Intelligent Ventilation Method, System and Device for Coal Mines

Abstract

The present application discloses a coal mine intelligent ventilation method, system and device. The method includes: obtaining ventilation monitoring data through a plurality of sensors; wherein the ventilation monitoring data includes at least two of wind speed data, humidity data, temperature data, air pressure data, gas data, carbon monoxide data, carbon dioxide data and dust data; using a preset simulation analysis model to perform simulation analysis on the ventilation monitoring data for ventilation safety monitoring; and matching the analysis result of the simulation analysis with a preset index to execute a control operation corresponding to the preset index. By the above method, the present application can improve the rationality, intelligence and automation of coal mine ventilation.

Description

Technical Field

[0001] This application relates to the technical field of coal mines, and particularly to a method, system, and device for intelligent ventilation in coal mines. Background Art

[0002] Since coal mine resources are an indispensable part of the energy industry, the exploitation of coal mine resources has attracted people's attention. One of the key factors for the smooth progress of coal mine exploitation is coal mine ventilation. When coal mines are exploited, harmful gases such as carbon monoxide, carbon dioxide, and dust often exist inside the coal mines. The ventilation system can effectively discharge these harmful substances from the mine, ensuring the health and safety of miners.

[0003] The coal mine ventilation system is a crucial link in coal mine safety. Therefore, it is particularly important to establish a stable, reliable, and simple ventilation system. The variability of coal mine production determines the dynamics of the coal mine ventilation system. High-tech means must be adopted, advanced technologies introduced, and an automated management system utilized to regularly test, inspect, and evaluate various ventilation parameters, timely collect and analyze the operation information of the ventilation system, formulate the optimal air flow adjustment plan for each production period, and predict the long-term operation status of the ventilation system to improve the management level of coal mine ventilation.

[0004] However, most of the current coal mine ventilation systems have problems such as low operating efficiency of ventilation fans in different mines, unreasonable ventilation facilities, inappropriate air volume adjustment methods, and low levels of intelligence and automation. Summary of the Invention

[0005] The main technical problem to be solved by this application is to provide a method, system, and device for intelligent ventilation in coal mines, which can improve the intelligence level of the coal mine ventilation system.

[0006] To solve the above technical problem, a technical solution adopted by this application is: to provide a method for intelligent ventilation in coal mines, which is applied to an intelligent ventilation system in coal mines and includes:

[0007] Determine the positions and quantities of several sensors based on preset safety requirements;

[0008] Determining the positions and quantities of several sensors based on preset safety requirements includes:

[0009] Classify sensors of the same type at different positions into different levels according to preset safety requirements; among them, sensors of different levels are arranged in a cyclic order from low to high;

[0010] Obtain ventilation monitoring data through several sensors; among them, the ventilation monitoring data includes at least two of wind speed data, humidity data, temperature data, air pressure data, gas data, carbon monoxide data, carbon dioxide data, and dust data;

[0011] Performing ventilation safety monitoring by simulating and analyzing ventilation monitoring data using a preset simulation analysis model;

[0012] Matching the analysis results of the simulation analysis with preset indicators to perform control operations corresponding to the preset indicators;

[0013] Matching the analysis results of the simulation analysis with preset indicators to perform control operations corresponding to the preset indicators, including:

[0014] If the analysis result is normal ventilation, controlling the sensors at the lowest level to work properly and read data;

[0015] If the analysis result is abnormal ventilation, obtaining the operation monitoring values of the ventilation monitoring data corresponding to the sensors, determining the values of the final-level indicators corresponding to the operation monitoring values, and controlling the opening and closing of sensors at different levels based on the values of the final-level indicators.

[0016] Optionally, before obtaining ventilation monitoring data through a number of sensors, including:

[0017] Determining the type of sensors based on the type of ventilation monitoring data;

[0018] Evenly distributing and setting the sensors in the coal mine intelligent ventilation system according to the determined positions and quantities.

[0019] Optionally, before obtaining ventilation monitoring data through a number of sensors, further including:

[0020] Obtaining the historical arrangement data stored in the preset storage space; where the historical arrangement data includes the historical positions and quantities of the sensors and the corresponding historical scenarios;

[0021] Comparing the historical scenario with the current scenario;

[0022] If they are the same, evenly distributing and setting the sensors in the coal mine intelligent ventilation system according to the historical positions and quantities;

[0023] If they are different, determining the positions and quantities of the sensors based on the preset safety requirements, evenly distributing and setting the sensors in the coal mine intelligent ventilation system according to the determined positions and quantities, and updating the historical arrangement data.

[0024] Optionally, the preset simulation analysis model includes a three-dimensional mine model and a three-dimensional ventilation model. Before performing ventilation safety monitoring by simulating and analyzing ventilation monitoring data using the preset simulation analysis model, including:

[0025] Simplifying the mine geographical data and importing it into model construction software to generate a basic mine model;

[0026] Obtain the proportional relationship and geometric relationship in the geographical relationship of the mine;

[0027] Adjust the basic mine model based on the proportional relationship and geometric relationship in the geographical relationship of the mine so that the adjusted geographical relationship conforms to the actual situation, and obtain a three-dimensional mine model;

[0028] After simplifying the roadway position data, import it into the model construction software to generate a basic roadway model;

[0029] Obtain the proportional relationship and geometric relationship in the spatial relationship of the roadway;

[0030] Adjust the basic roadway model based on the proportional relationship and geometric relationship in the spatial relationship of the roadway so that the adjusted spatial relationship conforms to the actual situation;

[0031] Input the specific parameters of the roadways in different sections into the adjusted basic roadway model to obtain a three-dimensional ventilation model;

[0032] Combine the three-dimensional mine model and the three-dimensional ventilation model to obtain a preset simulation analysis model;

[0033] Use the preset simulation analysis model to perform simulation analysis on the ventilation monitoring data for ventilation safety monitoring, including:

[0034] Input the ventilation monitoring data into the preset simulation analysis model;

[0035] In the preset simulation analysis model, adapt the ventilation monitoring data to the data in the three-dimensional mine model and the three-dimensional ventilation model respectively to obtain the analysis results.

[0036] Optionally, match the analysis results of the simulation analysis with the preset indicators to perform control operations corresponding to the preset indicators, including:

[0037] If the analysis result is normal ventilation, control the locking of the return airway or control the start of the ventilator;

[0038] If the analysis result is abnormal ventilation, determine the operating monitoring value corresponding to the final-level indicator according to the ventilation monitoring data in the analysis result;

[0039] Determine the range corresponding to the operating monitoring value; among them, the values of the final-level indicators corresponding to different ranges are different;

[0040] Determine the value of the corresponding final-level indicator according to the determined range and summarize it;

[0041] Combine the values of multiple final-level indicators with the preset weight ratio for weighting to calculate the value of the upper-level indicator;

[0042] Perform weighted calculation step by step until the first-level indicator;

[0043] Control is performed based on the action decision corresponding to the value of the first-level indicator.

[0044] Optionally, the return airway includes the main return airway, the primary return airway, the district return airway, and the working face return airway. The ventilators include the main ventilator and the local ventilator. Controlling based on the action decision corresponding to the value of the first-level indicator includes:

[0045] Based on the value of the first-level indicator, control at least one of the main return airway, the primary return airway, the district return airway, and the working face return airway to release the interlock; wherein, the larger the value of the first-level indicator, the greater the number of return airways with the interlock released;

[0046] Based on the value of the first-level indicator, control the rotation state of at least one of the main ventilator and the local ventilator; wherein, the rotation state includes speed increase, speed decrease, start, and stop.

[0047] To solve the above technical problems, another technical solution adopted by this application is: a coal mine intelligent ventilation system. The coal mine intelligent ventilation system executes the coal mine intelligent ventilation method provided by this application, and includes: a data acquisition module, which is used to acquire ventilation monitoring data through a number of sensors; a simulation analysis module, which is communicatively connected to the data acquisition module and is used to receive the ventilation monitoring data to perform simulation analysis on the ventilation monitoring data by using a preset simulation analysis model for ventilation safety monitoring; a control module, which is communicatively connected to the simulation analysis module and is used to receive the analysis result of the simulation analysis to match the analysis result of the simulation analysis with a preset indicator to execute a control operation corresponding to the preset indicator; the coal mine intelligent ventilation system further includes a fault diagnosis module and an early warning module. The fault diagnosis module, the early warning module, and the control module are communicatively connected. The fault diagnosis module judges whether the coal mine intelligent ventilation system has a fault based on the analysis result and sends the diagnosis result to the early warning module so that the early warning module issues an early warning message, and the control module controls the coal mine intelligent ventilation system based on the diagnosis result.

[0048] To solve the above technical problems, another technical solution adopted by this application is: a coal mine intelligent ventilation device, which includes the coal mine intelligent ventilation system provided by this application. The coal mine intelligent ventilation device further includes: a return airway, a ventilator, and sensors. The return airway adopts automatic air doors. The return airway and the ventilator are communicatively connected to the control module, and the sensors are communicatively connected to the control module so that the control module controls the return airway, the ventilator, and the sensors based on the analysis result.

[0049] The beneficial effects of the present application are as follows: Different from the prior art, ventilation monitoring data is obtained through several sensors, and a preset simulation analysis model is used to perform simulation analysis on the ventilation monitoring data for ventilation safety monitoring, and the coal mine intelligent ventilation system is controlled based on the analysis results. By performing simulation analysis on the ventilation monitoring data and matching the analysis results of the simulation analysis with preset indicators to execute control operations corresponding to the preset indicators, the rationality, intelligence, and automation in adjusting the coal mine ventilation can be improved, which is beneficial to enhancing the safety during the coal mine operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic circuit structure diagram based on an embodiment of the coal mine intelligent ventilation system of the present application;

[0051] Figure 2 is a first flowchart based on an embodiment of the coal mine intelligent ventilation method of the present application;

[0052] Figure 3 is a second flowchart based on an embodiment of the coal mine intelligent ventilation method of the present application;

[0053] Figure 4 is a third flowchart based on an embodiment of the coal mine intelligent ventilation method of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0055] As Figure 1 shown, the embodiment of the coal mine intelligent ventilation system of the present application may include: a data acquisition module, a simulation analysis module, and a control module. Specifically, the data acquisition module is used to obtain ventilation monitoring data through several sensors. The simulation analysis module is communicatively connected to the data acquisition module and is used to receive the ventilation monitoring data sent by the data acquisition module to perform simulation analysis on the ventilation monitoring data for ventilation safety monitoring. The control module is communicatively connected to the simulation analysis module and is used to receive the analysis results of the simulation analysis to match the analysis results of the simulation analysis with preset indicators to execute control operations corresponding to the preset indicators.

[0056] By performing simulation analysis on the ventilation monitoring data and controlling the coal mine intelligent ventilation system based on the analysis results, the rationality, intelligence, and automation in adjusting the mine ventilation of the coal mine intelligent ventilation system can be improved, which is beneficial to enhancing the safety during the coal mine operation.

[0057] In one implementation, the intelligent coal mine ventilation system may further include a fault diagnosis module and a warning module. Specifically, the fault diagnosis module, the warning module, the control module, and the simulation analysis module are communicatively connected. The fault diagnosis module can receive the analysis result of the simulation analysis module, determine whether the intelligent coal mine ventilation system has a fault based on the analysis result, and send the diagnosis result to the warning module so that the warning module issues a warning message to prompt the engineering staff to pay attention to the current fault. Or send the diagnosis result to the control module, and the control module can perform corresponding control on the intelligent coal mine ventilation system based on the diagnosis result.

[0058] By setting up the fault diagnosis module and the warning module, faults in the intelligent coal mine ventilation system can be detected in a timely manner, corresponding operations can be carried out, and the construction personnel can be notified in a timely manner. On the one hand, it can prompt the construction personnel to repair the faults in a timely manner, and on the other hand, it can also warn the construction personnel to take timely precautions in case of danger, improving the safety and stability in coal mine work.

[0059] The embodiment of the intelligent coal mine ventilation device of the present application may include the intelligent coal mine ventilation system in the above-mentioned embodiment of the intelligent coal mine ventilation system and execute the corresponding intelligent coal mine ventilation method.

[0060] In one implementation, in order to improve the automation in coal mine work, the intelligent coal mine ventilation device may further include a return airway, a ventilator, and sensors. Specifically, the return airway adopts automatic air doors, and the return airway and the ventilator are communicatively connected to the control module, and the sensors are communicatively connected to the control module, so that the control module performs intelligent control on the automatic air doors of the return airway, the rotation state of the ventilator, and the opening and closing of the sensors based on the analysis result, thereby controlling the ventilation condition of the entire intelligent coal mine ventilation system to improve the safety of the intelligent coal mine ventilation system.

[0061] As Figure 2 shown, the intelligent coal mine ventilation method in the embodiment of the intelligent coal mine ventilation method of the present application can be applied to the above-mentioned intelligent coal mine ventilation system and intelligent coal mine ventilation device. This embodiment may include the following steps: S100: Obtain ventilation monitoring data through a plurality of sensors; S200: Perform simulation analysis on the ventilation monitoring data by using a preset simulation analysis model to perform ventilation safety monitoring; S300: Match the analysis result of the simulation analysis with a preset index to execute a control operation corresponding to the preset index.

[0062] Ventilation monitoring data is obtained through a number of sensors, and a preset simulation analysis model is used to perform simulation analysis on the ventilation monitoring data for ventilation safety monitoring, and the coal mine intelligent ventilation system is controlled based on the analysis results. By performing simulation analysis on the ventilation monitoring data and matching the analysis results of the simulation analysis with preset indicators, corresponding control operations are executed, thereby being able to improve the rationality, intelligence, and automation of the coal mine intelligent ventilation system in the process of adjusting mine ventilation, which is beneficial to improving the safety during coal mine operation.

[0063] The following will explain this embodiment in detail. This embodiment may include:

[0064] S100: Obtain ventilation monitoring data through a number of sensors.

[0065] The ventilation monitoring data may include at least two of wind speed data, humidity data, temperature data, air pressure data, gas data, carbon monoxide data, carbon dioxide data, and dust data. By obtaining various types of data and then performing safety analysis on various types of data, compared with the analysis of a single type of data, the accuracy of the ventilation system judgment can be improved.

[0066] In one implementation, a data acquisition module may be used to obtain ventilation monitoring data through a number of sensors. Specifically, the sensors correspond to the ventilation monitoring data, that is, the type of ventilation monitoring data corresponds to the type of sensors. And the number and location of the sensors can be determined based on preset safety requirements.

[0067] For example, the sensors may include an ultrasonic anemometer for measuring the real-time wind speed in the roadway, so that the air volume in all roadways underground can be calculated in real time.

[0068] In one implementation, before obtaining ventilation monitoring data through a number of sensors, the following steps may be included:

[0069] S110: Determine the type of sensors based on the type of ventilation monitoring data.

[0070] S120: Determine the location and number of sensors based on preset safety requirements.

[0071] S130: Uniformly distribute and set the sensors in the coal mine intelligent ventilation system according to the determined location and number.

[0072] Before obtaining ventilation monitoring data, the number, location, and type of sensors can be determined based on the ventilation monitoring data and preset safety requirements, and then the sensors can be evenly distributed and set in the coal mine intelligent ventilation system according to the determined location and number. Specifically, the preset safety requirements can include accurate air volume measurement indicators, gas emission calculation indicators, and environmental status identification indicators. The sensors can correspond to the ventilation monitoring data and the preset safety requirements. Specifically, the type of the sensors can correspond to the type of the ventilation monitoring data, the number of the sensors can correspond to the indicators of the preset safety requirements, and the location of the sensors can correspond to the location required by the preset safety requirements.

[0073] For example, if the type of the ventilation monitoring data is wind speed data, the corresponding type of the sensor is a wind speed sensor, and the number of the wind speed sensors corresponding to the accurate air volume measurement indicator is 8, so 8 wind speed sensors are set when setting the wind speed sensors.

[0074] For another example, during the process of obtaining humidity data, the data can be obtained through humidity sensors, and there are five humidity sensors set in the entire ventilation system based on the preset safety requirements, which are evenly set around the ventilation system and at the center point respectively, so as to obtain the data of multiple humidity sensors and avoid the errors caused by the measurement data of a single sensor.

[0075] In one implementation, the sensors can be provided with remote monitoring interfaces, and the data read by the sensors can be sent to a remote simulation analysis module, so that the simulation analysis module can receive the ventilation monitoring data and perform simulation analysis on the ventilation monitoring data.

[0076] In one implementation, the sensors can also be provided with remote control interfaces, and the sensors are communicatively connected to a control module through the remote control interfaces. The control module can control the opening and closing of the sensors based on the current ventilation situation or different ventilation requirements.

[0077] In one implementation, for how to determine the location and number of sensors based on the preset safety requirements, the following steps can be referred to:

[0078] S121: Divide the sensors of the same type at different locations into different levels according to the preset safety requirements.

[0079] During the process of determining the number and location of sensors based on the preset safety requirements, the sensors of the same type at different determined locations can be divided into different levels according to the preset safety requirements. Specifically, the sensors of the same type at different levels can be arranged and distributed in a cyclic order from low to high. By dividing the sensors into different levels, it is convenient to perform intelligent control on the sensors at different levels according to different ventilation situations, so that the accuracy of the data obtained by the sensors at different levels is higher.

[0080] For example, the wind speed sensors are of the same type. The wind speed sensors are divided into three levels, and in the ventilation duct, the wind speed sensors are arranged in a cycle of the first level, the second level, the third level, the first level, the second level, the third level... Under different ventilation conditions, the opening and closing of sensors of different levels can be controlled. Thus, in the case of normal ventilation, the low-level sensors can be started to work normally to save energy consumption, and in the case of abnormal ventilation, multiple levels of sensors can be turned on simultaneously to improve the accuracy of the obtained ventilation monitoring data.

[0081] In one implementation, before obtaining ventilation monitoring data through a plurality of sensors, the following steps may further be included:

[0082] S140: Obtain historical arrangement data stored in a preset storage space.

[0083] The preset storage space may be a space pre-set in the coal mine intelligent ventilation system for storing data. The historical arrangement data may include the historical positions and historical quantities of the sensors and the corresponding historical scenarios.

[0084] S150: Compare the historical scenario with the current scenario.

[0085] S160: If they are consistent, evenly distribute the sensors in the coal mine intelligent ventilation system according to the historical positions and historical quantities.

[0086] S170: If they are inconsistent, determine the positions and quantities of the sensors based on the preset safety requirements, evenly distribute the sensors in the coal mine intelligent ventilation system according to the determined positions and quantities, and update the historical arrangement data.

[0087] After obtaining the historical scenario in the historical arrangement data, the historical scenario can be compared with the current scenario. If the historical scenario is consistent with the current scenario, the sensors can be evenly distributed in the coal mine intelligent ventilation system according to the historical positions and historical data corresponding to the historical scenario. If the historical scenario is inconsistent with the current scenario, the positions and quantities of the sensors can be determined based on the preset safety requirements corresponding to the current scenario. Thus, the sensors can be evenly distributed in the coal mine intelligent ventilation system according to the determined positions and quantities. At the same time, the historical arrangement data in the historical storage space can also be updated based on the determined positions and quantities of the sensors, so that in the subsequent process of re-arranging the sensors, the layout can be based on the latest historical arrangement data.

[0088] Before obtaining ventilation monitoring data through a number of sensors, historical arrangement data stored in a preset storage space can be obtained, so that during the process of arranging the sensors, settings can be made based on the historical arrangement data, which is beneficial to improving the efficiency of sensor arrangement.

[0089] S200: Use a preset simulation analysis model to perform simulation analysis on ventilation monitoring data for ventilation safety monitoring.

[0090] After obtaining ventilation monitoring data through a number of sensors, the ventilation monitoring data can be subjected to simulation analysis for ventilation safety monitoring. Specifically, the sensors can send the ventilation monitoring data to the simulation analysis module, and the simulation analysis module performs simulation analysis based on the received ventilation monitoring data to obtain an analysis result, and then the coal mine intelligent ventilation system can be controlled based on the analysis result.

[0091] In one implementation, as Figure 3 shown, before using a preset simulation analysis model to perform simulation analysis on ventilation monitoring data for ventilation safety monitoring, the following steps can be included:

[0092] S210: Simplify the mine geographical data and then import it into model construction software to generate a basic mine model.

[0093] S220: Adjust the basic mine model according to the geographical relationship of the mine to make the adjusted geographical relationship conform to the actual situation, and obtain a three-dimensional mine model.

[0094] S230: Simplify the roadway position data and then import it into model construction software to generate a basic roadway model.

[0095] S240: Adjust the basic roadway model according to the spatial relationship of the roadway to make the adjusted spatial relationship conform to the actual situation.

[0096] S250: Input the specific parameters of roadways in different sections into the adjusted basic roadway model to obtain a three-dimensional ventilation model.

[0097] S260: Combine the three-dimensional mine model and the three-dimensional ventilation model to obtain a preset simulation analysis model.

[0098] In one implementation, the simulation analysis module can include a model construction module for constructing a three-dimensional mine model and a three-dimensional ventilation model according to preset data to perform simulation analysis on the obtained ventilation monitoring data by using the three-dimensional mine model and the three-dimensional ventilation model. By simultaneously combining the three-dimensional mine model and the three-dimensional ventilation model to perform simulation analysis on ventilation monitoring data, the terrain and ventilation structure can be combined simultaneously, so that the analysis result of the simulation analysis is more accurate and conforms to the actual situation.

[0099] The preset data may include mine geographical data and roadway location data. Specifically, the mine geographical data may include geographical data obtained during the exploration process, such as longitude and latitude, altitude, and the thickness of rock and soil. The roadway location data may include the layout information of each roadway during the design of the intelligent coal mine ventilation system, such as coordinate information.

[0100] In one implementation, the preset data may include mine geographical data and pipeline location data. Based on the mine geographical data, a three-dimensional mine model can be constructed, and based on the pipeline location data, a three-dimensional ventilation model can be constructed. The preset data can be pre-stored in the intelligent coal mine ventilation system or obtained and uploaded based on survey equipment.

[0101] During the process of constructing the three-dimensional mine model, the mine geographical data can be simplified and then imported into the model construction software to generate a basic model. The basic model is adjusted according to the geographical relationship of the mine so that the adjusted geographical relationship is consistent with the actual situation, thereby obtaining the three-dimensional mine model.

[0102] In one implementation, for how to adjust the basic model according to the geographical relationship of the mine, the following steps included in S220 can be referred to:

[0103] S221: Obtain the proportional relationship and geometric relationship in the geographical relationship of the mine.

[0104] S222: Adjust the basic mine model based on the proportional relationship and geometric relationship in the geographical relationship of the mine.

[0105] After the mine geographical data is simplified and imported into the model construction software to obtain the basic mine model, the proportional relationship and geometric relationship in the mine geographical relationship can be obtained to adjust the basic mine model based on the proportional relationship and geometric relationship in the mine geographical relationship, so that the adjusted basic mine model fits the geographical situation of the actual mine more closely. Specifically, the proportional relationship may include the scale relationship between the mileage data in the actual mine geographical relationship and the mileage data in the basic mine model, and the geometric relationship may include the vertical relationship, parallel relationship, and intersection relationship in the mine geographical relationship.

[0106] During the process of constructing the three-dimensional ventilation model, the roadway location data can be simplified and then imported into the model construction software to generate a basic roadway model. The basic roadway model is adjusted according to the spatial relationship of the roadway so that the adjusted spatial relationship is consistent with the actual situation, thereby obtaining the three-dimensional ventilation model.

[0107] In one implementation, for how to adjust the basic roadway model according to the spatial relationship of the roadway, the following steps included in S240 can be referred to:

[0108] S241: Obtain the proportional relationship and geometric relationship in the spatial relationship of the roadway.

[0109] S242: Adjust the basic roadway model based on the proportional relationship and geometric relationship in the spatial relationship of the roadway.

[0110] After simplifying the roadway position data and importing it into the model construction software to obtain the basic roadway model, the proportional relationship and geometric relationship in the spatial relationship of the roadway can be obtained, so as to adjust the basic roadway model based on the proportional relationship and geometric relationship in the spatial relationship of the roadway, so that the adjusted basic roadway model can better fit the actual spatial distribution of the roadway. Specifically, the proportional relationship may include the proportional relationship between the actual roadway length and the roadway length in the basic model, and the geometric relationship may include the vertical relationship, parallel relationship, and intersection relationship between the roadways.

[0111] For example, the Ventsim ventilation simulation software can be used to construct a three-dimensional ventilation model, and the three-dimensional ventilation model can be established according to the actual size of the coal mine ventilation structure at a ratio of 1:1. By accurately inputting the specific parameters of the roadway position data and according to the internal calculation method of the software, the ventilation network can be accurately calculated, and the calculation results obtained have a certain degree of credibility.

[0112] In one implementation, during the process of constructing the three-dimensional ventilation model, the influence of frictional resistance can also be considered. Specifically, the frictional resistance coefficient can be reasonably valued, and the frictional resistance coefficient and the roadway position data are input into the software to obtain a three-dimensional ventilation model that is the same as the actual situation.

[0113] In one implementation, after the three-dimensional mine model and the three-dimensional ventilation model are constructed, the actual situation can be compared with the simulation results of the simulation model to judge the accuracy of the three-dimensional modeling.

[0114] During the process of performing simulation analysis using the three-dimensional mine model and the three-dimensional ventilation model, the computer program can be used to simulate and calculate the coal mine ventilation system. By establishing a calculation model, the operation of the ventilation system under different working conditions can be simulated, and the possible problems can be predicted. And by changing different parameters and boundary conditions, the performance and operation of the ventilation system can be studied, and its impact on the safety of miners and coal mine production can be evaluated.

[0115] In one implementation, for how to perform simulation analysis on ventilation monitoring data using a preset simulation analysis model for ventilation safety monitoring, the following steps included in S200 can be referred to:

[0116] S270: Input the ventilation monitoring data into the preset simulation analysis model.

[0117] S280: Adapt the ventilation monitoring data to the data in the 3D mine model and the 3D ventilation model respectively in the preset simulation analysis model to obtain the analysis result.

[0118] After the preset simulation analysis model is constructed using model construction software, the ventilation monitoring data read by the sensor can be input into the preset simulation analysis model, and then the ventilation monitoring data is adapted to the data in the 3D mine model and the 3D ventilation model respectively to obtain the analysis result. Specifically, the simulation data in the 3D mine model and the 3D ventilation model can be compared with the ventilation monitoring data obtained by actual measurement. If they are consistent, the analysis result of normal ventilation can be output. If they are inconsistent, the analysis result of abnormal ventilation can be output.

[0119] In one implementation, due to the existence of errors, a preset floating range can be set in the process of judging whether the simulation data is consistent with the actual measurement data. For example, a floating range of ±2% can be determined as consistent.

[0120] S300: Match the analysis result of the simulation analysis with the preset index to perform the control operation corresponding to the preset index.

[0121] After the simulation analysis module outputs the analysis result, the analysis result can be matched with the preset index to perform the corresponding control. Specifically, the control module can receive the analysis result output by the simulation analysis module, so as to control the intelligent coal mine ventilation system through the control module.

[0122] In one implementation, for how to control the intelligent coal mine ventilation system correspondingly based on the analysis result of the simulation analysis, the following steps included in S300 can be referred to:

[0123] S310: If the analysis result is normal ventilation, control the return airway to be locked or control the ventilator to start.

[0124] S320: If the analysis result is abnormal ventilation, match the analysis result with the preset index to perform the control operation corresponding to the preset index.

[0125] S330: If the analysis result is normal ventilation, control the sensors at the lowest level to work properly and read data.

[0126] S340: If the analysis result is abnormal ventilation, obtain the running monitoring value of the ventilation monitoring data corresponding to the sensor, determine the value of the final-level index corresponding to the running monitoring value, and control the opening and closing of sensors at different levels based on the value of the final-level index.

[0127] Since the return airway, the ventilator, and the sensors are communicatively connected to the control module, the control module can perform corresponding control on the return airway, the ventilator, and the sensors based on the analysis results, thereby enabling intelligent control of the coal mine intelligent ventilation system and improving the automation of the coal mine ventilation system.

[0128] The coal mine intelligent ventilation system may include a main return airway, a primary return airway, a district return airway, and a working face return airway. Automatic air doors may be used for the main return airway, the primary return airway, the district return airway, and the working face return airway. By using automatic air doors, the control module can control whether each return airway is locked based on the analysis results. Specifically, if the analysis result is normal ventilation, the control module controls the main return airway, the primary return airway, the district return airway, and the working face return airway to be locked, or the control module controls the ventilator to start. Thus, under normal ventilation conditions, the control module can ensure the normal operation of the return airway and the ventilator, thereby improving the automation and safety of the coal mine intelligent ventilation system.

[0129] If the analysis result is normal ventilation, the control module controls the sensors at the lowest level to operate normally and read data. When the sensors at the lowest level are operating normally, ventilation monitoring data of the coal mine intelligent system under normal ventilation conditions can be obtained, thereby saving energy to a certain extent.

[0130] For example, if the analysis result is normal ventilation, the control module can control the first-level wind speed sensors to operate normally to read wind speed data.

[0131] In one implementation, as Figure 4 shown, for the case where the analysis result is abnormal ventilation, how to match the analysis result with the preset indicators may include the following steps:

[0132] S3211: Determine the operation monitoring value corresponding to the final-level indicator based on the ventilation monitoring data in the analysis result.

[0133] S3212: Substitute the operation monitoring value into the preset decision-making algorithm to obtain the value of the final-level indicator and perform aggregation.

[0134] S3213: Weight the values of multiple said final-level indicators in combination with the preset weight ratio to calculate the value of the upper-level indicator.

[0135] S3214: Perform weighted calculation step by step until the first-level indicator.

[0136] S3215: Perform control based on the action decision corresponding to the value of the first-level indicator.

[0137] The intelligent coal mine ventilation system may further include a fault diagnosis module. The fault diagnosis module may be communicatively connected to the simulation analysis module to receive the analysis results, match the analysis results with preset indicators to obtain a matching result, and the control module may then perform a control operation corresponding to the preset indicators based on the matching result.

[0138] In the process of matching the analysis results with the preset indicators, in the fault diagnosis module, according to the production process module or business management classification, a series of multi-level index factors and influence weights that affect the normal operation or accurate calculation of the intelligent coal mine ventilation system are defined, the judgment algorithm and result collection of each final-level index are defined, the operation monitoring values of one or a group of ventilation equipment or sensors corresponding to each final-level index are defined, and the action decisions for different value ranges of each first-level index are defined, such as: the equipment or sensors for starting / stopping / speed regulation / warning, so as to execute the decision-making actions according to the decision results. By defining various index parameters, the fault diagnosis module can thus match the analysis results with the preset indicators, improving the automation control level of the intelligent coal mine ventilation system.

[0139] Specifically, first, determine the operation monitoring value corresponding to the final-level index according to the ventilation monitoring data in the analysis results, substitute the determined operation monitoring value into the defined judgment algorithm to obtain the value of the final-level index, and perform result collection. Then, weight the calculation results of multiple final-level indexes in combination with the preset weight ratio to calculate the value of the upper-level index, and then perform weighted calculation level by level until the first-level index. Finally, perform control based on the action decision corresponding to the value of the first-level index.

[0140] In one implementation, for how to substitute the operation monitoring value into the preset judgment algorithm to obtain the value of the final-level index, the following steps can be referred to:

[0141] S32121: Determine the range corresponding to the operation monitoring value.

[0142] S32122: Determine the value of the corresponding final-level index according to the determined range.

[0143] In the process of determining the value of the final-level index corresponding to the operation monitoring value, the operation monitoring value can be pre-divided into different ranges, and the values of the final-level indexes corresponding to different ranges are different. After determining the corresponding range based on the operation monitoring value, the value of the corresponding final-level index can be determined according to the determined range.

[0144] For example, the range of the operation monitoring value can be divided into 0 - 20, 21 - 40, 41 - 60, 61 - 80, 81 - 100, and the values of the corresponding final-level indexes can be 1, 2, 3, 4, 5 respectively. If the operation monitoring value is 66, the value of the corresponding final-level index is 4.

[0145] If the analysis result is abnormal ventilation, it is also possible to obtain the operation monitoring values of the ventilation monitoring data corresponding to the sensors, determine the values of the final-level indicators corresponding to the operation monitoring values, and control the opening and closing of sensors of different levels based on the values of the final-level indicators. Specifically, the larger the value of the final-level indicator, the more sensors of different levels there are. By controlling the opening and closing of sensors of different levels based on different ventilation monitoring data, the accuracy of sensor data reading under different ventilation conditions can be improved.

[0146] For example, if the operation monitoring value corresponding to the wind speed sensor is 22, the value of the corresponding final-level indicator is 2, and the sensors of the first level and the second level can be controlled to work simultaneously to read the wind speed data. If the operation monitoring value corresponding to the wind speed sensor is 88, the value of the corresponding final-level indicator is 5, and the sensors of the first level, the second level, the third level, the fourth level, and the fifth level can be controlled to work simultaneously to read the wind speed data.

[0147] In one implementation, for how to control based on the action decision corresponding to the value of the primary indicator, the following steps can be referred to:

[0148] S32151: Control at least one of the main return airway, the main return airway of the mining area, the return airway of the working face, and the return airway of the mining area to unlock based on the value of the primary indicator.

[0149] After calculating the primary indicator, it is possible to control at least one of the main return airway, the main return airway of the mining area, the return airway of the working face, and the return airway of the mining area to unlock based on the value of the primary indicator. Specifically, the larger the value of the primary indicator, the more serious the abnormal ventilation situation can be indicated, and the larger the number of return airways unlocked, until all return airways are unlocked to cope with the abnormal ventilation situation.

[0150] S32152: Control the rotation state of at least one of the main ventilator and the local ventilator based on the value of the primary indicator.

[0151] The intelligent coal mine ventilation system may also include a main ventilator and a local ventilator, and the main ventilator and the local ventilator are communicatively connected to the control module, so that the control module can control the rotation state of the main ventilator and the local ventilator after matching the analysis result with the preset indicator.

[0152] After calculating the first-level indicators, it is also possible to control the rotation state of at least one of the main ventilation fan and the local ventilation fan based on the values of the first-level indicators. Specifically, the rotation state can include speed increase, speed decrease, start, and stop. During the process of adjusting the rotation state of the ventilation fan, corresponding controls can be determined based on the abnormal ventilation situation. If the abnormal ventilation is insufficient ventilation, the main ventilation fan and the local ventilation fan can be speeded up one by one. If the abnormal ventilation is excessive ventilation or a failure occurs, the main ventilation fan and the local ventilation fan can be speeded down until they stop one by one.

[0153] In one implementation, the intelligent coal mine ventilation system may further include an early warning module. The early warning module can be communicatively connected to the fault diagnosis module to receive the matching result and give an early warning to the construction personnel based on the matching result. During the process of the early warning module giving an early warning to the construction personnel based on the matching result, the early warning module can issue an early warning to the construction personnel through at least one of the prompt methods such as lighting an indicator light, sounding a siren, information prompting, and voice broadcasting, so that the construction personnel can timely obtain the news of the abnormality of the intelligent coal mine ventilation system and perform relevant processing in a timely manner.

[0154] The above are only the embodiments of the present application, and do not limit the patent scope of the present application accordingly. All equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present application.

Claims

1. A coal mine intelligent ventilation method, applied to a coal mine intelligent ventilation system, characterized in that: include: Determine the location and quantity of several sensors based on preset safety requirements; The determining of the positions and quantities of the plurality of sensors based on the preset safety requirements includes: The sensors of the same type at different locations are divided into different levels according to the preset safety requirements; wherein the sensors of different levels are arranged and distributed cyclically from low to high; Acquiring ventilation monitoring data through a plurality of sensors; wherein the ventilation monitoring data includes at least two of wind speed data, humidity data, temperature data, air pressure data, gas data, carbon monoxide data, carbon dioxide data and dust data; Using a preset simulation analysis model to perform simulation analysis on the ventilation monitoring data to perform ventilation safety monitoring; Matching the analysis result of the simulation analysis with the preset index to execute the control operation corresponding to the preset index; The matching of the analysis result of the simulation analysis with the preset index to execute the control operation corresponding to the preset index includes: If the analysis result is normal ventilation, the sensor at the lowest level is controlled to work normally and read data; If the analysis result is abnormal ventilation, the operation monitoring value of the ventilation monitoring data corresponding to the sensor is obtained, the value of the final-level indicator corresponding to the operation monitoring value is determined, and the opening and closing of the sensors at different levels are controlled based on the value of the final-level indicator.

2. The method according to claim 1, characterized in that Before obtaining ventilation monitoring data through a plurality of sensors, it includes: determining a type of the sensor based on a type of the ventilation monitoring data; The sensors are evenly distributed in the coal mine intelligent ventilation system according to the determined positions and quantities.

3. The method according to claim 1, characterized in that Before obtaining ventilation monitoring data through a plurality of sensors, the method further includes: Acquire historical arrangement data stored in a preset storage space; wherein the historical arrangement data includes the historical position and historical quantity of the sensor and the corresponding historical scene; Contrast said historical scenario with the current scenario; If they are consistent, the sensors are evenly distributed and arranged in the coal mine intelligent ventilation system according to the historical positions and the historical quantities; If there is inconsistency, the position and quantity of the sensors are determined based on preset safety requirements, so that the sensors are evenly distributed in the coal mine intelligent ventilation system according to the determined positions and quantities, and the historical arrangement data is updated.

4. The method according to claim 1, characterized in that: The preset simulation analysis model includes a three-dimensional mine model and a three-dimensional ventilation model. Before using the preset simulation analysis model to simulate and analyze the ventilation monitoring data to perform ventilation safety monitoring, the method includes: Simplify the mine geographic data and import it into the model building software to generate a basic mine model; Obtaining proportional relationships and geometric relationships in the geographical relationship of the mine; The basic mine model is adjusted based on the proportional relationship and geometric relationship in the geographical relationship of the mine, so that the adjusted geographical relationship is consistent with the actual situation, thereby obtaining the three-dimensional mine model; After simplifying the tunnel location data, import it into the model building software to generate a basic model of the tunnel; Obtaining proportional relationships and geometric relationships in the spatial relationship of the lanes; Adjusting the basic model of the lane based on the proportional relationship and the geometric relationship in the spatial relationship of the lane so that the adjusted spatial relationship is consistent with the actual situation; Inputting specific parameters of lanes in different sections into the adjusted lane basic model to obtain the three-dimensional ventilation model; Combining the three-dimensional mine model with the three-dimensional ventilation model to obtain the preset simulation analysis model; The method of using a preset simulation analysis model to simulate and analyze the ventilation monitoring data to perform ventilation safety monitoring includes: Inputting the ventilation monitoring data into the preset simulation analysis model; In the preset simulation analysis model, the ventilation monitoring data is respectively matched with the data in the three-dimensional mine model and the three-dimensional ventilation model to obtain analysis results.

5. The method according to claim 1, characterized in that The matching of the analysis result of the simulation analysis with the preset index to execute the control operation corresponding to the preset index includes: If the analysis result is normal ventilation, the return air lane is controlled to be locked, or the ventilator is controlled to start; If the analysis result is abnormal ventilation, then Determine the operation monitoring value corresponding to the final level indicator according to the ventilation monitoring data in the analysis results; Determine the range corresponding to the operation monitoring value; wherein different ranges correspond to different values ​​of the final-level indicator; Determine the value of the corresponding final-level indicator according to the determined range and aggregate it; The values ​​of the multiple lower-level indicators are weighted in combination with a preset weight ratio to calculate the value of the upper-level indicator; Weighted calculation is performed step by step until the first-level indicator; Control is performed based on the action decision corresponding to the value of the first-level indicator.

6. The method according to claim 5, characterized in that The return air lanes include the main return air lanes, the main return air lanes, the mining area return air lanes, and the working face return air lanes. The ventilators include the main ventilators and the local ventilators. The control is performed based on the action decision corresponding to the value of the primary indicator, including: Based on the value of the primary index, at least one of the total return air lane, the main return air lane, the mining area return air lane, and the working face return air lane is controlled to be unlocked; wherein, the larger the value of the primary index is, the greater the number of return air lanes unlocked; The rotation state of at least one of the main fan and the local fan is controlled based on the value of the primary index; wherein the rotation state includes speed increase, speed decrease, opening and closing.

7. A coal mine intelligent ventilation system, the coal mine intelligent ventilation system executing the coal mine intelligent ventilation method according to any one of claims 1 to 6, characterized in that: include: A data acquisition module, used for acquiring ventilation monitoring data through a number of sensors; A simulation analysis module, which is in communication with the data acquisition module and is used to receive the ventilation monitoring data, and to perform simulation analysis on the ventilation monitoring data using a preset simulation analysis model to perform ventilation safety monitoring; A control module, which is in communication with the simulation analysis module and is used to receive the analysis result of the simulation analysis, so as to match the analysis result of the simulation analysis with a preset indicator, so as to perform a control operation corresponding to the preset indicator; The coal mine intelligent ventilation system also includes a fault diagnosis module and an early warning module. The fault diagnosis module, the early warning module and the control module are communicatively connected. The fault diagnosis module determines whether the coal mine intelligent ventilation system has a fault based on the analysis result, and sends the diagnosis result to the early warning module so that the early warning module issues an early warning message. The control module controls the coal mine intelligent ventilation system based on the diagnosis result.

8. An intelligent ventilation device for coal mines, characterized in that: It includes the coal mine intelligent ventilation system as described in claim 7, and the coal mine intelligent ventilation device also includes: a return air lane, a ventilator and a sensor, the return air lane adopts an automatic damper, the return air lane and the ventilator are communicatively connected to the control module, and the sensor is communicatively connected to the control module, so that the control module controls the return air lane, the ventilator and the sensor based on the analysis result.

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