Fireproof and high temperature monitoring and early warning control system for electrical control box

Abstract

The application discloses a kind of electrical control box fire prevention high temperature monitoring early warning control system, it is related to electrical equipment safety monitoring technical field, including thermal state acquisition module, thermal gradient construction module, thermal inertia analysis module, thermal diffusion prediction module, risk assessment module and hierarchical control module.The application is established by multiple space correlation thermal nodes in control box interior, the temperature gradient change between different nodes is calculated, dynamic thermal gradient matrix is formed, the problem that traditional temperature detection can only judge overall temperature height, cannot find local contact anomaly, terminal heating and early hotspot formation is solved, can identify abnormal heat source before overall temperature reaches dangerous threshold, improve fire early detection capability, reduce the risk of missed detection.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment safety monitoring technology, specifically to a fire prevention and high temperature monitoring and early warning control system for electrical control boxes. Background Technology

[0002] Existing fire prevention technologies for electrical control boxes mainly employ temperature sensors, smoke sensors, and infrared detection modules to monitor the internal status of the control box and trigger alarms or fire suppression measures when the temperature exceeds a set threshold or smoke is detected. For example, some intelligent distribution boxes use a combination of temperature and smoke sensors to determine the fire status and control the fire suppression module to perform fire suppression operations; some control cabinet fire prevention systems use multiple temperature sensors to collect the internal temperature of the cabinet and combine it with the ambient temperature for risk analysis.

[0003] However, the aforementioned technologies still have certain shortcomings: First, most existing solutions rely on single-point temperature threshold judgments, which can only identify problems that have already reached a clearly high-temperature state, but cannot identify potential faults during the slow temperature rise process; second, there are heat conduction relationships between different components inside the control box, and single temperature data cannot reflect the location of hotspot formation and the trend of heat diffusion; third, existing systems typically employ fixed alarm strategies, and cannot take proactive control measures such as adjusting heat dissipation, load adjustment, and fault isolation according to the degree of risk development. Therefore, a fire prevention and high-temperature monitoring and early warning control system is needed that can model the evolution process of the thermal state inside the control box and take control measures in advance based on the development trend of thermal risks. Summary of the Invention

[0004] The purpose of this invention is to provide a fireproof and high-temperature monitoring and early warning control system for electrical control boxes, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a fire prevention and high-temperature monitoring and early warning control system for an electrical control box, the control system comprising: The thermal status acquisition module is used to collect temperature data of multiple preset thermal nodes inside the electrical control box, operating current data of corresponding electrical components, ambient temperature data of the box, and air flow parameters inside the box. The thermal gradient construction module, connected to the thermal state acquisition module, is used to establish a thermal field model inside the control box based on the spatial coordinate relationship of each thermal node, calculate the thermal gradient value between any adjacent thermal nodes, and form a real-time thermal gradient matrix. The thermal inertia analysis module, connected to the thermal gradient construction module, is used to establish a thermal accumulation model based on the historical temperature change process of each thermal node, calculate the thermal inertia parameters of each thermal node, and generate the corresponding thermal state evolution curve. The thermal diffusion prediction module, connected to the thermal inertia analysis module, is used to construct a thermal diffusion prediction model based on the thermal gradient matrix, thermal inertia parameters, and airflow parameters, and to predict the temperature change trend and thermal diffusion path of each thermal node within multiple preset time windows in the future. The risk assessment module, connected to the heat diffusion prediction module, is used to calculate the thermal risk index based on the predicted heat diffusion path, hot spot propagation speed, heat accumulation degree and device temperature tolerance margin, and generate the corresponding risk level. The hierarchical control module, connected to the risk assessment module, is used to control the enclosure heat dissipation device, local heat insulation device, power supply switching device, fault branch isolation device and alarm device to execute corresponding control strategies according to different risk levels, so as to maintain the internal thermal state of the control box within a safe operating range.

[0006] Preferably, the thermal status acquisition module includes at least a busbar thermal node acquisition unit, a circuit breaker thermal node acquisition unit, a contactor thermal node acquisition unit, a PLC controller thermal node acquisition unit, a power module thermal node acquisition unit, a cable connection end thermal node acquisition unit, and a enclosure environment thermal node acquisition unit. Each thermal node corresponds to a unique spatial coordinate, and a three-dimensional thermal node distribution network is established inside the control box.

[0007] Preferably, the thermal gradient construction module calculates the thermal gradient value based on the spatial distance between thermal nodes and the real-time temperature difference, and establishes a thermal gradient matrix according to the topological relationship of the thermal nodes. When the thermal gradient change rate of a local area continuously exceeds the preset gradient threshold, the area is marked as a hotspot initiation area without waiting for the overall temperature to reach the alarm threshold.

[0008] Preferably, the thermal inertia analysis module uses a sliding time window to statistically analyze the short-cycle, medium-cycle, and long-cycle thermal accumulation of each thermal node, and calculates the thermal inertia coefficient based on the thermal accumulation change trend of each time window. The thermal inertia coefficient is used to correct the impact of instantaneous temperature on the risk assessment results, so as to distinguish between load heating and abnormal continuous heating state.

[0009] Preferably, the heat diffusion prediction module constructs a heat diffusion propagation network based on the thermal gradient matrix, airflow direction, airflow tassels, device thermal capacity, and internal heat conduction path of the enclosure, iteratively predicts the temperature distribution of each hot node at multiple future prediction times, and outputs the hot spot propagation direction, propagation speed, and hot spot coverage area.

[0010] Preferably, the risk assessment module constructs a comprehensive thermal risk assessment model based on the hotspot propagation speed, hotspot duration, thermal inertia coefficient, device temperature tolerance margin, thermal diffusion coverage area, and hotspot density, calculates the thermal risk index, and classifies the risk level into five levels: normal state, attention state, early warning state, dangerous state, and emergency state.

[0011] Preferably, the graded control module adopts a progressive control strategy based on the risk level, including increasing the speed of the cooling fan, adjusting the direction of the air guide mechanism, activating the local heat insulation mechanism, cutting off non-critical loads, isolating abnormal branches, and cutting off the main power supply to the control box. The latter control action is automatically executed if the thermal risk index continues to rise after the former control action is executed.

[0012] Preferably, the control system further includes a digital thermal fingerprint module, which is used to establish a thermal distribution benchmark model under normal operating conditions of the control box, continuously calculate the degree of deviation between the current thermal field model and the benchmark model during operation, and when the degree of deviation continuously exceeds a preset threshold, the corresponding area is identified as a potential aging area and maintenance warning information is generated in advance.

[0013] Preferably, the control system further includes an environmental compensation module, which is used to collect the external ambient temperature, solar irradiance and equipment load rate of the control box, and dynamically compensate the thermal gradient matrix and thermal inertia parameters to reduce the misjudgment of risk assessment results caused by high ambient temperature and load fluctuations, and improve the accuracy of hotspot identification.

[0014] Preferably, the control system further includes a learning module for recording historical data of thermal field evolution of each thermal node under different load conditions, establishing a thermal state evolution sample library, and dynamically correcting the model parameters of the thermal diffusion prediction model and risk assessment model based on historical thermal diffusion trajectories, so that the prediction results continuously and adaptively optimize with the operating status of the control box, thereby improving the early warning capability of fire formation and the accuracy of control strategy matching.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention establishes multiple spatially related thermal nodes inside the control box, calculates the temperature gradient changes between different nodes, and forms a dynamic thermal gradient matrix. This solves the problem that traditional temperature detection can only judge the overall temperature and cannot detect local contact abnormalities, terminal heating, and early hot spot formation. It can identify abnormal heat sources before the overall temperature reaches the dangerous threshold, improve the early detection capability of fires, and reduce the risk of missed detection. 2. This invention calculates the cumulative heat and thermal inertia coefficient by cumulatively analyzing the historical temperature change process, and determines the long-term thermal load status of the equipment. It solves the problem that existing systems cannot identify hidden risks such as insulation aging and material performance degradation caused by long-term low-temperature operation. It can detect equipment status of "normal temperature but continuous accumulation of thermal damage", realize predictive maintenance, and improve the reliability of equipment operation. 3. This invention combines thermal gradient direction, airflow state, component heat capacity, and historical heat propagation patterns to predict future hotspot diffusion paths, solving the problem that traditional systems can only detect current anomalies but cannot judge the development trend of anomalies. It can predict the direction of risk propagation in advance, providing a time window for the execution of control strategies and reducing the probability of fire accidents escalating. 4. This invention dynamically matches different control actions based on the risk index to achieve heat dissipation regulation, load adjustment, branch isolation, and alarm protection. It solves the problem that traditional fire protection systems use fixed alarm methods and cannot implement differentiated control according to the degree of risk. It realizes the transformation from "post-fault handling" to "pre-fault intervention", improves the safety of the control box, and avoids the impact on production caused by frequent power outages in the absence of risk. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the internal thermal node arrangement of the present invention; Figure 2 This is a schematic diagram of the risk classification closed-loop control of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0018] Please see Figure 1 and Figure 2 One embodiment of the present invention is an electrical control box fire prevention and high temperature monitoring and early warning control system, which includes a control box body, a thermal state acquisition module, a thermal gradient construction module, a thermal inertial analysis module, a thermal diffusion prediction module, a risk assessment module, a hierarchical control module, a digital thermal fingerprint module, an environmental compensation module, and a remote communication module.

[0019] The control box contains circuit breakers, AC contactors, intermediate relays, a PLC controller, a switching power supply, busbars, cable terminal blocks, and a cooling fan. Each electrical component is positioned according to its actual location, establishing a three-dimensional coordinate system, with each component corresponding to at least one thermal node. The thermal status acquisition module includes multiple digital temperature sensors, a current acquisition unit, a wind speed acquisition unit, and an ambient temperature acquisition unit. The digital temperature sensors are fixedly installed at busbar connections, circuit breaker terminals, the outside of contactor coils, the PLC controller housing, the power module heatsink, cable crimp terminals, and the top and bottom of the box. These acquisition nodes form a thermal node set N = {N1, N2, ..., N...}. n}

[0020] The controller reads all hot node data sequentially according to a preset sampling period and establishes a real-time thermal state matrix: T={T1,T2,…,T n}; where T i This represents the real-time temperature value of the i-th hot node. Simultaneously, the controller synchronously acquires the corresponding branch current I. i Ambient temperature T e The current operating status dataset is formed by considering the fan speed V and the airflow direction parameter F inside the box. The thermal gradient construction module is based on the spatial coordinates (X, Y, F) of each thermal node. i ,Y i Z i Calculate the spatial distance between any adjacent thermal nodes, and establish the thermal gradient matrix of the control box in combination with the temperature difference. Generate a local thermal gradient vector for each thermal node, and further obtain the thermal field distribution map inside the entire control box.

[0021] Subsequently, the thermal inertia analysis module continuously records the historical temperature changes of each hot node, integrates the heat accumulation within different time windows, establishes a thermal inertia database, and generates the thermal inertia coefficient for each hot node to characterize the long-term heat accumulation of the components. The heat diffusion prediction module predicts the temperature distribution at multiple future predicted times based on the current thermal gradient matrix, thermal inertia coefficient, airflow direction, and heat conduction path of the enclosure structure, outputting the hotspot diffusion direction, diffusion speed, and hotspot propagation path. The risk assessment module calculates the thermal risk index by comprehensively considering the hotspot propagation speed, hotspot duration, heat accumulation level, and the temperature tolerance margin of each device, and classifies the risk status into five levels: normal, watch out, warning, dangerous, and emergency.

[0022] The hierarchical control module executes control actions sequentially based on risk level, such as increasing the cooling fan speed, adjusting the air guide plate angle, shutting down non-critical loads, isolating abnormal branches, and disconnecting the main circuit. This achieves closed-loop control from prevention to protection, rather than waiting for smoke or open flames to appear before taking action. Compared to traditional solutions that rely on temperature and smoke-triggered alarms, this implementation method places greater emphasis on predicting thermal state evolution and proactive control.

[0023] Please see Figure 1 and Figure 2 The present invention provides an embodiment of a fire prevention and high temperature monitoring and early warning control system for an electrical control box. This embodiment focuses on the implementation process of the thermal gradient construction module and the thermal diffusion prediction module.

[0024] The control box contains 16 thermal nodes, each connected in a spatial adjacency network, with each node forming a heat transfer relationship with at least three adjacent nodes. After each data acquisition, the controller first calculates the temperature difference between all adjacent thermal nodes and generates a thermal gradient value based on the distance between them. When the thermal gradient in a certain area shows a continuous increase over multiple sampling periods, the system considers that a local hotspot initiation region has formed. Subsequently, the controller further analyzes the direction of thermal gradient changes within the hotspot's neighborhood, constructs a hotspot propagation vector, and analyzes the hotspot's movement trajectory through multiple consecutive time windows to predict the future propagation area of ​​the hotspot.

[0025] For example, during the operation of the control box, the contactor contacts begin to generate local heat due to the long-term increase in contact resistance. Although the overall average temperature inside the box is still within the normal range, the temperature difference between the hot contactor node and the adjacent busbar hot node continues to widen. The thermal gradient module identifies the rapid increase in local thermal gradient and marks the area as a primary hot spot.

[0026] Subsequently, the heat diffusion prediction module, combined with the airflow direction, discovered that the hotspot would preferentially propagate towards the busbar and cable terminals. Therefore, it started the local cooling fans in advance and adjusted the air guide mechanism to change the airflow direction, so that the cold air would preferentially pass through the hotspot area, thereby suppressing the hotspot diffusion.

[0027] Since the entire control process occurs before the overall temperature reaches the alarm threshold, it can effectively reduce the probability of fire.

[0028] Please see Figure 1 and Figure 2 One embodiment of the present invention is a fire prevention and high temperature monitoring and early warning control system for an electrical control box. The system establishes three thermal history databases for short-cycle, medium-cycle and long-cycle periods, respectively storing operating data for the past 1 hour, 24 hours and 30 days.

[0029] Each time the controller completes temperature acquisition, it writes the current thermal state into the thermal history database and simultaneously calculates the accumulated heat. For equipment operating for extended periods, even if the current temperature does not exceed the alarm threshold, if the temperature remains high for several consecutive operating cycles, the corresponding thermal inertia coefficient will gradually increase. For example, a circuit breaker operating at over 90% of its rated load for an extended period maintains a temperature of around 65°C. Although this does not reach the alarm threshold of 80°C, its thermal inertia coefficient continues to increase after several dozen days of continuous operation. Based on the thermal inertia analysis results, the risk assessment module determines that the internal insulation material of the circuit breaker has entered the thermal aging stage. Simultaneously, it predicts the potential formation of a persistent hotspot within the next week based on historical hotspot diffusion trajectories. Therefore, it generates a maintenance warning in advance and recommends replacing the circuit breaker. This implementation method avoids the missed alarm problems caused by traditional systems that rely solely on instantaneous temperature judgments.

[0030] Please see Figure 1 and Figure 2 The present invention provides an embodiment of a fire prevention and high temperature monitoring and early warning control system for an electrical control box. This embodiment illustrates the closed-loop control process of the risk assessment module and the hierarchical control module.

[0031] The system classifies risks into five levels based on the thermal risk index. When the thermal risk index is at level one, only the cooling fan speed is increased and the air guide direction is adjusted. If the risk index continues to increase, the phase change thermal storage component is activated to absorb local heat, while limiting the output power of non-critical branches. If the hotspot continues to spread to the busbar area, the corresponding branch contactor is automatically disconnected, isolating only the abnormal branch while keeping other branches running. If the prediction indicates that the hotspot will spread to the entire control box within a preset time window, the control module immediately cuts off the main power supply circuit and sends an emergency alarm message to the monitoring center. After the control is completed, the system continues to collect thermal status data. When the hotspot temperature returns to normal and the thermal gradient decreases to a safe range, the control measures are gradually lifted, allowing the control box to return to normal operation, forming a complete monitoring-prediction-control-feedback closed loop.

[0032] Please see Figure 1 and Figure 2 The present invention provides an embodiment of a fire prevention and high temperature monitoring and early warning control system for an electrical control box. In this embodiment, a digital thermal fingerprint model of the control box under normal operating conditions is established.

[0033] The first 30 days after the control box is put into operation serve as a learning phase. During this phase, the system collects thermal field distribution data under different load rates, ambient temperatures, and seasonal conditions, generating multiple standard thermal field templates. During operation, the system calculates the deviation between the current thermal field and the standard thermal field in real time. If a thermal node deviates from the standard thermal field for several consecutive operating cycles, the system determines that there are potential faults in that area, such as component aging, loose bolts, increased contact resistance, or duct blockage, and generates corresponding maintenance suggestions. For example, if the busbar connection point thermal node continuously deviates from the standard thermal field while the rated load remains constant, the system further combines the thermal gradient change trend and thermal inertia analysis results to determine that the contact resistance at the connection point has increased abnormally, prompting maintenance personnel to retighten the connections in advance, thereby preventing local overheating from developing into an arc fault or fire. This embodiment achieves long-term self-learning, self-correction, and anomaly identification of the control box's thermal state by constructing a digital thermal fingerprint model. This enables the system not only to detect current thermal anomalies but also to identify potential failure modes that evolve slowly over a long period, further improving early fire prediction capabilities and control accuracy. It also represents a significant departure from existing control schemes that use temperature, smoke, and flame as triggering conditions.

[0034] I. Specific implementation method of thermal status acquisition module: Establish a three-dimensional coordinate system inside the electrical control box. The origin O is the front left corner of the bottom of the control box: the X-axis represents the horizontal direction, the Y-axis represents the depth direction, and the Z-axis represents the height direction. Multiple hotspots are set according to the installation positions of the electrical components inside the control box.

[0035] For example: ; Each hot node is configured with: a digital temperature sensor; a current acquisition unit; and a node positioning unit.

[0036] The controller collects data according to a fixed sampling period Δt. For example: Sampling period: Δt=5s, then: control box thermal status data is generated once every 5 seconds.

[0037] form: ; in: T n Temperature of the nth thermal node; I n : Corresponding electrical branch current; V: Air velocity inside the enclosure; F: Direction of airflow.

[0038] II. Methods for Calculating Thermal Gradient Matrix 1. Calculation of spatial distance between nodes Any two hot nodes: N i (x i ,y i ,z i );N j (x j ,y j ,z j ).

[0039] Distance between: ; in: D ij Spatial distance between two hot nodes; x i y i z i Node N i Spatial coordinates; x j y j z j Node N j Spatial coordinates.

[0040] 2. Temperature gradient calculation Temperature difference between the two nodes: ; Thermal gradient: ; in: G ij Thermal gradient from node i to node j; unit: ℃ / mm.

[0041] 3. Establish the thermal gradient matrix ; The system updates the matrix in real time.

[0042] When the following conditions are met: ; Then it can be determined that the thermal gradient is growing abnormally.

[0043] Where: K g : Thermal gradient growth threshold.

[0044] For example: Let: ; When the thermal gradient of a node increases beyond a certain value for three consecutive sampling periods, an abnormal hotspot is determined to have occurred.

[0045] III. Specific Implementation of the Thermal Inertia Analysis Module Traditional temperature alarms only determine the current temperature. However, electrical faults often manifest as a slow, prolonged increase in temperature. Therefore, a thermal inertia model is established.

[0046] 1. Calculation of cumulative heat definition: ; in: H i : The cumulative heat at the i-th hot node; T i (k): Temperature of the kth sampling; T e Ambient temperature; Δt: Sampling interval.

[0047] 2. Calculation of thermal inertia coefficient definition: ; in: λ i Thermal inertia coefficient; H max The maximum allowable heat accumulation for this component. Range: 0 to 1.

[0048] IV. Specific Implementation of the Thermal Diffusion Prediction Model This application does not simply determine the current temperature, but predicts the future temperature.

[0049] Establish: ; in: Ti (t+Δt) represents the temperature at future time; α i : Thermal conductivity coefficient; β i : Airflow influence coefficient; γ i : Thermal inertia correction factor.

[0050] Parameter acquisition method: 1. Thermal conductivity coefficient α Calculated based on historical operating data: ; This indicates the rate of temperature increase per unit time.

[0051] 2. Air pollution factor β Based on wind speed: ; Where: V i Current wind speed; V max Maximum design wind speed.

[0052] 3. Thermal inertia correction γ Based on thermal inertia: ; The higher the accumulated heat, the faster the predicted temperature rise.

[0053] V. Calculation Process of Thermal Risk Index Establish a comprehensive risk assessment model: ; in: R: Heat risk index; A: Temperature rise rate indicator; B: Thermal gradient index; C: Thermal inertia index; D: Diffusion prediction index; w: weight.

[0054] 1. Temperature rise rate index ; Indicates the rate of temperature increase.

[0055] 2. Thermal gradient index ; 3. Thermal inertia index ; 4. Diffusion Indicators ; Wherein: S p : Predicted hotspot coverage area; S: Total internal area of ​​the control box.

[0056] VI. Risk Level and Control Strategies Based on R value: ; Working principle: This system first uses multiple sets of thermal status acquisition units located at different key positions inside the electrical control box to synchronously collect temperature, current, air velocity, and ambient temperature parameters of each electrical component during operation. Based on the spatial location of each thermal node within the control box, a three-dimensional thermal node distribution model is established. The system does not simply determine whether the temperature at a certain location exceeds a threshold; instead, it calculates thermal gradient information based on the temperature change relationship between adjacent thermal nodes, forming a real-time thermal field distribution state inside the control box. This allows for the identification of localized hotspot areas caused by poor contact, abnormal current, or reduced heat dissipation.

[0057] After obtaining real-time thermal field data, the system further analyzes the historical operating data of each thermal node. By establishing a thermal inertia model, it calculates the degree of heat accumulation in different areas and, combined with the temperature rise rate, duration, and environmental compensation parameters, determines whether the current anomaly is due to an instantaneous load change or a continuous thermal anomaly. Simultaneously, the system utilizes a heat diffusion prediction model to predict the hotspot diffusion trend within a preset timeframe based on the current thermal gradient direction, airflow state, component heat capacity, and historical heat propagation patterns. This allows for the early identification of potentially high-risk areas, avoiding the problem of traditional methods that only issue alarms after a fire has occurred.

[0058] Once the system calculates the thermal risk index, it automatically generates corresponding control strategies based on the risk level. In low-risk conditions, it maintains normal temperature by adjusting the operation of cooling fans. When a localized hotspot is detected to be expanding, measures such as enhanced cooling, air duct adjustment, and reduction of non-critical loads are initiated. When a fire risk is predicted, abnormal branch isolation, power supply protection, and remote alarm operations are implemented, achieving a shift from "passive alarm" to "proactive prediction and prevention combined."

[0059] 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 invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A fireproof and high-temperature monitoring and early warning control system for an electrical control box, characterized in that: The control system includes: The thermal status acquisition module is used to collect temperature data of multiple preset thermal nodes inside the electrical control box, operating current data of corresponding electrical components, ambient temperature data of the box, and air flow parameters inside the box. The thermal gradient construction module, connected to the thermal state acquisition module, is used to establish a thermal field model inside the control box based on the spatial coordinate relationship of each thermal node, calculate the thermal gradient value between any adjacent thermal nodes, and form a real-time thermal gradient matrix. The thermal inertia analysis module, connected to the thermal gradient construction module, is used to establish a thermal accumulation model based on the historical temperature change process of each thermal node, calculate the thermal inertia parameters of each thermal node, and generate the corresponding thermal state evolution curve. The thermal diffusion prediction module, connected to the thermal inertia analysis module, is used to construct a thermal diffusion prediction model based on the thermal gradient matrix, thermal inertia parameters, and airflow parameters, and to predict the temperature change trend and thermal diffusion path of each thermal node within multiple preset time windows in the future. The risk assessment module, connected to the heat diffusion prediction module, is used to calculate the thermal risk index based on the predicted heat diffusion path, hot spot propagation speed, heat accumulation degree and device temperature tolerance margin, and generate the corresponding risk level. The hierarchical control module, connected to the risk assessment module, is used to control the enclosure heat dissipation device, local heat insulation device, power supply switching device, fault branch isolation device and alarm device to execute corresponding control strategies according to different risk levels, so as to maintain the internal thermal state of the control box within a safe operating range.

2. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The thermal status acquisition module includes at least a busbar thermal node acquisition unit, a circuit breaker thermal node acquisition unit, a contactor thermal node acquisition unit, a PLC controller thermal node acquisition unit, a power module thermal node acquisition unit, a cable connection end thermal node acquisition unit, and a enclosure environment thermal node acquisition unit. Each thermal node corresponds to a unique spatial coordinate, and a three-dimensional thermal node distribution network is established inside the control box.

3. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The thermal gradient construction module calculates the thermal gradient value based on the spatial distance between thermal nodes and the real-time temperature difference, and establishes a thermal gradient matrix according to the topological relationship of the thermal nodes. When the thermal gradient change rate of a local area continuously exceeds the preset gradient threshold, the area is marked as a hotspot initiation area without waiting for the overall temperature to reach the alarm threshold.

4. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The thermal inertia analysis module uses a sliding time window to statistically analyze the short-cycle, medium-cycle, and long-cycle thermal accumulation of each thermal node. It calculates the thermal inertia coefficient based on the thermal accumulation change trend of each time window and corrects the impact of instantaneous temperature on the risk assessment results through the thermal inertia coefficient, so as to distinguish between load heating and abnormal continuous heating state.

5. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The heat diffusion prediction module constructs a heat diffusion propagation network based on the thermal gradient matrix, airflow direction, air tassels, device thermal capacity, and internal heat conduction path of the enclosure. It iteratively predicts the temperature distribution of each hot node at multiple future prediction times and outputs the hot spot propagation direction, propagation speed, and hot spot coverage area.

6. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The risk assessment module constructs a comprehensive thermal risk assessment model based on hotspot propagation speed, hotspot duration, thermal inertia coefficient, device temperature tolerance margin, thermal diffusion coverage area, and hotspot density. It calculates the thermal risk index and classifies the risk level into five levels: normal state, attention state, warning state, dangerous state, and emergency state.

7. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The graded control module adopts a progressive control strategy based on the risk level, including increasing the speed of the cooling fan, adjusting the direction of the air guide mechanism, activating the local heat insulation mechanism, cutting off non-critical loads, isolating abnormal branches, and cutting off the main power supply to the control box. The latter control action is automatically executed if the thermal risk index continues to rise after the former control action is executed.

8. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The control system also includes a digital thermal fingerprint module, which is used to establish a thermal distribution benchmark model under normal operating conditions of the control box. During operation, it continuously calculates the degree of deviation between the current thermal field model and the benchmark model. When the degree of deviation continuously exceeds a preset threshold, the corresponding area is identified as a potential aging area, and maintenance warning information is generated in advance.

9. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The control system also includes an environmental compensation module, which is used to collect the external ambient temperature, solar irradiance and equipment load rate of the control box, and dynamically compensate the thermal gradient matrix and thermal inertia parameters to reduce the misjudgment of risk assessment results caused by high ambient temperature and load fluctuations, and improve the accuracy of hotspot identification.

10. The fireproof and high-temperature monitoring and early warning control system for an electrical control box according to claim 1, characterized in that: The control system also includes a learning module, which records historical data of thermal field evolution of each hot node under different load conditions, establishes a thermal state evolution sample library, and dynamically corrects the model parameters of the thermal diffusion prediction model and risk assessment model based on historical thermal diffusion trajectories, so that the prediction results continuously and adaptively optimize with the operating status of the control box, thereby improving the early warning capability of fire formation and the accuracy of control strategy matching.

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