Electrical control cabinet and operation temperature control method

Abstract

The invention discloses an electrical control cabinet and an operation temperature control method, and relates to the technical field of electrical control cabinets, the system discloses a cabinet body, an air cooling system, a liquid cooling system, a compression type refrigeration system and an operation temperature controller are arranged in the cabinet body, and the operation temperature controller comprises an operation temperature monitoring unit, an electrical parameter mapping unit and an operation temperature control execution unit. Through the design and operation temperature control method of the electrical control cabinet, the temperature characteristics of each monitoring point in the electrical control cabinet can be predicted in advance, the multi-physical field simulation of the virtual control cabinet is combined, multiple different types of active heat dissipation systems are matched, and the multiple types of active heat dissipation systems are freely and cooperatively combined based on the virtual cabinet simulation. The capability limitation of a single heat dissipation mode is broken through, and the operation temperature of the electrical control cabinet is reasonably controlled while advanced regulation and control of the temperature of the cabinet body are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical control cabinets, and more particularly to an electrical control cabinet and an operating temperature control method. Background Art

[0002] Electrical control cabinets are core components in industrial automation, new energy equipment, and other fields. Their temperature control performance directly impacts equipment reliability and lifespan. Traditional temperature control technologies primarily rely on a single heat dissipation method: early cooling primarily relied on natural air cooling, with heat convection achieved through cabinet vents. However, this efficiency was significantly insufficient in scenarios with power densities ≥50W / cm². With the increasing use of high-power devices, active heat dissipation technologies such as liquid cooling and compression refrigeration have become widely adopted.

[0003] However, existing technologies typically operate independently, lacking a coordinated mechanism for optimal energy efficiency. For example, while existing technologies have proposed combining air cooling with semiconductor cooling, they lack a dynamic switching strategy, leading to energy waste exceeding 30% in load fluctuation scenarios. Furthermore, traditional cooling designs rely heavily on engineers' experience, employing a trial-and-error approach to adjust parameters like fan speed and liquid cooling flow. This results in development cycles lasting several months and struggles to address complex scenarios involving multi-physics coupling (e.g., fluid-heat-solid).

[0004] Based on the above content, the present invention proposes an electrical control cabinet and an operation temperature control method. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention aims to provide an electrical control cabinet and an operating temperature control method.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An electrical control cabinet comprises a cabinet body, wherein an air cooling system, a liquid cooling system, a compression refrigeration system, and an operation temperature controller are arranged in the cabinet body, wherein the operation temperature controller comprises an operation temperature monitoring unit, an electrical parameter mapping unit, and an operation temperature control execution unit;

[0008] The operating temperature monitoring unit is used to continuously generate a time series temperature set of each temperature monitoring point during the operation of the electrical control cabinet;

[0009] The electrical parameter mapping unit is used to determine the predicted temperature characteristics of each temperature monitoring point and establish a parameter mapping mechanism between the predicted temperature characteristics of each temperature monitoring point and the virtual electrical control cabinet;

[0010] The temperature control execution unit is used to select a temperature control solution to control the temperature of the cabinet.

[0011] Furthermore, a method for controlling the operating temperature of an electrical control cabinet comprises the following steps:

[0012] Step 1: During the operation of the electrical control cabinet, continuously obtain the temperature data of each temperature monitoring point in the cabinet, and continuously generate a time series temperature set for each temperature monitoring point;

[0013] Step 2: Generate a time series temperature set for each temperature monitoring point each time and determine the predicted temperature characteristics of each temperature monitoring point;

[0014] Step 3: Establish a mapping mechanism between the predicted temperature characteristics of each temperature monitoring point and the parameters of the virtual electrical control cabinet;

[0015] Step 4: Obtain the comprehensive feedback value of each temperature control matching solution, and execute the temperature control matching solution with the largest comprehensive feedback value to control the temperature of the cabinet.

[0016] Furthermore, the continuous generation method of the time series temperature set of the temperature monitoring point is: every time a temperature monitoring point obtains a temperature data, the previously continuously obtained i-1 temperature data are synchronously collected, and the i temperature data are integrated into the time series temperature set in the form of a time series set.

[0017] Furthermore, the predicted temperature characteristics of the temperature monitoring point are determined as follows: whenever a temperature monitoring point generates a time series temperature set, the time series temperature set is input into the temperature characteristic prediction model corresponding to the temperature monitoring point, and the temperature characteristic prediction model outputs the predicted temperature characteristics of the temperature monitoring point.

[0018] Furthermore, the method for obtaining the comprehensive feedback value of the temperature control matching scheme is as follows: select a temperature control matching scheme, start all active cooling systems included in the temperature control matching scheme in the virtual electrical control cabinet, and control the virtual electrical control cabinet to run for T time. After the operation is completed, calculate the average temperature performance value, calculate the ratio of the average temperature performance value to the total power consumption of all active cooling systems, and calculate the comprehensive feedback value of the temperature control matching scheme.

[0019] Furthermore, the average temperature performance value is calculated by determining the temperature characteristics of each temperature monitoring point in the virtual electrical control cabinet, importing the temperature characteristics of each temperature monitoring point into the temperature characteristic analytical model in turn, then determining the temperature performance value of each temperature monitoring point in turn, and summing and averaging the temperature performance values ​​of each temperature monitoring point to calculate the average temperature performance value.

[0020] Furthermore, the temperature feature analysis model is constructed by collecting multiple temperature features, constructing a neural network model, using the temperature features as basic data, training the neural network model, and assigning a temperature performance value to each temperature feature. The larger the temperature performance value, the better the temperature feature performance. Finally, a temperature feature analysis model is constructed.

[0021] Furthermore, each temperature monitoring point corresponds to a temperature feature prediction model. Each temperature feature prediction model is built based on the LSTM model, and each temperature feature prediction model is updated regularly.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention can make advance predictions on the temperature characteristics of each monitoring point in the electrical control cabinet through the design and operation temperature control method of the electrical control cabinet. Combined with the multi-physical field simulation of the virtual control cabinet and equipped with multiple different types of active cooling systems, the multiple types of active cooling systems can be freely coordinated and combined based on the virtual cabinet simulation, breaking through the capability limitation of a single cooling mode. While realizing advance control of the cabinet temperature, the electrical control cabinet can be reasonably controlled in terms of operating temperature. In this way, a closed-loop intelligent temperature control system of "data-driven prediction-physical simulation verification-energy efficiency optimization decision-making" is constructed, which realizes the technological leap from static design relying on empirical rules to dynamic optimization driven by digital twins, and significantly improves the temperature stability, energy efficiency and reliability of the operation of complex electrical control cabinets. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for controlling the operating temperature of an electrical control cabinet;

[0025] Figure 2 This is the internal structure diagram of the electrical control cabinet.

[0026] 100, cabinet; 200, air cooling system; 300, liquid cooling system; 400, compression refrigeration system; 500, operating temperature controller. DETAILED DESCRIPTION

[0027] Example 1: Reference Figure 1 , a method for controlling the operating temperature of an electrical control cabinet, the steps are as follows:

[0028] Step 1: During the operation of the electrical control cabinet, temperature data of each temperature monitoring point in the cabinet 100 is continuously acquired (temperature monitoring points are arranged according to the size of the cabinet and the heating characteristics of the components, such as near the heating components, at the inlet and outlet of the air duct, and in the corners of the cabinet. "Continuous acquisition" means continuous collection of high-frequency words (usually ≥1 time / second)), and a time series temperature set of each temperature monitoring point is continuously generated;

[0029] The continuous generation method of the time series temperature set of the temperature monitoring point is as follows: whenever a temperature monitoring point obtains a temperature data, the previously continuously obtained i-1 temperature data are synchronously collected, and the i temperature data are integrated into the time series temperature set in the form of a time series set.

[0030] Step 2: Generate a time series temperature set for each temperature monitoring point each time and determine the predicted temperature characteristics of each temperature monitoring point;

[0031] The method for determining the predicted temperature characteristics of the temperature monitoring point is as follows: whenever a temperature monitoring point generates a time series temperature set, the time series temperature set is input into the temperature characteristic prediction model corresponding to the temperature monitoring point, and the temperature characteristic prediction model outputs the predicted temperature characteristics of the temperature monitoring point;

[0032] Each temperature monitoring point corresponds to a temperature feature prediction model (for example, if temperature monitoring points A, B, C, D, and E are provided in the cabinet 100, there are five temperature feature prediction models in total). Each temperature feature prediction model is constructed based on the LSTM model, and each temperature feature prediction model is updated regularly. In this embodiment, taking temperature monitoring point A as an example, the construction method of the temperature feature prediction model corresponding to temperature monitoring point A is disclosed: all time series temperature sets generated by temperature monitoring point A are collected (to ensure the integrity and accuracy of the data), an LSTM model is constructed, and the time series temperature sets are divided into a training set, a validation set, and a test set in a ratio of 70%:15%:15%. Ensure the continuity of the time series when splitting the data to avoid future information leakage into the training set, extract basic features from each time series temperature data, such as average temperature, temperature extremes (maximum temperature, minimum temperature), temperature change rate, etc., and select the LSTM model as the prediction model (model structure design: Input layer: Determine the dimension of the input layer based on the results of feature engineering. For example, if the temperature data of the past 24 hours is used as input, and three features such as average temperature, temperature extremes, and temperature change rate are extracted at each time point, the input layer dimension is 24*3=72. Hidden layer: Design the LSTM hidden layer, including the number of LSTM units and the number of stacked layers. The optimal hidden layer structure can be determined through experiments. Output layer: The output layer should be the predicted temperature feature, which includes the average temperature, temperature extremes, temperature change rate, etc. within the prediction time period T. The dimension of the output layer should match the number of predicted features), select a suitable loss function (such as mean square error MSE) and optimizer (such as Adam), compile the model, and set the number of training rounds (epochs), batch size (batch size) and other hyperparameters, use the training set to train the model, and update the model parameters through the back-propagation algorithm. During the training process, use the validation set to monitor the performance of the model to prevent overfitting. Based on the performance feedback of the validation set, adjust the model structure or hyperparameters to optimize the model performance. Use the test set to evaluate the trained model, calculate the error indicators between the predicted results and the actual values ​​(such as root mean square error (RMSE) and mean absolute error (MAE), etc.), analyze the prediction performance of the model, ensure that the model can maintain good prediction accuracy on unseen data, and construct a temperature feature prediction model for temperature monitoring point A.

[0033] Step 3: Establish a mapping mechanism between the predicted temperature characteristics of each temperature monitoring point and the parameters of the virtual electrical control cabinet (for example, the coordinates of temperature monitoring point A are (X=90mm, Y=60mm, Z=50mm),

[0034] #Example: Parameter structure of temperature monitoring point A

[0035] pointA_params={

[0036] "coordinate":(90,60,50), # 3D coordinates in mm

[0037] "pred_features": {

[0038] "avg_temp": [25.0, 26.5, 28.0, ...], # Second-by-second average temperature sequence within T duration

[0039] "temp_extremes": [(24.5, 29.8), (25.1, 30.2), ...], # Highest / lowest temperature every 10 seconds

[0040] "change_rate": [0.3, 0.5, -0.2, ...] # Temperature change rate per second (℃ / s)

[0041] },

[0042] "time_stamp": "2025-02-11 14:30:00" # Prediction starting time}).

[0043] Construct a virtual electrical control cabinet: Step S1: Construct a 3D model of the control cabinet (using SolidWorks or ANSYS Design Modeler; cabinet dimensions: typical dimensions (width × depth × height) 600 mm × 800 mm × 2000 mm, taking into account structures such as ventilation holes and cable inlets; internal component layout: heating elements: IGBT module (power consumption 1000 W), power module (power consumption 500 W), PLC controller (power consumption 50 W); cooling components: air cooling system: top axial fan (air volume 20 CFM), guide plate; liquid cooling system: cold plate (material Al6061, flow channel cross-sectional area 5 mm × 10 mm), circulation pump (flow rate 2 L / min); compression refrigeration: evaporator (fin pitch 2 mm), condenser (fan speed 2000 rpm); sealing structure: dustproof rubber strip (thermal conductivity coefficient 0.15 W / m・K)).

[0044] Step S2: Define material properties (for example, if the component is a cabinet and its material is cold-rolled steel plate, define the thermal conductivity to be 45 W / m・K, the specific heat capacity to be 480 J / kg・K, and the density to be 7850 kg / m³; if the component is an IGBT module and its material is Al2O3 ceramic, define the thermal conductivity to be 20 W / m・K, the specific heat capacity to be 750 J / kg・K, and the density to be 3900 kg / m³).

[0045] Step S3: performing air cooling system simulation, liquid cooling system simulation, and compression refrigeration system simulation.

[0046] Air cooling system simulation:

[0047] Luent settings: turbulence model: SST k-ω (applicable to flow near the wall);

[0048] Inlet conditions: fan outlet speed 3m / s (corresponding to 120CFM air volume);

[0049] Outlet conditions: pressure outlet (0Pa);

[0050] Heat source: IGBT module heat flux density 20W / cm² (calculated using Joule heat formula Q=I²R);

[0051] Convection coefficient: natural convection on the cabinet surface (5W / m²・K);

[0052] Liquid cooling system simulation:

[0053] COMSOL Setup:

[0054] Physics: Laminar Flow + Heat Transfer in Solids;

[0055] Coolant inlet: temperature 25°C, flow rate 2L / min;

[0056] Contact between cold plate and component: thermal contact resistance 0.5K・cm² / W;

[0057] Optimization goal: maximize heat transfer (Q) while minimizing pump power (P), using dimensionless processing;

[0058] Compression Refrigeration System Simulation:

[0059] ANSYS Simplorer co-simulation:

[0060] Driving circuit modeling:

[0061] IGBT inverter: using IRF3710 model, switching frequency 20kHz;

[0062] Control algorithm: digital incremental PID, temperature control accuracy ±0.5℃;

[0063] Compressor Modeling:

[0064] ANSYS Maxwell: coupling electromagnetic force calculation with piston displacement;

[0065] Displacement: 10cc / rev, speed 3000rpm;

[0066] Evaporator / Condenser Modeling:

[0067] Evaporator: air side convection coefficient 50W / m²・K, refrigerant side phase change temperature 5℃;

[0068] Condenser: fan speed 2000 rpm, ambient temperature 35°C;

[0069] Multiphysics coupling:

[0070] Refrigerant pressure-temperature relationship: using the NIST REFPROP database;

[0071] Evaporator is coupled to the cabinet: through heat flux transfer;

[0072] Step S4: The virtual electrical control cabinet is constructed through verification and experimental comparison.

[0073] Step 4: Obtain the comprehensive feedback value of the temperature control matching for each temperature control matching scheme (the temperature control matching scheme is to randomly combine the air cooling system 200, the liquid cooling system 300, and the compression refrigeration system 400. If there are other active refrigeration systems, there will be more temperature control matching schemes, and the temperature control scheme will be determined based on the combination results. The combination of each temperature control matching scheme is not repeated. For example, the first temperature control matching scheme has only the air cooling system 200, the second temperature control matching scheme has only the liquid cooling system 300, the third temperature control matching scheme has only the compression refrigeration system 400, and the fourth temperature control matching scheme includes the air cooling system 200 and the compression refrigeration system 400. 00, the fifth temperature control matching scheme includes a liquid cooling system 300 and a compression refrigeration system 400, etc. Different temperature control matching scheme configurations correspond to different typical parameter configurations. For example, in the parameter configuration of the first temperature control matching scheme, the fan speed of the air cooling system 200 is 120 CFM, in the second temperature control matching scheme, the flow rate of the liquid cooling system 300 is 2 L / min, and the coolant temperature is 25°C. In the fourth temperature control matching scheme, the fan of the air cooling system 200 is set to 80% speed, and the compressor of the compression refrigeration system 400 is set to 60% power). The temperature control matching scheme with the largest comprehensive feedback value is executed to control the temperature of the cabinet 100.

[0074] The method for obtaining the comprehensive feedback value of the temperature control matching scheme is as follows: select a temperature control matching scheme, start all active cooling systems included in the temperature control matching scheme in the virtual electrical control cabinet (start according to the corresponding typical parameter configuration), and control the virtual electrical control cabinet to run for a period of T. After the operation is completed, determine the temperature characteristics of each temperature monitoring point in the virtual electrical control cabinet (the position of the temperature monitoring point in the virtual electrical control cabinet, that is, the actual coordinates of the temperature monitoring point in the corresponding cabinet body 100), and simultaneously determine the total power consumption of all active cooling systems in the virtual electrical control cabinet (that is, the air cooling system 200, the liquid cooling system 200). The temperature characteristics of each temperature monitoring point are sequentially imported into the temperature characteristic analytical model, and then the temperature performance value of each temperature monitoring point is determined in turn. The temperature performance value of each temperature monitoring point is summed and averaged to calculate the average temperature performance value. The average temperature performance value is then ratioed with the total power consumption of all active cooling systems (the ratio calculation is a dimensionless calculation) to calculate the comprehensive feedback value of the temperature control matching scheme.

[0075] The temperature feature analysis model is constructed by collecting multiple temperature features and building a neural network model. The multiple temperature features are divided into training set, validation set, and test set in a ratio of 60%:20%:20%. The neural network model is trained using the temperature features as the basic data. A temperature performance value is assigned to each temperature feature. The temperature performance value range is set between 1 and 30, and the temperature performance value is calibrated to the physical meaning. def map_to_performance(temp_feature, feature_type):

[0076] """Map to the range 1-30 according to the feature type"""

[0077] if feature_type == "avg_temp":

[0078] # The lower the temperature, the higher the performance value (assuming the ideal temperature is 25℃)

[0079] performance = 30 - min(30, max(1, int((temp_feature - 25) *2)))

[0080] elif feature_type == "change_rate":

[0081] # The lower the rate of change, the higher the performance value

[0082] performance = 30 - min(30, max(1, int(change_rate * 10)))

[0083] return max(1, min(30, performance)). The temperature performance value has a clear meaning. The larger the value, the better the temperature feature performance. The neural network model is repeatedly trained using the training set. The performance of the training phase is verified with the help of the validation set. The model parameters are adjusted in a timely manner according to the verification results. Hyperparameter tuning, overfitting prevention, and training monitoring are adopted. The final model is evaluated using a test set that did not participate in the training to ensure that the results do not rely on data peeking during the training process. Finally, a temperature feature analysis model is constructed.

[0084] The above method uses an LSTM neural network to predict the temperature characteristics of each monitoring point in the electrical control cabinet in advance. Combined with the multi-physical field simulation of the virtual control cabinet and equipped with multiple different types of active cooling systems, the multiple types of active cooling systems are freely coordinated based on the virtual cabinet simulation, breaking through the capability limitations of a single cooling mode. While achieving advanced control of the cabinet temperature, the electrical control cabinet can also be reasonably controlled in terms of operating temperature. This constructs a closed-loop intelligent temperature control system of "data-driven prediction-physical simulation verification-energy efficiency optimization decision-making", realizing a technological leap from static design relying on empirical rules to dynamic optimization driven by digital twins, significantly improving the temperature stability, energy efficiency and reliability of the operation of complex electrical control cabinets.

[0085] Example 2: Reference Figure 2 An electrical control cabinet includes a cabinet body 100, in which an air cooling system 200, a liquid cooling system 300, a compression refrigeration system 400, and an operation temperature controller 500 are arranged. The operation temperature controller 500 includes an operation temperature monitoring unit, an electrical parameter mapping unit, and an operation temperature control execution unit.

[0086] The operating temperature monitoring unit is used to continuously generate a time series temperature set of each temperature monitoring point during the operation of the electrical control cabinet.

[0087] The electrical parameter mapping unit is used to determine the predicted temperature characteristics of each temperature monitoring point and establish a parameter mapping mechanism between the predicted temperature characteristics of each temperature monitoring point and the virtual electrical control cabinet.

[0088] The temperature control execution unit is used to select a temperature control matching solution to control the temperature of the cabinet 100.

[0089] Among them, the air cooling system 200, the liquid cooling system 300, and the compression refrigeration system 400 are different active heat dissipation systems, which differ from each other in heat dissipation capacity, power consumption characteristics, and temperature control accuracy. The present invention only illustrates the above three active heat dissipation systems. In actual application, other active heat dissipation systems such as cooling semiconductor systems can also be installed in the electrical control cabinet.

[0090] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0091] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0092] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0096] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0097] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An electrical control cabinet, characterized in that: The cabinet (100) comprises an air cooling system (200), a liquid cooling system (300), a compression refrigeration system (400), and an operating temperature controller (500). The operating temperature controller (500) comprises an operating temperature monitoring unit, an electrical parameter mapping unit, and an operating temperature control execution unit. The operating temperature monitoring unit is used to continuously generate a time series temperature set of each temperature monitoring point during the operation of the electrical control cabinet; The electrical parameter mapping unit is used to determine the predicted temperature characteristics of each temperature monitoring point and establish a parameter mapping mechanism between the predicted temperature characteristics of each temperature monitoring point and the virtual electrical control cabinet; The operating temperature control execution unit is used to select a temperature control matching scheme to perform temperature control on the cabinet (100).

2. A method for controlling the operating temperature of an electrical control cabinet, applied to the electrical control cabinet according to claim 1, characterized in that: Here are the steps: Step 1: During the operation of the electrical control cabinet, continuously obtain the temperature data of each temperature monitoring point in the cabinet (100), and continuously generate a time series temperature set of each temperature monitoring point; Step 2: Generate a time series temperature set for each temperature monitoring point each time and determine the predicted temperature characteristics of each temperature monitoring point; Step 3: Establish a mapping mechanism between the predicted temperature characteristics of each temperature monitoring point and the parameters of the virtual electrical control cabinet; Step 4: Obtain the temperature control combination comprehensive feedback value of each temperature control combination scheme, and execute the temperature control combination scheme with the largest temperature control combination comprehensive feedback value to control the temperature of the cabinet (100).

3. The operating temperature control method of an electrical control cabinet according to claim 2, characterized in that: The continuous generation method of the time series temperature set of the temperature monitoring point is as follows: whenever a temperature monitoring point obtains a temperature data, the previously continuously obtained i-1 temperature data are synchronously collected, and the i temperature data are integrated into the time series temperature set in the form of a time series set.

4. The operating temperature control method of an electrical control cabinet according to claim 2, characterized in that: The predicted temperature characteristics of the temperature monitoring point are determined as follows: whenever a temperature monitoring point generates a time series temperature set, the time series temperature set is input into the temperature characteristic prediction model corresponding to the temperature monitoring point, and the temperature characteristic prediction model outputs the predicted temperature characteristics of the temperature monitoring point.

5. The operating temperature control method of an electrical control cabinet according to claim 2, characterized in that: The method for obtaining the comprehensive feedback value of the temperature control combination of the temperature control combination scheme is as follows: select a temperature control combination scheme, start all active cooling systems included in the temperature control combination scheme in the virtual electrical control cabinet, and control the virtual electrical control cabinet to run for a period of T. After the operation is completed, the average temperature performance value is calculated. The average temperature performance value is then ratioed to the total power consumption of all active cooling systems to calculate the comprehensive feedback value of the temperature control combination scheme.

6. The operating temperature control method of an electrical control cabinet according to claim 5, characterized in that: The average temperature performance value is calculated by determining the temperature characteristics of each temperature monitoring point in the virtual electrical control cabinet, importing the temperature characteristics of each temperature monitoring point into the temperature characteristic analysis model in sequence, and then determining the temperature performance value of each temperature monitoring point in sequence, summing and averaging the temperature performance values ​​of each temperature monitoring point, and calculating the average temperature performance value.

7. The operating temperature control method of an electrical control cabinet according to claim 6, characterized in that: The temperature feature analysis model is constructed by collecting multiple temperature features, building a neural network model, and using the temperature features as basic data to train the neural network model. Each temperature feature is assigned a temperature performance value. The larger the temperature performance value, the better the temperature feature performance. Finally, a temperature feature analysis model is constructed.

8. The operating temperature control method of an electrical control cabinet according to claim 4, characterized in that: Each temperature monitoring point corresponds to a temperature feature prediction model. Each temperature feature prediction model is built based on the LSTM model, and each temperature feature prediction model is updated regularly.

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