Integrated intelligent power distribution cabinet air conditioning system with dynamic load adjustment

Abstract

The application discloses a kind of integrated intelligent power distribution cabinet air conditioning systems of dynamic load regulation, it is related to power distribution cabinet temperature control technical field, specifically includes: power distribution cabinet cabinet, refrigeration execution module, parameter acquisition module and main control module;Parameter acquisition module is used to real-time acquisition in-cabinet electrical equipment load current data and temperature data;Main control module determines the working mode of system according to the data collected, and the working mode includes load current control mode;In load current control mode, main control module directly according to the load current data that is positively correlated with heat production, dynamically adjusts the refrigeration amount of refrigeration execution module, realizes pre-control, to overcome the response lag problem of traditional temperature control;In addition, the present application can also obtain the temperature distribution in the cabinet through the thermal imaging data acquisition unit, to achieve the partition accurate air supply of local hot spots.The present application can effectively suppress the temperature fluctuation in the cabinet, realize fast and accurate heat dissipation and reduce energy consumption, while improving the operation reliability of the system.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology for distribution cabinets, and more particularly to an integrated intelligent distribution cabinet air conditioning system with dynamic load regulation. Background Technology

[0002] During operation, the power electronic components inside the distribution cabinet generate a significant amount of heat. To ensure normal equipment operation, a dedicated air conditioning system is typically required for heat dissipation. Existing distribution cabinet air conditioning systems mostly employ a temperature sensor-based start-stop control method. The logic of this control method is to activate cooling when the cabinet temperature reaches a preset upper limit and stop cooling when it drops to the lower limit.

[0003] This control strategy inherently suffers from a lag, because a temperature increase is a result of increased load and heat generation, and the air conditioning system only begins to respond after the temperature has already risen significantly. When the load on the electrical equipment inside the cabinet suddenly changes, the heat generation increases dramatically, and the delayed response of the air conditioning system may cause the temperature inside the cabinet to exceed the safe range within a short period of time, affecting the stability and lifespan of the equipment. Furthermore, traditional overall cooling methods cannot effectively address localized hotspots in specific areas within the cabinet, potentially resulting in situations where the overall temperature meets the standard but individual components continue to overheat. At the same time, frequent start-ups and shutdowns and a single cooling capacity also lead to high energy consumption. Summary of the Invention

[0004] The main objective of this invention is to provide an integrated intelligent power distribution cabinet air conditioning system with dynamic load regulation.

[0005] Another object of the present invention is to provide an electronic device.

[0006] A third objective of this invention is to provide a non-transitory computer-readable storage medium.

[0007] To achieve the above objectives, a first aspect of the present invention provides an integrated intelligent power distribution cabinet air conditioning system with dynamic load regulation, comprising:

[0008] Distribution cabinet enclosure; A cooling execution module is installed in the power distribution cabinet and is used to cool the inside of the power distribution cabinet. The parameter acquisition module is used to collect real-time operating current data of the electrical equipment inside the distribution cabinet and real-time operating temperature data inside the cabinet. The main control module is electrically connected to the parameter acquisition module and the refrigeration execution module respectively; the main control module is used to determine the current working mode of the air conditioning system based on the collected real-time operating current data of the load and the real-time operating temperature data inside the cabinet. The operating mode includes a load current control mode. When the operating mode is the load current control mode, the main control module dynamically adjusts the cooling capacity of the cooling execution module according to the real-time operating current data of the load.

[0009] Furthermore, the operating mode also includes a temperature control mode. When the operating mode is the temperature control mode, the main control module compares the real-time operating temperature data inside the cabinet with the preset upper and lower temperature limits, and controls the start or stop of the refrigeration execution module.

[0010] Furthermore, when both the load rate of the real-time operating current data and the temperature rise rate of the real-time operating temperature data inside the cabinet exceed a preset threshold used to indicate that the power distribution cabinet will enter a high-power heating condition, the main control module determines that the working mode is switched to the load current control mode.

[0011] Furthermore, the main control module is equipped with a big data analysis model; in the load current control mode, the main control module takes the real-time operating current data of the load and the real-time operating temperature data inside the cabinet as input, calculates the target cold air volume through the big data analysis model, and adjusts the cooling capacity according to the target cold air volume.

[0012] Furthermore, the refrigeration execution module includes: Variable frequency compressors, condensers, evaporators, and throttling devices; The main control module dynamically adjusts the cooling capacity by regulating the operating frequency of the variable frequency compressor.

[0013] Furthermore, the parameter acquisition module also includes a thermal imaging data acquisition unit, used to acquire a two-dimensional digital temperature distribution matrix inside the power distribution cabinet; The main control module divides the internal cooling area of ​​the power distribution cabinet into independent cooling areas according to the two-dimensional digital temperature distribution matrix, and independently adjusts the airflow speed delivered to each independent cooling area.

[0014] Furthermore, it also includes a safety monitoring module, which includes a limit switch for detecting the open state of the distribution cabinet door; When the limit switch detects that the power distribution cabinet door is open, the main control module controls the cooling execution module to stop running and restarts after a delay after the power distribution cabinet door is closed.

[0015] Furthermore, the cooling execution module also includes a backup fan assembly; When the main control module detects a malfunction in the cooling execution module, it cuts off the control circuit of the cooling execution module and starts the backup fan assembly for heat dissipation.

[0016] Furthermore, it also includes an Internet of Things (IoT) communication unit, which is connected to the main control module; The IoT communication unit is used to send the real-time operating current data of the load, the real-time operating temperature data inside the cabinet, and the operating mode to a remote cloud platform.

[0017] Furthermore, it also includes an air filter installed in the air intake duct; The fan in the refrigeration execution module is a bidirectional variable frequency fan; The main control module is also used for: When the cumulative running time reaches the preset cleaning cycle, the bidirectional variable frequency fan is controlled to rotate in reverse to generate reverse airflow to remove the dust attached to the air filter.

[0018] To achieve the above objectives, a second aspect of the present invention provides an electronic device, comprising: a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing an integrated intelligent power distribution cabinet air conditioning system with dynamic load adjustment as described in the first aspect embodiment.

[0019] To achieve the above objectives, a third aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an integrated intelligent power distribution cabinet air conditioning system with dynamic load regulation as described in the first aspect embodiment.

[0020] The embodiments of the present invention have the following beneficial effects: This invention introduces a load current control mode to achieve predictive adjustment of cooling capacity. This adjustment method is directly related to the heat source of the equipment and has a faster response speed than traditional temperature control. It can effectively suppress drastic temperature fluctuations inside the cabinet caused by sudden load changes, ensuring the stability of the internal equipment operating environment, while avoiding energy waste caused by over-cooling. Attached Figure Description

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a structural diagram of an integrated intelligent power distribution cabinet air conditioning system with dynamic load regulation provided in an embodiment of the present invention; Figure 2 A flowchart illustrating an integrated intelligent power distribution cabinet air conditioning method for dynamic load regulation, provided as an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an integrated intelligent power distribution cabinet air conditioning device with dynamic load regulation provided in an embodiment of the present invention; Figure 4 This is a comparison chart of the experimental effects of dynamic load adjustment of the present invention. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] The following describes, with reference to the accompanying drawings, an integrated intelligent power distribution cabinet air conditioning system and method for dynamic load regulation according to embodiments of the present invention.

[0025] Example 1 This invention provides an integrated intelligent power distribution cabinet air conditioning system with dynamic load regulation, such as... Figure 1 and Figure 3 As shown, the system includes: The system includes a main control module, a parameter acquisition module, a refrigeration execution module, a pipeline circulation module, and a safety monitoring module.

[0026] The parameter acquisition module is located both inside and outside the distribution cabinet, and includes an ambient temperature sensor, a real-time internal temperature sensor, a load current monitoring unit, and a thermal imaging data acquisition unit. The ambient temperature sensor and the real-time internal temperature sensor are PT100 platinum resistance thermometers, used to periodically collect ambient temperature data and real-time internal operating temperature data. The load current monitoring unit uses an LMK-0.66 current transformer to collect real-time operating current data of the main circuit load in the distribution cabinet. The parameter acquisition module transmits all acquired data to the main control module.

[0027] The refrigeration execution module includes an air conditioning system and a backup fan assembly. The air conditioning system integrates a condenser and an evaporator, and receives control commands from the main control module to adjust the cooling capacity and airflow. The piping module includes an intake and return air piping system installed on the distribution cabinet, with multiple layers of air filters installed at the intake duct. The safety monitoring module includes an LXK3 limit switch located at the distribution cabinet door and an IoT communication unit.

[0028] Example 2 This invention relates to a method for operating an integrated intelligent distribution cabinet air conditioner with dynamic load regulation, the method comprising the following steps: S10, the parameter acquisition module acquires ambient temperature data, real-time operating temperature data inside the cabinet, real-time operating current data of the load, and thermal imaging data, and transmits the data to the main control module.

[0029] S101, in this embodiment, the parameter acquisition module acquires the temperature status parameters of the environment and the equipment body through temperature acquisition devices arranged outside and inside the distribution cabinet. To detect the temperature of the environment where the distribution cabinet is located, an ambient temperature sensor is installed on the shaded side of the outer protective plate 3 of the distribution cabinet or outside the air inlet duct to detect the air temperature parameters of the external working space where the distribution cabinet is located and output the ambient temperature data. Considering the physical characteristic of hot air rising naturally, a real-time temperature sensor inside the cabinet is installed above the heating area inside the distribution cabinet or at the inlet of the return air duct to detect the overall air temperature parameters accumulated by the long-term operation of electrical components within the sealed cabinet space and output the real-time operating temperature data inside the cabinet. Both the ambient temperature sensor and the real-time temperature sensor inside the cabinet use PT100 platinum resistance thermometers, utilizing the physical characteristic that the resistance value of the platinum wire changes regularly with temperature to convert the temperature physical quantity into a resistance electrical signal. For the temperature and resistance conversion circuit and sampling filtering algorithm of the PT100 platinum resistance thermometer, those skilled in the art can design it according to conventional analog-to-digital conversion technology; its specific hardware circuit wiring is well-known in the art and will not be described in detail here.

[0030] S102, as a preferred method, the parameter acquisition module synchronously acquires the load status of the main circuit of the distribution cabinet through the load current monitoring unit. The load current monitoring unit uses an LMK-0.66 current transformer. The LMK-0.66 current transformer is connected to the main incoming busbar or the high-power branch circuit busbar inside the distribution cabinet. Based on the law of electromagnetic induction, the LMK-0.66 current transformer converts the large current in the main circuit into a standard small current signal at a fixed ratio, and further converts it into a digital quantity through a signal conditioning circuit, forming real-time load operating current data. Since the heat dissipation power of electrical components such as circuit breakers, contactors, and frequency converters inside the distribution cabinet is usually positively correlated with the square of the actual current passing through them, continuously acquiring real-time load operating current data provides feedforward physical data support for the main control module to dynamically adjust the cooling air volume based on the heating trend.

[0031] S103, the parameter acquisition module controls the thermal imaging data acquisition unit to acquire the two-dimensional temperature distribution characteristics of the surfaces of components inside the distribution cabinet. The thermal imaging data acquisition unit is installed inside the cabinet door or at the top corner of the cabinet. To ensure the validity of the monitoring data, the field of view of the thermal imaging data acquisition unit covers the core heat-generating electrical components deployed inside the distribution cabinet. Based on the general principle of infrared radiation temperature measurement, the thermal imaging data acquisition unit captures the infrared radiation energy emitted by the surface of each electrical component and converts the infrared radiation energy into a two-dimensional digital temperature distribution matrix, forming thermal imaging data. This digital temperature distribution matrix consists of the actual temperature values ​​corresponding to multiple row and column coordinates. Specifically, the number of vertical pixel rows and horizontal pixel columns of this matrix is ​​determined based on the physical resolution of the infrared detector array inside the thermal imaging data acquisition unit. The thermal imaging data can concretize the overall temperature inside the cabinet into the local surface thermal distribution of each electrical component, realizing the transformation of temperature measurement from single-point temperature measurement to area array temperature measurement, thereby providing the system with dual evidence of location and temperature for identifying locally high-heat-generating components and executing zoned independent cooling control.

[0032] The S104 parameter acquisition module integrates ambient temperature data, real-time operating temperature data inside the cabinet, real-time operating current data of the load, and thermal imaging data, and periodically sends all the data to the main control module via an internal industrial communication bus. Due to inherent differences in the sampling frequencies of various sensors, the main control module incorporates a data caching circuit to ensure the completeness of subsequent multi-parameter linkage algorithm logic, aligning the received multi-dimensional sensing data in time. Specifically, the main control module adds a timestamp to each received data frame based on a unified system clock source and pre-sets a tolerance time window. The system divides the ambient temperature data, real-time operating temperature data inside the cabinet, real-time operating current data of the load, and thermal imaging data falling within the same tolerance time window into valid operating condition datasets at the same time cross-section, thereby eliminating errors caused by asynchronous data and providing a time-synchronized data foundation for the system to determine the operating mode and issue cooling control commands.

[0033] S20, the main control module determines the current working mode of the integrated intelligent power distribution cabinet air conditioning system with dynamic load adjustment. The working modes include temperature control mode and load current control mode.

[0034] S201, in this embodiment, the main control module receives the time-aligned valid operating condition dataset transmitted by the parameter acquisition module and parses the data to determine the current load operating status of the equipment. The system presets the temperature control mode to the default initial state. When the integrated intelligent distribution cabinet air conditioning system with dynamic load adjustment completes its initial power-on, or when the distribution cabinet is in a light-load operation phase, the main control module maintains the internal working mode flag in the temperature control mode. The temperature control mode mainly relies on real-time temperature data inside the cabinet for hysteresis feedback adjustment. Its control basis depends on the steady-state heat conduction principle in thermodynamics and is suitable for conventional working scenarios where the load current inside the cabinet is stable and the heat generation changes slowly.

[0035] S202, as a preferred method, involves the main control module monitoring the real-time load operating current data in the effective operating condition dataset to determine whether the system needs to switch to load current control mode. Due to the physical thermal inertia and hysteresis effect of the high-power components inside the distribution cabinet under heavy load conditions, relying solely on passive temperature feedback will lead to a delay in cooling replenishment due to thermal inertia. The main control module extracts the latest real-time load operating current data and performs a division operation with the pre-configured rated operating current constant of the main circuit of the distribution cabinet. Considering the logical completeness of the division operation, this rated operating current constant is fixed and calibrated at the factory and is always greater than 0. This processing logic avoids the abnormal situation where the denominator approaches 0 at the algorithm's underlying level. The main control module obtains the load rate parameter, representing the current power consumption of the main circuit, through this division operation.

[0036] S203, the main control module calls the internally stored load rate judgment threshold. This threshold is set between 60% and 80%, and its specific value is determined based on the heating characteristics of the inverters and high-power contactors within the distribution cabinet and the full-load temperature rise curve. To avoid biased judgments due to reliance on a single extreme value, the main control module introduces multi-dimensional logic based on physical correlations in the mode judgment. High load current inevitably leads to heat accumulation. The main control module extracts real-time temperature data from multiple consecutive time segments within the cabinet and calculates the temperature rise rate per unit time. The main control module compares the real-time calculated load rate parameter with the preset load rate judgment threshold, simultaneously considering the temperature rise rate. When the load rate parameter reaches or exceeds the load rate judgment threshold, and the temperature rise rate exceeds the preset temperature rise slope threshold, the main control module determines that the distribution cabinet is entering a high-power heating condition. At this point, simple temperature feedback is insufficient to match the dynamic heat source changes of the system, and the system immediately triggers state switching logic, updating the operating mode flag to load current control mode.

[0037] S204. To prevent frequent and disordered switching of system operating modes caused by transient surge currents in the power distribution network or high-frequency current spikes generated by the starting of large motors, the main control module introduces a time-debounce mechanism in the mode transition judgment. When the main control module initially detects that the above-mentioned multi-dimensional switching conditions are met, it activates an internal timer. Only after this state continuously meets the set confirmation time length will the main control module finally confirm and execute the mode switching action. The setting of this confirmation time length is based on the operating delay parameter of the main circuit breaker and the physical decay period of the transient starting current. After completing the final confirmation of the operating mode, the main control module locks the current operating mode flag and transmits the corresponding mode command to the lower-level processing unit to call the corresponding cooling capacity adjustment and multi-parameter linkage algorithm program.

[0038] S30, when in temperature control mode, the main control module compares the real-time operating temperature data inside the cabinet with the preset upper and lower limit temperatures. If the real-time operating temperature inside the cabinet is higher than the upper limit temperature, the air conditioning system in the cooling execution module is started to cool the inside of the cabinet. If the real-time operating temperature inside the cabinet is lower than the lower limit temperature, the air conditioning system is stopped.

[0039] S301, in some optional embodiments, when the system operating mode flag is in temperature control mode, the main control module continuously extracts real-time operating temperature data inside the cabinet from the effective operating condition dataset. The system internally has pre-set upper limit temperature representing the highest permissible operating environment and lower limit temperature representing the condition where cooling is not required. Based on the hysteresis characteristic in control theory, a temperature dead zone is formed between the upper and lower limits. The technical purpose of this setting is to avoid frequent start-stop cycles of the air conditioning system at critical temperature points, thereby shortening the compressor's mechanical life. The specific value of the upper limit temperature is generally set between 35°C and 40°C, determined based on the safe temperature resistance threshold of the most sensitive electrical components in the distribution cabinet. The value of the lower limit temperature is set between 25°C and 28°C. The main control module compares the real-time operating temperature data inside the cabinet with the upper and lower limits. If the real-time operating temperature data inside the cabinet is higher than the upper limit temperature, the main control module issues a start command, controlling the air conditioning system in the cooling execution module to start and output cold air to cool the cabinet. If the real-time operating temperature data inside the cabinet is lower than the lower limit temperature, the main control module issues a stop command, controlling the air conditioning system to stop operating.

[0040] S302, preferably, the air conditioning system in the refrigeration execution module adopts an integrated embedded design. To fully utilize space and improve the overall protection level of the distribution cabinet, the condenser and evaporator of the air conditioning system adopt a compact back-to-back physical layout, and are embedded in the side wall or top of the distribution cabinet. To avoid crosstalk between internal and external airflow and thermal bridging effects, a high-density polyurethane thermal insulation sealing layer is configured between the condenser and evaporator. The condenser is located on the outside and exchanges heat with the ambient air, while the evaporator is located on the inside and faces the heat-generating area inside the distribution cabinet. Through this embedded installation method, the outer shell of the air conditioning system penetrates the inner protective plate 2 of the cabinet and forms a flush fit with the outer protective plate of the distribution cabinet. This physical structure helps to shorten the transmission path of cold air into the cabinet, while reducing the risk of external dust and moisture entering the cabinet through external connection gaps. For the basic refrigeration cycle system of the air conditioning system compressor and throttling device, those skilled in the art can design it according to conventional vapor compression refrigeration technology, and its refrigerant cycle process is well known in the art and will not be described in detail here.

[0041] S303. During the continuous supply of low-pressure cold air to the distribution cabinet by the air conditioning system, condensation occurs near the air outlet because the evaporator surface temperature is typically lower than the dew point temperature of the air inside the cabinet. To mitigate the safety hazard of condensation dripping onto live electrical components and causing short circuits, the refrigeration execution module incorporates a guide bell structure at the air outlet of the air conditioning system. This guide bell structure features a gradually increasing cross-sectional area in the airflow direction. Based on the principle of continuity in fluid mechanics—that the velocity of a fluid in steady flow is inversely proportional to its cross-sectional area—the increased cross-sectional area of ​​the cold air as it flows through the guide bell structure naturally reduces its velocity. To prevent boundary layer separation and the resulting eddies that affect the uniform flow, the single-sided expansion angle of this guide bell structure is typically set between 15° and 30°. This physical deceleration process ensures that the concentrated cold air is uniformly diffused before entering the distribution cabinet, helping to mitigate the rapid temperature drop caused by direct cold airflow and thus eliminating the condensation conditions caused by the large accumulation of condensate at the outlet edge.

[0042] S304, at the lower edge of the cold air diffuser end of the guide bell structure, the system further incorporates a water-absorbing material layer. When extreme high humidity conditions cause trace amounts of condensation to still appear on the inner wall of the guide bell structure, these water droplets slide down the inclined inner wall of the guide bell structure under gravity and are intercepted and absorbed by the bottom water-absorbing material layer. The water-absorbing material layer is made of hydrophilic polymer fibers with a high water absorption rate. Combined with the micro-guide channels below the guide bell structure, the water absorbed by the water-absorbing material layer is guided through the air conditioning drain 1 at the bottom of the system to the evaporation water pan outside the distribution cabinet. To prevent backflow of high-temperature, high-humidity external air and dust along the air conditioning drain 1 and the drainage pipes from damaging the high protection level of the distribution cabinet, a U-shaped water seal structure or a physical one-way valve is installed in the middle section of the drainage pipes to achieve one-way discharge of condensate and physical isolation between internal and external airflow. The system utilizes the waste heat from the condenser exhaust to naturally evaporate the collected water. This physical anti-condensation mechanism, which combines the flow-guiding flared structure with the water-absorbing material layer, reduces the threat of condensate to electrical components from two dimensions: airflow equalization suppression and physical interception and diversion. This helps ensure the electrical operation safety of the system during temperature control and cooling.

[0043] S40, when in load current control mode, the main control module matches the target cooling air volume under the current mode based on the real-time operating current data of the load and the big data analysis model, and adjusts the cooling output of the cooling execution module. When the real-time operating current of the load is high, the cooling is enhanced, and when the load is low and the current is low, the cooling capacity is reduced to perform energy-saving operation.

[0044] S401, specifically in this embodiment, when the system operating mode flag is switched to the load current control mode, the main control module synchronously extracts the ambient temperature data, the real-time operating temperature data inside the cabinet, and the real-time operating current data of the load. Considering that the air humidity inside the cabinet has a direct physical impact on the specific heat capacity and dew point temperature of the air, thereby changing the efficiency of heat removal by the air, the parameter acquisition module is equipped with a conventional integrated temperature and humidity sensor to obtain the real-time relative humidity data inside the cabinet. Based on the basic thermophysical correlation, the above parameters are introduced as follows: the load current directly characterizes the heat source power of the core electrical components inside the distribution cabinet, the temperature difference between inside and outside the cabinet determines the basic heat conduction rate of the system, and the relative humidity is used to assist in correcting the effective enthalpy value of the cold air. To eliminate the interference of different physical dimensions and numerical magnitudes on the subsequent algorithm model weight allocation, the main control module uses the max-min normalization method to linearly scale each data, mapping its value to the dimensionless range of 0 to 1 to form a standardized input tensor.

[0045] In the S402, as a preferred approach, the main control module is equipped with a pre-built and trained big data analysis model. This model specifically employs a multi-layer feedforward neural network architecture. The internal hierarchical structure of this multi-layer feedforward neural network includes an input layer, at least two hidden layers (preferably two or three hidden layers to achieve a balance between nonlinear fitting capability and computational power), and an output layer. The number of neurons in the input layer matches the data dimension of the standardized input tensor, corresponding to the normalized ambient temperature, real-time operating temperature inside the cabinet, real-time load current, and real-time relative humidity inside the cabinet, respectively. The hidden layer nodes use a linear rectified function as the activation function to handle the nonlinear thermal coupling relationship between multiple physical parameters. The output layer contains a single neuron node, whose output corresponds to the target cooling air volume required to maintain the thermal balance of the distribution cabinet system under the current comprehensive operating conditions.

[0046] In S403, the standardized input tensor is fed into a multi-layer feedforward neural network via the input layer. Forward propagation calculations are performed in the hidden layers using predefined weights and bias matrices. To avoid the singularity risk associated with complex matrix inversion, the forward propagation process primarily relies on vector dot multiplication and addition. Through deep feature extraction of the physical data using the multi-layer feedforward neural network, the system implements multi-parameter linkage logic. The multi-layer feedforward neural network ultimately calculates and outputs the target cooling air volume value at the output layer. Based on the general calculation principle of weighted summation of network nodes, the logic for obtaining the target cooling air volume value is expressed by the following weight aggregation formula:

[0047] in, This represents the target cooling air volume calculated by the output layer; This represents the total number of neurons in the final hidden layer that are directly connected to the output layer. Indicates the last hidden layer The connection weight coefficients between each neuron and the output layer nodes; Indicates the last hidden layer The hidden feature value output by each neuron after being processed by a linear rectified function; This represents the bias constant of the output layer. The technical purpose of this calculation step is to directly map the complex thermal features extracted through multidimensional nonlinear coupling into specific physical control reference quantities that can be identified by the refrigeration execution module.

[0048] In step S404, after acquiring the target cooling air volume value, the main control module converts it into control commands for the refrigeration execution module. To avoid the algorithmic logic dead zone that exists in a single prediction model under extreme conditions, the main control module introduces multi-dimensional safety weighted judgment logic based on actual physical boundaries in this step. The main control module extracts the real-time load operating current data and the real-time operating temperature data inside the cabinet. When the real-time load operating current is high, causing an increase in the target cooling air volume value, or when the real-time operating temperature inside the cabinet exceeds the set upper limit temperature, the main control module directly executes the over-limit protection logic, controlling the refrigeration execution module to increase the operating frequency of the inverter compressor and the fan speed to forcibly enhance cooling. Under stable operating conditions with low load and low current and without triggering the upper temperature limit, the main control module controls the refrigeration execution module to proportionally reduce the fan speed and compressor frequency according to the target cooling air volume value, performing energy-saving operation. This mechanism, which integrates feedforward dynamic matching and physical boundary protection, helps overcome the hysteresis effect caused by simple temperature feedback control and achieves on-demand cooling.

[0049] To support the reliable operation of the big data analysis model in the main control module, the construction and training process of the multi-layer feedforward neural network (S405) is based on a supervised learning mechanism. The training sample dataset comes from the time-series synchronization status logs accumulated by the same model of distribution cabinet during long-term historical operation. The input features of the sample data include historical ambient temperature, historical cabinet temperature, historical load current, and historical relative humidity. The output label of the sample data is the empirical value of the actual air conditioning cooling volume that can maintain the cabinet temperature within the safe set range under the corresponding historical operating conditions. During the model training phase, mean squared error is used as the loss function, and the connection weight coefficients and bias constants within the multi-layer feedforward neural network are continuously updated iteratively through the backpropagation algorithm combined with the gradient descent optimizer. The training process continues until the value of the loss function converges to within the preset error threshold range, thus completing the solidification and calibration of the network parameters. To address the mechanical performance degradation and data drift issues caused by long-term equipment operation, the main control module has built-in model evaluation logic that periodically calculates the prediction residual between the predicted value of the target cooling volume and the actual required cooling volume. When the average residual exceeds the allowable confidence interval, the system automatically extracts recent valid operation logs as new samples to fine-tune the hidden layer weights of the multi-layer feedforward neural network online. This continuous learning logic ensures that the model deployed in the system has the long-term fitting ability to accurately map multi-dimensional physical parameters to cooling demand.

[0050] The S50 main control module divides the internal cooling cabinet into different cooling zones based on thermal imaging data and the heat generated by the components, and independently adjusts the output fan speed of the cooling execution module for each cooling zone to achieve zoned temperature control within the cabinet.

[0051] S501, in another embodiment, the main control module extracts the two-dimensional digital temperature distribution matrix transmitted by the thermal imaging data acquisition unit. Since a single surface temperature only reflects the current heat accumulation state, to assess the actual heat dissipation requirements of each area, the main control module introduces the rated heat dissipation attribute characteristics of electrical components as a supplementary judgment criterion in addition to the temperature dimension. The main control module internally stores a three-dimensional spatial layout mapping table of the distribution cabinet, which records the physical coordinates of core heat-generating electrical components such as circuit breakers, contactors, and frequency converters within the distribution cabinet, as well as their factory-calibrated nominal heat output parameters. Because two-dimensional images inherently lack depth information, the thermal imaging data acquisition unit is installed in a fixed position to avoid coordinate mapping failure. Based on the principle of perspective projection geometry, the main control module maps and aligns the pixel coordinates in the two-dimensional digital temperature distribution matrix with the actual physical coordinates in the three-dimensional spatial layout mapping table, thereby obtaining the real-time surface temperature and nominal heat output of the target electrical component corresponding to each spatial coordinate point. Based on the principle of physical heat load assessment, the main control module performs multi-dimensional weighted fusion of the real-time surface temperature and nominal heat output. This operation can prevent the system from misjudging non-core heat-generating components with high transient surface temperatures but low actual heat generation power as areas with high heat dissipation requirements, ensuring that the cooling distribution logic conforms to the true thermodynamic causal relationship.

[0052] S502, as a preferred method, the main control module, based on the aligned multidimensional thermal data mentioned above, divides the physical space inside the distribution cabinet into independent cooling zones using a grid. To achieve airflow guidance and cooling volume statistics, the main control module divides the overall volume inside the distribution cabinet into multiple independent, bounded three-dimensional cooling zones according to a preset physical space grid scale. The main control module extracts the spatial node data of all nodes within the same independent cooling zone and obtains the comprehensive heat load of that independent cooling zone through integral aggregation. The calculation logic of the comprehensive heat load of the zone is expressed by the following linear aggregation formula:

[0053] in, Indicates the first The combined heat load of each independent cooling zone; Indicates the division in the first The total number of effective spatial nodes within each independent cooling zone; Indicates the first in this region Each spatial node corresponds to the real-time surface temperature of an electrical component; Indicates the first in this region Each spatial node corresponds to the nominal heat generation of the electrical component; This represents the preset temperature characterization weighting coefficient; This represents the preset power characterization weighting coefficient. Wherein, and The values ​​of both are between 0 and 1, and the sum of their values ​​is set to 1. The specific ratios of the above weighting coefficients are comprehensively calibrated based on the specific heat capacity of the casing material and the factory thermal parameters of the mainstream electrical components in the distribution cabinet. The technical purpose of this calculation step is to quantify the dispersed point-based heating state into a block-level cooling urgency index, providing a numerical basis for the main control module to implement differentiated allocation of cooling resources.

[0054] S503: After obtaining the comprehensive heat load of all independent cooling zones, the main control module further calculates the target allocation wind speed required for each independent cooling zone. Since the total output cooling capacity provided by the refrigeration execution module is physically limited by the compressor displacement within the same time frame, the main control module allocates the target allocation wind speed proportionally based on the dynamic ratio of the comprehensive heat load of a single independent cooling zone to the total heat load of the distribution cabinet. Considering the completeness of the algorithm logic, to avoid abnormal situations where the wind speed allocation approaches zero due to extremely low heat load in a certain area, the main control module sets a basic wind speed lower limit threshold for each independent cooling zone to meet basic heat exchange requirements. Simultaneously, the main control module introduces an over-limit intervention mechanism based on temperature extreme values ​​in this step. When the main control module detects that the local real-time surface temperature within an independent cooling zone exceeds the set safety tolerance threshold, the system triggers the over-limit protection logic. The value range of this safety tolerance threshold is typically set between 65℃ and 80℃, specifically determined based on the derating operation curve of the core electrical insulation material. At this time, the main control module forcibly assigns the maximum allowable target allocation wind speed to that independent cooling zone. This multi-dimensional safety logic helps ensure that the equipment receives the necessary cooling intervention under localized overheating conditions.

[0055] The S504 main control module converts the target distribution airflow speed of each independent cooling zone into underlying hardware control signals to drive the cooling execution module to reconstruct the physical airflow field inside the distribution cabinet. The internal airflow distribution nodes of the cooling execution module are equipped with two-dimensional electric air outlet guide devices driven by multiple sets of micro stepper motors, and multiple dedicated zone fans are independently installed at different height levels inside the distribution cabinet. Based on the three-dimensional physical boundary coordinates of the high heat load area and the spatial reference point of the air outlet, the main control module calculates the corresponding airflow deflection angle using the principle of spatial vector angle calculation.

[0056] Specifically, the main control module establishes a three-dimensional Cartesian coordinate system with the center of the air outlet as the origin. It obtains the target position vector of the local high-heat area within this coordinate system. Then, by calculating the spatial angle between this target position vector and the current air outlet reference normal vector, it determines the required horizontal and pitch deflection angles. Subsequently, the main control module outputs pulse control signals to adjust the deflection attitude of the louvers of the two-dimensional electric air outlet guide device, directing the cold air output from the air conditioning system to this local high-heat area. Simultaneously with airflow angle guidance, the main control module outputs corresponding variable frequency control voltage signals to independently adjust the speed of the dedicated zone fan covering this high-heat-load area, thereby increasing the local air convection exchange velocity in that area. Through the aforementioned hardware execution actions that link the directional deflection of the air supply angle with independent local velocity boosting, the system constructs an in-cabinet zoned temperature control mechanism tailored to the different spatial heating characteristics.

[0057] S60, the pipeline circulation module guides air circulation and intercepts dust through the multi-layer air filter 4. The main control module drives the fan of the cooling execution module to generate reverse airflow to remove the dust attached to the surface of the air filter 4 according to the set cleaning cycle frequency.

[0058] S601, In this embodiment, to ensure the air cleanliness inside the distribution cabinet, the system is equipped with an air intake and return duct system. The main control module controls the duct flow module to guide external cold air into the cabinet through the air intake duct, and discharges or circulates the warm air that has absorbed heat inside the cabinet through the return duct. An air filter 4 is physically embedded at the air intake duct inlet side of the distribution cabinet, and a dustproof cotton 5 is installed inside the air filter 4. The dustproof cotton 5 is made of multi-layer composite resistance fiber material to intercept suspended particulate matter in the air. Based on the filtration and interception principle in aerodynamics, when the airflow entering the distribution cabinet passes through the air filter 4, dust particles are captured and attached by the mesh of the dustproof cotton 5, thereby ensuring that the dust content of the air entering the cabinet meets the preset industrial equipment safety operation standard requirements. This physical isolation structure helps reduce the risk of decreased insulation resistance caused by dust adhering to the surface of electrical components.

[0059] S602, as a preferred method, sees an increase in dust accumulation on the windward side of the dustproof cotton 5 as the system runs, leading to increased airflow resistance in the intake duct. To ensure sufficient ventilation, the dustproof cotton 5 needs to be cleaned or replaced periodically. To reduce the frequency of manual cleaning, maintenance, and replacement, the system incorporates self-cleaning filter control logic. The main control module has a preset cleaning cycle frequency parameter, the specific value of which is set or dynamically configured based on the dust concentration level of the environment where the distribution cabinet is located. Typically, the cleaning cycle in a conventional industrial environment is set between 168 hours and 336 hours. The main control module has a built-in timing unit to track the cumulative operating time of the dynamically load-adjustable integrated intelligent distribution cabinet air conditioning system. When the cumulative operating time reaches the set cleaning cycle frequency parameter, the main control module triggers a self-cleaning action command. For the basic timing statistics and data comparison logic, those skilled in the art can program it using conventional microcontroller clock configuration technology; the underlying timing control is well-known in the field and will not be elaborated upon here.

[0060] Before executing the self-cleaning action, the S603 main control module employs a reverse airflow cleaning mechanism to prevent dust from being blown into the electrical distribution cabinet during the cleaning process. The air intake duct system is equipped with a bidirectional variable frequency fan that supports forward and reverse rotation. Considering that a direct commutation of a high-power motor at high speed would generate destructive reverse electromotive force surges and mechanical torque impacts due to rotor inertia, the main control module outputs a gradual braking signal with slope control after receiving the self-cleaning action command. This gradual braking signal controls the bidirectional variable frequency fan to smoothly decelerate to a stop state and temporarily suspends the current temperature control or load current control mode. After confirming that the fan has stopped, the main control module sends a reverse phase-switching command to the fan drive circuit, driving the bidirectional variable frequency fan to rotate in the opposite direction at a preset cleaning speed. This commutation drive logic physically transforms the system's original suction state into an exhaust state.

[0061] S604, the fan reversal operation creates a physical airflow field inside the air inlet duct that is opposite to the normal cooling airflow direction. The reverse airflow blows at high speed from inside the distribution cabinet outwards, generating outward aerodynamic thrust as it penetrates the air filter 4. Based on the principle of fluid dynamics equilibrium, when this reverse aerodynamic thrust exceeds the mechanical adhesion between dust particles and the fibers of the dustproof cotton 5, the dust accumulated on the windward side of the dustproof cotton 5 is physically peeled off and blown out of the air inlet duct opening. To ensure effective dust removal while controlling motor heat consumption, the continuous blowing time of the reverse airflow is typically set between 30 and 60 seconds. After the set blowing time is completed, the main control module restores the bidirectional variable frequency fan to the forward airflow state and reactivates the suspended normal operating mode, allowing the integrated intelligent distribution cabinet air conditioning system with dynamic load adjustment to resume normal temperature regulation and environmental control. This self-cleaning filter mechanism uses reverse airflow to physically purge the air, ensuring the long-term unobstructed flow of the air intake duct and effectively reducing the frequency of equipment maintenance and dustproof cotton replacement for on-site maintenance personnel.

[0062] The S70 safety monitoring module monitors the equipment status. When the LXK3 limit switch detects that the distribution cabinet door is open, it disconnects the air conditioning system to prevent the loss of cooling. When a fault is detected in the air conditioning system, it cuts off the air conditioning system control loop and switches to the backup fan assembly for heat dissipation redundancy protection. At the same time, it generates an alarm signal and realizes remote data transmission and early warning closed loop through the Internet of Things communication unit.

[0063] S701, in this embodiment, considering that frequent opening of the distribution cabinet door during operation and maintenance will lead to a significant loss of cooling capacity, and that the intrusion of high-temperature and high-humidity external air will cause condensation on the low-temperature surface inside the cabinet, the system is equipped with an interlocking protection mechanism between the air conditioner and the cabinet door. A magnetic limit switch is installed between the physical door frame and the movable door panel of the distribution cabinet. The input port of the main control module continuously collects the on / off level signal of this limit switch. To prevent momentary fluctuations in the level signal caused by slight collisions or vibrations to the cabinet door, the main control module is equipped with digital anti-jitter filtering logic with a time window of 5 seconds. When the limit switch is detected to be in a stable open state, the main control module issues a stop command, cutting off the operation control signal of the refrigeration execution module.

[0064] After executing the shutdown command to open the cabinet door, the S702 main control module simultaneously activates the compressor shutdown delay protection mechanism. Based on the fluid pressure characteristics of the vapor compression refrigeration cycle, after the compressor stops, the high-pressure condensing side and the low-pressure evaporating side inside the system require a certain amount of time to achieve physical pressure balance. If power is briefly restored in an unbalanced state, the compressor motor will face a significant stall-start current surge. Therefore, the main control module has a built-in timer that forcibly sets a restart delay window typically between 3 and 5 minutes. During this delay window, even if the limit switch recloses, the main control module will still suspend the start command until the timer expires before resuming normal operation of the dynamically load-regulated integrated intelligent power distribution cabinet air conditioning system. This interlocking delay logic helps ensure the electrical safety and mechanical lifespan of the core hardware.

[0065] S703, as another preferred embodiment of the present invention, the integrated parameter acquisition module not only acquires environmental data but also monitors the underlying physical operating status of the cooling execution module in real time. To prevent rapid heat accumulation inside the distribution cabinet due to unexpected shutdown of the air conditioning system, the system constructs an emergency heat dissipation mechanism based on hardware redundancy.

[0066] The main control module determines the air conditioner's fault status based on two physical criteria: First, it monitors the compressor's operating current. When the actual current exceeds 120% of the rated current threshold and remains so for more than 10 seconds, it is considered an overload fault. This current threshold is determined based on the compressor motor's inverse-time overload protection characteristic curve. Second, it compares the real-time temperature difference between the inlet and outlet air ducts. When the temperature difference remains below 3°C for 15 minutes, it is considered a refrigerant leak or mechanical failure. This lower limit of the temperature difference is calibrated based on the refrigerant's basic enthalpy difference under standard operating conditions. Once the main control module confirms a fault, the system cuts off the power supply to the refrigeration execution module and automatically switches to start the backup DC cooling fan installed on the top of the cabinet. To ensure the redundancy system remains effective in the event of a main AC power supply failure, the backup DC cooling fan is connected to an independent DC control power supply inside the distribution cabinet. The backup fan exhausts air at a set speed to maintain basic forced convection heat transfer, while the main control module outputs audible and visual alarm signals to maintain the equipment's minimum heat dissipation requirements under extreme conditions.

[0067] The S704, designed to meet the centralized management and maintenance needs of modern industry, is an integrated intelligent power distribution cabinet air conditioning system with dynamic load adjustment. It establishes bidirectional data communication with a remote cloud platform via its integrated IoT communication unit. The main control module serializes and encapsulates locally collected information such as real-time operating temperature inside the cabinet, real-time load current, operating mode flags, and fault warning status using JSON data format, and sends this data to the cloud platform according to a set heartbeat cycle. In the command transmission link, the IoT communication unit receives remote start / stop or parameter modification commands from the cloud platform. To avoid logical conflicts between remote scheduling and on-site maintenance that could lead to personal safety hazards, the main control module has an internal authorization arbitration mechanism. The main control module prioritizes checking the lock status of the local physical control panel and only responds to remote scheduling commands when the local device is not in maintenance lock mode. Furthermore, the main control module has communication anomaly handling logic. If it does not receive a handshake response from the cloud platform within several consecutive heartbeat cycles, the main control module determines that the network is disconnected, and the system will automatically revert to the local temperature control mode to prevent equipment stagnation due to missing remote commands. This integrated monitoring system, which combines underlying hardware interlocking with upper-level data transmission, helps improve the transparency of equipment operation and maintenance and the efficiency of fault response.

[0068] Specific application examples: Implementation scenario setting: This embodiment selects the core frequency converter distribution cabinet of a large steel plant's continuous rolling production line as the application scenario. The workshop environment is a typical high-temperature (ambient temperature approximately 32°C) and high-dust industrial environment. The distribution cabinet is equipped with a high-power frequency converter, contactor, and main incoming line circuit breaker.

[0069] Hardware parameter settings: The ambient temperature sensor and the cabinet temperature sensor are both PT100 type; the current transformer is LMK-0.66 type; the system is set to a target temperature upper limit of 38℃ and a lower limit of 26℃; the self-cleaning cycle is set to 168h; the load rate judgment threshold is set to 70%.

[0070] Specific implementation steps: For basic monitoring and control under steady-state conditions, during the early shift equipment preheating and light-load rolling stages, the system operates in the default temperature control mode. The parameter acquisition module obtains ambient temperature data through a PT100 sensor installed on the shaded side of the cabinet, and acquires real-time operating temperature data inside the cabinet through another PT100 sensor installed at the return air vent. Simultaneously, all acquired data is appended with a unified timestamp and sent to the main control module's data buffer circuit for timing alignment, forming a time-synchronized and effective operating condition dataset.

[0071] In this mode, the main control module performs a hysteresis comparison based solely on the real-time temperature data inside the cabinet, comparing it with the preset upper limit of 38℃ and lower limit of 26℃. When the temperature inside the cabinet slowly rises to 38℃, the main control module issues a start command to control the air conditioning system to begin cooling; when the temperature is reduced to 26℃, a stop command is issued. During this stage, the airflow guide flare structure of the air conditioning outlet achieves uniform airflow diffusion by reducing the cold air velocity. Combined with the water-absorbing material layer at the bottom and the U-shaped water seal, it effectively prevents condensation dripping caused by the temperature difference between the inside and outside, ensuring electrical safety.

[0072] Dynamic feedforward regulation under high-intensity operation: Entering the afternoon high-intensity continuous rolling stage, the billet load increased sharply. The LMK-0.66 current transformer connected to the main incoming busbar detected a sudden increase in the real-time operating current data of the load. After calculation by the main control module, the load rate reached 85% (exceeding the 70% threshold), and the temperature rise rate per unit time also exceeded the preset slope. After confirmation by the time anti-jitter mechanism, the main control module determined that the system had entered a high-power heating condition and immediately switched the operating mode flag to the load current control mode.

[0073] In this mode, the main control module extracts normalized load current, internal and external temperature, and relative humidity data to form a standardized input tensor. This tensor is fed into a pre-trained multi-layer feedforward neural network model, and through forward propagation calculations in the input layer, two hidden layers, and the output layer, it directly maps the target cooling air volume required for the current operating condition. This calculation logic aggregates multi-dimensional nonlinear thermal characteristics into specific physical control quantities, and its core calculation process is expressed by the following weight aggregation formula:

[0074] in, Indicates the target cooling air volume; This represents the total number of neurons in the final hidden layer. For the first The connection weights between each neuron and the output layer; For the first The output feature values ​​of each neuron; This is the output layer bias constant. The main control module bases this on... By combining the numerical values ​​with physical boundary protection logic, a high-power cooling control command is output in advance before the temperature inside the cabinet rises significantly, thereby increasing the frequency of the inverter compressor and the speed of the fan to achieve precise cooling on demand and effectively overcome the lag effect of traditional control.

[0075] Localized overheating was addressed through thermal imaging intervention. During continuous full-load operation, the thermal imaging data acquisition unit installed inside the cabinet door captured a two-dimensional digital temperature distribution matrix, revealing that the surface temperature of the IGBT module area inside the inverter reached 68℃ (triggering the 65℃ safety tolerance threshold). The main control module immediately aligned the pixel coordinates of this matrix with the internally stored three-dimensional spatial layout mapping table, obtaining the real-time surface temperature and nominal heat generation parameters of this area. The system then calculated the comprehensive heat load of this independent cooling area, the calculation logic of which is expressed by the following linear aggregation formula:

[0076] in, For the first The overall heat load of each region; This represents the total number of nodes within the region. and These are the real-time surface temperature and nominal heat generation of the node, respectively; and The corresponding weighting coefficients. Because the area triggered a temperature exceedance, the main control module forcibly assigned it the maximum target wind speed. Subsequently, the main control module calculated the spatial vector angle and output a pulse signal to drive the louvers of the two-dimensional electric air outlet guide device to deflect, directing the cool air towards the inverter's physical area. Simultaneously, it independently increased the speed of the dedicated fan in that area, quickly dissipating the localized hot spots.

[0077] With maintenance and safety redundancy protection, after accumulating 168 hours of operation, the system automatically triggers a self-cleaning program during downtime. The main control module smoothly brakes the bidirectional variable frequency fan and drives it to rotate in the opposite direction for 45 seconds. The resulting reverse airflow physically peels off and blows away the steel slag dust from the outside of the air filter 4 and dustproof cotton 5. During one operation, the air conditioning system failed due to voltage fluctuations on the plant's AC bus. The main control module detected that the temperature difference between the inlet and outlet air was below 3°C for 15 consecutive minutes, immediately determining it as an air conditioning failure. It cut off the circuit and automatically switched to a backup fan powered by an independent DC control power supply for emergency cooling. Simultaneously, it sent alarm data in JSON format to the central control room via the IoT communication unit, ensuring the equipment was protected from thermal runaway.

[0078] Experimental verification scheme and effect comparison: To verify the superiority of the technical solution of the present invention, a typical high load surge condition (60 minutes) was selected in the above scenario, and the multi-parameter dynamic linkage scheme of the present invention was compared with the single temperature feedback control scheme of the traditional distribution cabinet air conditioner in parallel.

[0079] Experimental conditions: Initial state: The real-time temperature inside the cabinet is stable at 30℃, and the ambient temperature is constant.

[0080] Interference event: When the system was running for 20 minutes, the load current of the main circuit of the distribution cabinet suddenly increased from 40% to 90% and continued to operate at a high level.

[0081] Process observation and data comparison: The temperature change trajectory during the experiment is as follows Figure 4 As shown in the figure, the horizontal axis represents the continuous operating time of the system, measured in minutes, while the vertical axis represents the real-time monitored air temperature inside the distribution cabinet, measured in degrees Celsius. A vertical gray dotted line marks the 20-minute mark on the horizontal axis, which is the starting point of the intervention for the high-heat interference event in this experiment, where the load current suddenly increases significantly.

[0082] Control group (traditional temperature feedback control scheme): its temperature change trajectory corresponds to Figure 4 The solid black line in the diagram clearly shows that after the load surge at the 20-minute mark, the temperature sensor initially failed to detect any abnormalities due to the thermal inertia of the high-power components. It wasn't until around the 30-minute mark that the overall temperature inside the cabinet exceeded the 38°C upper limit, and the air conditioning system began operating at full load. Because of the significant heat buildup in the earlier stages, there was a physical lag in the cooling output, resulting in a severe overshoot in the cabinet temperature. The highest peak temperature accurately corresponds to 44°C on the vertical axis, putting the inverter at risk of derating or even tripping its thermal protection circuit breaker.

[0083] Experimental group (the scheme of this invention): its temperature change trajectory is as follows: Figure 4 The dark gray dashed line represents this. This line visually shows that at the moment of sudden load increase at the 20-minute mark, the main control module predicted the thermodynamic trend through the aforementioned neural network model and immediately increased the cooling capacity. Due to the early intervention of cooling capacity compensation, the enormous heat that the components were about to dissipate was effectively offset, and the temperature inside the cabinet only showed a gradual upward trend, with its highest peak firmly suppressed at 37.5°C on the vertical axis, before steadily falling back and maintaining within the set safe range.

[0084] Comparison conclusion: Through the Figure 4 The intuitive comparison of the two temperature change curves and the experimental data fully demonstrate that the present invention, by introducing load current as a feedforward judgment parameter and combining it with a neural network model and a zoned temperature control mechanism based on thermal imaging, overcomes the hysteresis effect and thermal overshoot problem caused by passive temperature measurement in traditional temperature control systems, effectively reduces the extreme thermal stress of the core electrical components of the distribution cabinet, and improves the continuity and safety of system operation.

[0085] Example 3 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0086] Example 4 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0089] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An integrated intelligent distribution cabinet air conditioning system with dynamic load regulation, characterized in that, include: Distribution cabinet enclosure; A cooling execution module is installed in the power distribution cabinet and is used to cool the inside of the power distribution cabinet. The parameter acquisition module is used to collect real-time operating current data of the electrical equipment inside the distribution cabinet and real-time operating temperature data inside the cabinet. The main control module is electrically connected to the parameter acquisition module and the refrigeration execution module respectively; the main control module is used to determine the current working mode of the air conditioning system based on the collected real-time operating current data of the load and the real-time operating temperature data inside the cabinet. The operating mode includes a load current control mode. When the operating mode is the load current control mode, the main control module dynamically adjusts the cooling capacity of the cooling execution module according to the real-time operating current data of the load.

2. The dynamic load-regulating intelligent distribution cabinet air conditioning system according to claim 1, characterized in that, The operating mode also includes a temperature control mode. When the operating mode is the temperature control mode, the main control module compares the real-time operating temperature data inside the cabinet with the preset upper and lower temperature limits, and controls the start or stop of the refrigeration execution module.

3. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, When the load rate of the real-time operating current data of the load and the temperature rise rate of the real-time operating temperature data inside the cabinet both exceed the preset thresholds used to indicate that the power distribution cabinet will enter a high-power heating condition, the main control module determines that the working mode is switched to the load current control mode.

4. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, The main control module is equipped with a big data analysis model. In the load current control mode, the main control module takes the real-time operating current data of the load and the real-time operating temperature data inside the cabinet as input, calculates the target cold air volume through the big data analysis model, and adjusts the cooling capacity according to the target cold air volume.

5. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, The refrigeration execution module includes: Variable frequency compressors, condensers, evaporators, and throttling devices; The main control module dynamically adjusts the cooling capacity by regulating the operating frequency of the variable frequency compressor.

6. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, The parameter acquisition module also includes a thermal imaging data acquisition unit, used to acquire a two-dimensional digital temperature distribution matrix inside the power distribution cabinet; The main control module divides the internal cooling area of ​​the power distribution cabinet into independent cooling areas according to the two-dimensional digital temperature distribution matrix, and independently adjusts the airflow speed delivered to each independent cooling area.

7. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, It also includes a safety monitoring module, which includes a limit switch for detecting the open state of the distribution cabinet door; When the limit switch detects that the power distribution cabinet door is open, the main control module controls the cooling execution module to stop running and restarts after a delay after the power distribution cabinet door is closed.

8. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, The cooling execution module also includes a backup fan assembly; When the main control module detects a malfunction in the cooling execution module, it cuts off the control circuit of the cooling execution module and starts the backup fan assembly for heat dissipation.

9. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, It also includes an Internet of Things (IoT) communication unit, which is connected to the main control module; The IoT communication unit is used to send the real-time operating current data of the load, the real-time operating temperature data inside the cabinet, and the operating mode to a remote cloud platform.

10. The integrated intelligent distribution cabinet air conditioning system with dynamic load regulation according to claim 1, characterized in that, It also includes air filters installed in the air intake duct; The fan in the refrigeration execution module is a bidirectional variable frequency fan; The main control module is also used for: When the cumulative running time reaches the preset cleaning cycle, the bidirectional variable frequency fan is controlled to rotate in reverse to generate reverse airflow to remove the dust attached to the air filter.

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