Self-adaptive electrical cabinet internal temperature, humidity and pressure balance control system

Abstract

The invention discloses a temperature, humidity and pressure balance control system in a self-adaptive electrical cabinet. The system comprises a sensor module, a control unit module, a temperature, humidity and pressure regulation and control module and an execution module. The sensor module comprises a temperature sensor, a humidity sensor and a pressure sensor and is used for monitoring the environment in the electrical cabinet; the control unit module comprises a memory and a microcontroller and is used for storing preset temperature, humidity and pressure values of the electric appliance cabinet, recording data acquired by the sensor module and monitoring the data in real time; the temperature, humidity and pressure regulation and control module is used for reducing errors of the microcontroller in the calculation process, constructing a temperature, humidity and pressure regulation and control model, optimizing the regulation and control model by adopting an RL-Meta algorithm, and improving the learning ability of the RL-Meta algorithm by using an RL-Meta-PID collaborative optimization equation; the execution module starts the ventilation device, the heating and refrigerating device and the pressure adjusting device to adjust the environment in the electrical cabinet after receiving the regulation and control strategy transmitted by the microcontroller. According to the invention, dynamic balance control of temperature, humidity and pressure in the cabinet is realized through intelligent algorithm regulation and control and a multi-module synergistic effect.

Description

Technical Field

[0001] The present invention relates to an environmental control system inside an electrical cabinet, and particularly to an adaptive temperature, humidity and pressure balance control system inside an electrical cabinet. Background Art

[0002] With the expansion and application of the power, communication, petrochemical, aerospace and other fields to extreme environments, as a key equipment carrier, the control of temperature, humidity and pressure inside the electrical cabinet is becoming increasingly critical for the reliability of electronic components. At the same time, the rapid development and deep integration of modern technologies such as information technology, Internet of Things, big data, cloud computing and artificial intelligence have promoted the development and innovation of electrical cabinets, bringing new functions and application scenarios to electrical cabinets.

[0003] Traditional electrical cabinets have many limitations in design and are difficult to meet the high requirements of modern industry for complex environments. First of all, traditional electrical cabinets usually adopt simple temperature and humidity control devices, such as fans, heaters and dehumidifiers, etc. These devices can only achieve rough adjustment of a single parameter and cannot accurately control and balance-adjust the three key parameters of temperature, humidity and pressure at the same time. This design may be barely able to cope in a conventional environment, but as the equipment application expands to extreme conditions such as high humidity and high pressure difference, its defects become more obvious. For example, in a high humidity environment, traditional electrical cabinets cannot effectively prevent moisture intrusion, which may cause short circuits or corrosion of electronic components; under high pressure difference conditions, the air pressure imbalance inside the cabinet may affect the normal operation of the equipment. Secondly, the control method of traditional electrical cabinets often adjusts based on preset fixed parameters, lacking flexibility and intelligence. When the environmental conditions change greatly, such as a rapid switch from high temperature to low temperature or a drastic fluctuation in humidity, the temperature, humidity and pressure control effect of traditional electrical cabinets is often not ideal. Due to the inability to dynamically adjust the control strategy, the equipment is in a non-optimal working state for a long time, accelerating the aging of components and shortening the service life. In addition, this traditional control method may also lead to excessive energy consumption, which does not meet the requirements of green manufacturing and sustainable development. Summary of the Invention

[0004] Object of the Invention: The object of the present invention is to provide an adaptive temperature, humidity and pressure balance control system inside an electrical cabinet, which can monitor the changes of temperature, humidity and air pressure in the environment in real time, and adjust the internal temperature, humidity and pressure control strategy according to the monitoring results, so as to achieve precise balance control of temperature, humidity and pressure, and provide more stable and reliable electrical equipment support for industrial production.

[0005] Technical Solution: An adaptive temperature, humidity and pressure balance control system inside an electrical cabinet described in the present invention includes a sensor module, a control unit module, a temperature, humidity and pressure control module, and an execution module;

[0006] The sensor module is used to monitor the environment inside the electrical cabinet. Among them, the temperature sensor is used to capture temperature changes and convert the temperature changes into electrical signals; the humidity sensor is used to sense humidity changes and convert the humidity changes into electrical signals; the pressure sensor is used to monitor pressure changes and convert the pressure changes into electrical signals, and the data after monitoring is transmitted to the control unit module;

[0007] The control unit module includes a memory and a microcontroller. The memory is used to store the preset values of the temperature, humidity, and pressure in the electrical cabinet, and record the data collected by the sensor module; the microcontroller is used to monitor the data collected by the sensor module in real time. If the data deviates from the threshold range, a corresponding regulation strategy is generated and the data is transmitted to the execution module;

[0008] The temperature, humidity, and pressure regulation module is used to reduce the error in the calculation process of the microcontroller, construct a temperature, humidity, and pressure regulation model, optimize the regulation model using an improved RL-Meta algorithm, and use the RL-Meta-PID collaborative optimization equation to improve the learning ability of the RL-Meta algorithm;

[0009] After receiving the regulation strategy transmitted by the microcontroller, the execution module starts the ventilation device, heating and cooling devices, and pressure regulating device to adjust the environment inside the electrical cabinet; the ventilation device includes a motor and a fan, and the motor speed is adjusted according to the electrical signal of the microcontroller; the heating and cooling devices select heating or cooling according to the calculation result of the microcontroller. If the temperature needs to be increased, the current of the heating device is turned on. If the temperature needs to be decreased, the current of the cooling device is turned on; the pressure device includes a pneumatic valve, and the valve is selected to be opened or closed according to the signal transmitted by the microcontroller to control the inlet and outlet of the gas.

[0010] Preferably, the memory stores the preset values of the temperature, humidity, and pressure in the electrical cabinet S set :

[0011] S set ={T set ,H set ,P set}

[0012] In the formula, T set represents the temperature set value, H set represents the relative humidity set value; P set represents the pressure set value;

[0013] The formula for the microcontroller to detect whether the temperature deviates from the threshold is as follows:

[0014]

[0015] In the formula, A T is the preset upper temperature limit, B T is the preset lower temperature limit, Tmeasured is the temperature value after being converted by the temperature sensor formula;

[0016] The formula for the microcontroller to monitor whether the humidity deviates from the threshold is as follows:

[0017]

[0018] In the formula, A RH is the preset upper humidity limit, B RH is the preset lower humidity limit, and RH measured is the relative humidity value after temperature compensation;

[0019] The formula for the microcontroller to monitor whether the pressure deviates from the threshold is as follows:

[0020]

[0021] In the formula, A P is the preset upper pressure limit, B P is the preset lower pressure limit, and P measured is the pressure value after temperature compensation.

[0022] Preferably, the construction process of the temperature-humidity-pressure regulation model is as follows:

[0023] (1) Define the coupling relationship between temperature, humidity, and pressure in the regulation space, and establish a state equation. The formula is as follows:

[0024]

[0025] In the formula, α i , β i , γ i are environmental coupling coefficients, T set , H set , P set are the temperature, humidity, and pressure set values stored in the memory, and T, H, P are the temperature, humidity, and pressure inside the cabinet;

[0026] (2) With the minimization of energy consumption and the maximization of regulation accuracy as the optimization objectives, design a fitness function. The temperature-humidity-pressure regulation comprehensive efficiency optimization function is as follows:

[0027]

[0028] In the formula, E total is the total energy consumption, w1 and w2 are weight coefficients, where w1 + w2 = 1, and ∈ is a constant to prevent division by zero; T k , H k , P[[ID=7s]] k [[ID=I2]]are the real-time temperature, humidity, and pressure.

[0029] (3) Encode each particle to represent the PID control parameters of the three channels of temperature, humidity, and pressure. Each particle position vector is:

[0030]

[0031] The dimension D = 9;

[0032] In the formula, ΔK represents the adjustment amplitude of the proportional term of the temperature PID controller, represents the encoding of temperature in the three dimensions of p, i, and d, represents the encoding of humidity in the three dimensions of p, i, and d, represents the encoding of pressure in the three dimensions of p, i, and d.

[0033] Preferably, the construction process of the RL-Meta algorithm is as follows:

[0034] The meta-learning objective function is:

[0035]

[0036] In the formula, θ * is the optimized global parameter, represents finding the parameter that minimizes the objective function, represents the expectation of the task distribution, represents the i-th task sampled from the task distribution, represents on the task the parameter after the inner-loop update is performed on the initial parameter θ, represents the task 's loss function; represents 's probability distribution.

[0037] Inner and outer loop updates:

[0038] Inner loop:

[0039]

[0040] Outer loop:

[0041]

[0042] In the formula, φ i is the parameter after the inner-loop update for the task ; represents taking the gradient of θ; α is the inner-loop learning rate, controlling the update amplitude of the task-specific parameters; β is the outer-loop learning rate, determining the global optimization rate of the meta-parameters; represents φ i 's loss function on the training set;

[0043] Policy Gradient and Action Generation:

[0044]

[0045] Wherein, ΔX t represents the state change amount at the current moment; π θ (s t ) represents the parameterized policy function; z t is the latent space noise, generated by meta-learning to enhance the temporal consistency of exploration; ξ is the noise weight coefficient to balance the deterministic policy and random exploration; σ is the standard deviation of the noise z t .

[0046] Preferably, the RL-Meta-PID collaborative optimization equation is:

[0047] X t+1 = X t + ΔX·η

[0048] Wherein, ΔX is the state change amount; η is the learning rate decay factor; X t is the PID parameter vector at the current moment; X t+1 is the updated PID parameter vector at the next moment.

[0049] Preferably, the learning decay mechanism of the improved RL-Meta algorithm is:

[0050]

[0051] η(t) = η init ·e -λt

[0052] Wherein, λ is the decay rate; γ is the energy consumption penalty coefficient to suppress high-energy consumption parameter combinations; ⊙ is the element-wise multiplication, is the gradient of the parameter with respect to the energy consumption; η init is the initial learning rate factor.

[0053] Preferably, the regulation formula for the motor speed of the ventilation device is:

[0054] n = n0 + k T ΔT

[0055] n = n0 + k H ΔH

[0056]

[0057] Wherein, n is the target speed; n0 is the base speed of the motor; k T is the regulation coefficient between temperature and speed; k His the regulation coefficient between humidity and rotational speed; f is the adjusted power supply frequency; p is the number of pole pairs of the motor; s is the slip ratio; ΔT is the difference between the cabinet internal environment temperature and the set temperature; ΔH is the difference between the cabinet internal environment humidity and the set humidity;

[0058] The formula for judging heating or cooling conditions is:

[0059] ΔT = T set - T

[0060] In the formula, the unit of T is Kelvin; ΔT is the difference between the cabinet internal environment temperature and the set temperature; when ΔT > 0, the cooling mode is started; when ΔT < 0, the heating mode is started; when ΔT = 0, it means to maintain the current state or standby; T is the cabinet internal environment temperature; T set is the standard temperature set in the memory.

[0061] Preferably, the regulation strategy of the refrigeration device is:

[0062] Q c = k c ΔT

[0063] In the formula, k c is the refrigerating capacity regulation coefficient;

[0064] The refrigeration formula is:

[0065] Q c = H × S

[0066] In the formula, H is the cooling load per unit area; S is the area;

[0067] The regulation strategy formula of the heating device is:

[0068] Q h = k h ∣hΔT∣

[0069] In the formula, k h is the heat output regulation coefficient;

[0070] Its heating formula is:

[0071] Q h = Q c × N

[0072] In the formula, N is the heating efficiency coefficient.

[0073] Preferably, the formula for the pressure regulating device to judge whether to open the gate is as follows:

[0074] ΔP = ∣P - P set ∣

[0075] In the formula, P is the air pressure value of the cabinet internal environment; P setis the standard air pressure value set in the memory; P set is the safety threshold set in the memory; if ΔP > P set open the gate; if ΔP ≤ P set keep the gate closed;

[0076] When ΔP > 0, the gate control formula is:

[0077] O = k P1 ΔP

[0078] In the formula, k P1 is the positive pressure control coefficient;

[0079] The formula for adjusting the balanced air pressure is:

[0080]

[0081] In the formula, P ex is the air pressure in the cabinet after the gas is output; P ex1 is the initial air pressure in the cabinet; V ex1 is the initial gas volume in the cabinet; P ex2 is the pressure of the output gas; V ex2 is the volume of the output gas;

[0082] When ΔP < 0, the gate control formula is:

[0083] O = k P2 ∣ΔP∣

[0084] In the formula, k P2 is the negative pressure control coefficient;

[0085] The formula for adjusting the balanced air pressure is:

[0086]

[0087] In the formula, P in is the air pressure in the cabinet after the gas is input; P in1 is the initial air pressure in the cabinet; V in1 is the initial gas volume in the cabinet; P in2 is the pressure of the input gas; V in2 is the volume of the input gas.

[0088] Preferably, the coupling formula for temperature, humidity and pressure control is:

[0089]

[0090] In the formula, k1 is the temperature adjustment coefficient; k2 is the air pressure adjustment coefficient; k3 is the rotation speed adjustment formula; C total is the total heat capacity of the gas and equipment in the electrical cabinet; n iTo maintain the engine speed when the humidity meets the threshold.

[0091] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages:

[0092] (1) Through the integrated high-precision sensors, the changes in temperature, humidity, and air pressure can be accurately monitored in real time. It can effectively improve the accuracy of the regulation strategy and ensure the stable operation of the equipment in extreme environments; (2) By adopting the optimized RL-Meta algorithm to construct a temperature-humidity-pressure regulation model, the comprehensive regulation of temperature, humidity, and air pressure is realized, achieving a rapid adjustment of temperature, humidity, and air pressure, reducing the risk of short circuits and aging of the electrical cabinet components caused by the sharp changes in temperature, humidity, and air pressure, extending the service life of the electrical cabinet, and improving the working efficiency of the electrical cabinet; (3) Through the temperature-humidity-pressure regulation comprehensive efficiency optimization system, the energy consumption generated by the electrical cabinet itself is reduced, meeting the requirements of green manufacturing and sustainable development. Description of the drawings

[0093] Figure 1 It is a schematic diagram of the system structure of the present invention.

[0094] Figure 2 It is a flow chart of the RL-Meta algorithm of the present invention.

[0095] Figure 3 It is an optimized flow chart of the RL-Meta algorithm of the present invention. Detailed implementation manners

[0096] The technical solution of the present invention will be further described below with reference to the drawings.

[0097] As Figure 1 shown, an adaptive temperature-humidity-pressure balance control system in an electrical cabinet includes a sensor module, a control unit module, a temperature-humidity-pressure regulation module, and an execution module.

[0098] The sensor module is used to monitor the environment inside the electrical cabinet. Among them, the temperature sensor is used to capture temperature changes and convert the temperature changes into electrical signals; the humidity sensor is used to sense humidity changes and convert the humidity changes into electrical signals; the pressure sensor is used to monitor pressure changes and convert the pressure changes into electrical signals, and the monitored data is transmitted to the control unit module.

[0099] The formula for the sensor module to monitor the environment inside the electrical cabinet is as follows:

[0100] The calculation formula of the temperature sensor:

[0101]

[0102] In the formula, T is the temperature of the environment inside the cabinet, R is the resistance value of the thermistor at the current temperature, and A, B, and C are the characteristic parameters of the sensor.

[0103] The temperature sensor converts the electrical signal V out :

[0104] V out = I const ·R t = I const ·R0(1 + At + Bt 2 )

[0105] Wherein, V out is the output voltage, I const is the output current of the constant current source, R t is the resistance value at the temperature of t °C, and R0 is the nominal resistance value at 0 °C.

[0106] The formula of the humidity sensor is:

[0107]

[0108] Wherein, V out is the output voltage of the sensor, V min and V out are the output voltages at 0% RH and 100% RH of the humidity, RH is the relative humidity, RH min is the minimum humidity, RH max is the maximum humidity.

[0109] The humidity sensor converts the electrical signal I out :

[0110]

[0111] Wherein, RH min is the minimum humidity, RH max is the maximum humidity, I out is the output current.

[0112] The formula of the pressure sensor is:

[0113]

[0114] Wherein, I out is the current value at present, P max is the upper limit of the sensor range, P min is the lower limit of the sensor range, and P is the real-time pressure detection value.

[0115] The pressure sensor converts the electrical signal I out :

[0116]

[0117] Wherein, I out is the output current value.

[0118] The control unit module includes a memory and a microcontroller. The memory is used to store the preset values of temperature, humidity and pressure in the electrical cabinet, and record the data collected by the sensor module; the microcontroller is used to monitor the data collected by the sensor module in real time. If the data deviates from the threshold range, a corresponding regulation strategy is generated and the data is transmitted to the execution module.

[0119] Among them, the memory stores the preset values of temperature, humidity and pressure S set :

[0120] S set ={T set ,H set ,P set}

[0121] In the formula, T set represents the temperature set value, H set represents the relative humidity set value; P set represents the pressure set value.

[0122] The formula for the microcontroller to detect whether the temperature deviates from the threshold is as follows:

[0123]

[0124] In the formula, A T is the preset upper temperature limit, B T is the preset lower temperature limit, T measured is the temperature value after formula conversion by the temperature sensor.

[0125] The formula for the microcontroller to monitor whether the humidity deviates from the threshold is as follows:

[0126]

[0127] In the formula, A RH is the preset upper humidity limit, B RH is the preset lower humidity limit, RH measured is the relative humidity value after temperature compensation.

[0128] The formula for the microcontroller to monitor whether the pressure deviates from the threshold is as follows:

[0129]

[0130] In the formula, A P is the preset upper pressure limit, B P is the preset lower pressure limit, P measured is the pressure value after temperature compensation.

[0131] The temperature, humidity, and pressure regulation module is used to reduce the error of the microcontroller during the calculation process, construct a temperature, humidity, and pressure regulation model, optimize the regulation model using the RL-Meta algorithm, and use the RL-Meta-PID collaborative optimization equation to improve the learning ability of the RL-Meta algorithm.

[0132] Among them, for the temperature, humidity, and pressure regulation model, the model construction is as follows:

[0133] Construct a dynamic temperature, humidity, and pressure model: Define the coupling relationship of temperature, humidity, and pressure in the regulation space, and establish a state equation. The equation is as follows:

[0134]

[0135] In the formula, α i , β i , γ i are environmental coupling coefficients, T set , H set , P set are the set values of temperature, humidity, and pressure stored in the memory.

[0136] Construct a multi-objective fitness function: Taking energy consumption minimization and regulation accuracy maximization as optimization objectives, design a fitness function. The comprehensive efficiency optimization function of temperature, humidity, and pressure regulation is as follows:

[0137]

[0138] In the formula, E total is the total energy consumption, w1 and w2 are weight coefficients, where w1 + w2 = 1, ∈ is a constant to prevent division by zero, T k , H k , P k are the real-time temperature, humidity, and pressure.

[0139] Particle coding rule: Encode each particle to represent the PID control parameters of the temperature, humidity, and pressure channels. The position vector of each particle is:

[0140]

[0141] The dimension D = 9. In the formula, ΔK represents the adjustment amplitude of the proportional term of the temperature PID controller, represents the encoding of temperature in the p, i, and d dimensions, represents the encoding of humidity in the p, i, and d dimensions, represents the encoding of pressure in the p, i, and d dimensions.

[0142] In order to enable the temperature, humidity, and pressure regulation model to calculate accurate regulation strategies more quickly and accurately, the RL-Met algorithm is used to optimize the temperature, humidity, and pressure regulation model. Construct the RL-Met algorithm model:

[0143] Meta learning objective function:

[0144]

[0145] Where θ * is the optimized global parameter, represents finding the parameter that minimizes the objective function, represents the expectation over the task distribution, represents the i-th task sampled from the task distribution, represents at task the parameter after performing the inner loop update on the initial parameter θ, represents task 's loss function, represents 's probability distribution.

[0146] Inner and outer loop updates:

[0147] Inner loop:

[0148]

[0149] Outer loop:

[0150]

[0151] Where φ i is the parameter after the inner loop update for task , represents taking the gradient with respect to θ, α is the inner loop learning rate, controlling the update magnitude of task-specific parameters, β is the outer loop learning rate, determining the global optimization rate of the meta-parameters, represents φ i 's loss function on the training set.

[0152] Policy gradient and action generation:

[0153]

[0154] Where ΔX t represents the state change at the current moment, π θ (s t ) represents the parameterized policy function, z t is the latent space noise, generated through meta learning to enhance the temporal consistency of exploration, ξ is the noise weight coefficient, balancing the deterministic policy and stochastic exploration, σ is the standard deviation of the noise z t .

[0155] Improve the learning ability of the RL-Met algorithm using the RL-Meta-PID collaborative optimization equation, and the RL-Meta-PID collaborative optimization equation is as follows:

[0156] X t+1 = X t + ΔX·η

[0157] In the formula, ΔX is the change in state, η is the learning rate decay factor, X t is the PID parameter vector at the current moment, and X t+1 is the updated PID parameter vector at the next moment.

[0158] Improve and optimize the RL-Met algorithm to optimize the problem of lag in the response of the RL-Met algorithm to dynamic environmental changes. The aim is to be able to quickly respond to changes in temperature, humidity, and pressure and timely regulate equipment in places with variable environments, ensuring that the cabinet environment meets the normal operation standards of the electrical cabinet.

[0159] State space design:

[0160] S t = [T k , H k , P k , T set , H set , P set , E total , E avg (t; α)]

[0161] E avg (t) = α·E avg (t - 1)+(1 - α)·CurrentError(t)

[0162] CurrentError(t) = |T(t)-T set | + |H(t)-H set | + |P(t)-P set |

[0163] In the formula, T k , H k , P k represent the actual values of the current temperature, humidity, and pressure, E total represents the cumulative energy consumption, and E avg (t; α) represents the mean historical error. α represents the error memory strength coefficient, and CurrentError(t) represents the sum of the absolute deviations between the actual values and the set values of the temperature, humidity, and pressure in three dimensions at the current moment.

[0164] Clarify the constraints on the action range:

[0165]

[0166] The learning decay mechanism of the optimized RL-Meta algorithm is as follows:

[0167]

[0168] η(t) = η init ·e -λt

[0169] where λ is the decay rate, γ is the energy consumption penalty coefficient to suppress high-energy consumption parameter combinations, ⊙ is element-wise multiplication, is the gradient of the parameter with respect to the energy consumption, and η init is the initial learning rate factor.

[0170] The iteration termination conditions are: reaching the maximum number of iterations; the standard deviation of the fitness value being less than the threshold; and the energy consumption reaching the maximum threshold.

[0171] After receiving the regulation strategy transmitted by the microcontroller, the execution module starts the ventilation device, heating and cooling devices, and pressure regulating device to adjust the environment inside the electrical cabinet; the ventilation device includes a motor and a fan, and the motor speed is adjusted according to the electrical signal of the microcontroller; the heating and cooling devices select heating or cooling according to the calculation result of the microcontroller. If the temperature needs to be increased, the current of the heating device is turned on, and if the temperature needs to be decreased, the current of the cooling device is turned on; the pressure device includes a pneumatic valve, and the opening or closing of the valve is selected according to the signal transmitted by the microcontroller to control the inlet and outlet of the gas.

[0172] Among them, the regulation formula for the motor speed n of the ventilation device is:

[0173] n = n0 + k T ΔT

[0174] n = n0 + k H ΔH

[0175]

[0176] where n is the target speed, n0 is the base speed of the motor, k T is the regulation coefficient between temperature and speed; k H is the regulation coefficient between humidity and speed, f is the adjusted power supply frequency, p is the number of motor poles, and s is the slip ratio.

[0177] The formula for the cold device to judge the heating or cooling condition is:

[0178] ΔT = T set -T

[0179] In the formula, the unit of T is Kelvin, ΔT is the difference between the cabinet internal environment temperature and the set temperature. When ΔT > 0, the refrigeration mode is started; when ΔT < 0, the heating mode is started; when ΔT = 0, it means maintaining the current state or standby. T is the cabinet internal environment temperature, and T set is the standard temperature set in the memory.

[0180] A circulation device similar to an air conditioner is used in the cabinet as the heating and refrigeration device, which consists of a compressor, a condenser, a throttling device, and an evaporator. When ΔT > 0, the control strategy formula for the refrigeration device is:

[0181] Q c = k c ΔT

[0182] In the formula, k c is the refrigeration capacity control coefficient.

[0183] The refrigeration formula is:

[0184] Q c = H × S

[0185] In the formula, H is the cooling load per unit area, and S is the area.

[0186] When ΔT < 0, the control strategy formula for the heating device is:

[0187] Q h = k h ∣hΔT∣

[0188] In the formula, k h is the heat output control coefficient.

[0189] The heating formula is:

[0190] Q h = Q c × N

[0191] In the formula, N is the heating efficiency coefficient.

[0192] The formula for the pressure regulating device to judge whether to open the gate is:

[0193] ΔP = ∣P - P set ∣

[0194] In the formula, P is the air pressure value of the cabinet internal environment, and P set is the standard air pressure value set in the memory, and P set is the safety threshold set in the memory. If ΔP > P set open the gate; if ΔP ≤ P set keep the gate closed.

[0195] When ΔP > 0, the gate control formula is:

[0196] O = k P1 ΔP

[0197] Wherein, k P1 is the positive pressure regulation coefficient.

[0198] The formula for adjusting the balanced air pressure is:

[0199]

[0200] Wherein, P ex is the air pressure in the cabinet after the output gas, P ex1 is the initial air pressure in the cabinet, V ex1 is the initial gas volume in the cabinet, P ex2 is the pressure of the output gas, V ex2 is the volume of the output gas.

[0201] When ΔP < 0, the gate control formula is:

[0202] O = k P2 ∣ΔP∣

[0203] Wherein, k P2 is the negative pressure regulation coefficient.

[0204] The formula for adjusting the balanced air pressure is:

[0205]

[0206] Wherein, P in is the air pressure in the cabinet after the input gas, P in1 is the initial air pressure in the cabinet, V in1 is the initial gas volume in the cabinet, P in2 is the pressure of the input gas, V in2 is the volume of the input gas.

[0207] Integrating the above formulas, the temperature, humidity and pressure regulation coupling formula is as follows:

[0208]

[0209] Wherein, k1 is the temperature regulation coefficient, k2 is the air pressure regulation coefficient, k3 is the rotation speed regulation formula, C total is the total heat capacity of the gas and equipment in the electrical cabinet, n i is the engine speed when the humidity can be maintained to meet the threshold.

Claims

1. An adaptive temperature, humidity and pressure balance control system inside an electrical cabinet, characterized in that, including; A sensor module for monitoring the environment inside the electrical cabinet. Among them, a temperature sensor is used to capture temperature changes and convert the temperature changes into electrical signals; a humidity sensor is used to sense humidity changes and convert the humidity changes into electrical signals; a pressure sensor is used to monitor pressure changes and convert the pressure changes into electrical signals, and the data after monitoring is transmitted to the control unit module; A control unit module, including a memory and a microcontroller. The memory is used to store the preset values of the temperature, humidity, and pressure in the electrical cabinet and record the data collected by the sensor module; the microcontroller is used to monitor the data collected by the sensor module in real time. If the data deviates from the threshold range, a corresponding regulation strategy is generated and the data is transmitted to the execution module; A temperature, humidity, and pressure regulation module for reducing the error in the calculation process of the microcontroller, constructing a temperature, humidity, and pressure regulation model, optimizing the regulation model using an improved RL-Meta algorithm, and using an RL-Meta-PID collaborative optimization equation to improve the learning ability of the RL-Meta algorithm; An execution module, which starts a ventilation device, a heating and cooling device, and a pressure regulating device to adjust the environment inside the electrical cabinet after receiving the regulation strategy transmitted by the microcontroller; the ventilation device includes a motor and a fan, and the motor speed is adjusted according to the electrical signal of the microcontroller; the heating and cooling device selects heating or cooling according to the calculation result of the microcontroller. If the temperature needs to be increased, the current of the heating device is turned on, and if the temperature needs to be decreased, the current of the cooling device is turned on; the pressure device includes a pneumatic valve, and the valve is selected to be opened or closed according to the signal transmitted by the microcontroller to control the inlet and outlet of the gas.

2. The self-adaptive temperature, humidity, and pressure balance control system inside the electrical cabinet according to claim 1, wherein The memory stores the preset values S of the temperature, humidity, and pressure of the electrical cabinet set : S set = {T set , H set , P set} where, T set represents the temperature set value, H set represents the relative humidity set value; P set represents the pressure set value; The formula for the microcontroller to detect whether the temperature deviates from the threshold is as follows: Where A T is the preset upper temperature limit, B T is the preset lower temperature limit, and T measured is the temperature value after being converted by the temperature sensor formula; The formula for the microcontroller to monitor whether the humidity deviates from the threshold is as follows: Where A RH is the preset upper humidity limit, B RH is the preset lower humidity limit, and RH measured is the relative humidity value after temperature compensation; The formula for the microcontroller to monitor whether the pressure deviates from the threshold is as follows: Where A P is the preset upper pressure limit, B P is the preset lower pressure limit, and P measured is the pressure value after temperature compensation.

3. An adaptive temperature, humidity and pressure balance control system in an electrical cabinet according to claim 1, wherein, The process of constructing the temperature, humidity, and pressure regulation model is as follows: (1) Define the coupling relationship between temperature, humidity, and pressure in the regulation space, and establish a state equation. The formula is as follows: where α i , β i , γ i are environmental coupling coefficients, T set , H set , P set are the temperature, humidity, and pressure set values stored in the memory, and T, H, P are the temperature, humidity, and pressure inside the cabinet; (2) With minimizing energy consumption and maximizing regulation accuracy as the optimization objectives, design a fitness function. The comprehensive efficiency optimization function for temperature, humidity, and pressure regulation is as follows: where E total is the total energy consumption, w1 and w2 are weight coefficients, where w1 + w2 = 1, and ∈ is a constant to prevent division by zero; T k , H k , P k are the real-time temperature, humidity, and pressure. (3) Encode each particle to represent the PID control parameters of the temperature, humidity, and pressure three channels. Each particle position vector is: Dimension D = 9; where ΔK represents the adjustment range of the proportional term of the temperature PID controller, represents the encoding of temperature in the three dimensions of p, i, and d, represents the encoding of humidity in the three dimensions of p, i, and d, represents the encoding of pressure in the three dimensions of p, i, and d.

4. An adaptive temperature, humidity and pressure balance control system in an electrical cabinet according to claim 1, characterized in that, The process of constructing the RL-Meta algorithm is as follows: The meta-learning objective function is: where θ * is the optimized global parameter, represents finding the parameter that minimizes the objective function, represents the expectation of the task distribution, represents the i-th task sampled from the task distribution, represents at task the parameter after the inner-loop update is performed on the initial parameter θ, represents task 's loss function; represents 's probability distribution. Internal and external loop updates: Inner loop: Outer loop: where φ i is the task parameters updated through the inner loop; denotes taking the gradient with respect to θ; α is the inner loop learning rate, controlling the update amplitude of task-specific parameters; β is the outer loop learning rate, determining the global optimization rate of meta-parameters; denotes φ i the loss function on the training set; Policy gradient and action generation: where, ΔX t represents the state change at the current moment; π θ (s t ) represents the parameterized policy function; z t is the latent space noise, generated by meta-learning to enhance the temporal consistency of exploration; ξ is the noise weight coefficient to balance the deterministic policy and random exploration; σ is the standard deviation of the noise z t .

5. An adaptive temperature, humidity and pressure balance control system in an electrical cabinet according to claim 1, characterized in that, The RL-Meta-PID collaborative optimization equation is: X t+1 = X t + ΔX·η Where, ΔX is the change in state; η is the learning rate decay factor; X t is the PID parameter vector at the current moment; X t+1 is the updated PID parameter vector at the next moment.

6. An adaptive temperature, humidity and pressure balance control system inside an electrical cabinet according to claim 1, characterized in that, The learning attenuation mechanism of the improved RL-Meta algorithm is: η(t) = η init ·e -λt where λ is the attenuation rate; γ is the energy consumption penalty coefficient to suppress high-energy consumption parameter combinations; ⊙ is the element-wise multiplication, is the gradient of the parameter with respect to the energy consumption; η init is the initial learning rate factor.

7. An adaptive temperature, humidity and pressure balance control system in an electrical cabinet according to claim 1, characterized in that, The regulation formula for the motor speed of the ventilation device is: n = n0 + k T ΔT n = n0 + k H ΔH Where n is the target rotational speed; n0 is the base rotational speed of the motor; k T is the regulation coefficient between temperature and rotational speed; k H is the regulation coefficient between humidity and rotational speed; f is the adjusted power supply frequency; p is the number of pole pairs of the motor; s is the slip ratio; ΔT is the difference between the cabinet internal environment temperature and the set temperature; ΔH is the difference between the cabinet internal environment humidity and the set humidity; The formula for judging the heating or cooling condition is: ΔT = T set - T Wherein, the unit of T is Kelvin; ΔT is the difference between the cabinet internal environment temperature and the set temperature; when ΔT>0, the refrigeration mode is started; when ΔT<0, the heating mode is started; when ΔT = 0, it means to maintain the current state or standby; T is the cabinet internal environment temperature; T set is the standard temperature set in the memory.

8. An adaptive temperature, humidity and pressure balance control system in an electrical cabinet according to claim 1, characterized in that, The regulation strategy of the cooling device is: Q c = k c ΔT where k c is the refrigerating capacity regulation coefficient; [[ID= Q c = H × S ​ ​ Q h = k h |hΔT| where k h is the coefficient for regulating the heating capacity; ​ Q h = Q c × N ​ 9. An adaptive temperature, humidity and pressure balance control system in an electrical cabinet according to claim 1, wherein, ​ ΔP = ∣P - P set ∣ Wherein, P is the air pressure value of the environment inside the cabinet; P set is the standard air pressure value set in the memory; P set is the safety threshold set in the memory; if ΔP > P set open the gate; if ΔP ≤ P set keep the gate closed; ​ O = k P1 ΔP where k P1 is the positive pressure regulation coefficient; ​ Wherein, P ex is the air pressure in the cabinet after the output gas; P ex1 is the initial air pressure in the cabinet; V ex1 is the initial gas volume in the cabinet; P ex2 is the pressure of the output gas; V ex2 is the volume of the output gas; When ΔP < 0, the gate control formula is as follows: O = k P2 |ΔP| where k P2 is the negative pressure regulation coefficient; The formula for adjusting the balanced air pressure is as follows: Wherein, P in is the air pressure in the cabinet after the input gas; P in1 is the initial air pressure in the cabinet; V in1 is the initial gas volume in the cabinet; P in2 is the pressure of the input gas; V in2 is the volume of the input gas.

10. An adaptive temperature, humidity and pressure balance control system inside an electrical cabinet according to claim 1, characterized in that The coupling formula for the temperature, humidity and pressure control is as follows: Wherein, k1 is the temperature adjustment coefficient; k2 is the air pressure adjustment coefficient; k3 is the rotation speed adjustment formula; C total is the total heat capacity of the gas and equipment in the electrical cabinet; n i is the engine speed when the humidity can be maintained to meet the threshold value.

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