximum stress value under the current working condition and its corresponding maximum stress value, and give a voice alarm prompt.

4. The reliability monitoring method for the heavy truck battery swapping cabinet structure according to claim 1, wherein Perform early warning preprocessing on the predicted value of the fatigue characteristics of the battery swapping cabinet, and give an early warning prompt or output the predicted value. Specifically: Judge whether the predicted value of the fatigue characteristics of the battery swapping cabinet exceeds the preset safety threshold; If not, output the fatigue cumulative damage value under the current working condition; If so, use a classification neural network to determine the fatigue characteristic category under the current working condition, output the fatigue cumulative damage value corresponding to the fatigue characteristic category under the current working condition, and give a voice alarm prompt.

5. A reliability monitoring device for the structure of a heavy truck battery swapping cabinet, characterized in that, Including: A virtual entity creation unit, which is used to obtain the physical data set collected by the sensor components configured on the heavy truck and its battery swapping cabinet, and establish a virtual entity corresponding to the heavy truck based on the physical data set; A fusion unit, which is used to obtain the virtual data set of the virtual entity, and fuse the virtual data set and the physical data set based on the digital twin technology to generate a simulation data set; A DNN unit, which is used to calculate the simulation data set using a preset DNN model to generate a predicted value of the maximum stress at the risk point and a predicted value of the fatigue characteristics of the battery swapping cabinet; An early warning unit, which is used to perform early warning preprocessing on the predicted value of the maximum stress at the risk point and the predicted value of the fatigue characteristics of the battery swapping cabinet, and give an early warning prompt or output the predicted value; Obtain the physical data set collected by the sensor components configured on the heavy truck and its battery swapping cabinet, and establish a virtual entity corresponding to the heavy truck based on the physical data set. Specifically: Obtain the physical data set collected by the sensor components configured in the areas of concern of the heavy truck and its battery swapping cabinet. The physical data set includes stress, mass, speed, acceleration, and displacement; According to the physical data set, use Trucksim dynamics software to build different heavy truck models, and select different road surface conditions to solve the vehicle dynamics parameters to generate a heavy truck Trucksim dynamics model; Based on the geometric model of the battery swapping cabinet, use the finite element software Altair HyperWorks to establish a finite element model of the battery swapping cabinet; Perform fusion processing on the heavy truck Trucksim dynamics model and the finite element model of the battery swapping cabinet to generate a virtual entity corresponding to the heavy truck. Among them, the output of the heavy truck Trucksim dynamics model is introduced into the finite element model of the battery swapping cabinet to combine the dynamic equation of the battery swapping cabinet with the physical characteristics of the internal structure; Obtain the virtual data set of the virtual entity, and fuse the virtual data set and the physical data set based on the digital twin technology to generate a simulation data set. Specifically: Obtain the virtual data set of the virtual entity, and the virtual data set includes amplitude virtual data, frequency virtual data, and stress virtual data; Combine the physical data sets under different working conditions with the virtual data set and input them into a preset simulation model to generate the stress value at the risk monitoring point; Extract the actual stress value of the key area, compare and verify the stress value of the risk monitoring point with the actual stress value of the key area, and generate a verification result; When the verification result is not passed, recalculate using the simulation model; When the verification result is passed, construct a stress data set of risk points and generate a simulation data set.

6. A reliability monitoring device for the structure of a heavy truck battery swapping cabinet, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the heavy truck battery swapping cabinet structure reliability monitoring method according to any one of claims 1 to 4.

7. A readable storage medium, characterized in that, A computer program is stored, and the computer program can be executed by the processor of the device where the storage medium is located to implement the heavy truck battery swapping cabinet structure reliability monitoring method according to any one of claims 1 to 4.

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