Heat-dissipation moisture-proof electric energy metering box and control method thereof

Abstract

The invention discloses a heat-dissipation moisture-proof electric energy metering box and a control method thereof, and relates to the technical field of electric energy metering boxes. The damp-proof layer forms a'resistance-absorption-regeneration 'closed loop through an activated carbon net groove and a heating piece, the heat dissipation layer forms a directional convection path through a partition plate air duct, a fan and a heat dissipation device, and heat and humidity cross interference is reduced spatially through the physical partition design of the damp-proof layer and the heat dissipation layer; meanwhile, working conditions are dynamically divided based on temperature and humidity sensing, a reinforcement learning algorithm integrating efficiency, energy consumption and part health degree is introduced, and a heat dissipation and moisture-proof operation strategy is dynamically deployed. According to the invention, through the cooperation of the space function partition and the intelligent control strategy, the limitation of the traditional fixed threshold control is broken through, the dynamic optimization of the heat and humidity management strategy is realized, and the environmental adaptability and long-period reliability of the outdoor electric energy metering box are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy metering box, more particularly to a heat-dissipation and moisture-proof electric energy metering box and a control method thereof. BACKGROUND

[0002] At present, the heat dissipation and moisture-proof control of the electric energy metering box in the complex outdoor environment is mainly based on the traditional threshold triggering mode, such as directly starting and stopping the fan and heating sheet through the temperature and humidity sensor, or using the preset time sequence segmented control strategy. Although this kind of scheme can realize the basic environmental regulation, the control logic is rigid and cannot optimize the operation mode according to the real-time temperature and humidity change rate, electric meter power consumption heat load and other dynamic parameters, resulting in that the temperature and humidity in the box fluctuate sharply under the working conditions of high temperature and high humidity, large diurnal temperature difference and the like, and the electrical components are accelerated to age.

[0003] However, the prior art has the following defects: firstly, the control strategy lacks adaptability, for example, under the high temperature and high humidity working condition in the plum rain season, the fixed threshold control will cause the fan and heating sheet to frequently start and stop, which increases the energy consumption and shortens the service life of the components; secondly, the heat and moisture coupling evaluation mechanism is not established, and the collaborative demand of heat dissipation and moisture-proof cannot be balanced, and the contradiction of "introducing moisture to aggravate the moisture-proof burden when dissipating heat, and disturbing the heat dissipation effect when preventing moisture" often occurs; thirdly, the component health degree management is ignored, and the traditional control does not consider the factors such as the aging degree of the heating sheet and the wear of the fan bearing into the strategy optimization, resulting in that the equipment fault warning is delayed.

[0004] Therefore, how to provide a heat-dissipation and moisture-proof electric energy metering box and a control method thereof, realize the adaptive and collaborative optimization of heat dissipation and moisture-proof, and improve the operation efficiency and reliability of the electric energy metering box under complex working conditions, is a problem to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the present application provides a heat-dissipation and moisture-proof electric energy metering box and a control method thereof, which breaks through the limitation of the traditional fixed threshold control through the cooperation of spatial function partition and intelligent control strategy, realizes the dynamic optimization of the heat and moisture management strategy, and improves the operation efficiency and reliability of the electric energy metering box under complex environment.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0007] On the one hand, the present application provides a heat-dissipation and moisture-proof electric energy metering box, which comprises a box body, the box body is divided into a moisture-proof layer and a heat-dissipation layer from top to bottom;

[0008] The box body is provided with a plurality of strip frames on the top, the strip frames are used for installing moisture-proof components; the moisture-proof components comprise a top plate, the size of the top plate is matched with that of the strip frame; an activated carbon mesh groove is fixedly connected below the top plate, a hydrophobic layer is arranged at the top end of the activated carbon mesh groove, and a heating sheet is installed on the bottom plate of the activated carbon mesh groove;

[0009] The heat dissipation layer includes an electric meter placement area, a sensor placement area, and a control device placement area; the electric meter placement area is used to install the electric meter; the sensor placement area is used to install the temperature and humidity sensor; the control device placement area is used to install the control module; the position of the bar frame corresponds to the installation position of the electric meter in the electric meter placement area; the control module is connected to the heating plate;

[0010] The side walls at both ends of the heat dissipation layer box are respectively provided with an air inlet and a first air outlet; a dustproof net is provided on one side of the air inlet; a fan and fan blades are provided on the side of the first air outlet; an installation groove is provided at the bottom of the heat dissipation layer, and the installation groove is used to install a radiator with a partition; the control module is connected to the fan and the radiator.

[0011] Preferably, the activated carbon mesh tank is supported by a cubic frame; the bracket of the cubic frame is a hollow structure for placing the connection line between the heating plate and the control module.

[0012] Preferably, the radiator includes a fan mounting base and a fan; the fan is fixed in the radiator through the fan mounting base;

[0013] The partitions are installed inside the radiator and are respectively arranged on the left and right sides and the top of the radiator. The partitions of the radiator form air ducts.

[0014] Preferably, an opening is provided on the inner side of the bottom of the heat dissipation layer, and the opening corresponds to the air duct formed by the left partition of the radiator to form an air inlet duct;

[0015] A second air outlet is provided at the bottom of one side of the first air outlet, and the second air outlet corresponds to the air duct formed by the partition plate on the right side of the radiator to form an air outlet air duct.

[0016] On the other hand, the present invention further discloses a control method for a heat dissipation and moisture-proof electric energy metering box, which is applied to the heat dissipation and moisture-proof electric energy metering box, comprising:

[0017] Acquire temperature and humidity sensor data based on temperature and humidity sensor;

[0018] Using intelligent algorithms, the control module executes a heat dissipation and moisture-proof collaborative optimization strategy based on the temperature and humidity sensor data.

[0019] Preferably, using an intelligent algorithm, the control module executes a heat dissipation and moisture-proof collaborative optimization strategy according to the temperature and humidity sensor data, including:

[0020] Based on the temperature and humidity sensor data, the temperature and humidity deviation rate, the change rate, and the power consumption and heat load of the electric meter are obtained, and the operating conditions are divided according to the temperature and humidity deviation rate, the change rate, and the power consumption and heat load of the electric meter;

[0021] Dynamically allocate the operating weight coefficients of the heat dissipation layer and moisture-proof layer according to the operating conditions;

[0022] Determine component control data for the heat dissipation layer and the moisture-proof layer according to the operation weight coefficient;

[0023] Based on historical component control data and component health index, the operation weight coefficient and control strategy library are optimized through reinforcement learning algorithm, and the working condition-strategy mapping relationship is dynamically updated.

[0024] Preferably, based on historical component control data and component health index, the operation weight coefficient and the control strategy library are optimized by a reinforcement learning algorithm, and the working condition-strategy mapping relationship is dynamically updated, including:

[0025] Historical component control data and component health index are input into a reinforcement learning algorithm. The state space of the reinforcement learning algorithm includes temperature and humidity deviation rate, change rate, meter power consumption and heat load, and component operating status. The action space includes heater power adjustment, fan speed adjustment, and radiator fan start and stop and speed adjustment. By setting a reward function that includes efficiency factor, energy consumption factor, and health factor, the operation weight coefficient and control strategy library are optimized, thereby dynamically updating the working condition-strategy mapping relationship.

[0026] The reward function R formula is as follows:

[0027]

[0028] Where 1-ΔT-ΔRH is the efficiency factor; is the energy consumption factor; is the health factor; w e is the weight coefficient of the efficiency factor; w p is the weight coefficient of energy consumption factor; w h is the weight coefficient of the health factor; ΔT is the temperature deviation rate; ΔRH is the humidity deviation rate; P total is the power of the heater, blower and fan; P max is the upper limit of rated total power; η load is the load factor; H i is the health of the i-th component, I(H i >0.8) is an indicator function, which takes the value of 1 when the health of the i-th component is greater than 0.8, and 0 otherwise.

[0029] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention discloses a heat dissipation and moisture-proof electricity meter box and its control method. The moisture-proof layer uses activated carbon mesh grooves + heating plates to construct a "resistance-absorption-regeneration" closed loop. The heat dissipation layer uses partition air ducts, fans and radiators to form a directional convection path. The physical partition design of the moisture-proof layer and the heat dissipation layer reduces the cross-interference of heat and moisture in space. At the same time, based on the dynamic division of working conditions based on temperature and humidity sensors, a reinforcement learning algorithm that integrates efficiency, energy consumption and component health is introduced to dynamically adjust the operation strategy of heat dissipation and moisture-proofing. The present invention breaks through the limitations of traditional fixed threshold control through the coordination of spatial functional zoning and intelligent control strategies, realizes dynamic optimization of heat and moisture management strategies, and significantly improves the environmental adaptability and long-term reliability of outdoor electricity meter boxes. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0031] Figure 1 This is an overall schematic diagram of the heat dissipation and moisture-proof electric energy metering box provided by the present invention;

[0032] Figure 2 A schematic diagram of the heat dissipation layer structure provided by the present invention;

[0033] Figure 3 This is a flow chart of the control method for the heat dissipation and moisture-proof electric energy metering box provided by the present invention.

[0034] In the figure, 1-top plate; 2-activated carbon mesh slot; 21-bracket; 31-air inlet; 311-dust screen; 312-air inlet duct; 32-first air outlet; 321-fan; 322-fan blades; 33-second air outlet; 4-radiator; 41-partition; 42-fan; 43-fan mounting base; 5-electric meter placement area; 6-control device placement area; 7-sensor placement area. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] like Figure 1-Figure 2As shown, an embodiment of the present invention provides a heat dissipation and moisture-proof electric energy metering box, comprising a box body, which is divided into a moisture-proof layer and a heat dissipation layer from top to bottom;

[0037] Several strip frames are provided on the top of the box body, which are used to install moisture-proof components. The moisture-proof components include a top plate 1, which is adapted to the size of the strip frames. An activated carbon mesh 2 is fixedly connected to the bottom of the top plate 1. A hydrophobic layer is provided on the top of the activated carbon mesh 2, and a heating plate is installed on the bottom plate of the activated carbon mesh 2.

[0038] The heat dissipation layer includes an electric meter placement area 5, a sensor placement area 7, and a control device placement area 6; the electric meter placement area 5 is used to install the electric meter; the sensor placement area 7 is used to install the temperature and humidity sensor; the control device placement area 6 is used to install the control module; the position of the bar corresponds to the installation position of the electric meter in the electric meter placement area 5; the control module is connected to the heating plate;

[0039] The side walls at both ends of the heat dissipation layer box are respectively provided with an air inlet 31 and a first air outlet 32; a dustproof net 311 is provided on one side of the air inlet 31; a fan 321 and fan blades 322 are provided on the side of the first air outlet 32; an installation groove is provided at the bottom of the heat dissipation layer, and the installation groove is used to install a radiator 4 with a partition 41; the control module is connected to the fan 321 and the radiator 4.

[0040] Preferably, the activated carbon mesh tank 2 is supported by a cubic frame; the bracket 21 of the cubic frame is a hollow structure, which is used to place the connection line between the heating plate and the control module.

[0041] Preferably, the radiator 4 includes a fan mounting base 43 and a fan 42; the fan 42 is fixed in the radiator 4 through the fan mounting base 43;

[0042] The partitions 41 are installed inside the radiator 4 and are respectively arranged on the left and right sides and the top of the radiator 4 . The partitions 41 of the radiator 4 form an air duct.

[0043] Preferably, an opening is provided on the inner side of the bottom of the heat dissipation layer, and the opening corresponds to the air duct formed by the left partition 41 of the radiator 4 to form an air inlet duct 312;

[0044] A second air outlet 33 is provided at the bottom of one side of the first air outlet 32 ​​. The second air outlet 33 corresponds to the air duct formed by the partition plate 41 on the right side of the radiator 4 , forming an air outlet air duct.

[0045] When the metering box in this embodiment is working, the temperature and humidity sensors collect environmental data in real time and transmit it to the control module. The control module controls the heating plate of the moisture-proof layer to heat the activated carbon mesh slot 22 according to the data to dry the activated carbon after absorbing moisture, and at the same time the hydrophobic layer blocks the intrusion of liquid water; in the heat dissipation layer, the air inlet 31 filters the air through the dustproof net 311, and a part of the air flow enters the air inlet duct 312 formed by the left partition 41 of the radiator 4 through the bottom opening of the heat dissipation layer, and is accelerated by the fan 42 in the radiator 4 and discharged through the second air outlet 33 corresponding to the right partition 41. The other part is directly discharged from the first air outlet 32 ​​after the speed is adjusted by the fan 321. The two air flows work together to take away the heat from the meter area.

[0046] On the other hand, reference Figure 3 The embodiment of the present invention further provides a control method for a heat dissipation and moisture-proof electric energy metering box, which is applied to the heat dissipation and moisture-proof electric energy metering box, comprising:

[0047] S1. Obtain temperature and humidity sensor data based on the temperature and humidity sensor.

[0048] This embodiment uses the SHT30-DIS-B temperature and humidity sensor to collect real-time placement layer environmental data at a frequency of 1 second per time. The sensor communicates with the control module (STM32F407 chip) through the SPI interface to ensure that the data transmission delay is ≤50ms.

[0049] S2. Using intelligent algorithms, the control module executes a collaborative optimization strategy for heat dissipation and moisture prevention based on the temperature and humidity sensor data, including:

[0050] S21. Based on the temperature and humidity sensor data, obtain the temperature and humidity deviation rate, change rate, and meter power consumption and heat load, and divide the operating conditions according to the temperature and humidity deviation rate, change rate, and meter power consumption and heat load.

[0051] Raw temperature and humidity sensor data is processed through a sliding average filter. Data is stored in a ring buffer structure, retaining the last 24 hours of history. When a data mutation is detected, a redundancy check mechanism is automatically triggered to cross-verify data reliability using adjacent sensors.

[0052] The temperature deviation rate formula is as follows:

[0053]

[0054] The humidity deviation rate formula is as follows:

[0055]

[0056] The temperature and humidity change rate formula is as follows:

[0057]

[0058] The power consumption heat load formula of the electric meter is as follows:

[0059] Q elec =P elec ;

[0060] In the formula, P elec is the real-time power consumption of the electric meter, which is directly obtained through the self-measuring function of the electric meter.

[0061] Specifically, the high-temperature and high-humidity working condition is that the temperature deviation rate ΔT>60% and the humidity deviation rate ΔRH>60%, and the temperature change rate k t ≤0.2, the humidity change rate k RH ≤0.2, and the electric meter power consumption heat load Q elec >0.6Q elec_rated .

[0062] The high-temperature and low-humidity working condition is that the temperature deviation rate ΔT>60% and the humidity deviation rate ΔRH<30%, and the temperature change rate k t ≤0.2, the humidity change rate k RH ≤0.2, and the electric meter power consumption heat load Q elec >0.6Q elec_rated .

[0063] The low-temperature and high-humidity working condition is that the temperature deviation rate ΔT<0.3 and the humidity deviation rate ΔRH>0.6, and the temperature change rate k t ≤0.2, the humidity change rate k RH ≤0.2, and the electric meter power consumption heat load Q elec <0.3Q elec_rated .

[0064] The low-temperature and low-humidity working condition is that the temperature deviation rate ΔT<0.3 and the humidity deviation rate ΔRH<0.3, and the temperature change rate k t ≤0.2, the humidity change rate k RH ≤0.2, and the electric meter power consumption heat load Q elec <0.3Q elec_rated .

[0065] The fluctuation working condition is that the temperature change rate k t >0.2 or the humidity change rate k RH >0.2, and the electric meter power consumption heat load Q elec fluctuates by more than 0.3Q elec_rated .

[0066] S22. Dynamically allocate the operation weight coefficients of the heat dissipation layer and the moisture-proof layer according to the operation working condition.

[0067] The calculation formula of the heat dissipation layer operation weight coefficient W s is as follows:

[0068]

[0069] Where, α is the deviation rate influencing factor; f(Q hp ) is the heat load influence function, β is the heat load weight coefficient; γ is the temperature change rate weight coefficient; δ is the humidity change rate weight coefficient.

[0070] Moisture-proof layer operation weight coefficient W h =1-W s .

[0071] Weight coefficient W of heat dissipation layer operation under fluctuating conditions s Introducing volatility factors at this time:

[0072]

[0073] S23. Determine component control data for the heat dissipation layer and the moisture-proof layer based on the operation weight coefficient.

[0074] The heat dissipation layer component control includes fan speed control and radiator fan control.

[0075] In fan speed control, the speed formula is:

[0076] Among them, U max is the maximum speed of the fan, ΔT rated is the rated temperature deviation rate. When Ws>0.7, the fan is started in full speed mode.

[0077] During radiator fan control, when Q elec >0.5Q elec_rated When , the radiator fan is started; the fan speed formula is

[0078] Among them, U fan_max The maximum fan speed.

[0079] The control logic of moisture-proof layer components includes heating plate power control and hydrophobic layer status monitoring.

[0080] The power formula for heating plate power control is:

[0081] Among them, P max is the maximum power of the heater, ΔRH rated is the rated humidity deviation rate. When the humidity deviation rate Δ RH When the temperature is >70%, start the pulse heating mode of the heating plate (the power is cycled from 200W to 100W to 200W, with a cycle of 5 minutes) to prevent the activated carbon from overheating and becoming ineffective.

[0082] The hydrophobic layer status monitoring process is as follows: when the heating plate is working, the interlayer humidity is monitored in real time through a humidity sensor (accuracy ±3% RH) buried in the hydrophobic layer. If the humidity of the hydrophobic layer is detected to be >40% RH, an alarm is triggered and the power of the heating plate is increased by 20% until the humidity drops below 20% RH.

[0083] S24. Based on historical component control data and component health index, the operation weight coefficient and control strategy library are optimized through reinforcement learning algorithms, and the working condition-strategy mapping relationship is dynamically updated, including:

[0084] The historical component control data and component health index are input into the reinforcement learning algorithm. The state space of the reinforcement learning algorithm includes the temperature and humidity deviation rate, change rate, meter power consumption and thermal load, and component operating status. The action space includes heater power adjustment, fan speed adjustment, radiator fan start and stop, and speed adjustment. By setting a reward function that includes efficiency factor, energy consumption factor, and health factor, the operation weight coefficient and control strategy library are optimized, thereby dynamically updating the working condition-strategy mapping relationship.

[0085] This embodiment adopts the deep Q network (DQN) combined with the experience replay mechanism. The neural network structure is a three-layer fully connected layer (input layer 12 nodes → hidden layer 64 nodes → output layer 8 nodes), the activation function is ReLU, and the optimizer is Adam (learning rate 0.001).

[0086] The reward function formula is as follows:

[0087]

[0088] Where 1-ΔT-ΔRH is the efficiency factor; is the energy consumption factor; is the health factor; w e is the weight coefficient of the efficiency factor; w p is the weight coefficient of energy consumption factor; w h is the weight coefficient of the health factor; ΔT is the temperature deviation rate; ΔRH is the humidity deviation rate; P total is the power of the heater, blower and fan; P max is the upper limit of rated total power; η load is the load factor; H i is the health of the i-th component, I(H i >0.8) is an indicator function, which takes the value of 1 when the health of the i-th component is greater than 0.8, and 0 otherwise.

[0089] The health of the i-th component is H i The calculation formula is as follows:

[0090] H1 is the health of the heating plate, H1=α1·f(δIheat )+β1·g(t heat ), current fluctuation coefficient function is the standard deviation of the heater's operating current, is the rated current of the heating plate, when When f(δI heat )=0.2;Run time decay function t heat t is the cumulative running time of the heating plate; heat_rated is the design life of the heater, when t heat ≥t heat_rated When g(t heat ) = 0. Weight coefficient α1 = 0.6 (current fluctuation weight), β1 = 0.4 (operation duration weight).

[0091] H2 is the health of the fan, H2=α2·f(δI fan )+β2·g(t fan )+γ2·h(n abnormal ) ; Current fluctuation coefficient function in: is the standard deviation of the fan operating current, the sampling period is 5 minutes, and the latest 100 sets of current data are calculated; is the rated current of the fan. When a sudden change in current is detected (fluctuation amplitude > 50%), f(δI fan )=0.1(emergency fault flag). Runtime decay function t fan is the cumulative running time of the fan; t fan_rated is the design life of the fan. The penalty function for abnormal start and stop times h(n abnormal )=max(0,1-0.05·n abnormal ), n abnormal The number of abnormal starts and stops of the fan in the last 24 hours (such as start failure, overload shutdown), when n abnormal ≥20, h(n abnormal ) = 0. Weight coefficient α2 = 0.5 (current fluctuation weight), β2 = 0.3 (operation time weight), γ2 = 0.2 (abnormal number weight).

[0092] H3 is the calculation formula for the health of the radiator fan, which is the same as the fan health, where the weight coefficients α3 = 0.4 (current fluctuation weight), β3 = 0.3 (operating time weight), and γ3 = 0.3 (abnormal number weight).

[0093] The historical control data (state-action-reward-next state) is stored in the experience pool (capacity 10,000), 32 data are randomly sampled for each training, and the Q value is updated by the TD error:

[0094] Q(S,A)←Q(S,A)+α[R+γmax A′ Q(S′, A′)-Q(S, A)].

[0095] The mapping table in the strategy library is updated every 100 valid training runs (reward value > 0). For example, when using the "fan 100% speed + radiator fan 80% speed + heater 150W" combination under high temperature and high humidity conditions, if the reward is > 0.8 for five consecutive times, the weight of this combination is increased by 20%.

[0096] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0097] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A heat dissipation and moisture-proof electric energy metering box, characterized in that: The box comprises a housing, wherein the housing is divided into a moisture-proof layer and a heat dissipation layer from top to bottom; The top of the box is provided with a plurality of strip frames, which are used to install moisture-proof components; the moisture-proof components include a top plate, the top plate is adapted to the size of the strip frames; the bottom of the top plate is fixedly connected to the activated carbon mesh slot, the top of the activated carbon mesh slot is provided with a hydrophobic layer, and the bottom plate of the activated carbon mesh slot is installed with a heating plate; The heat dissipation layer includes an electric meter placement area, a sensor placement area, and a control device placement area; the electric meter placement area is used to install the electric meter; the sensor placement area is used to install the temperature and humidity sensor; the control device placement area is used to install the control module; The position of the bar frame corresponds to the installation position of the electric meter in the electric meter placement area; the control module is connected to the heating plate; The side walls at both ends of the heat dissipation layer box are respectively provided with an air inlet and a first air outlet; a dustproof net is provided on one side of the air inlet; a fan and fan blades are provided on the side of the first air outlet; an installation groove is provided at the bottom of the heat dissipation layer, and the installation groove is used to install a radiator with a partition; the control module is connected to the fan and the radiator.

2. The heat dissipation and moisture-proof electric energy metering box according to claim 1, characterized in that: The activated carbon mesh tank is supported by a cubic frame; the bracket of the cubic frame is a hollow structure, which is used to place the connection line between the heating plate and the control module.

3. The heat dissipation and moisture-proof electric energy meter box according to claim 1, characterized in that: The radiator includes a fan mounting base and a fan; the fan is fixed in the radiator through the fan mounting base; The partitions are installed inside the radiator and are respectively arranged on the left and right sides and the top of the radiator. The partitions of the radiator form air ducts.

4. The heat dissipation and moisture-proof electric energy meter box according to claim 1, characterized in that: An opening is provided on the inner side of the bottom of the heat dissipation layer, and the opening corresponds to the air duct formed by the left partition of the radiator to form an air inlet duct; A second air outlet is provided at the bottom of one side of the first air outlet, and the second air outlet corresponds to the air duct formed by the partition plate on the right side of the radiator to form an air outlet air duct.

5. A control method for a heat dissipation and moisture-proof electric energy metering box, applied to a heat dissipation and moisture-proof electric energy metering box according to any one of claims 1 to 5, characterized in that: include: Acquire temperature and humidity sensor data based on temperature and humidity sensor; Using intelligent algorithms, the control module executes a heat dissipation and moisture-proof collaborative optimization strategy based on the temperature and humidity sensor data.

6. The control method of a heat dissipation and moisture-proof electric energy metering box according to claim 5, characterized in that: Using intelligent algorithms, the control module executes a collaborative optimization strategy for heat dissipation and moisture prevention based on the temperature and humidity sensor data, including: Based on the temperature and humidity sensor data, the temperature and humidity deviation rate, the change rate, and the power consumption and heat load of the electric meter are obtained, and the operating conditions are divided according to the temperature and humidity deviation rate, the change rate, and the power consumption and heat load of the electric meter; Dynamically allocate the operating weight coefficients of the heat dissipation layer and moisture-proof layer according to the operating conditions; Determine component control data for the heat dissipation layer and the moisture-proof layer according to the operation weight coefficient; Based on historical component control data and component health index, the operation weight coefficient and control strategy library are optimized through reinforcement learning algorithm, and the working condition-strategy mapping relationship is dynamically updated.

7. The control method of the heat dissipation and moisture-proof electric energy metering box according to claim 6, characterized in that: Based on historical component control data and component health index, the reinforcement learning algorithm optimizes the operation weight coefficient and control strategy library, and dynamically updates the working condition-strategy mapping relationship, including: Historical component control data and component health index are input into a reinforcement learning algorithm. The state space of the reinforcement learning algorithm includes temperature and humidity deviation rate, change rate, meter power consumption and heat load, and component operating status. The action space includes heater power adjustment, fan speed adjustment, and radiator fan start and stop and speed adjustment. By setting a reward function that includes efficiency factor, energy consumption factor, and health factor, the operation weight coefficient and control strategy library are optimized, thereby dynamically updating the working condition-strategy mapping relationship. The reward function R formula is as follows: Where 1-ΔT%-ΔRH% is the efficiency factor; is the energy consumption factor; is the health factor; w e is the weight coefficient of the efficiency factor; w p is the weight coefficient of energy consumption factor; w h is the weight coefficient of the health factor; ΔT% is the temperature deviation rate; ΔRH% is the humidity deviation rate; P total is the power of the heater, blower and fan; P max is the upper limit of rated total power; η load is the load factor; H i is the health of the i-th component, I(H i >0.8) is an indicator function, which takes the value of 1 when the health of the i-th component is greater than 0.8, and 0 otherwise.

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