Digital twin system simulation method and system

Abstract

The embodiment of the invention relates to the technical field of digital twinning, and discloses a digital twinning system simulation method and system.The method comprises the steps that firstly, parameter data of a physical entity of a robot is obtained in real time through Internet of Things data acquisition equipment; then, the data type of the parameter data is judged, a target data transmitter is selected based on the judgment result to transmit the parameter data of the physical entity of the robot to an operation parameter builder, and the data type of the parameter data comprises gentle data or jump data; then, a running parameter builder is used for building simulation running parameters according to the parameter data of the physical entity of the robot; and finally, operating a pre-constructed simulation model by using a simulation model device in a simulation operation environment according to the simulation operation parameters to obtain mapping simulation data of the physical entity of the robot. Therefore, the accuracy of the mapping simulation data is improved, and powerful support is provided for performance verification of a product in a real environment.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a digital twin system simulation method and system. Background Art

[0002] Digital twin is a simulation process that fully utilizes data such as physical models, sensor updates, and operation history, and integrates multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities. It mainly constructs an identical entity in the digital world by digitally simulating events (objects) in the physical world, thereby realizing the process of understanding, analyzing, and optimizing physical entities. In the design stage of a product, the use of digital twins can improve the accuracy of the design and verify the performance of the product in a real environment.

[0003] For example, during the design phase of a robot, due to limited space, developers were unable to run the physical robot indefinitely and were unable to obtain performance parameters such as the robot’s maximum coverage area and battery life. Summary of the invention

[0004] The main purpose of the present invention is to provide a digital twin system simulation method and system, aiming to solve the technical problem in the prior art that the performance of a product in a real environment cannot be effectively verified due to conditional limitations.

[0005] To achieve the above-mentioned purpose, in a first aspect, an embodiment of the present application provides a digital twin system simulation method, which is applied to a simulation system, wherein the simulation system includes an Internet of Things data acquisition device, a data transmitter, an operation parameter builder, and a simulation modeler, and the method includes:

[0006] IoT data collection equipment obtains parameter data of the robot’s physical entity in real time;

[0007] Determine the data type of the parameter data and select a target data transmitter based on the determination result to transmit the parameter data of the robot physical entity to the operation parameter builder, wherein the data type of the parameter data includes smooth data or jump data;

[0008] An operation parameter builder builds simulation operation parameters according to parameter data of the robot physical entity;

[0009] The simulation modeler runs the pre-built simulation model with the simulation running parameters in the simulation running environment to obtain the mapping simulation data of the robot physical entity.

[0010] In a possible implementation, the parameter data includes the temperature of the robot lower computer, and determining the data type of the parameter data includes:

[0011] Calculate the temperature change rate of the robot lower computer according to the current temperature of the robot lower computer and the historical temperature at the last preset time;

[0012] Determine that the temperature change rate of the lower computer is greater than or equal to a temperature change rate threshold, and determine that the data type of the parameter data is jump data;

[0013] It is determined that the temperature change rate of the lower computer is less than a temperature change rate threshold, and the data type of the parameter data is determined to be flat data.

[0014] In a possible implementation, the step of selecting a target data transmitter based on the judgment result to transmit the parameter data of the robot physical entity to the operation parameter builder includes:

[0015] In response to the data type of the parameter data being flat data, selecting a first data transmitter to transmit the parameter data of the robot physical entity to an operation parameter builder;

[0016] In response to the data type of the parameter data being jump data, a second data transmitter is selected to transmit the parameter data of the robot physical entity to the operation parameter builder, wherein the data transmission speed of the second data transmitter is greater than that of the first data transmitter.

[0017] In a possible implementation, in response to the data type of the parameter data being jump data, after selecting the second data transmitter to transmit the parameter data of the robot physical entity to the operation parameter builder, the method further includes:

[0018] During the parameter data transmission process, obtaining a data reading and writing rate value and a data reading and writing accuracy value of the second data transmitter;

[0019] Inputting the data reading and writing rate value and the data reading and writing accuracy value into a transmission status monitoring and evaluation unit to obtain a transmission efficiency characterization value of the second data transmitter;

[0020] When the transmission efficiency characterization value of the second data transmission device is less than or equal to the efficiency threshold, dividing the parameter data into a first parameter data packet and a second parameter data packet, wherein the data volume of the second parameter data packet is greater than the data volume of the first parameter data packet;

[0021] Data transmission rights are reallocated to the first data transmission transmitter and the second data transmission transmitter, so that the first parameter data packet is transmitted to the operation parameter builder by using the first data transmission transmitter, and the second parameter data packet is transmitted to the operation parameter builder by using the second data transmission transmitter.

[0022] In a possible implementation, the step of inputting the data read / write rate value and the data read / write accuracy rate value into the transmission status monitoring and evaluation unit to obtain the transmission efficiency characterization value of the second data transmitter includes:

[0023] Inputting the data read / write rate value and the data read / write accuracy rate value into a pre-trained transmission status monitoring and evaluation model to obtain the transmission efficiency characterization value of the second data transmitter, where the transmission status monitoring and evaluation model satisfies the following expression:

[0024]

[0025] where C is the transmission efficiency characterization value, R is the current data read / write rate value, R0 is the data reference read / write rate value, A is the current data read / write accuracy rate value, A0 is the data reference read / write accuracy rate value, T is the performance load of the data transmitter, 0 < T ≤ 1, the performance load characterizes the original performance of the data transmitter, Gr is the performance loss coefficient, 0 < Gr < 1, the performance loss coefficient Gr is positively correlated with the usage load of the data transmitter, β1 is the influence weight of the read / write rate value on the data transmission efficiency, and β2 is the influence weight of the read / write accuracy rate value on the data transmission efficiency.

[0026] In a possible implementation, the parameter data includes the temperature of the robot lower computer, and the operation parameter builder constructs simulation operation parameters according to the parameter data of the robot physical entity, including:

[0027] Inputting the current temperature of the lower computer into the chassis speed control model to obtain the target running speed of the chassis of the robot physical entity in the simulation environment, where the chassis speed control model satisfies the following expression:

[0028] V = -1.28(T - 25) + V0 (T ≥ 25°C), where V is the target running speed, T is the current temperature of the lower computer, and V0 is the initial running speed of the robot at 25°C.

[0029] In a possible implementation, the parameter data includes the temperature of the robot lower computer, and the operation parameter builder constructs simulation operation parameters according to the parameter data of the robot physical entity, including:

[0030] Performing temperature compensation on the current temperature of the lower computer according to the terrain complexity of the simulation robot model in the simulation operation environment to obtain the target temperature;

[0031] Inputting the target temperature of the lower computer into the chassis speed control model to obtain the target running speed of the chassis of the robot physical entity in the simulation environment, where the chassis speed control model satisfies the following expression:

[0032] V = -1.28(T M-25)+V0(T≥25℃), where V is the target operating speed, T M is the target temperature of the lower computer, and V0 is the initial speed of the robot at 25℃.

[0033] In a possible implementation, the mapping simulation data includes the robot operation coverage area and operation time, and the simulation modeler runs the pre-built simulation model with the simulation operation parameters under the simulation operation environment to obtain the mapping simulation data of the robot physical entity, including:

[0034] The simulation robot model is controlled to run continuously in the simulation environment at the target running speed of the lower computer at different temperatures until the power is exhausted to obtain the robot's running coverage area and running time.

[0035] In a possible implementation, after the simulation modeler runs the pre-built simulation model with the simulation running parameters under the simulation running environment to obtain the mapping simulation data of the robot physical entity, it also includes:

[0036] An expectation check is performed on the mapping simulation data, and when the mapping simulation data does not meet the expected data requirements, the parameters of the chassis speed control model are adjusted until the mapping simulation data meets the expected data requirements.

[0037] In the second aspect, a simulation system is also provided in an embodiment of the present application, and the simulation system includes an Internet of Things data acquisition device, a data transmitter, an operating parameter builder, a simulation modeler; and a memory and a processor, wherein the memory is used to store program code, and the processor is used to call the program code to execute the method described in the first aspect.

[0038] Different from the prior art, the digital twin system simulation method provided by the embodiment of the present application first uses the Internet of Things data acquisition device to obtain the parameter data of the robot physical entity in real time; then judges the data type of the parameter data and selects the target data transmission device based on the judgment result to transmit the parameter data of the robot physical entity to the operation parameter builder, and the data type of the parameter data includes smooth data or jump data; then uses the operation parameter builder to build the simulation operation parameters according to the parameter data of the robot physical entity; finally uses the simulation modeler to run the pre-built simulation model with the simulation operation parameters in the simulation operation environment to obtain the mapping simulation data of the robot physical entity. That is, different types of data transmission devices are selected for data transmission according to different data types. When the data type is jump data, a second data transmission device with a faster transmission speed is selected for data transmission so that the operation parameter builder can make a quick response adjustment of the simulation operation parameters according to the parameter data in time, thereby improving the accuracy of the mapping simulation data and providing strong support for the performance verification of the product in a real environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0040] Figure 1 This is a schematic diagram of the structure of the simulation system in some embodiments of the present application;

[0041] Figure 2 A schematic diagram of a flow chart of a digital twin system simulation method in some embodiments of the present application;

[0042] Figure 3 A schematic diagram of a flow chart of a digital twin system simulation method in some other embodiments of the present application;

[0043] Figure 4 This is a schematic diagram of the hardware structure of the simulation system in some embodiments of the present application.

[0044] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0047] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0048] Digital twin is a simulation process that fully utilizes data such as physical models, sensor updates, and operation history, and integrates multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities. It mainly constructs an identical entity in the digital world by digitally simulating events (objects) in the physical world, thereby realizing the process of understanding, analyzing, and optimizing physical entities. In the design stage of a product, the use of digital twins can improve the accuracy of the design and verify the performance of the product in a real environment.

[0049] For example, during the design phase of a robot, due to limited space, developers were unable to run the physical robot indefinitely and were unable to obtain performance parameters such as the robot’s maximum coverage area and battery life.

[0050] In view of the above problems, this application proposes a digital twin system simulation method, which is applied to the simulation system, such as Figure 1As shown, the simulation system includes an IoT data collection device 100, a data transmitter 200, an operation parameter builder 300, and a simulation modeler. The data transmitter 200 includes a first data transmitter 210 and a second data transmitter 220. The first data transmitter 210 is configured to have a lower data transmission performance than the second data transmitter 220 to save hardware costs. For example, the first data transmitter 210 selects a relatively stable data transmitter with a lower bandwidth requirement; the second data transmitter 220 selects a data transmitter with a fast transmission speed and a high bandwidth requirement.

[0051] The inventive concept of the present application is: the Internet of Things data acquisition device acquires parameter data of the physical entity in real time. If the parameter data is flat data (PH data, i.e. data with no obvious changes), a first data transmitter with a slower transmission speed is selected for data transmission. If the parameter data is jump data (TB data, i.e. data with more obvious changes), a second data transmitter with a faster transmission speed is selected for data transmission. Afterwards, for the jump data, the operation parameter builder can promptly and quickly receive the parameter data, and analyze and construct the corresponding simulation operation parameters, that is, it can promptly respond to and adjust the simulation operation parameters according to the parameter data, thereby improving the accuracy of the mapped simulation data and providing strong support for the performance verification of the product in a real environment.

[0052] It should be noted that although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here. Figure 2 and Figure 3 The method includes the following steps S100 to S400:

[0053] S100, the IoT data acquisition device obtains parameter data of the robot's physical entity in real time;

[0054] IoT data acquisition devices can include temperature sensors, pressure sensors, and image collectors (such as cameras, RGBD cameras, etc.) to collect parameter data of the robot's physical entity. For example, the robot can be allowed to run various functional modules in a suspended state to collect parameter data under different functional modules.

[0055] For example, when adjusting the speed of the robot, the temperature of the lower computer needs to be taken into consideration so as to implement the corresponding speed control strategy. Specifically, when the temperature of the lower computer is detected to be increased, the speed of the robot should be appropriately slowed down to ensure that its overall performance is not affected, especially to maintain the endurance of the robot.

[0056] S200, determining the data type of the parameter data and selecting a target data transmission device based on the determination result to transmit the parameter data of the robot physical entity to the operation parameter builder, wherein the data type of the parameter data includes smooth data or jump data;

[0057] In one embodiment, the parameter data includes the temperature of the robot lower computer, and the determining of the data type of the parameter data includes:

[0058] Calculate the temperature change rate of the robot lower computer according to the current temperature of the robot lower computer and the historical temperature at the last preset time;

[0059] Determine that the temperature change rate of the lower computer is greater than or equal to a temperature change rate threshold, and determine that the data type of the parameter data is jump data;

[0060] It is determined that the temperature change rate of the lower computer is less than a temperature change rate threshold, and the data type of the parameter data is determined to be flat data.

[0061] Specifically, it is first necessary to collect the current temperature data of the robot's lower computer, and at the same time, it is necessary to obtain the historical temperature data of the last preset time. This preset time can be set according to actual needs, such as recording the temperature every one minute, five minutes or longer. After obtaining the current temperature and the historical temperature of the last preset time, the temperature change rate can be calculated according to the time interval. If the temperature change rate is greater than or equal to the preset temperature change rate threshold, it means that the temperature of the lower computer has changed significantly in a short period of time. At this time, the data type of the parameter data is judged as "jump data". If the temperature change rate is less than the temperature change rate threshold, it means that the temperature change of the lower computer is relatively slow. At this time, the data type of the parameter data is judged as "smooth data".

[0062] It should be noted that parameter data plays a vital role in the operation and regulation of the robot. They have a profound impact on the adjustment of the robot's operating parameters or operating environment, but the importance of different parameters is not equal. Some key parameter data, whose changes are directly related to the performance and stability of the robot, therefore, these parameters must be given priority when adjusting the robot's operating parameters or operating environment. For example, the temperature of the robot's lower computer is a crucial parameter. As the "brain" of the robot, the stability of its temperature is directly related to the normal operation of the entire system. When the temperature of the lower computer changes significantly, it often means that the robot is experiencing high-intensity operation or is in unfavorable environmental conditions. At this time, if countermeasures are not taken in time, the robot's hardware may be damaged and even its overall performance may be affected. Therefore, when the temperature of the lower computer is detected to be abnormally high, it must respond quickly to reduce its operating load by adjusting the robot's travel speed, thereby effectively controlling the temperature of the lower computer. This adjustment not only helps to protect the robot's hardware, but also improves the accuracy of the mapping simulation data. Mapping simulation is an important part of product design and verification. The accuracy of its data is directly related to the performance of the product in the real application environment. By accurately controlling the robot's operating parameters and environmental conditions, simulation data that is closer to reality can be obtained, providing strong support for product performance verification.

[0063] Therefore, for parameter data with the data type of "jump data", the parameter data needs to be sent to the operation parameter builder as quickly and accurately as possible, so that the operation parameter builder can quickly respond to changes in the parameter data and adjust the simulation operation parameter characteristics in time.

[0064] For example, when the temperature of the robot lower computer changes significantly, the operation parameter builder needs to obtain this change in a timely and accurate manner to quickly adjust the operation parameter characteristics, thereby obtaining more accurate mapping simulation data.

[0065] Therefore, in one embodiment, the step of selecting a target data transmission device based on the judgment result to transmit the parameter data of the robot physical entity to the operation parameter builder includes:

[0066] In response to the data type of the parameter data being flat data, selecting a first data transmitter to transmit the parameter data of the robot physical entity to an operation parameter builder;

[0067] In response to the data type of the parameter data being jump data, a second data transmitter is selected to transmit the parameter data of the robot physical entity to the operation parameter builder, wherein the data transmission speed of the second data transmitter is greater than that of the first data transmitter.

[0068] Specifically, when it is determined that the data type of the parameter data is flat data, a first data transmission device with lower transmission performance is selected to transmit the parameter data of the robot physical entity to the operation parameter builder, and when it is determined that the data type of the parameter data is jump data, a second data transmission device with higher transmission performance is selected to transmit the parameter data of the robot physical entity to the operation parameter builder. In this way, the parameter data of the robot system can be ensured to be transmitted and processed in the most appropriate way at a reasonable cost, thereby improving the overall performance and reliability of the system.

[0069] For data types that occupy less memory, such as digital or text information such as temperature and pressure, efficient and stable transmission can usually be achieved through the second data transmission link. However, when processing data that occupies a large amount of memory, such as pictures and videos, the transmission process is often affected by the current performance status or load level of the second data transmission link. In this case, the transmission performance may be unstable, which is manifested as fluctuations in transmission rate or a decrease in overall efficiency.

[0070] Therefore, in one embodiment, the step S200: in response to the data type of the parameter data being jump data, after selecting the second data transmitter to transmit the parameter data of the robot physical entity to the operation parameter builder, further includes:

[0071] S210, in the parameter data transmission process, obtaining a data reading and writing rate value and a data reading and writing accuracy value of the second data transmitter;

[0072] S220, inputting the data reading and writing rate value and the data reading and writing accuracy value into a transmission status monitoring and evaluation unit to obtain a transmission efficiency characterization value of the second data transmitter;

[0073] S230, when the transmission efficiency characterization value of the second data transmission device is less than or equal to the efficiency threshold, dividing the parameter data into a first parameter data packet and a second parameter data packet, wherein the data volume of the second parameter data packet is greater than the data volume of the first parameter data packet;

[0074] S240. Reallocate data transmission rights to the first data transmission transmitter and the second data transmission transmitter, so that the first parameter data packet is transmitted to the operation parameter builder by using the first data transmission transmitter, and the second parameter data packet is transmitted to the operation parameter builder by using the second data transmission transmitter.

[0075] Specifically, during the process of using the second data transmitter to transmit the parameter data of the physical entity of the robot to the operating parameter builder, the data read / write rate value and the data read / write accuracy rate value of the second data transmitter are obtained in real time. Then, the data read / write rate value and the data read / write accuracy rate value are input into the transmission status monitoring and evaluation unit to obtain the transmission efficiency characterization value of the second data transmitter. When it is detected that the transmission efficiency characterization value of the second data transmitter is less than or equal to the efficiency threshold, it indicates that the data transmission performance of the second data transmitter has been significantly affected. Relying solely on the second data transmitter for data cannot meet the requirements of efficient data transmission. Therefore, in this case, the embodiments of the present application divide the parameter data into a first parameter data packet and a second parameter data packet; then, the data transmission permissions are reallocated to the first data transmitter and the second data transmitter, so as to use the first data transmitter to transmit the first parameter data packet to the operating parameter builder and use the second data transmitter to transmit the second parameter data packet to the operating parameter builder. In this way, after real-time monitoring of the data transmission status of the second data transmitter, if it is determined that the data transmission status cannot meet the requirements of efficient transmission, the data packet disassembling process is performed, greatly improving the efficiency and accuracy of data transmission.

[0076] In one embodiment, the inputting the data read / write rate value and the data read / write accuracy rate value into the transmission status monitoring and evaluation unit to obtain the transmission efficiency characterization value of the second data transmitter includes:

[0077] Inputting the data read / write rate value and the data read / write accuracy rate value into a pre-trained transmission status monitoring and evaluation model to obtain the transmission efficiency characterization value of the second data transmitter. The transmission status monitoring and evaluation model satisfies the following expression:

[0078]

[0079] Where C is the transmission efficiency characterization value, R is the current data read / write rate value, R0 is the reference data read / write rate value, A is the current data read / write accuracy rate value, A0 is the reference data read / write accuracy rate value, T is the performance load of the data transmitter, 0 < T ≤ 1, and the performance load characterizes the original performance size of the data transmitter. Gr is the performance loss coefficient, 0 < Gr < 1, and the performance loss coefficient Gr is positively correlated with the usage load of the data transmitter. β1 is the influence weight of the read / write rate value on the data transmission efficiency, and β2 is the influence weight of the read / write accuracy rate value on the data transmission efficiency.

[0080] Specifically, when constructing the transmission status monitoring and evaluation model, the embodiment of the present application considers the use load (such as the use time or the temperature generated during use) to cause loss to the data transmission performance of the data transmitter, and obtains the above-mentioned transmission status monitoring and evaluation model by collecting a large amount of data for model training and parameter calibration. And when the performance load T of the second data transmitter is 0.9, the performance loss coefficient of the second data transmitter is determined to be 0.1 after 30 hours of use, that is, the data reading and writing efficiency baseline value of the second data transmitter after 30 hours of use becomes 0.9*(1-0.1)=0.72, and the data reading and writing efficiency value is Then, the current read / write rate value, the benchmark read / write rate value, the current read / write accuracy value, and the benchmark read / write accuracy value can be used to obtain the final transmission efficiency characterization value. For the convenience of calculation, the embodiment of the present application assigns the same weight to the influence weight β1 of the read / write rate value on the transmission efficiency and the influence weight β2 of the read / write accuracy value on the transmission efficiency, that is, both are 50%.

[0081] S300, an operation parameter builder builds simulation operation parameters according to parameter data of the robot physical entity;

[0082] It is understandable that as the robot's physical entity continues to run, its parameter data (such as the temperature of the lower computer) will continue to change. In order to maintain the synchronization between the simulation model and the actual running state, it is necessary to adjust the simulation running parameters of the simulation model in a timely manner, such as by adjusting the running speed of the simulation model to match these changes.

[0083] In one embodiment, the parameter data includes the temperature of the robot lower computer, and the operation parameter builder constructs the simulation operation parameters according to the parameter data of the robot physical entity, including:

[0084] The current temperature of the lower computer is input into the chassis speed control model to obtain the target running speed of the robot's physical chassis in the simulation environment. The chassis speed control model satisfies the following expression:

[0085] V = -1.28 (T-25) + V0 (T ≥ 25 ° C), where V is the target running speed, T is the current temperature of the lower computer, and V0 is the initial running speed of the robot at 25 ° C.

[0086] Specifically, the expected operating speed of the physical chassis in the simulation environment is dynamically adjusted according to the real-time temperature data of the robot's physical entity lower computer. After determining the corresponding target speeds under various temperature conditions, the simulated robot model is controlled to operate at these target speeds in the simulated operating environment, thereby generating mapping simulation data of the robot's physical entity. This process ensures that the simulation model can closely simulate the operating conditions of the real world, thereby significantly improving the accuracy of the mapping simulation data.

[0087] When constructing the chassis speed control model, the embodiment of the present application obtains the above chassis speed control model by collecting a large amount of data for model training and parameter calibration. For example, the initial running speed of the robot at 25°C of the lower computer is set to 10cm / S. If the temperature of the lower computer rises, the chassis needs to be decelerated (at a rate of 1.28); in this way, the performance and life of the robot can be effectively guaranteed.

[0088] It can be understood that when the physical robot is in the suspended state, the resistance of the walking mechanism can be ignored, but in the actual operation process, the robot walking mechanism must be subject to a certain resistance, so the temperature of the lower meter will be higher than the temperature in the suspended state. Therefore, in order to improve the accuracy of the simulation test, it is necessary to perform temperature compensation on the sensing temperature of the physical robot lower computer to obtain the target temperature T M Then the target temperature T M Substitute it into the above chassis speed control model to calculate the target running speed of the physical entity chassis in the simulation environment. In the simulation environment, the generation of temperature will also be affected by the complexity of the terrain. The more complex the terrain, the more frequent the robot's movements change, the higher the heat generated by the lower computer, and therefore, the higher the temperature compensation amount. That is, the temperature compensation amount is positively correlated with the complexity of the terrain. For the convenience of calculation, a certain compensation coefficient can be set. The complexity of the terrain can be approximately characterized by the number of turns per unit time. For example, when the complexity of the terrain is level 1, the temperature compensation coefficient is 1.1 (the target temperature after compensation is 1.1 times the current temperature); when the complexity of the terrain is 1.5, the temperature compensation coefficient is 1.6 (the target temperature after compensation is 1.6 times the current temperature); when the complexity of the terrain is 2, the temperature compensation coefficient is 2 (the target temperature after compensation is 2 times the current temperature). In this way, through temperature compensation, the temperature performance in the actual environment can be simulated, so as to accurately obtain the mapping simulation data of the robot's physical entity.

[0089] S400, the simulation modeler runs the pre-built simulation model with the simulation running parameters in the simulation running environment to obtain mapping simulation data of the robot physical entity.

[0090] In one embodiment, the mapping simulation data includes the robot operation coverage area and operation time, and the simulation modeler runs the pre-built simulation model with the simulation operation parameters under the simulation operation environment to obtain the mapping simulation data of the robot physical entity, including:

[0091] The simulation robot model is controlled to run continuously in the simulation environment at the target running speed of the lower computer at different temperatures until the power is exhausted to obtain the robot's running coverage area and running time.

[0092] Specifically, first, according to the real-time temperature data of the robot's physical entity lower computer, the chassis speed control model calculates the corresponding target operating speed in the simulation environment. Then, the simulation robot model is controlled to continue running in the simulation environment at these target speeds until its virtual power is exhausted. In this process, the system records and calculates the robot's operating coverage area and total operating time. In this way, even in the actual situation where the test site is limited, key data such as the robot's operating coverage area and operating time can be efficiently obtained through the simulation model. By real-time monitoring of the temperature of the lower computer and dynamically adjusting the simulation operation parameters, it is ensured that the simulation process can be as close to the real environment as possible, thereby improving the accuracy and practicality of the mapped simulation data.

[0093] In other embodiments, if the robot operation coverage area and operation time obtained by simulation fail to meet the expected standards, that is, when the mapping simulation data is verified and found to fail to meet the established data requirements, further optimization measures can be taken. Specifically, the embodiment of the present application adjusts the parameter settings of the chassis speed control model and re-simulates the operation based on these new parameters. This process will be iterated until the mapping simulation data finally meets the expected data requirements. In this way, the optimal simulation results can be continuously approached to ensure that the simulation model can more accurately reflect the actual operating conditions of the robot's physical entity.

[0094] Based on this, a digital twin system simulation method provided by an embodiment of the present application first uses an Internet of Things data acquisition device to obtain parameter data of a robot physical entity in real time; then determines the data type of the parameter data and selects a target data transmitter based on the determination result to transmit the parameter data of the robot physical entity to an operation parameter builder, wherein the data type of the parameter data includes smooth data or jump data; then uses the operation parameter builder to build simulation operation parameters according to the parameter data of the robot physical entity; finally uses the simulation modeler to run the pre-built simulation model with the simulation operation parameters in a simulation operation environment to obtain the mapping simulation data of the robot physical entity. That is, different types of data transmitters are selected for data transmission according to different data types. When the data type is jump data, a second data transmitter with a faster transmission speed is selected for data transmission so that the operation parameter builder can make a quick response adjustment of the simulation operation parameters according to the parameter data in a timely manner, thereby improving the accuracy of the mapping simulation data and providing strong support for the performance verification of the product in a real environment.

[0095] Please see attached Figure 4 , Figure 4A schematic diagram of the hardware structure of a simulation system provided for some embodiments of the present application; the simulation system provided in the embodiments of the present application also includes a memory 110 and a processor 120, wherein the memory 110 is used to store program code, and the processor 120 is used to call the program code to execute the method described above.

[0096] Among them, the processor 120 is used to provide computing and control capabilities to control the simulation system to perform corresponding tasks, for example, to control the simulation system to execute the digital twin system simulation method in any of the above method embodiments, the method comprising: the Internet of Things data acquisition device obtains the parameter data of the robot physical entity in real time; the data type of the parameter data is determined and the target data transmitter is selected based on the determination result to transmit the parameter data of the robot physical entity to the operation parameter builder, the data type of the parameter data includes smooth data or jump data; the operation parameter builder constructs simulation operation parameters according to the parameter data of the robot physical entity; the simulation modeler runs a pre-constructed simulation model with the simulation operation parameters in a simulation operation environment to obtain the mapping simulation data of the robot physical entity.

[0097] The processor 120 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0098] The memory 110, as a non-transient computer-readable storage medium, can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the digital twin system simulation method in the embodiment of the present application. The processor 120 can implement the digital twin system simulation method in any of the above method embodiments by running the non-transient software programs, instructions and modules stored in the memory 110.

[0099] Specifically, the memory 110 may include a volatile memory (VM), such as a random access memory (RAM); the memory 110 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD) or other non-transitory solid-state storage device; the memory 110 may also include a combination of the above types of memory.

[0100] To sum up, the simulation system of the present application adopts the technical solution of any one of the above-mentioned digital twin system simulation method embodiments, and therefore, it has at least the beneficial effects brought by the technical solution of the above-mentioned embodiments, which will not be elaborated one by one here.

[0101] The embodiment of the present application also provides a computer-readable storage medium, such as a memory including a program code, and the program code can be executed by a processor to complete the digital twin system simulation method in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0102] The embodiment of the present application also provides a computer program product, which includes one or more program codes, and the program code is stored in a computer-readable storage medium. The processor of the simulation system reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the steps of the digital twin system simulation method provided in the above embodiment.

[0103] A person skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware or by hardware associated with a program code, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0104] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] Through the description of the above implementation methods, ordinary technicians in this field can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Ordinary technicians in this field can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0106] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A digital twin system simulation method, characterized in that: Applied to a simulation system, the simulation system includes an Internet of Things data acquisition device, a data transmitter, an operation parameter builder, and a simulation modeler, and the method includes: IoT data collection equipment obtains parameter data of the robot’s physical entity in real time; Determine the data type of the parameter data and select a target data transmitter based on the determination result to transmit the parameter data of the robot physical entity to the operation parameter builder, wherein the data type of the parameter data includes smooth data or jump data; An operation parameter builder builds simulation operation parameters according to parameter data of the robot physical entity; The simulation modeler runs the pre-built simulation model with the simulation running parameters in the simulation running environment to obtain the mapping simulation data of the robot physical entity.

2. The digital twin system simulation method according to claim 1, characterized in that: The parameter data includes the temperature of the robot lower computer, and the data type of the parameter data is determined, including: Calculate the temperature change rate of the robot lower computer according to the current temperature of the robot lower computer and the historical temperature at the last preset time; Determine that the temperature change rate of the lower computer is greater than or equal to a temperature change rate threshold, and determine that the data type of the parameter data is jump data; It is determined that the temperature change rate of the lower computer is less than a temperature change rate threshold, and the data type of the parameter data is determined to be flat data.

3. The digital twin system simulation method according to claim 1, characterized in that: The step of selecting a target data transmission device based on the judgment result to transmit the parameter data of the robot physical entity to the operation parameter builder includes: In response to the data type of the parameter data being flat data, selecting a first data transmitter to transmit the parameter data of the robot physical entity to an operation parameter builder; In response to the data type of the parameter data being jump data, a second data transmitter is selected to transmit the parameter data of the robot physical entity to the operation parameter builder, wherein the data transmission speed of the second data transmitter is greater than that of the first data transmitter.

4. A simulation system, characterized in that: The simulation system includes an Internet of Things data acquisition device, a data transmitter, an operation parameter builder, and a simulation modeler; and A memory and a processor, wherein the memory is used to store program codes, and the processor is used to call the program codes to execute the method according to any one of claims 1 to 3.

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