A twin physical modeling method

Abstract

The present invention relates to the field of computer simulation, and in particular to a twin physical modeling method. The present invention obtains image information of a monitoring target collected by a monitoring terminal to determine the number of active components of the monitoring target and the displacement parameters of the monitoring target within a predetermined period according to the image information, calculates complex characterization parameters of the twin simulation for the monitoring terminal, summarizes the complex characterization parameters of the twin simulation corresponding to each monitoring terminal, divides the complex category of joint simulation for each monitoring target in the current period, constructs a digital twin model of each monitoring target in a virtual space at a predetermined update frequency, and adaptively selects a joint simulation method, thereby alleviating the jamming caused by the joint simulation of multiple interacting complex digital twins in the virtual space in the process of concentrated dynamicization of physical entities within a short period, and improving the simulation fluency.

Description

Technical Field

[0001] The present invention relates to the field of computer simulation, and in particular to a twin physical modeling method. Background Art

[0002] With the development of technology, systems are becoming more and more complex, involving multiple disciplines such as mechanics, electronics, and control. In order to fully understand and optimize these systems, it is necessary to adopt multidisciplinary unified modeling and joint simulation technology. Digital twins are digital mappings of physical entities. They can be used to simulate, analyze, and optimize physical systems. The development of digital twin technology has promoted the demand for higher-precision and more real-time simulation of embedded systems.

[0003] For example, Chinese patent publication number: CN117555251A discloses a digital twin simulation device and a digital twin simulation system. The system includes a sensor module, a data acquisition module, a modeling and simulation module, and an analysis and optimization module. The sensor module is connected to the data acquisition module, the data acquisition module is connected to the modeling and simulation module, the modeling and simulation module is connected to the analysis and optimization module, and the sensor module is used to collect data in the physical world in real time.

[0004] However, there are still the following problems in the prior art:

[0005] The complexity of the model directly affects the required computing resources, which include more processor time, memory, and storage space. For complex models, a high-performance computing cluster may be required to complete the simulation in a reasonable time. In particular, when physical entities are intensively dynamic in a short period of time and the digital twin models mapped by the object entities are more complex, the simulation involving multiple interacting digital twins in the virtual space becomes difficult and prone to jamming. Summary of the invention

[0006] To this end, the present invention provides a twin physical modeling method to overcome the problem in the prior art that physical entities are concentrated and dynamic within a short period of time, and when the digital twin model mapped by the object entity is relatively complex, the simulation involving multiple interacting digital twins in the virtual space becomes difficult and prone to jamming.

[0007] To achieve the above object, the present invention provides a twin physical modeling method, which comprises:

[0008] Step S1, obtaining image information of the monitoring target collected by the monitoring terminal, so as to determine the number of movable parts of the monitoring target and the displacement parameter of the monitoring target within a predetermined period according to the image information, wherein the displacement parameter is calculated based on the displacement of several calibration points on the movable parts;

[0009] Step S2, calculating the twin simulation complex characterization parameters for the monitoring end based on the displacement parameters of the monitoring target and the number of movable parts;

[0010] Step S3, summarizing the complex characterization parameters of the twin simulation corresponding to each of the monitoring terminals to divide the complex categories of the joint simulation for each monitoring target in the current cycle;

[0011] Step S4, based on the joint simulation complexity category of each monitoring target in the current cycle, a joint simulation method is selected, and a digital twin model of each monitoring target is constructed in a virtual space at a predetermined update frequency. The joint simulation method includes:

[0012] Adjusting the number of grids of the digital twin model and reducing the update frequency of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals;

[0013] Or, based on the displacement parameters of each monitored target, a stable characteristic target is identified, and updating of the digital twin model corresponding to the stable characteristic target is stopped.

[0014] Furthermore, in step S1, the process of acquiring the displacement parameter of the monitoring target includes:

[0015] Determine the moving parts of the monitored target in the image information;

[0016] Setting a number of calibration points on the moving parts;

[0017] The sum of displacements of each calibration point within a predetermined period is calculated, and the sum of displacements is determined as the displacement parameter of the monitoring target.

[0018] Furthermore, in step S2, the complex characterization parameter of the twin simulation for the monitoring end is calculated according to formula (1),

[0019]

[0020] In formula (1), E represents the complex characterization parameter of the twin simulation at the monitoring end, X represents the displacement parameter, X0 represents the preset standard threshold of the displacement parameter, α represents the weight coefficient of the displacement parameter, N represents the number of active components, N0 represents the preset standard threshold of the number of active components, and β represents the weight coefficient of the number of active components.

[0021] Furthermore, in step S3, the complex categories of joint simulation for each monitoring target in the current cycle are divided into:

[0022] If the twin simulation complexity characterization parameter mean is greater than or equal to the preset twin simulation complexity characterization standard threshold, the joint simulation complexity category is determined to be a strong complexity category;

[0023] If the mean value of the twin simulation complexity characterization parameter is less than the preset twin simulation complexity characterization standard threshold, the joint simulation complexity category is determined to be a weak complexity category.

[0024] Furthermore, in step S4, based on the complexity category of the joint simulation of each monitoring target in the current cycle, the joint simulation method is selected, including:

[0025] If the joint simulation complexity category for each monitoring target in the current cycle is a strong complexity category, the number of grids of the digital twin model is adjusted based on the mean value of the complex characterization parameter of the twin simulation corresponding to each monitoring end, and the update frequency of the digital twin model is reduced;

[0026] If the joint simulation complexity category for each monitoring target in the current cycle is a weak complexity category, the stable feature target is identified based on the displacement parameters of each monitoring target, and it is determined whether to stop updating the digital twin model corresponding to the stable feature target.

[0027] Furthermore, in step S4, adjusting the number of grids of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals includes:

[0028] The reduction in the number of grids in the digital twin model is positively correlated with the mean values ​​of the complex characterization parameters of the twin simulation corresponding to each of the monitoring ends.

[0029] Further, in step S4, reducing the update frequency of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals includes:

[0030] The reduction value of the update frequency of the digital twin model is positively correlated with the mean value of the complex characterization parameters of the twin simulation corresponding to each of the monitoring ends.

[0031] Furthermore, in step S4, stable characteristic targets are identified based on the displacement parameters of each monitored target.

[0032] If the displacement parameter of the monitoring target is 0, the monitoring target is determined to be a stable characteristic target.

[0033] Furthermore, in step S4, based on identifying the stable feature target, it is determined whether to stop updating the digital twin model corresponding to the stable feature target.

[0034] If the monitoring target is a stable characteristic target, the updating of the digital twin model corresponding to the stable characteristic target is stopped.

[0035] Furthermore, in step S1, a single monitoring target needs to be configured with at least one monitoring terminal.

[0036] Compared with the prior art, the present invention obtains image information of the monitored target collected by the monitoring end to determine the number of active parts of the monitored target and the displacement parameters of the monitored target within a predetermined period according to the image information, calculates the complex characterization parameters of the twin simulation for the monitoring end, summarizes the complex characterization parameters of the twin simulation corresponding to each monitoring end, divides the complex category of joint simulation for each monitoring target in the current period, constructs a digital twin model of each monitoring target in the virtual space at a predetermined update frequency, and adaptively selects the joint simulation method. In the process of concentrated dynamicization of physical entities within a short period, the jamming caused by the joint simulation of multiple interacting complex digital twins in the virtual space is alleviated, thereby improving the simulation fluency.

[0037] In particular, the present invention calculates the complex characterization parameters of the twin simulation for the monitoring end by monitoring the displacement parameters and the number of active components of the monitoring target. The operating modes of various monitoring targets may be different. Therefore, the displacement parameters and the number of active components of the monitoring target may be different. In actual situations, the more active components of the monitoring target, the higher the complexity, and more computing resources are required for simulation, including more processor time, memory and storage space. Similarly, if the active components of the monitoring target move together in a short period of time, the digital twin model corresponding to the monitoring target needs to be updated during simulation, which also requires more computing resources. Therefore, the present invention calculates the complex characterization parameters of the twin simulation for the monitoring end to provide data support for the subsequent division of the complex categories of joint simulation for each monitoring target in the current cycle, so as to facilitate the adaptive selection of the joint simulation method, and then, when the physical entities are concentrated and dynamicized in a short period of time and the digital twin model mapped by the object entity is more complex, the jamming of the joint simulation is reduced and the simulation fluency is improved.

[0038] In particular, the present invention identifies stable characteristic targets through the displacement of each monitoring target. The activity frequency of each monitoring target is different. Therefore, the update of the digital twin model corresponding to the stable characteristic target in the monitoring target is stopped in time. In actual conditions, repeated updates of the model that remains stable within a predetermined period will increase unnecessary calculations and affect the simulation update rate. Therefore, the present invention stops updating the corresponding digital twin model by identifying stable characteristic targets. Furthermore, in the process of concentrated dynamicization of physical entities within a short period, the jamming caused by the joint simulation of complex digital twins involving multiple interactions in the virtual space can be alleviated, thereby improving the simulation fluency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of the steps of a twin physical modeling method according to an embodiment of the invention;

[0040] Figure 2 A logical decision diagram for classifying complex categories of joint simulation for embodiments of the invention;

[0041] Figure 3 A logic decision diagram for selecting a joint simulation method for an embodiment of the invention;

[0042] Figure 4 A logical decision diagram for identifying stable feature targets for embodiments of the invention;

[0043] Figure 5 A logical decision diagram for adjusting the update frequency according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] See also Figures 1 to 5 As shown, Figure 1 This is a schematic diagram of the steps of the twin physical modeling method of the embodiment of the invention. Figure 2 A logical decision diagram for classifying complex categories of joint simulation for an embodiment of the invention, Figure 3 A logical decision diagram for selecting a joint simulation method for an embodiment of the invention, Figure 4 A logical decision diagram for identifying stable feature targets for an embodiment of the invention, Figure 5 The embodiment of the invention adjusts the logic decision diagram of the update frequency. The twin physical modeling method of the embodiment of the invention includes:

[0046] Step S1, obtaining image information of the monitoring target collected by the monitoring terminal, so as to determine the number of movable parts of the monitoring target and the displacement parameter of the monitoring target within a predetermined period according to the image information, wherein the displacement parameter is calculated based on the displacement of several calibration points on the movable parts;

[0047] Step S2, calculating the twin simulation complex characterization parameters for the monitoring end based on the displacement parameters of the monitoring target and the number of movable parts;

[0048] Step S3, summarizing the complex characterization parameters of the twin simulation corresponding to each of the monitoring terminals to divide the complex categories of the joint simulation for each monitoring target in the current cycle;

[0049] Step S4, based on the joint simulation complexity category of each monitoring target in the current cycle, a joint simulation method is selected, and a digital twin model of each monitoring target is constructed in a virtual space at a predetermined update frequency. The joint simulation method includes:

[0050] Adjusting the number of grids of the digital twin model and reducing the update frequency of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals;

[0051] Or, based on the displacement parameters of each monitored target, a stable characteristic target is identified, and updating of the digital twin model corresponding to the stable characteristic target is stopped.

[0052] Specifically, the monitoring end is an image acquisition device used to collect image information of the monitoring target. The collected image information can be a depth image, which is used to build a digital twin model. This is an existing technology and will not be elaborated on.

[0053] Specifically, there is no specific limitation on the method of constructing the digital twin model of the monitoring target in the virtual space. The digital twin model of the corresponding monitoring target can be placed at the corresponding position in the virtual space according to the relative position relationship of each monitoring target in the actual space. This will not be elaborated here.

[0054] Specifically, there is no limitation on the updating method of the digital twin model. It is understandable that in the virtual space, the digital twin model needs to be continuously updated at a predetermined update frequency to synchronously reflect the activity status of the actual monitoring target. For example, the original model can be overwritten, or the digital twin model can be rebuilt based on the newly received image information. Of course, other forms can also be used. This is existing technology and will not be elaborated.

[0055] Specifically, in step S1, the process of acquiring the displacement parameter of the monitoring target includes:

[0056] Determine the moving parts of the monitored target in the image information;

[0057] Setting a number of calibration points on the moving parts;

[0058] The sum of displacements of each calibration point within a predetermined period is calculated, and the sum of displacements is determined as the displacement parameter of the monitoring target.

[0059] Specifically, there is no limitation on the method of setting calibration points on the movable parts. It can be understood that the purpose of setting the calibration points is to collect dynamic data of specific parts of various models, such as displacement. The calibration points can be set on the active nodes of the movable parts. The number of settings is determined by those skilled in the art and will not be repeated here.

[0060] Specifically, in step S2, the complex characterization parameter of the twin simulation for the monitoring end is calculated according to formula (1):

[0061]

[0062] In formula (1), E represents the complex characterization parameter of the twin simulation at the monitoring end, X represents the displacement parameter, X0 represents the preset standard threshold of the displacement parameter, α represents the weight coefficient of the displacement parameter, N represents the number of active components, N0 represents the preset standard threshold of the number of active components, and β represents the weight coefficient of the number of active components.

[0063] Specifically, in this embodiment, the displacement parameter standard threshold value X0 and the movable component standard threshold value N0 are obtained in advance, and the image information of several monitoring targets is obtained, and the displacement parameters and the number of movable components of each monitoring target are extracted. The displacement parameter average value ΔX0 and the number of movable components average value ΔN0 are solved, and it is set that X0 = r1 × ΔX0, N0 = r2 × ΔN0, r1 represents the first precision coefficient, r2 represents the second precision coefficient, 1.1 < r1 < 1.15, 1.15 < r2 < 1.25.

[0064] α is 0.55 and β is 0.45.

[0065] The present invention calculates the complex characterization parameters of the twin simulation for the monitoring end by monitoring the displacement parameters and the number of active components of the monitoring target. The operating modes of various monitoring targets may be different. Therefore, the displacement parameters and the number of active components of the monitoring target may be different. In actual situations, the more active components of the monitoring target, the higher the complexity, and more computing resources are required for simulation, including more processor time, memory and storage space. Similarly, if the active components of the monitoring target move together in a short period of time, the digital twin model corresponding to the monitoring target needs to be updated during simulation, which also requires more computing resources. Therefore, the present invention calculates the complex characterization parameters of the twin simulation for the monitoring end to provide data support for the subsequent division of the complex categories of joint simulation for each monitoring target in the current cycle, so as to facilitate the adaptive selection of the joint simulation method, and then, when the physical entities are concentrated and dynamicized in a short period of time and the digital twin model mapped by the object entity is more complex, the jamming of the joint simulation is reduced and the simulation fluency is improved.

[0066] Specifically, in step S3, the complex categories of joint simulation for each monitoring target in the current cycle are divided into:

[0067] If the twin simulation complexity characterization parameter mean is greater than or equal to the preset twin simulation complexity characterization standard threshold, the joint simulation complexity category is determined to be a strong complexity category;

[0068] If the mean value of the twin simulation complexity characterization parameter is less than the preset twin simulation complexity characterization standard threshold, the joint simulation complexity category is determined to be a weak complexity category.

[0069] The twin simulation complexity characterization standard threshold E0 is selected in the interval [1.15, 1.25].

[0070] Specifically, in step S4, based on the complexity category of the joint simulation of each monitoring target in the current cycle, the joint simulation method is selected to include:

[0071] If the joint simulation complexity category for each monitoring target in the current cycle is a strong complexity category, the number of grids of the digital twin model is adjusted based on the mean value of the complex characterization parameter of the twin simulation corresponding to each monitoring end, and the update frequency of the digital twin model is reduced;

[0072] If the joint simulation complexity category for each monitoring target in the current cycle is a weak complexity category, the stable feature target is identified based on the displacement parameters of each monitoring target, and it is determined whether to stop updating the digital twin model corresponding to the stable feature target.

[0073] Specifically, in step S4, adjusting the number of grids of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals includes:

[0074] The reduction in the number of grids in the digital twin model is positively correlated with the mean values ​​of the complex characterization parameters of the twin simulation corresponding to each of the monitoring ends.

[0075] In this embodiment, optionally,

[0076] The twin simulation complex characterization parameter mean value Ee is compared with the first twin simulation complex characterization parameter comparison threshold value Ee1 and the second twin simulation complex characterization parameter comparison threshold value Ee2.

[0077] If Ee>Ee2, the mesh number reduction amount is determined to be the first mesh number reduction amount c1, and c1=0.4c0;

[0078] If Ee1≤Ee≤Ee2, the reduction amount of the number of grids is determined to be the second reduction amount of the number of grids c2, and c2=0.3c0 is set;

[0079] If Ee<Ee1, the reduction amount of the number of grids is determined to be the third reduction amount of the number of grids c3, and c3=0.2c0 is set;

[0080] Among them, c0 represents the current number of grids, Ee1=1.3E0, Ee2=1.5E0.

[0081] Specifically, there is no limitation on the method of adjusting the number of meshes of the digital twin model. The number of meshes can be reduced based on relevant mesh simplification algorithms. This is a prior art and will not be elaborated on.

[0082] Specifically, in step S4, reducing the update frequency of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals includes:

[0083] The reduction value of the update frequency of the digital twin model is positively correlated with the mean value of the complex characterization parameters of the twin simulation corresponding to each of the monitoring ends.

[0084] In this embodiment, optionally,

[0085] The twin simulation complex characterization parameter mean value Ee is compared with the first twin simulation complex characterization parameter comparison threshold value Ee1 and the second twin simulation complex characterization parameter comparison threshold value Ee2.

[0086] If Ee>Ee2, the update frequency reduction value is determined to be the first update frequency reduction value z1, and z1=0.3z0;

[0087] If Ee1≤Ee≤Ee2, the update frequency reduction value is determined to be the second update frequency reduction value z2, and z2=0.2z0 is set;

[0088] If Ee<Ee1, the update frequency reduction value is determined to be the third update frequency z3, and z3=0.1z0;

[0089] Wherein, z0 represents the current update frequency, Ee1=1.3E0, E2=1.5E0.

[0090] Specifically, in step S4, stable characteristic targets are identified based on the displacement parameters of each monitoring target.

[0091] If the displacement parameter of the monitoring target is 0, the monitoring target is determined to be a stable characteristic target.

[0092] Specifically, in step S4, based on identifying the stable feature target, it is determined whether to stop updating the digital twin model corresponding to the stable feature target.

[0093] If the monitoring target is a stable characteristic target, the updating of the digital twin model corresponding to the stable characteristic target is stopped.

[0094] The present invention identifies stable characteristic targets through the displacement of each monitoring target. The activity frequency of each monitoring target is different. Therefore, the update of the digital twin model corresponding to the stable characteristic target in the monitoring target is stopped in time. In actual conditions, repeated updates of the model that remains stable within a predetermined period will increase unnecessary calculations and affect the simulation update rate. Therefore, the present invention stops updating the corresponding digital twin model by identifying stable characteristic targets. Furthermore, in the process of concentrated dynamicization of physical entities within a short period, the jamming caused by the joint simulation of complex digital twins involving multiple interactions in the virtual space is alleviated, thereby improving the simulation fluency.

[0095] Specifically, in step S1, a single monitoring target needs to be configured with at least one monitoring terminal.

[0096] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A twin physical modeling method, characterized in that: include: Step S1, obtaining image information of the monitoring target collected by the monitoring terminal, so as to determine the number of movable parts of the monitoring target and the displacement parameter of the monitoring target within a predetermined period according to the image information, wherein the displacement parameter is calculated based on the displacement of several calibration points on the movable parts; Step S2, calculating the complex characterization parameters of the twin simulation for the monitoring end based on the displacement parameters of the monitoring target and the number of movable parts; Step S3, summarizing the complex characterization parameters of the twin simulation corresponding to each of the monitoring terminals to divide the complex categories of the joint simulation for each monitoring target in the current cycle; Step S4, based on the joint simulation complexity category of each monitoring target in the current cycle, a joint simulation method is selected, and a digital twin model of each monitoring target is constructed in a virtual space at a predetermined update frequency. The joint simulation method includes: If the joint simulation complexity category for each monitoring target in the current cycle is a strong complexity category, the number of grids of the digital twin model is adjusted based on the mean value of the complex characterization parameter of the twin simulation corresponding to each monitoring end, and the update frequency of the digital twin model is reduced; If the complex category of the joint simulation for each monitoring target in the current cycle is a weakly complex category, a stable characteristic target is identified based on the displacement parameters of each monitoring target, and the updating of the digital twin model corresponding to the stable characteristic target is stopped; In step S2, the complex characterization parameter of the twin simulation for the monitoring end is calculated according to formula (1): （1） In formula (1), E represents the complex characterization parameter of the twin simulation at the monitoring end, X represents the displacement parameter, X0 represents the preset standard threshold of the displacement parameter, α represents the weight coefficient of the displacement parameter, N represents the number of active components, N0 represents the preset standard threshold of the number of active components, and β represents the weight coefficient of the number of active components; In step S3, the complex categories of joint simulation for each monitoring target in the current cycle are divided into: If the twin simulation complexity characterization parameter mean is greater than or equal to the preset twin simulation complexity characterization standard threshold, the joint simulation complexity category is determined to be a strong complexity category; If the mean value of the twin simulation complexity characterization parameter is less than the preset twin simulation complexity characterization standard threshold, the joint simulation complexity category is determined to be a weak complexity category.

2. The twin physical modeling method according to claim 1, characterized in that: In step S1, the process of acquiring the displacement parameter of the monitoring target includes: Determine the moving parts of the monitored target in the image information; Setting a number of calibration points on the moving parts; The sum of displacements of each calibration point within a predetermined period is calculated, and the sum of displacements is determined as the displacement parameter of the monitoring target.

3. The twin physical modeling method according to claim 1, characterized in that: In step S4, adjusting the number of grids of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals includes: The reduction in the number of grids in the digital twin model is positively correlated with the mean values ​​of the complex characterization parameters of the twin simulation corresponding to each of the monitoring ends.

4. The twin physical modeling method according to claim 1, characterized in that: In step S4, reducing the update frequency of the digital twin model based on the mean value of the complex characterization parameter of the twin simulation corresponding to each of the monitoring terminals includes: The reduction value of the update frequency of the digital twin model is positively correlated with the mean value of the complex characterization parameters of the twin simulation corresponding to each of the monitoring ends.

5. The twin physical modeling method according to claim 1, characterized in that: In step S4, stable characteristic targets are identified based on the displacement parameters of each monitored target. If the displacement parameter of the monitoring target is 0, the monitoring target is determined to be a stable characteristic target.

6. The twin physical modeling method according to claim 1, characterized in that: In step S4, based on identifying the stable feature target, it is determined whether to stop updating the digital twin model corresponding to the stable feature target. If the monitoring target is a stable characteristic target, the updating of the digital twin model corresponding to the stable characteristic target is stopped.

7. The twin physical modeling method according to claim 1, characterized in that: In step S1, a single monitoring target needs to be configured with at least one monitoring terminal.

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