A building electrical cabinet heat dissipation method and system based on temperature feedback

Abstract

The application relates to the technical field of heat dissipation of electrical equipment, and relates to a building electrical cabinet heat dissipation method and system based on temperature feedback, which comprises the following steps: confirming multiple electrical cabinets based on a building area, confirming multiple load elements based on the electrical cabinets, obtaining multiple element rated powers based on the multiple load elements, calculating total heat load based on the multiple element rated powers, an internal-external temperature difference, a cabinet outer surface area and a heat transfer coefficient, performing natural convection heat dissipation on the electrical cabinets, constructing a double-target function based on a cabinet internal temperature prediction value, solving the double-target function by using a pre-constructed multi-target optimization solver, and performing speed control on the target fan based on an optimal speed control amount. The application can improve the accuracy and controllability of building electrical cabinet heat dissipation.

Description

Technical Field

[0001] This invention relates to the field of heat dissipation technology for electrical equipment, and in particular to a heat dissipation method and system for building electrical cabinets based on temperature feedback. Background Technology

[0002] With the continuous improvement of building electrification, the number of electrical cabinets and the internal load density in building areas continue to increase, and the impact of heat dissipation on the safe operation of equipment is becoming increasingly prominent.

[0003] Currently, electrical cabinets are mainly cooled through natural convection or forced ventilation using fixed-speed fans. The former is suitable for scenarios with low loads but has limited cooling capacity; the latter, while providing a larger airflow, typically involves fans operating at a constant speed for extended periods, failing to consider real-time changes in the actual heat load. There is an urgent need for a method to cool electrical cabinets that can intelligently switch cooling modes based on the electrical cabinet's heat load status and control fan speed based on temperature prediction results. Summary of the Invention

[0004] This invention provides a method and system for heat dissipation of building electrical cabinets based on temperature feedback, the main purpose of which is to improve the accuracy and controllability of heat dissipation of building electrical cabinets.

[0005] To achieve the above objectives, the present invention provides a heat dissipation method for building electrical cabinets based on temperature feedback, comprising: The building area was identified, and multiple electrical cabinets were identified based on the building area; For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. Compare the total heat load with the preset heat load threshold. If the total heat load is less than the heat load threshold, then the electrical cabinet is subjected to natural convection cooling to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The target fan and a pre-built sensor array are used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence. Based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and the pre-built long short-term memory neural network, temperature prediction is performed to obtain the cabinet temperature prediction value. A dual objective function is constructed based on the predicted temperature inside the cabinet. The dual objective function is solved using a pre-built multi-objective optimization solver to obtain the optimal speed control value. The speed of the target fan is controlled based on the optimal speed control value to obtain the electrical cabinet after fan regulation. Based on the heat dissipation electrical cabinet or the electrical cabinet after fan regulation, heat dissipation of the electrical cabinets in the building area is achieved.

[0006] Optionally, the identification of multiple electrical cabinets based on the building area includes: A building floor plan is obtained based on the building area, and multiple power consumption areas are extracted based on the building floor plan. Multiple electrical devices are obtained based on the multiple electricity consumption areas; Based on the multiple electrical devices, multiple electrical cabinets are selected.

[0007] Optionally, the process of identifying multiple load elements based on the electrical cabinet, obtaining the rated power of multiple elements based on the multiple load elements, and detecting the internal and external temperature difference of the electrical cabinet based on a pre-built temperature sensing device to obtain the internal and external temperature difference includes: An electrical connection diagram is obtained based on the electrical cabinet. Multiple busbars are extracted from the electrical connection diagram, and the connection elements of the multiple busbars are traversed to obtain multiple load elements. Based on each of the multiple load elements, multiple device nameplates are read, and the rated power is queried based on the multiple device nameplates to obtain the rated power of multiple elements; The internal and external temperatures of the electrical cabinet are detected by a pre-built temperature sensing device to obtain the internal and external temperatures. The internal temperature is then subtracted from the external temperature to obtain the internal and external temperature difference.

[0008] Optionally, the step of obtaining the cabinet material and outer surface area based on the electrical cabinet, determining the heat transfer coefficient based on the cabinet material, and calculating the total heat load based on the rated power of the multiple components, the internal and external temperature difference, the outer surface area of ​​the cabinet, and the heat transfer coefficient includes: The electrical cabinet is 3D scanned to obtain a 3D point cloud of the electrical cabinet, and the outer surface area of ​​the electrical cabinet is calculated based on the 3D point cloud of the electrical cabinet. Based on the electrical cabinet, the cabinet material is determined, and a heat transfer coefficient is obtained by querying the heat transfer coefficient based on the cabinet material. The total heat load is calculated based on the rated power of the multiple components, the internal and external temperature difference, the external surface area of ​​the cabinet, and the heat transfer coefficient. The calculation formula is as follows:

[0009] in, Indicates the total heat load. Indicates the first The rated power of each load element. Indicates the first The heat loss coefficient of each load element Indicates the heat transfer coefficient. Indicates the outer surface area of ​​the cabinet. Indicates the temperature difference between inside and outside. This indicates the number of load elements.

[0010] Optionally, comparing the total heat load with a preset heat load threshold, if the total heat load is less than the heat load threshold, then natural convection cooling is applied to the electrical cabinet to obtain a heat-dissipating electrical cabinet, comprising: Obtain a heat load threshold, compare the total heat load with the heat load threshold, and if the total heat load is less than the heat load threshold, obtain a fan stop command; The electrical cabinet is driven based on the fan stop command, and the air inlet and outlet positions are obtained using the electrical cabinet. A natural convection heat dissipation circuit is constructed based on the air inlet and outlet positions, and the electrical cabinet is cooled based on the natural convection heat dissipation circuit to obtain a heat-dissipating electrical cabinet.

[0011] Optionally, the step of calculating the ventilation volume based on the total heat load and selecting a fan based on the ventilation volume to obtain a target fan includes: The maximum allowable temperature is obtained based on the electrical cabinet. The allowable temperature rise is calculated based on the maximum allowable temperature and a preset reference temperature. The air density value is collected based on a pre-built densitometer. The ventilation volume is calculated based on the total heat load, air density value, and allowable temperature rise value. The calculation formula is as follows:

[0012] in, Indicates ventilation volume. Indicates the total heat load. This indicates the air density value. This indicates the specific heat capacity of air at constant pressure. Indicates the allowable temperature rise value; Multiple types of fans are obtained. Based on the multiple types of fans, multiple upper limits of fan flow and multiple rated powers are obtained. The upper limits of fan flow are compared with the ventilation volume, and fans with upper limits of fan flow greater than the ventilation volume are selected to obtain multiple initial target fans. Based on the multiple initial target fans and the rated powers corresponding to the multiple initial target fans, the minimum power fan is obtained, and the minimum power fan is used as the target fan.

[0013] Optionally, the step of predicting the cabinet temperature based on the cabinet internal temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence, and a pre-constructed long short-term memory neural network to obtain the predicted cabinet internal temperature includes: The time window length is obtained, and segments are extracted from the load current sequence, ambient temperature sequence, and fan speed sequence using the time window length to obtain input features; Based on the temperature sequence inside the cabinet, the measured temperature value is extracted and used as the output label. A long short-term memory neural network is obtained, wherein the long short-term memory neural network includes an input layer, a hidden layer and an output layer, wherein the hidden layer includes a forget gate, an input gate and an output gate; The input features are input into the input layer, and based on the input layer, the input features are transmitted to the hidden layer in chronological order. The forget gate and the input gate are used to discard non-critical information and update the memory state of the input features in sequence, respectively. The output gate is used to generate the initial temperature prediction value. The initial temperature prediction value is compared with the output label to obtain the prediction error. The prediction error is then backpropagated to update the long short-term memory neural network to obtain the updated memory neural network. Temperature prediction is then performed based on the updated memory neural network to obtain the predicted temperature inside the cabinet.

[0014] Optionally, the step of constructing a dual-objective function based on the predicted temperature inside the cabinet, and solving the dual-objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity includes: The minimum component temperature limit is obtained based on the multiple load components. The temperature deviation value is calculated based on the predicted temperature inside the cabinet and the minimum component temperature limit. The temperature deviation value is used as the first optimization target. The current speed value of the target fan is obtained, and the fan energy consumption value is calculated based on the current speed value and the rated power of the target fan. The fan energy consumption value is used as the second optimization target. A dual-objective function is constructed based on the first optimization objective and the second optimization objective. The fan speed range is determined based on the target fan, and the fan speed range is used as the function constraint condition. The bi-objective function and function constraints are input into a pre-constructed multi-objective optimization solver to obtain a Pareto optimal solution set. The Pareto optimal solution set includes multiple Pareto optimal solutions, each of which includes a fan speed control value, a temperature deviation value, and a fan energy consumption value. Based on the Pareto optimal solution set, the fan speed control value corresponding to the Pareto optimal solution with the smallest temperature deviation value is selected, and this fan speed control value is taken as the optimal speed control value.

[0015] Optionally, the step of controlling the speed of the target fan based on the optimal speed control value to obtain the fan-regulated electrical cabinet includes: The optimal speed control quantity is converted into a drive signal, which includes a frequency command and a voltage command. Based on the target fan receiving the drive signal, the target fan is driven to operate according to the optimal speed control amount using the frequency command and voltage command, thus obtaining the fan control electrical cabinet.

[0016] To achieve the above objectives, the present invention also provides a heat dissipation system for building electrical cabinets based on temperature feedback, comprising: The heat load calculation module is used to identify the building area and, based on the building area, identify multiple electrical cabinets; For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. A heat dissipation and diversion module is used to compare the total heat load with a preset heat load threshold. If the total heat load is less than the heat load threshold, the electrical cabinet is subjected to natural convection heat dissipation to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The temperature prediction module is used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence using the target fan and a pre-built sensor array, and to perform temperature prediction based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and a pre-built long short-term memory neural network to obtain the predicted value of the cabinet temperature. The speed control module is used to construct a dual objective function based on the predicted temperature inside the cabinet, solve the dual objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity, control the speed of the target fan based on the optimal speed control quantity, and obtain the fan-controlled electrical cabinet. Based on the heat dissipation electrical cabinet or the fan-controlled electrical cabinet, heat dissipation of the electrical cabinets in the building area is realized.

[0017] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: Memory, storing at least one instruction; The processor executes the instructions stored in the memory to implement the above-described heat dissipation method for building electrical cabinets based on temperature feedback.

[0018] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described heat dissipation method for building electrical cabinets based on temperature feedback.

[0019] To address the problems described in the background art, this invention identifies a building area and, based on that area, identifies multiple electrical cabinets. This demonstrates that the invention manages electrical cabinets uniformly within a building area. For each of these cabinets, the following operations are performed: multiple load elements are identified based on the cabinet; the rated power of these elements is obtained; the internal and external temperature difference of the cabinet is detected using a pre-built temperature sensor; the cabinet material and external surface area are obtained; the heat transfer coefficient is determined based on the cabinet material; and the total heat load is calculated based on the rated power of the elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. This demonstrates that the invention considers multiple factors when calculating the total heat load. The total heat load is compared to a preset heat load threshold. If the total heat load is less than the threshold, natural convection cooling is applied to the electrical cabinet to obtain a heat-dissipated cabinet. This demonstrates that the invention prioritizes natural convection cooling when the heat load is low, avoiding unnecessary energy consumption. If the total heat load exceeds the threshold, the ventilation volume is calculated based on the total heat load, and a fan is selected based on the ventilation volume. The target fan is obtained, demonstrating that this invention selects a fan based on actual ventilation requirements after the heat load exceeds a threshold, ensuring the fan corresponds to the heat dissipation demand. The target fan and a pre-built sensor array are used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence, and fan speed sequence. Based on these sequences and a pre-built long short-term memory neural network, temperature prediction is performed to obtain the predicted cabinet temperature value. This invention uses load current, ambient temperature, and fan speed as inputs and employs a long short-term memory neural network to predict the cabinet temperature. The internal temperature is predicted by considering multiple factors simultaneously. A dual-objective function is constructed based on the predicted internal temperature. A pre-built multi-objective optimization solver is used to solve this function to obtain the optimal speed control value. The speed of the target fan is then controlled based on this optimal speed control value, resulting in a fan-controlled electrical cabinet. Based on this cooling electrical cabinet or the fan-controlled electrical cabinet, heat dissipation is achieved for the electrical cabinets in the building area. Therefore, this invention uses a dual-objective function to consider both temperature control and fan energy consumption in fan speed control, obtaining the speed control value through multi-objective optimization. Thus, this invention can improve the accuracy and controllability of heat dissipation for building electrical cabinets. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a heat dissipation method for building electrical cabinets based on temperature feedback, provided in an embodiment of the present invention. Figure 2 A functional block diagram of a building electrical cabinet heat dissipation system based on temperature feedback provided in an embodiment of the present invention; Figure 3This is a schematic diagram of the structure of an electronic device for implementing the heat dissipation method for building electrical cabinets based on temperature feedback, according to an embodiment of the present invention.

[0021] Explanation of reference numerals in the attached figures: 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0022] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0024] This application provides a method for heat dissipation of building electrical cabinets based on temperature feedback. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for heat dissipation of building electrical cabinets based on temperature feedback can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0025] Reference Figure 1 The diagram shown is a schematic flowchart of a heat dissipation method for building electrical cabinets based on temperature feedback, provided in an embodiment of the present invention. In this embodiment, the heat dissipation method for building electrical cabinets based on temperature feedback includes: S1. Identify the building area, and identify multiple electrical cabinets based on the building area.

[0026] It should be explained that the identification of multiple electrical cabinets based on the building area includes: A building floor plan is obtained based on the building area, and multiple power consumption areas are extracted based on the building floor plan. Multiple electrical devices are obtained based on the multiple electricity consumption areas; Based on the multiple electrical devices, multiple electrical cabinets are selected.

[0027] Furthermore, obtaining the building floor plan based on the building area refers to reading the layout plan of the building area. The building floor plan is a layout drawing that includes the location of equipment and power lines within the building area, used to determine the location and scope of the power consumption area. Extracting multiple power consumption areas based on the building floor plan means dividing the building area into multiple power consumption areas according to the functional zoning (e.g., office area, production area, equipment room, etc.) marked on the building floor plan. Each power consumption area refers to an area within the building area that requires electricity, defined according to its function. Obtaining multiple electrical devices based on the multiple power consumption areas means extracting the electrical equipment (e.g., lighting fixtures, electrical cabinets, etc.) installed in each power consumption area based on the location of the electrical equipment marked on the building floor plan. Filtering electrical cabinets based on the multiple electrical devices means filtering electrical cabinets from the electrical equipment. Each electrical cabinet refers to power distribution equipment used to provide power distribution protection.

[0028] S2. Based on the electrical cabinet, identify multiple load elements, obtain the rated power of multiple elements based on the multiple load elements, detect the internal and external temperature difference of the electrical cabinet based on the pre-built temperature sensing device, obtain the internal and external temperature difference, obtain the cabinet material and cabinet surface area based on the electrical cabinet, and determine the heat transfer coefficient based on the cabinet material. Calculate the total heat load based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient.

[0029] It should be understood that the process of identifying multiple load elements based on the electrical cabinet, obtaining the rated power of multiple elements based on the multiple load elements, and detecting the internal and external temperature difference of the electrical cabinet based on a pre-built temperature sensing device to obtain the internal and external temperature difference includes: An electrical connection diagram is obtained based on the electrical cabinet. Multiple busbars are extracted from the electrical connection diagram, and the connection elements of the multiple busbars are traversed to obtain multiple load elements. Based on each of the multiple load elements, multiple device nameplates are read, and the rated power is queried based on the multiple device nameplates to obtain the rated power of multiple elements; The internal and external temperatures of the electrical cabinet are detected by a pre-built temperature sensing device to obtain the internal and external temperatures. The internal temperature is then subtracted from the external temperature to obtain the internal and external temperature difference.

[0030] It needs to be explained that obtaining the electrical connection diagram based on the electrical cabinet refers to reading the internal electrical connection diagram of the electrical cabinet from the electrical design drawings of the electrical cabinet. The electrical connection diagram is a circuit diagram that includes the busbar routing and component connection methods within the electrical cabinet, used to determine the connection relationships and layout of the components inside the electrical cabinet. Extracting multiple busbars based on the electrical connection diagram refers to identifying multiple busbars within the electrical cabinet according to the circuits in the electrical connection diagram. The busbars are the lines in the electrical cabinet that connect to each load component and are used to provide power. Traversing the multiple busbars to connect components refers to sequentially reading the electrical components (e.g., circuit breakers, contactors, fuses, relays, etc.) connected to each busbar along its routing. The load components are the electrical components in the electrical cabinet that are connected to the busbars and consume electrical energy. Reading multiple equipment nameplates based on each of the multiple load components refers to reading the nameplate attached to each load component. The equipment nameplate is a label fixed to the electrical component, containing parameters such as the component's model, rated voltage, rated current, and rated power. The process of querying rated power based on the multiple device nameplates refers to retrieving the rated power value of the load element from the device nameplate. Rated power refers to the power consumed by the electrical element under rated operating conditions, measured in watts (W). The process of detecting the internal and external temperatures of the electrical cabinet based on a pre-built temperature sensing device refers to measuring the internal air temperature and the external ambient temperature using temperature sensors installed inside and outside the electrical cabinet, respectively. The temperature sensing device is a sensor used to measure temperature, such as a thermocouple or thermistor. The internal temperature refers to the temperature of the air inside the electrical cabinet, and the external temperature refers to the temperature of the surrounding environment outside the electrical cabinet, measured in degrees Celsius (°C). The process of subtracting the external temperature from the internal temperature to obtain the internal-external temperature difference refers to calculating the difference between the internal and external temperatures, where the internal-external temperature difference is the difference between the internal temperature of the electrical cabinet and the external ambient temperature.

[0031] Furthermore, the step of obtaining the cabinet material and outer surface area based on the electrical cabinet, determining the heat transfer coefficient based on the cabinet material, and calculating the total heat load based on the rated power of the multiple components, the internal and external temperature difference, the outer surface area of ​​the cabinet, and the heat transfer coefficient includes: The electrical cabinet is 3D scanned to obtain a 3D point cloud of the electrical cabinet, and the outer surface area of ​​the electrical cabinet is calculated based on the 3D point cloud of the electrical cabinet. Based on the electrical cabinet, the cabinet material is determined, and a heat transfer coefficient is obtained by querying the heat transfer coefficient based on the cabinet material. The total heat load is calculated based on the rated power of the multiple components, the internal and external temperature difference, the external surface area of ​​the cabinet, and the heat transfer coefficient. The calculation formula is as follows:

[0032] in, Indicates the total heat load. Indicates the first The rated power of each load element. Indicates the first The heat loss coefficient of each load element Indicates the heat transfer coefficient. Indicates the outer surface area of ​​the cabinet. Indicates the temperature difference between inside and outside. This indicates the number of load elements.

[0033] It should be understood that the three-dimensional scanning of the electrical cabinet refers to: using the three-dimensional laser scanner to perform a three-dimensional scan of the electrical cabinet to obtain original three-dimensional point cloud data, performing interpolation and hole-filling processing on the original three-dimensional point cloud data to obtain pre-processed point cloud data, and performing multi-station stitching on the pre-processed point cloud data to obtain the three-dimensional point cloud of the electrical cabinet. Based on the 3D point cloud of the electrical cabinet, n target points are randomly selected. For each of the n target points, m nearest neighbor points are searched, and the distance from each of the n target points to the m nearest neighbor points is calculated to obtain multiple neighbor distances. The average density of the point cloud is calculated based on the multiple neighbor distances, and the calculation formula is as follows:

[0034] in, The average density of the point cloud. For the number of target points, The number of neighboring points for each target point. For n target points, the th From the target point to its corresponding m neighboring points, the first... The distance between neighboring points; Multiply the average density of the point cloud by a preset empirical coefficient to obtain the slice thickness value; Based on the electrical cabinet, the height direction is determined. Multiple slicing planes are generated at equal intervals based on the slice thickness values ​​according to the height direction. A slicing layer is constructed based on every two adjacent slicing planes, resulting in multiple slicing layers. The following operation is performed on each of the multiple slicing layers: Based on the slice layer, the point cloud within the layer is obtained, and the point cloud within the layer is projected onto the middle plane of the slice layer to obtain the projected point cloud. Contour line fitting is performed on each projected point cloud to obtain a closed contour line. By summing the closed contour lines corresponding to each of the multiple slice layers, multiple closed contour lines are obtained; Triangulation is performed on two adjacent closed contour lines among the multiple closed contour lines to obtain the areas of multiple triangles. The areas of the multiple triangles are then summed to obtain the outer surface area of ​​the cabinet.

[0035] Furthermore, the 3D scanning of the electrical cabinet using the 3D laser scanner refers to deploying the 3D laser scanner at multiple measuring stations around the electrical cabinet (specific 3D spatial coordinate points where the 3D laser scanner is located) to perform laser scanning on the outer surface of the electrical cabinet. The 3D laser scanner is a measuring device used to measure 3D point cloud data of an object surface. The projected point cloud refers to the unprocessed point cloud dataset obtained by the 3D laser scanner. The interpolation and hole-filling processing of the original 3D point cloud data to obtain preprocessed point cloud data refers to first using a normal estimation algorithm to identify missing areas in the original point cloud data caused by occlusion, glass transmission, etc., and then using a hole-filling algorithm (e.g., fitting a local surface or linear interpolation) to fill in the missing areas. The preprocessed point cloud data refers to the original 3D point cloud data after the above hole-filling processing. The preprocessed point cloud data is stitched together from multiple stations to obtain the 3D point cloud of the electrical cabinet. This is because the coordinate systems of the preprocessed point cloud data are not unified (different origins) due to measurements taken at multiple stations. Now, the coordinates of the preprocessed point cloud data are unified by using the 3D coordinates of only one station location as the origin. For example, assuming three stations are set up around the electrical cabinet, the coordinate system of station 1 is used as the global coordinate system. The rotation matrix R12 and translation vector T12 from station 2 to station 1, and the rotation matrix R13 and translation vector T13 from station 3 to station 1 are obtained through least squares matching calculation. All point cloud coordinates of each station are then uniformly transformed to the coordinate system of station 1 to obtain the complete 3D point cloud of the electrical cabinet. The 3D point cloud of the electrical cabinet refers to the preprocessed point cloud data after the above coordinate system is unified. The step of randomly selecting n target points based on the 3D point cloud of the electrical cabinet refers to selecting n points as sample points from the 3D point cloud dataset of the electrical cabinet according to a uniform random distribution principle. The n target points refer to the target points randomly selected from the 3D point cloud of the electrical cabinet. The step of searching for the m nearest neighbor points of each of the n target points and calculating the distance from each of the n target points to the m nearest neighbor points to obtain multiple neighbor point distances refers to searching for the m nearest neighbor points of each target point in the 3D point cloud dataset of the electrical cabinet and calculating the Euclidean distance between the target point and each neighbor point. The m neighboring points refer to the m point cloud data points closest to the target point, and m can be set manually, for example, to 10. The m neighboring points represent the m distance values ​​between each target point and its m neighboring points. Calculating the average point cloud density based on these distances involves summing all distances from each target point to its neighboring points and dividing by the product of the number of target points and the number of neighboring points. The average point cloud density is the average spacing between adjacent points in the point cloud, representing the density of the point cloud in space, and is measured in mm. The average point cloud density is then multiplied by a preset empirical coefficient.Obtaining the slice thickness value involves multiplying the average density of the point cloud by an empirical coefficient (usually 4-8, which can be manually set according to needs, the purpose of which is to amplify the average density to generate a slice thickness of appropriate size). The slice thickness value refers to the thickness of the slice layer used to extract contour lines from the point cloud. Determining the height direction based on the electrical cabinet means determining the vertical direction as the slice direction based on the principal axis direction of the electrical cabinet's 3D point cloud. The height direction refers to the vertical direction from the bottom to the top of the electrical cabinet. Generating multiple slice planes at equal intervals based on the slice thickness value based on the height direction means generating a batch of slice planes parallel to the horizontal plane along the height direction with the slice thickness as the interval. The slice plane refers to the plane used to segment the point cloud. Constructing a slice layer based on every two adjacent slice planes means taking the 3D space region between two adjacent slice planes as a slice layer. The slice layer refers to the 3D space enclosed by two adjacent slice planes. Obtaining the point cloud within the slice layer and projecting the point cloud within the layer onto the middle plane of the slice layer to obtain the projected point cloud, and fitting a contour line to each projected point cloud to obtain a closed contour line refers to… All 3D point cloud points falling within the thickness range of the slice layer are projected vertically onto the mid-plane between the upper and lower slice planes of the slice layer. Boundary extraction algorithms (such as α-shape or convex hull algorithms) are used to extract the outer boundary points of the projected point cloud, and these points are connected to form a closed contour line representing the outer contour shape of the slice. The in-layer point cloud refers to the 3D point cloud falling within the slice layer, and the projected point cloud refers to the set of points formed by projecting all point cloud points within the slice layer onto the mid-plane of the slice layer. For example, assume the height range of a certain slice layer is 10.0m. The height of the intermediate plane is 10.1m, and the slice contains three point cloud points with three-dimensional coordinates of (1.0, 2.0, 10.05), (1.2, 2.3, 10.15), and (1.5, 2.5, 10.10). After being projected onto the intermediate plane, the coordinates of the three points become (1.0, 2.0, 10.1), (1.2, 2.3, 10.1), and (1.5, 2.5, 10.1). The closed contour line refers to the contour line extracted from the above contour, which represents the outer contour of the electrical cabinet corresponding to the height of the middle of the slice. Connecting two adjacent closed contour lines from the multiple closed contour lines refers to using the closed contour lines of two adjacent slice layers as the upper and lower boundaries, and using a triangulation algorithm to divide the surface into multiple non-overlapping triangles. The area of ​​each triangle refers to the area of ​​the multiple non-overlapping triangles divided by the above triangulation algorithm. Summing the areas of these multiple triangles to obtain the cabinet's outer surface area involves summing the areas of all triangular facets to obtain the total area of ​​the electrical cabinet's outer surface. The cabinet's outer surface area is the total area of ​​the electrical cabinet's outer surface, measured in meters (m²). 2The reason for calculating the surface area of ​​the electrical cabinet based on the aforementioned 3D point cloud is that electrical cabinets are not necessarily ideal cuboids. Some electrical cabinets may be designed with other shapes, such as frustums or screens, depending on the actual placement or space requirements. Furthermore, the surface of the electrical cabinet may contain irregular structures such as handles, hinges, and ventilation caps. Therefore, using the aforementioned 3D point cloud allows for more accurate calculation of the electrical cabinet's surface area and can adapt to electrical cabinets with different structures. Determining the cabinet material based on the electrical cabinet refers to reading the material type used in the cabinet's technical specifications, such as steel plate, stainless steel, or aluminum alloy. The cabinet material refers to the type of material used for the electrical cabinet's outer shell. The heat transfer query based on the cabinet material refers to querying the corresponding heat transfer coefficient value according to the cabinet material type. The heat transfer coefficient refers to the amount of heat transferred per unit area per unit time, with units of W / (m²). 2 ·K). The internal and external temperature difference refers to the temperature difference between the inside and outside of the electrical cabinet. The number of load elements refers to the number of elements connected to the electrical cabinet. The total heat load refers to the difference between the heat generated by all the heat-generating elements inside the electrical cabinet and the heat dissipated outward from the cabinet (when the external temperature is lower than the internal temperature, the internal and external temperature difference is negative, i.e., heat dissipation occurs). Furthermore, from the formula, it can be seen that: the rated power is in W, the heat loss coefficient is dimensionless, and the heat transfer coefficient is in W / (m²). 2 ·K), the unit of the cabinet's external surface area is m. 2 The unit for internal and external temperature difference is K, and the unit for external temperature difference is W. The heat loss coefficient refers to the proportion of electrical power consumed by a load element at its rated power that is converted into heat. The value ranges from 0 to 1 and is determined by the material properties and operating efficiency of the element itself. For example, the contact resistance loss of a circuit breaker is small, and the heat loss coefficient is about 0.1 to 0.3. The iron loss and copper loss of a transformer are large, and the heat loss coefficient is about 0.5 to 0.8. It can be determined experimentally.

[0036] S3. Compare the total heat load with the preset heat load threshold. If the total heat load is less than the heat load threshold, then perform natural convection heat dissipation on the electrical cabinet to obtain a heat dissipation electrical cabinet.

[0037] It should be explained that the comparison of the total heat load with a preset heat load threshold, and if the total heat load is less than the heat load threshold, then the electrical cabinet is subjected to natural convection cooling to obtain a heat-dissipating electrical cabinet, including: Obtain a heat load threshold, compare the total heat load with the heat load threshold, and if the total heat load is less than the heat load threshold, obtain a fan stop command; The electrical cabinet is driven based on the fan stop command, and the air inlet and outlet positions are obtained using the electrical cabinet. A natural convection heat dissipation circuit is constructed based on the air inlet and outlet positions, and the electrical cabinet is cooled based on the natural convection heat dissipation circuit to obtain a heat-dissipating electrical cabinet.

[0038] Furthermore, obtaining the heat load threshold refers to reading a preset heat load threshold. This heat load threshold is a critical value used to determine the specific heat dissipation method to be used for the electrical cabinet. It can be determined with reference to the national standard GB / T7251.1. Considering the actual situation, different regions are affected by different geographical environments and seasons, causing ambient temperatures to change in real time, resulting in fluctuations in the total heat load. Therefore, the two methods of natural convection cooling or fan cooling for the electrical cabinet may be used alternately (the fan is installed inside the electrical cabinet and only operates when the total heat load exceeds the heat load threshold; otherwise, natural convection cooling is used). The purpose of this invention is to provide two different, alternating heat dissipation methods for the electrical cabinet in advance. In this invention, the calculation and judgment of the total heat load are performed periodically, for example, once per hour, to determine which heat dissipation method to use. Comparing the total heat load with the heat load threshold means determining whether the total heat load is less than the heat load threshold; if it is less, it is determined that natural convection cooling can be used. The process of obtaining the fan stop command involves automatically generating a command to stop the fan when the total heat load is less than the heat load threshold, indicating a low heat load level. The fan stop command is the instruction to stop the fan. Driving the electrical cabinet based on the fan stop command means sending the command to the fan to stop it. Obtaining the air inlet and outlet positions using the electrical cabinet means determining the positions of the air inlet and outlet. The air inlet position refers to the opening where the air enters the electrical cabinet, and the air outlet position refers to the opening where the air exits. Constructing a natural convection cooling circuit based on the air inlet and outlet positions means that when the fan is off, hot air inside the electrical cabinet rises due to its decreased density and exits through the outlet, while cold air from outside, due to its higher density, flows in through the air inlet, forming an airflow path. Cooling the electrical cabinet based on the natural convection cooling circuit means using the natural convection cooling circuit to expel heat from inside the electrical cabinet, resulting in a cabinet with natural convection cooling. The aforementioned heat dissipation electrical cabinet refers to an electrical cabinet that dissipates heat through natural convection.

[0039] S4. If the total heat load is greater than the heat load threshold, calculate the ventilation volume based on the total heat load, select a fan based on the ventilation volume, and obtain the target fan.

[0040] Furthermore, the step of calculating the ventilation volume based on the total heat load and selecting a fan based on the ventilation volume to obtain the target fan includes: The maximum allowable temperature is obtained based on the electrical cabinet. The allowable temperature rise is calculated based on the maximum allowable temperature and a preset reference temperature. The air density value is collected based on a pre-built densitometer. The ventilation volume is calculated based on the total heat load, air density value, and allowable temperature rise value. The calculation formula is as follows:

[0041] in, Indicates ventilation volume. Indicates the total heat load. This indicates the air density value. This indicates the specific heat capacity of air at constant pressure. Indicates the allowable temperature rise value; Multiple types of fans are obtained. Based on the multiple types of fans, multiple upper limits of fan flow and multiple rated powers are obtained. The upper limits of fan flow are compared with the ventilation volume, and fans with upper limits of fan flow greater than the ventilation volume are selected to obtain multiple initial target fans. Based on the multiple initial target fans and the rated powers corresponding to the multiple initial target fans, the minimum power fan is obtained, and the minimum power fan is used as the target fan.

[0042] It should be understood that obtaining the maximum permissible temperature based on the electrical cabinet refers to reading the highest permissible operating temperature value inside the electrical cabinet. The maximum permissible temperature refers to the highest temperature limit at which each load component inside the electrical cabinet can operate safely. Furthermore, in this invention, the maximum permissible temperature only includes components performing normal conduction and does not apply to components such as fuses that may generate extreme high temperatures. The preset reference temperature refers to the standard reference temperature value of the environment in which the electrical cabinet is located, typically 40℃ (referencing national standard GB / T 7251.1). The reference temperature is a temperature used for reference, such as 25 degrees Celsius. Calculating the permissible temperature rise based on the maximum permissible temperature and the preset reference temperature means subtracting the reference temperature from the maximum permissible temperature. The permissible temperature rise refers to the maximum permissible increase in temperature inside the electrical cabinet relative to the reference ambient temperature. Collecting air density values ​​based on a pre-built densitometer means measuring the air density around the electrical cabinet using a densitometer. The densitometer is a device capable of detecting air density, such as an air density meter. The air density value refers to the air density around the electrical cabinet. The ventilation volume refers to the minimum airflow required to remove excess heat from the electrical cabinet, and as can be seen from the formula, the unit of air density is kg / m³. 3 The dimension of the specific heat capacity of air at constant pressure is J / (kg·K), and the dimension of the allowable temperature rise is K. Therefore, the unit of the ventilation volume is m³ / s. 3 / s, where "multiple fans" refers to fan types with different upper limits of flow rate and rated power. Obtaining multiple upper limits of flow rate and multiple rated power based on these multiple fans means querying the maximum ventilation flow rate and rated power of each fan. The upper limit of flow rate refers to the maximum airflow rate that the fan can provide, measured in meters per second (m³). 3 / s, where the rated power refers to the electrical power consumed by the fan under rated operating conditions. Comparing the upper limits of the flow rates of the multiple fans with the ventilation volume and selecting fans whose upper limits of flow rates are greater than the ventilation volume means comparing the upper limit of the flow rate of each fan with the ventilation volume one by one, retaining fans whose upper limits of flow rates are greater than the ventilation volume. The initial target fan refers to a fan whose flow rate meets the heat dissipation requirements. Obtaining the minimum power fan based on the multiple initial target fans and their corresponding rated powers means selecting the fan model with the lowest rated power from the multiple initial target fans. The minimum power fan refers to the fan with the lowest energy consumption among the multiple initial target fans. The target fan refers to a fan that meets the airflow requirements and has the lowest energy consumption.

[0043] S5. Using the target fan and the pre-built sensor array, collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence. Based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and the pre-built long short-term memory neural network, perform temperature prediction to obtain the cabinet temperature prediction value.

[0044] It should be explained that the temperature prediction based on the cabinet internal temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence, and pre-constructed long short-term memory neural network to obtain the cabinet internal temperature prediction value includes: The time window length is obtained, and segments are extracted from the load current sequence, ambient temperature sequence, and fan speed sequence using the time window length to obtain input features; Based on the temperature sequence inside the cabinet, the measured temperature value is extracted and used as the output label. A long short-term memory neural network is obtained, wherein the long short-term memory neural network includes an input layer, a hidden layer and an output layer, wherein the hidden layer includes a forget gate, an input gate and an output gate; The input features are input into the input layer, and based on the input layer, the input features are transmitted to the hidden layer in chronological order. The forget gate and the input gate are used to discard non-critical information and update the memory state of the input features in sequence, respectively. The output gate is used to generate the initial temperature prediction value. The initial temperature prediction value is compared with the output label to obtain the prediction error. The prediction error is then backpropagated to update the long short-term memory neural network to obtain the updated memory neural network. Temperature prediction is then performed based on the updated memory neural network to obtain the predicted temperature inside the cabinet.

[0045] It should be explained that the pre-built sensor array refers to a collection of multiple sensors pre-installed inside and outside the electrical cabinet. This sensor array includes temperature sensors, current sensors, and speed sensors, used to collect various parameters of the electrical cabinet in real time. The collection of cabinet temperature sequences, load current sequences, ambient temperature sequences, and fan speed sequences using the target fan and the pre-built sensor array refers to the collection of continuous time-series data of the cabinet's temperature, load current, ambient temperature, and fan speed using the sensor array. The cabinet temperature sequence refers to the real-time temperature value sequence inside the electrical cabinet collected by the temperature sensor; the load current sequence refers to the real-time current value sequence flowing through the electrical cabinet collected by the current sensor; the ambient temperature sequence refers to the temperature value sequence outside the electrical cabinet; and the fan speed sequence refers to the speed sequence of the target fan collected by the speed sensor. The acquisition of the time window length refers to reading a preset time window length, which is the time length used for segment extraction, such as 60 seconds in the past. This can be manually set according to actual needs. Extracting segments from the load current sequence, ambient temperature sequence, and fan speed sequence using the time window length means extracting segments from these sequences backwards within the time window length, using the current time as a reference. These three segments are then aligned by time and combined into a vector as input features. The input features are vectors containing segments corresponding to the load current, ambient temperature, and fan speed. Extracting the measured temperature value based on the cabinet temperature sequence and using it as the output label means extracting the measured temperature value at the current time from the cabinet temperature sequence. The output label is the target value used for training the LSTM network. Acquiring the Long Short-Term Memory (LSTM) neural network means loading a pre-built LSTM network. The LSTM neural network is a recurrent neural network with memory capabilities, used to establish long-term dependencies in the aforementioned load current sequence, ambient temperature sequence, and fan speed sequence. The Long Short-Term Memory (LSTM) neural network comprises an input layer, a hidden layer, and an output layer, wherein the hidden layer includes a forget gate, an input gate, and an output gate. The LSTM neural network is a recurrent neural network used for temperature prediction. In this embodiment, the LSTM neural network is trained using load current sequence, ambient temperature sequence, and fan speed sequence as input features, and the measured temperature inside the cabinet as the output label. After training, it predicts the temperature inside the cabinet at future times. Inputting the input features into the input layer means sending the input features to the input layer of the LSTM network. The input layer receives the input features and passes them to the hidden layer.The process involves transmitting the input features sequentially from the input layer to the hidden layer in chronological order. The forget gate and input gate are used to discard non-critical information and update the memory state of the input features sequentially. The output gate generates the initial temperature prediction value by determining which information in the input features needs to be discarded and which needs to be retained via the forget gate, and which needs to be written into the memory state (i.e., the internal vector used to store information) via the input gate. After updating the memory state through the forget gate and output gate, the output gate calculates the initial temperature prediction value based on the memory state, the sigmoid function, and the tanh function. This initial temperature prediction value refers to the electrical cabinet temperature predicted by the Long Short-Term Memory Neural Network based on the input features. For example, assuming a time window length of 60 seconds, 60 sampling points are extracted from the load current sequence (rising from 50A to 90A), the ambient temperature sequence (rising from 25.0℃ to 27.2℃), and the fan speed sequence (constantly 1500 r / min), respectively, to form the input feature (60×3 matrix). The measured temperature inside the cabinet, 43.0℃, is used as the output label. After updating the memory state of the input feature through the forget gate (discarding approximately 35% of non-critical information) and the input gate (retaining approximately 40% of current information) of the LSTM network, a 128-dimensional feature vector is generated (the specific features of this vector are determined by the number of neurons in the neural network). This vector is then mapped through the output layer to obtain the initial temperature prediction value of 42.5℃. The difference operation between the initial temperature prediction value and the output label to obtain the prediction error refers to subtracting the output label (i.e., the actual temperature value) from the initial temperature prediction value. The prediction error is the deviation between the initial temperature prediction value and the output label, which can be used for subsequent correction of the Long Short-Term Memory neural network. The step of updating the Long Short-Term Memory (LSTM) neural network based on the prediction error through backpropagation to obtain an updated memory neural network refers to updating the LSM based on the prediction error using a backpropagation algorithm. The updated memory neural network is an LSM that has undergone one backpropagation calculation and has been updated. The step of performing temperature prediction based on the updated memory neural network to obtain a predicted temperature value inside the cabinet refers to re-predicting the input features using the updated LSM neural network and predicting a corrected temperature value inside the cabinet. This predicted temperature value is the temperature value re-predicted after the LSM neural network is updated.

[0046] S6. Construct a dual objective function based on the predicted temperature inside the cabinet, solve the dual objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity, control the speed of the target fan based on the optimal speed control quantity, and obtain the electrical cabinet after fan regulation. Based on the heat dissipation electrical cabinet or the electrical cabinet after fan regulation, realize heat dissipation of the electrical cabinet in the building area.

[0047] Furthermore, the step of constructing a dual objective function based on the predicted temperature inside the cabinet, and solving the dual objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity includes: The minimum component temperature limit is obtained based on the multiple load components. The temperature deviation value is calculated based on the predicted temperature inside the cabinet and the minimum component temperature limit. The temperature deviation value is used as the first optimization target. The current speed value of the target fan is obtained, and the fan energy consumption value is calculated based on the current speed value and the rated power of the target fan. The fan energy consumption value is used as the second optimization target. A dual-objective function is constructed based on the first optimization objective and the second optimization objective. The fan speed range is determined based on the target fan, and the fan speed range is used as the function constraint condition. The bi-objective function and function constraints are input into a pre-constructed multi-objective optimization solver to obtain a Pareto optimal solution set. The Pareto optimal solution set includes multiple Pareto optimal solutions, each of which includes a fan speed control value, a temperature deviation value, and a fan energy consumption value. Based on the Pareto optimal solution set, the fan speed control value corresponding to the Pareto optimal solution with the smallest temperature deviation value is selected, and this fan speed control value is taken as the optimal speed control value.

[0048] It should be understood that obtaining the minimum component temperature limit based on the multiple load components means reading the minimum allowable operating temperature value from the instruction manual of each load component, and taking the smallest minimum allowable operating temperature value among all load components as the minimum component temperature limit. The minimum component temperature limit refers to the smallest minimum allowable operating temperature value among all load components within the electrical cabinet. Calculating the temperature deviation value based on the predicted cabinet temperature and the minimum component temperature limit means subtracting the minimum component temperature limit from the predicted cabinet temperature to obtain the temperature deviation value. The temperature deviation value is the temperature difference between the predicted cabinet temperature and the minimum component temperature limit. Using the temperature deviation value as the first optimization objective means using the temperature deviation value as one of the optimization objectives in the dual objective function, and the smaller the temperature deviation value, the better. In subsequent objective functions, the closer this value is to zero, the better the temperature control effect. The first optimization objective refers to the temperature-related optimization objective in the dual objective function. The step of obtaining the current rotational speed value based on the target fan refers to reading the fan rotational speed at the current moment from the fan rotational speed sensor. The current rotational speed value refers to the rotational speed of the target fan at the current moment. The step of calculating the fan energy consumption value based on the current rotational speed value and the rated power corresponding to the target fan refers to multiplying the cube of the ratio of the current rotational speed to the rated speed by the rated power, according to the principle that fan energy consumption is directly proportional to the cube of the rotational speed (i.e., the fan similarity law). For example, if the rated power of the target fan is 500W, the rated rotational speed is 3000r / min, and the current rotational speed is 2400r / min, then the fan energy consumption value is 500×(2400 / 3000). 3=256W, where the fan energy consumption value refers to the electrical power consumed by the fan at the current speed. Using the fan energy consumption value as the second optimization objective means using it as another optimization objective in a dual-objective function, with a lower value being better. The second optimization objective is the energy consumption-related optimization objective in the dual-objective function. Constructing a dual-objective function based on the first and second optimization objectives means using the temperature deviation value and the fan energy consumption value as two objectives to construct a dual-objective optimization function. The dual-objective function is an objective function containing both the temperature deviation value and the fan energy consumption value. Determining the fan speed range based on the target fan means determining the minimum and maximum allowable speeds of the target fan. The fan speed range refers to the speed interval within which the target fan can operate safely, which can be obtained by consulting the fan's instruction manual. Using the fan speed range as a function constraint means that during the optimization process, all candidate speed control variables must fall within the fan speed range; solutions exceeding the range are invalid. The function constraint is a condition that must be satisfied during the solution process. The bi-objective optimization solver refers to an algorithmic tool used to solve bi-objective optimization problems, such as a solver built based on a multi-objective optimization algorithm. The Pareto optimal solution set refers to the set of feasible solutions that satisfy all constraints in bi-objective optimization. Multiple Pareto optimal solutions refer to a set of feasible solutions generated by the objective optimization solver, each solution representing a wind turbine speed control scheme, including a set of speed control variables, temperature deviation values, and wind turbine energy consumption values. Selecting the wind turbine speed control variable corresponding to the Pareto optimal solution with the smallest temperature deviation from the Pareto optimal solution set refers to selecting the Pareto optimal solution corresponding to the set with the smallest temperature deviation from all Pareto optimal solutions and extracting its corresponding wind turbine speed control variable. The wind turbine speed control variable refers to the speed value used to control the wind turbine speed, and the optimal speed control variable refers to the wind turbine speed control variable corresponding to the Pareto optimal solution with the smallest temperature deviation.

[0049] It should be explained that the electrical cabinet for controlling the speed of the target fan based on the optimal speed control value, to obtain the fan control, includes: The optimal speed control quantity is converted into a drive signal, which includes a frequency command and a voltage command. Based on the target fan receiving the drive signal, the target fan is driven to operate according to the optimal speed control amount using the frequency command and voltage command, thus obtaining the fan control electrical cabinet.

[0050] Furthermore, the step of converting the optimal speed control quantity into a drive signal refers to: dividing the optimal speed control quantity by 60 to obtain a speed per second value; multiplying the speed per second value by the number of pole pairs of the target fan to obtain an electrical frequency value; obtaining the rated frequency and rated voltage of the target fan; dividing the electrical frequency value by the rated frequency to obtain a voltage regulation ratio; multiplying the voltage regulation ratio by the rated voltage to obtain a voltage command value; constructing a frequency command and a voltage command based on the electrical frequency value and the voltage command value; and summing the frequency command and voltage command to obtain a drive signal. Dividing the optimal speed control quantity by 60 to obtain a speed per second value means converting the speed per minute value to a speed per second value. Multiplying the speed per second value by the number of pole pairs of the target fan to obtain the electrical frequency value means, based on the relationship between AC motor speed and power supply frequency (… ,in Rotational speed per second For electrical frequency, The electrical frequency value is obtained by multiplying the rotational speed per second (RPS) by the number of pole pairs of the motor. The number of pole pairs refers to the number of magnetic poles in the rotating magnetic field of the fan motor, which is determined by the motor structure and is measured in pole pairs. The electrical frequency value refers to the AC frequency required to drive the fan motor and is measured in Hz. For example, if the optimal speed control is 2400 r / min, dividing by 60 gives 40 r / s, and the fan motor has 1 pole pair, then the electrical frequency value is 40 × 1 = 40 Hz. Obtaining the rated frequency and rated voltage of the target fan means reading the frequency and voltage of the fan under rated operating conditions. The rated frequency refers to the power supply frequency required by the fan motor at its rated speed, and the rated voltage refers to the power supply voltage required by the fan motor under rated operating conditions. Dividing the electrical frequency value by the rated frequency to obtain the voltage regulation ratio means calculating the ratio of the electrical frequency value to the rated frequency. The voltage regulation ratio is the proportion of the current electrical frequency value relative to the rated frequency. The step of multiplying the voltage regulation ratio by the rated voltage to obtain the voltage command value refers to the principle of constant voltage-frequency ratio control (V / F control) in variable frequency speed regulation, where the voltage amplitude should be adjusted in the same proportion as the frequency. The voltage command value refers to the voltage value used to drive the fan for corresponding voltage regulation. For example, if the rated voltage is 380V and the voltage regulation ratio is 0.8, then the voltage command value is 380 × 0.8 = 304V. The step of constructing frequency and voltage commands based on the electrical frequency and voltage command values ​​refers to encapsulating the calculated electrical frequency and voltage command values ​​into commands. The frequency and voltage commands are respectively commands to drive the target fan for frequency and voltage regulation, and the drive signal is the set of frequency and voltage commands. The phrase "based on the target fan receiving the drive signal and using the frequency command and voltage command to drive the target fan to operate according to the optimal speed control amount" means that the driver adjusts the power supply frequency of the target fan according to the frequency command and adjusts the power supply voltage of the target fan according to the voltage command, so that the target fan rotates at the set speed. The "fan speed control electrical cabinet" refers to the electrical cabinet installed inside the fan speed control unit.

[0051] To address the problems described in the background art, this invention identifies a building area and, based on that area, identifies multiple electrical cabinets. This demonstrates that the invention manages electrical cabinets uniformly within a building area. For each of these cabinets, the following operations are performed: multiple load elements are identified based on the cabinet; the rated power of these elements is obtained; the internal and external temperature difference of the cabinet is detected using a pre-built temperature sensor; the cabinet material and external surface area are obtained; the heat transfer coefficient is determined based on the cabinet material; and the total heat load is calculated based on the rated power of the elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. This demonstrates that the invention considers multiple factors when calculating the total heat load. The total heat load is compared to a preset heat load threshold. If the total heat load is less than the threshold, natural convection cooling is applied to the electrical cabinet to obtain a heat-dissipated cabinet. This demonstrates that the invention prioritizes natural convection cooling when the heat load is low, avoiding unnecessary energy consumption. If the total heat load exceeds the threshold, the ventilation volume is calculated based on the total heat load, and a fan is selected based on the ventilation volume. The target fan is obtained, demonstrating that this invention selects a fan based on actual ventilation requirements after the heat load exceeds a threshold, ensuring the fan corresponds to the heat dissipation demand. The target fan and a pre-built sensor array are used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence, and fan speed sequence. Based on these sequences and a pre-built long short-term memory neural network, temperature prediction is performed to obtain the predicted cabinet temperature value. This invention uses load current, ambient temperature, and fan speed as inputs and employs a long short-term memory neural network to predict the cabinet temperature. The internal temperature is predicted by considering multiple factors simultaneously. A dual-objective function is constructed based on the predicted internal temperature. A pre-built multi-objective optimization solver is used to solve this function to obtain the optimal speed control value. The speed of the target fan is then controlled based on this optimal speed control value, resulting in a fan-controlled electrical cabinet. Based on this cooling electrical cabinet or the fan-controlled electrical cabinet, heat dissipation is achieved for the electrical cabinets in the building area. Therefore, this invention uses a dual-objective function to consider both temperature control and fan energy consumption in fan speed control, obtaining the speed control value through multi-objective optimization. Thus, this invention can improve the accuracy and controllability of heat dissipation for building electrical cabinets.

[0052] like Figure 2 The diagram shown is a functional block diagram of a building electrical cabinet heat dissipation system based on temperature feedback provided in an embodiment of the present invention.

[0053] The temperature feedback-based heat dissipation system 100 for building electrical cabinets described in this invention can be installed in electronic devices. Depending on the functions implemented, the temperature feedback-based heat dissipation system 100 may include a heat load calculation module 101, a heat dissipation distribution module 102, a temperature prediction module 103, and a speed control module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, and which are stored in the memory of the electronic device.

[0054] The heat load calculation module 101 is used to identify the building area and, based on the building area, identify multiple electrical cabinets. For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. The heat dissipation and diversion module 102 is used to compare the total heat load with a preset heat load threshold. If the total heat load is less than the heat load threshold, the electrical cabinet is subjected to natural convection heat dissipation to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The temperature prediction module 103 is used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence using the target fan and the pre-built sensor array, and to perform temperature prediction based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and the pre-built long short-term memory neural network to obtain the cabinet temperature prediction value. The speed control module 104 is used to construct a dual objective function based on the predicted temperature inside the cabinet, solve the dual objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity, control the speed of the target fan based on the optimal speed control quantity, obtain the fan-controlled electrical cabinet, and realize heat dissipation of the electrical cabinet in the building area based on the heat dissipation electrical cabinet or the fan-controlled electrical cabinet.

[0055] In detail, the modules in the temperature feedback-based building electrical cabinet heat dissipation system 100 described in this embodiment of the invention employ the same methods as described above during use. Figure 1The heat dissipation method for building electrical cabinets based on temperature feedback described herein uses the same technical means and can produce the same technical effect, so it will not be repeated here.

[0056] like Figure 3 The diagram shown is a structural schematic of an electronic device for implementing a heat dissipation method for building electrical cabinets based on temperature feedback, according to an embodiment of the present invention.

[0057] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a heat dissipation method program for building electrical cabinets based on temperature feedback.

[0058] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a heat dissipation method program for a building electrical cabinet based on temperature feedback, but also to temporarily store data that has been output or will be output.

[0059] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a heat dissipation method program for building electrical cabinets based on temperature feedback), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0060] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0061] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0062] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0063] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0064] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0065] The heat dissipation method program for building electrical cabinets based on temperature feedback, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following: The building area was identified, and multiple electrical cabinets were identified based on the building area; For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. Compare the total heat load with the preset heat load threshold. If the total heat load is less than the heat load threshold, then the electrical cabinet is subjected to natural convection cooling to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The target fan and a pre-built sensor array are used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence. Based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and the pre-built long short-term memory neural network, temperature prediction is performed to obtain the cabinet temperature prediction value. A dual objective function is constructed based on the predicted temperature inside the cabinet. The dual objective function is solved using a pre-built multi-objective optimization solver to obtain the optimal speed control value. The speed of the target fan is controlled based on the optimal speed control value to obtain the electrical cabinet after fan regulation. Based on the heat dissipation electrical cabinet or the electrical cabinet after fan regulation, heat dissipation of the electrical cabinets in the building area is achieved.

[0066] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0067] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0068] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: The building area was identified, and multiple electrical cabinets were identified based on the building area; For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. Compare the total heat load with the preset heat load threshold. If the total heat load is less than the heat load threshold, then the electrical cabinet is subjected to natural convection cooling to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The target fan and a pre-built sensor array are used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence. Based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and the pre-built long short-term memory neural network, temperature prediction is performed to obtain the cabinet temperature prediction value. A dual objective function is constructed based on the predicted temperature inside the cabinet. The dual objective function is solved using a pre-built multi-objective optimization solver to obtain the optimal speed control value. The speed of the target fan is controlled based on the optimal speed control value to obtain the electrical cabinet after fan regulation. Based on the heat dissipation electrical cabinet or the electrical cabinet after fan regulation, heat dissipation of the electrical cabinets in the building area is achieved.

[0069] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0070] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0072] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A heat dissipation method for building electrical cabinets based on temperature feedback, characterized in that, The method includes: The building area was identified, and multiple electrical cabinets were identified based on the building area; For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. Compare the total heat load with the preset heat load threshold. If the total heat load is less than the heat load threshold, then the electrical cabinet is subjected to natural convection cooling to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The target fan and a pre-built sensor array are used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence. Based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and the pre-built long short-term memory neural network, temperature prediction is performed to obtain the cabinet temperature prediction value. A dual objective function is constructed based on the predicted temperature inside the cabinet. The dual objective function is solved using a pre-built multi-objective optimization solver to obtain the optimal speed control value. The speed of the target fan is controlled based on the optimal speed control value to obtain the electrical cabinet after fan regulation. Based on the heat dissipation electrical cabinet or the electrical cabinet after fan regulation, heat dissipation of the electrical cabinets in the building area is achieved.

2. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 1, characterized in that, The identification of multiple electrical cabinets based on the building area includes: A building floor plan is obtained based on the building area, and multiple power consumption areas are extracted based on the building floor plan. Multiple electrical devices are obtained based on the multiple electricity consumption areas; Based on the multiple electrical devices, multiple electrical cabinets are selected.

3. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 2, characterized in that, The process involves identifying multiple load components based on the electrical cabinet, obtaining the rated power of multiple components based on these load components, and performing internal and external temperature difference detection on the electrical cabinet using a pre-built temperature sensing device to obtain the internal and external temperature difference, including: An electrical connection diagram is obtained based on the electrical cabinet. Multiple busbars are extracted from the electrical connection diagram, and the connection elements of the multiple busbars are traversed to obtain multiple load elements. Based on each of the multiple load elements, multiple device nameplates are read, and the rated power is queried based on the multiple device nameplates to obtain the rated power of multiple elements; The internal and external temperatures of the electrical cabinet are detected by a pre-built temperature sensing device to obtain the internal and external temperatures. The internal temperature is then subtracted from the external temperature to obtain the internal and external temperature difference.

4. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 3, characterized in that, The process of obtaining the cabinet material and outer surface area based on the electrical cabinet, determining the heat transfer coefficient based on the cabinet material, and calculating the total heat load based on the rated power of the multiple components, the internal and external temperature difference, the outer surface area of ​​the cabinet, and the heat transfer coefficient includes: The electrical cabinet is 3D scanned to obtain a 3D point cloud of the electrical cabinet, and the outer surface area of ​​the electrical cabinet is calculated based on the 3D point cloud of the electrical cabinet. Based on the electrical cabinet, the cabinet material is determined, and a heat transfer coefficient is obtained by querying the heat transfer coefficient based on the cabinet material. The total heat load is calculated based on the rated power of the multiple components, the internal and external temperature difference, the external surface area of ​​the cabinet, and the heat transfer coefficient. The calculation formula is as follows: ； in, Indicates the total heat load. Indicates the first The rated power of each load element. Indicates the first The heat loss coefficient of each load element Indicates the heat transfer coefficient. Indicates the outer surface area of ​​the cabinet. Indicates the temperature difference between inside and outside. This indicates the number of load elements.

5. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 4, characterized in that, The process involves comparing the total heat load with a preset heat load threshold. If the total heat load is less than the heat load threshold, the electrical cabinet is subjected to natural convection cooling to obtain a heat-dissipating electrical cabinet, comprising: Obtain a heat load threshold, compare the total heat load with the heat load threshold, and if the total heat load is less than the heat load threshold, obtain a fan stop command; The electrical cabinet is driven based on the fan stop command, and the air inlet and outlet positions are obtained using the electrical cabinet. A natural convection heat dissipation circuit is constructed based on the air inlet and outlet positions, and the electrical cabinet is cooled based on the natural convection heat dissipation circuit to obtain a heat-dissipating electrical cabinet.

6. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 5, characterized in that, The process of calculating the ventilation volume based on the total heat load, selecting a fan based on the ventilation volume, and obtaining the target fan includes: The maximum allowable temperature is obtained based on the electrical cabinet. The allowable temperature rise is calculated based on the maximum allowable temperature and a preset reference temperature. The air density value is collected based on a pre-built densitometer. The ventilation volume is calculated based on the total heat load, air density value, and allowable temperature rise value. The calculation formula is as follows: ； in, Indicates ventilation volume. Indicates the total heat load. This indicates the air density value. This indicates the specific heat capacity of air at constant pressure. Indicates the allowable temperature rise value; Multiple types of fans are obtained. Based on the multiple types of fans, multiple upper limits of fan flow and multiple rated powers are obtained. The upper limits of fan flow are compared with the ventilation volume, and fans with upper limits of fan flow greater than the ventilation volume are selected to obtain multiple initial target fans. Based on the multiple initial target fans and the rated powers corresponding to the multiple initial target fans, the minimum power fan is obtained, and the minimum power fan is used as the target fan.

7. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 6, characterized in that, The temperature prediction based on the cabinet internal temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence, and a pre-constructed long short-term memory neural network, to obtain the predicted cabinet internal temperature value, includes: The time window length is obtained, and segments are extracted from the load current sequence, ambient temperature sequence, and fan speed sequence using the time window length to obtain input features; Based on the temperature sequence inside the cabinet, the measured temperature value is extracted and used as the output label. A long short-term memory neural network is obtained, wherein the long short-term memory neural network includes an input layer, a hidden layer and an output layer, wherein the hidden layer includes a forget gate, an input gate and an output gate; The input features are input into the input layer, and based on the input layer, the input features are transmitted to the hidden layer in chronological order. The forget gate and the input gate are used to discard non-critical information and update the memory state of the input features in sequence, respectively. The output gate is used to generate the initial temperature prediction value. The initial temperature prediction value is compared with the output label to obtain the prediction error. The prediction error is then backpropagated to update the long short-term memory neural network to obtain the updated memory neural network. Temperature prediction is then performed based on the updated memory neural network to obtain the predicted temperature inside the cabinet.

8. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 7, characterized in that, The process of constructing a dual-objective function based on the predicted temperature inside the cabinet, and solving the dual-objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity includes: The minimum component temperature limit is obtained based on the multiple load components. The temperature deviation value is calculated based on the predicted temperature inside the cabinet and the minimum component temperature limit. The temperature deviation value is used as the first optimization target. The current speed value of the target fan is obtained, and the fan energy consumption value is calculated based on the current speed value and the rated power of the target fan. The fan energy consumption value is used as the second optimization target. A dual-objective function is constructed based on the first optimization objective and the second optimization objective. The fan speed range is determined based on the target fan, and the fan speed range is used as the function constraint condition. The bi-objective function and function constraints are input into a pre-constructed multi-objective optimization solver to obtain a Pareto optimal solution set. The Pareto optimal solution set includes multiple Pareto optimal solutions, each of which includes a fan speed control value, a temperature deviation value, and a fan energy consumption value. Based on the Pareto optimal solution set, the fan speed control value corresponding to the Pareto optimal solution with the smallest temperature deviation value is selected, and this fan speed control value is taken as the optimal speed control value.

9. The heat dissipation method for building electrical cabinets based on temperature feedback as described in claim 8, characterized in that, The electrical cabinet for controlling the speed of the target fan based on the optimal speed control value, to obtain the fan-regulated electrical cabinet, includes: The optimal speed control quantity is converted into a drive signal, which includes a frequency command and a voltage command. Based on the target fan receiving the drive signal, the target fan is driven to operate according to the optimal speed control amount using the frequency command and voltage command, thus obtaining the fan control electrical cabinet.

10. A heat dissipation system for building electrical cabinets based on temperature feedback, characterized in that, The system includes: The heat load calculation module is used to identify the building area and, based on the building area, identify multiple electrical cabinets; For each of the plurality of electrical cabinets, the following operations are performed: Based on the electrical cabinet, multiple load elements are identified, and the rated power of multiple elements is obtained based on the multiple load elements. The internal and external temperature difference of the electrical cabinet is detected based on the pre-built temperature sensing device to obtain the internal and external temperature difference. The cabinet material and cabinet surface area are obtained based on the electrical cabinet, and the heat transfer coefficient is determined based on the cabinet material. The total heat load is calculated based on the rated power of the multiple elements, the internal and external temperature difference, the cabinet surface area, and the heat transfer coefficient. A heat dissipation and diversion module is used to compare the total heat load with a preset heat load threshold. If the total heat load is less than the heat load threshold, the electrical cabinet is subjected to natural convection heat dissipation to obtain a heat-dissipating electrical cabinet. If the total heat load is greater than the heat load threshold, the ventilation volume is calculated based on the total heat load, and the fan is selected based on the ventilation volume to obtain the target fan; The temperature prediction module is used to collect the cabinet temperature sequence, load current sequence, ambient temperature sequence and fan speed sequence using the target fan and a pre-built sensor array, and to perform temperature prediction based on the cabinet temperature sequence, load current sequence, ambient temperature sequence, fan speed sequence and a pre-built long short-term memory neural network to obtain the predicted value of the cabinet temperature. The speed control module is used to construct a dual objective function based on the predicted temperature inside the cabinet, solve the dual objective function using a pre-built multi-objective optimization solver to obtain the optimal speed control quantity, control the speed of the target fan based on the optimal speed control quantity, and obtain the fan-controlled electrical cabinet. Based on the heat dissipation electrical cabinet or the fan-controlled electrical cabinet, heat dissipation of the electrical cabinets in the building area is realized.

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