Intelligent optimization regulation and control system and method for coal mine gas extraction

Abstract

The invention discloses an intelligent optimization regulation and control system and method for coal mine gas extraction, and relates to the field of intelligent optimization regulation and control for gas extraction, and the system comprises a geological module, an equipment module, a calculation module, a control module and an environment module. Calculating a control parameter combination of the coal-mine gas extraction equipment, creating a Brownian motion control model of the coal-mine gas extraction equipment, generating corresponding state change data of the coal-mine gas extraction equipment through the Brownian motion control model, analyzing to obtain an interval comprehensive change value of the gas extraction equipment, and establishing a neural network model; according to the method, the opening degree instruction of the electric air supplement valve is analyzed, the electric air supplement valve is driven to adjust, the air supplement amount is adjusted, multi-dimensional parameter PID control can be adopted, the adaptability is high under the complex geological condition, the equipment state response is timely, and the control strategy optimization meets the requirement.

Description

Technical Field

[0001] The present invention relates to the field of intelligent optimization control of gas drainage, and specifically to a coal mine gas drainage intelligent optimization control system and method. Background Technique

[0002] With the development of the economy, the demand for energy is getting higher and higher. As one of the main energy sources in China, coal mining safety production is also an important matter of current concern, especially gas drainage. The traditional coal mine gas system mostly uses a water ring vacuum pump operating at power frequency, and the fixed speed results in high energy consumption. Therefore, the coal mine gas drainage intelligent optimization control system and method came into being.

[0003] Existing coal mine gas drainage intelligent optimization control systems and methods mostly adopt fixed-parameter PID control, which cannot consider equipment operation, environmental changes, and geological changes. At the same time, geological grain boundary migration will change the gas migration path, but the traditional method does not incorporate grain boundary dynamics into the control model, resulting in the disconnection between drainage parameters and actual needs, poor adaptability under complex geological conditions, lagging equipment state response, and insufficient optimization of control strategies.

[0004] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0005] To solve the technical problems raised in the above background technique, the present invention is proposed. Embodiments of the present invention provide a coal mine gas drainage intelligent optimization control system and method.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] In the first aspect, the present invention provides an intelligent optimization and control system for coal mine gas extraction, including: a geological module, an equipment module, a computing module, a control module, and an environmental module. The geological module collects coal mine samples through coring equipment, sets a temperature sensor at the collection point to obtain monitoring temperature change information, observes the coal body sample through a transmission electron microscope to obtain a grain boundary image, drills a hole at the coal mine collection point to install a strain gauge, obtains geological stress field data through a stress relief method, analyzes the composition and content of coal mine minerals and organic matter through X-ray fluorescence spectroscopy, and obtains coal seam inclination information to form geological collection information and transmit it to the computing module; the equipment module installs a piezoelectric acceleration sensor on the motor bearing seat and gear of the extraction equipment to convert mechanical vibration into an electrical signal, and a thermocouple is pre-embedded in the motor stator winding to obtain the motor winding temperature signal. The light beam passes through the coal powder in the pipeline to obtain the distribution of coal scattered light intensity, which constitutes the information collected by the extraction equipment and is transmitted to the calculation module; the environmental module arranges pressure measuring boreholes in the coal mine, installs gas pressure sensors in the boreholes, obtains gas pressure information, installs orifice flowmeters in gas extraction boreholes, and installs gas pressure sensors in the extraction pipelines to record gas extraction flow and gas pressure change information. In the main ventilation tunnels and the inlet and return air tunnels of the mining working face, the instantaneous speed of the air flow is measured by a hot wire anemometer, and the initial parameter data of various extraction environments such as gas pressure, temperature, humidity, and ventilation wind speed are collected, and the spatial position corresponding to each monitoring point is recorded to constitute the environmental collection information and transmitted to the calculation module; the control module includes a driving device for the electric air supply valve, which drives the electric air supply valve to adjust according to the opening instruction of the calculation module to adjust the air supply volume;

[0008] The calculation module obtains the geological collection information in the geological module to construct a grain boundary directional migration model, analyzes the grain boundaries of mineral strata in each region, obtains the extraction equipment collection information in the equipment module and the environmental collection information in the environment module, analyzes and calculates the control parameter combination of the coal mine gas extraction equipment, creates a Brownian motion control model of the coal mine gas extraction equipment, analyzes the status of the coal mine gas extraction equipment, establishes a neural network model, analyzes the opening instruction of the electric air supply valve, and transmits it to the control module.

[0009] In a second aspect, the present invention provides a method for intelligent optimization and control of coal mine gas extraction, comprising the following steps:

[0010] Step 1: Migration model construction and grain boundary analysis. The calculation module obtains the geological information collected in the geological module to construct a grain boundary directional migration model, and analyzes and obtains the grain boundary dynamic potential value of the mineral strata in each region;

[0011] Step 2: solving the equipment control parameter combination, the calculation module obtains the extraction equipment collection information in the equipment module and the environment collection information in the environment module, and analyzes and calculates the coal mine gas extraction equipment control parameter combination;

[0012] Step 3: The device status changes. The calculation module creates a Brownian motion control model for the coal mine gas drainage equipment, generates corresponding data on the status changes of the coal mine gas drainage equipment through the Brownian motion control model, and analyzes to obtain the interval comprehensive change value of the gas drainage equipment.

[0013] Step 4: Analysis of the opening command. The calculation module establishes a neural network model, analyzes the opening command of the electric air supply valve, and transmits it to the control module.

[0014] Step 5: Air supply control. The control module drives the electric air supply valve to adjust according to the opening command of the calculation module to adjust the air supply volume.

[0015] Furthermore, the analysis steps for the grain boundary movement aggregation potential value of each regional mineral strata are as follows:

[0016] Step 104: The calculation module normalizes the grain boundary migration concentration value of each regional mineral strata and the energy product of the grain boundary rate variation amplitude of each regional mineral strata. Based on the regular octahedron as the basic geometric model, it constructs the spatial framework of the mineral strata. The grain boundary migration concentration value of each regional mineral strata is linearly mapped to the edge length of the regular octahedron. A prism is created outside the regular octahedron, with the bottom surface of the prism coinciding with one face of the regular octahedron. The energy product of the grain boundary rate variation amplitude is mapped to the volume of the prism. The total volume formed by the prism and the regular octahedron is identified and labeled as the grain boundary movement aggregation potential value τ of each regional mineral strata.

[0017] Furthermore, the analysis steps for the grain boundary migration concentration value of each regional mineral strata and the grain boundary rate variation amplitude energy of each regional mineral strata are as follows:

[0018] Step 103: The calculation module obtains the temperature change information, geological stress field data, chemical potential of the coal body, grain boundary strike, and extension direction of each region collected by the geological module, and inputs them into the grain boundary directional migration model. The directional migration direction and rate of the grain boundary in each region are output. The calculation module draws a rose diagram of the grain boundary directional migration direction in each region, presented in polar coordinates. The radius of the polar coordinates is the frequency of the grain boundary appearing in this direction, and the angle of the polar coordinates is the grain boundary migration direction. The angular range of 0° to 360° of the polar coordinates is divided into several equal angular intervals with 15°. The ratio of the sum of the angular interval frequencies in the first 25% interval of the frequencies to the total frequency is calculated and labeled as the grain boundary migration concentration value of each regional mineral strata. The maximum migration rate and minimum migration rate of the grain boundary in each region are obtained. With time as the horizontal axis and the grain boundary migration rate in each region as the vertical axis, the maximum migration rate and minimum migration rate at each time point are plotted on the coordinate axis respectively, and the points are connected with curves to obtain the change curve graph of the maximum migration rate and minimum migration rate. The enclosed area formed by the maximum migration rate and minimum migration rate curves is obtained and labeled as the energy product of the grain boundary rate variation amplitude of each regional mineral strata.

[0019] Furthermore, the analysis steps of the grain boundary oriented migration model are as follows:

[0020] Step 101: The calculation module obtains the grain boundary image of the mineral stratum collected by the geology module. Using image analysis software, it measures the trend and extension direction of the grain boundary. The calculation module obtains the composition and content of coal mine minerals and organic matter collected by the geology module, and selects the ideal solution model to determine the parameters in the model, specifically the mole fraction of substances, activity coefficient, and interaction parameter. Substitute the determined parameters into the ideal solution model to obtain the chemical potential of various components of the coal mine, which is used as the key input parameter for the subsequent operation of the grain boundary oriented migration model. The calculation module divides the coal mine stratum into several regions;

[0021] Step 102: The calculation module adopts the phase field model, defines the grain boundary migration parameters, namely the grain boundary migration rate and grain boundary energy, clarifies the physical parameters, namely the elastic modulus and Poisson's ratio, and clarifies that the geological stress field generates a driving force for grain boundary migration through dislocation theory and crystal mechanics principles, establishes a quantitative relationship between stress and migration rate. According to the thermodynamic principle, establishes a proportional relationship between the chemical potential gradient and the driving force of grain boundary migration, and establishes an exponential relationship between temperature and grain boundary migration rate through the Arrhenius equation. Incorporate the influence of the geological stress field, chemical potential, and temperature on grain boundary migration into the energy equation of the phase field model. The energy equation includes energy terms such as grain boundary energy, strain energy, and chemical energy. Combine the relationship between various factors and grain boundary migration to establish a kinetic equation, and use the finite element method numerical method to discretize the equation, and iteratively solve to obtain the grain boundary migration simulation result. Extract historical observation data matching the simulation conditions from the database. The historical data covers information such as grain boundary migration direction, migration rate, and related geological stress field, chemical potential, and temperature at different regions and different time points. The data is consistent with the simulation in terms of spatial and temporal scales. Compare the simulated and actual migration directions, calculate the angular deviation, compare the migration rates to obtain the relative error, and construct a root mean square error index to judge the verification situation of the model. If it fails, adjust the parameters in the model, optimize the relationship equation between the chemical potential gradient and the migration driving force, re-simulate and compare, and iteratively calibrate until the deviation is less than the set threshold to obtain the grain boundary oriented migration model.

[0022] Furthermore, the analysis steps of the control parameter combination of the coal mine gas drainage equipment are as follows:

[0023] The calculation module obtains the extraction device acquisition information collected by the device module, calculates the root mean square value of the electrical signal of the motor bearing seat, marks it as the effective value of the motor vibration acceleration, performs a fast Fourier transform on the gear vibration signal to obtain the frequency spectrum fr, and obtains the gear meshing frequency fm according to the formula fm = z × n / 60, where z is the number of teeth and n is the number of revolutions. The ratio of the sideband energy of the gear meshing frequency β is calculated according to the formula, k is the sideband order identifier, and the value is 1, 2, 3. The motor winding temperature rise rate is obtained by analyzing the motor winding temperature signal. The scattered light intensity signal changing with time is collected by a high-speed photodetector and converted into an electrical signal. The fast Fourier transform converts the time-domain signal into a frequency-domain signal, and the frequency corresponding to the energy peak in the frequency spectrum diagram is obtained and marked as the pulverized coal concentration pulsation frequency. The effective value of the motor vibration acceleration, the ratio of the sideband energy of the gear meshing frequency, the motor winding temperature rise rate, and the pulverized coal concentration pulsation frequency are marked as the operation parameters of the extraction device;

[0024] The calculation module obtains the environmental acquisition information of the environment module, calculates the gas pressure gradient ψ through the gas pressure information, and obtains the average pipeline flow velocity v according to the gas extraction flow rate Q measured by the orifice flowmeter and the cross-sectional area A of the extraction borehole or pipeline according to the formula v = Q / A. According to Darcy's law, the coal mass coupling permeability k is obtained according to the formula k = -u × v / ψ. The instantaneous velocity g(t) of the air flow is calculated, and the average velocity The air flow pulsation intensity I is obtained according to the formula. The gas pressure gradient, the coal mass coupling permeability, and the air flow pulsation intensity are marked as the extraction environment parameters. The change value of the coal seam dip angle per unit distance in the coal seam dip angle information is calculated to obtain the single change value of the coal seam dip angle, which is marked as the extraction geological parameter. The operation parameters of the extraction device, the extraction environment parameters, and the extraction geological parameters are marked as the control parameter combination of the coal mine gas extraction device, constituting the basic operation framework of the device.

[0025] Furthermore, the analysis steps of the comprehensive state value change of the gas extraction device are as follows:

[0026] Step 303: For the change amount dS t (x) of the comprehensive state value of the gas extraction device in each region, through integration and accumulation in space and time, according to the formula the total amount of change in the comprehensive state of the gas extraction device from the initial time t0 to the current time t in each region is obtained and marked as the comprehensive state change value λ2 of the gas extraction device at intervals, where ξ is the number of the region.

[0027] Furthermore, the analysis steps of the change in the comprehensive state value of the gas extraction device are as follows:

[0028] Step 301: Create a Brownian motion control model of the coal mine gas extraction device according to the control parameter combination of the coal mine gas extraction device. The Brownian motion control model includes: dS t(x) = ρ(x, t)S t (x)dt + δ(x, t)S t (x)dB t (x), dS t (x) represents the change amount of the comprehensive state value of the gas drainage equipment at time interval dt and position x, and S t (x) represents the comprehensive state value of the gas drainage equipment at time t and position x. The operating state of the equipment is reflected by the combination of the control parameters of the drainage equipment. ρ(x, t) is the drift rate at time t and position x, which reflects the average change trend of the equipment state parameters. δ(x, t) is the volatility at time t and position x, representing the influence degree of the drainage geological parameters and the drainage environment parameters on the equipment state, and dB t (x) is a random disturbance term, affected by the drainage environment parameters and the drainage geological parameters;

[0029] Step 302: Analyze the comprehensive state of the gas drainage equipment and the random disturbance term of the control process under random disturbance for the Brownian motion control model through the Duhamel formula. The Duhamel formula includes:

[0030]

[0031] c represents a constant used to define the integration range, χ represents the distance between the position δ and the target position x in space, and s represents the time integration variable. is the distribution function of the operating parameters of the drainage equipment at position δ in space. The effective value of the motor vibration acceleration, the ratio of the sideband energy of the gear meshing frequency, the motor winding temperature rise rate, and the z - score normalization of the pulverized coal concentration pulsation frequency are used to obtain the normalized value x at position δ. * i (δ), i = 1, 2, 3, 4 corresponding to four parameters. According to the Gaussian function, G i (δ) Gaussian functions of each parameter. The Gaussian functions corresponding to the four parameters are multiplied and combined. According to the formula to obtain γ(δ) is the initial function of the drainage environment of the drainage equipment at position δ in space, obtained by constructing the gas pressure, temperature, humidity, and ventilation wind speed in the initial parameter data of the drainage environment according to the method of constructing the above - mentioned operating parameter distribution function. f(δ, s) is the external excitation function that changes with time s and space δ, obtained by constructing the drainage environment parameters and the drainage geological parameters according to the method of constructing the above - mentioned operating parameter distribution function.

[0032] Furthermore, the steps for analyzing and adjusting the supplementary air volume are as follows:

[0033] The control quantity u(t) is converted into the opening degree command of the electric air supply valve, which is specifically implemented by a preset mapping table. The generated opening degree command is sent to the control module to adjust the air supply volume, so as to adjust the pressure in the gas drainage pipeline and make it approach the set value.

[0034] Further, the analysis steps of the control quantity u(t) are as follows:

[0035] Measure the actual pressure Pactual in the gas drainage pipeline controlled by the electric air supply valve, obtain the set pressure value Pset from the database, calculate the pressure deviation e(t), e(t)=Pset - Pactual, where t represents the current moment. Obtain the comprehensive variable value λ2 of the interval of the gas drainage equipment and the dynamic aggregation potential value τ of the grain boundaries of the mineral strata in the operation area of the gas drainage equipment, and perform z-score normalization on them together with the pressure deviation e(t). The normalized values are used to establish a multi-dimensional feature vector X(t)=[λ2norm(k), τnorm(k), enorm(t)]. Use the historical data in the database to construct a multi-dimensional feature vector X(k), establish a neural network model. The number of nodes in the input layer is the dimension of the feature vector, specifically 3. The number of nodes in the hidden layer is set to 10, and the number of nodes in the output layer is 3, corresponding to ΔKλ, ΔKτ, and ΔKe respectively. Use the historical data to train the neural network so that the network can learn the relationship between the feature vector and the PID parameter adjustment amount under different working conditions. Input the feature vector X(t) at the current moment into the trained mapping model to obtain the corresponding PID parameter adjustment amounts ΔKλ(t), ΔKτ(t), and ΔKe(t). Update the parameters of the PID controller: Kλ(t)=Kλ(t - 1)+ΔKλ(t), Kτ(t)=Kτ(t - 1)+ΔKτ(t), Ke(t)=Ke(t - 1)+Δeτ(t). Adopt the incremental PID control algorithm Δu(t)=Kλ(t)[y(t)-y(t - 1)]+Kτ(t)y(t)+Ke(t)[y(t)-2y(t - 1)+y(t - 2)], where Δu(t) is the increment of the control quantity at the t-th moment, and y(t), y(t - 1), and y(t - 2) are the pressure deviations at the t-th, (t - 1)-th, and (t - 2)-th moments respectively. Accumulate the calculated control quantity increment Δu(t) to the control quantity u(t - 1) at the previous moment to obtain the control quantity u(t) at the current moment.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. The present invention obtains geological acquisition information in the geological module through a calculation module to construct a grain boundary directional migration model, analyzes and obtains the grain boundary movement potential values of the mineral strata in each region. The calculation module obtains the acquisition information of the gas drainage equipment in the equipment module and the environmental acquisition information in the environmental module, analyzes and calculates the control parameter combination of the coal mine gas drainage equipment. The calculation module creates a Brownian motion control model for the coal mine gas drainage equipment, and generates corresponding state change data of the coal mine gas drainage equipment through the Brownian motion control model, analyzes and obtains the interval comprehensive change value of the gas drainage equipment, can consider equipment operation, environmental changes and geological changes. At the same time, the migration of geological grain boundaries will change the gas migration path, and can incorporate the grain boundary dynamics into the control model to ensure that the drainage parameters match the actual requirements.

[0038] 2. The present invention establishes a neural network model through the calculation module, analyzes the opening instruction of the electric air supply valve, and transmits it to the control module. The control module drives the electric air supply valve to adjust according to the opening instruction of the calculation module to adjust the air supply volume. It can adopt multi-dimensional parameter PID control, and has strong adaptability under complex geological conditions, timely response to equipment status, and optimized control strategy to meet requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. The following drawings are not deliberately drawn to scale in actual size, and the focus is on showing the gist of the present invention.

[0040] Figure 1 It is the system block diagram of the present invention;

[0041] Figure 2 It is the method flow chart of the present invention;

[0042] Figure 3 It is the method flow chart for migration model construction and grain boundary analysis. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts also belong to the scope of protection of the present invention.

[0044] Embodiment 1: As Figure 1 shown, a coal mine gas drainage intelligent optimization and regulation system includes a geological module, an equipment module, a calculation module, a control module, and an environmental module.

[0045] The geological module collects coal mine samples through a coring device, records the collection location and depth at the same time, sets temperature sensors at the collection point locations to obtain information on monitored temperature changes, observes the coal body samples through a transmission electron microscope to obtain grain boundary images, drills holes at the coal mine collection point locations to install strain gauges, and obtains geological stress field data through the stress relief method. The geological stress field data includes the principal stress direction and stress magnitude. Analyze the composition and content of coal mine minerals and organic matter through X-ray fluorescence spectroscopy, and obtain the coal seam dip angle information to form geological collection information and transmit it to the calculation module;

[0046] The equipment module installs piezoelectric acceleration sensors on the motor bearing seats and gears of the gas drainage equipment to convert mechanical vibrations into electrical signals, embeds thermocouples in the motor stator windings to obtain motor winding temperature signals, and passes a laser beam through the pulverized coal in the pipeline to obtain the coal scattered light intensity distribution, forming the gas drainage equipment collection information and transmitting it to the calculation module;

[0047] The environmental module arranges pressure measuring boreholes in the coal mine shaft, installs gas pressure sensors in the boreholes to obtain gas pressure information, installs orifice flow meters in the gas drainage boreholes, and installs gas pressure sensors in the gas drainage pipelines at the same time to record the gas drainage flow rate and gas pressure change information. At the positions of the main ventilation roadways and the intake and return air roadways of the mining and excavation working faces, measure the instantaneous velocity of the air flow through a hot wire anemometer, and collect various initial parameters of the gas drainage environment such as gas pressure, temperature, humidity, and ventilation air velocity, and record the spatial positions corresponding to each monitoring point to form environmental collection information and transmit it to the calculation module;

[0048] The control module includes a driving device for an electric air make-up valve, which drives the electric air make-up valve to adjust according to the opening instruction of the calculation module to adjust the air make-up volume;

[0049] The calculation module obtains the geological collection information in the geological module to construct a grain boundary directional migration model, analyzes the grain boundaries of each regional mineral strata, obtains the gas drainage equipment collection information in the equipment module and the environmental collection information in the environmental module, analyzes and calculates the control parameter combinations of the coal mine gas drainage equipment, creates a Brownian motion control model for the coal mine gas drainage equipment, analyzes the state of the coal mine gas drainage equipment, establishes a neural network model, analyzes the opening instruction of the electric air make-up valve, and transmits it to the control module;

[0050] Example 2: As Figure 2 shown, a method for intelligent optimization and regulation of coal mine gas drainage includes the following steps:

[0051] Step 1: Migration model construction and grain boundary analysis. The calculation module obtains the geological collection information in the geological module to construct a grain boundary directional migration model and analyzes to obtain the grain boundary movement aggregation potential values of each regional mineral strata;

[0052] Step 2: Solving the combination of equipment control parameters. The calculation module obtains the collection information of the extraction equipment in the equipment module and the environmental collection information in the environmental module, and analyzes and calculates the combination of the control parameters of the coal mine gas extraction equipment;

[0053] Step 3: Equipment state change. The calculation module creates a Brownian motion control model for the coal mine gas extraction equipment, generates corresponding state change data of the coal mine gas extraction equipment through the Brownian motion control model, and analyzes to obtain the interval comprehensive change value of the gas extraction equipment;

[0054] Step 4: Analysis of the opening command. The calculation module establishes a neural network model, analyzes the opening command of the electric air supply valve, and transmits it to the control module;

[0055] Step 5: Air supply control. The control module drives the electric air supply valve to adjust according to the opening command of the calculation module, and adjusts the air supply volume;

[0056] Among them, the steps of constructing the migration model and grain boundary analysis are as follows:

[0057] Step 101: As Figure 3 shown, the calculation module obtains the grain boundary image of the mineral strata collected by the geological module, uses image analysis software to measure the trend and extension direction of the grain boundary, the calculation module obtains the composition and content of the coal mine minerals and organic matter collected by the geological module, selects the ideal solution model to determine the parameters in the model, specifically the mole fraction of the substance, activity coefficient, interaction parameter, substitutes the determined parameters into the ideal solution model, obtains the chemical potential of various components of the coal mine, and uses it as the key input parameter for the subsequent operation of the grain boundary directional migration model. The calculation module divides the coal mine strata into several regions;

[0058] Step 102: The calculation module uses the phase field model to define the grain boundary migration parameters, namely the grain boundary mobility and the grain boundary energy, and specifies the physical parameters, namely the elastic modulus and the Poisson's ratio. It clarifies that the geological stress field generates a driving force for grain boundary migration through dislocation theory and crystal mechanics principles, establishes a quantitative relationship between stress and migration rate, and based on the principle of thermodynamics, establishes a proportional relationship between the chemical potential gradient and the grain boundary migration driving force. An exponential relationship between temperature and grain boundary migration rate is established through the Arrhenius equation. The influences of the geological stress field, chemical potential, and temperature on grain boundary migration are incorporated into the energy equation of the phase field model. The energy equation includes energy terms such as grain boundary energy, strain energy, and chemical energy. A kinetic equation is established by combining the relationships between various factors and grain boundary migration. The equation is discretized using the finite element numerical method, and the grain boundary migration simulation results are obtained through iterative solution. Historical observation data matching the simulation conditions are extracted from the database. The historical data cover the grain boundary migration directions, migration rates, and related geological stress fields, chemical potentials, and temperatures, etc. at different regions and different time points. The data are consistent with the simulation in terms of spatial and temporal scales. The included angle deviation is calculated by comparing the simulated and actual migration directions, and the relative error is obtained by comparing the migration rates. A root mean square error index is constructed to judge whether the model passes the verification. If it does not pass, the parameters in the model are adjusted, the relationship equation between the chemical potential gradient and the migration driving force is optimized, and the simulation is redone and compared. Iterative calibration is performed until the deviation is less than the set threshold to obtain the grain boundary directional migration model;

[0059] Step 103: The calculation module obtains the temperature change information, geological stress field data, chemical potential of the coal body, and the trend and extension direction of the grain boundaries collected by the geological module, and inputs them into the grain boundary directional migration model to output the directional migration directions and rates of the grain boundaries in each region. The calculation module draws a rose diagram of the grain boundary directional migration directions in each region, presented in polar coordinates. The radius of the polar coordinates is the frequency of the grain boundary in this direction, and the angle of the polar coordinates is the grain boundary migration direction. The angular range of the polar coordinates from 0° to 360° is divided into several equal angular intervals with 15°. The ratio of the sum of the angular interval frequencies in the first 25% interval of the frequencies to the total frequency is calculated and marked as the grain boundary migration concentration value of the mineral strata in each region. The maximum and minimum migration rates of the grain boundaries in each region are obtained, and with time as the horizontal axis and the grain boundary migration rates in each region as the vertical axis, the maximum and minimum migration rates at each time point are plotted on the coordinate axes respectively, and the points are connected with curves to obtain the change curve diagrams of the maximum and minimum migration rates. The enclosed area formed by the maximum and minimum migration rate curves is obtained and marked as the grain boundary rate amplitude energy product of the mineral strata in each region;

[0060] Step 104: The calculation module normalizes the grain boundary migration concentration values and the energy product of the grain boundary rate amplitude variation in each area of the mineral stratum. Based on the regular octahedron as the basic geometric model, it constructs the spatial framework of the mineral stratum. It linearly maps the grain boundary migration concentration values in each area to the edge length of the regular octahedron. A prism is created outside the regular octahedron, with the bottom surface of the prism coinciding with one face of the regular octahedron. The energy product of the grain boundary rate amplitude variation is mapped to the volume of the prism. It identifies the total volume formed by the prism and the regular octahedron, and labels it as the grain boundary movement aggregation potential value τ of each area of the mineral stratum;

[0061] Among them, the steps for solving the equipment control parameter combination are as follows:

[0062] The calculation module obtains the extraction equipment acquisition information collected by the equipment module, calculates the root mean square value of the electrical signal of the motor bearing seat, and marks it as the effective value of the motor vibration acceleration. It obtains the frequency spectrum fr of the gear vibration signal through fast Fourier transform. According to the formula fm = z×n / 60, it obtains the gear meshing frequency fm, where z is the number of teeth and n is the number of revolutions. According to the formula Calculate to obtain the ratio β of the sideband energy of the gear meshing frequency. k is the sideband order identifier, and its value is 1, 2, or 3. Analyze the motor winding temperature signal to obtain the motor winding temperature rise rate. Collect the scattered light intensity signal that changes with time through a high-speed photodetector, convert it into an electrical signal, and use fast Fourier transform to convert the time-domain signal into a frequency-domain signal. Obtain the frequency corresponding to the energy peak in the frequency spectrum diagram, and mark it as the pulverized coal concentration pulsation frequency. Mark the effective value of the motor vibration acceleration, the ratio of the sideband energy of the gear meshing frequency, the motor winding temperature rise rate, and the pulverized coal concentration pulsation frequency as the extraction equipment operation parameters;

[0063] The calculation module obtains the environmental acquisition information of the environment module, calculates the gas pressure gradient ψ through the gas pressure information, obtains the average flow velocity v of the pipeline according to the gas extraction flow rate Q measured by the orifice flowmeter and the cross-sectional area A of the extraction borehole or pipeline, using the formula v = Q / A. According to Darcy's law, based on the formula k = -u×v / ψ, it obtains the coal mass coupling permeability k. By measuring the instantaneous velocity g(t) of the air flow and calculating the average velocity According to the formula Obtain the air flow pulsation intensity I. T is the maximum value of time. Mark the gas pressure gradient, the body coupling permeability, and the air flow pulsation intensity as the extraction environment parameters. Calculate the change value of the coal seam dip angle per unit distance in the coal seam dip angle information to obtain the single-variable value of the coal seam dip angle, and mark it as the extraction geological parameter. Mark the extraction equipment operation parameters, the extraction environment parameters, and the extraction geological parameter as the coal mine gas extraction equipment control parameter combination, which constitutes the basic operation framework of the equipment to ensure that the basic extraction capacity and basic performance of the gas extraction system can meet the basic requirements of coal mine gas extraction;

[0064] Among them, the steps for analyzing the equipment state change are as follows:

[0065] Step 301: Create a Brownian motion control model for the coal mine gas drainage equipment according to the combination of coal mine gas drainage equipment control parameters. The Brownian motion control model includes: dS t (x) = ρ(x, t)S t (x)dt + δ(x, t)S t (x)dB t (x), dS t (x) represents the change amount of the comprehensive state value of the gas drainage equipment in the time interval dt and at the position x. S t (x) represents the comprehensive state value of the gas drainage equipment at the time t and the position x. The operation state of the equipment is reflected by the combination of drainage equipment control parameters. ρ(x, t) is the drift rate at the time t and the position x, reflecting the average change trend of the equipment state parameters. δ(x, t) is the volatility at the time t and the position x, representing the influence degree of the drainage geological parameters and drainage environment parameters on the equipment state. dB t (x) is a random disturbance term, affected by the drainage environment parameters and drainage geological parameters;

[0066] Step 302: Analyze the comprehensive state of the gas drainage equipment under random disturbance and the random disturbance term of the control process for the Brownian motion control model through the Duhamel formula. The Duhamel formula includes:

[0067]

[0068] c represents a constant used to define the integration range. χ represents the distance between the position δ and the target position x in space. s represents the time integration variable. is the distribution function of the operation parameters of the drainage equipment at the position δ in space. The effective value of the motor vibration acceleration, the ratio of the sideband energy of the gear meshing frequency, the motor winding temperature rise rate, and the z-score normalization of the pulverized coal concentration pulsation frequency are used to obtain the normalized value x at the position δ * i (δ), i = 1, 2, 3, 4 correspond to four parameters. According to the Gaussian function obtain G i (δ) Gaussian functions of each parameter. The Gaussian functions corresponding to the four parameters are multiplied and combined. λ is the standard deviation of the data, and η is the mean value of the data. According to the formula obtain γ(δ) is the initial function of the drainage environment of the drainage equipment at the position δ in space. It is obtained by constructing the initial parameters of the drainage environment data such as gas pressure, temperature, humidity, and ventilation wind speed according to the method of the above operation parameter distribution function. f(δ, s) is the external excitation function that changes with time s and space δ. It is obtained by constructing the drainage environment parameters and drainage geological parameters according to the method of the above operation parameter distribution function;

[0069] Specifically, a Brownian motion control model of coal mine gas drainage equipment is created based on the combination of control parameters of coal mine gas drainage equipment. By simulating the dynamic behavior of coal mine gas drainage equipment, the adaptability and intelligent level of the gas drainage system are effectively improved, so as to achieve the purpose of improving the gas drainage efficiency and ensuring the safety conditions of the underground operation environment. The core of the Brownian motion control model lies in simulating the random changes of the state of coal mine gas drainage equipment over time and space. In the model, the change amount of the comprehensive state value of the gas drainage equipment is mainly affected by three factors, namely the distribution function of the operation parameters of the drainage equipment The initial function γ(δ) of the drainage environment and the external excitation function f(δ, s) simulate the uncertainty of coal mine gas drainage, such as changes in equipment operation status, changes in drainage environment, and random fluctuations in drainage geology, etc., which have an impact on the operation status of coal mine gas drainage equipment. At the same time, the Duhamel formula plays a bridging role, enabling the system to convert the random changes in Brownian motion control into specific state change data. Through the Duhamel formula, the state change trend of coal mine gas drainage equipment under random disturbances is calculated, providing accurate data support for gas drainage control.

[0070] Step 303: For the change amount dS t (x) of the comprehensive state value of the gas drainage equipment in each region, through integration and accumulation in space and time, according to the formula The total amount of change in the comprehensive state of the gas drainage equipment from the initial time t0 to the current time t within each region is obtained as λ1, marked as the interval comprehensive state change value λ2 of the gas drainage equipment, and ξ is the number of the region;

[0071] Among them, the steps of make-up air control are as follows:

[0072] Measure the actual pressure Pactual in the gas drainage pipeline controlled by the electric air make-up valve, obtain the set pressure value Pset from the database, calculate the pressure deviation e(t), where e(t) = Pset - Pactual, and t represents the current moment. Obtain the comprehensive variable value λ2 of the interval of gas drainage equipment and the crystal boundary movement aggregation potential value τ of the mineral formation in the operation area of the gas drainage equipment, perform z-score normalization on them together with the pressure deviation e(t), and establish a multi-dimensional feature vector X(t) = [λ2norm(k), τnorm(k), enorm(t)] after normalization. Use the historical data in the database to construct a multi-dimensional feature vector X(k), establish a neural network model, where the number of nodes in the input layer is the dimension of the feature vector, specifically 3, the number of nodes in the hidden layer is set to 10, and the number of nodes in the output layer is 3, corresponding to ΔKλ, ΔKτ, and ΔKe respectively. Use the historical data to train the neural network so that the network can learn the relationship between the feature vector and the PID parameter adjustment amount under different working conditions. Input the feature vector X(t) at the current moment into the trained mapping model to obtain the corresponding PID parameter adjustment amounts ΔKλ(t), ΔKτ(t), and ΔKe(t). Update the parameters of the PID controller: Kλ(t) = Kλ(t - 1) + ΔKλ(t), Kτ(t) = Kτ(t - 1) + ΔKτ(t), Ke(t) = Ke(t - 1) + Δeτ(t). Adopt the incremental PID control algorithm Δu(t) = Kλ(t)[y(t) - y(t - 1)] + Kτ(t)y(t) + Ke(t)[y(t) - 2y(t - 1) + y(t - 2)], where Δu(t) is the increment of the control quantity at the t-th moment, and y(t), y(t - 1), y(t - 2) are the pressure deviations at the t-th, (t - 1)-th, and (t - 2)-th moments respectively. Accumulate the calculated control quantity increment Δu(t) to the control quantity u(t - 1) at the previous moment to obtain the control quantity u(t) at the current moment. Convert the control quantity u(t) into an opening command for the electric air make-up valve, which is specifically realized by a preset mapping table, and send the generated opening command to the control module to adjust the air make-up volume, thereby adjusting the pressure in the gas drainage pipeline to make it approach the set value;

[0073] The above is a description of the present invention and should not be construed as a limitation thereof. Although several exemplary embodiments of the present invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined by the claims. It should be understood that the above is a description of the present invention and should not be considered limited to the specific embodiments disclosed, and modifications to the disclosed embodiments as well as other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. An intelligent optimization and control system for coal mine gas drainage, comprising: A geological module, an equipment module, a calculation module, a control module, and an environment module, characterized in that the geological module collects coal mine samples, monitors temperature change information through a temperature sensor, obtains a grain boundary image by observing coal body samples, obtains geological stress field data by the stress relief method at the coal mine collection point location, and transmits the geological collection information to the calculation module after obtaining it; the equipment module converts the piezoelectric acceleration sensors installed on the motor bearing seat and gear of the gas drainage equipment into electrical signals, obtains the motor winding temperature signal, obtains the gas drainage equipment collection information, and transmits it to the calculation module; the environment module obtains gas pressure information, records the gas drainage flow rate and gas pressure change information, collects various initial parameter data of the drainage environment, and records the spatial position corresponding to each monitoring point to form environment collection information, and transmits it to the calculation module; the control module includes a driving device for an electric air supply valve, and drives the electric air supply valve to adjust according to the opening instruction of the calculation module to adjust the air supply volume. The calculation module obtains the geological collection information in the geological module to construct a grain boundary directional migration model, analyzes the grain boundaries of each regional mineral strata, obtains the gas drainage equipment collection information in the equipment module and the environment collection information in the environment module, analyzes and calculates the control parameter combination of the coal mine gas drainage equipment, creates a Brownian motion control model for the coal mine gas drainage equipment, analyzes the state of the coal mine gas drainage equipment, establishes a neural network model, analyzes the opening instruction of the electric air supply valve, and transmits it to the control module.

2. An intelligent optimization and control method for coal mine gas drainage, characterized in that Applied to implement a coal mine gas drainage intelligent optimization control system as claimed in claim 1, comprising the following steps: Step 1: Migration model construction and grain boundary analysis. The calculation module obtains the geological collection information in the geological module to construct a grain boundary directional migration model, and analyzes to obtain the grain boundary movement aggregation potential values of each regional mineral strata. Step 2: Solving the control parameter combination of the equipment. The calculation module obtains the gas drainage equipment collection information in the equipment module and the environment collection information in the environment module, and analyzes and calculates the control parameter combination of the coal mine gas drainage equipment. Step 3: Equipment state change. The calculation module creates a Brownian motion control model for the coal mine gas drainage equipment, and generates corresponding coal mine gas drainage equipment state change data through the Brownian motion control model, and analyzes to obtain the interval comprehensive change value of the gas drainage equipment. Step 4: Analysis of the opening instruction. The calculation module establishes a neural network model, analyzes the opening instruction of the electric air supply valve, and transmits it to the control module. Step 5: Air supply control. The control module drives the electric air supply valve to adjust according to the opening instruction of the calculation module to adjust the air supply volume.

3. An intelligent optimization and control method for coal mine gas drainage according to claim 2, characterized in that, The analysis steps of the grain boundary movement aggregation potential values of each regional mineral strata are as follows: Step 104: The calculation module normalizes the grain boundary migration concentration values of each regional mineral strata and the energy product of the grain boundary rate amplitude variation of each regional mineral strata. Based on the regular octahedron as the basic geometric model, it constructs the spatial framework of the mineral strata. It linearly maps the grain boundary migration concentration values of each regional mineral strata to the edge length of the regular octahedron. A prism is created outside the regular octahedron, with the bottom surface of the prism coinciding with one face of the regular octahedron. The energy product of the grain boundary rate amplitude variation is mapped to the volume of the prism. It identifies the total volume formed by the prism and the regular octahedron, and labels it as the grain boundary movement aggregation potential value τ of each regional mineral strata.

4. An intelligent optimization and regulation method for coal mine gas drainage according to claim 3, characterized in that The steps for analyzing the grain boundary migration concentration values of each regional mineral strata and the grain boundary rate amplitude energy of each regional mineral strata are as follows: Step 103: The calculation module obtains the temperature change information, geological stress field data, chemical potential of the coal body, grain boundary strike, and extension direction of each region collected by the geological module, and inputs them into the grain boundary directional migration model, and outputs the directional migration direction and rate of the grain boundary in each region. The calculation module draws a rose diagram of the grain boundary directional migration direction in each region, presented in polar coordinates. The radius of the polar coordinates is the frequency of the grain boundary appearing in this direction, and the angle of the polar coordinates is the grain boundary migration direction. The angular range of 0° to 360° of the polar coordinates is divided into several equal angular intervals at 15°. It calculates the ratio of the sum of the angular interval frequencies in the first 25% interval of the frequency to the total frequency, and labels it as the grain boundary migration concentration value of each regional mineral strata. It obtains the maximum migration rate and the minimum migration rate of the grain boundary in each region. With time as the horizontal axis and the grain boundary migration rate in each region as the vertical axis, it plots the maximum migration rate and the minimum migration rate at each time point on the coordinate axis respectively, and connects them with a curve to obtain the variation curve diagram of the maximum migration rate and the minimum migration rate. It obtains the enclosed area formed by the maximum migration rate and the minimum migration rate curves, and labels it as the energy product of the grain boundary rate amplitude variation of each regional mineral strata.

5. The intelligent optimization control method for coal mine gas drainage according to claim 4, characterized in that The analysis steps of the grain boundary directional migration model are as follows: Step 101: The calculation module obtains the grain boundary image of the mineral strata collected by the geological module, uses image analysis software to measure the grain boundary strike and extension direction. The calculation module obtains the composition and content of the coal mine minerals and organic matter collected by the geological module, selects the ideal solution model to determine the parameters in the model, specifically the mole fraction of the substance, activity coefficient, and interaction parameter. Substitute the determined parameters into the ideal solution model to obtain the chemical potential of various components of the coal mine, as the key input parameter for the subsequent operation of the grain boundary directional migration model. The calculation module divides the coal mine strata into several regions; Step 102: The calculation module uses the phase-field model to define the grain boundary migration parameters, namely the grain boundary mobility and the grain boundary energy, and specifies the physical parameters, namely the elastic modulus and the Poisson's ratio. It clarifies the geological stress field to generate a driving force for grain boundary migration through dislocation theory and crystal mechanics principles, establishes a quantitative relationship between stress and migration rate, and based on the thermodynamic principle, establishes a proportional relationship between the chemical potential gradient and the grain boundary migration driving force. Through the Arrhenius equation, an exponential relationship between temperature and grain boundary migration rate is established. The influences of the geological stress field, chemical potential, and temperature on grain boundary migration are incorporated into the energy equation of the phase-field model. The energy equation includes energy terms such as grain boundary energy, strain energy, and chemical energy. Combining the relationships between various factors and grain boundary migration, a kinetic equation is established. The equation is discretized by the finite element numerical method and iteratively solved to obtain the grain boundary migration simulation results. Historical observation data matching the simulation conditions are extracted from the database. The historical data cover the grain boundary migration direction, migration rate, and related geological stress field, chemical potential, temperature, etc. at different regions and different time points. The data are consistent with the simulation in terms of spatial and temporal scales. The included angle deviation is calculated by comparing the simulated and actual migration directions, and the relative error is obtained by comparing the migration rates. A root mean square error index is constructed to judge whether the model passes the verification. If not, the parameters in the model are adjusted, the relationship equation between the chemical potential gradient and the migration driving force is optimized, and the simulation is redone and compared. Iterative calibration is performed until the deviation is less than the set threshold to obtain the grain boundary directional migration model.

6. The intelligent optimization and regulation method for mine gas drainage according to claim 2, characterized in that The steps for analyzing the combined control parameters of the coal mine gas drainage equipment are as follows: The calculation module obtains the collection information of the drainage equipment collected by the equipment module, calculates the root mean square value of the electrical signal of the motor bearing seat, and marks it as the effective value of the motor vibration acceleration. The gear vibration signal is subjected to fast Fourier transform to obtain the frequency spectrum fr. According to the formula fm = z × n / 60, the gear meshing frequency fm is obtained, where z is the number of teeth and n is the number of revolutions. The ratio β of the sideband energy of the gear meshing frequency is calculated. The motor winding temperature rise rate is obtained by analyzing the motor winding temperature signal. The scattered light intensity signal changing with time is collected by a high-speed photodetector and converted into an electrical signal. The fast Fourier transform converts the time-domain signal into a frequency-domain signal, and the frequency corresponding to the energy peak in the frequency spectrum diagram is obtained and marked as the pulverized coal concentration pulsation frequency. The effective value of the motor vibration acceleration, the ratio β of the sideband energy of the gear meshing frequency, the motor winding temperature rise rate, and the pulverized coal concentration pulsation frequency are marked as the operation parameters of the drainage equipment; The calculation module obtains the environmental acquisition information of the environmental module, calculates the gas pressure gradient ψ through the gas pressure information, obtains the average flow velocity v of the pipeline according to the gas drainage flow rate Q measured by the orifice flowmeter and the cross-sectional area A of the drainage borehole or pipeline using the formula v = Q / A, and obtains the coal seam coupling permeability k according to Darcy's law using the formula k = -u×v / ψ. By measuring the instantaneous velocity g(t) of the air flow and calculating the average velocity According to the formula, the air flow pulsation intensity I is obtained. The gas pressure gradient, the body coupling permeability, and the air flow pulsation intensity are marked as drainage environmental parameters. The change value of the coal seam dip within a unit distance in the coal seam dip information is calculated to obtain the single change value of the coal seam dip, which is marked as the drainage geological parameter. The drainage equipment operation parameters, the drainage environmental parameters, and the drainage geological parameters are marked as the control parameter combination of the coal mine gas drainage equipment, constituting the basic operation framework of the equipment.

7. The intelligent optimization control method for coal mine gas drainage according to claim 2, characterized in that, The steps for analyzing the interval comprehensive variable value of the gas drainage equipment are as follows: Step 303: For the change amount dS of the comprehensive state value of the gas drainage equipment in each area t (x), through integration and accumulation in space and time, according to the formula Obtain the total change in the comprehensive state of the gas drainage equipment from the initial time t0 to the current time t in each area, denoted as the interval comprehensive state change value λ2 of the gas drainage equipment, and ξ is the number of the area.

8. An intelligent optimization and control method for coal mine gas drainage according to claim 7, characterized in that, The steps for analyzing the change of the comprehensive state value of the gas drainage equipment are as follows: Step 301: Create a Brownian motion control model for coal mine gas drainage equipment according to the combination of coal mine gas drainage equipment control parameters. The Brownian motion control model includes: dS t (x) = ρ(x, t)S t (x)dt + δ(x, t)S t (x)dB t (x), dS t (x) represents the change amount of the comprehensive state value of the gas drainage equipment in the time interval dt and at the position x. S t (x) represents the comprehensive state value of the gas drainage equipment at time t and position x. The operation state of the equipment is reflected by the combination of drainage equipment control parameters. ρ(x, t) is the drift rate at time t and position x, reflecting the average change trend of the equipment state parameters. δ(x, t) is the volatility at time t and position x, representing the influence degree of the drainage geological parameters and drainage environment parameters on the equipment state. dB t (x) is a random disturbance term, affected by the drainage environment parameters and drainage geological parameters; Step 302: Analyze the random disturbance terms of the comprehensive state and control process of the gas drainage equipment under random disturbance for the Brownian motion control model through the Duhamel formula. The Duhamel formula includes: c represents a constant used to define the integration range, χ represents the distance between the position δ and the target position x in space, and s represents the time integration variable. is the distribution function of the operating parameters of the extraction equipment at the position δ in space. The effective value of the motor vibration acceleration, the ratio of the sideband energy of the gear meshing frequency, the motor winding temperature rise rate, and the z-score normalization of the pulverized coal concentration pulsation frequency are used to obtain the normalized value x at the position δ. * i (δ), where i = 1, 2, 3, 4 correspond to four parameters. According to the Gaussian function, G is obtained. i (δ) Gaussian functions of each parameter. The Gaussian functions corresponding to the four parameters are multiplied and combined. According to the formula is obtained. γ(δ) is the initial function of the extraction environment of the extraction equipment at the position δ in space, which is obtained by using the gas pressure, temperature, humidity, and ventilation wind speed in the initial parameter data of the extraction environment according to the method for constructing the above operating parameter distribution function. f(δ, s) is the external excitation function that changes with time s and space δ, which is obtained by using the extraction environment parameters and extraction geological parameters according to the method for constructing the above operating parameter distribution function.

9. The intelligent optimization and control method for coal mine gas drainage according to claim 2, wherein, The steps for analyzing the adjusted air make-up volume are as follows: The control quantity u(t) is converted into an opening command for the electric air make-up valve, which is specifically realized by a preset mapping table. The generated opening command is sent to the control module to adjust the air make-up volume, thereby adjusting the pressure in the gas drainage pipeline to approach the set value.

10. The intelligent optimization control method for coal mine gas drainage according to claim 9, characterized in that, The steps for analyzing the control quantity u(t) are as follows: Measure the actual pressure \(P_{actual}\) in the gas drainage pipeline controlled by the electric air make-up valve, obtain the set pressure value \(P_{set}\) from the database, calculate the pressure deviation \(e(t)\), where \(e(t)=P_{set} - P_{actual}\), \(t\) represents the current moment. Obtain the comprehensive variable value \(\lambda_2\) of the intervals of gas drainage equipment and the dynamic aggregation potential value \(\tau\) of the grain boundaries of the mineral strata in the operation area of the gas drainage equipment, and perform z-score normalization on them together with the pressure deviation \(e(t)\). The normalized values are used to establish a multi-dimensional feature vector \(X(t)=[\lambda_{2norm}(k),\tau_{norm}(k),e_{norm}(t)]\). Use the historical data in the database to construct a multi-dimensional feature vector \(X(k)\), establish a neural network model. The number of nodes in the input layer is the dimension of the feature vector, specifically 3. The number of nodes in the hidden layer is set to 10, and the number of nodes in the output layer is 3, corresponding to \(\Delta K_{\lambda}\), \(\Delta K_{\tau}\), and \(\Delta K_{e}\) respectively. Use the historical data to train the neural network so that the network can learn the relationship between the feature vector and the PID parameter adjustment amount under different working conditions. Input the feature vector \(X(t)\) at the current moment into the trained mapping model to obtain the corresponding PID parameter adjustment amounts \(\Delta K_{\lambda}(t)\), \(\Delta K_{\tau}(t)\), and \(\Delta K_{e}(t)\). Update the parameters of the PID controller: \(K_{\lambda}(t)=K_{\lambda}(t - 1)+\Delta K_{\lambda}(t)\), \(K_{\tau}(t)=K_{\tau}(t - 1)+\Delta K_{\tau}(t)\), \(K_{e}(t)=K_{e}(t - 1)+\Delta e_{\tau}(t)\). Adopt the incremental PID control algorithm \(\Delta u(t)=K_{\lambda}(t)[y(t)-y(t - 1)]+K_{\tau}(t)y(t)+K_{e}(t)[y(t)-2y(t - 1)+y(t - 2)]\), where \(\Delta u(t)\) is the increment of the control amount at the \(t\)-th moment, and \(y(t)\), \(y(t - 1)\), \(y(t - 2)\) are the pressure deviations at the \(t\)-th, \(t - 1\)-th, and \(t - 2\)-th moments respectively. Accumulate the calculated control amount increment \(\Delta u(t)\) to the control amount \(u(t - 1)\) at the previous moment to obtain the control amount \(u(t)\) at the current moment.

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