Mine ventilation intelligent adjusting method and system

Abstract

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

Description

Technical Field

[0001] The present invention relates to the field of mine ventilation, and in particular to an intelligent adjustment method and system for mine ventilation. Background Art

[0002] Mine ventilation systems are core infrastructure for ensuring underground work safety. Their primary function is to continuously deliver fresh air to all working areas, dilute harmful gases like gas and dust, regulate ambient temperature and humidity, and provide safe working conditions for miners. As mines increase in depth and mining area, ventilation networks become increasingly complex, and traditional ventilation control methods are no longer sufficient to meet production safety requirements.

[0003] Traditional ventilation adjustment is mostly based on static models or manual experience. The wind resistance in the tunnel will change dynamically with factors such as mining progress, rock movement, support changes and dust accumulation. The static model cannot reflect the dynamic fluctuations of wind resistance in real time, resulting in a large deviation between the air volume calculation results and the actual working conditions on site. It is easy to have local air volume shortage or excess problems, which will lead to safety hazards such as gas accumulation and hypoxia.

[0004] At the same time, the control of fan speed and damper opening also relies heavily on manual operation, with low adjustment accuracy and delayed response. It is difficult for operators to accurately allocate air volume according to the real-time needs of each lane in the complex network, which often leads to energy waste or insufficient ventilation in key areas. Traditional methods lack a scientific closed-loop feedback mechanism, and are unable to monitor the control effect and correct deviations in a timely manner. It is difficult to respond quickly when the wind pressure in the ventilation network is unbalanced, further exacerbating the instability of the system operation.

[0005] In complex ventilation networks, the air volume coupling relationship of each tunnel branch is complex. Traditional static calculation methods cannot clarify the mutual influence between loops, resulting in a lack of reliable basis for air volume optimization configuration, which seriously restricts the safety and economy of the ventilation system. Therefore, a more scientific and convenient ventilation method is urgently needed to solve the problem that mine ventilation adjustment in existing technologies is not intelligent and safe enough. Summary of the Invention

[0006] In view of the defects in the prior art, the present invention provides a method and system for intelligent adjustment of mine ventilation, which solves the problem that mine ventilation adjustment in the prior art is not intelligent and safe enough.

[0007] In order to achieve the above-mentioned purpose, one aspect of the present invention provides an intelligent adjustment method for mine ventilation, comprising: constructing a dynamic topological model with tunnels as edges and fans and dampers as nodes based on the mine CAD drawings; collecting the actual wind pressure and actual wind volume of each tunnel branch in real time to invert and obtain the current wind resistance, and using the current wind resistance to update the wind resistance properties of the edges in the dynamic topological model; using the updated dynamic topological model to calculate the optimal wind volume of the tunnel branch; based on the optimal wind volume, using the particle swarm algorithm to solve and obtain the target control parameters of the fan and the damper; and according to the target control parameters, adjusting the fan and the damper through the fuzzy PID algorithm.

[0008] The present invention inverts the wind resistance based on the measured wind pressure and air volume and updates the topological model, ensuring that the model always fits the actual working conditions of the system, solving the problem of deviation between traditional static models and on-site conditions, dynamically calculating the optimal air volume and combining it with the particle swarm algorithm for optimization, which can quickly adapt to changes in tunnel conditions and realize on-demand distribution of air volume. The adaptive adjustment mechanism of the fuzzy PID algorithm can accurately respond to the nonlinear characteristics of fans and dampers, improve parameter control accuracy and system stability, and overall improve the intelligence and safety of mine ventilation.

[0009] Optionally, the calculating the optimal air volume of the lane branch using the updated dynamic topology model includes: Based on the tunnel connection relationship in the dynamic topology model, multiple independent ventilation circuits are divided by the breaking circle method, and a clockwise reference direction is set for the ventilation circuit; the ventilation circuit includes one or more tunnel branches, and the safe air volume threshold of the tunnel branch is obtained and the safe air volume threshold is set as the initial air volume of the tunnel branch; the wind pressure imbalance of each circuit is calculated based on the initial air volume and the clockwise reference direction; and the optimal air volume of the tunnel branch is calculated based on the wind pressure imbalance.

[0010] The present invention's circle-breaking method divides independent ventilation circuits and sets a clockwise reference direction, clearly sorting out complex network structures and avoiding cross-circuit interference. This lays the foundation for accurate calculations, using the safe air volume threshold as the initial value to ensure that the starting point of the calculation meets safety regulations and guarantees ventilation. The wind pressure imbalance quantifies the difference between circuit supply and demand, and the wind resistance fluctuation coefficient is combined to dynamically correct the air volume, adapting the result to real-time wind resistance changes. The iterative optimization mechanism continuously reduces the deviation, and the resulting optimal air volume accurately matches the needs of each lane, improving the accuracy of the optimal air volume calculation for the lane.

[0011] Optionally, the calculating the optimal air volume of the branch roadway according to the wind pressure imbalance comprises: calculating a wind resistance fluctuation coefficient of the ventilation circuit according to the current wind resistance and the initial wind resistance; calculating an air volume correction step of each branch roadway according to the wind pressure imbalance, the current wind resistance, the air volume of each branch roadway and the wind resistance fluctuation coefficient; updating the air volume of each branch roadway according to the air volume correction step and the clockwise reference direction to obtain an updated air volume; and performing iteration based on the updated air volume to obtain the optimal air volume of the branch roadway.

[0012] The application can quantify the change degree of the current wind resistance relative to the initial value by calculating the wind resistance fluctuation coefficient, provides a dynamic basis for air volume adjustment, avoids the deviation caused by the static wind resistance assumption, calculates the air volume correction step in combination with the wind pressure imbalance, the current wind resistance and other parameters, ensures that the adjustment range is scientific and reasonable, updates the air volume according to the clockwise reference direction to ensure the consistency of the air volume adjustment in the circuit, continuously reduces the deviation through iteration optimization, makes the air volume continuously approach the balanced state, and improves the scientificity and reliability of the optimal air volume.

[0013] Optionally, the obtaining the target control parameters of the fan and the air door based on the optimal air volume by using the particle swarm algorithm comprises: obtaining a current fan speed and a current air door opening degree, and selecting an initial population according to the current fan speed and the current air door opening degree; calculating a fan wind pressure and an air door wind resistance based on the initial population; updating the dynamic topology model by using the fan wind pressure and the air door wind resistance; establishing an air volume balance equation of the node and a resistance equation of the branch roadway by using the updated dynamic topology model; simultaneously solving the air volume balance equation and the resistance equation to obtain an actual air volume of the branch roadway; calculating a target function by taking the minimum difference between the actual air volume of the branch roadway and the optimal air volume and the minimum fan speed as the target; and obtaining the target control parameters of the fan and the air door by using the particle swarm algorithm based on the target function.

[0014] The application selects the initial population based on the current equipment state, determines the search range in combination with the adjustment threshold, ensures that the optimization starting point is consistent with the actual working condition, updates the topology model and establishes the air volume balance and resistance equation, simultaneously solves the actual air volume, makes the calculation consistent with the physical law of the ventilation system, the target function takes the minimum air volume difference and the minimum fan speed into account, realizes the balance between safety and energy saving, the particle swarm algorithm continuously optimizes the parameter combination through iteration optimization, and finally obtains the target control parameters which can accurately match the optimal air volume demand and reduce the energy consumption, improves the scientificity of the ventilation control and reduces the energy consumption cost.

[0015] Optionally, selecting the initial population based on the current fan speed and the current damper opening includes: setting a fan speed change threshold and a damper opening change threshold; determining the adjustable range of the fan and the damper based on the current fan speed, the current damper opening, the fan speed change threshold and the damper opening change threshold; and determining the initial population based on the adjustable range using Latin hypercube sampling.

[0016] This method sets speed and damper opening change thresholds, and determines the adjustable range based on the current device status. This prevents system fluctuations caused by excessive parameter adjustments and ensures control safety. Latin hypercube sampling is used to generate the initial population within the adjustment range, ensuring that the samples evenly cover all possible parameter combinations, reducing the risk of local optimal traps and improving the particle swarm algorithm's convergence speed, solution efficiency, and accuracy.

[0017] Optionally, the combined air volume balance equation and the resistance equation to solve the actual air volume of the tunnel branch includes: setting the optimal air volume as the initial iterative air volume of the tunnel branch; calculating the initial actual air volume based on the initial iterative air volume using the air volume balance equation and the resistance equation; performing iterative calculation based on the initial actual air volume, and calculating the rate of change based on the results of two adjacent iterative calculations; and outputting the actual air volume of the tunnel branch based on the rate of change.

[0018] The present invention uses the optimal air volume as the initial iteration value to ensure that the calculation starting point is close to the target and reduce convergence time. The initial actual air volume is calculated based on the air volume balance and resistance equation, so that the result conforms to the physical laws of the ventilation system. Through iterative calculation and monitoring of the change rate of adjacent results, the air volume deviation is dynamically corrected until the accuracy requirements are met. It ensures that the output actual air volume is consistent with the current fan wind pressure and damper wind resistance status, thereby improving the accuracy of solving the actual air volume of the tunnel branch.

[0019] Optionally, the calculation of the initial actual air volume based on the initial iterative air volume using the air volume balance equation and the resistance equation includes: based on the initial iterative air volume, calculating the residual of the node according to the air volume balance equation to obtain an initial node residual vector; based on the initial iterative air volume, calculating the residual of the tunnel branch according to the resistance equation to obtain an initial branch residual vector; merging the initial node residual vector and the initial branch residual vector to obtain an initial overall residual vector; calculating the partial derivative of the initial overall residual vector with respect to the initial iterative air volume, and constructing a Jacobian matrix using the partial derivative; calculating the initial air volume correction according to the Jacobian matrix and the initial overall residual vector; and calculating the initial actual air volume based on the initial air volume correction.

[0020] The present invention calculates the node and lane branch residuals separately and merges them into an overall residual vector, comprehensively quantifies the deviation between the current air volume and the equilibrium state, avoids the omission of single-dimensional errors, and constructs a Jacobian matrix to clearly characterize the correlation between the residual and the air volume, providing a mathematical basis for the calculation of the correction amount. The air volume correction amount is solved based on the Jacobian matrix and the residual vector to ensure that the adjustment direction and amplitude are scientific and reasonable, making the air volume correction more targeted. The initial actual air volume obtained by using the correction amount not only conforms to the physical constraints of the air volume balance and the resistance equation, but also improves the scientificity and accuracy of the initial actual air volume calculation.

[0021] Optionally, adjusting the fan and the damper by a fuzzy PID algorithm according to the target control parameters includes: setting a fuzzy subset and PID initial parameters of a fuzzy PID controller according to the equipment characteristics of the fan and the damper; adjusting the fan and the damper based on the PID initial parameters, and obtaining the fan deviation change rate and the damper deviation change rate of two consecutive samples based on the adjustment result and the target control parameters; respectively calculating the membership of the fan deviation change rate and the damper deviation change rate to the fuzzy subset; based on the membership, performing reasoning using a preset fuzzy rule base to obtain a PID parameter correction; respectively calculating the fan control quantity and the damper control quantity based on the PID parameter correction and the PID initial parameters; and adjusting the fan and the damper using a PID controller according to the fan control quantity and the damper control quantity.

[0022] The present invention sets fuzzy subsets and initial parameters in combination with equipment characteristics to ensure that the regulation and control adapt to the inherent properties of the equipment, quantifies the dynamic trend of regulation through the deviation change rate, realizes precise fuzzy conversion through membership calculation, and infers PID parameter correction amounts based on the fuzzy rule base to make parameter adjustment more in line with actual working conditions. By calculating the control amount through the correction parameters, a closed-loop regulation is formed. The overall process can adapt to the nonlinear characteristics of the fan and damper, dynamically optimize the regulation accuracy, reduce overshoot and fluctuation, and improve the response speed and stability of the fan and damper adjustment.

[0023] Optionally, the intelligent mine ventilation adjustment method further includes: collecting the measured air volume after the adjustment; and increasing the fans in key areas to rated power and issuing an alarm based on a comparison result between the measured air volume and the optimal air volume.

[0024] The present invention collects the adjusted air volume in real time to visually verify the control effect and promptly discover the adjustment deviation. When the deviation between the measured and optimal air volume exceeds the limit for multiple consecutive adjustments, the fan is immediately increased to the rated power to ensure basic ventilation, and an alarm is issued to indicate the abnormality, thereby improving the safety and scientificity of mine ventilation.

[0025] Another aspect of the present invention provides an intelligent mine ventilation adjustment system, comprising: a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a mine ventilation intelligent adjustment method as described in any one of the previous aspects of the present invention.

[0026] The intelligent mine ventilation adjustment system of the present invention has a compact structure, stable performance, high integration and simple composition. It can stably execute the intelligent mine ventilation adjustment method provided in the previous aspect of the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a method for intelligently adjusting mine ventilation according to an embodiment of the present invention; Figure 2 This is a structural schematic diagram of an intelligent mine ventilation adjustment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0029] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0030] See Figure 1 , in an optional implementation, such as Figure 1 The intelligent adjustment method for mine ventilation shown includes the following steps: Step S1: construct a dynamic topology model based on the mine CAD drawing, with tunnels as edges and fans and dampers as nodes.

[0031] In this embodiment, the mine CAD drawings are first used as an accurate and comprehensive basic data source. With the help of advanced computer graphics recognition and intelligent analysis technology, the CAD drawings are deeply traversed element by element and layer by layer according to the preset graphic feature recognition rules. For the lines representing the tunnels, their starting and ending point coordinates are extracted through vector analysis algorithms to accurately determine the spatial direction of the tunnels.

[0032] Based on the drawing scale and line length vector data, combined with annotated dimension information, the actual length of the roadway is accurately calculated. For the enclosed areas of the roadway cross-section, graphic contour recognition and geometric parameter calculation are used to obtain the cross-sectional shape (such as rectangle, trapezoid, circle, etc.) and corresponding dimensions (width, height, diameter, etc.). Combined with the annotated information on the roadway support material and surface roughness, the roadway roughness is determined through empirical formulas or database mapping. These extracted basic parameters such as the roadway's spatial direction, length, cross-sectional dimensions, and roughness serve as the core basis for constructing topological edges. According to topological modeling specifications, each roadway with independent ventilation path significance is abstracted and converted into an edge element in the dynamic topological model.

[0033] Then, based on Darcy's law of fluid mechanics, the basic parameters of the tunnel are substituted (the cross-sectional area is calculated from the cross-sectional dimensions, the tunnel length is the extracted value, and the roughness corresponds to the drag coefficient), and the empirical formula is used. (in is the friction coefficient related to roughness, is the tunnel length, is the cross-sectional area of ​​the roadway) and initially calculate the wind resistance properties of each edge The calculated wind resistance value and the basic parameters involved in the calculation are stored together as the inherent parameters of the edge, providing an initial resistance basis for subsequent ventilation network solution.

[0034] After completing the construction of the tunnel edge, continue to use the graphic recognition and equipment parameter analysis module to identify the exclusive position identification of fans and dampers on the CAD drawings (such as specific legends, annotated coordinates). For fans, analyze their rated air volume, rated air pressure, speed adjustment range, motor power and other equipment parameters in the drawing property column or associated annotations. For dampers, analyze the opening adjustment range (minimum closing angle, maximum opening angle), local wind resistance characteristic curves (or empirical formulas) under different openings, structural type and other parameters. According to the definition rules of topological nodes, fans and dampers are abstracted as nodes of the dynamic topological model respectively. An exclusive attribute library is established for each node to record the equipment type of the node (distinguishing between fans, dampers and specific subtypes) and complete control parameters (such as fan speed gear, damper opening value, etc.). Through the spatial coordinate association and connection relationship algorithm, the connection method between the node and the tunnel edge is determined (such as the fan is connected in series at the air inlet end of a tunnel edge, and the damper is connected in parallel at the intersection node of two tunnel edges). The connection relationship between the node and the tunnel edge in the topological model is accurately recorded, thereby completing the construction of the dynamic topological model from the mine CAD drawing to the digital and structured model, and finally obtaining the dynamic topological model.

[0035] Step S2: In real time, the actual wind pressure and actual wind volume of each lane branch are collected to invert and obtain the current wind resistance, and the wind resistance attribute of the edge in the dynamic topology model is updated using the current wind resistance.

[0036] In this embodiment, during the operation of the ventilation system, the wind pressure sensor and wind speed sensor arranged in the lane are used to collect the actual wind pressure of each lane in real time according to the set sampling period. And the actual wind speed data, using the conversion relationship between wind speed and wind volume (wind volume =Equal to the wind speed sensor measurement value multiplied by the tunnel cross-sectional area), to obtain the actual air volume of the corresponding tunnel According to the wind pressure-air volume relationship in fluid mechanics, the formula , inverse calculation of the actual wind resistance of the current tunnel , the current wind resistance obtained by inversion is replaced and updated with the wind resistance attribute initially stored at the corresponding lane edge in the dynamic topology model, so that the dynamic topology model can reflect the actual state of the lane wind resistance in real time.

[0037] Step S3: Calculate the optimal air volume of the lane branch using the updated dynamic topology model.

[0038] Calculating the optimal air volume of the lane branch using the updated dynamic topology model specifically includes the following sub-steps: Step S301 : dividing a plurality of independent ventilation loops by a circle-breaking method based on the lane connection relationship in the dynamic topology model, and setting a clockwise reference direction for the ventilation loops.

[0039] In this embodiment, based on the established dynamic topology model, the connection relationships between all lane edges are first sorted out to clarify how the lanes are interconnected to form the overall ventilation network. Next, a loop-breaking method is used to identify and break redundant connections within the complex ventilation network that could form loops (specifically, closed loops in the network are traversed and lane edges that do not affect connectivity are removed). The entire ventilation network is gradually divided into multiple independent ventilation loops without intersecting loops. After each independent ventilation loop is divided, a clockwise reference direction is uniformly set for it. Subsequent operations such as ventilation network solution and air volume and pressure analysis use this clockwise reference direction to determine the calculated airflow direction and loop routing rules. This ensures consistent and standardized analysis and calculation of each ventilation loop, providing a clear and unified loop foundation for subsequent ventilation system air volume control and resistance analysis.

[0040] Step S302: The ventilation circuit includes one or more lane branches, and a safety air volume threshold of the lane branch is obtained and set as the initial air volume of the lane branch.

[0041] In this embodiment, after the independent ventilation circuits are divided, the tunnel branch composition included in each ventilation circuit is analyzed to determine the specific conditions of one or more tunnel branches. Then, based on factors such as mine ventilation safety regulations, tunnel usage (e.g., for personnel passage, equipment operation, gas emission, and other functional requirements), support conditions, cross-sectional dimensions, and the amount of harmful gas generated and the number of personnel in the service area (e.g., mining working face, chamber, etc.), a safe air volume threshold for each tunnel branch to ensure safe production is determined through theoretical calculations (combined with relevant ventilation safety standard formulas), reference to historical empirical data, simulation analysis, or requirements of the Coal Mine Safety Regulations. This threshold must meet the requirements of diluting harmful gases and providing a suitable working environment while preventing problems such as a sudden increase in tunnel ventilation resistance and excessive energy consumption due to excessive air volume. Finally, the calculated safe air volume threshold is assigned to the corresponding tunnel branch as its initial air volume, providing a basis for subsequent ventilation system air volume optimization, real-time monitoring, and control, ensuring that each tunnel branch has an air volume configuration that meets safety requirements in its initial state.

[0042] Step S303: Calculate the wind pressure imbalance of each circuit based on the initial air volume and the clockwise reference direction.

[0043] In this embodiment, for each independent ventilation loop, all lane branches (i.e. lane routing branches) in the loop are traversed in sequence according to the lane branch structure it contains. During the traversal process, the wind pressure sign is determined based on the relationship between the actual direction of the branch airflow and the clockwise reference direction of the loop: if the actual direction of the branch airflow is consistent with the reference direction, the wind pressure loss of the branch is recorded as (in For loop branch The current wind resistance, is the initial air volume. If the actual direction is opposite, the wind pressure loss is recorded as By performing the above determination and calculation on all branches in the circuit, the wind pressure imbalance of the circuit is obtained. The wind pressure imbalance satisfies the following formula: For ventilation circuit The wind pressure imbalance, For ventilation circuit The number of lane branches, For ventilation circuit Lane branch The current wind resistance, For ventilation circuit Lane branch The initial air volume, is the initial air volume The clockwise reference direction is 1, and the reverse is -1, which is used to quantify the difference in wind pressure supply and demand within the circuit.

[0044] Step S304: Calculate the optimal air volume of the lane branch according to the wind pressure imbalance.

[0045] Calculating the optimal air volume of the lane branch according to the wind pressure imbalance specifically includes the following sub-steps: Step S30401: Calculate the wind resistance fluctuation coefficient of the ventilation circuit according to the current wind resistance and the initial wind resistance.

[0046] In this embodiment, for ventilation circuit, traverse the first The wind resistance fluctuation coefficient of a single branch is calculated based on the current wind resistance and the initial wind resistance.

[0047] The wind resistance fluctuation coefficient of a single lane branch satisfies the following formula: in, For loop Lane branch The wind resistance fluctuation coefficient, is the influence coefficient, For loop Lane branch The current wind resistance, For loop Lane branch The initial wind resistance, For loop The formula quantifies the degree of fluctuation of the current wind resistance relative to the initial wind resistance.

[0048] Influence coefficient By collecting a large amount of wind resistance data in different types of ventilation tunnels, comparing the designed wind resistance with the actual wind resistance, and analyzing the impact of wind resistance fluctuations on the performance of the ventilation system, we can find a coefficient that can better reflect the impact of wind resistance fluctuations on subsequent calculations through statistical analysis of these data. The general value is 0.12.

[0049] The wind resistance fluctuation coefficient of the ventilation circuit satisfies the following formula: In the above formula, the wind resistance fluctuation coefficient of the loop is taken as the average of the wind resistance fluctuation coefficients of each lane branch.

[0050] Step S30402: Calculate the air volume correction step size of each lane branch according to the wind pressure imbalance, the current wind resistance, the air volume of each lane branch, and the wind resistance fluctuation coefficient.

[0051] In this embodiment, the air volume correction step size satisfies the following formula: in, For loop The air volume correction step size is For loop The wind resistance fluctuation coefficient, For loop The wind pressure imbalance value, For loop The number of branches, For loop Lane branch The current wind resistance value, For loop Lane branch Initial wind resistance value.

[0052] The above formula is for the tunnel ventilation resistance , about its air volume Taking the differential, we can get , drawing on the iterative process of the Hardy-Cross method, the nonlinear resistance equation is replaced by a linear approximation. Considered as the amount of correction required for the air volume , Approximately regarded as the wind pressure imbalance of the circuit , for a circuit. This embodiment takes into account the wind pressure changes of each branch. It is necessary to comprehensively consider the impact of the resistance characteristics of all branches on the wind pressure imbalance. The denominator is obtained by multiplying the wind resistance-air volume of each branch. , it reflects the comprehensive influence of the wind resistance and air volume of each branch in the entire circuit on the wind pressure imbalance. During the calculation, because.

[0053] Step S30403: Update the air volume of the lane branch according to the air volume correction step and the clockwise reference direction to obtain an updated air volume.

[0054] In this embodiment, if the circuit Middle lane branch The wind direction is consistent with the clockwise reference direction of the circuit. The wind volume of this branch needs to increase the corresponding wind volume correction step. If the wind direction is opposite to the reference direction, the wind volume needs to be reduced by the corresponding wind volume correction step. According to this rule, the circuit All lane branches (total After the air volume adjustment is completed, the updated air volume set is obtained. .

[0055] Step S30404: Iterate based on the updated air volume to obtain the optimal air volume of the lane branch.

[0056] In this embodiment, after completing a single air volume update to obtain the updated air volume, it is re-substituted into the ventilation circuit wind pressure calculation process, and the wind pressure imbalance of each circuit is calculated again. If the imbalance does not reach the preset accuracy threshold (such as being less than a certain minimum value, indicating that the wind pressure is basically balanced), then based on the updated air volume, the "calculate wind pressure imbalance → adjust the air volume according to the reference direction and correction step size → obtain a new updated air volume" operation is repeated. If the imbalance meets the accuracy requirements, the iteration terminates. Through multiple rounds of this iterative process, the air volume distribution of each tunnel branch is continuously optimized, so that the relationship between wind pressure and air volume in the ventilation system gradually becomes stable and reasonable. The air volume finally obtained is the optimal air volume for the tunnel branch that can balance the wind pressure of the ventilation network and meet the ventilation safety and efficiency requirements of the mine.

[0057] Step S4: Based on the optimal air volume, a particle swarm algorithm is used to solve and obtain target control parameters of the fan and the damper.

[0058] Wherein, based on the optimal air volume, using the particle swarm algorithm to solve and obtain the target control parameters of the fan and the damper specifically includes the following sub-steps: Step S401 , obtaining the current fan speed and the current damper opening, and selecting an initial population according to the current fan speed and the current damper opening.

[0059] In the present embodiment, the current rotation period or pulse signal is obtained by a rotation speed sensor (such as a Hall rotation speed sensor, an optical rotation speed sensor, etc.), and the current fan rotation speed value is obtained through signal conversion and calculation. For the damper, the rotation angle, linear displacement, etc. of the damper blade or door plate are monitored by using an opening degree sensor (such as an angular displacement sensor, a pull-wire displacement sensor, etc. adapted to the mechanical structure of the damper), and the angle value of the current damper opening degree is obtained by combining the mechanical transmission parameters (such as the transmission ratio, the corresponding relationship between the stroke and the opening degree) of the damper.

[0060] The selecting of the initial population according to the current fan rotation speed and the current damper opening degree specifically includes the following sub-steps: Step S40101, setting a fan rotation speed variation threshold and a damper opening degree variation threshold.

[0061] In the present embodiment, the fan rotation speed variation threshold and the damper opening degree variation threshold are set based on the equipment characteristics of the fan and the damper, the dynamic response requirements of the ventilation system, and the on-site safe operation specifications.

[0062] For the fan, the stability of the rotation speed adjustment, the tolerance range of the motor load variation, and the influence degree on the air volume output are comprehensively considered, and the fan rotation speed variation threshold is set to 10% of the current fan rated rotation speed (or the current running rotation speed), that is, the change amount of the fan rotation speed needs to be controlled within the threshold value each time the adjustment is made, so as to avoid problems such as the air volume of the ventilation system fluctuating sharply, the equipment mechanical impact being too large, etc. caused by sudden rotation speed change.

[0063] For the damper, according to the adjustment accuracy of the mechanical structure, the requirement for the smoothness of the air resistance change, and the adaptive adjustment requirement of the roadway ventilation resistance, the damper opening degree variation threshold is set to ±15 degrees, so as to ensure that the damper opening degree is changed within the range of the angle during the adjustment process, so that the roadway air resistance and the air volume distribution can be smoothly transitioned, and the stable operation of the ventilation system is ensured.

[0064] Step S40102, determining the adjustable range of the fan and the damper according to the current fan rotation speed, the current damper opening degree, the fan rotation speed variation threshold, and the damper opening degree variation threshold.

[0065] In the present embodiment, for the fan, the upper and lower limits of the adjustable range are obtained by taking the current actual running rotation speed as the reference and combining the set rotation speed variation threshold: the adjustable upper limit is the current fan rotation speed plus 10% of the current rotation speed (which needs to be simultaneously not more than the upper limit of the rated rotation speed of the fan equipment itself), and the adjustable lower limit is the current fan rotation speed minus 10% of the current rotation speed (which needs to be not less than the minimum stable running rotation speed of the fan equipment), so as to clearly define the numerical interval of the fan rotation speed that can be adjusted under the premise of ensuring the stability of the system and the safety of the equipment.

[0066] For the damper, based on the current actual opening, its adjustable range is determined according to the set opening change threshold of ±15 degrees: the adjustable upper limit is the current damper opening plus 15 degrees (not exceeding the maximum opening allowed by the damper mechanical structure), and the adjustable lower limit is the current damper opening minus 15 degrees (not less than the minimum opening allowed by the damper mechanical structure, usually the opening value corresponding to full closure), thereby accurately defining the adjustable angle range of the damper opening.

[0067] Step S40103: Determine an initial population using Latin hypercube sampling based on the adjustable range.

[0068] For fan speed, the adjustable range is evenly divided into subintervals equal to the population size using the Latin hypercube sampling rule. One speed value is randomly selected from each subinterval as a candidate solution. For damper opening, the same sampling process is performed within the adjustable range: one opening value is randomly selected from each subinterval. The selected fan speed value is then combined with the damper opening value in a one-to-one correspondence to form an initial population of fan-damper parameter pairs. The population size is set based on the complexity of the ventilation system, generally not less than 30 and not more than 50 individuals. This sampling method ensures that the initial population is evenly distributed within the adjustable range, covering high, medium, and low adjustment gears, while avoiding sample concentration in a local area. This provides a sufficiently diverse initial solution space for subsequent optimization iterations of the particle swarm algorithm, improving the search efficiency and global optimization capability of the target control parameters.

[0069] Step S402 : Calculate the fan wind pressure and damper wind resistance based on the initial population.

[0070] For fan pressure, we first collect multiple sets of measured wind pressure data of this model of fan at different speeds through experiments to form a speed-wind pressure sample data set, and then use polynomial fitting to establish a fitting relationship model between fan speed and wind pressure based on the data set. For damper resistance, we also collect multiple sets of measured wind resistance data of the damper at different openings through experiments to form an opening-wind resistance sample data set, and then use power function fitting to establish a fitting relationship model between damper opening and wind resistance. When calculating the fan pressure and damper resistance of individuals in the initial population, the individual fan speed parameters and damper opening parameters are respectively input into the corresponding fitting model, and the corresponding wind pressure and wind resistance calculation results can be output, thereby realizing the calculation of fan pressure and damper resistance based on fitting method.

[0071] Step S403: updating the dynamic topology model using the fan wind pressure and the damper wind resistance.

[0072] In this embodiment, after completing the wind pressure of each wind turbine in the initial population (the Typhoon wind pressure ) and each damper resistance ( Air door resistance ), the model parameters are updated based on the association between fans, dampers and tunnel branches in the dynamic topology model. For fans, based on their installation position in the ventilation network (corresponding to the associated tunnel branch or ventilation loop), the calculated wind pressure is used as the output wind pressure parameter of the fan node, replacing the original wind pressure attribute of the fan in the model, thereby reflecting the actual wind pressure contribution of the fan to the ventilation network at the current speed. For dampers, according to the position information of the tunnel branch where they are located, the calculated wind resistance is updated to the wind resistance parameter of the corresponding tunnel branch (if the damper is connected in series to a tunnel branch, the local wind resistance of the branch is directly replaced; if it is connected in parallel, the total wind resistance of the branch is recalculated according to the wind resistance parallel rule), accurately correcting the resistance characteristics of the tunnel branch.

[0073] Step S404: using the updated dynamic topology model to establish the wind volume balance equation of the node and the resistance equation of the lane branch.

[0074] In this embodiment, for the Nodes (nodes are core units of the ventilation network such as fans, dampers or lane intersections) are used to establish a balance relationship based on the principle of air volume conservation. As the key hub of air volume flow in the ventilation system, nodes must maintain a dynamic balance between inflow and outflow. All lane branches associated with the node are sorted out one by one, and the number of inflow branches is counted. and the number of outgoing branches , for the The strip flows into the branch and extracts its air volume (This data comes from the wind volume iteration result in the topology model, and the real-time monitoring value from the on-site sensor before iteration). Branch out and extract its air volume , the wind volume balance equation of the node satisfies the following formula: For the The air flow balance equation for each node is: is the number of inflow branches, For the The air volume flowing into the branch, is the number of outgoing branches, For the This equation strictly constrains the algebraic sum of the inflow and outflow air volumes at a node to be zero, making the model accurately conform to the physical law of air conservation.

[0075] For the A ventilation circuit (the circuit consists of tunnels, fans and other components to form a closed ventilation path, The identification is consistent with the logic of the circuit division in the previous article). The resistance balance equation is constructed based on the fluid mechanics resistance characteristics and the fan air supply mechanism. The fan in the circuit provides air pressure for the system. (Calculated by the fan speed-wind pressure correlation formula, reflecting the fan work output), There is wind resistance in the branch lanes (including resistance equipment such as dampers) , when the air volume When the flow passes through this branch, resistance loss will be generated , combined with the wind direction function (Positive sign for inflow into the node, negative sign for outflow, to match the clockwise reference direction of the loop) Corrects the sign of the resistance loss.

[0076] Traverse within a loop The resistance loss of all lane branches is accumulated, and the resistance equation of the lane branch satisfies the following formula: For the The resistance equation for a circuit is, For the The wind pressure provided by the fan in each circuit is For loop The number of lane branches, For lane branches of wind resistance, For lane branches The direction of air volume is positive when it flows into the node and negative when it flows out of the node.

[0077] The above formula constrains the algebraic sum of the wind pressure provided by the fan in the loop and the resistance loss of each branch to be zero, accurately simulating the mechanical balance relationship between the fan air supply and branch resistance in the ventilation loop.

[0078] Step S405 , the air volume balance equation and the resistance equation are solved to obtain the actual air volume of the lane branch.

[0079] Solving the air volume balance equation and the resistance equation to obtain the actual air volume of the lane branch specifically includes the following sub-steps: Step S40501: setting the optimal air volume as the initial iterative air volume of the lane branch.

[0080] In this embodiment, in the initial stage of solving the actual air volume of the tunnel branch by simultaneously using the air volume balance equation and the resistance equation, it is necessary to set basic air volume parameters for the iterative calculation, and directly assign the optimal air volume of the tunnel branch obtained through multiple rounds of iterative optimization as the initial iterative air volume of each tunnel branch to ensure that the iterative calculation starts from the optimal air volume benchmark that meets the wind pressure balance and safety requirements of the ventilation network, avoiding slow iteration convergence or falling into local optimality due to the initial value deviating from the reasonable range.

[0081] Step S40502: Calculate the initial actual air volume based on the initial iterative air volume using the air volume balance equation and the resistance equation.

[0082] Calculating the initial actual air volume based on the initial iterative air volume using the air volume balance equation and the resistance equation specifically includes the following sub-steps: Step S4050201, based on the initial iterative air volume, calculate the residual of the node according to the air volume balance equation to obtain an initial node residual vector.

[0083] Substitute the initial iterative air volume into the air volume balance equation of the node Calculate the difference between the total inflow air volume and the total outflow air volume. This difference is the value of the The residual of each node reflects the imbalance of the node air volume under the current initial iterative air volume. Traverse all nodes and arrange the residuals of each node in the order of node number to form the initial node residual vector. Each element in the vector corresponds to the residual value of a node.

[0084] Step S4050202: Based on the initial iterative air volume, the residual of the tunnel branch is calculated according to the resistance equation to obtain an initial branch residual vector.

[0085] In this embodiment, the ventilation circuit Traverse the contained The initial iterative air volume is substituted into the resistance equation of the loop. Calculate the difference between the fan pressure and the algebraic sum of all branch resistance losses. This difference is the first The residual of each loop corresponding to the lane branch reflects the imbalance degree of the loop air pressure under the current initial iterative air volume. All ventilation loops and their branches are traversed, and the residuals of each branch are arranged in the order of loop and branch numbers to form an initial branch residual vector. Each element in the vector corresponds to the residual value of a lane branch.

[0086] Step S4050203: merge the initial node residual vector and the initial branch residual vector to obtain an initial overall residual vector.

[0087] In this embodiment, the initial node residual vector and the initial branch residual vector are merged according to the logical association order of nodes and lane branches in the dynamic topology model to construct an initial overall residual vector.

[0088] First, the residual values ​​of each node in the initial node residual vector (arranged in order of node number) are used as the first half of the vector elements, and then the residual values ​​of each lane branch in the initial branch residual vector (arranged in order of loop number and branch sequence within the loop) are used as the second half of the vector elements. A complete vector structure is formed through orderly splicing.

[0089] Step S4050204, calculating the partial derivatives of the initial overall residual vector with respect to the initial iterative wind volume, and constructing a Jacobian matrix using the partial derivatives.

[0090] In this embodiment, for the initial iterative air volumes of all lane branches in the dynamic topology model, partial derivatives of the overall residual vector with respect to each initial iterative air volume are calculated, and a Jacobian matrix is ​​constructed based on these partial derivatives.

[0091] Traverse each element in the initial overall residual vector and calculate its effect on the Ventilation circuit Partial derivative of the initial iterative air volume of the lane branch: For the node residual element, if the node is connected to the loop Lane branch If there is an inflow or outflow association, the partial derivative takes the value of +1 (inflow) or -1 (outflow), and if there is no association, it is 0. For the branch residual element, if the corresponding loop Lane branch The residual and partial derivatives of the resistance equation are calculated according to the derivative rules of the resistance equation. Finally, all partial derivatives are arranged in order according to the matrix dimension of the overall residual vector element number × the lane branch number, forming a matrix with the dimension of ( is the total number of nodes, is the total number of branches), each element in the matrix accurately reflects the correlation between the corresponding residual and the change of air volume.

[0092] Step S4050205, calculating the initial air volume correction amount according to the Jacobian matrix and the initial overall residual vector.

[0093] In this embodiment, the Jacobian matrix and the initial overall residual vector are substituted into the correction amount solution formula, and the linear equation system is solved by implementation, where is the Jacobian matrix, is the initial air volume correction vector to be determined, The initial overall residual vector is obtained by using a numerical calculation method such as Gaussian elimination or LU decomposition to solve the equation set, and the initial air volume correction amount corresponding to each roadway branch is obtained. The numerical value of the correction amount reflects the air volume adjustment range required to reduce the residual, and the sign indicates the air volume adjustment direction (a positive value indicates increasing the air volume, and a negative value indicates reducing the air volume). Finally, the global residual information is converted into specific air volume correction parameters.

[0094] Step S4050206, calculating the initial actual air volume according to the initial air volume correction amount.

[0095] In this embodiment, if the correction amount is positive, it indicates that the corresponding air volume needs to be increased on the basis of the initial iteration air volume to reduce the residual; if the correction amount is negative, the corresponding air volume needs to be reduced. According to this rule, the air volume of all roadway branches is adjusted one by one, and finally a set containing the initial actual air volume of each branch is formed.

[0096] Step S40503, based on the initial actual air volume, performing iteration calculation, and calculating the change rate according to the results of two adjacent iterations.

[0097] In this embodiment, after obtaining the initial actual air volume, the complete process of “substituting the air volume into the balance equation and the resistance equation to calculate the residual, constructing the Jacobian matrix, solving the air volume correction amount, and calculating the new actual air volume” is repeated, and multiple rounds of iteration calculation are carried out. For the first roadway branch of the first ventilation circuit, the actual air volume of the first iteration and the actual air volume of the second iteration are recorded. The change rate satisfies the following formula: Step S40504, based on the change rate, outputting the actual air volume of the roadway branch.

[0098] In this embodiment, first, the convergence precision threshold (such as less than 0.5% or 1%) is set according to the ventilation system control precision requirement. After each iteration, the convergence precision threshold is compared with the change rate. Step S406, calculating a target function with the minimum difference between the actual air volume of the roadway branch and the optimal air volume and the minimum speed of the fan as the target.

[0099] Step S406, calculating a target function with the minimum difference between the actual air volume of the roadway branch and the optimal air volume and the minimum speed of the fan as the target.​​​​​

[0100] In this embodiment, in order to eliminate the dimensional difference between the air volume and the fan speed, normalization processing is required to map them to the interval [0, 1] before calculating the objective function.

[0101] The objective function satisfies the following formula: is the objective function, is the air volume difference weight, is the number of ventilation circuits, ventilation circuit The number of branches, ventilation circuit Lane branch The actual air volume, ventilation circuit Lane branch The optimal air volume, is the fan speed weight, is the number of fans, For fans speed.

[0102] The weight of the air volume difference in the above formula and fan speed weight Determined according to the severity of mine ventilation safety, for example, if the amount of harmful gas overflowing from the mine is large, the air volume difference weight The value is 0.8-1, and the air volume difference weight is relatively simple when the mine tunnel is relatively simple and the overflow of harmful gases is relatively small. The value can be selected from 0.6 to 0.8. The specific value depends on the actual situation. However, mine ventilation is still a problem that needs to be solved at this stage, so the weight of the air volume difference Should not be lower than 0.6.

[0103] Step S407 , iteratively obtaining target control parameters of the fan and the damper using a particle swarm algorithm based on the objective function.

[0104] In this embodiment, after the objective function is determined, the particle swarm algorithm is started to perform iterative optimization. The particle swarm is initialized, and each particle corresponds to a set of parameter combinations of fan speed and damper opening, whose value range is limited by the adjustable range determined in the early stage. During the iteration process, the particles are based on their own historical optimal position and the global optimal position of the population, combined with the objective function. Fitness is calculated to measure the quality of parameter combinations. By continuously updating particle speeds and positions, the population gradually converges to the region with the smallest objective function value. Iteration continues until the preset convergence conditions (such as the number of iterations reaching the upper limit, the rate of change of the objective function value being less than a threshold, etc.) are met. The final output is the fan speed and damper opening combination that optimizes the objective function, i.e., the target control parameters for the fan and damper. This achieves the coordinated optimization of the ventilation system between precise air volume matching and low-energy operation.

[0105] Step S5: According to the target control parameters, the fan and the damper are adjusted by a fuzzy PID algorithm.

[0106] According to the target control parameter, adjusting the fan and the damper by using the fuzzy PID algorithm specifically includes: Step S501 : setting the fuzzy subset and PID initial parameters of the fuzzy PID controller according to the equipment characteristics of the fan and the damper.

[0107] In this example, fuzzy PID controller parameter configuration is performed based on equipment characteristics (such as the fan's speed-to-air pressure response curve and the damper's opening-to-air resistance adjustment characteristics). For fan speed control and damper opening adjustment, the dynamic response patterns of the equipment under different operating conditions are analyzed (e.g., the delay in the effect of fan speed changes on air pressure and the stabilization time of air resistance after damper opening adjustment). Based on this information, fuzzy subsets are defined: speed deviations, opening deviations, and their corresponding rates of change are categorized into "negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PB)" fuzzy language values, covering the typical deviation range of equipment control. Initial parameters for the PID controller are also set based on the equipment's rated parameters, historical commissioning experience, and theoretical calculations.

[0108] The initial parameters include the scale factor , integral coefficient , differential coefficient , refer to the wind pressure adjustment sensitivity setting of the fan at rated speed Initial value, combined with the static wind resistance error setting of the air door opening Initial value, set according to the dynamic change rate of the device response The initial value lays the foundation for the fuzzy PID controller to achieve precise control of fan speed and damper opening.

[0109] Step S502 : adjusting the fan and damper based on the PID initial parameters, and obtaining the fan deviation change rate and the damper deviation change rate of two consecutive samples based on the adjustment result and the target control parameters.

[0110] In this embodiment, according to the PID control logic, with the target control parameters (fan target speed, damper target opening) as the benchmark, the current fan actual speed and damper actual opening are collected in real time, the adjustment amount is calculated and output, and the fan and damper are driven to adjust the operating state. Cycle and cycle), obtain the deviation between the actual speed of the fan and the target speed 、 , and the deviation between the actual damper opening and the target opening 、 , and then calculate the fan deviation change rate and damper deviation change rate .

[0111] The fan deviation change rate satisfies the following formula: The damper deviation change rate satisfies the following formula: In the above formula, is the interval between two samples. The fan deviation change rate and damper deviation change rate calculated by the above formula reflect the dynamic change trend of the deviation during the fan and damper adjustment process.

[0112] Step S503 , respectively calculating the membership degree of the fan deviation change rate and the damper deviation change rate to the fuzzy subset.

[0113] In this embodiment, based on the fuzzy subsets (negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), positive large (PB)) set for the fuzzy PID controller in the early stage, the membership degrees of the two change rates to each fuzzy subset are calculated respectively.

[0114] The predefined membership function is used to calculate the fan deviation change rate. Substitute the specific numerical value into the function and calculate its membership degree for each fuzzy language value (NB, NM, NS, Z, PS, PM, PB) to obtain a set of membership values. ( Corresponding to each fuzzy subset), similarly, for the rate of change of the damper deviation , using the same membership function, calculate its membership to each fuzzy subset These membership values ​​quantify the degree of fit between the deviation change rate and the fuzzy language value, and provide key fuzzy input for the subsequent fuzzy PID controller to adjust the PID parameters according to fuzzy rules.

[0115] Predefined membership functions prioritize triangular or trapezoidal membership functions for the range of wind turbine speed deviation change rates (determined based on historical operating data or equipment parameters). These functions are then divided into intervals using the fuzzy subsets of negative large (NB), negative medium (NM), negative small (NS), zero (Z), positive small (PS), positive medium (PM), and positive large (PB). During wind turbine startup or sudden load changes, the membership function coverage of negative large (NB) and positive large (PB) needs to be expanded to accommodate larger deviation change rates. During stable operation, the membership function range near zero (Z) is narrowed to increase sensitivity to small deviation changes. This ensures that the fuzzy partitioning can both adapt to the wind turbine's dynamic characteristics under a wide range of operating conditions and accurately capture deviation changes to meet control requirements.

[0116] Step S504 : Based on the membership degree, reasoning is performed using a preset fuzzy rule base to obtain a PID parameter correction amount.

[0117] In this embodiment, the fuzzy rule base uses the deviation change rate membership combination → PID parameter correction direction and amplitude as the core logic. Each rule corresponds to a specific fuzzy input combination and output conclusion (e.g., if the fan deviation change rate is positive (PB), the proportional coefficient correction amount is negative (NM); if the damper deviation change rate is negative (NS), the integral coefficient correction amount is positive (PS)). The rule design combines the dynamic response characteristics of the fan damper (e.g., the fan speed regulation lag requires enhanced differential action, and the damper opening nonlinearity requires optimization of the proportional coefficient) and control experience. During the reasoning process, the fan deviation change rate membership is calculated. and throttle deviation change rate membership Perform fuzzy matching, determine the activation strength of each rule by taking the smallest operation, and then use the weighted average method or the maximum membership method to clarify the output conclusion of the activation rule, and finally obtain the proportional coefficient correction value of the fan , integral coefficient correction of the fan , differential coefficient correction of the fan Similarly, the proportional coefficient correction value of the corresponding damper is obtained , the integral coefficient correction of the damper , differential coefficient correction of the damper ,These correction quantities quantify the adjustment range of PID parameters and provide a basis for achieving online adaptive optimization of controller parameters.

[0118] Step S505 , calculating the fan control variable and the damper control variable based on the PID parameter correction value and the PID initial parameter.

[0119] In this embodiment, the initial value of the proportional coefficient of the fan is respectively , integral coefficient correction of the fan , differential coefficient correction of the fan The updated fan control quantity is obtained by superposition. Similarly, the initial value of the proportional coefficient of the damper is added to the proportional coefficient correction value of the damper. , the integral coefficient correction of the damper , differential coefficient correction of the damper The updated damper control value is obtained by superposition.

[0120] Step S506: Regulate the fan and the damper using a PID controller according to the fan control variable and the damper control variable.

[0121] In this embodiment, after obtaining the fan and damper control variables, the PID controller's execution phase converts the control signals into actual regulatory actions for the equipment. For the fan, the PID controller outputs the calculated fan control variable to the fan motor's speed control module. This module adjusts the fan speed in real time by varying the motor's power supply frequency or voltage amplitude based on the magnitude and direction of the control variable. If the control variable is positive and increasing, the motor speed is increased to increase the air pressure output; if the control variable is negative, the motor speed is reduced to reduce energy consumption, ensuring that the actual fan speed approaches the target control parameter. For the damper, the PID controller transmits the damper control variable to the damper actuator (e.g., an electric linear actuator or servo motor). Based on the control variable's instructions, the actuator mechanically adjusts the rotation angle of the damper blades or door panels: a positive control variable increases the opening to reduce wind resistance, while a negative control variable decreases the opening to increase wind resistance, ensuring that the actual damper opening accurately matches the target value. During the entire adjustment process, the PID controller continuously receives real-time feedback signals from the fan speed sensor and the damper opening sensor, dynamically corrects the control quantity, and forms a closed-loop control of measurement-comparison-adjustment to ensure that the fan and damper operate stably under the action of the control quantity.

[0122] Step S6: collecting the measured air volume after the adjustment.

[0123] In this embodiment, after the fan and damper are adjusted according to the control amount, wind speed sensors arranged in each tunnel collect wind speed data in real time according to a preset sampling period. Combined with the tunnel cross-sectional dimensions, the actual air volume of each tunnel branch is obtained by multiplying the wind speed and cross-sectional area to form a measured air volume set.

[0124] Step S7: Based on the comparison result between the measured air volume and the optimal air volume, the fans in the key areas are increased to the rated power and an alarm is issued.

[0125] In this embodiment, the actual wind pressure and actual air volume of each tunnel are collected in real time to invert the current wind resistance. The purpose is to continuously update the current wind resistance. Data collection is carried out at a predetermined time (depending on demand). After each inversion of wind resistance, the fan and damper are adjusted once. When the difference between the measured air volume and the optimal air volume after multiple consecutive adjustments (for example, 3 times) does not meet the preset safety threshold, the system is judged to be abnormal. At this time, the safety mechanism is immediately triggered, and the fans associated with the key areas are forced to increase to the rated power to ensure the basic air volume. A fault alarm is issued through the sound and light alarm device to prompt the operation and maintenance personnel to check for abnormal wind resistance or equipment failure.

[0126] like Figure 2 As shown, on the other hand, the present invention also provides a mine ventilation intelligent adjustment system, including: a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute relevant steps of a relevant embodiment of a mine ventilation intelligent adjustment method of the present invention.

[0127] In the intelligent mine ventilation control system provided by the present invention, each functional component can be integrated into a single processing component, each component can exist physically separately, or two or more components can be integrated into a single component. The integrated components can be implemented in either hardware or software form.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A mine ventilation intelligent adjustment method, characterized in that: include: Based on the mine CAD drawings, a dynamic topology model is constructed with tunnels as edges and fans and dampers as nodes; Inverting the actual wind pressure and actual wind volume of each lane branch in real time to obtain the current wind resistance, and using the current wind resistance to update the wind resistance attribute of the edge in the dynamic topology model; Calculating the optimal air volume of the lane branch using the updated dynamic topology model; Based on the optimal air volume, the target control parameters of the fan and the damper are obtained by using a particle swarm algorithm; According to the target control parameters, the fan and the damper are adjusted by a fuzzy PID algorithm.

2. A mine ventilation intelligent adjustment method according to claim 1, characterized in that: The calculating the optimal air volume of the lane branch by using the updated dynamic topology model includes: Dividing a plurality of independent ventilation loops by a circle-breaking method based on the lane connection relationship in the dynamic topology model, and setting a clockwise reference direction for the ventilation loops; The ventilation circuit includes one or more lane branches, obtaining a safe air volume threshold of the lane branch and setting the safe air volume threshold as the initial air volume of the lane branch; Calculating the wind pressure imbalance of each circuit based on the initial wind volume and the clockwise reference direction; The optimal air volume of the tunnel branch is calculated according to the wind pressure imbalance.

3. The intelligent mine ventilation adjustment method according to claim 2, characterized in that: Calculating the optimal air volume of the lane branch according to the wind pressure imbalance includes: Calculating a wind resistance fluctuation coefficient of the ventilation circuit according to the current wind resistance and the initial wind resistance; Calculating the air volume correction step length of each lane branch according to the wind pressure imbalance, the current wind resistance, the air volume of each lane branch, and the wind resistance fluctuation coefficient; updating the air volume of the lane branch according to the air volume correction step and the clockwise reference direction to obtain an updated air volume; Iteration is performed based on the updated air volume to obtain the optimal air volume of the tunnel branch.

4. The intelligent mine ventilation adjustment method according to claim 1, characterized in that: The target control parameters of the fan and the damper obtained by using the particle swarm algorithm based on the optimal air volume include: Obtaining a current fan speed and a current damper opening, and selecting an initial population based on the current fan speed and the current damper opening; Calculating the fan wind pressure and the damper wind resistance based on the initial population; Updating the dynamic topology model using the fan wind pressure and the damper wind resistance; Using the updated dynamic topology model to establish the wind volume balance equation of the node and the resistance equation of the lane branch; The air volume balance equation and the resistance equation are solved to obtain the actual air volume of the lane branch; Calculating an objective function with the goal of minimizing the difference between the actual air volume of the lane branch and the optimal air volume and minimizing the speed of the fan; The target control parameters of the fan and the damper are iteratively obtained using a particle swarm algorithm based on the objective function.

5. The intelligent mine ventilation adjustment method according to claim 4, characterized in that: The selecting of the initial population according to the current fan speed and the current damper opening comprises: Set the fan speed change threshold and the damper opening change threshold; Determining an adjustable range of the fan and the damper according to the current fan speed, the current damper opening, the fan speed change threshold, and the damper opening change threshold; An initial population is determined based on the adjustable range using Latin hypercube sampling.

6. The intelligent mine ventilation adjustment method according to claim 4, characterized in that: The simultaneous solution of the air volume balance equation and the resistance equation to obtain the actual air volume of the lane branch includes: Setting the optimal air volume as the initial iterative air volume of the lane branch; Calculating an initial actual air volume based on the initial iterative air volume using the air volume balance equation and the resistance equation; Performing an iterative calculation based on the initial actual air volume, and calculating a rate of change based on results of two adjacent iterative calculations; The actual air volume of the tunnel branch is output based on the change rate.

7. A mine ventilation intelligent adjustment method according to claim 6, characterized in that: Calculating the initial actual air volume based on the initial iterative air volume using the air volume balance equation and the resistance equation includes: Based on the initial iterative air volume, the residual of the node is calculated according to the air volume balance equation to obtain an initial node residual vector; Based on the initial iterative air volume, the residual of the roadway branch is calculated according to the resistance equation to obtain an initial branch residual vector; Merging the initial node residual vector and the initial branch residual vector to obtain an initial overall residual vector; Calculating partial derivatives of the initial overall residual vector with respect to the initial iterative wind volume, and constructing a Jacobian matrix using the partial derivatives; Calculating an initial air volume correction amount according to the Jacobian matrix and the initial overall residual vector; The initial actual air volume is calculated according to the initial air volume correction amount.

8. The intelligent mine ventilation adjustment method according to claim 5, characterized in that: The adjusting the fan and the damper by using a fuzzy PID algorithm according to the target control parameter includes: Setting the fuzzy subset and PID initial parameters of the fuzzy PID controller according to the device characteristics of the fan and the damper; Adjusting the fan and damper based on the PID initial parameters, and obtaining the fan deviation change rate and the damper deviation change rate of two consecutive samples based on the adjustment result and the target control parameter; respectively calculating the membership degree of the fan deviation change rate and the damper deviation change rate to the fuzzy subset; Based on the membership degree, a PID parameter correction amount is obtained by reasoning using a preset fuzzy rule base; Calculate the fan control amount and the damper control amount based on the PID parameter correction amount and the PID initial parameter respectively; The fan and the damper are adjusted using a PID controller according to the fan control amount and the damper control amount.

9. The intelligent mine ventilation adjustment method according to claim 1, characterized in that: The intelligent mine ventilation adjustment method further includes: collecting the measured air volume after the adjustment; Based on the comparison result between the measured air volume and the optimal air volume, the fans in the key areas are increased to the rated power and an alarm is issued.

10. An intelligent mine ventilation adjustment system, characterized in that: include: A processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a mine ventilation intelligent adjustment method as described in any one of claims 1 to 9.

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