A method and system for simulating and modeling the temperature field of an electrical control cabinet

Abstract

This application relates to the field of simulation modeling technology, and particularly to a simulation modeling method and system for the temperature field of an electric control cabinet. The method includes: first, performing basic simulation modeling on the electric control cabinet; then setting relevant dynamic parameters and temperature values therein; obtaining fitting coefficients by fitting according to the dynamic parameter sequence and temperature value of each node, and obtaining feature importance scores and out-of-bag errors based on the random forest algorithm; combining the three to obtain dynamic sensitivity intensity; obtaining the coupled heat conduction factor of the node based on the p-value between one node and all other nodes, the transfer entropy of temperature, the spatial distance, and the dynamic sensitivity intensity; obtaining the predicted temperature at the next moment through the temperature sequence, and using the coupled conduction factor to weight and obtain the corrected temperature in combination with the current temperature, thereby completing the real-time simulation of the temperature field. This application significantly improves the accuracy and adaptability of the simulation of the temperature field of the electric control cabinet, and solves the problem of working condition deviation caused by static assumptions in traditional simulation models.

Description

Technical Field

[0001] This application relates to the technical field of simulation modeling, and particularly to a method and system for simulating and modeling the temperature field of an electrical control cabinet. Background Art

[0002] Electrical control cabinets are used to centrally arrange electrical components and control devices, and to achieve functions such as starting, stopping, protecting, and monitoring equipment such as motors. Their structural design needs to consider issues such as heat dissipation and electromagnetic compatibility to ensure the stable operation of the system. Temperature field simulation modeling uses computer technology to establish a mathematical model and perform numerical simulations based on information such as the geometric structure, material properties, and heat source distribution of the electrical control cabinet, in order to predict the temperature distribution inside the electrical control cabinet. Through simulation modeling, potential heat dissipation problems can be discovered in advance during the design stage, the heat dissipation scheme can be optimized, the number of tests and costs in actual production can be reduced, the product research and development cycle can be shortened, and an efficient and low-cost means for the analysis and optimization of complex systems is provided.

[0003] During the actual operation of the electrical control cabinet, dynamic factors such as load fluctuations and changes in environmental temperature and humidity will cause rapid changes in the internal temperature field. Traditional simulation models are difficult to accurately reflect the actual working conditions due to the lack of a real-time data correction mechanism. For example, load fluctuations will cause changes in the heating power of the components inside the electrical control cabinet, and changes in environmental temperature and humidity will affect the heat dissipation efficiency. The combined effect of these factors makes the traditional simulation model have a large error in predicting the internal temperature field of the electrical control cabinet, resulting in design deviations and operation and maintenance risks. Summary of the Invention

[0004] In order to solve the technical problem of large design deviations in the temperature field caused by the lack of real-time data correction, this application provides a method and system for simulating and modeling the temperature field of an electrical control cabinet. The specific technical solutions adopted are as follows:

[0005] In a first aspect, this application proposes a method for simulating and modeling the temperature field of an electrical control cabinet. The method includes the following steps:

[0006] Model the electrical control cabinet through modeling software, and perform model simulation based on the simulation parameters of the electrical control cabinet to obtain a basic simulation model;

[0007] Generate random values of dynamic parameters within a fixed range at each moment in the basic simulation model, and form sequences of all data of each dynamic parameter up to the current moment; After discretizing the model, use the heat conduction principle to obtain the temperature value of each node at each moment, forming a temperature sequence; The dynamic parameters include environmental temperature, environmental humidity, and gas flow rate.

[0008] For each dynamic parameter sequence and temperature sequence of each node, fit them to a fitting equation to obtain their fitting coefficients; for all dynamic parameters, use the random forest algorithm to obtain their feature importance scores and out-of-bag errors respectively; based on all the fitting coefficients, feature importance scores of the dynamic parameters combined with the out-of-bag errors, obtain the dynamic sensitivity intensity.

[0009] Input the temperature sequences of any two nodes, and based on the test algorithm, output the p-value; calculate the transfer entropy of the temperature sequences of the two nodes; calculate the coupling heat conduction factor of a node by combining the mean of the p-value, spatial distance, and corresponding transfer entropy of the node with the remaining all nodes and the dynamic sensitivity coefficient of the node.

[0010] Based on the temperature sequence, predict the predicted temperature of the node at the next moment through the model; use the normalized coupling heat conduction factor as the weight to weight the temperature of the node at the current moment and the predicted temperature at the next moment to obtain the corrected temperature at the next moment; replace the temperature field temperature with the corrected temperature of all nodes at the next moment to realize the real-time simulation of the temperature field in a dynamic environment.

[0011] In the above solution, the present application integrates multiple linear regression and random forest algorithms, quantifies the linear influence and non-linear pattern of environmental parameters on a single node, constructs a conduction network through transfer entropy and Granger causality test, reveals the directionality and intensity of heat conduction between nodes, and solves the limitations of traditional methods in the fuzzy identification of sensitive nodes and the dependence on empirical assumptions for conduction paths; combined with the ARIMA prediction correction model, it realizes the real-time iterative optimization of the temperature field. By dynamically weighting and fusing the current temperature and the predicted temperature, it effectively compensates for the cumulative errors caused by mesh discretization and idealization of material properties in traditional finite element simulations. That is, through dynamic modeling and multi-dimensional data fusion correction mechanisms, it significantly improves the accuracy and adaptability of the temperature field simulation of the electric control cabinet, and solves the problem of working condition deviation caused by static assumptions in traditional simulation models.

[0012] In one embodiment, the simulation parameters include thermal conductivity, surface radiation emissivity, heat source position, heat source power, and surface convection coefficient.

[0013] In one embodiment, the value range of the environmental temperature is 21~25°C, the value range of the environmental humidity is 45~55%, and the value range of the air flow velocity is 0.5~2 m / s.

[0014] In one embodiment, the method of fitting each dynamic parameter sequence and temperature sequence of each node to a fitting equation to obtain their fitting coefficients is as follows:

[0015] Set the environmental temperature, environmental humidity, and gas flow velocity of each node as independent variables, and the temperature sequence as the dependent variable, output the fitting equation through the multi-source linear regression algorithm, and obtain the fitting coefficients of each independent variable.

[0016] In one embodiment, the dynamic sensitivity intensity is positively correlated with the feature importance score and the fitting coefficient, and negatively correlated with the out-of-bag error.

[0017] In one embodiment, the method of inputting the temperature sequences of any two nodes and outputting the p-value based on the test algorithm is as follows:

[0018] Denote any one node as the target point, and use the temperature sequence of the target point and another node as the input; set the null hypothesis that the historical temperature value of the target point has no predictive ability for the current temperature of another node, and the alternative hypothesis that the historical temperature value of the target point has predictive ability for the current temperature of another node; use the Granger causality test algorithm to output the p-value between the two.

[0019] In one embodiment, the coupling heat conduction factor is negatively correlated with the p-value and the spatial distance, and positively correlated with the transfer entropy and the dynamic sensitivity intensity.

[0020] In one embodiment, the expression of the coupling heat conduction factor is:

[0021] , represents the p-value between the i-th node and the j-th node, represents the spatial distance between the i-th node and the j-th node, represents the transfer entropy between the temperature sequences of the i-th node and the j-th node, represents the exponential function with the natural constant as the base, N represents the number of nodes, represents the dynamic sensitivity intensity of the i-th node, represents the coupling heat conduction factor of the i-th node.

[0022] In one embodiment, the method of using the normalized coupling heat conduction factor as a weight to weight the temperature of the node at the current moment and the predicted temperature at the next moment to obtain the corrected temperature at the next moment is as follows:

[0023] , represents the temperature of the i-th node at the current moment, represents the predicted temperature of the i-th node at the next moment, represents the coupling heat conduction factor of the i-th node, represents the corrected temperature of the i-th node at the next moment, is the normalization function.

[0024] In a second aspect, an embodiment of the present application further provides an electric control cabinet temperature field simulation and modeling system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned electric control cabinet temperature field simulation and modeling methods are implemented.

[0025] The beneficial effects of the present application are as follows:

[0026] The present application combines multiple linear regression and random forest algorithms to quantify the linear impact and non-linear pattern of environmental parameters on a single node, and constructs a conduction network through transfer entropy and Granger causality test to reveal the directionality and intensity of heat conduction between nodes, solving the limitations of traditional methods in the fuzzy identification of sensitive nodes and the dependence on empirical assumptions for conduction paths; combined with the ARIMA prediction correction model, real-time iterative optimization of the temperature field is achieved. By dynamically weighting and fusing the current temperature and the predicted temperature, the cumulative errors caused by mesh discretization and idealized material properties in traditional finite element simulations are effectively compensated. That is, through dynamic modeling and multi-dimensional data fusion correction mechanisms, the accuracy and adaptability of the electric control cabinet temperature field simulation are significantly improved, and the problem of working condition deviation caused by static assumptions in traditional simulation models is solved. Description of the Drawings

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a flowchart of an electric control cabinet temperature field simulation and modeling method provided by an embodiment of the present application. Detailed Embodiments

[0029] To further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following describes in detail the specific embodiments, structures, features, and effects of an electric control cabinet temperature field simulation and modeling method and system proposed according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art belonging to the technical field of the present application.

[0031] An Embodiment of a Method and System for Simulating and Modeling the Temperature Field of an Electric Control Cabinet

[0032] The following specifically describes the specific solutions of a method and system for simulating and modeling the temperature field of an electric control cabinet provided by this application with reference to the accompanying drawings.

[0033] Please refer to Figure 1 , which shows a flowchart of a method for simulating and modeling the temperature field of an electric control cabinet provided by an embodiment of this application. The method includes the following steps:

[0034] Step S001, perform basic simulation modeling on the electric control cabinet.

[0035] First, use 3D modeling software to create a 3D model based on the design drawing of the electric control cabinet, and simplify secondary details. For example, complex components are equivalent to regular geometric bodies, such as ignoring screws, small holes, etc., and retaining key heat dissipation characteristics. The 3D modeling software used can be SolidWorks for modeling.

[0036] For the established 3D model, perform mesh division to discretize the model into several small subdomains. It should be noted that the type and size of the mesh are determined according to the complexity of the model and the requirements of simulation accuracy. In this embodiment, tetrahedral meshes are used to divide the geometric model.

[0037] Set the simulation parameters of the electric control cabinet in the modeling to simulate the model, including the high thermal conductivity of the metal cabinet body and the surface radiation emissivity of the components. In this embodiment, the aluminum electric control cabinet body used has a thermal conductivity of 237 W / m·K; its surface radiation emissivity is 0.8; in addition, the position and power of the heat sources inside the electric control cabinet also need to be simulated. The heat sources mainly include the losses of electrical components, such as resistance losses, inductance losses, and switching losses of switching devices, etc. Calculate their heat according to the electrical parameters and working states of the components, and set them as corresponding heat source terms, and distribute the heat sources to the corresponding surfaces, thereby obtaining the heat generated by each heat source; then considering the heat dissipation method of the electric control cabinet and the surrounding environmental conditions, set the corresponding boundary conditions. Common boundary conditions include convective heat transfer boundary conditions, radiative heat transfer boundary conditions, and adiabatic boundary conditions, etc. In this embodiment, convective heat transfer boundary conditions are adopted, and the convective coefficient on the cabinet surface is determined to be 3 - 10 W / m²·K.

[0038] So far, the basic simulation modeling of the temperature field of the electric control cabinet is obtained.

[0039] Step S002, set relevant dynamic parameters and temperature values in the basic simulation modeling.

[0040] Traditional simulation requires the assumption of constant load, environmental temperature, humidity, and air flow velocity. However, in actual operation, electrical control cabinets often face dynamic interferences such as sudden load changes (e.g., motor start and stop), day-night temperature difference, humidity fluctuations, and changes in fan speed, resulting in a significant time-varying characteristic of the temperature field. Ignoring these dynamic factors will underestimate the local high-temperature risk or overestimate the heat dissipation efficiency. Through dynamic modeling, the thermal inertia effect, temperature response delay, and multi-parameter coupling effect can be accurately captured, thus optimizing the heat dissipation design;

[0041] The dynamic load is achieved through random power injection, and its fluctuation follows a normal distribution. Based on the central limit theorem, the load power varies randomly around the calibration value, which is the rated power. A random value within the range of mean ± 3σ is generated for each time step, and the heat source power is updated in real-time through the built-in function of the simulation software. In this embodiment, the built-in function used is the randmnormal function of COMSOL, and for the 10kW frequency converter load setting, the power fluctuates randomly between 8.5kW and 11.5kW.

[0042] Using the same generation method as the dynamic load fluctuation, random temperature, humidity, and air flow velocity are generated. Specifically, to ensure that the electrical control cabinet is in a good ideal working environment, the calibration value ranges of the environmental temperature and humidity are 21~25°C and 45~55% respectively, and the specific values in this embodiment are 25°C and 50%; similarly, to ensure the heat dissipation requirements inside the electrical control cabinet and the change in fan speed, the value range of the air flow velocity is 0.5~2m / s, and the specific value in this embodiment is 1m / s.

[0043] Define a fixed time step in the transient simulation settings; in this embodiment, the time step takes an empirical value of 0.1s, that is, the load, temperature, humidity, and air flow velocity all generate random values within the range at a time step of 0.1s. Use the field variable monitoring function of the simulation software to synchronously record the instantaneous values of the load power, environmental temperature, environmental humidity, and air flow velocity at each time step; and associate a unified time stamp. Subsequently, the load power, environmental temperature, environmental humidity, and air flow velocity recorded from the initial acquisition time to the current time are subjected to overall normalization processing to obtain the load power sequence, environmental temperature sequence, environmental humidity sequence, and gas flow velocity sequence.

[0044] According to the basic principle of heat conduction, the heat generated by the heat source, the thermal conductivity of the material and the heat conduction process in space are comprehensively considered to establish a heat conduction equation that describes the heat transfer inside the electric control cabinet. Then, the finite element analysis method is used to discretize the complex geometric model into many small units. Each node in the model is used as the basic point for solving the temperature. In each time step, according to the change of the power of each heat source, the heat conduction equation is numerically solved by the finite element analysis software to obtain the temperature value of each node at that moment. As the simulation time progresses, the above solution process is repeated continuously, and finally the temperature distribution of each node inside the electric control cabinet at each moment is obtained, thereby obtaining the temperature field.

[0045] For each node, the temperature values ​​recorded by the node from the initial collection time to the current time constitute the temperature sequence of the node.

[0046] So far, the load power sequence, ambient temperature sequence, ambient humidity sequence, gas flow rate sequence and the temperature sequence of each node have been obtained.

[0047] Step S003, obtain the fitting coefficient according to the dynamic parameter sequence and temperature value of each node, and obtain the feature importance score and out-of-bag error based on the random forest algorithm; the three are combined to obtain the dynamic sensitivity strength.

[0048] For each node, changes in data such as ambient temperature, ambient humidity, and airflow velocity will have a significant impact on its temperature field. When the ambient temperature rises, the node heat dissipation becomes more difficult and the temperature rises accordingly. When the ambient temperature decreases, it helps the node to dissipate heat and lowers its temperature. Humidity changes affect the thermal conductivity of the air. When the humidity increases, the thermal conductivity of the air deteriorates, and the node heat dissipation efficiency decreases. When the temperature rises, the humidity decreases, which is conducive to heat dissipation and the node temperature decreases. Changes in airflow velocity change the intensity of convective heat transfer. When the airflow velocity increases, the convective heat transfer is enhanced, the node heat dissipation efficiency is improved, and when the temperature decreases, the airflow velocity decreases, the heat dissipation efficiency decreases, and the node temperature increases. The combined effect of these data makes the node temperature field present a complex dynamic change characteristic.

[0049] Based on the above analysis, taking node i as an example, the ambient temperature sequence, ambient humidity sequence, air flow velocity sequence and temperature sequence of node i are taken as input, the ambient temperature, ambient humidity and gas flow velocity are set as independent variables, and the temperature is set as the dependent variable, and the fitting equation is obtained by fitting. In this embodiment, the multivariate linear regression algorithm is used to output the fitting equation, and the fitting coefficients of each independent variable are obtained, which are respectively recorded as The ambient temperature, ambient humidity and air flow velocity are uniformly recorded as dynamic parameters; then the ambient temperature sequence, ambient humidity sequence and air flow velocity sequence are used as input, and the feature importance score of each dynamic parameter is obtained using the random forest algorithm, which are recorded as , and obtain the out-of-bag error a through the steps in the random forest algorithm. The fitting coefficients respectively represent the linear influence intensity of each dynamic parameter on the node temperature, while the importance score reflects the non-linear influence intensity of different dynamic parameters on the node temperature. The out-of-bag error characterizes the prediction error of the random forest model on the samples not participating in the training.

[0050] Calculate the dynamic sensitivity intensity of each node through the feature importance score, fitting coefficient and out-of-bag error of different dynamic parameters; the dynamic sensitivity intensity is positively correlated with the feature importance score and fitting coefficient, and negatively correlated with the out-of-bag error.

[0051] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the change directions of the two variables are the same. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large; the specific relationship is determined by the actual application, and this application does not make special restrictions.

[0052] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the change directions of the two variables are opposite. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small; the specific relationship is determined by the actual application, and this application does not make special restrictions.

[0053] Preferably, in this embodiment, the expression of the dynamic sensitivity intensity is:

[0054] , represents the fitting coefficient of the nth dynamic parameter, represents the feature importance score of the nth dynamic parameter, represents the out-of-bag error in the random forest algorithm, represents the exponential function with the natural constant as the base, represents the temperature dynamic sensitivity intensity of the node.

[0055] Among them, the out-of-bag error represents the prediction error of the model on the samples not participating in the training, reflecting the generalization ability of the model to the relationship between dynamic environmental parameters and the temperature field. The larger its value, the more complex non-linear relationship or noise interference between the environmental parameters and the node temperature that is not captured by the model, and the less reliable the subsequent calculation of the sensitivity of the node temperature to the environmental parameters; and the greater the linear influence intensity and non-linear influence intensity of the dynamic parameter, the stronger the contribution of the dynamic parameter to the dynamic sensitivity of the node temperature, and the more significant the influence of the node temperature on environmental fluctuations.

[0056] The dynamic sensitivity characterizes the comprehensive sensitivity intensity of the temperature field of a node to dynamic parameters. The larger its value, the more easily the temperature of the node is affected by environmental fluctuations, and it may cause local overheating or unstable heat dissipation due to dynamic load, temperature and humidity changes, or air flow speed fluctuations.

[0057] Thus, the dynamic sensitivity intensities of each node are obtained.

[0058] Step S004: Obtain the coupling heat conduction factor of a node based on the p-value, transfer entropy of temperature, spatial distance, and dynamic sensitivity intensity between one node and all the other nodes.

[0059] In the temperature field simulation of the electrical control cabinet, heat is transferred between nodes through heat conduction. When the temperature of adjacent nodes is higher, heat will be transferred to the nodes with lower temperature, causing the temperature of that node to rise; conversely, if the temperature of adjacent nodes is lower, it will absorb the heat of that node, resulting in a decrease in its temperature. This conduction effect does not exist in isolation but is the result of the combined action of multiple nodes. For example, inside the electrical control cabinet, the nodes close to the heat source will have their temperatures increased due to the heat generated by the heat source, while the nodes far from the heat source balance their temperatures through heat conduction with the surrounding nodes. At the same time, the thermal conductivity of different materials will also affect the conduction effect between nodes. Materials with high thermal conductivity will accelerate the heat transfer between nodes, making the temperature distribution more uniform or change rapidly. In addition, the geometric position of the nodes and the surrounding environmental conditions will also affect the conduction effect. For example, nodes located in positions with good heat dissipation will affect the temperature fields of themselves and surrounding nodes through conduction with other nodes and heat dissipation methods such as convection and radiation with the environment.

[0060] Based on the above analysis, any one node is denoted as the target point, and the temperature sequences of the target point and any one node other than the target point are used as inputs, and the p-value is output based on the test algorithm. Preferably, in this embodiment, the null hypothesis is set that the historical temperature value of the target point has no predictive ability for the current temperature of any one node other than the target point, and the alternative hypothesis is that the historical temperature value of the target point has predictive ability for the current temperature of any one node other than the target point. No predictive ability indicates no conduction path, and having predictive ability indicates the existence of a conduction path. The Granger causality test algorithm is used to output the p-value between the two.

[0061] Calculate the spatial distance between any two nodes. In this embodiment, the Euclidean distance is used to represent it, and the temperature sequences of the two nodes are used as inputs to calculate the transfer entropy between the temperature sequences of the two nodes.

[0062] Obtain the coupling heat conduction factor of the target point based on the p-value, spatial distance, and transfer entropy between the target point and all nodes other than the target point, combined with the dynamic sensitivity intensity of the target point.

[0063] The coupling heat conduction factor is negatively correlated with the p-value and the spatial distance, and positively correlated with the transfer entropy and the dynamic sensitivity intensity.

[0064] Preferably, in this embodiment, the expression of the coupling heat conduction factor of the target point is:

[0065] , represents the p-value between the i-th node and the j-th node, represents the spatial distance between the i-th node and the j-th node, represents the transfer entropy between the temperature sequences of the i-th node and the j-th node, represents the exponential function with the natural constant as the base, N represents the number of nodes, represents the dynamic sensitivity intensity of the i-th node, represents the coupling heat conduction factor of the i-th node; the i-th node is the target point.

[0066] Among them, the dynamic sensitivity intensity is used to measure the sensitivity of the temperature fluctuation of the node to the change of environmental parameters. The larger its value, the stronger the sensitivity of the node itself, and its conduction coupling effect is amplified; the p-value measures the strength of the heat conduction path corresponding to the temperatures of two nodes. The larger its value, the weaker the significance of the existence of the heat path conduction; the spatial distance reflects the spatial proximity of two nodes. The farther the two nodes are apart, the less likely heat conduction is to occur; the transfer entropy quantifies the amount of information transfer between two nodes, reflecting the direction and strength of heat conduction between nodes. The larger its value, the stronger the heat conduction information flow, that is, when the non-target point is the heat source, its temperature fluctuation will affect the temperature of the target point through the conduction path, avoiding the confusion of bidirectional conduction.

[0067] The larger the value of the coupling conduction factor, the more it characterizes that the node is both a sensitive node, vulnerable to environmental fluctuations; and a conduction hub, and the temperature fluctuation diffuses through the conduction path, indicating that the node is more likely to be a node close to the heat source and connecting multiple high heat conduction paths, and is more likely to cause a chain temperature fluctuation due to a load mutation.

[0068] Thus, the coupling heat conduction factor of each node is obtained.

[0069] Step S005, obtain the predicted temperature at the next moment through the temperature sequence, and use the coupling conduction factor to weight and obtain the corrected temperature in combination with the current moment temperature, so as to complete the real-time simulation of the temperature field.

[0070] Normalize the coupling heat conduction factors of all nodes. In this embodiment, the softmax function is used for normalization, making the sum of the coupling heat conduction factors of all nodes at the current moment equal to 1. Input the temperature sequence of the nodes, use the ARIMA model to predict the predicted temperature of the nodes at the next moment, and use the coupling heat conduction factors of the nodes as weights to weight the predicted temperature at the next moment and the temperature at the current moment to obtain the corrected temperature at the next moment.

[0071] Preferably, in this embodiment, the expression of the corrected temperature is:

[0072] , represents the temperature of the i-th node at the current moment, represents the predicted temperature of the i-th node at the next moment, represents the coupling heat conduction factor of the i-th node, represents the corrected temperature of the i-th node at the next moment, is the normalization function.

[0073] According to the time change, calculate the corrected temperature at the next moment, and replace the temperature of the current temperature field with it to realize the real-time simulation of the temperature field in a dynamic environment.

[0074] Based on the same inventive concept as the above method, an electric control cabinet temperature field simulation and modeling system is also provided in an embodiment of the present invention, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for simulating and modeling the temperature field of an electric control cabinet.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

[0076] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for simulating and modeling the temperature field of an electric control cabinet, characterized in that The method includes the following steps: Model the electrical control cabinet using modeling software, and perform model simulation based on the simulation parameters of the electrical control cabinet to obtain a basic simulation model; Generate random values of dynamic parameters within a fixed range at each moment in the basic simulation model. All data of each dynamic parameter from the initial acquisition moment to the current moment form their respective sequences. After discretizing the model, use the heat conduction principle to obtain the temperature value of each node at each moment, forming a temperature sequence. The dynamic parameters include ambient temperature, ambient humidity, and gas flow rate; Fit the dynamic parameter sequence and temperature sequence of each node into a fitting equation respectively to obtain their fitting coefficients. Use the random forest algorithm for all dynamic parameters to obtain their feature importance scores and out-of-bag errors respectively. Based on all the fitting coefficients, feature importance scores of the dynamic parameters combined with the out-of-bag errors, obtain the dynamic sensitivity intensity; Input the temperature sequences of any two nodes, and output the p-value based on the test algorithm. Calculate the transfer entropy of the temperature sequences of the two nodes. Calculate the coupling heat conduction factor of a node by combining the mean value of the p-value, spatial distance, and corresponding transfer entropy between one node and all the other nodes with the dynamic sensitivity intensity of the one node; Predict the predicted temperature of the node at the next moment based on the temperature sequence through the model. Use the normalized coupling heat conduction factor as the weight to weight the temperature of the node at the current moment and the predicted temperature at the next moment to obtain the corrected temperature at the next moment. Replace the temperature of the temperature field with the corrected temperature of all nodes at the next moment to realize the real-time simulation of the temperature field in a dynamic environment; 2. The method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, wherein The simulation parameters include thermal conductivity, surface radiation emissivity, heat source position, heat source power, and surface convection coefficient; 3. A method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, characterized in that, The value range of the ambient temperature is 21~25°C, the value range of the ambient humidity is 45~55%, and the value range of the air flow velocity is 0.5~2 m / s; 4. A method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, characterized in that, The method of respectively fitting the dynamic parameter sequence and temperature sequence of each node into a fitting equation to obtain their fitting coefficients is as follows: Set the ambient temperature, ambient humidity, and gas flow rate of each node as independent variables, and the temperature sequence as the dependent variable. Output the fitting equation through the multi-source linear regression algorithm to obtain the fitting coefficients of each independent variable; 5. A method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, characterized in that, The dynamic sensitivity intensity has a positive correlation with the feature importance score and the fitting coefficient, and a negative correlation with the out-of-bag error; 6. The method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, wherein, The method of inputting the temperature sequences of any two nodes and outputting the p-value based on the test algorithm is as follows: Denote any one node as the target point, and use the temperature sequences of the target point and another node as the input. Set the null hypothesis as that the temperature history value of the target point has no predictive ability for the current temperature of the other node, and the alternative hypothesis as that the temperature history value of the target point has predictive ability for the current temperature of the other node. Use the Granger causality test algorithm to output the p-value between the two; 7. The method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, wherein The coupling heat conduction factor has a negative correlation with the p-value and spatial distance, and a positive correlation with the transfer entropy and dynamic sensitivity intensity; 8. The method for simulating and modeling the temperature field of an electric control cabinet according to claim 7, characterized in that, The expression of the coupling heat conduction factor is: , represents the p-value between the i-th node and the j-th node, represents the spatial distance between the i-th node and the j-th node, represents the transfer entropy between the temperature sequences of the i-th node and the j-th node, represents the exponential function with the natural constant as the base, and N represents the number of nodes, represents the dynamic sensitivity intensity of the i-th node, represents the coupling heat conduction factor of the i-th node.

9. The method for simulating and modeling the temperature field of an electric control cabinet according to claim 1, wherein, The method of using the normalized coupling heat conduction factor as the weight to weight the temperature of the node at the current moment and the predicted temperature at the next moment to obtain the corrected temperature at the next moment is as follows: , represents the temperature of the i-th node at the current moment, represents the predicted temperature of the i-th node at the next moment, represents the coupled heat conduction factor of the i-th node, represents the corrected temperature of the i-th node at the next moment, is the normalization function.

10. An electric control cabinet temperature field simulation and modeling system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for simulating and modeling the temperature field of an electric control cabinet as described in any one of claims 1-9.

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