Electrical equipment virtual debugging system and method for determining data exchange priority in electrical equipment virtual debugging system

Abstract

The invention relates to an electrical equipment virtual debugging system and a data exchange priority determination method thereof, and relates to the technical field of electrical engineering. The method comprises the following steps: acquiring multiple pieces of target physical field data in the electrical equipment virtual debugging system, and extracting feature information of each piece of target physical field data; the feature information comprises at least one feature type of real-time performance, priority, importance and parameter value of the data; based on at least one of the feature information and a real-time data demand of the electrical equipment virtual debugging system, determining a color identifier of each piece of target physical field data; the color identifier comprises at least one of a color type and a color gradient; determining data exchange priorities of the target physical field data based on the color identifiers; according to the method, the classification of the related exchange priorities of the cross-module interactive data based on the color identification is realized, the unnecessary cross-module data exchange of the system in the multi-physics field coupling simulation process is conveniently reduced, and the computing resources and the data transmission efficiency are optimized.

Description

Technical Field

[0001] The present application relates to the field of electrical engineering technology, and in particular to a virtual debugging system for electrical equipment and a method for determining a data exchange priority therein. Background Art

[0002] The operation of electrical equipment involves not only electrical characteristics, but also the coupled effects of multiple physical fields such as temperature, mechanical vibration, and airflow. Virtual debugging systems and digital twin technologies in related technologies mostly focus on simulation in the electrical field and lack comprehensive integration of multiple physical fields. This results in the inability of virtual debugging systems in related technologies to intuitively identify and distinguish the priority, real-time nature, and importance of different data; for example, the processing priorities of real-time electrical fault alarm data and temperature data should be different, but if there is no clear distinction, critical data may be delayed in processing.

[0003] Therefore, it is urgent to provide a method that can identify and distinguish the data exchange priorities of multiple data in the virtual debugging system of electrical equipment. Summary of the invention

[0004] Based on this, it is necessary to provide an electrical equipment virtual debugging system that can identify and distinguish the data exchange priorities of multiple data, as well as a method, device, computer equipment, computer-readable storage medium and computer product for determining the data exchange priority in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for determining data exchange priority in an electrical equipment virtual debugging system, comprising:

[0006] Acquire multiple target physical field data in the electrical equipment virtual debugging system, and extract characteristic information of each of the target physical field data; the characteristic information includes at least one characteristic type of the real-time performance, priority, importance, and parameter value of the data;

[0007] Based on at least one of the characteristic information and the real-time data requirement of the electrical equipment virtual debugging system, determine the color identification of each of the target physical field data; the color identification includes at least one of a color type and a color gradient;

[0008] The data exchange priority of each target physical field data is determined based on the color identification.

[0009] In one embodiment, determining the color identification of each target physical field data based on the characteristic information includes:

[0010] Comparing the characteristic value of the characteristic information of each target physical field data with a preset characteristic threshold; wherein the preset characteristic threshold includes at least one sub-characteristic threshold;

[0011] When the characteristic value of the target physical field data is greater than or equal to a first sub-characteristic threshold, determining that the color identification of the target physical field data is a first color type;

[0012] When the characteristic value of the target physical field data is greater than or equal to the second sub-feature threshold and less than the first sub-feature threshold, determining that the color identification of the target physical field data is a second color type;

[0013] When the characteristic value of the target physical field data is greater than or equal to the third sub-feature threshold and less than the second sub-feature threshold, determining that the color identification of the target physical field data is a third color type;

[0014] When the characteristic value of the target physical field data is less than the third sub-characteristic threshold, determining that the color identification of the target physical field data is a fourth color type;

[0015] The first color type, the second color type, the third color type and the fourth color type are arranged clockwise in the hue wheel, or the first color type, the second color type, the third color type and the fourth color type are arranged counterclockwise in the hue wheel.

[0016] In one embodiment, determining the color identification of each target physical field data based on the characteristic information includes:

[0017] Obtaining a standardized value within a preset range corresponding to each of the feature types of each of the feature information, and a weight corresponding to each of the feature types;

[0018] Determining a comprehensive weight of each of the feature information based on the standardized value and the weight of each of the feature types;

[0019] The color type of the corresponding target physical field data is determined based on the size of each comprehensive weight; wherein, as the comprehensive weight increases successively, the color type is arranged clockwise or counterclockwise in the color wheel.

[0020] In one embodiment, determining the color identification of each target physical field data based on the characteristic information includes:

[0021] Obtaining a standardized value within a preset range corresponding to each of the feature types of each of the feature information, and a weight corresponding to each of the feature types;

[0022] Determining a comprehensive weight of each of the feature information based on the standardized value and the weight of each of the feature types;

[0023] Determine the color value corresponding to each of the comprehensive weights based on a preset color mapping function, and convert the color value into an RGB value;

[0024] The color type of the corresponding target physical field data is determined based on each of the RGB values.

[0025] In one embodiment, determining the color identification of each target physical field data based on the real-time data requirement of the electrical equipment virtual debugging system includes:

[0026] In a case where the target physical field data belongs to the data required by the real-time data, determining that the color identification of the target physical field data is a first color type;

[0027] In a case where the target physical field data does not belong to the data required by the real-time data, the color identification of the target physical field data is determined to be a second color type.

[0028] In one embodiment, it also includes:

[0029] In the target physical field data belonging to the same color type, obtaining the parameter value size of the target physical field data having the same data type; the data type includes electrical parameters, temperature data, vibration data and airflow data;

[0030] The color gradient of the corresponding target physical field data is determined based on the parameter value; wherein, as the parameter value increases successively, the color gradient increases or decreases successively.

[0031] In one embodiment, determining the color identification of each target physical field data based on the characteristic information includes:

[0032] Acquire the parameter value of the target physical field data having the same data type; the data type includes electrical parameters, temperature data, vibration data and airflow data;

[0033] For the target physical field data of the same data type, the color gradient of the corresponding target physical field data is determined based on the size of the parameter value; wherein, as the size of the parameter value increases successively, the color gradient increases or decreases successively.

[0034] In one embodiment, it also includes:

[0035] The color type of the target physical field data is determined based on the data type of the target physical field data.

[0036] In one embodiment, determining the data exchange priority of each target physical field data based on the color identification includes:

[0037] As the color types of the target physical field data correspond to the clockwise arrangement in the color wheel, the data exchange priority of the target physical field data increases or decreases in sequence;

[0038] As the color gradient of the target physical field data increases sequentially, the data exchange priority of the target physical field data increases sequentially or decreases sequentially.

[0039] In a second aspect, the present application also provides an electrical equipment virtual debugging system, including a physical field model module and a data management and exchange layer; the physical field model in the physical field model module includes an electromagnetic field model, a thermal field model, a mechanical field model and a fluid field model;

[0040] The data management and exchange layer is used to realize the transmission of each target physical field data between multiple physical field models based on the data exchange priority of each target physical field data in the physical field model module;

[0041] The data exchange priority of the target physical field data is determined by the method for determining the data exchange priority in the electrical equipment virtual debugging system in the first aspect.

[0042] In a third aspect, the present application further provides a device for determining data exchange priority in a virtual debugging system of an electrical device, comprising:

[0043] A data acquisition module is used to acquire multiple target physical field data in the electrical equipment virtual debugging system, and extract characteristic information of each of the target physical field data; the characteristic information includes at least one characteristic type of the data's real-time performance, priority, importance, and parameter value;

[0044] A color identification determination module, used to determine the color identification of each of the target physical field data based on at least one of the characteristic information and the real-time data requirement of the electrical equipment virtual debugging system; the color identification includes at least one of a color type and a color gradient;

[0045] A priority determination module is used to determine the data exchange priority of each target physical field data based on the color identification.

[0046] In a fourth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any step in the first aspect when executing the computer program.

[0047] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements any step in the first aspect when executed by a processor.

[0048] In a sixth aspect, the present application further provides a computer program product, comprising a computer program, which implements any step in the first aspect when executed by a processor.

[0049] In the electrical equipment virtual debugging system provided by the present application, and the method, device, computer equipment, computer-readable storage medium and computer product for determining the data exchange priority therein, the method for determining the data exchange priority in the electrical equipment virtual debugging system is to obtain multiple target physical field data in the electrical equipment virtual debugging system and extract characteristic information of each target physical field data; the characteristic information includes at least one characteristic type of the real-time nature, priority, importance, and parameter value of the data; based on the characteristic information and at least one of the real-time data requirements of the electrical equipment virtual debugging system, determine the color identification of each target physical field data; the color identification includes at least one of the color type and the color gradient; determine the data exchange priority of each target physical field data based on the color identification; so as to realize the classification of data interacting across modules based on the color identification, so as to dynamically adjust the data exchange priority according to the characteristics such as the real-time nature, priority and importance of the data, which is conducive to helping the system reduce unnecessary cross-module data exchange during the multi-physical field coupling simulation process and optimize computing resources and data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 Schematic diagram of the architecture of a virtual commissioning system for electrical equipment based on digital twins in one embodiment;

[0052] Figure 2 An application environment diagram of a method for determining data exchange priority in an electrical equipment virtual debugging system in one embodiment;

[0053] Figure 3 A schematic flow chart of a method for determining data exchange priority in a virtual debugging system of an electrical device in one embodiment;

[0054] Figure 4 A schematic diagram of physical field coupling and data synchronization in one embodiment;

[0055] Figure 5 A schematic diagram of a data management and exchange layer in one embodiment;

[0056] Figure 6 A schematic diagram of assigning colors to electrical data in one embodiment;

[0057] Figure 7 A schematic diagram of assigning colors to temperature data in one embodiment;

[0058] Figure 8 A schematic diagram of assigning colors to vibration data in one embodiment;

[0059] Fig. 9 A schematic diagram of data transmission and synchronization in one embodiment;

[0060] Fig.10 A structural block diagram of a device for determining data exchange priority in a virtual debugging system of an electrical device in one embodiment;

[0061] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] In the related art, the electrical equipment virtual debugging system is a tool based on computer simulation technology, which is used to debug and verify the design and control of the electrical system through a virtual environment before the actual equipment is put into operation.

[0064] Digital Twin Technology is a technology that connects and interacts with physical entities in real time through virtual models. It collects data from physical devices or systems through sensors and Internet of Things (IoT) devices, and transmits this data to virtual models to form digital copies of the physical objects (i.e., "digital twins"). Virtual models can not only reflect the state, performance, and behavior of physical entities, but also simulate their reactions under different conditions for prediction, analysis, and optimization.

[0065] Since the operation of electrical equipment involves not only electrical characteristics, but also the coupling effects of multiple physical fields such as temperature, mechanical vibration, and airflow. Virtual debugging systems and digital twin technologies in related technologies mostly focus on simulation in the electrical field and lack comprehensive integration of multiple physical fields; therefore, virtual debugging systems in related technologies cannot intuitively identify and distinguish the priority, real-time nature, and importance of different data. For example, the processing priority of real-time electrical fault alarm data and temperature data should be different, but if there is no clear distinction, critical data may be delayed in processing.

[0066] Please refer to Figure 1 , shows a schematic diagram of the architecture of a virtual debugging system for electrical equipment based on digital twins, including a simulation core module, a physical field model module, a data management and exchange layer, a user interface, and a calculation and execution module; wherein the physical field model module may specifically include an electromagnetic field model, a thermal field model, a mechanical field model, and a fluid field model. In order to solve the problem of multi-physical field coupling in electrical equipment, the virtual debugging system for electrical equipment based on digital twins needs to integrate the development and integration of multi-physical field coupling simulation platforms, real-time data acquisition and analysis, intelligent optimization algorithms, and efficient computing technologies. Through these methods, the performance of the virtual debugging system and digital twin technology can be comprehensively improved, so that the equipment can accurately reflect the interaction between multi-physical fields during the design, debugging, monitoring and optimization process. The development and integration of multi-physical field coupling simulation platforms is used to create a flexible, scalable, and modular platform to achieve the ability to simultaneously simulate the coupling effects of different physical fields such as electromagnetic, thermal, mechanical, and fluid.

[0067] In order to identify and distinguish the data exchange priority of physical field data that needs to be interacted across modules in the electrical equipment virtual debugging system, the embodiment of the present application provides a method for determining the data exchange priority in the electrical equipment virtual debugging system, which can be applied to Figure 2 In the application environment shown, Figure 1 The electrical equipment virtual commissioning system based on digital twin can be Figure 2 The terminal 102 and / or server 104 shown in FIG. 1 are operated. The terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. The server 104 can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0068] In an exemplary embodiment, Figure 3As shown, a method for determining data exchange priority in a virtual debugging system of electrical equipment is provided, and the method is applied to Figure 1 The terminal in is taken as an example to illustrate, including the following steps 201 to 203. Among them:

[0069] Step 201, obtain multiple target physical field data in the electrical equipment virtual debugging system, and extract characteristic information of each target physical field data; the characteristic information includes at least one characteristic type of the data's real-time performance, priority, importance, and parameter value.

[0070] Among them, the electrical equipment virtual debugging system can be an electrical equipment virtual debugging system based on digital twins, which is used to debug and verify the design and control of electrical systems related to electrical equipment through a virtual environment before the actual electrical equipment is put into operation.

[0071] Among them, the physical field data includes data related only to its own physical field, which will not be transmitted to other physical fields, and also includes data that will be transmitted to other physical fields; the target physical field data here refers to the physical field data among multiple physical field data that needs to be transmitted or interacted between at least two physical fields. Physical field data is also the parameters of different physical fields, such as the electric field strength in the electromagnetic field, the temperature in the thermal field, the stress in the mechanical field, etc.

[0072] The characteristic information of the target physical field data may be some characteristic information set in advance for the target physical field data by the management personnel based on the needs; the characteristic information may include, for example, the real-time, priority, importance, parameter value, etc. of the target physical field data. The characteristic information of a target physical field data may include only one characteristic type, or may include two or more characteristic types, and this application does not make any specific limitation on this.

[0073] Exemplarily, from all the physical field data in the digital twin-based electrical equipment virtual debugging system, the target physical field data that will interact with other physical fields are screened out, and then the characteristic information of each target physical field data is extracted to learn the specific characteristic types included in the characteristic information of each target physical field data. For example, the characteristic information of some target physical field data only involves one of real-time, priority, importance, and parameter value size, and the characteristic information of some target physical field data simultaneously involves two or more of real-time, priority, importance, and parameter value size.

[0074] Step 202 : determining a color identification of each target physical field data based on at least one of the characteristic information and the real-time data requirement of the electrical equipment virtual debugging system; the color identification includes at least one of a color type and a color gradient.

[0075] Among them, color identification is used to represent the data exchange priority of each target physical field data when it is transmitted in the digital twin-based electrical equipment virtual debugging system. For example, different data exchange priorities can be represented by different color identifications, and different data exchange priorities can be represented by the depth and brightness of the same color.

[0076] The present application provides an optional implementation scheme in which the color identification may include at least one of a color type and a color gradient, wherein the color type may include, for example, red, yellow, green, blue and other color types, and the color gradient may include, for example, different shades or brightness of red, different shades or brightness of blue, etc.

[0077] The real-time data demand of the electrical equipment virtual debugging system refers to the transmission demand for at least part of the target physical field data during the current working period of the electrical equipment virtual debugging system. The amount of target physical field data is large, and the electrical equipment virtual debugging system does not need all the target physical field data to be interactively transmitted during the same working period.

[0078] Exemplarily, after obtaining characteristic information of each target physical field data based on executing step 201, the color identification of each target physical field data can be determined based on at least one of the characteristic information and the real-time data requirements of the electrical equipment virtual debugging system based on the current working status of the electrical equipment virtual debugging system.

[0079] Then, step 203 may be executed to determine the corresponding data exchange priority of each target physical data in the current working time period based on the color identification.

[0080] For example, the data exchange priority of the target physical data identified by the red type is higher than the data exchange priority of the target physical data identified by the green type; for example, the data exchange priority of the target physical data identified by the dark red type is higher than the data exchange priority of the target physical data identified by the light pink type.

[0081] In the method for determining the data exchange priority in the electrical equipment virtual debugging system provided by the present application, multiple target physical field data in the electrical equipment virtual debugging system are obtained, and characteristic information of each target physical field data is extracted; the characteristic information includes at least one characteristic type of the real-time nature, priority, importance, and parameter value of the data; based on the characteristic information and at least one of the real-time data requirements of the electrical equipment virtual debugging system, the color identification of each target physical field data is determined; the color identification includes at least one of the color type and the color gradient; the data exchange priority of each target physical field data is determined based on the color identification; so as to realize the classification of data interacting across modules based on the color identification, so as to dynamically adjust the priority of data exchange according to the characteristics of the data such as real-time nature, priority and importance, which is conducive to helping the system reduce unnecessary cross-module data exchange during the multi-physical field coupling simulation process and optimize computing resources and data transmission efficiency.

[0082] In an exemplary embodiment, the above step 202, determining the color identification of each target physical field data based on the characteristic information, can be implemented by executing steps 221 to 225, wherein:

[0083] Step 221, comparing the characteristic value of the characteristic information of each target physical field data with the value of a preset characteristic threshold; wherein the preset characteristic threshold includes at least one sub-characteristic threshold;

[0084] Step 222, when the characteristic value of the target physical field data is greater than or equal to the first sub-characteristic threshold, determine that the color identification of the target physical field data is a first color type;

[0085] Step 223, when the characteristic value of the target physical field data is greater than or equal to the second sub-characteristic threshold and less than the first sub-characteristic threshold, determine that the color identification of the target physical field data is a second color type;

[0086] Step 224, when the characteristic value of the target physical field data is greater than or equal to the third sub-characteristic threshold value and less than the second sub-characteristic threshold value, determine that the color identification of the target physical field data is a third color type;

[0087] Step 225, when the characteristic value of the target physical field data is less than the third sub-characteristic threshold, determining that the color identification of the target physical field data is a fourth color type;

[0088] The first color type, the second color type, the third color type and the fourth color type are arranged clockwise in the hue wheel, or the first color type, the second color type, the third color type and the fourth color type are arranged counterclockwise in the hue wheel.

[0089] Exemplarily, an implementation method for determining the corresponding color identification based on the characteristic information of the target physical field data is provided, which is to calculate the characteristic value corresponding to the characteristic type included in the characteristic information of each target physical field data, and compare the characteristic value of each target physical field data with a preset preset characteristic threshold to assign different color identifications to the target physical field data with different comparison results.

[0090] It should be noted that the present application does not specifically limit the number of sub-feature thresholds included in the preset feature threshold; for example, only one sub-feature threshold may be included. In this case, the comparison results of the feature value of the target physical field data and the preset feature threshold may include two types, one is that the feature value is greater than or equal to the preset feature threshold, and the other is that the feature value is less than the preset feature threshold. In this case, the target physical field data can be divided into two cases for color identification, for example, one type of target physical field data is color-identified in red, and the other type of target physical field data is color-identified in blue. For example, it is also possible to select the preset feature threshold to include two or more sub-feature thresholds.

[0091] An optional embodiment is provided in which the preset preset feature threshold includes three sub-feature thresholds, namely a first sub-feature threshold, a second sub-feature threshold and a third sub-feature threshold, wherein the first sub-feature threshold is greater than the second sub-feature threshold, and the second sub-feature threshold is greater than the third sub-feature threshold. Based on this, the characteristic value of the characteristic information of each target physical field data is compared with the first sub-feature threshold, the second sub-feature threshold, and the third sub-feature threshold to obtain the comparison result of each target physical field data compared with the preset sub-feature thresholds; specifically, when the comparison result represents the characteristic value of the target physical field data, which is greater than or equal to the first sub-feature threshold, the color identification of the relevant target physical field data can be selected as the first color type; when the comparison result represents the characteristic value of the target physical field data, which is greater than or equal to the second sub-feature threshold and less than the first sub-feature threshold, the color identification of the relevant target physical field data can be selected as the second color type; when the comparison result represents the characteristic value of the target physical field data, which is greater than or equal to the third sub-feature threshold and less than the second sub-feature threshold, the color identification of the relevant target physical field data can be selected as the third color type; when the comparison result represents the characteristic value of the target physical field data, which is less than the third sub-feature threshold, the color identification of the relevant target physical field data can be selected as the fourth color type.

[0092] That is, based on the characteristic value of each target physical field data compared to the size of each sub-feature threshold, different color identifiers are assigned to the corresponding target physical field data in different size intervals. Among them, the above-mentioned first color type may be, for example, a red type, the second color type may be, for example, an orange type, the third color type may be, for example, a yellow type, and the fourth color type may be, for example, a green type. In this embodiment, the first color type, the second color type, the third color type, and the fourth color type are arranged counterclockwise in the hue ring. However, this is only an optional implementation provided by the present application, and the first color type, the second color type, the third color type, and the fourth color type may also be selected, specifically presented as being arranged clockwise in the hue ring. Among them, two adjacent color types may be adjacently arranged in the hue ring of twelve colors, or may be spaced in the hue ring of twelve colors, and the present application does not make specific restrictions on this. For example, in the hue ring of twelve colors, the five colors of red, orange, yellow, green, and blue are arranged counterclockwise in sequence, and the above-mentioned first color type, second color type, third color type, and fourth color type may be the four colors of red, orange, yellow, and green, respectively, or the four colors of red, orange, green, and blue, etc.

[0093] It should be noted that, for a target physical field data, the characteristic value of its corresponding characteristic information is obtained by summing up the characteristic types of the target physical field data; the present application does not specifically limit the specific calculation method of the characteristic value of the target physical field data, and the method for calculating the characteristic value of the target physical field data can be set based on needs.

[0094] For the calculation method of the characteristic value of the characteristic information corresponding to the target physical field data, an optional implementation method is provided, in which each characteristic type may include three situations: high, medium, and low. The high characteristic type may be assigned a first score, the medium characteristic type may be assigned a second score, and the low characteristic type may be assigned a third score. The characteristic value of the characteristic information corresponding to a target physical field data may be the average of the scores corresponding to the characteristic types it includes. In the case where each characteristic type includes different classification levels, the scores corresponding to each classification level of each characteristic type may be set separately based on demand.

[0095] An optional implementation method is provided, where for two target physical field data, for example, the feature information corresponding to the first target physical field data and the second target physical field data respectively includes a feature type of priority, and does not include other feature types, wherein the priority corresponding to the first target physical field data is high, and the priority corresponding to the second target physical field data is medium. At this time, the characteristic value corresponding to the first target physical field data is higher than the characteristic value corresponding to the second target physical field data.

[0096] An optional implementation method is provided, for two target physical field data, for example, the feature information of the first target physical field data includes a feature type with a high priority and a feature type with a medium importance, and the feature information of the second target physical field data includes a feature type with a high priority and a feature type with a low real-time performance. In this case, the characteristic value corresponding to the first target physical field data can be set to be higher than the characteristic value corresponding to the second target physical field data.

[0097] In an exemplary embodiment, the above step 202, determining the color identification of each target physical field data based on the characteristic information, can be implemented by executing steps 311 to 313, wherein:

[0098] Step 311, obtaining a standardized value within a preset range corresponding to each feature type of each feature information, and a weight corresponding to each feature type;

[0099] Step 312, determining the comprehensive weight of each feature information based on the standardized value and weight of each feature type;

[0100] Step 313, determining the color type of the corresponding target physical field data based on the size of each comprehensive weight; wherein, as the comprehensive weight increases successively, the color type is arranged clockwise or counterclockwise in the hue wheel.

[0101] Exemplarily, an implementation method for determining the corresponding color identification based on the feature information of the target physical field data is provided, which is to first perform feature standardization processing on each feature type in the feature information corresponding to each target physical field data so that they can all be converted to a unified scale, that is, the standardization processing is to ensure that different characteristics (feature types) can be fairly compared when assigning colors. For example, each feature type can be standardized to a range of [0, 1]. Specifically, the relevant standardized value can be obtained through the preset maximum and minimum values ​​corresponding to each feature type, as well as the value of the feature type itself.

[0102] For example, assuming that the value of real-time itself is T, the value of priority itself is P, the value of importance itself is I, and the value of parameter value itself is S; then based on the preset maximum value T corresponding to real-time max , minimum value T min and real-time value T, calculate the normalized value T' corresponding to the real-time; and, based on the preset maximum value P corresponding to the priority max , minimum value P min and the priority value P, and calculate the normalized value P' corresponding to the priority; and, based on the preset maximum value I corresponding to the importance max , minimum value Imin and importance value I, calculate the standardized value I' corresponding to the importance; and, based on the preset maximum value S corresponding to the parameter value, max , minimum value S min The parameter value is taken as S, and the standardized value S' corresponding to the parameter value is calculated.

[0103] After obtaining the standardized value corresponding to each feature type in the feature information corresponding to each target physical field data, the weight value of the feature information corresponding to each target physical field data is further obtained; specifically, the comprehensive weight of the feature information corresponding to each target physical field data can be calculated through the weight corresponding to each feature type and the calculated standardized value.

[0104] For example, suppose the weight of real-time itself is W T , the weight of the priority itself is W P , the weight of the importance itself is W I , the weight of the parameter value itself is W S , then the comprehensive weight of the characteristic information corresponding to a target physical field data can be calculated based on W T , T', W P , P', W I , I', W S , S'. The value range of the comprehensive weight can also be in the range of [0, 1].

[0105] Then, the color type of each target physical field data can be determined based on the size of the comprehensive weight corresponding to the characteristic information of each target physical field data. For example, it can be set so that as the comprehensive weight increases, the color type is arranged clockwise in the hue ring; or, as the comprehensive weight increases, the color type is arranged counterclockwise in the hue ring. In addition, it can also be set so that as the comprehensive weight increases, the color gradient increases; or, as the comprehensive weight increases, the color gradient decreases.

[0106] In an exemplary embodiment, the above step 202, determining the color identification of each target physical field data based on the characteristic information, can be implemented by executing steps 321 to 324, wherein:

[0107] Step 321, obtaining a standardized value within a preset range corresponding to each feature type of each feature information, and a weight corresponding to each feature type;

[0108] Step 322, determining the comprehensive weight of each feature information based on the standardized value and weight of each feature type;

[0109] Step 323, determining the color value corresponding to each comprehensive weight based on a preset color mapping function, and converting the color value into an RGB value;

[0110] Step 324, determining the color type of the corresponding target physical field data based on each RGB value.

[0111] Exemplarily, an implementation method of determining a corresponding color identification based on characteristic information of target physical field data is provided as follows:

[0112] Exemplarily, an implementation method for determining the corresponding color identification based on the feature information of the target physical field data is provided, which is to first perform feature standardization processing on each feature type in the feature information corresponding to each target physical field data so that they can all be converted to a unified scale, that is, the standardization processing is to ensure that different characteristics (feature types) can be fairly compared when assigning colors. For example, each feature type can be standardized to a range of [0, 1]. Specifically, the relevant standardized value can be obtained through the preset maximum and minimum values ​​corresponding to each feature type, as well as the value of the feature type itself.

[0113] For example, assuming that the value of real-time itself is T, the value of priority itself is P, the value of importance itself is I, and the value of parameter value itself is S; then based on the preset maximum value T corresponding to real-time max , minimum value T min and real-time value T, calculate the normalized value T' corresponding to the real-time; and, based on the preset maximum value P corresponding to the priority max , minimum value P min and the priority value P, and calculate the normalized value P' corresponding to the priority; and, based on the preset maximum value I corresponding to the importance max , minimum value I min and importance value I, calculate the standardized value I' corresponding to the importance; and, based on the preset maximum value S corresponding to the parameter value, max , minimum value S min The parameter value is taken as S, and the standardized value S' corresponding to the parameter value is calculated.

[0114] After obtaining the standardized value corresponding to each feature type in the feature information corresponding to each target physical field data, the weight value of the feature information corresponding to each target physical field data is further obtained; specifically, the comprehensive weight of the feature information corresponding to each target physical field data can be calculated through the weight corresponding to each feature type and the calculated standardized value.

[0115] For example, suppose the weight of real-time itself is WT , the weight of the priority itself is W P , the weight of the importance itself is W I , the weight of the parameter value itself is W S , then the comprehensive weight of the characteristic information corresponding to a target physical field data can be calculated based on W T , T', W P , P', W I , I', W S , S'. The value range of the comprehensive weight can also be in the range of [0, 1].

[0116] Furthermore, based on the comprehensive weight corresponding to the characteristic information of each target physical field data, the corresponding adapted color value is obtained. For example, the color value corresponding to each comprehensive weight can be calculated through a preset color mapping function. The color value can be in the range of [0, 1], and then based on the preset conversion function of the color value and the RGB value, the associated RGB value is determined based on the color value corresponding to the characteristic information of each target physical field data. The RGB value specifically includes R (Red) value, G (Green) value and B (Blue) value.

[0117] Then, the RGB value calculated based on each target physical field data can be used as the color type of the target physical field data.

[0118] In an exemplary embodiment, the above step 202 is performed to determine the color identification of each target physical field data based on the real-time data requirements of the electrical equipment virtual debugging system, which can be achieved by executing steps 331-332, wherein:

[0119] Step 331, when the target physical field data is data required for real-time data, determining that the color identification of the target physical field data is a first color type;

[0120] Step 332, when the target physical field data does not meet the real-time data requirement, determine that the color identification of the target physical field data is a second color type.

[0121] Exemplarily, in the current working time period of the electrical equipment virtual debugging system, if a real-time data requirement is set, the data included in the real-time data requirement can be identified to determine which of the multiple target physical field data belong to the real-time data requirement; the target physical field data belonging to the real-time data requirement is the data with a higher priority in the current working time period, and the target physical field data not in the real-time data requirement is the data with a lower priority in the current working time period. For example, for the target physical field data that belongs to the real-time data requirement, its corresponding color identification is determined to be the first color type, and for the target physical field data that does not belong to the real-time data requirement, its corresponding color identification is determined to be the second color type.

[0122] Furthermore, after color identification of the color type of each target physical field data as to whether it belongs to the real-time data requirement, further steps that can be performed include step 333 and step 334, wherein:

[0123] Step 333, obtaining parameter values ​​of target physical field data with the same data type in the target physical field data of the same color type; the data types include electrical parameters, temperature data, vibration data, and airflow data;

[0124] Step 334, determining the color gradient of the corresponding target physical field data based on the parameter value; wherein, as the parameter value increases, the color gradient increases or decreases.

[0125] Exemplarily, when determining whether each target physical field data belongs to the real-time data requirement within the current working time period based on color type among multiple target physical field data, the corresponding priorities of multiple target physical field data belonging to the same color type can be further classified; that is, the priorities of multiple target physical field data related to the first color type are further classified, and the priorities of multiple target physical field data related to the second color type are further classified.

[0126] Specifically, it is possible to select the parameter value size of the target physical field data with the same data type in the target physical field data belonging to the same color type, and then sort the priority of the related target physical field data based on the parameter value size; for example, as the parameter value size increases successively, the color gradient increases successively; or, as the parameter value size increases successively, the color gradient decreases successively.

[0127] In an exemplary embodiment, the above step 202, determining the color identification of each target physical field data based on the characteristic information, can be implemented by executing steps 341-342, wherein:

[0128] Step 341, obtaining parameter values ​​of target physical field data having the same data type; the data type includes electrical parameters, temperature data, vibration data, and airflow data;

[0129] Step 342, for the target physical field data of the same data type, determine the color gradient of the corresponding target physical field data based on the parameter value; wherein, as the parameter value increases successively, the color gradient increases or decreases successively.

[0130] Exemplarily, in the case where the characteristic information of most target physical field data includes the characteristic type of parameter value size, an optional implementation method is provided for determining the color identification of each target physical field data based on the characteristic information, which is to obtain the parameter value size of each target physical field data with the same data type, and then determine the color gradient of each target physical field data of the same data type based on the parameter value size of each target physical field data; for example, for each target physical field data of the same data type, as the parameter value size increases successively, the color gradient increases successively; or as the parameter value size increases successively, the color gradient decreases successively.

[0131] That is, the data exchange priority of each target physical field data of the same data type can be identified by different color gradients.

[0132] In an exemplary embodiment, the method further includes: determining a color type of the target physical field data based on a data type of the target physical field data.

[0133] Exemplarily, in order to distinguish target physical field data of different data types, after determining the color gradient corresponding to each target physical field data, the color type corresponding to the target physical field data of each data type can be further determined; for example, the color type of each target physical field data belonging to electrical parameters is red, and each target physical field data belonging to electrical parameters has a red type color gradient, that is, different target physical field data belonging to electrical parameters can be identified by using gradient red of different saturations; the color type of each target physical field data belonging to airflow data is blue, and each target physical field data belonging to airflow data has a blue type color gradient, that is, different target physical field data belonging to electrical parameters can be identified by using gradient blue of different saturations.

[0134] In an exemplary embodiment, the data exchange priority of each target physical field data is determined based on the color identification in the above step 203, which can be achieved by executing steps 231 and 232, wherein:

[0135] Step 231, as the color types of the target physical field data correspond to the clockwise arrangement in the color wheel, the data exchange priority of the target physical field data increases or decreases in sequence;

[0136] Step 232 , as the color gradient of the target physical field data increases successively, the data exchange priority of the target physical field data increases successively or decreases successively.

[0137] Exemplarily, the data exchange priority of each target physical field data may be determined based on the color type of each target physical field data and the arrangement order in the twelve-color hue wheel; or, the data exchange priority of each target physical field data may be determined based on the color gradient of each target physical field data, that is, the color brightness or saturation (depth). In addition, as mentioned above, for multiple target physical field data whose priorities are determined based on color type, if there are several target physical field data with the same priority level, for example, all of them are the second level, then the multiple target physical field data in the second level may be further prioritized based on the color gradient of the target physical field data of the second level.

[0138] With respect to the electrical equipment virtual debugging system provided in the present application, and the method for determining the data exchange priority therein, an optional implementation method is also provided, which can be referred to Figure 1 , the architecture diagram of the electrical equipment virtual commissioning system shown in FIG.

[0139] The simulation core module is responsible for executing the calculation and coupling of each physical field. It performs the core calculation of the coupling of multiple physical fields through the coupling algorithm engine, and performs coupling simulation through numerical methods (such as finite element method, finite volume method, etc.); the coupling algorithm engine supports data exchange and feedback between different physical fields; in the embodiments provided in this application, a dedicated solver is provided for each physical field, and different physical fields (electromagnetic field, thermal field, mechanical field, fluid field) use different mathematical models and solution methods according to their characteristics.

[0140] For example, the electromagnetic field uses an electromagnetic field solver (such as Maxwell's equations and Poynting's theorem); the thermal field uses a thermodynamic solver based on heat conduction, convection and radiation; the mechanical field uses a solver based on elasticity and nonlinear mechanics; the fluid field uses a fluid dynamics solver and uses a coordination algorithm to deal with the coupling problems of different physical fields.

[0141] Physical field model module, the simulation of each physical field is managed through a dedicated module. At the same time, each module has independent input and output interfaces to exchange data with other modules to ensure simulation accuracy and efficiency. The physical field model module includes electromagnetic field module, thermal field module, mechanical field module, and fluid module.

[0142] Data management and exchange layer, such as Figure 4 As shown, it is responsible for transmitting information between different physical field models to ensure data synchronization and consistency. The parameters of different physical fields (such as electric field strength in electromagnetic fields, temperature in thermal fields, and stress in mechanics) may have different units and representation methods, so standardized data formats and unified interfaces are required; the data exchange mechanism supports cross-module data exchange and adopts an efficient data transmission mechanism. In the process of multi-physical field coupling simulation, the timing and feedback mechanism of the calculation process of each physical field are precisely controlled to maintain the synchronization of the calculation steps of each physical field.

[0143] Traditional data transmission mechanisms include memory sharing, message queues, and databases. When multiple modules share memory, synchronization problems may occur, especially when multiple processes or threads access the same memory at the same time, which may lead to data consistency and conflict problems. There is a certain delay in the transmission of message queues, especially in high concurrency situations. The efficiency of message delivery will decrease, affecting the real-time performance of the system. The read and write speed of the database is slower than that of memory sharing, especially in simulation environments that require frequent exchange of large amounts of data, and there is a performance bottleneck.

[0144] This application can "spatially" classify cross-module data according to different characteristics through spatial gradient color identification; each type of data can be given a unique color gradient, and the color gradient can be used to represent the different characteristics of the data (such as timeliness, importance, priority, etc.), and the data can be efficiently managed and exchanged based on factors such as the real-time nature of the data, data volume, and module requirements.

[0145] Spatial gradient colors can help clearly distinguish different types of data. For example, real-time data can be marked as red (high priority), while historical data can be marked as blue (low priority). Through color gradients, the priority of data exchange can be dynamically adjusted according to the data requirements of different modules, and resource allocation can be optimized. By spatially classifying data, unnecessary cross-module data exchange can be effectively reduced. For example, a module only needs to interact with specific types of data, and color can be used to distinguish and exchange only specific categories of data, avoiding the exchange of full data and improving efficiency.

[0146] For example Figure 5 As shown, electromagnetic field data, fluid field data, thermal field data and mechanical field data can have different color identifications (color identifications), for example, the color identification of electromagnetic field data is red, the color identification of fluid field data is blue, the color identification of thermal field data is green, and the color identification of mechanical field data is yellow. Among them, it can be selected to color-identify only the real-time data that needs to be interacted across modules.

[0147] Furthermore, specific steps for data classification by spatial gradient color are provided as follows, which may include, for example, the embodiments shown in the following steps S1 to S6.

[0148] S1, data analysis and definition of classification rules, including:

[0149] Data identification: Identify all possible cross-module data types in the system, including multi-physics field data such as electrical parameters, temperature, vibration, and airflow;

[0150] Data feature definition: Define some features for each type of data, including:

[0151] Real-time: For example, electrical parameters may require real-time updates, while temperature data can have a longer update interval;

[0152] Priority: Some data (such as electrical fault alarms) may be more important than other data (such as ambient temperature);

[0153] Importance: For example, electromagnetic field data is critical to a real-time control system, while fluid flow rate data may only be part of an optimization process;

[0154] Data size: The amount of data to be transferred (e.g. large data volume or small data volume).

[0155] S2, assign color ranges, including color identification design. According to the different characteristics of the data, different color types or color gradients are assigned to each data type. For example:

[0156] Red: for real-time, high-priority, or extremely important data;

[0157] Orange: for slightly less urgent or important data;

[0158] Yellow: used for data that is updated less frequently or does not directly affect control decisions;

[0159] Green: used for non-critical data, which is updated less frequently and does not affect the overall operation of the system;

[0160] Gradient colors: For a specific range of data, you can use gradient colors. For example, the data gradually changes color from the lowest value to the maximum value to help distinguish the size or range of the data.

[0161] S3, data identification and classification, including dynamic identification, giving color identification to data in different modules. Data is transmitted with its color identification to indicate its priority, real-time nature, importance, etc.;

[0162] For example:

[0163] Real-time data: can be red or dark red, ensuring the highest priority and the system can process it in real time;

[0164] Historical data: can use milder tones, such as green or blue, to indicate that its transmission can be processed later;

[0165] Multi-dimensional classification: In addition to color, data can be identified by other dimensions, such as the module where the data comes from, the type of data, the physical fields involved, etc., to enable flexible management in complex systems.

[0166] Provides a way to achieve a unified scale for various types of data, including:

[0167] First, the features of various types of data need to be standardized so that they are on a unified scale. Standardization is to ensure that different characteristics (such as real-time, priority, etc.) can be fairly compared when assigning colors.

[0168] Assume the following data characteristics: real-time (T), priority (P), importance (I), parameter value size or data size (S);

[0169] Standardize each feature so that it is in the range of [0, 1]. The relevant calculation method can be: , , , Among them, T min 、T max etc. are the minimum and maximum values ​​of the corresponding features.

[0170] In order to assign a color range to the data, a comprehensive "weight" value is further calculated, taking into account each feature. Assume that the weight of each feature is: W T , W P , W I , W S ; then the comprehensive weight W total The calculation method can be: Among them, W T , W P , W I , W S is the weight of each feature, which determines the influence of each feature on the final result; the comprehensive weight W total The values ​​of will be in the range [0, 1], and the data is mapped to a color range according to this weight, for example:

[0171] Red: corresponds to the highest comprehensive weight (i.e. close to 1), indicating extremely high priority and importance;

[0172] Orange: corresponds to a medium comprehensive weight (e.g. 0.6-0.8), indicating important but less urgent data;

[0173] Yellow: corresponds to a lower comprehensive weight (e.g. 0.3-0.6), indicating data with low update frequency or that does not directly affect decision-making;

[0174] Green: corresponds to the lowest comprehensive weight (close to 0), indicating non-critical data with low priority.

[0175] Furthermore, a color mapping function C(W total ), W total The value is mapped to a color value, assuming that the color value is in the range [0, 1], where 0 represents green and 1 represents red; for example .

[0176] Specifically, the RGB model can be used, mapped as: green (0, 255, 0), yellow (255, 255, 0), orange (255, 165, 0), red (255, 0, 0).

[0177] For data in a specific range (such as temperature, current, etc.), if you want to use gradient colors to represent the size or range of the data, perform color gradient calculations through the following steps. Assuming that we have data ranging from X min To X max , for each data point X i , use the following formula to calculate the corresponding gradient color: . Use the above formula to convert the value from the lowest value X min To the maximum value X max Mapped to a gradient from green to red.

[0178] A specific embodiment is further provided as follows:

[0179] Assume the following features and weights: real-time T = 0.9 (near real-time data), priority P = 0.8 (higher priority), importance I = 0.7 (medium importance), data size S = 0.5 (small data volume); and assume that the weights of each feature are: W T =0.4, W P =0.3, W I =0.2, W S = 0.1; further standardize the data: , , , ; Further calculate the comprehensive weight:

[0180] W total =0.4*0.9+0.3*0.8+0.2*0.7+0.1*0.5=0.36+0.24+0.14+0.05=0.79

[0181] Mapping the comprehensive weight to the color range, since W total =0.79, and it is mapped between red and green. The resulting color is close to orange or red. The above formula is used to calculate the comprehensive weight based on different characteristics of the data (such as real-time, priority, importance, data size, etc.), and the data is mapped to different color ranges. The gradient color is mapped according to the specific value of the data, which is suitable for situations where changes in data size need to be reflected.

[0182] S4, formulate data transmission strategy, including:

[0183] Color-based transmission priority: Different transmission strategies are formulated according to the color of the data. For example, real-time data (such as red) is quickly transmitted using a memory shared channel, while less urgent historical data (such as green) is transmitted using a message queue or database.

[0184] Dynamic adjustment: Adjust the transmission mode of different types of data according to the system load and real-time status. For example, when the system load is too high, delay the processing of low-priority data (such as yellow and green) to ensure that high-priority data (such as red and orange) is not affected.

[0185] S5, data transmission and synchronization, including:

[0186] Memory sharing and color identification synchronization: For data with high real-time requirements (such as red), memory sharing or direct mapping is used to ensure fast synchronization of data between modules;

[0187] Message queue and color identification synchronization: For data with lower real-time requirements (such as yellow and green), asynchronous transmission is performed through the message queue. Each message in the message queue can be prioritized according to the color identification of the data to ensure that high-priority data is processed first;

[0188] Synchronization of database processing and color identification: For historical data or large amounts of data, they can be stored in the database, and color identification can help decide when to extract and process these data from the database.

[0189] S6, data exchange and processing, including:

[0190] Data exchange: Data based on color identification is exchanged between modules within the system through appropriate channels. For example, data marked in red can be transmitted to the control module for real-time processing, while data marked in green can be pulled and processed later;

[0191] Data processing and feedback: After the data passes through each module, the system will feedback the processing results based on the color of the data. The change of color (such as from red to green) can also be used to indicate the status or completion of data processing. For example, after high-priority data is processed, other modules can be informed through color feedback.

[0192] The process of data classification through spatial gradient colors can effectively improve the efficiency, clarity and real-time performance of data exchange. The main steps include defining data features and classification rules, assigning colors, managing data transmission according to priority and real-time performance, dynamically adjusting transmission strategies, and using color identification for feedback and optimization. This method can help the system better process and exchange data in complex multi-physics field coupling simulations, and improve the system's response speed and overall performance.

[0193] Please refer to Figure 6-Figure 8 For example, when the characteristic types of electrical parameters specifically include high real-time performance, high priority, high importance, and medium data size, the assignable color is red; when the characteristic types of temperature data specifically include low real-time performance, medium priority, medium importance, and low data size, the assignable color is green; when the characteristic types of vibration data specifically include medium real-time performance, medium priority, low importance, and high data size, the assignable color is orange.

[0194] Please refer to Fig. 9 , provides an optional data transmission and synchronization view, in which, for real-time data, historical data and non-critical data, the color of real-time data is red, the color of historical data is green, and the color of non-critical data is yellow. When real-time data, historical data and non-critical data are exchanged in the electrical equipment virtual commissioning system, real-time data can adopt a fast transmission control strategy, historical data can adopt a delayed transmission control strategy, and non-critical data can adopt an asynchronous transmission control strategy.

[0195] Among them, real-time data adopts fast transmission, specifically involving: memory sharing, fast synchronization, real-time control processing and real-time decision-making; historical data adopts delayed transmission, specifically involving: database, database extraction, delayed processing, optimized processing; non-critical data adopts asynchronous transmission, specifically involving: message queue, asynchronous processing, low priority processing, system monitoring.

[0196] Data flow and workflow: users select models of different physical fields, set initial conditions (such as current, temperature, pressure, etc.), and set boundary conditions (such as temperature boundary, electromagnetic boundary, fluid boundary, etc.). The simulation core module starts each physical field solver for calculation based on the data and conditions entered by the user, and couples the feedback results of each physical field.

[0197] Result analysis and optimization, the simulation results will be analyzed and optimized by the optimization module. Users can further adjust the design parameters according to the analysis results. The association between data classifications is carried out by spatial gradient colors. Through the analysis and classification of data features, combined with an effective data exchange mechanism, a closed-loop collaborative workflow is formed in various links such as data transmission, processing, and visualization. Spatial gradient colors are not only a visualization tool for data classification, but also an important way to optimize data flow, improve system efficiency and user interaction experience.

[0198] Simulation feedback and iteration,Based on the optimization results, the simulation platform can,feed back the optimized design through a visual interface, and the,user can choose to further adjust the model or parameters and perform,iterative simulation.

[0199] In summary, the electrical equipment virtual debugging system provided by this application, and the method for determining the data exchange priority therein, can improve the performance of the electrical equipment virtual debugging system based on digital twins, and solve the problem of multi-physics field coupling in electrical equipment. Specifically, by integrating a multi-physics field coupling simulation platform, real-time data acquisition and analysis, intelligent optimization algorithms, and efficient computing technologies, a flexible and scalable system architecture is provided to ensure that the interaction between multiple physical fields can be accurately reflected during the design, debugging, monitoring, and optimization of the equipment. In addition, the application classifies cross-module data by spatial gradient color identification, so as to dynamically adjust the priority of data exchange according to the real-time nature, priority, and importance of the data. For example, real-time data is represented by red, and historical data is represented by blue, which helps the system reduce unnecessary cross-module data exchange during multi-physics field coupling simulation and optimize computing resources and data transmission efficiency.

[0200] Furthermore, the electrical equipment virtual debugging system provided by the present application and the method for determining the data exchange priority therein have the following four beneficial effects:

[0201] First, by using dedicated physical field models and solvers, each physical field (electromagnetic, thermal, mechanical, fluid, etc.) can be accurately simulated based on its characteristics. The electromagnetic field uses Maxwell's equations and Poynting's theorem, the thermal field uses heat conduction and convection equations, the mechanical field uses elastic mechanics solvers, and the fluid field relies on fluid dynamics models. The application of this targeted solver improves the coupling accuracy between the physical fields, ensuring that the mutual influence of multiple physical fields in the simulation process can be accurately reflected. The coordination algorithm can effectively handle the coupling problems between the physical fields and ensure that the simulation steps of each module are synchronized. By optimizing the calculation steps of different physical fields, the time lag or inconsistency of the calculation of each physical field is avoided, and the simulation efficiency and accuracy are improved.

[0202] Second, traditional data exchange mechanisms (such as memory sharing, message queues, and databases) often face synchronization problems, latency problems, and performance bottlenecks, especially in high-concurrency environments. Memory sharing may cause access conflicts between multiple processes or threads, message queues are less efficient at high concurrency, and databases have slow read and write speeds. In this technical solution, cross-module data is dynamically classified according to timeliness, priority, and module requirements through spatial gradient color identification, reducing unnecessary data exchange and significantly improving data transmission efficiency.

[0203] Third, the color gradient can be used to indicate the different priorities and importance of data. For example, real-time data is marked in red, and high-priority data information can be transmitted first; while historical data may be marked in blue, with a lower priority, reducing its consumption of system resources. In this way, computing resources and network bandwidth can be allocated efficiently, avoiding wasting time and computing power on unnecessary data exchange.

[0204] Fourth, during multi-physics simulation, the calculation results of each physical field will affect the state of other physical fields. Therefore, the data exchange layer is responsible for ensuring the synchronization and consistency of parameters (such as electric field strength, temperature, stress, etc.) between different physical fields. Through a unified data format and standardized interface, calculation errors or deviations in simulation results caused by inconsistent data can be avoided.

[0205] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0206] Based on the same inventive concept, the embodiment of the present application also provides a device for determining data exchange priority in an electrical equipment virtual debugging system for implementing the method for determining data exchange priority in the electrical equipment virtual debugging system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more devices for determining data exchange priority in an electrical equipment virtual debugging system provided below can refer to the limitations of the method for determining data exchange priority in an electrical equipment virtual debugging system in the above text, and will not be repeated here.

[0207] In an exemplary embodiment, Fig.10 As shown, a device 80 for determining data exchange priority in an electrical equipment virtual debugging system is provided, comprising: a data acquisition module 81, a color identification determination module 82 and a priority determination module 83, wherein:

[0208] The data acquisition module 81 is used to acquire multiple target physical field data in the electrical equipment virtual debugging system and extract characteristic information of each target physical field data; the characteristic information includes at least one characteristic type of the data's real-time performance, priority, importance, and parameter value;

[0209] A color identification determination module 82 is used to determine the color identification of each target physical field data based on at least one of the characteristic information and the real-time data requirement of the electrical equipment virtual debugging system; the color identification includes at least one of a color type and a color gradient;

[0210] The priority determination module 83 is used to determine the data exchange priority of each target physical field data based on the color identification.

[0211] In an exemplary embodiment, the color identification determination module 82 is used to determine the color identification of each target physical field data based on the feature information, specifically for: comparing the characteristic value of the feature information of each target physical field data with the size of a preset feature threshold; wherein the preset feature threshold includes at least one sub-feature threshold; when the characteristic value of the target physical field data is greater than or equal to the first sub-feature threshold, the color identification of the target physical field data is determined to be a first color type; when the characteristic value of the target physical field data is greater than or equal to the second sub-feature threshold and less than the first sub-feature threshold, the color identification of the target physical field data is determined to be a second color type; when the characteristic value of the target physical field data is greater than or equal to the third sub-feature threshold and less than the second sub-feature threshold, the color identification of the target physical field data is determined to be a third color type; when the characteristic value of the target physical field data is less than the third sub-feature threshold, the color identification of the target physical field data is determined to be a fourth color type; wherein the first color type, the second color type, the third color type and the fourth color type are arranged clockwise in the hue wheel, or the first color type, the second color type, the third color type and the fourth color type are arranged counterclockwise in the hue wheel.

[0212] In an exemplary embodiment, the color identification determination module 82 is used to determine the color identification of each target physical field data based on the feature information, specifically for: obtaining the standardized value within a preset range corresponding to each feature type of each feature information, and the weight corresponding to each feature type; determining the comprehensive weight of each feature information based on the standardized value and weight of each feature type; determining the color type of the corresponding target physical field data based on the size of each comprehensive weight; wherein, as the comprehensive weight increases successively, the color type is arranged clockwise or counterclockwise in the hue wheel.

[0213] In an exemplary embodiment, the color identification determination module 82 is used to determine the color identification of each target physical field data based on the feature information, specifically for: obtaining the standardized value within a preset range corresponding to each feature type of each feature information, and the weight corresponding to each feature type; determining the comprehensive weight of each feature information based on the standardized value and weight of each feature type; determining the color value corresponding to each comprehensive weight based on a preset color mapping function, and converting the color value into an RGB value; determining the color type of the corresponding target physical field data based on each RGB value.

[0214] In an exemplary embodiment, the color identification determination module 82 is used to determine the color identification of each target physical field data based on the real-time data requirements of the electrical equipment virtual debugging system, and is specifically used to: when the target physical field data is data that meets the real-time data requirements, determine the color identification of the target physical field data as a first color type; when the target physical field data is not data that meets the real-time data requirements, determine the color identification of the target physical field data as a second color type.

[0215] In an exemplary embodiment, the color identification determination module 82 is also used to obtain the parameter value size of the target physical field data having the same data type in the target physical field data belonging to the same color type; the data type includes electrical parameters, temperature data, vibration data and airflow data; the color gradient of the corresponding target physical field data is determined based on the parameter value size; wherein, as the parameter value size increases successively, the color gradient increases or decreases successively.

[0216] In an exemplary embodiment, the color identification determination module 82 is used to determine the color identification of each target physical field data based on the characteristic information, and is specifically used to: obtain the parameter value size of the target physical field data with the same data type; the data type includes electrical parameters, temperature data, vibration data and airflow data; for the target physical field data of the same data type, determine the color gradient of the corresponding target physical field data based on the parameter value size; wherein, as the parameter value size increases successively, the color gradient increases or decreases successively.

[0217] In an exemplary embodiment, the color identification determination module 82 is further used to determine the color type of the target physical field data based on the data type of the target physical field data.

[0218] In an exemplary embodiment, the priority determination module 83 is used to determine the data exchange priority of each target physical field data based on color identification, specifically: as the color type of the target physical field data corresponds to the clockwise arrangement in the color wheel, the data exchange priority of the target physical field data increases or decreases successively; as the color gradient of the target physical field data increases successively, the data exchange priority of the target physical field data increases or decreases successively.

[0219] Each module in the device for determining data exchange priority in the above-mentioned electrical equipment virtual debugging system can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0220] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for determining the data exchange priority in a virtual debugging system of an electrical device is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0221] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0222] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in the method for determining data exchange priority in the electrical equipment virtual debugging system provided in the present application are implemented.

[0223] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining data exchange priority in the electrical equipment virtual debugging system provided in the present application are implemented.

[0224] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps in the method for determining data exchange priority in a virtual debugging system for electrical equipment provided in the present application.

[0225] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0226] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0227] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0228] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining data exchange priority in an electrical equipment virtual debugging system, characterized in that: The method comprises: Acquire multiple target physical field data in the electrical equipment virtual debugging system, and extract characteristic information of each of the target physical field data; the characteristic information includes at least one characteristic type of the real-time performance, priority, importance, and parameter value of the data; Based on at least one of the characteristic information and the real-time data requirement of the electrical equipment virtual debugging system, determine the color identification of each of the target physical field data; the color identification includes at least one of a color type and a color gradient; The data exchange priority of each target physical field data is determined based on the color identification.

2. The method according to claim 1, characterized in that The determining the color identification of each target physical field data based on the characteristic information includes: Comparing the characteristic value of the characteristic information of each target physical field data with a preset characteristic threshold; wherein the preset characteristic threshold includes at least one sub-characteristic threshold; When the characteristic value of the target physical field data is greater than or equal to a first sub-characteristic threshold, determining that the color identification of the target physical field data is a first color type; When the characteristic value of the target physical field data is greater than or equal to the second sub-feature threshold and less than the first sub-feature threshold, determining that the color identification of the target physical field data is a second color type; When the characteristic value of the target physical field data is greater than or equal to the third sub-feature threshold and less than the second sub-feature threshold, determining that the color identification of the target physical field data is a third color type; When the characteristic value of the target physical field data is less than the third sub-characteristic threshold, determining that the color identification of the target physical field data is a fourth color type; The first color type, the second color type, the third color type and the fourth color type are arranged clockwise in the hue wheel, or the first color type, the second color type, the third color type and the fourth color type are arranged counterclockwise in the hue wheel.

3. The method according to claim 1, characterized in that The determining the color identification of each target physical field data based on the characteristic information includes: Obtaining a standardized value within a preset range corresponding to each of the feature types of each of the feature information, and a weight corresponding to each of the feature types; Determining a comprehensive weight of each of the feature information based on the standardized value and the weight of each of the feature types; The color type of the corresponding target physical field data is determined based on the size of each comprehensive weight; wherein, as the comprehensive weight increases successively, the color type is arranged clockwise or counterclockwise in the color wheel.

4. The method according to claim 1, characterized in that: The determining the color identification of each target physical field data based on the characteristic information includes: Obtaining a standardized value within a preset range corresponding to each of the feature types of each of the feature information, and a weight corresponding to each of the feature types; Determining a comprehensive weight of each of the feature information based on the standardized value and the weight of each of the feature types; Determine the color value corresponding to each of the comprehensive weights based on a preset color mapping function, and convert the color value into an RGB value; The color type of the corresponding target physical field data is determined based on each of the RGB values.

5. The method according to claim 1, characterized in that Determining the color identification of each target physical field data based on the real-time data requirement of the electrical equipment virtual debugging system includes: In a case where the target physical field data belongs to the data required by the real-time data, determining that the color identification of the target physical field data is a first color type; In a case where the target physical field data does not belong to the data required by the real-time data, the color identification of the target physical field data is determined to be a second color type.

6. The method according to any one of claims 2 to 5, characterized in that: Also includes: In the target physical field data belonging to the same color type, obtaining the parameter value size of the target physical field data having the same data type; the data type includes electrical parameters, temperature data, vibration data and airflow data; The color gradient of the corresponding target physical field data is determined based on the parameter value; wherein, as the parameter value increases successively, the color gradient increases or decreases successively.

7. The method according to claim 1, characterized in that The determining the color identification of each target physical field data based on the characteristic information includes: Acquire the parameter value of the target physical field data having the same data type; the data type includes electrical parameters, temperature data, vibration data and airflow data; For the target physical field data of the same data type, the color gradient of the corresponding target physical field data is determined based on the size of the parameter value; wherein, as the size of the parameter value increases successively, the color gradient increases or decreases successively.

8. The method according to claim 7, characterized in that Also includes: The color type of the target physical field data is determined based on the data type of the target physical field data.

9. The method according to claim 1, characterized in that: The determining the data exchange priority of each target physical field data based on the color identification includes: As the color types of the target physical field data correspond to the clockwise arrangement in the color wheel, the data exchange priority of the target physical field data increases or decreases in sequence; As the color gradient of the target physical field data increases sequentially, the data exchange priority of the target physical field data increases sequentially or decreases sequentially.

10. A virtual debugging system for electrical equipment, characterized in that: It includes a physical field model module and a data management and exchange layer; the physical field model in the physical field model module includes an electromagnetic field model, a thermal field model, a mechanical field model and a fluid field model; The data management and exchange layer is used to realize the transmission of each target physical field data between multiple physical field models based on the data exchange priority of each target physical field data in the physical field model module; The data exchange priority of the target physical field data is determined by the method for determining the data exchange priority in the electrical equipment virtual debugging system according to any one of claims 1 to 9.

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