Intelligent ventilation method, system and device for coal mine

Abstract

The invention discloses an intelligent ventilation method, system and device for a coal mine. The method comprises the following steps: acquiring ventilation monitoring data through a plurality of sensors; wherein the ventilation monitoring data comprises 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; performing simulation analysis on the ventilation monitoring data by using a preset simulation analysis model so as to perform ventilation safety monitoring; and matching an analysis result of the simulation analysis with a preset index so as to execute a control operation corresponding to the preset index. In this way, the rationality, intelligence and automation of coal mine ventilation can be improved.

Description

Technical Field

[0001] The present application relates to the field of coal mine technology, and in particular to a method, system and device for intelligent ventilation in coal mines. Background Art

[0002] As coal resources are an indispensable part of the energy industry, the mining of coal resources has also been paid attention to by people, and one of the key factors for the smooth mining of coal mines is coal mine ventilation. When coal mines are mined, there are often harmful gases inside the coal mines, such as carbon monoxide, carbon dioxide and dust. The ventilation system can effectively discharge these harmful substances from the mines to ensure the health and life safety of the miners.

[0003] Coal mine ventilation system is a key 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 dynamic nature of coal mine ventilation system. It is necessary to adopt high-tech means, introduce advanced technology, and use automated management systems to regularly test, inspect and evaluate various ventilation parameters, timely collect and analyze the operation information of the ventilation system, formulate the optimal airflow adjustment plan for each production period, and predict the long-term operation status of the ventilation system to improve the level of coal mine ventilation management.

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

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

[0006] In order to solve the above technical problems, a technical solution adopted in the present application is: to provide a coal mine intelligent ventilation method, which is applied to the coal mine intelligent ventilation system, including: obtaining ventilation monitoring data through a number 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 simulate and analyze the ventilation monitoring data for ventilation safety monitoring; matching the analysis results of the simulation analysis with preset indicators to execute control operations corresponding to the preset indicators.

[0007] Optionally, before obtaining ventilation monitoring data through a plurality of sensors, including: determining a type of sensor based on a type of ventilation monitoring data; Determine the location and quantity of sensors based on pre-set safety requirements; The sensors are evenly distributed in the coal mine intelligent ventilation system according to the determined positions and quantities.

[0008] Optionally, 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 historical positions and historical quantities of sensors and corresponding historical scenes; Contrast historical scenarios with current ones; If they are consistent, the sensors are evenly distributed and set in the coal mine intelligent ventilation system according to the historical positions and historical quantities; If there is any inconsistency, the location and number of 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 location and number, and the historical arrangement data is updated.

[0009] Optionally, the location and number of sensors are determined based on preset safety requirements, including: Sensors of the same type at different locations are divided into different levels according to preset security requirements; sensors of different levels are arranged in a cyclic distribution from low to high.

[0010] Optionally, matching the analysis result of the simulation analysis with a preset indicator to perform a control operation corresponding to the preset indicator includes: If the analysis result is normal ventilation, the sensor at the lowest level is controlled to work normally and read the 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 sensors of different levels are controlled based on the value of the final-level indicator.

[0011] Optionally, 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 for ventilation safety monitoring, the following steps are included: After simplifying the mine geographic data, import it into the model building software to generate a basic model; Obtain the proportional and geometric relationships in the geographical relationship of the mine; The basic model is adjusted based on the proportional relationship and geometric relationship so that the adjusted geographical relationship is consistent with the actual situation, thereby obtaining a three-dimensional mine model; After simplifying the lane location data, import it into the model building software to generate a basic model; Obtain the proportional relationship and geometric relationship in the spatial relationship of the lane; Adjust the basic model based on the proportional relationship and geometric relationship so that the adjusted spatial relationship is consistent with the actual situation; The specific parameters of the tunnels in different sections are input into the adjusted basic model to obtain a three-dimensional ventilation model; Combining the 3D mine model with the 3D ventilation model to obtain a preset simulation analysis model; Use the preset simulation analysis model to simulate and analyze the ventilation monitoring data for ventilation safety monitoring, including: Input 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 the analysis results.

[0012] Optionally, matching the analysis result of the simulation analysis with a preset indicator to perform a control operation corresponding to the preset indicator includes: If the analysis result is normal ventilation, the return air channel is controlled to be locked, or the ventilator is controlled to start; If the analysis result is abnormal ventilation, the operation monitoring value corresponding to the final level indicator is determined based on the ventilation monitoring data in the analysis result; Determine the range corresponding to the operation monitoring value; wherein different ranges correspond to different values ​​of the final level indicators; Determine the value of the corresponding final-level indicator according to the determined range and aggregate it; The values ​​of multiple lower-level indicators are weighted according to the 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.

[0013] Optionally, the return air lane includes a total return air lane, a main return air lane, a mining area return air lane, and a working face return air lane, and the ventilator includes a main ventilator and a local ventilator. The control is performed based on the action decision corresponding to the value of the primary indicator, including: Based on the value of the first-level 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 first-level index, the greater the number of return air lanes unlocked; The rotation state of at least one of the main ventilator and the local ventilator is controlled based on the value of the primary index; wherein the rotation state includes speed increase, speed decrease, opening and closing.

[0014] To solve the above-mentioned technical problems, another technical solution adopted in the present application is: a coal mine intelligent ventilation system, comprising: a data acquisition module, used to acquire ventilation monitoring data through a number of sensors; a simulation analysis module, which is communicated with the data acquisition module, and is used to receive ventilation monitoring data, so as to use a preset simulation analysis model to perform simulation analysis on the ventilation monitoring data for ventilation safety monitoring; a control module, which is communicated with the simulation analysis module, and is used to receive the analysis results of the simulation analysis, so as to match the analysis results of the simulation analysis with preset indicators, so as to perform control operations corresponding to the preset indicators; 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 communicated with each other, the fault diagnosis module determines whether the coal mine intelligent ventilation system has a fault based on the analysis results, and sends the diagnosis results 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 results.

[0015] In order to solve the above-mentioned technical problems, another technical solution adopted in the present application is: a coal mine intelligent ventilation device, including the coal mine intelligent ventilation system provided by the present application, the coal mine intelligent ventilation system executes the coal mine intelligent ventilation method provided by the present application, and the coal mine intelligent ventilation device also includes: return air lane, ventilator and sensor, the return air lane adopts automatic damper, the return air lane and ventilator are communicatively connected with the control module, and the sensor is communicatively connected with the control module, so that the control module controls the return air lane, ventilator and sensor based on the analysis results.

[0016] The beneficial effects of the present application are: different from the prior art, ventilation monitoring data is obtained through a number of sensors, and a preset simulation analysis model is used to simulate and analyze the ventilation monitoring data to perform ventilation safety monitoring, and the coal mine intelligent ventilation system is controlled based on the analysis results. By simulating and analyzing the ventilation monitoring data and matching the analysis results of the simulation analysis with preset indicators to perform control operations corresponding to the preset indicators, the rationality, intelligence and automation of the process of adjusting the ventilation of the coal mine can be improved, which is conducive to improving the safety of the coal mine operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the circuit structure of an embodiment of the present application based on the coal mine intelligent ventilation system; Figure 2 This is a first flow chart of an embodiment of the present application based on the intelligent ventilation method for coal mines; Figure 3 This is a second flow chart of an embodiment of the present application based on the intelligent ventilation method for coal mines; Figure 4 This is the third flow chart of the present application based on the embodiment of the intelligent ventilation method for coal mines. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] like Figure 1 As shown, the embodiment of the intelligent ventilation system for coal mines 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 acquire ventilation monitoring data through a number of 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, so as 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, so as to match the analysis results of the simulation analysis with preset indicators, so as to perform control operations corresponding to the preset indicators.

[0020] By simulating and analyzing the ventilation monitoring data and controlling the coal mine intelligent ventilation system based on the analysis results, the rationality, intelligence and automation of the coal mine intelligent ventilation system in adjusting mine ventilation can be improved, which is beneficial to improving the safety of coal mine operations.

[0021] In one implementation, the intelligent ventilation system for coal mines may further include a fault diagnosis module and an early warning module. Specifically, the fault diagnosis module, the early warning module, the control module and the simulation analysis module are in communication connection. The fault diagnosis module may receive the analysis result of the simulation analysis module, determine whether the intelligent ventilation system for coal mines has a fault based on the analysis result, and send the diagnosis result to the early warning module so that the early warning module issues an early warning message to remind the engineering personnel to pay attention to the current fault. Or the diagnosis result may be sent to the control module, and the control module may control the intelligent ventilation system for coal mines accordingly based on the diagnosis result.

[0022] By setting up the fault diagnosis module and the early warning module, faults in the intelligent ventilation system of coal mines can be discovered in time, and corresponding operations can be performed, while the construction personnel can be notified in time. On the one hand, it can prompt the construction personnel to repair the faults in time, and on the other hand, it can also warn the construction personnel to avoid risks in time when danger occurs, thereby improving the safety and stability of coal mine work.

[0023] The coal mine intelligent ventilation device embodiment of the present application may include the coal mine intelligent ventilation system in the above-mentioned coal mine intelligent ventilation system embodiment, and execute the corresponding coal mine intelligent ventilation method.

[0024] In one implementation, in order to improve the automation of coal mine work, the coal mine intelligent ventilation device may also include a return air lane, a ventilator and a sensor. Specifically, the return air lane uses an automatic damper, and 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 intelligently controls the automatic damper of the return air lane, the rotation state of the ventilator and the opening and closing of the sensor based on the analysis results, thereby controlling the ventilation condition of the entire coal mine intelligent ventilation system to improve the safety of the coal mine intelligent ventilation system.

[0025] like Figure 2 As shown, the intelligent ventilation method for coal mines in the embodiment of the intelligent ventilation method for coal mines of the present application can be applied to the above-mentioned intelligent ventilation system for coal mines and the intelligent ventilation device for coal mines. This embodiment may include the following steps: S100: Acquire ventilation monitoring data through a number of sensors; S200: Use a preset simulation analysis model to perform simulation analysis on the ventilation monitoring data to perform ventilation safety monitoring; S300: Match the analysis results of the simulation analysis with preset indicators to perform control operations corresponding to the preset indicators.

[0026] Ventilation monitoring data is acquired through a number of sensors, and a preset simulation analysis model is used to simulate and analyze the ventilation monitoring data for ventilation safety monitoring, and the coal mine intelligent ventilation system is controlled based on the analysis results. By simulating and analyzing the ventilation monitoring data and matching the simulation analysis results with the preset indicators to perform control operations corresponding to the preset indicators, the rationality, intelligence and automation of the coal mine intelligent ventilation system in adjusting the mine ventilation process can be improved, which is conducive to improving the safety of coal mine operations.

[0027] The present embodiment is described in detail below. The present embodiment may include: S100: Acquire ventilation monitoring data through a plurality of sensors.

[0028] 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 acquiring multiple types of data and then performing safety analysis on the multiple types of data, the accuracy of ventilation system judgment can be improved compared to the analysis of a single data.

[0029] In one implementation, the data acquisition module may be used to acquire ventilation monitoring data through a plurality of sensors. Specifically, the sensors correspond to the ventilation monitoring data, that is, the type of ventilation monitoring data corresponds to the type of sensor. The number and location of the sensors may be determined based on preset safety requirements.

[0030] For example, the sensor may include an ultrasonic anemometer for measuring the real-time wind speed in the tunnel, so that the wind volume in all tunnels in the mine can be calculated in real time.

[0031] In one implementation, before obtaining ventilation monitoring data through a plurality of sensors, the following steps may be included: S110: Determine the type of sensor based on the type of ventilation monitoring data.

[0032] S120: Determine the location and quantity of sensors based on preset safety requirements.

[0033] S130: Evenly distribute the sensors in the coal mine intelligent ventilation system according to the determined positions and quantities.

[0034] 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 in the coal mine intelligent ventilation system according to the determined locations and numbers. Specifically, the preset safety requirements may include accurate wind measurement indicators, gas outflow calculation indicators, and environmental status identification indicators. Sensors can correspond to ventilation monitoring data and preset safety requirements. Specifically, the type of sensor can correspond to the type of ventilation monitoring data, the number of sensors can correspond to the indicators of the preset safety requirements, and the location of the sensor can correspond to the location required by the preset safety requirements.

[0035] For example, the type of ventilation monitoring data is wind speed data, and the corresponding sensor type is a wind speed sensor, and the number of corresponding wind speed sensors in the precise wind measurement index is 8, so when setting the wind speed sensor, it is set to 8.

[0036] For example, in the process of obtaining humidity data, data can be obtained through humidity sensors. The entire ventilation system is equipped with five humidity sensors based on preset safety requirements, which are evenly arranged around the ventilation system and at the center point. This allows data from multiple humidity sensors to be obtained, avoiding errors caused by measuring data with a single sensor.

[0037] In one implementation, the sensor may be provided with a remote monitoring interface, and the data read by the sensor may be sent to a remote simulation analysis module, so that the simulation analysis module may receive the ventilation monitoring data and perform simulation analysis on the ventilation monitoring data.

[0038] In one implementation, the sensor may also be provided with a remote control interface, through which the sensor is communicatively connected to a control module, and the control module may control the opening and closing of the sensor based on current ventilation conditions or different ventilation requirements.

[0039] In one implementation, the following steps may be referred to for determining the location and quantity of sensors based on preset safety requirements: S121: Classify sensors of the same type at different locations into different levels according to preset security requirements.

[0040] In the process of determining the number and position of sensors based on preset safety requirements, the sensors of the same type at different positions determined can be divided into different levels according to the preset safety requirements. Specifically, sensors of the same type at different levels can be arranged and distributed in a cyclic manner from low to high. By dividing the sensors into different levels, it is convenient to intelligently control the sensors of different levels according to different ventilation conditions, so that the accuracy of the data obtained by the sensors of different levels is higher.

[0041] For example, wind speed sensors are sensors of the same type and are divided into three levels. In the ventilation duct, the wind speed sensors are arranged in a cycle of first level, second level, third level, first level, second level, third level... Under different ventilation conditions, the opening and closing of sensors of different levels can be controlled. In this way, under normal ventilation conditions, low-level sensors can be started to work normally to save energy. Under abnormal ventilation conditions, multiple levels of sensors can be turned on at the same time to improve the accuracy of the ventilation monitoring data obtained.

[0042] In one implementation, before obtaining ventilation monitoring data through a plurality of sensors, the following steps may also be included: S140: Acquire historical arrangement data stored in a preset storage space.

[0043] The preset storage space may be a space pre-set in the coal mine intelligent ventilation system for storing data, and the historical arrangement data may include the historical position and historical number of sensors and corresponding historical scenes.

[0044] S150: Compare historical scenarios with current scenarios.

[0045] S160: If they are consistent, the sensors are evenly distributed in the coal mine intelligent ventilation system according to the historical positions and historical quantities.

[0046] S170: 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.

[0047] After obtaining the historical scenes in the historical arrangement data, the historical scenes can be compared with the current scenes. If the historical scenes are consistent with the current scenes, 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 scenes. If the historical scenes are inconsistent with the current scenes, the positions and number of sensors can be determined based on the preset safety requirements corresponding to the current scenes, so that the sensors can be evenly distributed in the coal mine intelligent ventilation system according to the determined positions and numbers. At the same time, the historical arrangement data in the historical storage space can also be updated based on the determined positions and numbers of sensors, so that in the subsequent process of rearranging the sensors, the layout can be based on the latest historical arrangement data.

[0048] Before obtaining ventilation monitoring data through a plurality of sensors, historical arrangement data stored in a preset storage space may be obtained, so that in the process of arranging the sensors, settings may be made based on the historical arrangement data, which is beneficial to improving the efficiency of sensor arrangement.

[0049] S200: Perform simulation analysis on ventilation monitoring data using a preset simulation analysis model to perform ventilation safety monitoring.

[0050] After the ventilation monitoring data is acquired through a number of sensors, simulation analysis can be performed on the ventilation monitoring data to perform ventilation safety monitoring. Specifically, the sensor 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 analysis results, and then the coal mine intelligent ventilation system can be controlled based on the analysis results.

[0051] In one implementation, Figure 3 As shown, before using a preset simulation analysis model to perform simulation analysis on ventilation monitoring data to perform ventilation safety monitoring, the following steps may be included: S210: Simplify the mine geographic data and import them into the model building software to generate a basic model.

[0052] S220: 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 a three-dimensional mine model.

[0053] S230: Simplify the lane location data and import them into the model building software to generate a basic model.

[0054] S240: Adjust the basic model according to the spatial relationship of the lanes so that the adjusted spatial relationship is consistent with the actual situation.

[0055] S250: Inputting specific parameters of the tunnels in different sections into the adjusted basic model to obtain a three-dimensional ventilation model.

[0056] S260: Combining the three-dimensional mine model with the three-dimensional ventilation model to obtain a preset simulation analysis model.

[0057] In one implementation, the simulation analysis module may include a model construction module for constructing a three-dimensional mine model and a three-dimensional ventilation model according to preset data, so as to use the three-dimensional mine model and the three-dimensional ventilation model to simulate and analyze the acquired ventilation monitoring data. By combining the three-dimensional mine model and the three-dimensional ventilation model to simulate and analyze the ventilation monitoring data, the terrain and the ventilation structure can be combined at the same time, so that the analysis results of the simulation analysis are more accurate and in line with reality.

[0058] The preset data may include mine geographic data and tunnel location data. Specifically, the mine geographic data may include geographic data acquired during the exploration process, such as longitude and latitude, altitude, rock and soil thickness, etc. The tunnel location data may include the layout information of each tunnel during the design of the coal mine intelligent ventilation system, such as coordinate information.

[0059] In one implementation, the preset data may include mine geographic data and pipeline location data. A three-dimensional mine model may be constructed based on the mine geographic data, and a three-dimensional ventilation model may be constructed based on the pipeline location data. The preset data may be pre-stored in the coal mine intelligent ventilation system, or may be acquired and uploaded based on survey equipment.

[0060] In the process of constructing a 3D mine model, the mine geographic data can be simplified and imported into the model building 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 a 3D mine model.

[0061] In one implementation, how to adjust the basic model according to the geographical relationship of the mine may refer to the following steps included in S220: S221: Obtain the proportional relationship and geometric relationship in the geographical relationship of the mine.

[0062] S222: Adjust the basic model based on the proportional relationship and the geometric relationship.

[0063] After simplifying the mine geographic data and importing it into the model building software to obtain the basic model, the proportional relationship and geometric relationship in the mine geographic relationship can be obtained to adjust the basic model based on the proportional relationship and geometric relationship, so that the adjusted basic model is more in line with the actual mine geographical situation. Specifically, the proportional relationship may include the scale relationship between the mileage data in the actual mine geographic relationship and the mileage data in the basic model, and the geometric relationship may include the vertical relationship, parallel relationship, and intersection relationship in the mine geographic relationship.

[0064] In the process of constructing the three-dimensional ventilation model, the tunnel location data can be simplified and then imported into the model building software to generate a basic model. The basic model is adjusted according to the spatial relationship of the tunnel so that the adjusted spatial relationship is consistent with the actual situation, thereby obtaining a three-dimensional ventilation model.

[0065] In one implementation, how to adjust the basic model according to the spatial relationship of the lanes may refer to the following steps included in S240: S241: Obtain the proportional relationship and geometric relationship in the spatial relationship of the lane.

[0066] S242: Adjust the basic model based on the proportional relationship and the geometric relationship.

[0067] After simplifying the lane position data and importing it into the model building software to obtain the basic model, the proportional relationship and geometric relationship in the spatial relationship of the lane can be obtained, so as to adjust the basic model based on the proportional relationship and geometric relationship, so as to make the adjusted basic model more consistent with the spatial distribution of the actual lane. Specifically, the proportional relationship may include the proportional relationship between the actual lane length and the lane length in the basic model, and the geometric relationship may include the vertical relationship, parallel relationship, and intersection relationship between the lanes.

[0068] For example, Ventsim ventilation simulation software can be used to construct a three-dimensional ventilation model. The three-dimensional ventilation model can be established based on the actual size of the coal mine ventilation structure at a 1:1 ratio. By accurately inputting the specific parameters of the location data of each tunnel and using the calculation method within the software, the ventilation network can be accurately calculated, and the calculation results obtained have a certain degree of credibility.

[0069] In one implementation, the influence of frictional resistance can also be considered in the process of constructing the three-dimensional ventilation model. Specifically, a reasonable value of the frictional resistance coefficient can be selected, and the frictional resistance coefficient and the tunnel position data can be input into the software to obtain a three-dimensional ventilation model that is the same as the actual situation.

[0070] 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 determine the accuracy of the three-dimensional modeling.

[0071] In the process of simulation analysis using 3D mine models and 3D ventilation models, the coal mine ventilation system can be simulated and calculated through computer programs. By establishing a calculation model, the operation of the ventilation system under different working conditions can be simulated and possible problems can be predicted. In addition, the performance and operation of the ventilation system can be studied by changing different parameters and boundary conditions, and its impact on miner safety and coal mine production can be evaluated.

[0072] In one implementation, how to use a preset simulation analysis model to perform simulation analysis on ventilation monitoring data to perform ventilation safety monitoring may refer to the following steps included in S200: S270: Input the ventilation monitoring data into a preset simulation analysis model.

[0073] S280: In a 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.

[0074] After the preset simulation analysis model is built using the model building software, the ventilation monitoring data read by the sensor can be input into the preset simulation analysis model, and then the ventilation monitoring data can be adapted to the data in the three-dimensional mine model and the three-dimensional ventilation model to obtain the analysis results. Specifically, the simulation data in the three-dimensional mine model and the three-dimensional ventilation model can be compared with the ventilation monitoring data obtained by actual measurement. If they are consistent, the analysis result can be output as normal ventilation. If they are inconsistent, the analysis result can be output as abnormal ventilation.

[0075] In one implementation, due to the existence of errors, a preset floating range may be set in the process of determining whether the simulation data is consistent with the actual measurement data. For example, a floating range of ±2% may be determined to be consistent.

[0076] S300: Matching the analysis result of the simulation analysis with the preset index to execute a control operation corresponding to the preset index.

[0077] After the simulation analysis module outputs the analysis results, the analysis results can be matched with the preset indicators to perform control corresponding to the preset indicators. Specifically, the analysis results output by the simulation analysis module can be received by the control module, so as to control the coal mine intelligent ventilation system through the control module.

[0078] In one implementation, how to control the coal mine intelligent ventilation system accordingly based on the analysis results of the simulation analysis may refer to the following steps included in S300: S310: If the analysis result is normal ventilation, the return air duct is controlled to be closed, or the ventilation fan is controlled to start.

[0079] S320: If the analysis result is abnormal ventilation, the analysis result is matched with a preset index to execute a control operation corresponding to the preset index.

[0080] S330: If the analysis result is normal ventilation, the sensor at the lowest level is controlled to work normally and read data.

[0081] S340: 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 sensors of different levels are controlled based on the value of the final-level indicator.

[0082] Since the return air lane, ventilator and sensor are communicatively connected with the control module, the control module can perform corresponding control on the return air lane, ventilator and sensor based on the analysis results, thereby realizing intelligent control of the coal mine intelligent ventilation system and improving the automation of the coal mine ventilation system.

[0083] The intelligent ventilation system of a coal mine may include a total return air lane, a main return air lane, a mining area return air lane, and a working face return air lane. The total return air lane, the main return air lane, the mining area return air lane, and the working face return air lane may use automatic dampers. By using automatic dampers, the control module can be used to control whether each return air lane is locked based on the analysis result. Specifically, if the analysis result is normal ventilation, the control module controls the total return air lane, the main return air lane, the mining area return air lane, and the working face return air lane to be locked, or the control module controls the fan to start, so that the return air lane and the fan can be controlled to work normally under normal ventilation conditions, so as to improve the automation and safety of the intelligent ventilation system of a coal mine.

[0084] If the analysis result is normal ventilation, the control module will control the sensor at the lowest level to work normally and read the data. When the sensor at the lowest level works normally, the ventilation monitoring data of the coal mine intelligent system under normal ventilation can be obtained, thereby saving energy consumption to a certain extent.

[0085] For example, if the analysis result is normal ventilation, the control module can be used to control the first-level wind speed sensor to work normally to read wind speed data.

[0086] In one implementation, Figure 4 As shown, when the analysis result is abnormal ventilation, how to match the analysis result with the preset index may include the following steps: S3211: Determine the operation monitoring value corresponding to the final level indicator based on the ventilation monitoring data in the analysis results.

[0087] S3212: Substitute the operation monitoring value into the preset judgment algorithm to obtain the value of the final level indicator and aggregate it.

[0088] S3213: Weighting the values ​​of the multiple lower-level indicators in combination with a preset weight ratio to calculate the value of the upper-level indicator.

[0089] S3214: Weighted calculation at each level until the first-level indicator.

[0090] S3215: Control is performed based on the action decision corresponding to the value of the first-level indicator.

[0091] The intelligent ventilation system of coal mines may also include a fault diagnosis module, which can communicate with the simulation analysis module to receive analysis results and match the analysis results with preset indicators to obtain matching results. The control module can then perform control operations corresponding to the preset indicators based on the matching results.

[0092] In the process of matching the analysis results with the preset indicators, the fault diagnosis module defines a series of multi-level indicator factors and influence weights that affect the normal operation or accurate calculation of the intelligent ventilation system in coal mines according to the production process module or business management, defines the judgment algorithm and result collection of each final indicator, defines the operation monitoring value of one or a group of ventilation equipment or sensors corresponding to each final indicator, and defines the action decision of each first-level indicator with different value ranges, such as: start / stop / speed regulation / warning equipment or sensors, so as to execute the decision action according to the decision result. By defining various indicator parameters, the fault diagnosis module can match the analysis results with the preset indicators and improve the automation control level of the intelligent ventilation system in coal mines.

[0093] Specifically, first determine the operation monitoring value corresponding to the final-level indicator based on the ventilation monitoring data in the analysis results, substitute the determined operation monitoring value into the defined judgment algorithm, obtain the value of the final-level indicator, and aggregate it. Then, weight the calculation results of multiple final-level indicators in combination with the preset weight ratio to calculate the value of the upper-level indicator, and then perform weighted calculation step by step until the first-level indicator is reached, and finally control is performed based on the action decision corresponding to the value of the first-level indicator.

[0094] In one implementation, the following steps may be referred to for how to substitute the operation monitoring value into the preset determination algorithm to obtain the value of the final level indicator: S32121: Determine the range corresponding to the operation monitoring value.

[0095] S32122: Determine the value of the corresponding final-level indicator according to the determined range.

[0096] In the process of determining the value of the final-level indicator corresponding to the operation monitoring value, the operation monitoring value can be divided into different ranges in advance, and the values ​​of the final-level indicators corresponding to different ranges are different. After the corresponding range is determined based on the operation monitoring value, the value of the corresponding final-level indicator can be determined according to the determined range.

[0097] 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 corresponding final-level indicator values ​​can be 1, 2, 3, 4, and 5. If the operation monitoring value is 66, the corresponding final-level indicator value is 4.

[0098] If the analysis result is abnormal ventilation, the operation monitoring value of the ventilation monitoring data corresponding to the sensor can also be obtained, the value of the final index corresponding to the operation monitoring value can be determined, and the opening and closing of sensors of different levels can be controlled based on the value of the final index. Specifically, the larger the value of the final index, the greater the number of sensors of different levels. By controlling the opening and closing of sensors of different levels based on different ventilation monitoring data, the accuracy of sensor reading data under different ventilation conditions can be improved.

[0099] For example, if the operation monitoring value corresponding to the wind speed sensor is 22, the corresponding final index value is 2, and the first and second level sensors 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 corresponding final index value is 5, and the first, second, third, fourth and fifth level sensors can be controlled to work simultaneously to read the wind speed data.

[0100] In one implementation, the following steps may be referred to for how to control the action decision corresponding to the value of the primary indicator: S32151: Based on the value of the first-level indicator, control at least one of the total return air channel, main return air channel, mining area return air channel, and working face return air channel to unlock.

[0101] After calculating 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 can be controlled to be unlocked based on the value of the primary index. Specifically, the larger the value of the primary index, the more serious the abnormal ventilation situation is, and the larger the number of return air lanes unlocked, until all return air lanes are unlocked to deal with the abnormal ventilation situation.

[0102] S32152: Control the rotation state of at least one of the main ventilation fan and the local ventilation fan based on the value of the primary indicator.

[0103] The intelligent ventilation system of coal mines may also include main fans and local fans, which are communicatively connected to the control module, so that the control module can control the rotation status of the main fans and local fans after matching the analysis results with preset indicators.

[0104] After calculating the primary index, the rotation state of at least one of the main fan and the local fan can also be controlled based on the value of the primary index. Specifically, the rotation state may include speed increase, speed decrease, opening and closing. In the process of adjusting the rotation state of the fan, the corresponding control can be determined based on the abnormal ventilation situation. If the abnormal ventilation is insufficient ventilation, the main fan and the local fan can be increased in speed one by one. If the abnormal ventilation is excessive ventilation or a malfunction occurs, the main fan and the local fan can be decelerated one by one until they are closed.

[0105] In one implementation, the coal mine intelligent ventilation system may further include an early warning module, which may be connected to the fault diagnosis module in communication to receive the matching result and issue an early warning to the construction personnel based on the matching result. In the process of the early warning module issuing an early warning to the construction personnel based on the matching result, the early warning module may issue an early warning to the construction personnel through at least one prompting method including lighting of an indicator light, whistle, information prompt, and voice broadcast, so that the construction personnel can obtain the abnormal information of the coal mine intelligent ventilation system in a timely manner and carry out relevant processing in a timely manner.

[0106] The above descriptions are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also 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: 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; The analysis result of the simulation analysis is matched with the preset index to execute the control operation corresponding to the preset index.

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; Determining the location and quantity of the sensors based on preset safety requirements; 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 2 or 3, characterized in that: The determining the position and quantity of the 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 in a cyclic manner from low to high.

5. The method according to claim 4, 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 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.

6. 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: After simplifying the mine geographic data, import it into the model building software to generate a basic model; Obtaining proportional relationships and geometric relationships in the geographical relationship of the mine; The basic model is adjusted based on the proportional relationship and the geometric relationship so that the adjusted geographical relationship is consistent with the actual situation, thereby obtaining the three-dimensional mine model; After simplifying the lane location data, import it into the model building software to generate a basic model; Obtaining proportional relationships and geometric relationships in the spatial relationship of the lanes; Adjusting the basic model based on the proportional relationship and the geometric relationship so that the adjusted spatial relationship is consistent with the actual situation; Inputting specific parameters of laneways in different sections into the adjusted 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.

7. 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.

8. The method according to claim 7, 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.

9. A coal mine intelligent ventilation system, 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.

10. An intelligent ventilation device for coal mines, characterized in that: It includes the coal mine intelligent ventilation system as described in claim 9, and the coal mine intelligent ventilation system executes the coal mine intelligent ventilation method as described in any one of claims 1-8, 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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