Multi-mode physical simulation method and system based on artificial intelligence

Abstract

The present invention relates to the technical field of electronic digital data processing, and in particular to a multi-mode physical simulation method and system based on artificial intelligence, comprising: obtaining a plurality of state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter; obtaining a unique factor corresponding to each state parameter in each physical field, and then determining an important evaluation of each physical field using the unique factor; obtaining a response speed evaluation corresponding to each state parameter in each physical field, and obtaining a stability evaluation of each state parameter within a preset time period; determining a transmission compression index of the state parameter using the response speed evaluation, stability evaluation, unique factor and important evaluation, and then performing multi-mode physical simulation using the transmission compression index; the present invention determines and performs multi-mode physical simulation based on the transmission compression index, effectively ensuring the normal operation of a complex simulation system and the credibility of the simulation.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a multi-mode physical simulation method and system based on artificial intelligence. Background Art

[0002] Multi-mode physical simulation combines multi-physics simulation with physical electrical digital data processing simulation to simulate complex systems involving the interaction of multiple physical phenomena. It integrates information from multiple different sensors and combines multiple simulation modes (combining physical models, mathematical models, and actual hardware testing, etc.) to more accurately simulate the actual working conditions and environment of the system. It includes multiple components such as sensor data simulation, environmental effect simulation, and hardware in-loop testing.

[0003] Compared with traditional simulation, multi-mode physical simulation has higher fidelity and credibility because it introduces the physical effects of the actual system and the real environment. Moreover, because it can adjust model parameters and add or delete conditions according to needs, it has greater flexibility to adapt to different scenarios and can integrate more sensors and actuators. However, because the multi-mode physical simulation system needs to be combined with more simulation systems, it also requires more physical parameters and actual environmental parameters to be collected, making it difficult to ensure the normal operation of complex simulation systems. That is, the multi-mode physical simulation system has the disadvantages of collecting many types of data and large amounts of data. Summary of the invention

[0004] In order to solve the technical problem, the purpose of the present invention is to provide a multi-mode physical simulation method and system based on artificial intelligence, which can effectively ensure the normal operation of complex simulation systems and thus ensure the credibility of simulation.

[0005] The technical solution adopted is specifically as follows: a multi-mode physical simulation method based on artificial intelligence is provided, including: obtaining multiple state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter; obtaining a unique factor corresponding to each state parameter in each physical field, and then using the unique factor to determine the importance evaluation of each physical field; obtaining a response speed evaluation corresponding to each state parameter in each physical field, and obtaining a stability evaluation of each state parameter within a preset time period; using the response speed evaluation, the stability evaluation, the unique factor and the importance evaluation to determine the transmission compression index of the state parameter, and then using the transmission compression index to perform multi-mode physical simulation.

[0006] In one embodiment of the present invention, the use of the response speed evaluation, the stability evaluation, the unique factor and the importance evaluation to determine the transmission compression index of the state parameter includes: using the response speed evaluation and the stability evaluation to determine the compression transmission ratio corresponding to each state parameter in each physical field; using the unique factor and the importance evaluation to determine the transmission requirement index of the state parameter; using the compression transmission ratio and the transmission requirement index to determine the transmission compression index of the state parameter.

[0007] In one embodiment of the present invention, it also includes: when the transmission compression index is greater than or equal to the first index threshold, the state parameter is used as high-compression data, and the high-compression data is periodically transmitted to the corresponding simulation model; when the transmission compression index is less than the first index threshold, the state parameter is used as low-compression data, and the low-compression data is directly transmitted to the corresponding simulation model.

[0008] In one embodiment of the present invention, the method of obtaining the unique factor corresponding to each state parameter in each physical field includes: obtaining a first word vector corresponding to the target physical place, and obtaining a second word vector corresponding to the target state parameter in the physical field; obtaining the number of physical fields that call the target state parameter; using a preset correction factor, the first word vector, the second word vector and the number of physical fields to determine the target unique factor corresponding to the target state parameter in the target physical field, and then determining the unique factor corresponding to each state parameter in each physical field.

[0009] In one embodiment of the present invention, the use of the unique factor to determine the importance evaluation of each of the physical fields includes: obtaining parameter setting values ​​corresponding to target state parameters in the target physical field in the target simulation scheme; obtaining the standard deviation between the parameter setting values ​​in multiple simulation schemes, and obtaining the total value of the state parameters in the target physical field; using the parameter setting values, the standard deviation, the unique factor and the total value to determine the target importance evaluation of the target physical field, and then determine the importance evaluation of each of the physical fields.

[0010] In one embodiment of the present invention, the obtaining of the response speed evaluation corresponding to each state parameter in each physical field, and the obtaining of the stability evaluation of each state parameter within a preset time period, include: obtaining a first time point when a target state parameter in the target physical field changes, and obtaining a second time point when an associated state parameter corresponding to the target state parameter changes; and determining the response speed evaluation of the target state parameter in the target physical field using the first time point and the second time point.

[0011] In one embodiment of the present invention, it also includes: obtaining multiple response chains composed of mutually responsive state parameters; obtaining similarity scores corresponding to each of the response chains, and obtaining intersections and unions corresponding to the state parameters that have changed in each of the response chains; using the similarity scores, the intersections and the unions, determining the universal factor of each of the response chains; traversing all the response chains, and determining the response chain with the largest universal factor as the target response chain, wherein the target state parameters existing in the target response chain, and the associated state parameters associated with the target state parameters.

[0012] In one embodiment of the present invention, the method of obtaining multiple state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter, includes: obtaining relevant information of each physical field in the preset simulation scheme; obtaining multiple state parameters corresponding to each physical field, wherein the state parameters include at least one environmental parameter and one physical parameter.

[0013] In one embodiment of the present invention, it also includes: when the transmission requirement index is greater than or equal to the second index threshold, the state parameter is used as high-standard data, and the high-standard data is preferentially transmitted to the corresponding simulation model; when the transmission requirement index is less than the second index threshold, the state parameter is used as low-standard data, and the low-standard data is delayed and transmitted to the corresponding simulation model.

[0014] In order to solve the above technical problems, another technical solution adopted by the present invention is to provide a multi-mode physical simulation system based on artificial intelligence, including: a first acquisition module, used to obtain multiple state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter; a second acquisition module, used to obtain a unique factor corresponding to each state parameter in each physical field, and then use the unique factor to determine the importance evaluation of each physical field; a third acquisition module, used to obtain a response speed evaluation corresponding to each state parameter in each physical field, and obtain a stability evaluation of each state parameter within a preset time period; a determination module, using the response speed evaluation, the stability evaluation, the unique factor and the importance evaluation, to determine the transmission compression index of the state parameter, and then use the transmission compression index to perform multi-mode physical simulation.

[0015] The present invention has the following beneficial effects: in the present application, a plurality of state parameters corresponding to each physical place in the preset simulation scheme are obtained, wherein the state parameters include at least one environmental parameter and at least one physical parameter; a unique factor corresponding to each state parameter in each physical field is obtained, and then the unique factor is used to determine the important evaluation of each physical field; a response speed evaluation corresponding to each state parameter in each physical field is obtained, and a stability evaluation of each state parameter in a preset time period is obtained; the transmission compression index of the state parameter is determined by using the response speed evaluation, stability evaluation, unique factor and important evaluation, and then the multi-mode physical simulation is performed by using the transmission compression index. Through the technical solution provided by the present application, it is possible to first analyze each physical field and the corresponding physical quantity in the simulation system, determine the transmission compression index and perform multi-mode physical simulation based on the transmission compression index, effectively ensuring the normal operation of the complex simulation system, and then ensuring the credibility of the simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 these drawings without paying creative work.

[0017] Figure 1 A schematic flow chart of a multi-mode physical simulation method based on artificial intelligence provided by an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of the structure of a multi-mode physical simulation system based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a multi-mode physical simulation method and system based on artificial intelligence proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] The specific scheme of a multi-mode physical simulation method based on artificial intelligence provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] See also Figure 1 , which shows a flow chart of a multi-mode physical simulation method based on artificial intelligence provided by an embodiment of the present invention.

[0023] like Figure 1 As shown, a multi-mode physical simulation method based on artificial intelligence includes the following steps:

[0024] S10. Obtain multiple state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter.

[0025] Among them, the simulation scheme refers to a multi-mode physical simulation system that simulates the physical effects and real environment of the actual system to adapt to a variety of application scenarios; the simulation system allows users to adjust model parameters according to needs, add or delete conditions, thereby providing a high degree of flexibility. When conducting simulations, there will be some focuses, and some simulation directions are more concerned, especially environmental parameters and physical parameters; environmental parameters such as temperature, humidity, light, etc., these parameters affect the authenticity of the simulation environment; physical parameters such as pressure, speed, mass, etc., these parameters are directly related to the physical behavior of the simulation model.

[0026] Specifically, relevant information of each physical field in a preset simulation scheme is obtained; each physical field is analyzed to determine a corresponding plurality of state parameters, wherein the state parameters include at least one environmental parameter and one physical parameter.

[0027] In the simulation task, the following steps are used to determine the important evaluation of each physical field.

[0028] S20. Obtain the unique factor corresponding to each state parameter in each physical field, and then use the unique factor to determine the important evaluation of each physical field.

[0029] Determine the importance evaluation of each physical field based on the unique factors corresponding to each state parameter in each physical field. The specific steps are as follows:

[0030] First, obtain a first word vector corresponding to the target physical place, and obtain a second word vector corresponding to the target state parameter in the physical field.

[0031] Get the number of physics fields that call the target state parameter.

[0032] A target unique factor corresponding to a target state parameter in a target physical field is determined using a preset correction factor, a first word vector, a second word vector, and the number of physical fields.

[0033] Determine the target unique factor corresponding to the target state parameter in the target physical field based on the correction factor, the first word vector, the second word vector, the number of physical fields, and the following formula: :

[0034]

[0035] in, Represents the target state parameters in the physical field The corresponding second word vector; Representing physical fields The corresponding first word vector; Indicates calling the target state parameters in the physical field The number of physical fields; Indicates calculating the cosine similarity of two vectors; Represents the maximum and minimum normalization function, which is used for positive proportion normalization; Indicates the correction factor, which is used to adjust the screening threshold. .

[0036] Furthermore, word embedding is achieved by In The model was constructed using a database related to physical chemistry and other fields.

[0037] Next, the unique factors corresponding to each state parameter in each physical field are determined.

[0038] Specifically, by calculating the influence of each state parameter on each physical field and judging according to a preset threshold, if the unique factor of a state parameter is greater than or equal to the threshold, it is identified as the unique factor corresponding to the state parameter in the physical field.

[0039] More specifically, we traverse all state parameters, calculate their unique factors for each physical field, and set a threshold If the unique factor of a state parameter to the physical field is greater than or equal to this threshold, then the unique factor is identified as the unique factor corresponding to the state parameter in the physical field. The threshold can be preset. .

[0040] Next, obtaining parameter setting values ​​corresponding to target state parameters in a target physical field within a target simulation scheme;

[0041] The parameter setting value usually refers to the pre-set value for a specific physical field or state parameter during simulation. These values ​​are used to define the initial conditions, boundary conditions or dynamic conditions in the simulation environment.

[0042] Next, the standard deviations between parameter setting values ​​in multiple simulation schemes are obtained, as well as the total values ​​of state parameters in the target physical field.

[0043] Among them, obtaining the standard deviation between parameter setting values ​​in multiple simulation schemes and obtaining the total value of state parameters in the target physical field are two operations that are usually used to analyze and evaluate the diversity and stability of simulation schemes. The standard deviation is a statistic used to measure the degree of dispersion of a set of data, that is, the deviation of the data value from the average value; the total value of the state parameter in the target physical field refers to the accumulation or sum of all relevant state parameter values ​​in a specific physical field.

[0044] In addition, the standard deviation between parameter setting values ​​reflects the degree of variation of parameter setting values ​​in different schemes. If the standard deviation is large, it means that the setting values ​​of the parameter in different schemes are quite different, and further analysis of the cause may be required; if the standard deviation is small, it means that the setting values ​​of the parameter in different schemes are relatively consistent.

[0045] For example, suppose there are three simulation schemes A, B, and C, all of which involve the temperature parameter setting of a physical field. If the temperature setting of scheme A is 300K, that of scheme B is 310K, and that of scheme C is 305K, the mean and standard deviation of the three temperature setting values ​​can be calculated to evaluate the consistency of the temperature parameters of different schemes.

[0046] By analyzing the standard deviation between parameter setting values, we can understand the diversity and stability of different simulation schemes in key parameters, thus providing a basis for the selection, optimization and result analysis of simulation schemes.

[0047] Finally, the target importance rating of the target physics field is determined using parameter settings, standard deviations, unique factors, and total values, and the importance rating of each physics field is then determined.

[0048] Determine the target physics based on the parameter settings, standard deviation, unique factors, and total values ​​and the following formula Important evaluation of the target :

[0049]

[0050] in, Representing physical fields Medium status parameters Unique factors of Representing physical fields The total number of state parameter types in; Represents the simulation scheme Internal physics Medium status parameters Parameter setting value; It represents the standard deviation of the parameter setting values ​​in many schemes, which is used to describe the state parameters in many schemes in the simulation task. The discreteness of the set value, that is, whether to pay attention to the impact of this physical quantity on the simulation results; is the normalization function, used for direct proportion normalization.

[0051] S30, obtaining a response speed evaluation corresponding to each state parameter in each physical field, and obtaining a stability evaluation of each state parameter within a preset time period.

[0052] Among them, the step of obtaining the response speed evaluation corresponding to each state parameter in each physical field may include: obtaining a first time point when the target state parameter in the target physical field changes, and obtaining a second time point when the associated state parameter corresponding to the target state parameter changes.

[0053] The first time point and the second time point are used to determine the speed evaluation of the response to the target state parameter in the target physical field.

[0054] Specifically, the response speed evaluation is determined based on the following formula: .

[0055]

[0056] in, Indicates the status parameter The first point in time when a change occurs; Indicates the target state parameter The corresponding association status parameters At the second time point when the change occurs, the associated state parameter corresponding to the target state parameter refers to the state parameter The associated state parameters in the universal response chain, where the universal response chain is described in detail below and will not be repeated here; It means finding the mean function, that is, first finding the time difference between the changes of many related physical quantities; Represents an exponential function with a natural constant as base, used for inverse normalization.

[0057] The stability evaluation is determined based on the following formula :

[0058]

[0059] In the formula, Indicates time Time state parameters The corresponding data value; Indicates the status parameter The mode of the data values ​​obtained over a period of time (for example, 12 seconds by default); Indicates the distance time The most recent time point at which the mode data value was obtained; Indicates time The corresponding time point; Indicates time Time state parameters The length of time that the corresponding data value remains unchanged, which is the duration from when it changes to the value to now; Represents an exponential function with a natural constant as base, used for inverse normalization.

[0060] In addition, during the simulation process, data changes have a chain effect, and changes in one data will quickly cause changes in other data. For example, fluctuations in voltage and current will immediately affect the gear speed, which in turn will cause changes in motor temperature. However, not all data changes have the same significant or rapid effects. For example, changes in ambient temperature usually have a limited impact on motor temperature and are relatively stable over a certain period of time.

[0061] Based on this principle, the data transmission strategy can be optimized to prioritize the transmission of critical and responsive data, while delaying the transmission of data with less impact or infrequent changes. To implement this strategy, take the following steps:

[0062] First, multiple response chains consisting of mutually responding state parameters are obtained.

[0063] Among them, the response chain refers to a chain of causal relationships between a series of state parameters, in which the change of one parameter will cause changes in other parameters in the chain; in other words, when one or more state parameters change, these changes will cause a chain reaction of other state parameters, forming a response chain.

[0064] Specifically, at a preset first time interval (For example, Within 1 second, observe and record all the changed state parameters, and group these parameters into a group, called a "state parameter group"; uniquely number each state parameter group, for example, number them as Then, the state parameter response in each simulation scheme is converted into a string of long codes, and each state parameter group corresponds to a unit in the code; if the change interval between two state parameter groups exceeds another preset second time threshold (For example, the default seconds), they are regarded as independent sub-code groups, thus forming multiple strings of sub-codes; the data changes in all simulation schemes are encoded in this way to obtain multiple strings of long codes and numerous sub-codes, among which, in all simulation schemes, the same number is used as much as possible to represent the same state parameter group to ensure the consistency of the coding.

[0065] Secondly, obtain the similarity scores corresponding to the two response chains, and obtain the intersection and union corresponding to the changed state parameters in the two response chains.

[0066] Among them, by comparing the similarities between two response chains, the similarity score between them can be calculated.

[0067] The intersection of two response chains refers to the state parameters contained in both chains. For example, if response chain A contains parameters X and Y, and response chain B contains parameters Y and Z, then Y is their intersection.

[0068] The union of two response chains refers to all unique state parameters contained in the two response chains, without repeated calculations. Continuing the above example, if the union of response chains A and B is parameters X, Y, and Z.

[0069] Next, the universality factor of each response chain is determined using similarity scores, intersections, and unions.

[0070] Specifically, each response chain is determined based on the similarity score, intersection, union and the following formula Universal factors :

[0071]

[0072] in, Representation scheme Subcode in The corresponding response chain; Indicates calculation of two response chains of Similarity score; Indicates different sub-codes The maximum value of the corresponding function value, that is, selecting the response chain in each solution Most similar response chain; Representing the response chain With the responder chain Medium status parameter group The intersection of; Representing the response chain With the responder chain Medium status parameter group The union of; Indicates the calculation of many state parameter groups The mean of the corresponding function value, that is, the mean of the similarity (intersection to union) of many state parameter groups; Indicates calculating the mean of the function values ​​corresponding to different schemes, that is, calculating the response chain first The similarity between the response chain and the corresponding state parameter group in each scheme is then analyzed through the average method to analyze the universality of the response chain; Used for normalization of positive proportion.

[0073] Finally, all response chains are traversed to determine the response chain with the largest universal factor as the target response chain, including the target state parameters existing in the target response chain and the associated state parameters associated with the target state parameters.

[0074] Among them, multiple state parameters in the target response chain are interrelated and have a significant impact on the simulation results.

[0075] First, multiple response chains consisting of mutually responsive state parameters are obtained; multiple state parameters that change in a short period of time in the simulation environment are grouped together, and each group is numbered, and then the data response of each state parameter group is encoded into a string of long codes. If the interval between the changes of two sets of data exceeds the preset time, they are regarded as independent coding groups and form sub-codings. Secondly, the similarity scores corresponding to the two response chains are obtained, and the intersection and union of the state parameters in the response chain are analyzed; then, based on the similarity scores, intersections and unions, the universal factor of each response chain is determined, and finally, all response chains are traversed to determine the response chain with the largest universal factor as the target response chain.

[0076] S40. Determine the transmission compression index of the state parameter by using the response speed evaluation, stability evaluation, unique factor and importance evaluation, and then perform multi-mode physical simulation by using the transmission compression index.

[0077] Firstly, the compression transmission ratio corresponding to each state parameter in each physical field is determined by using response speed evaluation and stability evaluation.

[0078] Specifically, based on the response speed evaluation, stability evaluation and the following formula, the compression transmission ratio corresponding to each state parameter in each physical field is determined: :

[0079]

[0080] In the formula, Representing physical fields For state parameters The response speed evaluation is used to weight the stability evaluation; Indicates time Status parameters Evaluation of the stability of the data.

[0081] Secondly, the transmission requirement indicators of the state parameters are determined using unique factors and important evaluations.

[0082] Specifically, the transmission requirement index of the state parameter is determined based on the unique factors, the important evaluation and the following formula:

[0083]

[0084] in, Representation data Critical evaluation of the corresponding physical fields to be transmitted; Representation data Unique factor corresponding to the state parameter.

[0085] Next, the transmission compression index of the state parameter is determined by using the compression transmission ratio and the transmission requirement index.

[0086] Specifically, the transmission compression index of the state parameter is determined based on the compression transmission ratio, the transmission requirement index and the following formula:

[0087]

[0088] in, Indicates status parameter data Transmission requirements indicators; Indicates status parameter data Compression transmission ratio; Indicates time Corresponding data compression transmission ratio, time Refers to data Time period before the corresponding time The corresponding moment; Indicates the total duration of the traversal comparison, which is preset to 9 seconds; Used for inverse normalization.

[0089] Furthermore, the above method may also include the following operations: when the transmission compression index is greater than or equal to the first index threshold, the state parameter is used as high-compression data, and the high-compression data is periodically transmitted to the corresponding simulation model; when the transmission compression index is less than the first index threshold, the state parameter is used as low-compression data, and the low-compression data is directly transmitted to the corresponding simulation model.

[0090] Specifically, all state parameter data are traversed to calculate the transmission compression index. For example, if the transmission compression index is greater than or equal to the first index threshold , preset , then the data is recorded as high-compression data; otherwise, it is recorded as low-compression data; the high-compression data is compressed and transmitted to the corresponding simulation model at regular intervals, and the low-compression data needs to be transmitted in real time as much as possible.

[0091] The transmission interval is: , where Indicates the time threshold, which is preset as Second; Indicates status parameter data The transmission compression index.

[0092] The total amount of data transmitted in one transmission cycle is:

[0093]

[0094] in, is the transmission interval time; Indicates the number of The collected data corresponding to each moment; Indicates the number of seconds; this transmission cycle refers to the time before the current transmission time. time period, A represents the total amount of transmitted data in one transmission cycle.

[0095] Furthermore, the above method may also include the following operations: when the transmission requirement index is greater than or equal to the second index threshold, the state parameter is used as high-standard data, and the high-standard data is preferentially transmitted to the corresponding simulation model; when the transmission requirement index is less than the second index threshold, the state parameter is used as low-standard data, and the low-standard data is delayed and transmitted to the corresponding simulation model.

[0096] Specifically, all data are traversed and the corresponding transmission requirement index is calculated. For example, if the transmission requirement index is greater than or equal to the second index threshold , preset , then record this data as high-standard data; otherwise, record it as low-standard data. High-standard data is transmitted to the corresponding model first, such as real-time transmission to the simulation model; low-standard data can be properly delayed, such as once every two data acquisitions.

[0097] In summary, the present application constructs a compression rate based on the stability of state parameter acquisition data. Identifying and compressing non-essential data can reduce the amount of data that needs to be transmitted. Through selective compression, key feature data can be retained. Effective management and compression of data while retaining key information can ensure the credibility of simulation results. This method allows the system to allocate resources to more important data transmission and processing tasks, thereby improving the efficiency of the simulation system.

[0098] The present invention also provides a multi-mode physical simulation system 500 based on artificial intelligence, such as Figure 2 As shown, it includes: a first acquisition module 510, a second acquisition module 520, a third acquisition module 530 and a determination module 540.

[0099] In the present invention, the first acquisition module 510 is used to obtain multiple state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter; the second acquisition module 520 is used to obtain the unique factor corresponding to each state parameter in each physical field, and then use the unique factor to determine the importance evaluation of each physical field; the third acquisition module 530 is used to obtain the response speed evaluation corresponding to each state parameter in each physical field, and obtain the stability evaluation of each state parameter within a preset time period; the determination module 540 uses the response speed evaluation, stability evaluation, unique factor and importance evaluation to determine the transmission compression index of the state parameter, and then uses the transmission compression index to perform multi-mode physical simulation.

[0100] Among them, the first acquisition module 510 is responsible for extracting multiple state parameters corresponding to each physical place from the preset simulation scheme, and these parameters include at least one environmental parameter and one physical parameter. The second acquisition module 520: is used to identify the unique factors of each state parameter in each physical field, and use these factors to determine the importance evaluation of the physical field. The third acquisition module 530: obtains the response speed evaluation and stability evaluation within a preset time period of each state parameter in each physical field. Determination module 540: Combines the response speed evaluation, stability evaluation, unique factors and importance evaluation to determine the transmission compression index of the state parameter. Then, these indicators are used to perform multi-mode physical simulation. All modules work together to optimize data transmission and compression during the simulation process to ensure the accuracy and efficiency of the simulation.

[0101] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A multi-mode physical simulation method based on artificial intelligence, characterized in that: The multi-mode physical simulation method based on artificial intelligence includes: Acquire a plurality of state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter; Obtaining a unique factor corresponding to each state parameter in each physical field, and then using the unique factor to determine the importance evaluation of each physical field; Obtaining a response speed evaluation corresponding to each state parameter in each physical field, and obtaining a stability evaluation of each state parameter within a preset time period; Determine the transmission compression index of the state parameter by using the response speed evaluation, the stability evaluation, the unique factor and the importance evaluation, and then perform multi-mode physical simulation by using the transmission compression index; The obtaining of the unique factor corresponding to each state parameter in each physical field includes: Obtaining a first word vector corresponding to a target physical place, and obtaining a second word vector corresponding to a target state parameter in the physical field; Obtaining the number of physical fields that call the target state parameter; Determine a target unique factor corresponding to the target state parameter in the target physical field by using a preset correction factor, the first word vector, the second word vector, and the number of physical fields, and further determine a unique factor corresponding to each state parameter in each physical field; The determining of the significance evaluation of each of the physical fields using the unique factors comprises: Obtaining parameter setting values ​​corresponding to target state parameters in a target physical field within a target simulation scheme; Obtaining the standard deviation between the parameter setting values ​​in multiple simulation schemes, and obtaining the total value of the state parameter in the target physical field; The target importance evaluation of the target physical field is determined using the parameter setting value, the standard deviation, the unique factor and the total value, thereby determining the importance evaluation of each of the physical fields.

2. The multi-mode physical simulation method based on artificial intelligence according to claim 1 is characterized in that: The step of determining the transmission compression index of the state parameter by using the response speed evaluation, the stability evaluation, the unique factor and the importance evaluation includes: Determine the compression transmission ratio corresponding to each state parameter in each physical field by using the response speed evaluation and the stability evaluation; Determining a transmission requirement indicator of the state parameter using the unique factor and the importance evaluation; The transmission compression index of the state parameter is determined by using the compression transmission ratio and the transmission requirement index.

3. The multi-mode physical simulation method based on artificial intelligence according to claim 2 is characterized in that: Also includes: When the transmission compression index is greater than or equal to a first index threshold, the state parameter is used as high-compression data, and the high-compression data is periodically transmitted to a corresponding simulation model; When the transmission compression index is less than a first index threshold, the state parameter is used as low-compression data, and the low-compression data is directly transmitted to the corresponding simulation model.

4. The multi-mode physical simulation method based on artificial intelligence according to claim 1 is characterized in that: The step of obtaining a response speed evaluation corresponding to each state parameter in each physical field, and obtaining a stability evaluation of each state parameter within a preset time period, includes: Acquire a first time point at which a target state parameter in a target physical field changes, and acquire a second time point at which an associated state parameter corresponding to the target state parameter changes; The first time point and the second time point are used to determine a response speed evaluation of the target state parameter in the target physical field.

5. The multi-mode physical simulation method based on artificial intelligence according to claim 4 is characterized in that: Also includes: Get multiple response chains consisting of mutually responding status parameters; Obtain the similarity scores corresponding to the two response chains, and obtain the intersection and union corresponding to the changed state parameters in the two response chains; Determining a universality factor for each of the response chains using the similarity score, the intersection, and the union; All response chains are traversed to determine the response chain with the largest universal factor as the target response chain, wherein the target state parameter exists in the target response chain, and the associated state parameter associated with the target state parameter.

6. The multi-mode physical simulation method based on artificial intelligence according to claim 1 is characterized in that: The step of obtaining a plurality of state parameters corresponding to each physical place in the preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter, comprises: Get relevant information about each physical field in the preset simulation scheme; A plurality of state parameters corresponding to each physical field are obtained and determined, wherein the state parameters include at least one environmental parameter and one physical parameter.

7. The multi-mode physical simulation method based on artificial intelligence according to claim 2 is characterized in that: Also includes: When the transmission requirement index is greater than or equal to a second index threshold, the state parameter is used as high-standard data, and the high-standard data is preferentially transmitted to the corresponding simulation model; When the transmission requirement index is less than the second index threshold, the state parameter is used as low-standard data, and the low-standard data is transmitted to the corresponding simulation model with a delay.

8. A multi-mode physical simulation system based on artificial intelligence, characterized in that: include: A first acquisition module, used to acquire a plurality of state parameters corresponding to each physical place in a preset simulation scheme, wherein the state parameters include at least one environmental parameter and at least one physical parameter; A second acquisition module is used to acquire a unique factor corresponding to each state parameter in each physical field, and then use the unique factor to determine the importance evaluation of each physical field; The obtaining of the unique factor corresponding to each state parameter in each physical field includes: Obtaining a first word vector corresponding to a target physical place, and obtaining a second word vector corresponding to a target state parameter in the physical field; Obtaining the number of physical fields that call the target state parameter; Determine a target unique factor corresponding to the target state parameter in the target physical field by using a preset correction factor, the first word vector, the second word vector, and the number of physical fields, and further determine a unique factor corresponding to each state parameter in each physical field; The determining of the significance evaluation of each of the physical fields using the unique factors comprises: Obtaining parameter setting values ​​corresponding to target state parameters in a target physical field within a target simulation scheme; Obtaining the standard deviation between the parameter setting values ​​in multiple simulation schemes, and obtaining the total value of the state parameter in the target physical field; Determine a target importance evaluation of the target physical field using the parameter setting value, the standard deviation, the unique factor and the total value, and further determine an importance evaluation of each of the physical fields; A third acquisition module is used to obtain a response speed evaluation corresponding to each state parameter in each physical field, and obtain a stability evaluation of each state parameter within a preset time period; A determination module determines a transmission compression index of the state parameter using the response speed evaluation, the stability evaluation, the unique factor and the importance evaluation, and then uses the transmission compression index to perform multi-mode physical simulation.

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