Cable distribution box dehumidification predictive control system and method based on intelligent dehumidifier

Abstract

The application discloses a cable distribution box dehumidification prediction control system based on an intelligent dehumidifier, which comprises an intelligent dehumidifier terminal, a dynamic sampling control array and an edge computing gateway. The intelligent dehumidifier terminal is installed in the box body of the cable distribution box, and a terminal row is arranged in the box body. The dynamic sampling control array and the edge computing gateway are arranged outside or inside the box body. The surface state of the terminal row, the boundary disturbance amount of the box body, the electrical load amount and the surface historical contamination accumulation amount of the terminal row are obtained through the dynamic sampling control array. The surface state comprises a dew point temperature and a surface temperature. The edge computing gateway realizes the generation of various calculation and control instructions. The application solves the technical blind area problem that the local condensation in the vortex dead angle is ahead of the overall environment, and improves the system robustness under the strong disturbance working condition such as frequent opening of the box door by combining the feedforward and feedback collaborative control.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and particularly to a dehumidification prediction control system and method for a cable distribution box based on an intelligent dehumidifier. Background Art

[0002] A cable distribution box is a key device for realizing cable branching, transfer, and distribution in a distribution network, and is widely used in scenarios such as urban distribution networks, industrial parks, and residential communities. Due to outdoor installation, scattered distribution, and large quantity, the control of its operating environment has always been a difficult problem in the industry. The cable distribution box is exposed to the outdoor environment for a long time and faces complex climate tests. In the southern region, the humidity is high in spring, and the condensation problem is particularly prominent during the period of returning south. In coastal areas, the salt spray corrosion is serious, and the temperature difference between day and night is large in high-altitude areas, which all exacerbate the deterioration of the equipment environment. Condensation failure is one of the main failure types of cable distribution boxes. Some power supply enterprises have dozens of failures per year, causing great economic losses.

[0003] Existing cable distribution boxes generally have problems with poor sealing. The semi-closed louver design has extremely poor ventilation effect, and the cable holes are not tightly blocked, resulting in continuous infiltration of water vapor. The humidity inside the brick-concrete structure foundation is high, and the bottom of the box is severely oxidized and rusted. Most products do not have heating and dehumidification functions when leaving the factory, and completely rely on the external environment, being in a passive protection state. Therefore, condensation will occur, which causes corrosion of cable joints, increases contact resistance, and leads to local overheating. Condensation on the surface of the insulator reduces the insulation strength and causes flashover discharge. Rusting of metal components shortens the mechanical life, and in severe cases, it may cause combustion and explosion. Condensation also has concealment and accumulation, and it is not easy to be discovered in the initial stage. Once a failure occurs, it often causes sudden power outages. Generally speaking, the harm of condensation is very serious.

[0004] That is to say, if mainly solved by hardware devices, there will be some problems. For example, single-factor control only monitors humidity without considering multi-dimensional factors such as temperature, dew point, and equipment load; or there is no prediction ability at all; the fixed-frequency operation has high energy consumption and lacks energy-saving optimization; the condensate needs to be discharged through an external pipeline, which is easy to be blocked and frozen; it is completely isolated from the electrical state of the equipment and does not achieve linkage protection. Even opening the door of the cable distribution box is also a very serious influencing factor. The present application solves at least one or several of the existing technical problems. Summary of the Invention

[0005] The present invention aims at the deficiencies in the prior art and provides a dehumidification prediction control system and method for a cable distribution box based on an intelligent dehumidifier.

[0006] In order to solve the above technical problems, the present invention is solved by the following technical solutions: A dehumidification predictive control system for cable junction boxes based on intelligent dehumidifiers includes an intelligent dehumidifier terminal, a dynamic sampling control array, and an edge computing gateway. The intelligent dehumidifier terminal is installed inside the cable junction box, and terminal blocks are provided inside the box. The dynamic sampling control array and the edge computing gateway are respectively located outside or inside the box. The surface condition of the terminal block, the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block are obtained through a dynamic sampling control array. The surface condition includes dew point temperature and surface temperature. The edge computing gateway is configured as follows: The disturbance at the enclosure boundary is mapped to the potential energy of humid air intrusion, which characterizes the ability of humid air outside the enclosure to migrate to the surface of the terminal block and continue to act. The electrical load is mapped to the surface temperature as thermal perturbation potential energy, which characterizes the instantaneous thermal driving force for condensation formation. The hygroscopic gain coefficient is determined based on the historical accumulation of dirt on the surface, and then a competition index is constructed by combining the potential energy of humid air intrusion. The relevant competition index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. A critical slowing model is constructed based on the competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation formation, the remaining time exhibits a nonlinear and sharp contraction. Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence and correct the safety margin time. The safety margin threshold is dynamically expanded based on the depth of continuous influence to construct a safety margin buffer. The buffer delays the reaching of the condensation formation critical value by accumulating thermal disturbance energy. The remaining time predicted by the critical slowing model is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated based on the rate of change of the terminal block surface temperature and the dew point temperature, thereby realizing multiple control.

[0007] As one possible implementation, the container boundary disturbance includes the container's airtightness characteristics, the container door opening angle, the pressure difference between the inside and outside of the container, and meteorological data outside the container; mapping the container boundary disturbance to the potential energy of moist air intrusion includes the following steps: Based on the total area of ​​the cabinet door, the opening angle of the cabinet door is converted into an equivalent opening area; The intrusion mass flow rate of transient humid air when the box door is opened was determined by the pressure difference between the inside and outside of the box, the airtightness characteristics of the box, and the equivalent opening area. The intrusion potential energy of moist air is calculated by the intrusion mass flow rate, where the intrusion potential energy of moist air is the cumulative effect based on the historical path of the door opening angle. The equivalent opening area is expressed as: ; Intrusive mass flow rate is expressed as: ; The potential energy of moist air intrusion is expressed as: in, This indicates the total area of ​​the cabinet doors. Represents the flow coefficient. Represents the equivalent opening area. Indicates the opening angle of the cabinet door. This indicates the air density outside the enclosure. This indicates the pressure difference between the inside and outside of the box. Indicates the surface adsorption time constant. This represents a surface moisture absorption efficiency function based on the amount of dirt accumulated over a historical period. This represents the potential energy of moist air intrusion. This represents any point in history from the initial moment to the current moment t. Indicates the intrusive mass flow rate. Indicates the current moment.

[0008] In one possible implementation, the electrical load includes the charging and discharging current of the energy storage battery; then, mapping the electrical load to the surface temperature as thermal disturbance potential energy includes the following steps: The Joule heat power of the contact resistance is determined based on the charging and discharging current of the energy storage battery and the contact resistance connected to the terminal block. The heat balance equation of the terminal block is constructed and combined with Joule heat power to obtain the net heat flow; Based on the target protection time, critical evaporation power and net heat flux of the critical liquid film thickness for condensation, the thermal disturbance potential energy is determined. When the thermal disturbance potential energy is greater than 1, the thermal driving force inhibits condensation; when the thermal disturbance potential energy is greater than 0 and less than 1, it is in a state of wet heat competition; when the thermal disturbance potential energy is less than 0, the terminal block surface is in a state of accelerated condensation due to heat loss. The thermal disturbance potential energy is the dynamic balance between Joule heat power and convective heat dissipation power. Joule thermal power, expressed as: ; The heat balance equation is expressed as: Thermal perturbation potential energy is expressed as: ; Where I represents the charging and discharging current of the energy storage battery. This represents the contact resistance and is a function of the surface temperature Ts of the terminal block. This represents the critical evaporation power based on the target protection time and the critical liquid film thickness at which condensation occurs; Indicates net heat flow. Indicates Joule thermal power, Indicates the convective heat transfer coefficient. This indicates the heat exchange area, which is the contact area between the terminal block and the environment. Indicates ambient temperature. This indicates the latent heat loss during phase transition. Represents the critical evaporation power, in The power required to evaporate the thick liquid film within the time limit, i.e., the target protection time. This indicates the density of the liquid, i.e., the density of condensation. Indicates the critical liquid film thickness. It represents the latent heat of vaporization.

[0009] As one possible implementation, the step of obtaining and determining the degree of sustained impact and correcting the safety margin time by separately acquiring and using the extreme values ​​of the humid air intrusion potential energy and the thermal disturbance potential energy includes the following steps: Within the historical time window, the first extreme value of the potential energy of humid air intrusion and the second extreme value of the potential energy of thermal disturbance are obtained respectively. Based on the first extreme value, the second extreme value, and the effective moisture absorption coefficient since the last cleaning time, the degree of continuous influence of historical high humidity intrusion on whether condensation can occur at the current time is obtained. By using the degree of sustained influence and the intensity coefficient of memory effect, the safety margin time before the condensation criticality is dynamically corrected to obtain the corrected safety margin time. The degree of sustained impact is expressed as: ; The adjusted safety margin time is expressed as: ; in, Indicates the degree of sustained impact. Indicates the first extreme value. Indicates the second extreme value. Indicates the effective moisture absorption coefficient. Indicates protection parameters, The memory effect strength coefficient, i.e. the influence weight, represents the nonlinear extension effect of historical high humidity intrusion on the current safety margin.

[0010] As one possible implementation method, the critical slowing-down model is determined in the following way: A nonlinear mapping relationship is constructed based on the competition index and the remaining time; this nonlinear mapping relationship is the critical slowdown model. The critical slowing-down model is expressed as: ; in, , This represents the potential energy of moist air intrusion. This represents the potential energy of thermal perturbation. Represents the characteristic time of the system. This represents the critical contraction index. This indicates the competition index. Indicates the current moment.

[0011] As one possible implementation method, the following steps are also included: Based on the door opening angle, the momentum direction vector of the intruding humid air is obtained. Combined with the terminal block position data, the potential energy accumulation area inside the box is identified. The potential energy accumulation area includes vortex dead angles or low flow velocity areas. The safety margin time of the terminal block local area corresponding to the potential energy accumulation zone is shortened to achieve spatially differentiated condensation risk early warning.

[0012] As one possible implementation, the multiple control includes a feedforward control component and a feedback control component, specifically: The normalized remaining time factor is obtained based on the ratio of remaining time to target protection time. When the normalized residual time factor is not greater than 1, the feedforward control component is expressed as: , Indicates proportional gain. The nonlinear sensitivity coefficients of the critical slowing model are represented by the feedforward component, which varies with the coefficients. The decrease is non-linear, so as to saturate and intervene in advance near the critical value; when the normalized residual time factor is greater than 1, it is in dehumidification standby state. Based on the dew point temperature of the local terminal block and the corresponding surface temperature of the terminal block, the excess surface temperature of the terminal block is obtained. The feedback control component is then determined using this excess surface temperature, expressed as: , Represents differential gain. This indicates the surface excess temperature, and the feedback control component only responds to the rate of change of the excess temperature.

[0013] As one possible implementation method, the effective moisture absorption coefficient is determined through the following steps: Based on the time since the last cleaning and the rate of dust deposition in the environment, the mass of accumulated dirt on the surface is determined, and then the effective moisture absorption coefficient is determined. The mass of accumulated surface contamination is expressed as: ; The effective moisture absorption coefficient is expressed as: ; Wherein, represents the moisture absorption coefficient of the clean surface. The moisture absorption gain factor is the average humidity over the past 24 hours, which is the seasonal baseline humidity. This is the humidity correction factor; Indicates the time since the last cleaning. This indicates the mass of surface dirt accumulation over time. This represents the saturated deposition mass, i.e., the limit value for dust deposition. This represents the dust deposition rate constant. Indicates the effective moisture absorption coefficient. This indicates the moisture absorption coefficient of the clean surface, which is the baseline value. Indicates the local correction factor. Indicates seasonal baseline humidity. This indicates the average relative humidity over the past 24 hours.

[0014] A method for predictive control of dehumidification in cable junction boxes based on intelligent dehumidifiers includes the following steps: The surface condition of the terminal block, the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block are obtained through a dynamic sampling control array. The surface condition includes dew point temperature and surface temperature. The edge computing gateway is configured as follows: The disturbance at the enclosure boundary is mapped to the potential energy of humid air intrusion, which characterizes the ability of humid air outside the enclosure to migrate to the surface of the terminal block and continue to act. The electrical load is mapped to the surface temperature as thermal perturbation potential energy, which characterizes the instantaneous thermal driving force for condensation formation. The hygroscopic gain coefficient is determined based on the historical accumulation of dirt on the surface, and then a competition index is constructed by combining the potential energy of humid air intrusion. The relevant competition index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. A critical slowing model is constructed based on the competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation formation, the remaining time exhibits a nonlinear and sharp contraction. Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence and correct the safety margin time. The safety margin threshold is dynamically expanded based on the depth of continuous influence to construct a safety margin buffer. The buffer delays the reaching of the condensation formation critical value by accumulating thermal disturbance energy. The remaining time predicted by the critical slowing model is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated based on the rate of change of the terminal block surface temperature and the dew point temperature, thereby realizing multiple control.

[0015] This invention, by adopting the above technical solutions, has significant technical effects: Based on the critical slowing model, a nonlinear mapping between the competition index and the remaining time is constructed. When approaching the condensation critical value, the exponential contraction characteristic of the remaining time is captured, and the early warning advance is extended from a fixed threshold to a dynamic time, thus solving the problem of inaccurate prediction near the critical point. The depth of sustained impact is determined by historical potential energy extremes, the safety margin time is dynamically adjusted and a buffer zone is constructed, and the dehumidification intensity is controlled by combining the competitive state of thermal disturbance potential energy and humid air intrusion potential energy. Based on the door opening angle and terminal block position, potential energy accumulation areas are identified, and a differentiated safety margin shortening strategy is implemented to solve the technical blind spot where local condensation in the eddy current dead angle precedes the overall environment. The effective moisture absorption coefficient is corrected by introducing the amount of surface dirt accumulation, which adapts to the critical drift of condensation caused by dust deposition. Combined with feedforward feedback collaborative control, the system robustness under strong disturbance conditions such as frequent opening of the box door is improved. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an overall schematic diagram of the system of the present invention; Figure 2 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0019] Example 1: A dehumidification predictive control system for cable junction boxes based on intelligent dehumidifiers, such as Figure 1 As shown, the system includes an intelligent dehumidifier terminal 100, a dynamic sampling control array 200, and an edge computing gateway 300. The intelligent dehumidifier terminal is installed inside the housing 10 of a cable junction box, and the housing 10 is equipped with terminal blocks 20. The dynamic sampling control array 200 and the edge computing gateway 300 are respectively located outside or inside the housing. The surface condition of the terminal block, the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block are obtained through a dynamic sampling control array. The surface condition includes dew point temperature and surface temperature. The edge computing gateway is configured as follows: The disturbance at the enclosure boundary is mapped to the potential energy of humid air intrusion, which characterizes the ability of humid air outside the enclosure to migrate to the surface of the terminal block and continue to act. The electrical load is mapped to the surface temperature as thermal perturbation potential energy, which characterizes the instantaneous thermal driving force for condensation formation. The hygroscopic gain coefficient is determined based on the historical accumulation of dirt on the surface, and then a competition index is constructed by combining the potential energy of humid air intrusion. The relevant competition index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. A critical slowing model is constructed based on the competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation formation, the remaining time exhibits a nonlinear and sharp contraction. Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence and correct the safety margin time. The safety margin threshold is dynamically expanded based on the depth of continuous influence to construct a safety margin buffer. The buffer delays the reaching of the condensation formation critical value by accumulating thermal disturbance energy. The remaining time predicted by the critical slowing model is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated based on the rate of change of the terminal block surface temperature and the dew point temperature, thereby realizing multiple control.

[0020] This application utilizes a combination of dynamic sampling and edge computing to predict and control condensation formation within cable junction boxes, ensuring safe equipment operation. It primarily consists of three parts: an intelligent dehumidifier terminal, a dynamic sampling control array, and an edge computing gateway. The functions of each part are as follows: the intelligent dehumidifier terminal is installed inside the cable junction box to perform dehumidification operations; the dynamic sampling control array and the edge computing gateway can be flexibly positioned inside or outside the box, respectively responsible for data acquisition and core algorithm processing.

[0021] The dynamic sampling control array collects four types of key data: the surface condition of the terminal block (including dew point temperature and surface temperature), the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block.

[0022] Potential energy mapping: The disturbance at the enclosure boundary is mapped to the potential energy of humid air intrusion (characterizing the ability of external humid air to migrate to the terminal block), and the electrical load and surface temperature are mapped to the potential energy of thermal disturbance (characterizing the instantaneous thermal driving force of condensation formation). Competition Index Construction: The hygroscopic gain coefficient is determined based on the historical accumulation of dirt on the surface, and the competition index is constructed by combining the potential energy of humid air intrusion. This index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. Condensation prediction model: A critical slowing model is constructed using a competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation, the remaining time shrinks rapidly and nonlinearly. Safety margin correction: Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence, thereby correcting the safety margin time and constructing a safety margin buffer. The accumulation of thermal disturbance energy delays the attainment of the condensation critical value. Multiple control logics: The predicted remaining time is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated by combining the terminal block surface temperature change rate and dew point temperature to achieve dynamic control.

[0023] The solution proposed in this invention significantly improves both prediction accuracy and critical response speed. By constructing a critical slowdown model based on a competition index, it solves the technical problem of inaccurate predictions by traditional linear models near the condensation critical point. When the competition index approaches the critical value, the system can capture the nonlinear, sharp contraction characteristics of the remaining time. Compared to fixed threshold control, the advance time for condensation warning is extended from a traditional fixed time to a dynamic time (adapted based on historical intrusion depth), effectively overcoming the response lag caused by critical slowdown and enabling precise matching between the timing of dehumidification intervention and the evolution trend of condensation risk.

[0024] Furthermore, it achieves an optimal balance between energy consumption control and equipment lifespan. Based on the extreme values ​​of the potential energy of humid air intrusion and thermal disturbance, the depth of sustained impact is determined, and the safety margin time is dynamically adjusted and a safety margin buffer is constructed, realizing a shift from excessive dehumidification to precise defense. During the metastable period following historical high humidity intrusion, the safety margin time is extended nonlinearly through the memory effect intensity coefficient to ensure adequate protection is maintained until the material is completely dry; while during the stable drying period, the safety margin is automatically shortened, simultaneously reducing thermal stress cycles on terminal blocks and extending the service life of electrical connectors.

[0025] It can also enhance the ability to prevent and control spatially differentiated risks. By fusing data on the door opening angle and terminal block position, it identifies potential energy accumulation areas (eddy current dead zones, low flow velocity zones) and implements differentiated early warnings to shorten the safety margin time for local areas. This solves the technical blind spot of traditional uniform control where overall safety is achieved but local condensation occurs, and is especially suitable for cable junction boxes with complex internal structures, reducing the rate of local condensation accidents.

[0026] Furthermore, quantitative control of the energy competition state in humidity and heat can be achieved. The charging and discharging current of the energy storage battery is mapped to thermal disturbance potential energy via Joule heat power, realizing multi-physics field coupling control of electrothermal humidity. When the thermal disturbance potential energy is greater than 1, the dehumidification intensity is automatically reduced, utilizing the self-heating of electrical equipment to suppress condensation; when in a humidity-heat competition state (0 < thermal disturbance potential energy < 1), feedforward-feedback composite control is activated. This control strategy based on the energy competition index, while ensuring insulation safety, fully utilizes the operating characteristics of the equipment to achieve a dynamic balance between energy saving and protection.

[0027] Another crucial improvement in this application is enhanced adaptability to polluted environments. An effective hygroscopic coefficient based on historical surface contamination accumulation is introduced, resolving the drift problem of condensation critical conditions caused by dust deposition. By correcting the competition index through a contamination hygroscopic gain factor, the control system can adapt to changes in the critical liquid film thickness for condensation from clean to highly polluted environments. This ensures accurate condensation prediction in harsh environments such as coastal areas and industrial dusty environments, preventing premature condensation or misjudgment of insulation flashover due to surface contamination.

[0028] Ultimately, the robustness of the feedforward and feedback coordination can be improved. The feedforward control component, based on the nonlinear sensitivity coefficient of the critical slowing model, intervenes in advance when the competition index approaches the critical value, overcoming system inertia; the feedback control component responds only to the rate of change of excess temperature on the terminal block surface, suppressing measurement noise interference.

[0029] In one embodiment, the intelligent dehumidifier uses a high-tech product that organically combines advanced compressor refrigeration technology with intelligent control technology. It is suitable for smaller enclosed spaces and uses a compression refrigeration system for dehumidification. Through the product's air circulation system, humid air in the environment is continuously drawn into the product, condensed into water, and then discharged from the dehumidified space. At the same time, dry air is discharged. It can quickly and effectively reduce the air humidity in smaller enclosed spaces and prevent condensation. It also has an effective preventive effect on short circuits, insulation deterioration, corrosion, and aging of electrical control cabinets and electronic devices caused by moisture and condensation.

[0030] This intelligent dehumidifier features an LCD display that provides a wealth of information. It is flexible, convenient to install, and easy to maintain. This intelligent dehumidifier has numerous functions, including: measuring ambient temperature and humidity (external sensor); measuring antifreeze temperature (external sensor) to prevent water pipes from freezing; automatic defrosting; RS485 communication; fault alarm; heater output; display of cumulative dehumidification time and cumulative dehumidification capacity; historical record and fault alarm information query functions; and self-test function.

[0031] This application includes one or more sets of sensors, which can be installed in various places inside or outside the enclosure to sense temperature, humidity, door opening status, etc. under various conditions; the sensor information is collected and summarized through a dynamic sampling control array.

[0032] It should be emphasized that the intelligent dehumidifier in this application is not limited to a specific product model; any product or equivalent product that can achieve the solution described in this application is acceptable.

[0033] In one embodiment, the container boundary disturbance includes the container's airtightness characteristics, the container door opening angle, the pressure difference between the inside and outside of the container, and meteorological data outside the container; mapping the container boundary disturbance to the potential energy of moist air intrusion includes the following steps: Based on the total area of ​​the cabinet door, the opening angle of the cabinet door is converted into an equivalent opening area; The intrusion mass flow rate of transient humid air when the box door is opened was determined by the pressure difference between the inside and outside of the box, the airtightness characteristics of the box, and the equivalent opening area. The intrusion potential energy of moist air is calculated by the intrusion mass flow rate, where the intrusion potential energy of moist air is the cumulative effect based on the historical path of the door opening angle. The equivalent opening area is expressed as: ; Intrusive mass flow rate is expressed as: ; The potential energy of moist air intrusion is expressed as: in, This indicates the total area of ​​the cabinet doors. Represents the flow coefficient. Represents the equivalent opening area. Indicates the opening angle of the cabinet door. This indicates the air density outside the enclosure. This indicates the pressure difference between the inside and outside of the box. Indicates the surface adsorption time constant. This represents a surface moisture absorption efficiency function based on the amount of dirt accumulated over a historical period. This represents the potential energy of moist air intrusion. This represents any point in history from the initial moment to the current moment t. Indicates the intrusive mass flow rate. Indicates the current moment.

[0034] This embodiment transforms the routine operation of opening the junction box door into a quantifiable indicator for assessing humid air intrusion. In practice, the opening angle is first converted into an equivalent opening area based on the door's geometry, establishing a mapping relationship between the door's posture and the fluid boundary. Then, considering the internal and external pressure difference and airtightness characteristics, the transient mass flow rate of humid air intrusion during maintenance is calculated. Finally, by integrating historical opening paths, the humid air intrusion potential energy, representing the cumulative effect, is obtained. On one hand, the coupled calculation of the equivalent opening area and transient mass flow rate achieves effective quantification of pressure difference-driven processes. On the other hand, the introduction of historical path cumulative effects makes the condensation risk under different maintenance modes comparable. For example, although the frequency and magnitude of multiple small-angle inspection openings and a single large-angle maintenance opening differ, their humid air intrusion potential energy can be directly compared, providing a basis for optimizing inspection cycles and developing differentiated dehumidification strategies. Furthermore, since the door angle, internal and external pressure difference, and meteorological data are all easily obtainable monitoring parameters, they are readily integrated into existing systems to achieve dynamic early warning and intelligent control of condensation risks in cable junction boxes.

[0035] In addition, in one specific embodiment, the following steps are also included: Based on the door opening angle, the momentum direction vector of the intruding humid air is obtained. Combined with the terminal block position data, the potential energy accumulation area inside the box is identified. The potential energy accumulation area includes vortex dead angles or low flow velocity areas. The safety margin time of the terminal block local area corresponding to the potential energy accumulation zone is shortened to achieve spatially differentiated condensation risk early warning.

[0036] Specifically, the calculation can be performed in the following ways: The inflow direction vector is determined by the door opening angle, specifically: in, Indicates the opening angle of the cabinet door (0° is closed, 90° is fully open). This indicates the azimuth angle of the door hinge (the azimuth angle of the door hinge relative to the coordinate system of the box body). Indicates the unit direction vector (pointing inwards from the box); The incident velocity of the intruding moist air is determined based on Bernoulli's equation, and is expressed as: ,in, This indicates the incident velocity (m / s). It is the flow coefficient (related to the shape of the door gap, dimensionless, usually 0.6-0.8). The pressure difference between the inside and outside of the box (Pa). This indicates the density of moist air outside the enclosure (kg / m³). The momentum direction vector (momentum flux) is represented as: ,in, It is the momentum direction vector (kg·m / s², i.e., N). This indicates the intrusion mass flow rate (kg / s). The equivalent opening area (m²) Differential safety margin time correction shortens the safety margin time for identified potential energy accumulation regions, specifically as follows: ,in, This represents the safety margin time after local area correction. Basic safety margin time.

[0037] This embodiment, based on the calculation of the potential energy of humid air intrusion, further achieves spatially refined identification and differentiated early warning of condensation risk inside cable junction boxes. Specifically, based on the door opening angle, the momentum direction vector of the intruding humid air is determined. Combined with the spatial position data of the terminal blocks, potential energy accumulation zones inside the box are identified through flow field simulation or empirical models, including eddy current dead zones, low-velocity areas, and other areas where humid air easily accumulates. Furthermore, for the local areas of the terminal blocks corresponding to these potential energy accumulation zones, the safety margin time is shortened, achieving spatially differentiated condensation risk early warning.

[0038] This embodiment overcomes the limitations of traditional methods that treat the internal cavity of the cable junction box as a homogeneous region for overall evaluation. By introducing coupled analysis of momentum direction vectors and equipment spatial layout, it can accurately locate high-risk condensation points. At the same time, it dynamically adjusts the safety margin time based on the identification results of potential energy accumulation areas, expanding the early warning strategy from a single time dimension to a combined spatiotemporal dimension. This avoids excessive early warning in non-accumulation areas and ensures early intervention in key areas such as eddy current dead zones, improving the pertinence and economy of cable junction box insulation status monitoring.

[0039] In one embodiment, the electrical load includes the charging and discharging current of the energy storage battery; mapping the electrical load to surface temperature as thermal perturbation potential energy includes the following steps: The Joule heat power of the contact resistance is determined based on the charging and discharging current of the energy storage battery and the contact resistance connected to the terminal block. The heat balance equation of the terminal block is constructed and combined with Joule heat power to obtain the net heat flow; Based on the target protection time, critical evaporation power and net heat flux of the critical liquid film thickness for condensation, the thermal disturbance potential energy is determined. When the thermal disturbance potential energy is greater than 1, the thermal driving force inhibits condensation; when the thermal disturbance potential energy is greater than 0 and less than 1, it is in a state of wet heat competition; when the thermal disturbance potential energy is less than 0, the terminal block surface is in a state of accelerated condensation due to heat loss. The thermal disturbance potential energy is the dynamic balance between Joule heat power and convective heat dissipation power. Joule thermal power, expressed as: ; The heat balance equation is expressed as: Thermal perturbation potential energy is expressed as: ; Where I represents the charging and discharging current of the energy storage battery. This represents the contact resistance and is a function of the surface temperature Ts of the terminal block. This represents the critical evaporation power based on the target protection time and the critical liquid film thickness at which condensation occurs; Indicates net heat flow. Indicates Joule thermal power, Indicates the convective heat transfer coefficient. This indicates the heat exchange area, which is the contact area between the terminal block and the environment. Indicates ambient temperature. This indicates the latent heat loss during phase transition. Represents the critical evaporation power, in The power required to evaporate the thick liquid film within the time limit, i.e., the target protection time. This indicates the density of the liquid, i.e., the density of condensation. Indicates the critical liquid film thickness. It represents the latent heat of vaporization.

[0040] This embodiment transforms the charging and discharging characteristics of the energy storage battery into an active control resource for preventing condensation on the terminal block. It achieves a synergistic effect of suppressing condensation through current-carrying heating without requiring additional heating devices. By dividing the thermal disturbance potential energy into three intervals, it quantifies and measures the condensation risk, providing clear criteria for optimizing the thermal management strategy of the energy storage system. Furthermore, since the Joule heat power changes in real time with the charging and discharging current, this method can dynamically track the condensation suppression capability under different operating conditions, making it particularly suitable for the intermittent charging and discharging characteristics of energy storage batteries. This effectively improves the timeliness and accuracy of terminal block insulation reliability assessment.

[0041] In another embodiment, the step of obtaining and determining the degree of sustained impact and correcting the safety margin time by separately acquiring and using the extreme values ​​of the humid air intrusion potential energy and the thermal disturbance potential energy includes the following steps: Within the historical time window, the first extreme value of the potential energy of humid air intrusion and the second extreme value of the potential energy of thermal disturbance are obtained respectively. Based on the first extreme value, the second extreme value, and the effective moisture absorption coefficient since the last cleaning time, the degree of continuous influence of historical high humidity intrusion on whether condensation can occur at the current time is obtained. By using the degree of sustained influence and the intensity coefficient of memory effect, the safety margin time before the condensation criticality is dynamically corrected to obtain the corrected safety margin time. The degree of sustained impact is expressed as: ; The adjusted safety margin time is expressed as: ; in, Indicates the degree of sustained impact. Indicates the first extreme value. Indicates the second extreme value. Indicates the effective moisture absorption coefficient. Indicates protection parameters, The memory effect strength coefficient, i.e. the influence weight, represents the nonlinear extension effect of historical high humidity intrusion on the current safety margin.

[0042] In this embodiment, the first extreme value of the air intrusion potential energy represents the strongest historical humid air intrusion event, and the second extreme value of the thermal disturbance potential energy represents the historical optimal condensation suppression condition; while historical high humidity intrusion refers to the extreme value event of humid air intrusion potential energy that occurs within the historical time window, that is, the typical working condition in which a large amount of humid air corresponding to the first extreme value intrudes into the box body.

[0043] The entire process can be understood as follows: When the cable junction box door is opened during past maintenance, external humid air enters the box under the drive of pressure difference, forming a transient mass flow rate peak. This peak value, after being integrated over time, yields the humid air intrusion potential energy, and the first extreme value is the maximum intrusion potential energy value within that time window, corresponding to the most severe humid air intrusion event in history. This high-humidity intrusion event does not end instantaneously, but its effects are continuous. For example, some of the intruded humid air forms an adsorbed water film on the terminal block surface and the box wall, while some diffuses and permeates inside the insulation material. This residual moisture will lower the condensation critical condition in subsequent moments, making the terminal blocks more prone to condensation in lower ambient humidity.

[0044] The introduction of the effective moisture absorption coefficient in this embodiment is precisely to quantify the degree of this continuous impact: the closer to the last cleaning time, the thinner the surface adsorbed water film, and the weaker the residual effect of historical high humidity intrusion; as the operating time accumulates, the synergistic effect of surface dirt and moisture absorption strengthens, and the contribution of the same historical high humidity intrusion event to the deterioration of the current condensation risk is greater. Therefore, historical high humidity intrusion essentially transforms discrete historical extreme events into continuous current risk weights, realizing a technical leap from instantaneous state judgment to cumulative effect assessment in condensation early warning.

[0045] Based on this, this embodiment can trace the path of moisture absorption history on the terminal block surface through historical extreme values ​​and the effective moisture absorption coefficient; the time-varying characteristics of the effective moisture absorption coefficient (decaying with cleaning cycles) accurately reflect the deteriorating effect of surface contamination accumulation on the critical condensation condition, making the safety margin time more applicable to actual working conditions; and the memory effect intensity coefficient can achieve differentiated responses to extreme historical events. The entire embodiment can improve the robustness of condensation early warning under complex operation and maintenance history.

[0046] In this process, the effective moisture absorption coefficient is determined through the following steps: Based on the time since the last cleaning and the rate of dust deposition in the environment, the mass of accumulated dirt on the surface is determined, and then the effective moisture absorption coefficient is determined. The mass of accumulated surface contamination is expressed as: ; The effective moisture absorption coefficient is expressed as: ; Wherein, represents the moisture absorption coefficient of the clean surface. The moisture absorption gain factor is the average humidity over the past 24 hours, which is the seasonal baseline humidity. This is the humidity correction factor; Indicates the time since the last cleaning. This indicates the mass of surface dirt accumulation over time. This represents the saturated deposition mass, i.e., the limit value for dust deposition. This represents the dust deposition rate constant. Indicates the effective moisture absorption coefficient. This indicates the moisture absorption coefficient of the clean surface, which is the baseline value. Indicates the local correction factor. Indicates seasonal baseline humidity. This indicates the average relative humidity over the past 24 hours.

[0047] Finally, the critical slowing-down model was determined in the following way: A nonlinear mapping relationship is constructed based on the competition index and the remaining time; this nonlinear mapping relationship is the critical slowdown model. The critical slowing-down model is expressed as: ; in, , This represents the potential energy of moist air intrusion. This represents the potential energy of thermal perturbation. Represents the characteristic time of the system. This represents the critical contraction index. This indicates the competition index. Indicates the current moment.

[0048] Traditional linear models are inaccurate and prone to significant errors in practice, leading to highly imprecise judgments, especially in critical zones where calculated or inferred data becomes largely useless. Nonlinear mapping relationships, however, can more accurately determine the accumulation of system vulnerability before condensation occurs. The identification of critical slowdown characteristics provides a physical basis for setting early warning thresholds, and its sensitivity to the competition index allows it to distinguish critical approach velocities under different humid and hot competition conditions, providing a dynamic decision-making window for graded early warning and proactive intervention in cable junction boxes and energy storage systems.

[0049] In one embodiment, the multiple control includes a feedforward control component and a feedback control component, specifically: The normalized remaining time factor is obtained based on the ratio of remaining time to target protection time. When the normalized residual time factor is not greater than 1, the feedforward control component is expressed as: , Indicates proportional gain. The nonlinear sensitivity coefficients of the critical slowing model are represented by the feedforward component, which varies with the coefficients. The decrease is non-linear, so as to saturate and intervene in advance near the critical value; when the normalized residual time factor is greater than 1, it is in dehumidification standby state. Based on the dew point temperature of the local terminal block and the corresponding surface temperature of the terminal block, the excess surface temperature of the terminal block is obtained. The feedback control component is then determined using this excess surface temperature, expressed as: , Represents differential gain. This indicates the surface excess temperature, and the feedback control component only responds to the rate of change of the excess temperature.

[0050] By employing multiple control mechanisms, such as feedforward control components and feedback control components, the predictability and precision of the dehumidification control in cable junction boxes can be coordinated.

[0051] In one embodiment, the proportional gain and the derivative gain are adaptively adjusted in the following manner: Based on the risk level of the potential energy accumulation zone, the proportional gain of the control loop for the terminal block corresponding to the high-risk zone is increased: ,in, This is the spatial risk compensation coefficient. For coupling weights; Based on the surface temperature of the terminal block With the ambient temperature The temperature difference is used to dynamically adjust the differential gain. ,in, This is the temperature coupling attenuation coefficient, used to suppress feedback response sensitivity and avoid false triggering in low-temperature environments. Of course, other methods of determination may exist in this field, which will not be elaborated upon here.

[0052] Example 2: A method for predictive control of dehumidification in cable junction boxes based on intelligent dehumidifiers, such as... Figure 2 As shown, it includes the following steps: S100: The surface condition of the terminal block, the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block are obtained through a dynamic sampling control array. The surface condition includes dew point temperature and surface temperature. The edge computing gateway is configured as follows: S210. Map the disturbance at the enclosure boundary to the potential energy of humid air intrusion. The potential energy of humid air intrusion characterizes the ability of humid air outside the enclosure to migrate to the surface of the terminal block and continue to act. S220. The electrical load is mapped to the surface temperature as thermal disturbance potential energy, which characterizes the instantaneous thermal driving force for condensation formation. S230. The moisture absorption gain coefficient is determined based on the historical accumulation of dirt on the surface, and then the competition index is constructed by combining the potential energy of humid air intrusion. The relevant competition index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. S240. A critical slowing model is constructed based on the competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation formation, the remaining time exhibits a nonlinear and rapid contraction. S250. Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence and correct the safety margin time. The safety margin threshold is dynamically expanded based on the depth of continuous influence to construct a safety margin buffer. The buffer delays the reaching of the condensation formation critical value by accumulating thermal disturbance energy. S260. The remaining time predicted by the critical slowing model is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated based on the rate of change of the terminal block surface temperature and the dew point temperature, thereby realizing multiple control.

[0053] Various changes and modifications made without departing from the spirit and scope of this invention, and all equivalent technical solutions, also fall within the scope of this invention.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0055] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] This invention is described with reference to flowchart illustrations and / or block diagrams of the method, terminal device (system), and computer program product according to the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] It should be noted that: The phrase "an embodiment" or "an embodiment" mentioned in the specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0060] Furthermore, it should be noted that the shapes and names of the parts and components described in the specific embodiments described in this specification may differ. All equivalent or simple variations made to the structure, features, and principles described in this patent concept are included within the protection scope of this patent. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to replace them, as long as they do not depart from the structure of this invention or exceed the scope defined in these claims, they should all fall within the protection scope of this invention.

Claims

1. A dehumidification prediction and control system for a cable junction box based on an intelligent dehumidifier, comprising an intelligent dehumidifier terminal, a dynamic sampling control array, and an edge computing gateway, wherein the intelligent dehumidifier terminal is installed inside the cable junction box, and terminal blocks are provided inside the box; the dynamic sampling control array and the edge computing gateway are respectively located outside or inside the box, characterized in that: The surface condition of the terminal block, the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block are obtained through a dynamic sampling control array. The surface condition includes dew point temperature and surface temperature. The edge computing gateway is configured as follows: The disturbance at the enclosure boundary is mapped to the potential energy of humid air intrusion, which characterizes the ability of humid air outside the enclosure to migrate to the surface of the terminal block and continue to act. The electrical load is mapped to the surface temperature as thermal perturbation potential energy, which characterizes the instantaneous thermal driving force for condensation formation. The hygroscopic gain coefficient is determined based on the historical accumulation of dirt on the surface, and then a competition index is constructed by combining the potential energy of humid air intrusion. The relevant competition index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. A critical slowing model is constructed based on the competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation formation, the remaining time exhibits a nonlinear and sharp contraction. Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence and correct the safety margin time. The safety margin threshold is dynamically expanded based on the depth of continuous influence to construct a safety margin buffer. The buffer delays the reaching of the condensation formation critical value by accumulating thermal disturbance energy. The remaining time predicted by the critical slowing model is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated based on the rate of change of the terminal block surface temperature and the dew point temperature, thereby realizing multiple control.

2. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 1, characterized in that, The container boundary disturbance includes the container's airtightness characteristics, door opening angle, internal and external pressure difference, and meteorological data from outside the container; mapping the container boundary disturbance to the potential energy of moist air intrusion includes the following steps: Based on the total area of ​​the cabinet door, the opening angle of the cabinet door is converted into an equivalent opening area; The intrusion mass flow rate of transient humid air when the box door is opened was determined by the pressure difference between the inside and outside of the box, the airtightness characteristics of the box, and the equivalent opening area. The intrusion potential energy of moist air is calculated by the intrusion mass flow rate, where the intrusion potential energy of moist air is the cumulative effect based on the historical path of the door opening angle. The equivalent opening area is expressed as: ; Intrusive mass flow rate is expressed as: ; The potential energy of moist air intrusion is expressed as: in, This indicates the total area of ​​the cabinet doors. Represents the flow coefficient. Represents the equivalent opening area. Indicates the opening angle of the cabinet door. This indicates the air density outside the enclosure. This indicates the pressure difference between the inside and outside of the box. Indicates the surface adsorption time constant. This represents a surface moisture absorption efficiency function based on the amount of dirt accumulated over a historical period. This represents the potential energy of moist air intrusion. This represents any point in history from the initial moment to the current moment t. Indicates the intrusive mass flow rate. Indicates the current moment.

3. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 1, characterized in that, The electrical load includes the charging and discharging current of the energy storage battery; mapping the electrical load to surface temperature into thermal disturbance potential energy includes the following steps: The Joule heat power of the contact resistance is determined based on the charging and discharging current of the energy storage battery and the contact resistance connected to the terminal block. The heat balance equation of the terminal block is constructed and combined with Joule heat power to obtain the net heat flow; Based on the target protection time, critical evaporation power and net heat flux of the critical liquid film thickness for condensation, the thermal disturbance potential energy is determined. When the thermal disturbance potential energy is greater than 1, the thermal driving force inhibits condensation; when the thermal disturbance potential energy is greater than 0 and less than 1, it is in a state of wet heat competition; when the thermal disturbance potential energy is less than 0, the terminal block surface is in a state of accelerated condensation due to heat loss. The thermal disturbance potential energy is the dynamic balance between Joule heat power and convective heat dissipation power. Joule thermal power, expressed as: ; The heat balance equation is expressed as: Thermal perturbation potential energy is expressed as: ; Where I represents the charging and discharging current of the energy storage battery. This represents the contact resistance and is a function of the surface temperature Ts of the terminal block. This represents the critical evaporation power based on the target protection time and the critical liquid film thickness at which condensation occurs; Indicates net heat flow. Indicates Joule thermal power, Indicates the convective heat transfer coefficient. This indicates the heat exchange area, which is the contact area between the terminal block and the environment. Indicates ambient temperature. This indicates the latent heat loss during phase transition. Represents the critical evaporation power, in The power required to evaporate the thick liquid film within the time limit, i.e., the target protection time. This indicates the density of the liquid, i.e., the density of condensation. Indicates the critical liquid film thickness. It represents the latent heat of vaporization.

4. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 1, characterized in that, The process of obtaining and determining the degree of sustained impact and adjusting the safety margin time by separately acquiring and using the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance includes the following steps: Within the historical time window, the first extreme value of the potential energy of humid air intrusion and the second extreme value of the potential energy of thermal disturbance are obtained respectively. Based on the first extreme value, the second extreme value, and the effective moisture absorption coefficient since the last cleaning time, the degree of continuous influence of historical high humidity intrusion on whether condensation can occur at the current time is obtained. By using the degree of sustained influence and the intensity coefficient of memory effect, the safety margin time before the condensation criticality is dynamically corrected to obtain the corrected safety margin time. The degree of sustained impact is expressed as: ; The adjusted safety margin time is expressed as: ; in, Indicates the degree of sustained impact. Indicates the first extreme value. Indicates the second extreme value. Indicates the effective moisture absorption coefficient. Indicates protection parameters, The memory effect strength coefficient, i.e. the influence weight, represents the nonlinear extension effect of historical high humidity intrusion on the current safety margin.

5. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 1, characterized in that, The critical slowing-down model is determined in the following way: A nonlinear mapping relationship is constructed based on the competition index and the remaining time; this nonlinear mapping relationship is the critical slowdown model. The critical slowing-down model is expressed as: ; in, , This represents the potential energy of moist air intrusion. This represents the potential energy of thermal perturbation. Represents the characteristic time of the system. This represents the critical contraction index. This indicates the competition index. Indicates the current moment.

6. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 1, characterized in that, It also includes the following steps: Based on the door opening angle, the momentum direction vector of the invading humid air is obtained. Combined with the terminal block position data, the potential energy accumulation area inside the box is identified. The potential energy accumulation area includes vortex dead angles or low flow velocity areas. The safety margin time of the terminal block local area corresponding to the potential energy accumulation zone is shortened to achieve spatially differentiated condensation risk early warning.

7. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 1, characterized in that, The multiple control includes feedforward control components and feedback control components, specifically: The normalized remaining time factor is obtained based on the ratio of remaining time to target protection time. When the normalized residual time factor is not greater than 1, the feedforward control component is expressed as: , Indicates proportional gain. The nonlinear sensitivity coefficients of the critical slowing model are represented by the feedforward component, which varies with the coefficients. The decrease is non-linear, so as to intervene in saturation near the critical value in advance; When the normalized residual time factor is greater than 1, it is in dehumidification standby mode; Based on the dew point temperature of the local terminal block and the corresponding surface temperature of the terminal block, the excess surface temperature of the terminal block is obtained. The feedback control component is then determined using this excess surface temperature, expressed as: , Represents differential gain. This indicates the surface excess temperature, and the feedback control component only responds to the rate of change of the excess temperature.

8. The cable junction box dehumidification predictive control system based on an intelligent dehumidifier according to claim 4, characterized in that, The effective moisture absorption coefficient is determined through the following steps: Based on the time since the last cleaning and the rate of dust deposition in the environment, the mass of accumulated dirt on the surface is determined, and then the effective moisture absorption coefficient is determined. The mass of accumulated surface contamination is expressed as: ; The effective moisture absorption coefficient is expressed as: ; Wherein, represents the moisture absorption coefficient of the clean surface. The moisture absorption gain factor is the average humidity over the past 24 hours, which is the seasonal baseline humidity. This is the humidity correction factor; Indicates the time since the last cleaning. This indicates the mass of surface dirt accumulation over time. This represents the saturated deposition mass, i.e., the limit value for dust deposition. This represents the dust deposition rate constant. Indicates the effective moisture absorption coefficient. This indicates the moisture absorption coefficient of the clean surface, which is the baseline value. Indicates the local correction factor. Indicates seasonal baseline humidity. This indicates the average relative humidity over the past 24 hours.

9. A method for predictive control of dehumidification in cable junction boxes based on intelligent dehumidifiers, characterized in that, Includes the following steps: The surface condition of the terminal block, the amount of disturbance at the enclosure boundary, the amount of electrical load, and the amount of historical dirt accumulation on the surface of the terminal block are obtained through a dynamic sampling control array. The surface condition includes dew point temperature and surface temperature. The edge computing gateway is configured as follows: The disturbance at the enclosure boundary is mapped to the potential energy of humid air intrusion, which characterizes the ability of humid air outside the enclosure to migrate to the surface of the terminal block and continue to act. The electrical load is mapped to the surface temperature as thermal perturbation potential energy, which characterizes the instantaneous thermal driving force for condensation formation. The hygroscopic gain coefficient is determined based on the historical accumulation of dirt on the surface, and then a competition index is constructed by combining the potential energy of humid air intrusion. The relevant competition index is the dimensionless ratio of the wet-side driving force to the thermal-side resistance. A critical slowing model is constructed based on the competition index to predict the remaining time for condensation formation. When the competition index approaches the critical value for condensation formation, the remaining time exhibits a nonlinear and sharp contraction. Based on the nonlinear contraction characteristics, the extreme values ​​of the potential energy of humid air intrusion and the potential energy of thermal disturbance are extracted to determine the depth of continuous influence and correct the safety margin time. The safety margin threshold is dynamically expanded based on the depth of continuous influence to construct a safety margin buffer. The buffer delays the reaching of the condensation formation critical value by accumulating thermal disturbance energy. The remaining time predicted by the critical slowing model is compared with the safety margin time. When the remaining time is less than the safety margin time, a dehumidification intensity command is generated. At the same time, a feedback correction command is generated based on the rate of change of the terminal block surface temperature and the dew point temperature, thereby realizing multiple control.

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