Transmission detection method, system and device and storage medium

Abstract

The invention relates to a transmission detection method, system and device and a storage medium, relates to the technical field of vehicles, and at least solves the technical problem of how to improve the detection efficiency of a vehicle transmission in related technologies. The method comprises the steps that first operation data are obtained, the first operation data are operation data of a transmission entity under a target working condition, the transmission entity comprises a transmission body and a controller, and the operation data comprise transmission body multi-dimensional data and controller multi-dimensional data; the multi-dimensional data of the transmission body comprises dynamic dimension data and thermal dimension data; second operation data are obtained, the second operation data are operation data of transmission digital twinborn models under the target working condition, and the transmission digital twinborn models comprise a transmission body digital twinborn model and a controller digital twinborn model; based on the first operation data and the second operation data, a detection result is obtained, and the detection result is used for indicating whether the transmission entity breaks down or not.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a transmission detection method, system, device and storage medium. Background Art

[0002] With the rapid development of vehicle technology, users' requirements for vehicle transmission safety are also increasing. For example, vehicle transmission testing can be used to ensure vehicle safety.

[0003] At present, vehicle transmission fault detection mainly relies on the professional experience of technicians and basic analysis of sensor data. This method is highly dependent on personal expertise, but lacks accuracy and foresight, and is difficult to capture subtle changes or potential problems. In addition, this method cannot warn of faults in advance, which reduces the detection efficiency of vehicle transmissions.

[0004] Therefore, how to improve the detection efficiency of vehicle transmissions has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a transmission detection method, system, device and storage medium to at least solve the technical problem of how to improve the detection efficiency of vehicle transmission in the related art. The technical solution of the present application is as follows: According to the first aspect of the present application, a transmission detection method is provided, including: obtaining first operating data, the first operating data is the operating data of the transmission entity under the target working condition, the transmission entity includes: a transmission body and a controller, the operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller, the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data. Obtaining second operating data, the second operating data is the operating data of the transmission digital twin model under the target working condition, the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model. Based on the first operating data and the second operating data, a detection result is obtained, and the detection result is used to indicate whether a fault occurs in the transmission entity.

[0006] In a possible implementation, the dynamic dimension data includes motion state data and power transfer data, and the thermal dimension data includes temperature state data.

[0007] In a possible implementation, the controller multi-dimensional data includes: transmission control data and status monitoring data, the transmission control data is used to adjust the transmission body multi-dimensional data, and the status monitoring data is the status data of the transmission body.

[0008] In a possible implementation, the method further includes: acquiring a dynamic model of the transmission body and a thermodynamic model of the transmission body, and generating a digital twin model of the transmission body based on the dynamic model and the thermodynamic model.

[0009] In a possible implementation, the dynamic model includes: a dual-mass flywheel model, a clutch calculation model, a gear shift model and a gear shaft system model. The thermodynamic model includes: a hydraulic system model.

[0010] In one possible implementation, the controller digital twin model includes: a target gear selection model, a pre-gear selection model, a gear shift timing model, a clutch control model, a gear shift control model, a starting control model, a creep control model, a clutch thermal control model and a transmission fault diagnosis model.

[0011] In a possible implementation, the first operating data and the second operating data are operating data at the current moment. After the above-mentioned method of "obtaining a detection result based on the first operating data and the second operating data", the method also includes: when the detection result is used to indicate that the transmission entity has not failed, obtaining the first historical operating data and the second historical operating data, the first historical operating data being the operating data of the transmission entity in the target working condition at the historical moment. The second historical operating data is the operating data of the transmission digital twin model in the target working condition at the historical moment. Based on the first historical operating data and the first operating data, the first predicted operating data is determined, the first predicted operating data is the operating data of the transmission entity in the target working condition at the target future moment, and the target future moment is later than the current moment. Based on the second historical operating data and the second operating data, the second predicted operating data is determined, the second predicted operating data is the operating data of the transmission digital twin model in the target working condition at the target future moment. Based on the first predicted operating data and the second predicted operating data, a prediction result is obtained, and the prediction result is used to indicate whether the transmission entity fails at the target future moment.

[0012] According to the second aspect involved in the present application, a transmission detection system is provided, and the system includes: a digital twin platform, a digital twin module and a transmission entity. The transmission entity is used to send first operating data to the digital twin platform, and the first operating data is the operating data of the transmission entity under the target working condition. The operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller. The multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data. The digital twin module is used to send second operating data to the digital twin platform, and the second operating data is the operating data of the digital twin model of the transmission under the target working condition. The digital twin platform is used to receive the first operating data sent by the transmission entity and then the second operating data sent by the digital twin module, and obtain the detection result based on the first operating data and the second operating data, and the detection result is used to indicate whether the transmission entity has a fault.

[0013] In one possible implementation, the digital twin platform is also used to store first operating data, second operating data, first historical operating data, and second historical operating data, the first operating data and the second operating data being operating data at a current moment, the first historical operating data being operating data of the transmission entity in a target operating condition at a historical moment, and the second historical operating data being operating data of the transmission digital twin model in a target operating condition at a historical moment.

[0014] In a possible implementation, the transmission entity includes: a transmission body. The digital twin module includes: a transmission body digital twin model. The digital twin module is further used to obtain a dynamic model of the transmission body and a thermodynamic model of the transmission body. The digital twin module is further used to generate a transmission body digital twin model based on the dynamic model and the thermodynamic model.

[0015] According to the third aspect provided by the present application, a detection device for a transmission is provided, and the device includes an acquisition module and a processing module. The acquisition module is used to acquire first operating data, and the first operating data is the operating data of the transmission entity under the target working condition, and the transmission entity includes: a transmission body and a controller. The operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller, and the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data. The acquisition module is also used to acquire second operating data, and the second operating data is the operating data of the digital twin model of the transmission under the target working condition, and the digital twin model of the transmission includes: a digital twin model of the transmission body and a digital twin model of the controller. The processing module is used to obtain a detection result based on the first operating data and the second operating data, and the detection result is used to indicate whether a fault occurs in the transmission entity.

[0016] According to the fourth aspect provided by the present application, a transmission detection device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to execute instructions to implement a method as in the first aspect and any possible implementation manner thereof.

[0017] According to the fifth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by the processor of the transmission detection device, the transmission detection device can execute the method of the first aspect and any possible implementation method thereof.

[0018] According to the sixth aspect provided by the present application, a computer program product is provided, the computer program product comprising computer instructions, when the computer instructions are executed on a detection device of a transmission, the detection device of the transmission implements the method of the first aspect and any possible implementation manner thereof.

[0019] Beneficial effects of the present invention: (1) Obtaining the actual operating data of the transmission entity under the same working conditions (i.e., the first operating data) and the simulation data of the transmission digital twin model (i.e., the second operating data), and comparing the actual operating data with the simulation data, can determine whether the transmission entity has a fault. In this way, the simulation data can be used to evaluate the operating status of the actual data, so that the status detection and fault diagnosis of the transmission entity can be accurately performed, thereby improving the detection efficiency of the transmission. In addition, since the operating data includes multi-dimensional data of the transmission body and multi-dimensional data of the controller, the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data, and the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model, that is, the operating data in this application includes different data in the transmission, as well as multi-dimensional data, so that when the transmission is detected through the operating data, more data can be used to detect the transmission, thereby improving the accuracy of the detection.

[0020] (2) By dividing the multi-dimensional data of the transmission body into thermal dimension data and power dimension data and performing classification analysis, the working status of each part of the transmission body can be made more transparent and the collected data can be more accurate. This method not only helps to quickly locate the specific location of the abnormality when an abnormality is detected in the transmission body, but also provides strong support for the control and optimization of the transmission. This provides a solid foundation for building a more sophisticated digital twin model of the transmission body, which helps to achieve efficient control strategy optimization and fault prevention.

[0021] (3) By dividing the multi-dimensional data of the controller into two categories: transmission control data and status monitoring data, and performing classification analysis, the working status of each part of the controller can be more refined and the collected data can be more accurate. This provides a solid foundation for building a more refined controller digital twin model.

[0022] (4) By integrating the thermodynamic model and the kinetic model into a unified framework, a comprehensive digital twin model of the transmission can be formed. This approach not only covers the characteristics of each subsystem, but also takes into account the mutual influence between them, which can improve the simulation accuracy of the system and help understand and optimize the design and operation of the transmission, ensuring its efficiency and reliability in practical applications.

[0023] (5) By refining the thermodynamic model and the kinetic model, specific sub-models are constructed to describe the thermodynamic and kinetic processes inside the transmission. These refined sub-models can be used to simulate various physical processes of the transmission with high precision, and to comprehensively analyze the microscopic physical processes at the level of the transmission itself. These refined models provide a solid foundation for the efficient simulation of the digital twin model of the transmission itself, which can ensure the efficiency and reliability of the transmission in practical applications and provide support for more efficient transmission management and fault detection.

[0024] (6) By subdividing the controller digital twin model into multiple specific sub-models, each sub-model focuses on simulating specific functions of the controller or simulating different control processes of the controller, the operating state of the transmission control system can be simulated more accurately.

[0025] (7) By analyzing the historical and current operating data of the physical transmission and its digital twin under specific operating conditions, it is possible to predict the future performance of both. Then, by comparing the actual data with the corresponding simulated data, early signs of possible failure can be identified more accurately. This prediction is based on an understanding of past and present data trends and can assess the likelihood of future transmission failure so that preventive measures can be taken before failure occurs, thereby improving the efficiency and durability of the transmission.

[0026] It should be noted that the technical effects brought about by any implementation method in the second to sixth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0027] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0029] Figure 1 is a schematic diagram of a transmission detection system according to an exemplary embodiment; Figure 2 is a flow chart of a transmission detection method according to an exemplary embodiment; Figure 3 is a schematic diagram of an example of data interaction on a digital twin platform according to an exemplary embodiment; Figure 4 is a flow chart of another transmission detection method according to an exemplary embodiment; Figure 5 is a schematic diagram of an example of a transmission digital twin model according to an exemplary embodiment; Figure 6 is a flow chart of another transmission detection method according to an exemplary embodiment; Figure 7 is a schematic structural diagram of a transmission detection device according to an exemplary embodiment; Figure 8 It is a schematic structural diagram of another transmission detection device according to an exemplary embodiment. DETAILED DESCRIPTION

[0030] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0032] Before introducing the transmission detection method of the embodiment of the present application in detail, the implementation environment and application scenarios of the embodiment of the present application are first introduced.

[0033] With the rapid development of vehicle technology, the demand for transmission detection is increasing, especially for dual clutch transmission (DCT). Traditional DCT transmission fault diagnosis and maintenance methods mostly rely on manual inspection and experience judgment. This method is not only inefficient, but also difficult to detect potential faults in a timely and accurate manner, thus affecting the performance and service life of the transmission. With the development of vehicle intelligence and networking, traditional transmission fault diagnosis and maintenance methods can no longer meet the vehicle's demand for high-precision and intelligent maintenance.

[0034] At present, the data of the transmission can be monitored through the monitoring strategy of the transmission configured in the vehicle, thereby realizing the fault prediction of the transmission. However, the currently monitored data is relatively simple and cannot accurately detect whether the vehicle transmission has a fault.

[0035] In order to solve the above problems, the embodiment of the present application provides a transmission detection method, which is applied to the scene of detecting the transmission. The method includes: obtaining the actual operation data (i.e., the first operation data) of the transmission entity under the same working condition and the simulation data (i.e., the second operation data) of the transmission digital twin model, and comparing the actual operation data with the simulation data, so as to determine whether the transmission entity has a fault. In this way, the simulation data can be used to evaluate the operation state of the actual data, so that the state detection and fault diagnosis of the transmission entity can be accurately performed, thereby improving the detection efficiency of the transmission. In addition, since the operation data includes multi-dimensional data of the transmission body and multi-dimensional data of the controller, the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data, and the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model, that is, the operation data in the present application includes different data in the transmission, as well as multi-dimensional data, so that when the transmission is detected by the operation data, more data can be used to detect the transmission, thereby improving the accuracy of the detection.

[0036] Figure 1 is a schematic diagram of a transmission detection system according to an exemplary embodiment. Figure 1 As shown, the transmission detection system includes: a transmission entity 101, a digital twin module 102 and a digital twin platform 103. The digital twin platform 103 can communicate wirelessly with the transmission entity 101 and the digital twin module 102.

[0037] Specifically, the transmission entity 101 can be used to send first operating data to the digital twin platform 103. The first operating data is the operating data of the transmission entity 101 under the target working condition. The transmission entity 101 can include: a transmission body and a controller of the transmission (i.e., a controller). In addition, the transmission entity 101 can also include: a sensor of the transmission and an actuator of the transmission. The transmission entity 101 can also be used to send the first operating data to the digital twin platform 103.

[0038] The digital twin module 102 includes: a digital twin model of the transmission body and a digital twin model of the controller. The digital twin module 102 can be used to send the second operating data to the digital twin platform 103, and the second operating data is the operating data of the digital twin model of the transmission body and the digital twin model of the controller under the target working condition. In addition, the digital twin module 102 can also be used to send the second operating data to the digital twin platform 103. The digital twin module 102 is also used to obtain the dynamic model of the transmission body and the thermodynamic model of the transmission body, wherein the dynamic model includes: a dual mass flywheel model, a clutch calculation model, a shift model (i.e., a shift system and a gear shaft system calculation model) and a gear shaft system model (i.e., a gear shaft system shift dynamics model), and the thermodynamic model includes: a hydraulic system model. The digital twin module 102 is also used to generate a digital twin model of the transmission body based on the dynamic model and the thermodynamic model.

[0039] The digital twin platform 103 can be used to receive the first operating data sent by the transmission entity 101 and the second operating data sent by the digital twin module 102. The digital twin platform 103 can also be used to obtain a detection result based on the first operating data and the second operating data to detect whether the transmission entity 101 has a fault. The digital twin platform 103 is also used to store the first operating data, the second operating data, the first historical operating data and the second historical operating data, wherein the first operating data and the second operating data are operating data at the current moment, the first historical operating data are operating data of the transmission entity 101 in the target working condition at the historical moment, and the second historical operating data are operating data of the digital twin module 102 in the target working condition at the historical moment.

[0040] In the embodiment of the present application, the transmission entity 101 may include a transmission body, a transmission sensor, a transmission controller and a transmission actuator.

[0041] Among them, the transmission body includes: dual clutch, input shaft, output shaft, gear set and shift mechanism and other components. The transmission sensor includes at least one of the following: oil temperature sensor, speed sensor, pressure sensor, and the transmission sensor can be used to monitor the speed, temperature and pressure and other parameters of the transmission in real time. The transmission controller is used to receive sensor signals and generate corresponding controller data and control strategies according to the sensor signals. The transmission controller data includes at least one of the following: target gear selection data, pre-gear selection data, shift timing data, clutch control data, gear disengagement control data, start control data, creep control data. The actuator of the transmission can perform operations such as shifting and clutching according to the controller data of the controller.

[0042] The digital twin module 102 may be composed of a controller digital twin model and a transmission body digital twin model, which is used to simulate the operating state and performance of the transmission. The controller digital twin model includes: a target gear selection model, a pre-gear selection model, a shift timing model, a clutch control model, a gear disengagement control model, a start control model, a creep control model, a clutch thermal control model and a transmission fault diagnosis model. The controller digital twin model is used to simulate the shift strategy, clutch control, torque distribution, etc. according to the algorithm and logic of the controller. The transmission body digital twin model includes: a dual-mass flywheel model, a clutch calculation model, a hydraulic system model, a shift model (i.e., a shift system and gear shaft system calculation model) and a gear shaft system model (i.e., a gear shaft system shift dynamics model). The transmission body digital twin model is used to simulate gear transmission, clutch engagement and disengagement, shaft system rotation, etc. according to the physical structure and kinematics of the transmission.

[0043] The digital twin platform 103 includes a data interaction module, a digital twin database and a data analysis module, which are used to realize data transmission, storage and analysis. The data interaction module receives data of the transmission entity 101 (such as control signals and sensor signals sent by the transmission entity 101) and simulation data of the digital twin module 102 (such as simulation data of the controller digital twin model and simulation data of the transmission body digital twin model sent by the digital twin module 102) through wireless communication. The digital twin database is divided into an offline database and a real-time database. The offline database is used to store factory data, design performance indicators, historical operation data of the transmission entity 101 and historical simulation data of the digital twin module 102, and the real-time database stores the real-time operation data of the transmission entity 101 and the real-time operation data of the digital twin module 102. The data analysis module is used to analyze and process the data in the digital twin database to realize functions such as fault diagnosis, control optimization, performance prediction, and model calibration. The data analysis module can timely discover potential faults and take corresponding maintenance measures by comparing and analyzing the operating status and performance parameters of the transmission entity and the transmission digital twin model. At the same time, the data analysis module can calibrate the digital twin model based on real-time data to ensure the synchronization between the model and the entity.

[0044] As a possible implementation method, the digital twin module 102 can collect the status data of the transmission entity 101 in real time and transmit it to the data interaction module of the digital twin platform 103 through the network, and store it in the database after preprocessing. Subsequently, the digital twin module 102 simulates the transmission behavior through an algorithm and generates simulation data. Then, the data interaction module synchronously transmits the actual operation data and simulation data to the data analysis module of the digital twin platform 103. Next, the data analysis module analyzes the received data, uses machine learning and artificial intelligence technology to predict performance, diagnose potential faults, and generate reports. Finally, the data analysis module adjusts the control strategy based on the diagnosis results and calibrates and optimizes the digital twin model to ensure its accuracy and reliability.

[0045] For example, Figure 2 is a flow chart of a transmission detection method according to an exemplary embodiment. Figure 2As shown, the digital twin module 102 can establish a digital twin model of the transmission body and a digital twin model of the controller according to the design parameters of the transmission body and the controller, respectively. Then, the digital twin module 102 sets the initial parameters of the digital twin model of the controller and the digital twin model of the transmission body according to the design parameters (i.e., the digital twin model of the transmission body is established according to the transmission body and the initial parameters are set, and the digital twin model of the controller is established according to the controller of the transmission and the initial parameters are set). Then, the digital twin module 102 can calibrate the digital twin model, and can adjust the model parameters to improve the accuracy of the model by comparing the output data of the digital twin model with the physical transmission. After that, the sensor of the transmission obtains the operating status data of the transmission entity 101 in real time, and the controller receives the signal from the sensor in real time, and performs the control operation of the transmission entity according to these signals. The sensor of the transmission and the controller of the transmission both obtain the data of the transmission entity 101 (i.e., the sensor of the transmission obtains the data of the transmission entity, and the controller of the transmission obtains the data of the transmission entity), and transmits and stores the above data to the data interaction module of the digital twin platform 103 after processing.

[0046] Next, the digital twin module 102 receives the data and signals from the data interaction module, and the digital twin module 102 executes the same operating state as the transmission entity 101, and accordingly executes the same operating state as the transmission entity. In the digital twin module 102, the controller digital twin model can perform simulation operation according to the received data and signals, and generate simulation data corresponding to the controller of the transmission (i.e., collect the simulation data of the controller digital twin model). At the same time, the transmission body digital twin model also performs simulation calculations based on the received data to simulate the actual operating state of the transmission entity 101 (i.e., collect the simulation data of the transmission body digital twin model). After being processed, the simulation data of the digital twin module 102 is transmitted and stored in the data interaction module of the digital twin platform 103.

[0047] Since the data interaction module in the digital twin platform 103 is responsible for realizing the interaction between the data of the transmission entity 101 and the simulation data of the digital twin module 102. Therefore, after the data interaction module in the digital twin platform 103 receives the data from the transmission entity 101 and the digital twin module 102 (i.e., the simulation data of the transmission digital twin model), the transmission entity data and the simulation data of the transmission digital twin model are interactively processed, and the interactive data is sent to the digital twin database including the offline database and the real-time database, and the digital twin database stores the historical operation data and real-time operation data of the transmission entity and the transmission digital twin model. Then, the data analysis module in the digital twin platform 103 can perform in-depth mining and analysis on the data in the database, and realize the functions of fault diagnosis, performance prediction, etc. by analyzing the data differences and trends between the transmission entity 101 and the digital twin module 102. According to the analysis results, corresponding maintenance suggestions or control strategies can be generated and fed back to the DCT transmission entity module or maintenance personnel through the digital twin platform 103 (i.e., the data in the offline database and the real-time database are compared and analyzed to realize the functions of fault diagnosis, control optimization, performance prediction, model calibration, etc.).

[0048] It should be noted that the offline database includes factory data, design performance indicators, historical operation data of the transmission entity and historical simulation data of the transmission digital twin model, and the real-time database includes real-time operation data of the transmission entity and real-time simulation data of the transmission digital twin model.

[0049] In an embodiment of the present application, the digital twin module 102 can be deployed in a digital twin database in a digital twin platform 103. The digital twin platform 103 can serve as a central node, responsible for receiving and processing sensor signals and control signals from each transmission entity 101, which are transmitted to a cloud server or data center via a network.

[0050] like Figure 3 As shown in the figure, the data interaction module receives and stores the control signals and sensor signals of the transmission entity, and forwards the above data to the digital twin database for storage. The data interaction module can obtain real-time data streams from each transmission entity through the network interface and convert these data into a structured format for processing. At the same time, the module is also responsible for receiving simulation data from the digital twin model and sending it back to the transmission entity to adjust the transmission entity.

[0051] The digital twin database is used to store data. The digital twin database includes an offline database and a real-time database. The offline database can store historical data for data analysis, fault diagnosis and other tasks. The real-time database can store real-time data, and the real-time database is used to save the current sensor data and controller data, as well as the data of the current digital twin model of the transmission body and the data of the digital twin model of the controller to support instant analysis and decision-making. Among them, the digital twin module is deployed in the digital twin database in the digital twin platform. The data digital twin module can receive the transmission entity data and the data of the digital twin module from the data interaction module. The digital twin model in the digital twin module simulates the behavior of the transmission entity through the simulation algorithm, performs complex calculations and simulations based on the physical model, historical data and other input parameters, and generates detailed simulation data. The simulation data can be transmitted to the data interaction module through the network, and further forwarded to the real-time database or sent directly back to the transmission entity.

[0052] The data analysis module can obtain data from the digital twin database and generate optimization suggestions using the fault diagnosis algorithm to adjust and optimize the transmission entity. At the same time, the data analysis module can also use the performance prediction model to generate prediction results to predict whether the transmission entity will fail in the future. Then, the data analysis module can forward the prediction results and optimization suggestions to the digital twin database, and then transmit them to the transmission entity module through the data interaction module.

[0053] It should be noted that in the embodiment of the present application, the transmission is a dual-clutch transmission, and the present application mainly tests the dual-clutch transmission.

[0054] In summary, this application has established a transmission detection system based on digital twin technology. By analyzing the operation data of the transmission entity and the simulation data of the transmission digital twin model, potential failures of the transmission can be discovered in a timely manner and corresponding maintenance measures can be taken. At the same time, by optimizing the transmission control strategy, the operation efficiency and reliability of the transmission can be improved.

[0055] For ease of understanding, the transmission detection method provided in the present application is specifically introduced below with reference to the accompanying drawings. Figure 4 is a flow chart of another transmission detection method according to an exemplary embodiment. Figure 4 As shown, the method comprises the following steps: S401, obtaining first operation data.

[0056] The first operating data is operating data of the transmission entity under the target working condition, and the transmission entity includes: a transmission body and a controller.

[0057] It should be noted that the target operating condition is the operating state of the transmission under specific operating conditions. The specific operating conditions may include parameters such as the load, speed, temperature, and pressure of the transmission. For example, the target operating condition is when the oil temperature of the transmission is set to 85 degrees Celsius and the speed is set to 2500 revolutions per minute.

[0058] In the embodiment of the present application, the operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller.

[0059] The transmission entity also includes a transmission sensor for real-time monitoring of the transmission's operating status, and the transmission body multi-dimensional data is the operating data collected by the transmission sensor. The transmission body includes at least one of the following: a dual clutch, an input shaft, an output shaft, a gear set, and a shift mechanism. The transmission sensor includes at least one of the following: an oil temperature sensor, a speed sensor, and a pressure sensor.

[0060] It should be noted that the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data. The power dimension data includes the motion state data and power transmission data of the transmission body, and the thermal dimension data includes the temperature state data of the transmission body.

[0061] Exemplarily, the motion state data in the power dimension data may include: fork displacement data and flywheel rotation speed data, the power transmission data in the power dimension data may include: clutch pressure data, spring force data and clutch torque data, and the thermal dimension data may include: lubrication flow data and oil temperature data.

[0062] It is understandable that by dividing the multi-dimensional data of the transmission body into thermal dimension data and power dimension data and performing classification analysis, the working status of each part of the transmission body can be made more transparent and the collected data can be more accurate. This method not only helps to quickly locate the specific location of the abnormality when an abnormality is detected in the transmission body, but also provides strong support for the control and optimization of the transmission. This provides a solid foundation for building a more sophisticated digital twin model of the transmission body, which helps to achieve efficient control strategy optimization and fault prevention.

[0063] As a possible implementation manner, the detection device may collect multi-dimensional data of the transmission body and / or multi-dimensional data of the controller corresponding to the transmission entity under the target operating condition to obtain the first operating data.

[0064] It should be noted that the controller is responsible for receiving sensor signals, generating controller multi-dimensional data based on the signals, and controlling the transmission through a preset algorithm. The controller multi-dimensional data includes: transmission control data and status monitoring data. The transmission control data is used to adjust the multi-dimensional data of the transmission body, and the status monitoring data is the status data of the transmission body.

[0065] For example, the transmission control data may include target gear selection data, pre-gear selection data, shift timing data, clutch control data, gear shift control data, start control data, and creep control data. The state monitoring data may include clutch thermal control data and transmission fault diagnosis data.

[0066] It is understandable that by dividing the multi-dimensional data of the controller into two categories: transmission control data and status monitoring data, and performing classification analysis, the working status of each part of the controller can be more refined and the collected data can be more accurate. This provides a solid foundation for building a more refined controller digital twin model, which helps to achieve efficient control strategy optimization and fault prevention.

[0067] S402: Acquire second operation data.

[0068] Among them, the second operating data is the operating data of the transmission digital twin model under the target working conditions, and the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model.

[0069] As a possible implementation method, the detection device can collect multi-dimensional data of the transmission body and / or multi-dimensional data of the controller corresponding to the transmission digital twin model under the target operating conditions to obtain second operating data.

[0070] It should be noted that the controller digital twin model is used to simulate the control process of the controller. The controller digital twin model includes: target gear selection model, pre-gear selection model, shift timing model, clutch control model, gear shift control model, start control model, creep control model, clutch thermal control model and transmission fault diagnosis model.

[0071] It can be understood that by subdividing the controller digital twin model into multiple specific sub-models, each sub-model focuses on simulating specific functions of the controller or simulating different control processes of the controller, the operating state of the transmission control system can be simulated more accurately.

[0072] As a possible implementation method, the detection device can determine the data type that the controller digital twin model should output based on the multi-dimensional data of the controller of the transmission entity. Then, the detection device can establish a controller digital twin model that can accurately reflect the actual behavior of the controller based on the required output data.

[0073] In some embodiments, the detection device can obtain a thermodynamic model of the transmission body and a dynamic model of the transmission body, and generate a digital twin model of the transmission body based on the thermodynamic model of the transmission body and the dynamic model of the transmission body.

[0074] The digital twin model of the transmission is used to simulate the physical behavior of the transmission. The dynamics model includes: dual-mass flywheel model, clutch calculation model, shift model and gear shaft system model. The thermodynamics model includes: hydraulic system model.

[0075] It should be noted that the hydraulic system model includes multiple sub-models, including: a main pressure model, a shift pressure model, a clutch pressure model, a lubrication flow model, and an oil temperature model.

[0076] It is understandable that by refining the thermodynamic model and the kinetic model, specific sub-models are constructed to describe the thermodynamic and kinetic processes inside the transmission. Using these refined sub-models, various physical processes of the transmission can be simulated with high precision, and the microscopic physical processes at the level of the transmission itself can be fully analyzed. These refined models provide a solid foundation for the efficient simulation of the digital twin model of the transmission itself, which can ensure the efficiency and reliability of the transmission in practical applications and provide support for more efficient transmission management and fault detection.

[0077] It should be noted that the thermodynamic model and the dynamic model of the transmission body can be determined based on the thermodynamic dimension data and the dynamic dimension data of the multi-dimensional data of the transmission body.

[0078] Optional, such as Figure 5 As shown in FIG. 1 , the digital twin model of the transmission includes: a digital twin model of the transmission body and a digital twin model of the controller. The digital twin model of the transmission includes a digital twin model of the controller, a digital twin model of the transmission body, and the interaction between each sub-model.

[0079] Among them, the controller digital twin model includes: target gear selection model, pre-gear selection model, shift timing model, clutch control model, gear shift control model, start control model, creep control model, clutch thermal control model, transmission fault diagnosis model. The controller digital twin model can be constructed through mathematical modeling based on the actual control logic of the controller, and can reproduce the decision-making process of the controller, involving aspects such as shift point selection and clutch pressure adjustment. In addition, the controller digital twin model can be used to construct a digital twin model of the transmission body.

[0080] It should be noted that the digital twin model of the transmission body is constructed based on physical principles and can accurately simulate the working process of the transmission body, involving gear transmission, clutch friction, shaft system dynamic response, etc. The digital twin model of the transmission body includes: dual-mass flywheel model, clutch calculation model, shifting system and gear shaft system calculation model (i.e., shifting model), gear shaft system shifting dynamics model (i.e., gear shaft system model), and hydraulic system model.

[0081] The dual mass flywheel model can be a primary flywheel model, a spring force transmission model, and a secondary flywheel model established according to the components included in the dual mass flywheel. The output data of the dual mass flywheel model can be used as input data of the clutch calculation model to affect the construction of the clutch calculation model.

[0082] The hydraulic system model can establish the main pressure model, shift pressure model, clutch pressure model, lubrication flow model, and oil temperature model according to the working principle of the hydraulic system of the transmission body. The main pressure model is responsible for providing the basic pressure required by the entire hydraulic system. The output data of the main pressure model can be used as the input data of the clutch pressure model to establish the clutch pressure model. In addition, the output data of the main pressure model can also be used as the input data of the shift pressure model and the lubrication flow model, affecting the establishment of the shift pressure model and the lubrication flow model. In addition, the shift pressure model and the lubrication flow model can affect each other, such as the output data of the lubrication flow model can be used as the input data of the shift pressure model, and the output data of the shift pressure model can be used as the input data of the lubrication flow model. In addition, the output data of the lubrication flow model can be used as the input data of the oil temperature model. The output data of the hydraulic system model can be used as the input data of the shift system and gear shaft system calculation model, affecting the construction of the shift system and gear shaft system calculation model. The hydraulic system model and the gear shaft system shift dynamics model, dual mass flywheel model, and clutch calculation model can affect each other.

[0083] The clutch calculation model can establish a clutch dynamic friction coefficient data-driven model, a clutch dynamics model, a clutch-transmitted torque model, and a clutch drag torque data-driven model according to the working principle of the clutch of the transmission body. The output data of the clutch dynamic friction coefficient data-driven model can be used as input data of the clutch dynamics model to establish the clutch dynamics model. The output data of the clutch drag torque data-driven model can also be used as input data of the clutch-transmitted torque model to establish the clutch-transmitted torque model, and the clutch-transmitted torque model and the clutch dynamics model can influence each other. The output data of the clutch calculation model can be used as input data of the shifting system and gear shaft system calculation model to establish the shifting system and gear shaft system calculation model.

[0084] The shifting system and gear shaft system calculation model can establish the shift fork displacement calculation model, the force analysis model of the gear shifting process, the force analysis model of the gear engagement process, the shift fork shaft self-locking resistance calculation model, and the synchronization resistance calculation model according to the working principle of the shifting system of the transmission body. The shift fork displacement calculation model and the force analysis model of the gear shifting process, the force analysis model of the gear engagement process, and the shift fork shaft self-locking resistance calculation model can influence each other, and the force analysis model of the gear engagement process and the synchronization resistance calculation model can influence each other. The shifting system and gear shaft system calculation model can affect the establishment of the gear shaft system shifting dynamics model. The shifting system and gear shaft system calculation model mainly describes how to control the shifting process. The gear shaft system shifting dynamics model mainly focuses on the dynamic behavior of gears and shafts during the shifting process.

[0085] It is understandable that by integrating the thermodynamic model and the dynamic model into a unified framework, a comprehensive digital twin model of the transmission body can be formed. This approach not only covers the characteristics of each subsystem, but also takes into account the mutual influence between them, which can improve the simulation accuracy of the system, and can also help understand and optimize the design and operation of the transmission, ensuring its efficiency and reliability in practical applications.

[0086] S403: Obtain a detection result based on the first operation data and the second operation data.

[0087] The detection result is used to indicate whether a transmission entity fails.

[0088] As a possible implementation, the detection device may pre-store a preset threshold. Then, the detection device may obtain the timestamp of the first operating data, and sort the first operating data and the corresponding second operating data according to the timestamp. Then, the detection device may calculate the deviation between the first operating data and the second operating data at the same time, and compare the deviation with the preset threshold. When the deviation between the first operating data and the second operating data is greater than the preset threshold, it is determined that the transmission entity has failed. When the deviation between the first operating data and the second operating data is less than or equal to the preset threshold, it is determined that the transmission entity has not failed.

[0089] It should be understood that the deviation between the first operating data and the second operating data is greater than the preset threshold value, which means that there is a deviation between the first operating data and the second operating data that is greater than the preset threshold value. The deviation between the first operating data and the second operating data is less than or equal to the preset threshold value, which means that the deviation of each data between the first operating data and the second operating data is less than or equal to the preset threshold value.

[0090] Exemplarily, if both the first operating data and the second operating data include: flywheel rotation speed, clutch pressure and oil temperature, the deviation includes: flywheel rotation speed deviation, clutch pressure deviation and oil temperature deviation. In the case where there is a deviation greater than a preset threshold value among the flywheel rotation speed deviation, clutch pressure deviation and oil temperature deviation (e.g., the flywheel rotation speed deviation is greater than the preset threshold value, the clutch pressure deviation and the oil temperature deviation are less than the preset threshold value), it is determined that the transmission entity has failed. In the case where the flywheel rotation speed deviation, the clutch pressure deviation and the oil temperature deviation are all less than or equal to the preset threshold value, it is determined that the transmission entity has not failed.

[0091] It should be noted that, when both the first operating data and the second operating data include multiple data, each data may correspond to a preset threshold. For example, the first operating data and the second operating data both include: flywheel rotation speed, clutch pressure and oil temperature, then the preset threshold may include: flywheel rotation speed threshold, clutch pressure threshold and oil temperature threshold. Alternatively, each data may correspond to the same preset threshold, which is not limited in the embodiments of the present application.

[0092] The technical solution provided by the above embodiment brings at least the following beneficial effects: by obtaining the actual operating data (i.e., the first operating data) of the transmission entity and the simulation data (i.e., the second operating data) of the transmission digital twin model under the same working conditions, and comparing the actual operating data with the simulation data, it is possible to determine whether the transmission entity has a fault. In this way, the simulation data can be used to evaluate the operating state of the actual data, so that the state detection and fault diagnosis of the transmission entity can be accurately performed, thereby improving the detection efficiency of the transmission. In addition, since the operating data includes multi-dimensional data of the transmission body and multi-dimensional data of the controller, the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data, and the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model, that is, the operating data in this application includes different data in the transmission, as well as multi-dimensional data, so that when the transmission is detected through the operating data, more data can be used to detect the transmission, thereby improving the accuracy of the detection.

[0093] It should be noted that, based on confirming that the transmission is fault-free at the current moment, the detection device can use the historical operating data of the transmission entity and the historical operating data of the transmission digital twin model to predict the future working status and evaluate whether there is a potential failure risk, so as to take preventive measures in advance, so as to realize the evaluation of the future working status and operating trend of the transmission entity, and ensure that the transmission is always in the best working condition.

[0094] In some embodiments, after obtaining the detection result (i.e., S403) based on the first operating data and the second operating data, if the detection result indicates that the transmission entity has not failed, the detection device can obtain the first historical operating data and the second historical operating data, wherein the first historical operating data is the operating data of the transmission entity in the target working condition at the historical moment, and the second historical operating data is the operating data of the transmission digital twin model in the target working condition at the historical moment. Then, the detection device can determine the first predicted operating data based on the first historical operating data and the first operating data, and determine the second predicted operating data based on the second historical operating data and the second operating data. Then, the detection device can obtain the prediction result based on the first predicted operating data and the second predicted operating data to determine whether the transmission entity has failed at the target future moment.

[0095] Among them, the first predicted operating data is the operating data of the transmission entity in the target working condition at the target future moment, and the second predicted operating data is the operating data of the transmission digital twin model in the target working condition at the target future moment, and the target future moment is later than the current moment.

[0096] As a possible implementation method, the detection device can use advanced machine learning algorithms to predict the future performance of the transmission based on the historical operating data and real-time collected data of the transmission entity, as well as the historical operating data and real-time collected data of the digital twin model. The prediction results will be used to evaluate the working status and future operating trends of the transmission.

[0097] It should be noted that the embodiments of the present application do not limit the type of machine learning algorithm. For example, the machine learning algorithm may be a linear regression model, or a random forest model, or an extreme random tree model.

[0098] It is understood that by analyzing the historical and current operating data of the physical transmission and its digital twin under specific operating conditions, it is possible to predict the performance of both in the future. Then, by comparing the actual data with the corresponding simulated data, early signs that may lead to failure can be identified more accurately. This prediction is based on the understanding of past and present data trends, and can assess the possibility of future transmission failures so that preventive measures can be taken before failure occurs, which can improve the efficiency and durability of the transmission.

[0099] The following is an introduction to the transmission detection method provided by the embodiment of the present application with reference to specific examples. Figure 6 As shown, including the following S1-S7: S1. Data collection: First, data collection is performed through various sensors and data input interfaces installed on the DCT transmission, and various data during the operation of the DCT transmission are collected in real time, including key parameters such as temperature, pressure, and speed.

[0100] S2. Data transmission: The collected actual operation data of the DCT transmission entity is transmitted to the data interaction module of the digital twin platform. Through the network connection, these data are preliminarily processed and stored in the cloud server to provide basic data support for subsequent analysis.

[0101] S3. Data processing: Data cleaning, feature extraction, fault pattern identification and other data processing are performed within the digital twin platform to provide support for the subsequent use of the data analysis module to perform in-depth processing and analysis of the received data.

[0102] S4. Performance prediction: Based on key characteristic parameters, historical operating data and real-time collected data, advanced machine learning algorithms are used to predict the future performance of the DCT transmission and evaluate the working status and future operating trends of the DCT transmission.

[0103] S5. Fault diagnosis: The fault diagnosis module performs detailed analysis on the actual operation data of the DCT transmission entity and the simulation data of the digital twin module, and uses expert systems or artificial intelligence technology to detect and locate possible fault causes.

[0104] S6. Result output: The results of the performance prediction and fault diagnosis of the DCT transmission are presented in the form of reports or data output. These results not only provide detailed prediction reports, but also include specific fault diagnosis conclusions and recommended measures.

[0105] S7, Feedback and Optimization: Based on the above prediction and diagnosis results, corresponding feedback information is generated, and the control strategy of the DCT transmission is adjusted in real time. At the same time, the digital twin model is calibrated and optimized according to the diagnosis results to ensure that its accuracy and reliability are continuously improved.

[0106] In summary, the present application realizes feature extraction and selection of the operating state of the DCT transmission through digital twin technology, and constructs a digital twin model of the DCT transmission based on the operating state of the DCT transmission to obtain corresponding simulation data. Then, by comparing the simulation data with the operating data of the DCT transmission entity, the functions of real-time monitoring and abnormality detection, fault location and analysis, performance prediction, and control optimization of the DCT transmission can be realized, thereby improving the operating efficiency, reliability, and driving safety of the transmission. At the same time, the present application also provides a method for constructing a transmission detection system, which provides a reference for the development of similar systems.

[0107] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the detection device of the transmission includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0108] The embodiment of the present application can divide the transmission detection device into functional modules according to the above method. For example, the transmission detection device can include various functional modules corresponding to the various functional divisions, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0109] Figure 7 FIG. 1 is a schematic diagram of a transmission detection device according to an exemplary embodiment. Figure 7 The transmission detection device 700 includes an acquisition module 701 and a processing module 702 .

[0110] The acquisition module 701 is used to acquire the first operating data, which is the operating data of the transmission entity under the target working condition. The transmission entity includes: a transmission body and a controller. The operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller. The multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data.

[0111] The acquisition module 701 is also used to acquire second operating data, which is the operating data of the transmission digital twin model under the target working condition. The transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model.

[0112] The processing module 702 is used to obtain a detection result based on the first operating data and the second operating data, where the detection result is used to indicate whether a transmission entity fails.

[0113] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0114] Figure 8 FIG. 1 is a schematic diagram of the structure of another transmission detection device according to an exemplary embodiment. Figure 8 As shown, the transmission detection device 800 includes but is not limited to: a processor 801 and a memory 802 .

[0115] The memory 802 is used to store executable instructions of the processor 801. It can be understood that the processor 801 is configured to execute instructions to implement the transmission detection method in the above embodiment.

[0116] It should be noted that those skilled in the art can understand that Figure 8 The structure of the transmission detection device shown in the figure does not constitute a limitation on the transmission detection device. The transmission detection device may include a transmission detection device. Figure 8 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0117] The processor 801 is the control center of the transmission detection device. It uses various interfaces and lines to connect various parts of the entire transmission detection device. By running or executing software programs and / or modules stored in the memory 802, and calling data stored in the memory 802, it executes various functions of the transmission detection device and processes data, thereby monitoring the transmission detection device as a whole. The processor 801 may include one or more processing units. Optionally, the processor 801 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 801.

[0118] The memory 802 may be used to store software programs and various data. The memory 802 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 802 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0119] In an exemplary embodiment, a computer-readable storage medium including instructions is further provided, such as a memory 802 including instructions. The instructions can be executed by a processor 801 of a detection device of a transmission to implement the method in the above embodiment.

[0120] In actual implementation, Figure 7 The functions of the acquisition module 701 and the processing module 702 in Figure 8The processor 801 in the embodiment calls the computer program stored in the memory 802. The specific execution process can refer to the description of the method part in the above embodiment, which will not be repeated here.

[0121] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0122] In an exemplary embodiment, the embodiment of the present application further provides a computer program product including one or more instructions, and the one or more instructions can be executed by the processor 801 of the detection device of the transmission to complete the method in the above embodiment.

[0123] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the transmission detection device, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0124] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0125] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0128] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0129] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A transmission detection method, characterized in that: The method comprises: Acquire first operating data, wherein the first operating data is operating data of a transmission entity under a target working condition, wherein the transmission entity includes: a transmission body and a controller, wherein the operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller, wherein the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data; Acquire second operating data, where the second operating data is operating data of a transmission digital twin model under the target operating condition, wherein the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model; Based on the first operating data and the second operating data, a detection result is obtained, and the detection result is used to indicate whether a fault occurs in the transmission entity.

2. The method according to claim 1, characterized in that The dynamic dimension data includes motion state data and power transfer data, and the thermal dimension data includes temperature state data.

3. The method according to claim 1, characterized in that The controller multi-dimensional data includes: transmission control data and status monitoring data. The transmission control data is used to adjust the transmission body multi-dimensional data. The status monitoring data is the status data of the transmission body.

4. The method according to claim 1, characterized in that: The method further comprises: Acquiring a dynamic model of the transmission body and a thermodynamic model of the transmission body; Based on the dynamic model and the thermodynamic model, a digital twin model of the transmission body is generated.

5. The method according to claim 4, characterized in that The dynamic model includes: a dual-mass flywheel model, a clutch calculation model, a gear shift model and a gear shaft system model; the thermodynamic model includes: a hydraulic system model.

6. The method according to claim 1, characterized in that The controller digital twin model includes: a target gear selection model, a pre-gear selection model, a gear shift timing model, a clutch control model, a gear shift control model, a starting control model, a creep control model, a clutch thermal control model and a transmission fault diagnosis model.

7. The method according to any one of claims 1 to 6, characterized in that The first operating data and the second operating data are operating data at the current moment; After obtaining the detection result based on the first operation data and the second operation data, the method further includes: When the detection result indicates that the transmission entity has not failed, obtaining first historical operating data and second historical operating data, wherein the first historical operating data is operating data of the transmission entity at the target operating condition at a historical moment; and the second historical operating data is operating data of the transmission digital twin model at the target operating condition at the historical moment; Determine first predicted operating data based on the first historical operating data and the first operating data, wherein the first predicted operating data is operating data of the transmission entity at the target operating condition at a target future time, and the target future time is later than the current time; Determine second predicted operating data based on the second historical operating data and the second operating data, where the second predicted operating data is operating data of the transmission digital twin model under the target operating condition at the target future moment; A prediction result is obtained based on the first predicted operation data and the second predicted operation data, and the prediction result is used to indicate whether the transmission entity fails at the target future time.

8. A transmission detection system, characterized in that: The system comprises: a digital twin platform, a digital twin module and a transmission entity; The transmission entity is used to send first operating data to the digital twin platform, wherein the first operating data is operating data of the transmission entity under a target working condition, wherein the operating data includes: multi-dimensional data of the transmission body and multi-dimensional data of the controller, wherein the multi-dimensional data of the transmission body includes: power dimension data and thermal dimension data; The digital twin module is used to send second operating data to the digital twin platform, where the second operating data is operating data of the transmission digital twin model under the target operating condition; The digital twin platform is used to receive the first operating data sent by the transmission entity and then the second operating data sent by the digital twin module, and obtain a detection result based on the first operating data and the second operating data, and the detection result is used to indicate whether the transmission entity fails.

9. The system according to claim 8, characterized in that The digital twin platform is also used to store the first operating data, the second operating data, the first historical operating data and the second historical operating data, the first operating data and the second operating data are operating data at the current moment, the first historical operating data are operating data of the transmission entity in the target operating condition at the historical moment, and the second historical operating data are operating data of the transmission digital twin model in the target operating condition at the historical moment.

10. The system according to claim 8 or 9, characterized in that The transmission entity includes: a transmission body and a controller; the digital twin module includes: a transmission body digital twin model and a controller digital twin model; The digital twin module is further used to obtain a dynamic model of the transmission body and a thermodynamic model of the transmission body; The digital twin module is also used to generate a digital twin model of the transmission body based on the dynamic model and the thermodynamic model.

11. A transmission detection device, characterized in that: The device comprises: an acquisition module, configured to acquire first operating data, wherein the first operating data is operating data of a transmission entity under a target working condition, wherein the transmission entity comprises: a transmission body and a controller; the operating data comprises: multi-dimensional data of the transmission body and multi-dimensional data of the controller, wherein the multi-dimensional data of the transmission body comprises: power dimension data and thermal dimension data; An acquisition module, used for acquiring second operating data, where the second operating data is operating data of a transmission digital twin model under the target operating condition, wherein the transmission digital twin model includes: a transmission body digital twin model and a controller digital twin model; The processing module is used to obtain a detection result based on the first operating data and the second operating data, wherein the detection result is used to indicate whether a fault occurs in the transmission entity.

12. A transmission detection device, characterized in that: include: processor; A memory for storing the processor executable instructions; wherein the processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

13. A computer-readable storage medium, characterized in that: When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of a detection device for a transmission, the detection device for the transmission can perform the method according to any one of claims 1 to 7.

14. A computer program product, characterized in that The computer program product comprises computer program instructions which, when executed, implement the method according to any one of claims 1 to 7.

Images