Intelligent fusion terminal integrated temperature regulating and radiating system based on low-voltage comprehensive distribution box of smart grid

Abstract

The application discloses a low-voltage comprehensive distribution box intelligent fusion terminal integrated temperature regulation and heat dissipation system based on a smart power grid, and belongs to the technical field of temperature regulation systems. The system comprises an intelligent fusion terminal, which is a core control unit of the system and is internally provided with an adaptive control algorithm; a mobile scanning type acquisition unit, which is in communication connection with the intelligent fusion terminal and is used for acquiring temperature and humidity data in a distribution box; a heat dissipation execution unit, which comprises a fan and a heating sheet, receives control instructions of the intelligent fusion terminal and executes heat dissipation or dehumidification operation; and a cluster collaborative control unit, which is arranged at an edge computing node of a distribution network station area, is in communication with intelligent fusion terminals of multiple distribution boxes, selects 5%-10% of distribution boxes with similar working conditions as sample boxes, constructs a consistency and difference model based on sample box data, and deduces heat dissipation control parameters of to-be-controlled boxes. The application has the advantages of dynamic acquisition of temperature and humidity, high heat dissipation control collaboration, and control of multiple devices through a small amount of search.

Description

Technical Field

[0001] This invention belongs to the field of temperature regulation system technology, and particularly relates to an integrated temperature regulation and heat dissipation system for a smart fusion terminal of a low-voltage integrated distribution box based on a smart grid. Background Technology

[0002] In the process of building smart grids, low-voltage integrated distribution boxes, as the core hub connecting the power grid and users, integrate a large number of electronic components such as smart fusion terminals, circuit breakers, and power modules. Their stable operation directly determines the power supply reliability and intelligence level of the distribution network. However, low-voltage integrated distribution boxes are mostly installed outdoors, facing problems such as large fluctuations in ambient temperature and dense heat generation from the components inside the box. Abnormal temperature and humidity can easily lead to component performance degradation, shortened lifespan, and even short circuits, tripping, and other faults.

[0003] Existing low-voltage distribution box cooling systems suffer from two major drawbacks: First, temperature and humidity data acquisition is incomplete. Traditional systems often use single or fixed multi-point sensors, failing to cover core heat-generating components and corners of the enclosure, leading to distorted assessments of cooling needs and significant control deviations. Second, the coordination of cooling control is poor. Cooling actuators and terminals lack linkage, and fixed threshold start / stop modes cannot adapt to dynamic heat changes, easily resulting in insufficient cooling or energy waste. To solve these problems, current approaches require configuring a separate complete system for each low-voltage integrated distribution box. With large-scale applications (e.g., over 100 distribution boxes in a distribution area), hardware costs surge, and maintenance costs rise accordingly, hindering technology adoption. Therefore, there is an urgent need for an intelligent temperature-regulating cooling system that can address these shortcomings and is cost-effective. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides an integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid. This system has the advantages of dynamically acquiring temperature and humidity, high coordination in heat dissipation control, and the ability to control multiple devices with a small number of searches, thus solving the problems of the prior art.

[0005] This invention is implemented as follows: an integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, comprising: The intelligent fusion terminal, as the core control unit of the system, has a built-in adaptive control algorithm. A mobile scanning acquisition unit is communicatively connected to the intelligent fusion terminal and is used to collect temperature and humidity data inside the power distribution box; The heat dissipation execution unit includes a fan and a heating element, receives control commands from the intelligent fusion terminal and performs heat dissipation or dehumidification operations; The cluster collaborative control unit is deployed at the edge computing node of the distribution network area and communicates with the intelligent fusion terminal of multiple distribution boxes. 5%-10% of distribution boxes with similar operating conditions are selected as sample boxes. Based on the sample box data, a consistency and difference model is built to derive the heat dissipation control parameters of the box to be controlled.

[0006] As a preferred embodiment of the present invention, the mobile scanning acquisition unit includes a mobile actuator, a sensing module, and a path planning module; the mobile actuator consists of a micro stepper motor, a silent guide rail, and a photoelectric encoder; the sensing module integrates a temperature and humidity sensor and an infrared temperature probe; the path planning module presets a basic cruising path and a key scanning path; the mobile scanning acquisition unit is only configured in the sample box.

[0007] As a preferred embodiment of the present invention, the adaptive control algorithm includes a data preprocessing module, a comprehensive judgment value calculation module, a dynamic threshold calculation module, a movement speed control module, and a scene decision module; The data preprocessing module uses Kalman filtering and moving average filtering algorithms to process the collected data. The comprehensive judgment value calculation module obtains the comprehensive judgment value J using the following formula: J=α×T1+β×T2+γ×(T3-T0)+δ×(HH) 0) -η×Q; Where T1 is the average temperature of the core component, T2 is the internal temperature of the smart terminal, T3 is the average internal temperature of the box, T0 is the external ambient temperature of the box, H is the average internal humidity of the box, H0 is the critical humidity for condensation, Q is the heat generated by the motor of the moving part, α=0.4, β=0.3, γ=0.2, δ=0.1, η=0.02; the sample box acquires data through a mobile scanning acquisition unit to calculate J, and the J of the box to be controlled is derived by the cluster collaborative control unit based on J.

[0008] As a preferred embodiment of the present invention, the dynamic threshold calculation module calculates the dynamic threshold Y using the following formula: Y = Y0 + ε × (P / P) n )+ζ×(T0-25); Wherein, Y includes the heat dissipation start threshold Y1, the adjustment threshold Y2, and the stop threshold Y3. 10 =30, Y 20 =20, Y 30 =10, P is the real-time load power of the distribution box, P n The rated load power of the distribution box is ε=20-40, ζ=0.5-1.0; the dynamic threshold Y is the cluster shared threshold Y, which is calculated based on the average load of the sample box and the box to be controlled.

[0009] As a preferred embodiment of the present invention, the moving speed control module calculates the speed V of the moving component using the following formula: V = V0 + k × |J - Y_avg|; where V0 = 10 mm / s, k = 0.3-0.8 mm / (s·unit JY), Y_avg = (Y1 + Y2 + Y3) / 3, and the value range of V is 5-50 mm / s; the moving speed control only applies to the moving actuator of the sample box.

[0010] As a preferred embodiment of the present invention, the temperature and humidity measurement accuracy of the sensing module is ±0.1℃ / ±1%RH, and the sampling frequency is adjustable within the range of 1-1000Hz.

[0011] As a preferred embodiment of the present invention, when J≥Y1, the high temperature strong heat dissipation scenario is activated; when Y2≤J<Y1, the medium temperature regulation scenario is activated; when Y3≤J<Y2, the normal temperature critical scenario is activated; when 0≤J<Y3, the normal temperature mode is activated; and when J<0, the low temperature condensation scenario is activated.

[0012] As a preferred embodiment of the present invention, the consistency model constructed by the cluster collaborative control unit is determined by the coefficient of variation (CV) of the sample box J, where CV = σ(J) / μ(J). When CV ≤ 0.1, the cluster operating conditions are determined to be consistent, and J = μ(J) is taken as the basic judgment value of the box to be controlled.

[0013] As a preferred embodiment of the present invention, the differential model constructed by the cluster collaborative control unit introduces an environmental difference coefficient θ, and the judgment value of the controlled box J_control = J_group_average × θ + ε × (P_control - P_sample_average) / P_control n θ = 1 + 0.02 × ΔT0, where ΔT0 is the ambient temperature difference between the control box and the sample box, Pcontrol is the real-time load of the control box, and Psample is the average load of the sample box.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The system utilizes a mobile scanning acquisition unit configured in the sample box, combining basic cruise and focused scanning to precisely move a high-precision sensing module along the guide rail. This ensures comprehensive coverage of core heat-generating components such as circuit breaker terminals and power modules, as well as corners of the enclosure, resolving the blind spot problem of traditional fixed sensors. Combined with dual data preprocessing using Kalman filtering and moving average filtering, interference factors such as motor vibration are effectively eliminated, ensuring accurate capture of temperature and humidity changes. This provides reliable data support for subsequent heat dissipation requirement assessment, preventing equipment failure risks caused by incomplete monitoring from the outset.

[0015] 2. By quantifying heat dissipation requirements through comprehensive judgment values ​​and dynamically adjusting thresholds based on load and environment, the heat dissipation execution unit breaks free from the traditional fixed threshold start-stop mode. Based on threshold gradients, a stepped control logic is formed for high-temperature strong heat dissipation, medium-temperature regulation, and normal-temperature sleep, which ensures the stability of the core component's operating environment and avoids ineffective operation of the execution components, achieving the optimal balance between heat dissipation effect and energy consumption, and extending the service life of the execution components.

[0016] 3. The cluster management architecture is highly efficient, highlighting the cost advantage of controlling multiple devices with a small number of devices. The cluster collaborative control unit collects data by selecting sample boxes with similar operating conditions. Based on a consistency and difference model, it derives the control parameters of the boxes to be controlled. The boxes to be controlled do not need to be configured with complex data acquisition hardware; they can respond accurately simply by receiving commands through simplified terminals. This mode significantly reduces the hardware investment for large-scale applications. At the same time, when a single sample box malfunctions, the stability of cluster control can be ensured through data from other sample boxes, reducing the manpower and time costs of operation and maintenance, and adapting to the management needs of large-scale distribution boxes in a distribution area. Attached Figure Description

[0017] Figure 1 This is the module connection frame of the integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, provided in this embodiment of the invention. Figure 1 ; Figure 2 This is the module connection frame of the integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, provided in this embodiment of the invention. Figure 2 ; Figure 3 This is the module connection frame of the integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, provided in this embodiment of the invention. Figure 3 ; Figure 4 This is a module diagram of the scenario decision-making module provided in an embodiment of the present invention; Figure 5 This is a flowchart of an integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, provided in an embodiment of the present invention. Detailed Implementation

[0018] To further understand the invention's content, features, and effects, the following embodiments are provided, and detailed descriptions are given in conjunction with the accompanying drawings.

[0019] The structure of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] like Figures 1-5 As shown in the embodiment of the present invention, the integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid includes: The intelligent fusion terminal, as the core control unit of the system, has a built-in adaptive control algorithm. The mobile scanning acquisition unit, communicating with the intelligent fusion terminal, is used to collect temperature and humidity data within the power distribution box. The mobile scanning acquisition unit includes a mobile actuator, a sensing module, and a path planning module. The mobile actuator consists of a miniature stepper motor, a silent guide rail, and a photoelectric encoder. The sensing module integrates a temperature and humidity sensor and an infrared temperature probe. The path planning module presets a basic cruising path and a key scanning path. The mobile scanning acquisition unit is only configured within the sample box. The stepper motor of the mobile actuator drives the sensing module to move along the guide rail, and the photoelectric encoder provides real-time position feedback, ensuring the module accurately stops at key heat points (such as circuit breaker terminals). The basic path of the path planning module covers the entire box, while the key path focuses on high-heat areas, achieving a comprehensive scanning and focused monitoring acquisition mode, improving data coverage.

[0021] The heat dissipation unit includes a fan and a heating element, which receives control commands from the smart fusion terminal and performs heat dissipation or dehumidification operations. The cluster collaborative control unit, deployed at the edge computing nodes of the distribution network area, communicates with intelligent fusion terminals of multiple distribution boxes. Distribution boxes with 5%-10% similar operating conditions are selected as sample boxes. Based on the sample box data, a consistency and difference model is built to derive the heat dissipation control parameters of the boxes to be controlled. For example, in a distribution area with 100 identical low-voltage distribution boxes (rated power 10kW), 8 boxes with load fluctuations of 30%-35% and installed outdoors facing south are selected as sample boxes, configured with SHT30 sensor modules and 28HS45 stepper motors; the remaining 92 boxes to be controlled only retain simplified TTU terminals, 12V fans, and DS18B20 ambient temperature sensors. The cluster unit is deployed on the Huawei AR550 edge gateway and communicates via power line carrier.

[0022] Specifically, the adaptive control algorithm includes a data preprocessing module, a comprehensive judgment value calculation module, a dynamic threshold calculation module, a movement speed control module, and a scene decision module. The data preprocessing module uses Kalman filtering and moving average filtering algorithms to process the collected data. The comprehensive judgment value calculation module obtains the comprehensive judgment value J using the following formula: J = α×T1 + β×T2 + γ×(T3-T0) + δ×(HH) 0) -η×Q; where T1 is the average temperature of the core component, T2 is the internal temperature of the smart terminal, T3 is the average internal temperature of the box, T0 is the external ambient temperature of the box, H is the average internal humidity of the box, H0 is the critical humidity for condensation, Q is the heat generated by the motor of the moving part, α=0.4, β=0.3, γ=0.2, δ=0.1, η=0.02; the sample box acquires data through the mobile scanning acquisition unit to calculate J, and the J of the box to be controlled is derived by the cluster collaborative control unit based on J.

[0023] The adaptive control algorithm operates according to the following process: First, Kalman filtering is used to remove instantaneous outliers caused by motor vibration (such as a sudden 5°C jump in T1), and then moving average filtering is used to smooth the data; the above formula quantifies the heat dissipation demand through multi-parameter weighting, and the η×Q term cancels out the motor heating interference; finally, the dynamic threshold and speed formula are combined to output the execution command.

[0024] Furthermore, the dynamic threshold calculation module calculates the dynamic threshold Y using the following formula: Y = Y0 + ε × (P / P n )+ζ×(T0-25); where Y includes the heat dissipation start threshold Y1, adjustment threshold Y2, and stop threshold Y3, Y 10 =30, Y 20 =20, Y 30 =10, P is the real-time load power of the distribution box, P n The rated load power of the distribution box is ε=20-40, ζ=0.5-1.0; the dynamic threshold Y is the cluster shared threshold Y, calculated based on the average load of the sample box and the box to be controlled.

[0025] J is a real-time dynamic comprehensive judgment value reflecting the heat dissipation / dehumidification needs of the distribution box (the higher the value, the more urgent the need; a negative value indicates a risk of condensation). Y1 (heat dissipation start threshold), Y2 (adjustment threshold), and Y3 (stop threshold) are gradient control standards that are dynamically updated according to the load / environment. The two achieve fine-grained control through interval matching. The core logic is: based on J falling within different threshold intervals, corresponding intensity of execution actions are triggered. Simultaneously, the sample box is additionally linked to the moving scan speed, while the controlled box only performs heat dissipation / dehumidification actions. The specific comparison method and control strategy are as follows: J≥Y1: High-temperature, high-heat-dissipation scenario: J exceeds the heat dissipation activation threshold, indicating that the temperature / humidity inside the chamber is significantly high, requiring the activation of the strongest heat dissipation. At this time, the sample chamber fan runs at 80%-100% full load speed, and the moving scanning unit collects data along the key path at the highest speed to ensure real-time capture of temperature changes of core components; the control chamber simultaneously starts its fan at full load speed to quickly reduce the temperature inside the chamber.

[0026] Y2≤J<Y1: Medium-temperature regulation scenario: J is between the regulation threshold and the start-up threshold. The environment inside the chamber is in an intermediate state where heat dissipation is required but full load is not necessary. The core is to balance the heat dissipation effect and energy consumption. At this time, the sample chamber fan speed is reduced to 50%-80%, and the moving scan speed is simultaneously reduced to a medium level; the control chamber adjusts the fan speed by the same proportion to avoid the actuators from operating under high load ineffectively.

[0027] Y3≤J<Y2: Critical scenario at normal temperature: J is close to the normal range, requiring only low-load standby to prevent a rapid rise in temperature / humidity due to minor environmental fluctuations. At this time, the sample box fan is in standby at 30% low speed, the moving scanning unit resumes its basic cruise path, and the sensing frequency remains at a low level; the standby box is also in standby at low speed, balancing emergency response and energy saving.

[0028] 0≤J<Y3: Normal temperature mode: J is below the stop threshold, the environment inside the chamber fully meets the equipment operation requirements, and no active heat dissipation / dehumidification is required. At this time, both the sample chamber and the control chamber turn off the fans, heating elements and other actuators; the sample chamber only retains the lowest frequency data acquisition function, and the control chamber enters low-power sleep mode, only synchronously feeding back its own load and environmental status.

[0029] J < 0: Low-temperature condensation scenario: A negative J value indicates that the humidity inside the chamber is high and the temperature is low, posing a risk of moisture damage to components. Condensation issues should be addressed first (this scenario does not rely on threshold subdivisions below Y3). At this time, both the sample chamber and the control chamber have their fans turned off, and the heating element is activated for dehumidification until J rises back above 0, at which point the logic switches back to the normal threshold comparison logic.

[0030] In summary, the entire comparison and control process is a real-time dynamic closed loop: the cluster unit updates the J value and the Y1 / Y2 / Y3 thresholds every second. Once the J value fluctuates across intervals due to increased load or environmental changes, the system will immediately switch the corresponding control action to avoid lag or misjudgment caused by fixed threshold control.

[0031] In summary, the cluster collaborative control unit, as the core node, first selects sample boxes based on load fluctuations (within ±5%) and installation environment (same orientation, same obstruction). The moving scanning unit of the sample box dynamically collects data and calculates core parameters. The cluster unit builds a model based on the sample data, derives the control parameters of the box to be controlled, and issues commands. The box to be controlled only responds through the execution unit, forming a low-cost closed loop with minimal data collection and multiple controls. For example, the sample box uploads its J value once per second, the box to be controlled uploads its own P control and T0 values ​​once per second, and the cluster unit updates the control commands once per second, achieving full-domain collaboration.

[0032] The P-control (its own real-time load power) and T0 (its own external ambient temperature) uploaded by the controlled box every second are the core input parameters of the cluster collaborative control. Their role is to support the accurate derivation of the cooling demand of the controlled box, which is reflected in two aspects: First, it provides individual difference data for the differential model: the controlled box does not need to be equipped with sensors (to reduce costs). Its cooling demand judgment value J-control needs to be corrected by the sample box J-group. P-control is used to compensate for the load difference between the controlled box and the sample box (higher load means more heat generation, J-control needs to be increased), and T0 is used to correct the ambient temperature difference through the environmental difference coefficient θ (higher T0 means greater cooling difficulty, J-control needs to be optimized), avoiding deviation caused by a one-size-fits-all control. Second, it ensures the real-time performance of dynamic control: the upload frequency can capture the dynamic changes of load and ambient temperature (such as the increase of P-control during peak power consumption) without increasing communication energy consumption due to high-frequency upload. Combined with the J value uploaded by the sample box, the cluster unit can dynamically update the J-control and Y-group thresholds to ensure that the cooling command of the controlled box is in line with the real-time operating conditions (such as when P-control suddenly increases, J-control is adjusted synchronously, and the fan speed is increased in time).

[0033] The stepper motor of the mobile actuator receives commands from the smart terminal, driving the sensing module to move along the U-shaped guide rail. The photoelectric encoder provides real-time position signals (e.g., triggering a signal every 0.25mm of movement), ensuring the module accurately stops at key monitoring points such as circuit breaker terminals and power modules. The path planning module dynamically switches paths based on the J value; when J≥Y1, the key path is prioritized, and when J<Y3, the basic path is executed, balancing acquisition accuracy and efficiency. The basic path is the upper left corner, upper right corner, lower right corner, and lower left corner of the enclosure (time-consuming), while the key path is the circuit breaker A-phase terminal, circuit breaker B-phase terminal, and power module heat sink (time-efficient).

[0034] Furthermore, the moving speed control module calculates the moving component speed V using the following formula: V = V0 + k × |J - Y_avg|; where V0 = 10 mm / s, k = 0.3-0.8 mm / (s·JY), Y_avg = (Y1 + Y2 + Y3) / 3, and the value range of V is 5-50 mm / s. The moving speed control only applies to the moving actuator of the sample box. Specifically, the sample box moving speed V is positively correlated with the absolute difference of J-Yavg—the larger the difference, the further the temperature and humidity deviate from the ideal state, requiring a higher speed to quickly capture data changes; the smaller the difference, the lower the speed to save energy. The 5-50 mm / s range of V balances acquisition efficiency and motor lifespan.

[0035] Specifically, the temperature and humidity measurement accuracy of the sensing module is ±0.1℃ / ±1%RH, and the sampling frequency is adjustable within the range of 1-1000Hz. The high accuracy of ±0.1℃ / ±1%RH ensures the capture of minute temperature and humidity changes (such as the slow rise of H before the condensation threshold). The adjustable sampling frequency of 1-1000Hz adapts to different scenarios—lower frequencies are used for energy saving during routine inspections, and higher frequencies are switched when J approaches Y1 to avoid missing sudden temperature changes. For example, the daily sampling frequency of the sample box is set to 1Hz, collecting one set of data every second. When T1 is detected to rise from 50℃ to 55℃ within 2 seconds (dT1 / dt=2.5℃ / s), the system automatically increases the sampling frequency to 1000Hz, capturing the instantaneous peak value of T1 at 57℃, providing a basis for speed adjustment. In low-temperature condensation scenarios, when H rises from 50%RH to 54%RH, the sensor accurately detects a 0.2%RH change, triggering a dehumidification warning in advance to prevent components from becoming damp.

[0036] The consistency model constructed by the cluster collaborative control unit is determined by the coefficient of variation (CV) of sample bin J, where CV = σ(J) / μ(J). When CV ≤ 0.1, the cluster operating conditions are considered consistent, and J = μ(J) is taken as the basic judgment value for the control bin. The consistency model judges the uniformity of operating conditions by the dispersion of the statistical sample bin J values: σ(J) is the standard deviation (reflecting data fluctuation), and μ(J) is the mean (reflecting the average state). CV ≤ 0.1 indicates that the sample bin data differences are small and the operating conditions are consistent. Its mean can be used as a basic reference for the control bin, ensuring the reliability of the model derivation. For example, if the J values ​​of the 8 sample boxes are 48, 50, 49, 47.5, 48.8, 49.2, 47.9, and 48.6 respectively, we can calculate μ(J sample) = 48.88, σ(J sample) = 0.72, and CV = 0.72 / 48.88 ≈ 0.0147 ≤ 0.1, which indicates that the consistency is qualified. The J group mean = 48.88 is taken as the base value of the control box. If a sample box has a fault and J sample = 60, σ(J sample) rises to 4.5, and CV = 0.092 is still ≤ 0.1, the system marks the sample box but does not remove it to ensure the stability of the model.

[0037] Furthermore, the differential model constructed by the cluster collaborative control unit introduces an environmental difference coefficient θ, and the judgment value of the control box J_control = J_group_average × θ + ε × (P_control - P_sample_average) / P_control n θ = 1 + 0.02 × ΔT0, where ΔT0 is the ambient temperature difference between the controlled chamber and the sample chamber, Pcontrol is the real-time load of the controlled chamber, and Psample is the average load of the sample chamber. Through these settings, the difference model corrects for individual differences between the controlled chamber and the sample chamber: Jgroup mean is the mean of sample chamber Jsample (derived from the consistency model, e.g., the average of 8 sample chamber Jsample is 48.88), serving as the basic reference value for the controlled chamber; Jcontrol is the final heat dissipation requirement judgment value for the controlled chamber. The influence of the ambient temperature difference ΔT0 is compensated by the θ coefficient, and the effect is achieved by ε × (Pcontrol - Psample mean) / Psample.n The compensation for the "load difference" ultimately enables J-control to accurately match the actual operating conditions of the controlled box, overcoming the limitations of directly substituting the parameters of the controlled box with the sample mean. For example: ΔT0 = 3℃ for a controlled box and a sample box (sample box T0 = 35℃, controlled box T0 = 38℃), θ = 1 + 0.02 × 3 = 1.06; P-control = 0.36Pn, P-sample mean = 0.33Pn, ε = 30, Pn = 10kW. Substituting into the formula, we get J-control = 48.88 × 1.06 + 30 × (0.36 × 10 - 0.33 × 10) / 10 = 51.81 + 30 × 0.3 / 10 = 51.81 + 0.9 = 52.71.

[0038] Working principle of the invention: Calculation prerequisites (unified setting of scenario and parameters) 1. Core Scenarios A cluster consisting of 100 low-voltage distribution boxes, including 8 sample boxes and 92 boxes to be controlled, with a rated load P for all distribution boxes. n =10kW (uniform specifications to eliminate equipment differences); real-time collection of the overall ambient temperature of the cluster T0=35℃, and the adjustment coefficient is taken as the median value of the text recommendation range (to ensure calculation stability).

[0039] 2. Fixed preset parameters (derived from technical texts and commonly used engineering values)

[0040] 3. Real-time data collection Real-time loads of 8 sample boxes (unit: kW): 3.2, 3.4, 3.3, 3.5, 3.2, 3.3, 3.4, 3.3; Total real-time load of 92 control boxes (unit: kW): 302.375; Sample chamber temperature and humidity data: T1=58℃, T2=42℃, T3=38℃, H=65%RH, humidity baseline value H0=55%RH; Sample box motor operating data: operating current 0.1A, running time 10s, motor efficiency 0.85; II. Step-by-step detailed calculation Step 1: Calculate the average load P of the cluster (the core basis and the key to the shared threshold of the cluster). Since the dynamic threshold is calculated based on the average load of the sample bins and the bins to be controlled, the total load of the cluster must be calculated first, and then the average value must be calculated to ensure that the threshold can represent the working condition of the entire cluster.

[0041] Total load of the sample box: 3.2+3.4+3.3+3.5+3.2+3.3+3.4+3.3=26.6kW; Total cluster load: Total load of sample boxes + Total load of boxes to be controlled = 26.6 + 302.375 = 328.975kW; Average cluster load P: Total cluster load ÷ Total number of cluster boxes = 328.975 ÷ 100 = 3.28975 kW.

[0042] Step 2: Calculate the relative load P / P n (Standardized load, eliminating rated power differences) Relative load transforms the actual load into a dimensionless value of 0-1, allowing devices with different rated loads to share a common set of calculation logic. Since the cluster devices have uniform specifications, its core function here is to standardize the impact of load on thresholds. P / P n =3.28975÷10=0.328975.

[0043] Step 3: Calculate the cluster-shared dynamic thresholds Y1 / Y2 / Y3 (core control standard) Y = Y0 + ε × (P / P) n )+ζ×(T0-25) is calculated, only replacing Y0 (corresponding to Y) 10 / Y 20 / Y 30 These three components are shared by the entire cluster and do not require separate computation.

[0044] Fixed items should be calculated in advance: T0-25=35-25=10℃; ε×(P / P n =30 × 0.328975 = 9.86925; ζ×(T0-25)=0.8×10=8; Calculate the three thresholds separately: Heat dissipation start threshold Y1 (Y0=Y 10 =30): Y1=30+9.86925+8=47.86925; Adjusting the threshold Y2 (Y0=Y 20 =20): Y2=20+9.86925+8=37.86925; Stop threshold Y3 (Y0=Y 30 =10): Y3=10+9.86925+8=27.86925; Step 4: Calculate the comprehensive judgment value J of the sample box (quantifying heat dissipation requirements). The J value is a comprehensive reflection of the real-time operating conditions of the sample box. It is necessary to compensate for the motor heating interference. The formula is: J=α×T1+β×T2+γ×(T3-T0)+δ×(H-H0)-η×Q, where Q is the effective heat generation of the motor.

[0045] Calculate the effective heat generation of the motor Q: Q = voltage × current × running time × motor efficiency = 12V × 0.1A × 10s × 0.85 = 10.2J; Step-by-step calculation of the J value: α×T1=0.4×58=23.2; β×T2=0.3×42=12.6; γ×(T3-T0)=0.2×(38-35)=0.6; δ×(H-H0)=0.1×(65-55)=1; η×Q=0.02×10.2=0.204; Finally, J = 23.2 + 12.6 + 0.6 + 1 - 0.204 = 37.196; Step 5: Calculate the sample box moving scanning speed V (adjust the acquisition efficiency to suit the requirements). Speed ​​calculation depends on Y_avg (the average value of Y1 / Y2 / Y3). Y_avg=(Y1+Y2+Y3)÷3=(47.86925+37.86925+27.86925)÷3=37.86925; Calculate the movement speed V: V=V0+k×|J-Y_avg|=10+0.5×|37.196-37.86925|=10+0.5×0.67325=10.336625mm / s.

[0046] Step 6: Calculate the comprehensive judgment value J_control of the control box (adapted to the individual operating conditions of the control box). The control box does not require data collection. Based on the cluster model, J_control is derived using the formula: J_control = J_cluster_average × θ + ε × (P_control - P) ÷ P n (θ is the environmental difference coefficient. Here, we take a certain box under control with ΔT0=3℃, θ=1+0.02×3=1.06; the real-time load of the box under control is P_control=3.6kW).

[0047] Calculate the J-group mean (mean of J values ​​in the sample bins; since the sample bins are under consistent operating conditions, the J value from step 4 is used as the J-group mean): J-group mean = 37.196 Calculation J control: J=37.196×1.06+30×(3.6-3.28975)÷10=39.42776+30×0.31025÷10=40.35851.

[0048] III. Final control action judgment (based on calculation results matching threshold range) All sample boxes and control boxes perform corresponding actions based on the J value and the interval matching of the cluster's shared thresholds Y1 / Y2 / Y3, thereby achieving full-domain collaborative control.

[0049] Sample box (J=37.196): Y3 (27.86925) ≤ J < Y2 (37.86925), turn on the normal temperature critical scene, the fan is in standby mode at 30% low speed, and the moving scanning unit cruises at a basic speed of 10.336625mm / s.

[0050] Standby box (J control = 40.35851): Y2 (37.86925) ≤ J < Y1 (47.86925), activate medium temperature regulation scenario, fan runs at 50%-80% speed, balancing heat dissipation and energy consumption.

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, characterized in that: include: The intelligent fusion terminal, as the core control unit of the system, has a built-in adaptive control algorithm. A mobile scanning acquisition unit is communicatively connected to the intelligent fusion terminal and is used to collect temperature and humidity data inside the power distribution box; The heat dissipation execution unit includes a fan and a heating element, receives control commands from the intelligent fusion terminal and performs heat dissipation or dehumidification operations; The cluster collaborative control unit is deployed at the edge computing node of the distribution network area and communicates with the intelligent fusion terminal of multiple distribution boxes. 5%-10% of distribution boxes with similar operating conditions are selected as sample boxes. Based on the sample box data, a consistency and difference model is built to derive the heat dissipation control parameters of the box to be controlled.

2. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 1, is characterized in that: The mobile scanning acquisition unit includes a mobile actuator, a sensing module, and a path planning module; the mobile actuator consists of a micro stepper motor, a silent guide rail, and a photoelectric encoder; the sensing module integrates a temperature and humidity sensor and an infrared temperature probe; the path planning module presets a basic cruising path and a key scanning path; the mobile scanning acquisition unit is only configured in the sample box.

3. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 2, is characterized in that: The adaptive control algorithm includes a data preprocessing module, a comprehensive judgment value calculation module, a dynamic threshold calculation module, a movement speed control module, and a scene decision module. The data preprocessing module uses Kalman filtering and moving average filtering algorithms to process the collected data. The comprehensive judgment value calculation module obtains the comprehensive judgment value J using the following formula: J=α×T1+β×T2+γ×(T3-T0)+δ×(HH) 0) -η×Q; Where T1 is the average temperature of the core component, T2 is the internal temperature of the smart terminal, T3 is the average internal temperature of the box, T0 is the external ambient temperature of the box, H is the average internal humidity of the box, H0 is the critical humidity for condensation, Q is the heat generated by the motor of the moving part, α=0.4, β=0.3, γ=0.2, δ=0.1, η=0.02; the sample box acquires data through a mobile scanning acquisition unit to calculate J, and the J of the box to be controlled is derived by the cluster collaborative control unit based on J.

4. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 3, is characterized in that: The dynamic threshold calculation module calculates the dynamic threshold Y using the following formula: Y = Y0 + ε × (P / P) n )+ζ×(T0-25); Wherein, Y includes the heat dissipation start threshold Y1, the adjustment threshold Y2, and the stop threshold Y3. 10 =30, Y 20 =20, Y 30 =10, P is the real-time load power of the distribution box, P n The rated load power of the distribution box is ε=20-40, ζ=0.5-1.0; the dynamic threshold Y is the cluster shared threshold Y, which is calculated based on the average load of the sample box and the box to be controlled.

5. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 4, is characterized in that: The moving speed control module calculates the speed V of the moving component using the following formula: V = V0 + k × |J - Y_avg|; Wherein, V0=10mm / s, k=0.3-0.8mm / (s·unit JY), Y_avg=(Y1+Y2+Y3) / 3, and the value range of V is 5-50mm / s; the moving speed control only acts on the moving actuator of the sample box.

6. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box intelligent fusion terminal based on a smart grid as described in claim 1 or 5, characterized in that: The temperature and humidity measurement accuracy of the sensing module is ±0.1℃ / ±1%RH, and the sampling frequency can be adjusted within the range of 1-1000Hz.

7. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 5, is characterized in that: When J≥Y1, activate the high-temperature, high-heat-dissipation scenario. When Y2≤J<Y1, activate the medium temperature control scenario; When Y3≤J<Y2, activate the room temperature critical scenario; When 0 ≤ J < Y3, switch to room temperature mode; When J < 0, the low-temperature condensation scenario is activated.

8. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 7, is characterized in that: The consistency model constructed by the cluster collaborative control unit is determined by the coefficient of variation (CV) of the sample box J. CV = σ(J) / μ(J). When CV ≤ 0.1, the cluster operating conditions are considered consistent, and J = μ(J) is taken as the basic judgment value of the box to be controlled.

9. The integrated temperature control and heat dissipation system for a low-voltage integrated distribution box based on a smart grid, as described in claim 8, is characterized in that: The differential model constructed by the cluster collaborative control unit introduces an environmental difference coefficient θ, and the judgment value of the control box J_control = J_group_average × θ + ε × (P_control - P_sample_average) / P_control n θ = 1 + 0.02 × ΔT0, where ΔT0 is the ambient temperature difference between the control box and the sample box, Pcontrol is the real-time load of the control box, and Psample is the average load of the sample box.

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