Intelligent dehumidification system for electrical equipment of water plant

Abstract

The invention discloses an intelligent dehumidification system for electrical equipment of a water plant, and the system comprises one or more dehumidification modules which are used for reducing the internal humidity of the electrical equipment of the water plant; the control module is used for receiving a signal of the monitoring unit in real time, generating a control instruction and executing start-stop control of the dehumidification module; the communication unit is used for connecting network equipment, transmitting and receiving data frames, analyzing the MAC address and forwarding data; the monitoring unit is used for collecting the temperature, humidity and pressure inside and outside the electric control cabinet in real time, corresponding dehumidification system data are monitored and calculated in real time through an intelligent system, a target dehumidification value is scientifically calculated in combination with multiple parameters, and intelligent dehumidification is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent dehumidification, and in particular to an intelligent dehumidification system for electrical equipment in a water plant. Background Art

[0002] A water plant, usually referring to a waterworks or water supply plant, is a facility dedicated to supplying tap water. The main functions of a water plant include extracting raw water from a water source (such as a river, lake, reservoir or groundwater well), removing impurities and harmful substances from the water through a series of treatment processes (such as sedimentation, filtration, disinfection, etc.), and then delivering the treated water to the city water supply system for residential and industrial use.

[0003] Dehumidification in water plants is a key measure to ensure the safe and efficient operation of water supply systems. It effectively prevents corrosion of electrical equipment and metal structures by reducing environmental humidity, and maintains the insulation performance and operating efficiency of equipment. At the same time, appropriate humidity control helps to ensure water quality safety, prevent microbial growth, and ensure the hygienic standards of water supply.

[0004] There are usually two conventional dehumidification methods for water plants. One is the desiccant adsorption dehumidification method with low dehumidification efficiency. The adsorption capacity of the desiccant is limited and new desiccant needs to be replaced regularly, otherwise the dehumidification effect will be greatly reduced, which will increase production costs and pollute the environment.

[0005] The other is the heating, ventilation and dehumidification method, which consumes a lot of electricity and has unstable effects: First, the absolute humidity does not change. After heating, the relative humidity in the cabinet decreases, but because of the increase in saturated water vapor pressure, the absolute humidity in the space does not change, and the water molecules are still in the cabinet. Second, once the temperature threshold is reached and the exhaust fan is turned on, external air will enter the cabinet. Therefore, if the environment outside the cabinet is a high humidity environment (water plant purification workshop), the dehumidification effect will be greatly reduced. Third, heating and dehumidification are greatly affected by temperature. In the hot and humid weather in summer, when the ambient temperature is 30-40°C, there are great safety hazards in heating. Summary of the invention

[0006] In order to solve the above problem, the present invention provides an intelligent dehumidification system for electrical equipment in a water plant to solve the problem.

[0007] In order to achieve the above objectives, this application provides the following technical solutions:

[0008] An intelligent dehumidification system for electrical equipment in a water plant, comprising an electric control cabinet, and one or more dehumidification modules, wherein the dehumidification modules are used to reduce the humidity inside the electrical equipment in the water plant;

[0009] The control module receives the signal from the monitoring unit in real time, generates control instructions, and executes the start and stop control of the dehumidification module;

[0010] Communication unit, used to connect to network devices, transmit and receive data frames, resolve MAC addresses, and forward data;

[0011] The monitoring unit is used to collect the temperature, humidity and pressure inside and outside the electric control cabinet in real time.

[0012] An intelligent dehumidification system for electrical equipment in a water plant also includes:

[0013] A particle swarm optimization algorithm neural network intelligent dehumidification prediction model, wherein the prediction model is used to obtain a dehumidification target value, and a control module controls the opening and closing of the dehumidification module according to the dehumidification target value;

[0014] The algorithm execution unit is used to obtain input parameters of the prediction model, wherein the input parameters include saturated water vapor pressure, actual water vapor pressure, dew point temperature, water content, required dehumidification capacity, and dehumidification capacity per unit power.

[0015] The saturated water vapor pressure is calculated by the following formula:

[0016]

[0017] In the formula, e sw is the saturated water vapor pressure value, e0 is the saturated water vapor pressure at 0°C, t is the cabinet temperature monitored by the internal temperature and humidity sensor or the inlet temperature monitored by the external temperature and humidity sensor, a is the first constant coefficient, and b is the second constant coefficient;

[0018] The actual water vapor pressure is calculated by the following formula:

[0019]

[0020] In the formula, e act is the actual water vapor pressure, is the relative humidity;

[0021] The dew point temperature is calculated by the following formula:

[0022]

[0023] Where, t d is the dew point temperature;

[0024] The water content is calculated by the following formula:

[0025]

[0026] In the formula, D is the water content, e0 is the saturated water vapor pressure at 0°C, and e is the atmospheric pressure inside the cabinet monitored by the pressure sensor inside the cabinet;

[0027] The required dehumidification capacity is calculated by the following formula:

[0028] D a =D1-D2;

[0029] Where D a is the required dehumidification capacity, D1 is the air inlet moisture content, specifically, when t takes the temperature monitoring value of the external temperature and humidity sensor, D1=D, and D2 is the preset moisture content;

[0030] The dehumidification capacity per unit power is calculated by the following formula:

[0031]

[0032] Where D p is the dehumidification capacity per unit power, d is the diameter of the ventilation fan, ρ is the air density, Δp is the difference between the monitoring value of the internal pressure sensor and the monitoring value of the external pressure sensor, and P is the output power of the dehumidification module.

[0033] The steps for constructing the particle swarm optimization algorithm neural network intelligent dehumidification prediction model are as follows:

[0034] Step 1: Construct the objective function;

[0035] Step 2: construct a parameter sequence for fitting particle i;

[0036] Step 3: construct an optimization function based on the objective function, and obtain the optimal number of iterations for fitting particles through the optimization function;

[0037] Step 4: According to the optimal number of iterations of the fitted particles, a particle swarm optimization algorithm neural network intelligent dehumidification prediction model is established, wherein the prediction model includes a real-time monitoring target model for the required dehumidification amount, a condensation rate target model, and a unit power dehumidification amount target model.

[0038] The objective function f a (x) is constructed as follows:

[0039] f a (x) = t d ×D p ×δ;

[0040] Where, t d is the dew point temperature, D a is the required dehumidification capacity, D p is the dehumidification capacity per unit power, δ is the rate of change of the absolute moisture content of the wet air after passing through the dehumidification system. The calculation formula is as follows:

[0041]

[0042] The fitting particle i parameter sequence is:

[0043] In the formula, represents the parameter sequence of fitting particle i, represents the position of the fitting particle i at the k+1th iteration, represents the velocity of the fitted particle i at the k+1 iteration;

[0044] in, Obtained by the following formula:

[0045]

[0046] In the formula, represents the position of the fitted particle i at the kth iteration, represents the velocity of the fitting particle i at the kth iteration;

[0047] Obtained by the following formula:

[0048]

[0049] In the formula, w represents the inertia weight, c1 and c2 represent the population size of different discrete fitting particle swarms, r1 and r2 represent the population rates of different discrete fitting particle swarms, respectively. Indicates the historical optimal position of the fitted particle i at the kth iteration; Indicates the optimal historical position of all fitted particles at the kth iteration.

[0050] The optimization function f(x) is constructed as follows:

[0051]

[0052] The particle swarm optimization algorithm neural network intelligent dehumidification prediction model is constructed as follows:

[0053]

[0054] Where D a (x) represents the real-time monitoring target model of the dehumidification amount required by the system; T d (x) represents the system condensation rate target model; D p (x) represents the target model of dehumidification per unit power, n represents the optimal number of iterations for fitting particle i, D 1_n (x) represents the air inlet moisture content of the fitted particle i after n iterations, D 2_n (x) represents the preset water content of the fitted particle i after n iterations, t d_n (x) represents the dew point temperature of the fitted particle i after n iterations, t n (x) represents the temperature inside the cabinet, D a_n(x) represents the required dehumidification amount of the fitting particle i after n iterations, P n (x) represents the output power of the dehumidification module of the fitting particle i after n iterations.

[0055] Compared with the prior art, the beneficial technical effects of the present invention are:

[0056] The present invention realizes intelligent dehumidification by real-time monitoring and calculation of corresponding dehumidification system data through an intelligent system, and scientifically calculates target dehumidification values ​​in combination with multiple parameters. Furthermore, modular dehumidification can be performed according to different types of electrical equipment, with strong flexibility. It not only reduces the absolute humidity of the electrical environment, but also has practicality and scalability, stable effect, energy saving, and intelligent start and stop of equipment. Finally, the system of the present invention is highly integrated, low in cost, and has good dehumidification effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a schematic diagram of the system structure arrangement of the present invention.

[0059] Figure numerals: 1. electric control cabinet; 2. cabinet body; 3. cabinet door; 4. outer shell; 5. clearance opening; 6. square opening; 7. refrigeration fin; 8. cold end heat sink; 9. cold end fan; 10. hot end heat sink; 11. hot end fan; 12. water collecting bucket; 13. drain pipe; 14. ventilation check valve; 15. ventilation fan; 16. PLC control unit; 17. intermediate relay; 18. internal temperature and humidity sensor; 19. internal pressure sensor; 20. external temperature and humidity sensor; 21. external pressure sensor; 22. equipment rack; 23. DC power supply. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be described clearly and completely below 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 creative work are within the scope of protection of the present invention.

[0061] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0062] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] Example

[0064] Reference Figure 1 , which is an intelligent dehumidification system for water plant electrical equipment disclosed in the present invention, including an electric control cabinet 1 and a control module, a communication unit, a monitoring unit, an algorithm execution unit, a dehumidification module and a DC power supply 23 for powering the dehumidification module arranged on the electric control cabinet 1, wherein the dehumidification module is provided with one or more.

[0065] Furthermore, the electric control cabinet 1 includes a cabinet body 2 and a cabinet door 3 hinged on the lower side of the cabinet body 2. The dehumidification module is used to dehumidify the electrical equipment of the water plant. The dehumidification module includes a shell 4 fixed to the inner side wall of the cabinet body 2. A clearance opening 5 is provided at a position of the cabinet body 2 near the shell 4. Specifically, a square opening 6 is provided at a side position of the shell 4 near the clearance opening 5, and ventilation holes are provided on other end faces of the shell 4. A refrigeration fin 7 is fixed in the square opening 6, and the cold end of the refrigeration fin 7 faces the inside of the cabinet body 2, and a cold end heat sink 8 is arranged at the cold end of the refrigeration fin 7, and a cold end fan 9 is arranged at the end of the cold end heat sink 8 facing away from the refrigeration fin 7; the hot end of the refrigeration fin 7 faces the outside world, and a hot end heat sink 10 is arranged at the hot end of the refrigeration fin 7, and a hot end fan 11 is arranged at the end of the hot end heat sink 10 facing away from the refrigeration fin 7, and the hot end heat sink 10 and the hot end fan 11 are exposed to the outside world through the clearance opening 5.

[0066] The gap between the cold end of the refrigeration fin 7 and the cold end heat sink 8 and the gap between the hot end of the refrigeration fin 7 and the hot end heat sink 10 are filled with thermal conductive silicone grease for heat transfer and conduction.

[0067] In this embodiment, the cooling plate 7 is a TEC1-12706a cooling plate 7, the cold end fan 9 is an independent 4 cm DC fan, the hot end fan 11 is an independent 9 cm DC fan, and the hot end heat sink 10 is larger than the cold end heat sink 8. This arrangement ensures that the heat source of the cooling plate 7 can dissipate heat quickly and the cooling plate 7 can work efficiently.

[0068] Furthermore, a water collecting bucket 12 is fixedly connected to the lower surface of the shell 4 , and a vertically arranged drainage pipe 13 is fixedly connected to the lower surface of the water collecting bucket 12 , and the bottom end of the drainage pipe 13 passes through the electric control cabinet 1 .

[0069] In this embodiment, a ventilation check valve 14 and a ventilation fan 15 are provided on the cabinet door 3 , and the ventilation check valve 14 is in communication with the cabinet body 2 inside and outside, and the ventilation fan 15 is fixed on the outside of the ventilation check valve 14 .

[0070] In this embodiment, the communication unit is a switch, which enables each module to achieve efficient communication of Ethernet by connecting network devices, receiving data frames, parsing MAC addresses, learning address correspondences, intelligently forwarding data, and controlling traffic;

[0071] The control module includes a PLC control unit 16 and an intermediate relay 17 coupled to each other, wherein the PLC control unit 16 is connected to a 220V AC power supply and is responsible for signal collection of the monitoring unit and sending of action instructions, and the intermediate relay 17 is used to control the start and stop of the dehumidification module.

[0072] Specifically, the PLC control unit 16 uses Schneider BMX CPS2000PLC, the BMXP342020 module is used to communicate with the host computer through Ethernet, the BMXDDO1602 is a digital output module, the output point is connected to the intermediate relay 17, and the BMXAMI0810 is an analog input module;

[0073] The monitoring unit uses a 4-wire 4-20mA current signal. Specifically, the monitoring unit includes an internal temperature and humidity sensor 18 , an internal pressure sensor 19 , and an external temperature and humidity sensor 20 and an external pressure sensor 21 arranged on the outside of the cabinet door 3 .

[0074] Specifically, a vertically arranged equipment rack 22 is installed in the electric control cabinet 1 , and the PLC control unit 16 , the intermediate relay 17 , the DC power supply 23 , the internal temperature and humidity sensor 18 , and the internal pressure sensor 19 are arranged on the side of the equipment rack 22 .

[0075] The present invention collects temperature, humidity, pressure and other data in real time through the temperature and humidity sensor, the external temperature and humidity sensor 20, the internal pressure sensor 19 and the external pressure sensor 21;

[0076] Specifically, the present invention uses a particle swarm optimization algorithm to optimize the weights and thresholds of the neural network, accurately outputs the actual required water removal amount and the number of dehumidification modules required, and obtains a particle swarm optimization algorithm neural network (PSO-BP) intelligent dehumidification prediction model:

[0077] In the present invention, the algorithm execution unit uses the saturated water vapor pressure, actual water vapor pressure, dew point temperature, water content, required dehumidification capacity, and dehumidification capacity per unit power as input parameters of the prediction model to obtain the dehumidification target value. The control module starts and closes the dehumidification module and intelligently switches the operating time according to the dehumidification target value, thereby realizing the intelligent input and output of multiple groups of equipment, thereby meeting the system dehumidification effect and improving the dehumidification efficiency of the dehumidification system.

[0078] The calculation formula of saturated water vapor pressure is as follows:

[0079]

[0080] In the formula, e sw is the saturated water vapor pressure value, e0 is the saturated water vapor pressure at 0℃, and its value is 6.11, t is the cabinet temperature monitored by the internal temperature and humidity sensor or the inlet temperature monitored by the external temperature and humidity sensor, a is the first constant coefficient, and its value is 7.5, b is the second constant coefficient, and its value is 237.3;

[0081] The actual water vapor pressure is calculated as follows:

[0082]

[0083] In the formula, e act is the actual water vapor pressure, is the relative humidity;

[0084] The dew point temperature is calculated as follows:

[0085]

[0086] Where, t d is the dew point temperature;

[0087] The calculation formula of water content is as follows:

[0088]

[0089] In the formula, D is the water content, and e is the atmospheric pressure inside the cabinet monitored by the pressure sensor inside the cabinet;

[0090] The required dehumidification capacity is calculated as follows:

[0091] D a =D1-D2 (Formula 5);

[0092] Where D ais the required dehumidification capacity, D1 is the water content of the air inlet, specifically, when t in formula 1 takes the temperature monitoring value of the external temperature and humidity sensor, D1=D, and D2 is the preset water content;

[0093] The calculation formula of dehumidification capacity per unit power is as follows:

[0094]

[0095] Where D p is the dehumidification capacity per unit power, d is the diameter of the ventilation fan, ρ is the air density, Δp is the difference between the monitoring value of the internal pressure sensor and the monitoring value of the external pressure sensor, and P is the output power of the dehumidification module;

[0096] In the present invention, the particle swarm optimization algorithm neural network (PSO-BP) intelligent dehumidification prediction model is obtained by the following steps:

[0097] Step 1: Construct the objective function:

[0098] f a (x) = t d ×D p ×δ (Formula 7);

[0099] In the formula, f a (x) is the objective function, δ is the rate of change of the absolute moisture content of the wet air after passing through the dehumidification system, and its calculation formula is shown in Formula 8:

[0100]

[0101] Step 2: Construct the parameter sequence of fitting particle i:

[0102]

[0103] In the formula, represents the parameter sequence of fitting particle i, represents the position of the fitting particle i at the k+1th iteration, represents the velocity of the fitted particle i at the k+1 iteration;

[0104] in, Obtained by the following formula:

[0105]

[0106] In the formula, represents the position of the fitted particle i at the kth iteration, represents the velocity of the fitting particle i at the kth iteration;

[0107] Obtained by the following formula:

[0108]

[0109] In the formula, w represents the inertia weight, c1 and c2 represent the population size of different discrete fitting particle swarms, r1 and r2 represent the population rates of different discrete fitting particle swarms, respectively. Indicates the historical optimal position of the fitted particle i at the kth iteration; Indicates the optimal historical position of all fitted particles at the kth iteration.

[0110] Step 3: construct an optimization function based on the objective function, and obtain the optimal number of iterations for fitting particles through the optimization function;

[0111] Step 4: According to the optimal number of iterations of the fitted particles, a particle swarm optimization algorithm neural network intelligent dehumidification prediction model is established, wherein the prediction model includes a required dehumidification real-time monitoring target model, a condensation rate target model, and a unit power dehumidification target model;

[0112] In step 3, the optimization function f(x) is constructed as follows:

[0113]

[0114] In step 4, the particle swarm optimization algorithm neural network (PSO-BP) intelligent dehumidification prediction model is constructed as follows:

[0115]

[0116] Where D a (x) represents the real-time monitoring target model of the dehumidification amount required by the system; T d (x) represents the system condensation rate target model; D p (x) represents the target model of dehumidification per unit power, n represents the optimal number of iterations for fitting particle i, D 1_n (x) represents the air inlet moisture content of the fitted particle i after n iterations, D 2_n (x) represents the preset water content of the fitted particle i after n iterations, t d_n (x) represents the dew point temperature of the fitted particle i after n iterations, t n (x) represents the temperature inside the cabinet, D a_n (x) represents the required dehumidification amount of the fitting particle i after n iterations, P n (x) represents the output power of the dehumidification module of the fitting particle i after n iterations.

[0117] The working principle and beneficial effects of the present invention are:

[0118] The present invention obtains the input parameters of the prediction model through the algorithm execution module, and inputs the input parameters into the prediction model to obtain the dehumidification target value. When the dehumidification target value is higher than the dehumidification preset value, it means that the water vapor content in the environment is high. At this time, the dehumidification module is turned on. When the dehumidification module is working, the refrigeration plate 7 faces the equipment rack 22 on one side as a cold source and on the other side as a heat source. The refrigeration plate 7 transfers the heat in the body to the outside of the cabinet 2, and the cold end fan 9 blows the air in the cabinet 2 into the cold end heat sink 8 whose temperature is much lower than that of the cabinet 2, so that the air temperature drops suddenly. The moisture in the air condenses due to the decrease in saturated vapor pressure. Water droplets adhere to the cold end heat sink 8, flow along the direction of the heat sink fins to the water collecting bucket 12, and are discharged from the electrical control cabinet 1 through the drain pipe 13.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent dehumidification system for electrical equipment in a water plant, comprising an electric control cabinet (1), characterized in that: Also includes: One or more dehumidification modules, the dehumidification modules are used to reduce the humidity inside the electrical equipment of the water plant; The control module receives the signal from the monitoring unit in real time, generates control instructions, and executes the start and stop control of the dehumidification module; Communication unit, used to connect to network devices, transmit and receive data frames, resolve MAC addresses, and forward data; The monitoring unit is used to collect the temperature, humidity and pressure inside and outside the electric control cabinet (1) in real time.

2. The intelligent dehumidification system for water plant electrical equipment according to claim 1 is characterized in that: The electric control cabinet (1) comprises a cabinet body (2) and a cabinet door (3) hinged on the lower side of the cabinet body (2); the dehumidification module comprises a shell (4) fixed to the inner side wall of the cabinet body (2); a clearance opening (5) is provided at a position of the cabinet body (2) close to the shell (4); a square opening (6) is provided at a side position of the shell (4) close to the clearance opening (5); ventilation holes are provided on the end faces of the shell (4); a refrigeration fin (7) is fixed in the square opening (6); the cold end of the refrigeration fin (7) faces the inside of the cabinet body (2); a cold end heat sink (8) is provided at the cold end of the refrigeration fin (7); and the cold end heat sink (8) is provided at the cold end of the refrigeration fin (7). A cold end fan (9) is arranged at one end of the heat plate (8) facing away from the refrigeration plate (7), the hot end of the refrigeration plate (7) faces the outside, a hot end heat sink (10) is arranged at the hot end of the refrigeration plate (7), a hot end fan (11) is arranged at one end of the hot end heat sink (10) facing away from the refrigeration plate (7), and the hot end heat sink (10) and the hot end fan (11) are exposed to the outside through a clearance opening (5); a water collecting hopper (12) is fixedly connected to the lower surface of the outer shell (4), and a vertically arranged drainage pipe (13) is fixedly connected to the lower surface of the water collecting hopper (12), and the bottom end of the drainage pipe (13) passes through the electric control cabinet (1).

3. The intelligent dehumidification system for water plant electrical equipment according to claim 1 is characterized in that: The cabinet door (3) is provided with a ventilation check valve (14) and a ventilation fan (15); the ventilation check valve (14) is in communication with the cabinet body (2) inside and outside; and the ventilation fan (15) is fixed on the outside of the ventilation check valve (14).

4. The intelligent dehumidification system for water plant electrical equipment according to claim 1, characterized in that: Also includes: A particle swarm optimization algorithm neural network intelligent dehumidification prediction model, the prediction model is used to obtain the dehumidification target value, and the control module controls the opening and closing of the dehumidification module according to the dehumidification target value; an algorithm execution unit is used to obtain the input parameters of the prediction model, the input parameters include saturated water vapor pressure, actual water vapor pressure, dew point temperature, water content, required dehumidification capacity, and dehumidification capacity per unit power.

5. The intelligent dehumidification system for water plant electrical equipment according to claim 4, characterized in that: The saturated water vapor pressure is calculated by the following formula: In the formula, e sw is the saturated water vapor pressure value, e0 is the saturated water vapor pressure at 0°C, t is the cabinet temperature monitored by the internal temperature and humidity sensor or the inlet temperature monitored by the external temperature and humidity sensor, a is the first constant coefficient, and b is the second constant coefficient; The actual water vapor pressure is calculated by the following formula: In the formula, e act is the actual water vapor pressure, is the relative humidity; The dew point temperature is calculated by the following formula: Where, t d is the dew point temperature; The water content is calculated by the following formula: In the formula, D is the water content, e0 is the saturated water vapor pressure at 0°C, and e is the atmospheric pressure inside the cabinet monitored by the pressure sensor inside the cabinet; The required dehumidification capacity is calculated by the following formula: D a =D1-D2; Where D a is the required dehumidification capacity, D1 is the air inlet moisture content, specifically, when t takes the temperature monitoring value of the external temperature and humidity sensor, D1=D, and D2 is the preset moisture content; The dehumidification capacity per unit power is calculated by the following formula: Where D p is the dehumidification capacity per unit power, d is the diameter of the ventilation fan, ρ is the air density, Δp is the difference between the monitoring value of the internal pressure sensor and the monitoring value of the external pressure sensor, and P is the output power of the dehumidification module.

6. The intelligent dehumidification system for water plant electrical equipment according to claim 5, characterized in that: The steps for constructing the particle swarm optimization algorithm neural network intelligent dehumidification prediction model are as follows: Step 1: Construct the objective function; Step 2: construct a parameter sequence for fitting particle i; Step 3: construct an optimization function based on the objective function, and obtain the optimal number of iterations for fitting particles through the optimization function; Step 4: According to the optimal number of iterations of the fitted particles, a particle swarm optimization algorithm neural network intelligent dehumidification prediction model is established, wherein the prediction model includes a real-time monitoring target model for the required dehumidification amount, a condensation rate target model, and a unit power dehumidification amount target model.

7. The intelligent dehumidification system for water plant electrical equipment according to claim 6, characterized in that: The objective function f a (x) is constructed as follows: f a (x)=t d ×D p ×δ; Where, t d is the dew point temperature, D a is the required dehumidification capacity, D p is the dehumidification capacity per unit power, δ is the rate of change of the absolute moisture content of the wet air after passing through the dehumidification system. The calculation formula is as follows:

8. The intelligent dehumidification system for water plant electrical equipment according to claim 7, characterized in that: The fitting particle i parameter sequence is: In the formula, represents the parameter sequence of fitting particle i, represents the position of the fitting particle i at the k+1th iteration, represents the velocity of the fitting particle i at the k+1 iteration; in, Obtained by the following formula: In the formula, represents the position of the fitted particle i at the kth iteration, represents the velocity of the fitting particle i at the kth iteration; Obtained by the following formula: In the formula, w represents the inertia weight, c1 and c2 represent the population size of different discrete fitting particle swarms, r1 and r2 represent the population rates of different discrete fitting particle swarms, respectively. Indicates the historical optimal position of the fitted particle i at the kth iteration; Indicates the optimal historical position of all fitted particles at the kth iteration.

9. The intelligent dehumidification system for water plant electrical equipment according to claim 8, characterized in that: The optimization function f(x) is constructed as follows:

10. The intelligent dehumidification system for water plant electrical equipment according to claim 9, characterized in that: The particle swarm optimization algorithm neural network intelligent dehumidification prediction model is constructed as follows: Where D a (x) represents the real-time monitoring target model of the dehumidification amount required by the system; T d (x) represents the system condensation rate target model; D p (x) represents the target model of dehumidification per unit power, n represents the optimal number of iterations for fitting particle i, D 1_n (x) represents the air inlet moisture content of the fitted particle i after n iterations, D 2_n (x) represents the preset water content of the fitted particle i after n iterations, t d_n (x) represents the dew point temperature of the fitted particle i after n iterations, t n (x) represents the temperature inside the cabinet, D a_n (x) represents the required dehumidification amount of the fitting particle i after n iterations, P n (x) represents the output power of the dehumidification module of the fitting particle i after n iterations.

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