Wind flow heat and humidity coupling model construction and dynamic regulation and control method and system

Abstract

The invention relates to the technical field of mine ventilation and air conditioning, and discloses an air flow heat and humidity coupling model construction and dynamic regulation and control method and system which are suitable for temperature, humidity and thermal comfort control of key areas such as a mining face and an electromechanical chamber in a deep mine. The method comprises the steps that multi-source temperature and humidity sensing data and a wind network topology are collected, and a heat and humidity disturbance phase space grid is generated; deducing a compliance evolution path through a heat and humidity conduction quasi-array generator based on the grid, and outputting a heat and humidity compliance quasi-array; under the constraint of the quasi matrix, generating a control strategy partial order lattice through a partial order lattice optimizer for multi-chamber conflict resolution; according to the partial order lattice, multiple actuators guided by a lattice path execute dynamic closed-loop regulation and control; the problems that under deep mine multi-source disturbance and strong coupling, a traditional method is difficult to describe the heat and humidity environment in a multi-dimensional coupling mode, and regulation and control are prone to conflict or overrun are solved, it is achieved that the temperature and humidity of a key area are stabilized within a compliant comfortable interval, and operation safety and equipment stability are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine heat and humidity regulation, and more particularly to a wind flow heat and humidity coupling model construction and dynamic regulation method and system. BACKGROUND

[0002] In deep mine production operations, mine heat and humidity regulation technology is mainly used to cope with the coupled influence of surrounding rock heat exchange, equipment heat dissipation and concentrated heat source superposition on mine air flow temperature and humidity, and to maintain the stability of temperature, humidity and wet-bulb black globe temperature index in key areas such as mining faces and pump rooms. This technology needs to combine with the mine air network topology, consider the conduction effect of ground climate periodic fluctuations on the inlet air temperature, and through the construction of a wind flow heat and humidity coupling model, realize the prediction of the heat and humidity state of key areas, heat and humidity load distribution and dynamic closed-loop control, to meet the compliance operation requirements of mechanical and electrical equipment chambers and the operation comfort and efficiency requirements of mining faces.

[0003] However, the existing mine heat and humidity regulation method has the problem of being difficult to simultaneously adapt to multi-source disturbance, strong coupling characteristics and rigid compliance constraints. The problem arises from the fact that the deep mine heat and humidity environment is influenced by the multi-factor deep coupling of surrounding rock heat exchange, equipment heat dissipation and concentrated heat source, and the inlet air temperature is influenced by the conduction of ground climate periodic fluctuations through the air network topology to each air consumption area. The existing method lacks dynamic response capability to such multi-source disturbance, and does not fully integrate the conduction characteristics of the air network topology and the rigid requirements of the compliance constraints. This leads to the fact that the temperature and humidity in key areas are easy to exceed the specified threshold, and the operation area is difficult to stably maintain the comfort control target, thereby affecting the safe operation of mechanical and electrical equipment and the stable operation of the operation area. SUMMARY

[0004] To solve the technical problem that the traditional method is difficult to multi-dimensionally couple the heat and humidity environment and regulate in the multi-source disturbance and strong coupling scenario of deep mine, realize the stability of temperature and humidity in key areas in the compliance and comfort interval, and guarantee the safe operation of underground operation and the stable operation of mechanical and electrical equipment, the present application provides the following technical solutions:

[0005] A wind flow heat and humidity coupling model construction and dynamic regulation method, comprising:

[0006] Step S10: Collect multi-source temperature and humidity sensing data and air network topology, fuse the multi-source temperature and humidity sensing data and air network topology, generate and output a heat and humidity disturbance phase space grid;

[0007] Step S20: Based on the heat and humidity disturbance phase space grid, a heat and humidity conduction quasi-array generator is used to deduce a compliance evolution path, and output a heat and humidity compliance quasi-array;

[0008] Step S30: Under the constraint of the heat and humidity compliance quasi-array, a multi-chamber conflict resolution partial order lattice optimizer is used to generate a regulation strategy partial order lattice;

[0009] Step S40: According to the regulation strategy partial order lattice, the dynamic closed-loop regulation instruction is executed by the lattice path guidance multi-actuator; the multi-actuator includes a damper actuator, a refrigeration unit actuator and a fan variable frequency actuator.

[0010] Further, the fusion processing of the multi-source temperature and humidity sensing data and the air network topology includes:

[0011] The multi-source temperature and humidity sensing data are processed in space-time alignment to obtain space-time alignment data;

[0012] The space-time weight is calculated, and the space-time weight includes a time weight and a space weight; the time weight is determined according to the time difference between the data collection time and the current time and the seasonal cycle change rule, and the space weight is determined according to the straight line distance between the sensor and the space node and the air resistance coefficient in the air network topology;

[0013] The temperature, humidity, wind speed and heat source intensity in the space-time alignment data are respectively multiplied by the corresponding time weight and space weight to obtain weighted temperature data, weighted humidity data, weighted wind speed data and weighted heat source intensity data;

[0014] The weighted temperature data, the weighted humidity data, the weighted wind speed data and the weighted heat source intensity data are taken as four-element elements of the disturbance dimension to form a heat and humidity disturbance phase space grid.

[0015] Further, the heat and humidity disturbance phase space grid is a three-dimensional tensor structure, and the tensor dimension is a space node dimension, a time step dimension and a disturbance dimension; the space node dimension includes all key area positions in the mine that need to be monitored, the time step length is divided based on the seasonal periodic fluctuation rule of the mine ground climate, and the disturbance dimension element is temperature, humidity, wind speed and heat source intensity.

[0016] Further, the heat and humidity conduction quasi-array generator includes:

[0017] The heat and humidity state parameters of each space node at different time steps are extracted from the heat and humidity disturbance phase space grid;

[0018] The transfer rule of the heat and humidity state is established based on the heat and humidity state parameters;

[0019] Taking the heat and humidity state of the initial time step as the starting point, a breadth-first search strategy is used to generate a future heat and humidity state evolution path;

[0020] Each generated heat and humidity state evolution path is checked for compliance to obtain a compliant evolution path;

[0021] Based on the compliant evolution path, a heat and humidity compliance quasi-array is constructed by a quasi-array construction algorithm.

[0022] Further, the quasi-array construction algorithm includes:

[0023] define the base set as a set of all compliance evolution paths that pass the compliance check;

[0024] define the independent set family as a subset of the base set that satisfies the matroid axioms, including the hereditary axiom and the exchange axiom;

[0025] determine the matroid base as the independent set with the largest number of elements in the independent set family;

[0026] traverse all elements in the independent set family to verify whether they satisfy the hereditary axiom and the exchange axiom;

[0027] The structure of the hot and humid compliance matroid is composed of the base set, the independent set family, and the matroid base.

[0028] Further, the multi-chamber conflict resolution partial order lattice optimizer comprises:

[0029] determine the hot and humid regulation targets of the multi-chamber based on the hot and humid compliance matroid;

[0030] generate a regulation strategy candidate set, the regulation strategy comprising a damper opening degree, a refrigeration power, and a fan frequency, and the regulation strategy candidate set satisfying the hot and humid compliance matroid constraint;

[0031] perform multi-chamber conflict identification on the regulation strategy candidate set, the conflict identification comprising: substituting each regulation strategy in the regulation strategy candidate set into a multi-chamber hot and humid network model to calculate the wet-bulb black globe temperature index of each chamber after implementing the strategy, comparing the wet-bulb black globe temperature index of each chamber after implementing the strategy with the chamber wet-bulb black globe temperature index target set, and counting the number of chambers exceeding the target threshold; if the number of exceeding chambers is greater than 0, marking the strategy as a conflict strategy, otherwise marking it as a non-conflict strategy, to obtain a conflict strategy subset and a non-conflict strategy subset;

[0032] optimize the conflict strategy subset through a multi-chamber conflict resolution algorithm to obtain a resolved strategy subset;

[0033] combine the non-conflict strategy subset and the resolved strategy subset to obtain a compliance regulation strategy set;

[0034] construct a regulation strategy partial order lattice based on the compliance regulation strategy set and the partial order relation set; the partial order relation is defined as that strategy A Pareto dominates strategy B, and the partial order relation set is obtained by pairwise comparison of all strategies in the compliance regulation strategy set.

[0035] Further, the construction of the regulation strategy partial order lattice comprises:

[0036] each regulation strategy in the compliance regulation strategy set is taken as an element of the lattice, and the element attribute comprises a damper opening degree, a refrigeration power, and a fan frequency;

[0037] define a lattice operation, the lattice operation including an intersection operation and a union operation, the intersection operation being to take the minimum value of element attributes in two control strategies, and the union operation being to take the maximum value of element attributes in two control strategies;

[0038] determine a lattice bottom as the minimum result of the intersection operation in all strategies, and a lattice top as the maximum result of the union operation in all strategies.

[0039] Further, the lattice path guided multi-actuator includes:

[0040] screen a target lattice path from the control strategy partial order lattice through a lattice path priority evaluation algorithm, the target lattice path needing to simultaneously satisfy a safety margin and a comfort approximation efficiency constraint;

[0041] generate an initial control instruction based on the target lattice path, and convert parameters in a strategy of the lattice bottom into control signals of a damper actuator, a refrigeration unit actuator, and a fan variable frequency actuator;

[0042] after sending the initial control instruction, collect temperature and humidity monitoring data of a key area, and calculate a real-time wet-bulb black globe temperature index;

[0043] based on the real-time wet-bulb black globe temperature index, update the control instruction through a closed-loop feedback adjustment rule, including a comfort interval maintenance, a compliance interval optimization, and an over-limit emergency rollback rule;

[0044] avoid action conflicts through a cooperative time sequence control;

[0045] built-in fault tolerance mechanism to re-screen a backup lattice path in case of equipment failure or sensor abnormality.

[0046] Further, the closed-loop feedback adjustment rule includes:

[0047] rule one, if the real-time wet-bulb black globe temperature index and the relative humidity are in the comfort interval, maintain the current control strategy, generate a maintenance instruction, and the instruction content is a maintenance parameter of the current actuator control signal;

[0048] rule two, if the real-time wet-bulb black globe temperature index is in the compliance but not comfort interval, move to a more aggressive strategy along the target lattice path;

[0049] rule three, if the real-time wet-bulb black globe temperature index exceeds 34℃, immediately generate a rollback instruction to rollback to the last safe lattice layer strategy on the target lattice path, and record the over-limit reason.

[0050] A wind flow heat and humidity coupling model construction and dynamic control system is used to implement the wind flow heat and humidity coupling model construction and dynamic control method, and the system includes:

[0051] Data fusion and mesh generation module: for collecting multi-source temperature and humidity sensing data and wind network topology, fusing multi-source temperature and humidity sensing data and wind network topology, generating and outputting thermal and humid disturbance phase space mesh;

[0052] Compliance matrix generation module: based on the thermal and humid disturbance phase space mesh, the thermal and humid conduction matrix generator is used to deduce the compliance evolution path, the compliance of the evolution path is verified, and the thermal and humid compliance matrix is output;

[0053] Regulation strategy partial order lattice generation module: for determining multi-chamber thermal and humid regulation targets under the constraint of thermal and humid compliance matrix, generating and optimizing regulation strategy candidate set, constructing partial order relationship between regulation strategies, and generating regulation strategy partial order lattice through multi-chamber conflict resolution partial order lattice optimizer.

[0054] Compared with the prior art, the beneficial effects of the present application are: the present application accurately solves the problem that traditional methods are difficult to multi-dimensionally couple thermal and humid environment and are prone to conflict or over-limit under the condition of multi-source disturbance (surrounding rock heat exchange, equipment heat dissipation, ground climate cycle fluctuation) and strong coupling. The three-dimensional thermal and humid disturbance phase space mesh is constructed by fusing multi-source data and wind network topology through the time and space weight dynamic weighting method, the three-dimensional time and space coupling effect is retained, and the limitation of the traditional model in describing one-sidedness is solved. The thermal and humid conduction matrix generator enumerates the compliance evolution path and constructs the matrix, breaking through the blindness of traditional single path regulation. The multi-chamber conflict resolution partial order lattice optimizer accurately resolves the problem of "one-chamber compliance and multi-chamber over-limit", and also constructs a strategy partial order lattice containing a Pareto optimal relationship, solving the pain point of traditional strategy without structure. The grid path guided multi-actuator realizes "safe and gradual" regulation and structured rollback, avoids parameter oscillation, ensures that the temperature and humidity do not exceed 34℃ and approach the 26℃ / 85% comfortable interval, and guarantees the safety of underground operation and the stability of mechanical and electrical equipment. The present application realizes the systematic enumeration of the compliance evolution path of the complex coupled industrial system by introducing the matrix theory, realizes the structured optimization of the regulation strategy under multi-constraint conflict by constructing the partial order lattice, and designs the grid path guided multi-actuator gradual closed-loop mechanism, providing an innovative engineering paradigm with completeness, rollback and robustness for dealing with multi-variable strong coupling and multi-objective conflict industrial control scenes. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0056] Figure 1 A flow chart of a wind flow thermal and humid coupling model construction and dynamic regulation method in the present application;

[0057] Figure 2 The hierarchical grid structure and the Pareto optimal relationship diagram of the partial order lattice of the regulation strategy in the application;

[0058] Figure 3 The functional module diagram of the wind flow heat and humidity coupling model construction and dynamic regulation system in the application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0060] Embodiment 1:

[0061] Please refer to Figure 1 The embodiment provides a wind flow heat and humidity coupling model construction and dynamic regulation method, which comprises the following steps:

[0062] Step S10: Collecting multi-source temperature and humidity sensing data and wind network topology, fusing the multi-source temperature and humidity sensing data and the wind network topology, generating and outputting a heat and humidity disturbance phase space grid.

[0063] The multi-source temperature and humidity sensing data comprises four types of core data, i.e., temperature data, humidity data, wind speed data and heat source intensity data. The temperature data refers to real-time temperature values of spatial nodes in each monitoring area in a mine, which are collected by distributed temperature and humidity sensors arranged in key areas such as mining faces, central pump rooms and air intake shafts. The collection frequency is determined by variance analysis of historical heat and humidity fluctuation data, and the greater the variance is, the higher the collection frequency is. The humidity data refers to real-time relative humidity values of each spatial node, which are collected synchronously with the temperature data by the distributed temperature and humidity sensors. The value range is the historical measured relative humidity interval in the mine, which is determined by the maximum value and the minimum value of the mine humidity data in the past three years. The wind speed data refers to real-time airflow speeds in each air path, which are collected by wind speed sensors installed at the center of the air path section. The collection needs to avoid the vortex area in the air path, and the position of the vortex area is determined by CFD simulation of the mine airflow field. The heat source intensity data refers to real-time heating power of equipment in the mine, which is converted from real-time current and voltage data collected by the equipment controller in combination with the rated power of the equipment. The specific conversion formula is heat source intensity = equipment rated power x (real-time current / rated current) x (real-time voltage / rated voltage), wherein the equipment rated power is obtained from the equipment technical parameter manual, and the real-time current and voltage are collected from the current and voltage sensors of the equipment.

[0064] The wind network topology refers to the connection relationship and physical parameter set of each space node (including chambers, air shafts, etc.) and air paths in the mine ventilation network, which is obtained by combining the mine ventilation system design diagram with the field measurement data. The physical parameters included in the wind network topology are air path length, air path cross-sectional area, and air path resistance coefficient. The air path length is obtained by field measurement with a laser range finder, the air path cross-sectional area is calculated by measuring the length and width of the rectangular cross-section or the diameter of the circular cross-section, and the air path resistance coefficient is obtained by inversely calculating the wind speed and wind pressure data collected in the air path using the ventilation resistance formula h = RQ2, h is the wind pressure collected by the wind pressure sensor, Q is the air volume calculated by the product of the wind speed and the air path cross-sectional area, and R is the air path resistance coefficient.

[0065] Specifically, the fusion processing of the multi-source temperature and humidity sensor data and the wind network topology is:

[0066] Firstly, the multi-source temperature and humidity sensor data is processed in space-time alignment. Firstly, a unified time step is determined, and the time step length is divided according to the seasonal periodic fluctuation law of the mine ground climate, so as to completely capture the influence of different seasons (such as high temperature and high humidity in summer and low temperature and low humidity in winter) on the underground thermal and humid environment as the core principle, combined with the periodic variation period of temperature and humidity in the historical thermal and humid data of the mine in the past five years, to ensure that the time step can cover the climate cycle characteristics and meet the data timeliness requirements of real-time regulation. Subsequently, the space node division standard in the mine is determined, taking the key operation and equipment areas such as mining faces, central pump rooms, air intake shafts, and air return lanes as the core, combined with the airflow conduction path in the wind network topology, to determine the range of space nodes to be monitored, each space node corresponding to a specific physical area position in the mine. For multi-source temperature and humidity sensor data, the data collected by different sensors at different times is matched to the corresponding space node according to the determined unified time step: in the same time step, if there are multiple sensors collecting the same data (such as temperature data collected by different position sensors in the same area), the data is filtered and fused based on the physical distance between the sensor and the center of the space node, and the historical data accuracy of the sensor, to finally obtain the temperature, humidity, wind speed, and heat source intensity data of each space node at each time step, i.e. the space-time alignment data.

[0067] Second, calculate the space-time weight. The time weight is determined according to the time difference between the data collection time and the current time and the seasonal cycle change rule. Specifically, an exponential decay model is used in combination with a seasonal cosine correction factor to determine the weight. The greater the time difference, the more significant the weight decay. At the same time, according to the differences in heat and moisture conduction characteristics in different seasons such as summer and winter, the seasonal correction coefficient is obtained by historical data regression to ensure that the weight can reflect the influence of the climate cycle on the timeliness of the data. The spatial weight is determined according to the straight-line distance between the sensor and the space node and the wind network topology wind path resistance coefficient. Based on the inverse distance weighting method and the introduction of the wind path resistance correction term, the closer the sensor and the space node, the greater the weight. At the same time, reference is made to the wind network topology wind path resistance coefficient in this area. The greater the resistance, the stronger the barrier to heat and moisture transfer, and the corresponding space weight needs to be adjusted in inverse proportion to the resistance coefficient proportion.

[0068] Third, weight the space-time aligned data. Multiply the temperature, humidity, wind speed, and heat source intensity in the space-time aligned data by the corresponding time weight w_t and space weight w_s to obtain weighted temperature data, weighted humidity data, weighted wind speed data, and weighted heat source intensity data.

[0069] Fourth, construct a heat and moisture disturbance phase space grid. The weighted temperature data, weighted humidity data, weighted wind speed data, and weighted heat source intensity data are used as four-element elements of the disturbance dimension to form a three-dimensional tensor structure of the heat and moisture disturbance phase space grid. The tensor dimensions are space node x time step x disturbance dimension.

[0070] In the three-dimensional tensor structure of the heat and moisture disturbance phase space grid, the space node dimension covers all key area positions in the mine that need to be monitored, such as mining faces, central pump rooms, air intake shafts, and air return shafts. The specific number of nodes is determined according to the actual size of the mine and the monitoring requirements. The time step length is divided according to the seasonal periodic fluctuation rule of the mine ground climate to ensure that the influence of climate cycle changes on the mine heat and moisture environment can be completely captured. The four-element elements of the disturbance dimension correspond to temperature, humidity, wind speed, and heat source intensity, respectively. The value range of each element is determined based on historical measured data and equipment technical parameters to ensure the engineering practicability of the data.

[0071] The step is to construct a thermal and humidity disturbance phase space grid by collecting multi-source temperature and humidity sensing data and wind network topology. The core advantage is that it can simultaneously retain the three-dimensional spatio-temporal coupling effects of the surrounding rock thermal inertia, equipment instantaneous thermal shock and climate period conduction. Traditional grid models can only depict spatial distribution or time variation, and cannot simultaneously carry the connectivity of the wind network topology. The step of fusing multi-source temperature and humidity sensing data and wind network topology balances the spatio-temporal distribution difference of equipment instantaneous thermal shock, and solves the technical problem that traditional models cannot multi-dimensionally couple and depict the mine thermal and humidity environment. At the same time, the thermal and humidity disturbance phase space grid supports the calculation of local heat conduction differential operators and the projection of global climate modes, and can provide complete and accurate input data basis for the subsequent step S20 of deducing the thermal and humidity compliance evolution path, ensuring the compliance and robustness of the subsequent evolution path, and providing accurate initial data support for the dynamic regulation and control of the mine thermal and humidity.

[0072] Step S20: Based on the thermal and humidity disturbance phase space grid, a thermal and humidity conduction pseudo-array generator is used to deduce a compliance evolution path, and a thermal and humidity compliance pseudo-array is output.

[0073] The thermal and humidity conduction pseudo-array generator refers to an algorithm module for extracting thermal and humidity state evolution rules from the thermal and humidity disturbance phase space grid, and generating a path set that meets the compliance conditions based on the rules. Its core function is to convert the evolution process of thermal and humidity state into the independent set construction process of pseudo-array by introducing the pseudo-array theory framework, so as to realize the systematic enumeration of all possible compliance paths.

[0074] Specifically, the thermal and humidity conduction pseudo-array generator includes the following steps:

[0075] Based on the thermal-hygroscopic perturbation phase space grid, the thermal-hygroscopic state parameters of each space node at different time steps need to be extracted first. The thermal-hygroscopic state parameters include the temperature, humidity, wind speed, and heat source intensity four-tuple, which are directly obtained from the perturbation dimension of the thermal-hygroscopic perturbation phase space grid. Through the spatio-temporal correlation analysis of these parameters, the transfer rule of thermal-hygroscopic state is established, which is used to describe how the thermal-hygroscopic state at a certain time step evolves into the thermal-hygroscopic state at the next time step. Specifically, the transfer rule of thermal-hygroscopic state is established by the following way: for any space node, its temperature value at time step t+1 is calculated from the temperature value, humidity value, wind speed value, and heat source intensity value at time step t through the heat conduction equation, which is T(t+1)=T(t)+k1×(Theat source-T(t))×Δt+k2×(Tneighbor-T(t))×Δt×vwind, where k1 is the heat source conduction coefficient, which is obtained by fitting the experimental data of device heat dissipation characteristics; k2 is the space conduction coefficient, which is obtained by inverse calculation based on the wind resistance coefficient in the wind network topology; Δt is the time step interval; Tneighbor is the temperature value of the adjacent space node; vwind is the wind speed value; T(t+1) is the temperature value at time t+1, and Theat source is the heat source intensity value. The transfer rules of humidity value, wind speed value, and heat source intensity value are established based on the humidity diffusion equation, fluid mechanics conservation equation, and device power change curve, respectively.

[0076] Using the above transfer rules, the initial thermal-hygroscopic state in the thermal-hygroscopic perturbation phase space grid is taken as the starting point to generate all possible future thermal-hygroscopic state evolution paths. Each evolution path is composed of a series of consecutive thermal-hygroscopic perturbation phase space grid slices, and each slice corresponds to the thermal-hygroscopic state at a time step. The generation process adopts the breadth-first search strategy to expand each thermal-hygroscopic state at a time step until the maximum prediction length is reached. The maximum prediction length is determined according to the decision cycle of mine thermal-hygroscopic regulation, which is usually 24 to 72 hours.

[0077] Each generated evolution path is checked for compliance to obtain the compliant evolution path. The core of the compliance check is to judge whether the temperature of all space nodes in the path at all time steps meets the red line requirements of 30℃ and 34℃. Specifically, for key areas such as mining faces and central pump rooms, the temperature should not exceed 30℃; for non-operation areas such as air intake shafts and air return tunnels, the temperature should not exceed 34℃. At the same time, the wet-bulb black-bulb temperature index needs to be calculated based on humidity and wind speed. The calculation of the wet-bulb black-bulb temperature index uses the formula in the national standard, which is wet-bulb black-bulb temperature index=0.7×wet-bulb temperature+0.2×black-bulb temperature+0.1×dry-bulb temperature, where the wet-bulb temperature is calculated from the temperature and humidity through the thermodynamic formula, and the black-bulb temperature is corrected from the temperature and wind speed. When the wet-bulb black-bulb temperature index exceeds the corresponding safety threshold, the path is determined as non-compliant.

[0078] Based on the compliance evolution paths, a thermal-hygro compliance matroid is constructed by a matroid construction algorithm. Specifically, the matroid construction algorithm is:

[0079] First, define the basis set of the thermal-hygro compliance matroid. The basis set is the set of all compliance evolution paths that have passed the compliance check, that is, each element in the basis set is a compliance evolution path.

[0080] Second, define the independent set family. The independent set family is composed of subsets of the basis set that satisfy the matroid axioms. Among them, the heredity axiom requires that if a set of compliance evolution paths is an independent set, all its subsets are also independent sets; the commutativity axiom requires that if two independent sets A1 and B1, and the number of elements in A1 is less than B1, there is a compliance evolution path that belongs to B1 but not to A1, and the new set obtained by adding it to A1 is still an independent set.

[0081] Third, determine the matroid base. The matroid base is the independent set with the largest number of elements in the independent set family, that is, the matroid base is a set composed of the longest compliance evolution paths, and the longest compliance evolution path refers to the evolution path that remains compliant within the preset maximum prediction duration.

[0082] Fourth, verify the matroid properties. By traversing all elements in the independent set family, verify whether they satisfy the heredity and commutativity axioms, and ensure that the constructed thermal-hygro compliance matroid meets the mathematical definition of matroid.

[0083] The structure of the thermal-hygro compliance matroid is composed of the basis set, the independent set family, and the matroid base. Among them, the size of the basis set depends on the number of compliance evolution paths, the more the number, the more the evolution possibilities that remain compliant under the current thermal-hygro disturbance conditions; the independent set family realizes the structured organization of the compliance evolution paths through the heredity and commutativity axioms, so that the operation on the path set can follow the mathematical rules of the matroid theory; the matroid base as a set of the longest compliance evolution paths provides a benchmark reference for the subsequent optimization of the control strategy.

[0084] The heat and humidity disturbance phase space grid is converted into a heat and humidity compliance quasi-array by the heat and humidity conduction quasi-array generator, which has the core function of systematically enumerating all possible compliance evolution paths to provide comprehensive decision-making basis for mine heat and humidity dynamic regulation and control. Compared with traditional heat and humidity prediction models, this step has three significant advantages: first, the quasi-array structure realizes the structured description of the compliance path set, overcoming the limitation of traditional models that can only output a single path, allowing the selection of control strategies to be based on multiple path comparisons, improving the robustness of decision-making; second, the heredity of the quasi-array ensures that any compliance path is a compliance sub-path, meaning that any segment of the path can be used as a reference during regulation and control, enhancing the flexibility of path application; third, the commutativity of the quasi-array ensures that local strategies in different compliance paths can be replaced without affecting the overall compliance, providing a mathematical basis for the optimization of control strategies in subsequent steps, allowing local control measures to be effectively adapted in a global context. The heat and humidity compliance quasi-array generated by this step can fully retain all compliance evolution possibilities under strong disturbance conditions, providing key support for solving the regulation and control problems caused by the strong nonlinearity and multi-variable coupling of mine heat and humidity environments, allowing the subsequent generation of control strategies to be optimized based on a comprehensive coverage of the compliance space, avoiding the misregulation caused by incomplete path enumeration.

[0085] For example, assume that in the heat and humidity disturbance phase space grid of a certain mine, the temperature of the mining face at the initial time step is 28°C, the humidity is 80%, the wind speed is 1.5 m / s, and the heat source intensity is 50 kW. Through the state transition rules of the heat and humidity conduction quasi-array generator, the heat and humidity evolution paths for the next 24 hours are generated. In the compliance verification, all paths with a mining face temperature exceeding 30°C are removed, and 10 compliance evolution paths are retained. Through the quasi-array construction algorithm, it is determined that these 10 paths form the basis set of the heat and humidity compliance quasi-array, and the 3 paths with a length of 24 hours form the quasi-array base. Based on this heat and humidity compliance quasi-array, the subsequent steps can extract the optimal evolution path as the regulation and control target, ensuring that the mine heat and humidity environment remains within the compliance interval.

[0086] Step S30: Generate a regulation and control strategy partial order lattice through a multi-chamber conflict resolution partial order lattice optimizer under the constraint of the heat and humidity compliance quasi-array.

[0087] The multi-chamber conflict resolution partial order lattice optimizer refers to an algorithm module that, with the compliance evolution paths in the heat and humidity compliance quasi-array as the constraint boundary, identifies conflicts between regulation and control strategies based on the differences in heat and humidity regulation and control requirements of different chambers such as the central pump house, mining face, and air intake shaft, optimizes the strategy set through conflict resolution algorithms, and finally constructs a regulation and control strategy lattice with algebraic structure based on partial order relationships. Its core function is to solve the conflict problem of multi-chamber regulation and control targets and provide a structured organization method for regulation and control strategies that is evolvable and rollbackable.

[0088] Specifically, the partial order lattice optimizer for multi-chamber conflict resolution comprises:

[0089] First, determine the thermal and humidity regulation targets of the multi-chamber based on the thermal and humidity compliance hypergraph. Each independent set in the thermal and humidity compliance hypergraph corresponds to a compliance evolution path, and each compliance evolution path contains the temperature, humidity, and wind speed target values of each chamber at different time steps. These target values are converted into the wet-bulb black-bulb temperature index target thresholds of each chamber. The calculation of the wet-bulb black-bulb temperature index uses the industry standard formula: wet-bulb black-bulb temperature index = 0.7 x wet-bulb temperature + 0.2 x black-bulb temperature + 0.1 x dry-bulb temperature, where the wet-bulb temperature is calculated from the temperature and humidity in the path using the thermodynamic equilibrium equation, the black-bulb temperature is calculated from the temperature and wind speed in the path using the convective heat transfer coefficient, and the convective heat transfer coefficient is calculated based on the cross-sectional area of the air path in the wind network topology and the wind speed. Through this process, a set of wet-bulb black-bulb temperature index targets for the multi-chamber is obtained, which provides a reference basis for the generation of subsequent regulation strategies.

[0090] Second, generate a candidate set of regulation strategies. The regulation strategy is composed of a three-tuple of damper opening, refrigeration power, and fan frequency as the basic unit, and each unit needs to meet the constraints of the thermal and humidity compliance hypergraph, i.e., the thermal and humidity states of each chamber after the implementation of the strategy need to fall within the compliance evolution path. Specifically, the value range and calculation method of each parameter are as follows:

[0091] (1) Damper opening: the value range is 0% (fully closed) to 100% (fully open), and its calculation is based on the air path resistance coefficient in the wind network topology and the wind speed target value in the compliance evolution path, using the ventilation volume balance equation Q = K x a x S x where Q is the target air volume, obtained by multiplying the wind speed target value and the air path cross-sectional area; K is the flow coefficient, obtained by fitting the historical ventilation data of the wind network; a is the damper opening; S is the air path cross-sectional area; AP is the pressure difference between the two ends of the air path, calculated from the fan pressure data in the wind network topology; and R is the air path resistance coefficient;

[0092] (2) Refrigeration power: the value range is the minimum rated power to the maximum rated power of the equipment, and its calculation is based on the difference between the temperature target value and the current temperature in the compliance evolution path, the heat source intensity, using the heat balance equation P = c x m x AT / At2 + Pheat source, where c is the specific heat capacity of air, taking the industry standard value; m is the air mass of the chamber, calculated from the chamber volume and air density; AT is the temperature difference; At2 is the regulation time step; and Pheat source is the heat source intensity in the path;

[0093] (3) Fan frequency: the value range is the minimum rated frequency to the maximum rated frequency of the equipment, which is calculated based on the wind speed target value in the compliance evolution path and the fan characteristic curve fitted by the performance parameters provided by the fan manufacturer and the on-site measured wind speed-frequency data.

[0094] Based on the above parameter calculation method, the value range of each parameter is traversed to generate a candidate set of control strategies that satisfy the thermal-hygroscopic compliance quasi-array constraints. This set contains all possible three-tuple strategies that can make the multi-chamber thermal-hygroscopic state fall within the compliance evolution path.

[0095] Subsequently, the multi-chamber conflict identification is performed on the candidate set of control strategies. A conflict is defined as follows: if a control strategy is implemented, it can make the wet-bulb black-bulb temperature index of some chambers reach the target threshold, but it can also cause the wet-bulb black-bulb temperature index of at least one other chamber to exceed the target threshold. Therefore, the strategy conflicts with the multi-chamber control target. Specifically, the conflict identification method is as follows: each strategy in the candidate set of control strategies is substituted into the multi-chamber thermal-hygroscopic network model to calculate the wet-bulb black-bulb temperature index of each chamber after the strategy is implemented. The wet-bulb black-bulb temperature index of each chamber after the strategy is implemented is compared with the target set of wet-bulb black-bulb temperature indices of each chamber, and the number of chambers that exceed the target threshold is counted. If the number of excesses is greater than 0, the strategy is marked as a conflict strategy, otherwise it is marked as a non-conflict strategy. Through this process, a conflict strategy subset and a non-conflict strategy subset are obtained. The multi-chamber thermal-hygroscopic network model uses the control volume method to discretize the chambers and calculates the heat and moisture transfer through wind network air distribution, which is a common modeling method for mine heat and moisture control.

[0096] Next, the conflict strategy subset is optimized by a multi-chamber conflict resolution algorithm to obtain a resolved strategy subset. Specifically, the multi-chamber conflict resolution algorithm is as follows:

[0097] First, determine the chamber weight coefficient. According to the importance of the chamber operation and the thermal-hygroscopic sensitivity, the weight coefficient w_i of each chamber is calculated using the analytic hierarchy process, i is the chamber number, the sum of the weight coefficients of all chambers is 1, and the rationality of the weight coefficient is verified by consistency check.

[0098] Second, calculate the conflict degree C1 of the conflict strategy. The conflict degree is defined as the weighted sum of the deviations of the wet-bulb black-bulb temperature indices of each chamber from the target threshold caused by the strategy, and the calculation formula is C1 = Σ w_i × |T_i - T_i0| / T_i0, T_i is the wet-bulb black-bulb temperature index of chamber i after the strategy is implemented, T_i0 is the target threshold of chamber i, and the larger the conflict degree, the more serious the conflict of the strategy.

[0099] Third step, strategy parameter adjustment. Sort the conflict strategies by conflict degree from large to small, and adjust the strategies with high conflict degree first. The adjustment rules are as follows: if the wet-bulb black-bulb temperature index of a chamber exceeds the standard due to the strategy parameters, adjust the damper opening. If the chamber that exceeds the standard is on the air intake side, appropriately increase the opening to increase the air volume. If it is on the return air side, appropriately reduce the opening to reduce the influx of hot air. Adjust the refrigeration power. If the temperature of the chamber that exceeds the standard is too high, increase the refrigeration power. If the humidity is abnormal, adjust the dehumidification module in combination with the dehumidification module power. The dehumidification module power and the refrigeration power are linked, and the linkage coefficient is determined through temperature and humidity coupling experiments. Adjust the fan frequency. If the air speed of the chamber that exceeds the standard is insufficient, increase the frequency to increase the air volume, and vice versa.

[0100] Fourth step, verify the adjusted strategy. Substitute the adjusted strategy into the multi-chamber thermal-hygroscopic network model again to calculate the wet-bulb black-bulb temperature index of each chamber. If all chambers meet the target threshold, the strategy is included in the post-dissipation strategy subset. If there are still exceedances, repeat steps three to four until the strategy is compliant or the maximum number of adjustments is reached. The maximum number of adjustments is determined by historical adjustment efficiency data.

[0101] Combine the non-conflict strategy subset and the post-dissipation strategy subset to obtain a compliant control strategy set. Based on this set, construct the partial order relation between the control strategies. The partial order relation is defined as Pareto dominance: if strategy A and strategy B both belong to the compliant control strategy set, and for all chambers, the wet-bulb black-bulb temperature index T_Ai after implementing strategy A is not worse than that of strategy B, i.e. T_Ai < T_Bi (cooling) or T_Ai ≥ T_Bi (heating), and the specific direction is determined by the multi-chamber wet-bulb black-bulb temperature index target set, and at least one chamber has a better wet-bulb black-bulb temperature index after implementing strategy A than after implementing strategy B, then strategy A is said to be Pareto dominant over strategy B, denoted as A ≥ B. By comparing all strategies in the compliant control strategy set pairwise, the partial order relation set is obtained.

[0102] Finally, based on the compliant control strategy set and the partial order relation set, construct the control strategy partial order lattice. Specifically, the construction method of the control strategy partial order lattice structure is as follows:

[0103] First step, determine the elements of the lattice. Take each strategy in the compliant control strategy set as an element of the lattice, and the attributes of the element are the specific values of the damper opening, the refrigeration power, and the fan frequency.

[0104] Second, define the lattice operation. Intersection operation (conservative superposition): for any two strategies A, B, the intersection operation result is strategy C, and the value of each parameter of strategy C is the minimum value of the corresponding parameters of A and B, such as the damper opening degree min(α_A, α_B), the refrigeration power min(P_A, P_B), and the fan frequency min(f_A, f_B), which ensures that the intersection operation result is a more conservative strategy, ensuring control safety, where α_A and α_B are the damper opening degree parameters in strategies A and B, P_A and P_B are the refrigeration power parameters in strategies A and B, and f_A and f_B are the fan frequency parameters in strategies A and B; and union operation (aggressive synergy): for any two strategies A, B, the union operation result is strategy D, and the value of each parameter of strategy D is the maximum value of the corresponding parameters of A and B, which ensures that the union operation result is a more aggressive strategy and pursues a better thermal and humid comfort state.

[0105] Third, determine the vertices and paths of the lattice. The bottom of the lattice is the minimum result of the intersection operation of all strategies, that is, the most conservative strategy with the smallest parameter values; the top of the lattice is the maximum result of the union operation of all strategies, that is, the most aggressive strategy with the largest parameter values; the set of lattice vertices is the Pareto optimal frontier, that is, there is no other strategy that can Pareto dominate these vertex strategies; and the lattice path is a sequence of strategies from the bottom to the top of the lattice, gradually increasing the control intensity along the partial order relationship, each path corresponds to a control intensity gradient, and an exemplary structure is as follows: Figure 2 , where the arrow direction represents Pareto dominance, and the black small rectangles mark the Pareto optimal frontier nodes.

[0106] The structural characteristics of the control strategy partial order lattice are: the elements form an ordered hierarchy through the Pareto dominance relationship, the lattice operation supports the safe merging and aggressive optimization of strategies, and the lattice path provides a traceable and rollback control direction. The number of elements depends on the size of the compliant control strategy set, and the larger the size, the more control options exist under the thermal and humid compliance matroid constraints; the density of the partial order relationship reflects the difference between strategies, and the higher the density, the higher the optimization precision of the strategies.

[0107] The step generates a regulation strategy partial order set by a multi-chamber conflict resolution partial order lattice optimizer, which has the core role of solving the target conflict problem of multi-chamber thermal and humid regulation, and providing a structured strategy system for subsequent dynamic regulation. Compared with traditional multi-objective optimization methods, it has three core advantages: first, it breaks through the limitation of traditional methods to output a point set without structure, and realizes the ordered organization of strategies through the algebraic structure of the partial order lattice, so that the superiority and inferiority relationship, merging rule and evolution path of the strategies are clear and identifiable, avoiding the blindness of regulation decision; second, the multi-chamber conflict resolution algorithm combines chamber weight and thermal and humid compliance matroid constraints to ensure that the resolved strategies meet the regulation needs of the core chamber and do not deviate from the compliance evolution path, solving the problem of “trade-off” in traditional conflict resolution; third, lattice operation and lattice path design naturally adapt to the engineering logic of “safety first, step-by-step trial and error” in underground regulation. The conservative superposition of intersection operation can avoid the risk of over-limit caused by aggressive regulation, and the gradient evolution of lattice path supports gradual regulation from conservative to aggressive, while the traditional PID or MPC control lacks such a structured safety rollback mechanism. The regulation strategy partial order set generated by this step can provide a clear strategy selection space and evolution direction for the dynamic closed-loop regulation of step S40, ensuring that the temperature and humidity of the key area are stable in the compliance comfort interval while considering the safety and economy of regulation.

[0108] For example, assume that a certain mine includes a mining face, a central pump house, and an air intake shaft as three key chambers, and the wet bulb black globe temperature index target thresholds determined by the thermal and humid compliance matroid are 28°C, 30°C, and 32°C respectively. The generated regulation strategy candidate set includes 5 strategies, among which strategy 1 (air door opening degree 60%, refrigeration power 80kW, fan frequency 45Hz) after implementation, the mining face temperature is 27°C (compliant), the central pump house temperature is 31°C (over standard), and the air intake shaft temperature is 31°C (compliant), which is marked as a conflict strategy; through the multi-chamber conflict resolution algorithm, the central pump house weight is 0.4, the mining face is 0.5, and the air intake shaft is 0.1, and the conflict degree of strategy 1 is 0.4x|31-30| / 30+0.5x|27-28| / 28≈0.023; adjusting the refrigeration power of strategy 1 to 85kW, the resolved strategy 1' is obtained, and after implementation, the temperatures of the three chambers are 26.5°C, 29.8°C, and 31.5°C (all compliant) respectively. The non-conflict strategies 2-5 and the resolved strategy 1' are combined into a compliance regulation strategy set, and through Pareto dominance judgment, strategy 3 (air door opening degree 70%, refrigeration power 90kW, fan frequency 50Hz) is superior to strategy 1' (all three chambers have lower temperatures), strategy 5 (air door opening degree 50%, refrigeration power 70kW, fan frequency 40Hz) is the lattice bottom (most conservative), strategy 3 is the lattice top (most aggressive), strategy 3 and strategy 4 (air door opening degree 65%, refrigeration power 85kW, fan frequency 48Hz) constitute the Pareto optimal frontier, and finally a regulation strategy partial order set is formed.

[0109] Step S40: According to the regulatory policy partial order lattice, the dynamic closed-loop control instructions are executed by the lattice path guided multi-actuator.

[0110] The lattice path guided multi-actuator refers to a control module that coordinates the actions of three types of execution units, i.e., damper actuators, refrigeration unit actuators, and fan variable frequency actuators, in the direction of the lattice path in the regulatory policy partial order lattice, and realizes closed-loop feedback adjustment by combining real-time monitoring of key area thermal and humid data. The core function is to execute the control strategy according to the "safety progressive" principle, and at the same time has an over-limit fallback mechanism to ensure that the 34℃ absolute red line is not broken and gradually approaches the 26℃ / 85% comfort target.

[0111] Specifically, the lattice path guided multi-actuator step includes:

[0112] First, the target lattice path is selected from the regulatory policy partial order lattice. The target lattice path needs to meet the dual requirements of safety margin and comfort approximation efficiency, which is determined by the lattice path priority evaluation algorithm. Specifically, the lattice path priority evaluation algorithm is:

[0113] First, calculate the safety margin of each lattice path. The safety margin is defined as the minimum difference between the key area wet-bulb globe temperature index and the 34℃ red line for all strategies on the path, and the calculation formula is M_s = min(34-T_wbgt(s)), s is the control strategy on the path, T_wbgt(s) is the wet-bulb globe temperature index after the implementation of strategy s, and M_s is the safety margin. The larger the safety margin, the stronger the anti-disturbance ability of the path. This parameter is calculated by extracting the historical implementation data of each strategy in the regulatory policy partial order lattice. When the historical data is insufficient, the thermal and humid conduction model is used to simulate and obtain.

[0114] Second, calculate the comfort approximation efficiency of each lattice path. The comfort approximation efficiency is defined as the rate at which the wet-bulb globe temperature index approaches 26℃ from the initial value during the process of the path from the bottom to the top of the lattice. The calculation formula is E_c = (T_init-26) / N, T_init is the wet-bulb globe temperature index corresponding to the bottom strategy, N is the number of strategy steps required to reach 26℃ for the first time from the bottom of the path, and E_c is the comfort approximation efficiency. The higher the efficiency, the faster the comfort target can be achieved. N is determined by traversing the strategy sequence on the path.

[0115] Third, determine the target lattice path. The priority score S1 of each path is calculated using the weighted sum method, and the score formula is S1 = w1 x M_s + w2 x E_c, w1 is the safety margin weight, and w2 is the comfort approximation efficiency weight, wherein w1 and w2 are determined by the analytic hierarchy process, and w1 > w2 is set to prioritize safety. The path with the highest score is selected as the target lattice path.

[0116] Based on the target grid path, an initial control instruction is generated. The initial control instruction corresponds to the grid bottom strategy of the target grid path, that is, the most conservative strategy, and the damper opening degree, refrigeration power, and fan frequency parameters in the grid bottom strategy need to be converted into control signals of three types of actuators:

[0117] (1) Damper actuator control signal: the damper actuator is an electric proportional actuator, and the control signal is a PWM duty cycle. The corresponding relationship between the duty cycle and the damper opening degree is determined through actuator calibration experiments, and the expression is D = a x a + b, where D is the duty cycle, a is the damper opening degree, and a and b are calibration coefficients, which are obtained by fitting the duty cycle data at different opening degrees.

[0118] (2) Refrigeration unit actuator control signal: the refrigeration power is realized by adjusting the compressor speed, and the control signal is the speed command. The relationship between the speed and the refrigeration power is determined based on the unit characteristic curve, which is obtained by correcting the power-speed data provided by the manufacturer and the field measured data, and the expression is n = e x P cool + f, where n is the compressor speed, P cool is the refrigeration power, and e and f are characteristic coefficients.

[0119] (3) Fan frequency actuator control signal: the fan frequency is realized by adjusting the frequency converter, and the control signal is the frequency command, which directly corresponds to the fan frequency parameter in the strategy. The frequency command range is consistent with the rated frequency of the frequency converter, and the value is the calculated value of the fan frequency in the strategy.

[0120] The initial control instruction is sent to the corresponding actuator, and after the actuator acts according to the instruction, the temperature and humidity monitoring data of the key areas are collected. The key areas include the mining face, the central pump house, the air inlet well, etc. The temperature and humidity monitoring data are collected by the distributed temperature and humidity sensors deployed in each area, and the collection frequency is consistent with the control time step. The control time step is determined by the strategy step number of the target grid path and the control period, and the control period is determined based on the dynamic response speed experiment of the thermal and humid environment. At the same time, the real-time wet-bulb black-bulb temperature index T_wbgt is calculated based on the collected temperature and humidity monitoring data, and the calculation method and steps S30 are consistent, that is, T_wbgt = 0.7 x T_wet + 0.2 x T_black + 0.1 x T_dry, where T_wet is the wet-bulb temperature, T_black is the black-bulb temperature, and T_dry is the dry-bulb temperature, which are calculated or corrected from the real-time temperature and humidity data.

[0121] Based on the real-time wet-bulb black-bulb temperature index, the control instruction is updated by a closed-loop feedback adjustment rule. Specifically, the closed-loop feedback adjustment rule is:

[0122] Rule one, comfort interval maintenance. If the real-time wet-bulb black globe temperature index is within 26℃±1℃ and the relative humidity is within 85%±3% (comfort interval), the current control strategy is maintained, and a maintenance instruction is generated, with the content being the holding parameter of the current actuator control signal;

[0123] Rule two, compliance interval optimization. If the real-time wet-bulb black globe temperature index is within 27℃-34℃ (compliance but not comfort interval), move to a more aggressive strategy along the target grid path, with the moving step determined by the temperature and humidity deviation, the step formula being Δs=k×(T_target-T_real), Δs being the number of strategy moving steps, T_target being the 26℃ comfort target, T_real being the real-time wet-bulb black globe temperature index, and k being the step coefficient determined through the bias-step fitting relationship of historical control data, to ensure that the temperature and humidity change smoothly after each adjustment without sudden changes;

[0124] Rule three, emergency rollback beyond limit. If the real-time wet-bulb black globe temperature index exceeds 34℃, immediately generate a rollback instruction to return to the last safe grid layer strategy on the target grid path, and record the reasons for exceeding the limit such as equipment sudden heat impact, sensor abnormality, etc. for subsequent optimization of the target grid path.

[0125] During the control instruction updating process, the coordinated timing control of multiple actuators is used to avoid action conflicts. For example, when adjusting the frequency of the fan, the frequency change signal is sent to the fan frequency conversion actuator 0.5 control time steps in advance, and after the fan speed stabilizes, the stabilization time is determined through the air flow response experiment of the wind network, and then the damper opening degree and refrigeration power are adjusted to avoid temperature and humidity fluctuations caused by sudden air flow changes. The coordinated timing is determined by the actuator action timing table, which is developed based on the disturbance minimization principle of multi-actuator linkage experiment to ensure that the superposition effect of each actuator action meets the control expectation.

[0126] In addition, to deal with equipment failure or sensor abnormality, the actuator has a built-in fault tolerance mechanism. When a certain actuator feedbacks a fault signal such as a jammed damper or a stopped refrigeration unit, the backup grid path of the faulty actuator parameter is immediately selected and excluded from the control strategy bias sequence grid, a new target grid path is determined according to the grid path priority evaluation algorithm, and a control instruction suitable for the backup path is generated; when the sensor data is abnormal (such as data fluctuation exceeding the threshold value, which is determined by the sensor accuracy level and the historical data standard deviation), a multi-sensor data fusion algorithm is used to obtain reliable temperature and humidity data, to ensure that the closed-loop feedback does not make decisions based on abnormal data, and the fusion weight of the multi-sensor data fusion algorithm is determined based on the reliability coefficient of the sensor, which is calculated through the sensor calibration record.

[0127] The step is realized by a multi-actuator guided by a lattice path, and its core function is to convert the structured strategy in the strategy partial order lattice into physical execution actions, while ensuring the safety and effectiveness of the control through real-time feedback. Compared with traditional PID control or model predictive control (MPC), the step has three unique advantages: first, a structured safety rollback mechanism, traditional control can only avoid over-limiting through parameter limiting, while the step based on the lattice structure of the strategy partial order lattice can directly rollback to a proven safe strategy, the rollback logic is more explicit and has stronger anti-disturbance ability, completely solving the problem of parameter oscillation after over-limiting in traditional control; second, a gradual control logic, through step control of the target lattice path, a smooth transition from conservative to aggressive is realized, avoiding parameter mutation in traditional control for rapid convergence to the target, which is consistent with the characteristics of large inertia and slow response of the underground hot and humid environment, reducing the loss of frequent start and stop of equipment; third, multi-actuator coordination and timing optimization, traditional control usually uses parallel action, which is easy to cause control deviation due to interference between actuators, while the step makes the actions of each actuator form a complementary effect through timing coordination, improving the control accuracy, for example, first adjusting the fan flow to establish the airflow basis, and then adjusting the refrigeration power to control the temperature, avoiding waste of refrigeration power when the air volume is insufficient. Through the step, the temperature and humidity in the key area can be stabilized under strong disturbances such as transient heat shock of equipment and seasonal climate change, without breaking the 34℃ absolute red line, and gradually approaching the 26℃ / 85% comfort target, meeting the dual needs of the health of underground workers and the stable operation of equipment.

[0128] For example, the initial real-time wet-bulb black-bulb temperature index of a certain mine working face is 32℃ (compliant but not comfortable), according to the control strategy partial order lattice, a target lattice path with a safety margin M_s=2℃ and a comfortable approximation efficiency E_c=0.8℃ / step is selected by the lattice path priority evaluation algorithm. The initial control instruction corresponds to the lattice bottom strategy: air door opening 50% (PWM duty cycle 50%), refrigeration power 70kW (compressor speed 1500r / min), fan frequency 40Hz. The collected real-time wet-bulb black-bulb temperature index after execution is 31℃, according to the closed-loop feedback adjustment rule, the step size Δs=0.5×(26-31)=2 steps (k=0.5, fitted from historical data), move 2 steps along the target lattice path, the new strategy is air door opening 60% (duty cycle 60%), refrigeration power 85kW (speed 1800r / min), fan frequency 45Hz. After execution, the real-time wet-bulb black-bulb temperature index drops to 28℃, and after 3 steps of adjustment according to the rule, the comfortable target of 26℃ is reached, during which the real-time wet-bulb black-bulb temperature index temporarily rises to 34.2℃ due to the sudden increase in the intensity of local heat source, immediately rollback to the last safe lattice layer strategy (wet-bulb black-bulb temperature index 33.5℃), and continue to approach the comfortable target after the heat source intensity is restored, finally realize the stable control of temperature and humidity in the key area.

[0129] Example 2:

[0130] This embodiment, based on Embodiment 1, provides a system for constructing and dynamically controlling a coupled airflow, heat, and humidity model, such as... Figure 3 As shown, it includes:

[0131] Data fusion and mesh generation module: used to collect multi-source temperature and humidity sensor data and wind network topology, fuse the multi-source temperature and humidity sensor data and wind network topology, generate and output thermal and humidity disturbance phase space mesh;

[0132] Compliance matte generation module: Based on the thermal and moisture perturbation phase space grid, the compliance evolution path is deduced through the thermal and moisture conduction matte generator, the compliance of the evolution path is verified, and the thermal and moisture compliance matte is output.

[0133] The partial order lattice generation module for control strategies is used to determine the control objectives of multiple chambers under the constraints of thermal and humidity compliance matroid, generate and optimize the candidate set of control strategies, construct the partial order relationship between control strategies, and generate the partial order lattice of control strategies through the partial order lattice optimizer for multi-chamber conflict resolution.

[0134] Dynamic closed-loop control module: Based on the partial order grid of the control strategy, it uses multiple actuators guided by the grid path to select the target grid path, generate and update control instructions, coordinate the actions of multiple actuators, execute dynamic closed-loop control instructions, and stabilize the temperature and humidity of key areas within the compliant and comfortable range.

Claims

1. A method for constructing and dynamically controlling a coupled airflow-thermal-humidity model, characterized in that, The method includes: Step S10: Collect multi-source temperature and humidity sensor data and wind network topology, fuse the multi-source temperature and humidity sensor data and wind network topology, and generate and output the thermal and humidity disturbance phase space grid. Step S20: Based on the thermal and moisture perturbation phase space grid, deduce the compliance evolution path through the thermal and moisture conduction matte generator and output the thermal and moisture compliance matte; Step S30: Under the thermal and humidity compliance matrix constraint, generate a partial order lattice for the control strategy through a partial order lattice optimizer for multi-chamber conflict resolution; Step S40: Based on the partial order grid of the control strategy, execute dynamic closed-loop control commands through multiple actuators guided by the grid path; the multiple actuators include damper actuators, chiller unit actuators and fan frequency converter actuators.

2. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 1, characterized in that, The process of fusing multi-source temperature and humidity sensor data with wind network topology includes: Spatiotemporal alignment processing is performed on multi-source temperature and humidity sensor data to obtain spatiotemporal aligned data; The spatiotemporal weights are calculated, which include time weights and spatial weights. The time weights are determined based on the time difference between the data acquisition time and the current time and the seasonal cycle variation. The spatial weights are determined based on the straight-line distance between the sensor and the spatial node and the wind resistance coefficient in the wind network topology. The temperature, humidity, wind speed, and heat source intensity in the spatiotemporally aligned data are multiplied by their corresponding time weights and spatial weights to obtain weighted temperature data, weighted humidity data, weighted wind speed data, and weighted heat source intensity data. Weighted temperature data, weighted humidity data, weighted wind speed data, and weighted heat source intensity data are used as quadruples of perturbation dimensions to form a thermal and humid perturbation phase space grid.

3. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 2, characterized in that, The thermal and humid disturbance phase space grid is a three-dimensional tensor structure. The tensor dimensions are spatial node dimension, time step dimension, and disturbance dimension. The spatial node dimension includes the location of all key areas that need to be monitored within the mine. The time step length is divided based on the seasonal periodic fluctuation pattern of the mine's surface climate. The disturbance dimension elements are temperature, humidity, wind speed, and heat source intensity.

4. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 3, characterized in that, The thermal and moisture conduction pseudoarray generator includes: Extract the thermal and humidity state parameters of each spatial node at different time steps from the thermal and humidity disturbance phase space grid; Establish rules for the transfer of thermal and moisture states based on thermal and moisture state parameters; Starting from the initial thermal and humid state at the initial time step, a breadth-first search strategy is used to generate the future thermal and humid state evolution path; Each generated thermal and humid state evolution path is verified for compliance to obtain a compliant evolution path; Based on the compliance evolution path, a thermal and humidity compliance matte is constructed using a matte construction algorithm.

5. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 4, characterized in that, The matroid construction algorithm includes: Define the base set as the set of all compliance evolution paths that have undergone compliance verification; Define the family of independent sets as a subset of a basic set that satisfies the matroid axioms, which include the heritability axiom and the commutativity axiom; The matroid basis is determined to be the independent set with the largest number of elements in the family of independent sets; Iterate through all elements in the family of independent sets and verify whether they satisfy the axioms of inheritance and commutativity. The structure of the thermo-humidity compliant matte consists of a base set, a family of independent sets, and a matte base.

6. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 5, characterized in that, The partial order lattice optimizer for multi-chamber conflict resolution includes: Determine the thermal and humidity control targets for multiple chambers based on thermal and humidity compliance matrix; Generate a candidate set of control strategies, including damper opening, cooling power and fan frequency, and the candidate set of control strategies satisfies thermal and humidity compliance matrix constraints. Multi-chamber conflict identification is performed on the candidate set of control strategies. The conflict identification is as follows: each control strategy in the candidate set is substituted into the multi-chamber thermal-humidity network model to calculate the wet-bulb black-ball temperature index after implementing the strategy in each chamber. The wet-bulb black-ball temperature index after implementing the strategy in each chamber is compared with the target set of wet-bulb black-ball temperature indices for each chamber. The number of chambers exceeding the target threshold is counted. If the number of exceeding the threshold is greater than 0, the strategy is marked as a conflict strategy. Otherwise, it is marked as a non-conflict strategy, thus obtaining a subset of conflict strategies and a subset of non-conflict strategies. The conflict strategy subset is optimized by a multi-chamber conflict resolution algorithm to obtain the resolved strategy subset; The non-conflict strategy subset is merged with the resolved strategy subset to obtain the compliance control strategy set; Based on the set of compliance control strategies and the set of partial order relations, a partial order lattice of control strategies is constructed; the partial order relation is defined as the Pareto priority of strategy A over strategy B, and the set of partial order relations is obtained by comparing all strategies in the set of compliance control strategies pairwise.

7. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 6, characterized in that, The constructed control strategy partial order lattice includes: Each control strategy in the set of compliant control strategies is used as an element of the grid, and the element attributes include damper opening, cooling power, and fan frequency; Define lattice operations, which include intersection and union operations. The intersection operation is to take the minimum value of the element attributes in two control strategies, and the union operation is to take the maximum value of the element attributes in two control strategies. The cell base is determined as the minimum result of the intersection operation among all strategies, and the cell top is determined as the maximum result of the union operation among all strategies.

8. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 7, characterized in that, The grid-path guided multi-executor includes: The target lattice path is selected from the partial order lattice of the control strategy by the lattice path priority evaluation algorithm. The target lattice path must simultaneously satisfy the safety margin and the comfort approximation efficiency constraints. Initial control commands are generated based on the target grid path, and the parameters in the grid bottom strategy are converted into control signals for damper actuators, chiller unit actuators, and fan frequency converter actuators. After sending the initial control command, collect temperature and humidity monitoring data of key areas and calculate the real-time wet-bulb black-bulb temperature index; Based on the real-time wet-bulb black bulb temperature index, the control instructions are updated through closed-loop feedback adjustment rules, including maintaining the comfort range, optimizing the compliance range, and emergency retreat rules for exceeding limits. Avoid action conflicts through collaborative timing control; Built-in fault tolerance mechanism to reselect alternative grid paths in case of equipment failure or sensor malfunction.

9. The method for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model according to claim 8, characterized in that, The closed-loop feedback adjustment rules include: Rule 1: If the real-time wet-bulb temperature index and relative humidity are within the comfort range, maintain the current control strategy and generate a maintenance command. The command content is the maintenance parameter of the current actuator control signal. Rule 2: If the real-time wet-bulb black ball temperature index is in the compliant but not comfortable range, then move along the target grid path towards the aggressive strategy. Rule 3: If the real-time wet-bulb black sphere temperature index exceeds 34°C, a rollback command is immediately generated to roll back to the previous safe cell layer strategy on the target cell path, and the reason for exceeding the limit is recorded.

10. A system for constructing and dynamically controlling a wind-flow-thermal-humidity coupling model, used to implement the wind-flow-thermal-humidity coupling model construction and dynamic control method according to any one of claims 1-9, characterized in that, The system includes: Data fusion and mesh generation module: used to collect multi-source temperature and humidity sensor data and wind network topology, fuse the multi-source temperature and humidity sensor data and wind network topology, generate and output thermal and humidity disturbance phase space mesh; Compliance matte generation module: Based on the thermal and moisture perturbation phase space grid, the compliance evolution path is deduced through the thermal and moisture conduction matte generator, the compliance of the evolution path is verified, and the thermal and moisture compliance matte is output. The partial order lattice generation module for control strategies is used to determine the control objectives of multiple chambers under the constraints of thermal and humidity compliance matroid, generate and optimize the candidate set of control strategies, construct the partial order relationship between control strategies, and generate the partial order lattice of control strategies through the partial order lattice optimizer for multi-chamber conflict resolution. Dynamic closed-loop control module: Based on the partial order grid of the control strategy, it uses multiple actuators guided by the grid path to select the target grid path, generate and update control instructions, coordinate the actions of multiple actuators, execute dynamic closed-loop control instructions, and stabilize the temperature and humidity of key areas within the compliant and comfortable range.

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