An adaptive intelligent explosion-proof lighting system for mines

Abstract

The present invention belongs to the field of mine lighting technology, and specifically is an adaptive intelligent explosion-proof lighting system for mines, comprising: a positioning base station, a lighting network, a personnel identification card, a three-dimensional GIS module, an environmental monitoring module, and a central processing module. This system constructs a visibility compensation component based on an empirical formula for visibility and smoke, thereby compensating for changes in visibility when adjusting the luminous power, without the need for additional light sensors and motion sensors. In addition, a simulated K shortest path algorithm is proposed, which organically combines the Yen algorithm, fire simulation, particle swarm algorithm, and hazard assessment to achieve dynamic and comprehensive intelligent escape path planning. Ultimately, the central processing module controls the light-emitting units along the escape path to flash in sequence from far to near relative to the escape path endpoint to indicate the escape path.

Description

Technical Field

[0001] The present invention relates to the technical field of mine lighting, and in particular to an adaptive intelligent explosion-proof lighting system for mines. Background Art

[0002] The mine lighting system is an engineering system that provides lighting inside the mine. The system needs to meet the special environmental requirements inside the mine, such as dustproof, waterproof, corrosion-resistant and explosion-proof, to ensure long-term stable operation in such a special environment. Existing mine intelligent lighting systems generally use a light sensor plus motion sensor solution to control the brightness and switch of lamps, and generally do not have an escape indication function and do not consider the impact of visibility. Existing escape route planning methods are generally based on static analysis and planning, and do not take into account the dynamic evolution of the fire situation over time and the role of the refuge chamber, nor do they specify how to indicate the escape route. Summary of the Invention

[0003] The present invention overcomes the shortcomings of the prior art and provides an adaptive intelligent explosion-proof lighting system for mining, which constructs a visibility compensation component based on the empirical formula of visibility and smoke, thereby compensating for changes in visibility when adjusting the luminous power, and does not require additional light sensors and motion sensors. In addition, a simulated K shortest path algorithm is proposed, which uses the Yen algorithm to generate K shortest path groups, and uses a particle swarm simulation path verification algorithm and a hazard assessment to perform two rounds of screening to generate the final escape path. In this process, the refuge chamber is also regarded as a potential end point. The simulated K shortest path algorithm organically combines the Yen algorithm, fire simulation, particle swarm algorithm and hazard assessment to achieve dynamic and comprehensive intelligent escape path planning. Finally, the central processing module controls the light-emitting units along the escape path to flash in sequence from far to near according to the distance relative to the end point of the escape path to indicate the escape path.

[0004] The adaptive intelligent mining explosion-proof lighting system includes: a positioning base station, a lighting network, a personnel identification card, a three-dimensional GIS module, an environmental monitoring module and a central processing module;

[0005] The three-dimensional GIS module is electrically connected to the central processing module, the environmental monitoring module is electrically connected to the central processing module, the positioning base station is communicatively connected to the three-dimensional GIS module, the lighting network is communicatively connected to the central processing module, the personnel identification card is communicatively connected to the positioning base station, and the lighting network is communicatively connected to the positioning base station;

[0006] The lighting network is installed in the mine and includes multiple lighting groups. Each lane in the mine corresponds to a lighting group. The lighting group includes a first camera, a second camera, and multiple lighting modules. The lighting modules are fixedly installed on the ceiling of the lane at equal intervals. The lighting modules include device identification cards and light-emitting units. The first camera and the second camera are respectively located at opposite ends of the lane, and are arranged diagonally. The device identification cards report the device location to the central processing module.

[0007] The first camera and the second camera collect videos in the lane and upload them to the central processing module and the environmental monitoring module;

[0008] The environmental monitoring module collects and processes the gas concentration, smoke concentration and tunnel video in real time to generate an underground environmental report and transmits it to the central processing module, and issues a fire warning to the central processing module based on the underground environmental report;

[0009] The central processing module controls the switch of the light-emitting unit and automatically adjusts the power of the light-emitting unit according to changes in brightness and smoke concentration;

[0010] After receiving the fire warning, the central processing module plans the escape route by using the simulated K shortest path algorithm, and controls the light-emitting units along the escape route to flash in sequence from far to near according to the distance relative to the end point of the escape route.

[0011] Furthermore, the personnel identification card reports the personnel location to the three-dimensional GIS module in real time;

[0012] The central processing module uploads the personnel location and equipment location to the three-dimensional GIS module;

[0013] The three-dimensional GIS module collects the mine design drawing, and constructs a GIS three-dimensional simulation model based on the mine design drawing, personnel positions and equipment positions, and inputs the model into the central processing module.

[0014] Furthermore, the central processing module corrects the illumination of the light-emitting unit to comply with the "Mine Power Design Standards", records the power of the light-emitting unit at this time as the benchmark power, the average brightness of the video in the tunnel of the lighting group where the light-emitting unit is located at this time is the benchmark brightness, and the smoke concentration in this tunnel at this time is the benchmark smoke concentration.

[0015] Furthermore, the process of controlling the switch of the light-emitting unit includes the following steps:

[0016] Step E1: The central processing module calculates the displacement vector of the personnel identification card according to the GIS three-dimensional simulation model;

[0017] Step E2: The central processing module uses the displacement vector of the personnel identification card as the angle bisector to demarcate a sector area with an angle of 188° and a radius of 100 meters, and turns on the light-emitting units within this sector area.

[0018] Furthermore, the process of automatically adjusting the power of the light-emitting unit according to the brightness change and the smoke concentration change includes the following steps:

[0019] Step T1: The central processing module receives the video of the tunnel of the lighting group where the light-emitting units in the sector area are located, calculates the average brightness of the tunnel at that time, and extracts the smoke concentration of the tunnel at that time from the underground environment report;

[0020] Step T2: Adjust the power of the light-emitting unit according to the changes in brightness and smoke concentration. The power adjustment formula is as follows:

[0021]

[0022]

[0023] Where W is the adjusted light unit power, W' is the reference power, L' is the reference brightness, L is the average brightness at this time, C is the visibility compensation component, ρ' is the reference smoke concentration, and ρ is the smoke concentration at this time.

[0024] Furthermore, the process of planning an escape route using the simulated K shortest path algorithm includes the following steps:

[0025] Step S1: The central processing module constructs an underground undirected weighted graph based on the GIS three-dimensional simulation model, extracts lanes from the GIS three-dimensional simulation model as edges of the underground undirected weighted graph, and extracts the intersections between lanes, mine exits, and refuge chambers as vertices of the underground undirected weighted graph. The distances between lanes extracted from the GIS three-dimensional simulation model are used as the weights of the edges of the underground undirected weighted graph.

[0026] Step S2: The central processing module uses Pyrosim to deduce the fire situation based on the underground environmental report and the GIS three-dimensional simulation model to generate a deduction result. Based on the deduction result, a dynamic underground undirected weighted graph is constructed for the underground undirected weighted graph. The elements of the dynamic underground undirected weighted graph include vertices and edges. Each element has a spread attribute, whose value is either spread or not spread. The value of the spread attribute changes over time according to the deduction result.

[0027] Step S3: The central processing module marks the location of the personnel identification card in the underground undirected weighted graph based on the GIS three-dimensional simulation model, extracts the locations of the mine exits and refuge chambers as the end point list, and sorts them according to their distance from the location of the personnel identification card;

[0028] Step S4: Select a location from the destination list in order as the path destination, use the location of the personnel identification card as the path starting point, and use the Yen algorithm to generate K shortest path groups;

[0029] Step S5: Use the particle swarm simulation path verification algorithm to screen the K shortest path groups and generate the remaining K shortest path groups;

[0030] Step S6: Execute steps S4 to S5 for each position in the destination list, and aggregate all remaining K shortest path groups into an escape path group;

[0031] Step S7: Select the paths in the escape path group whose path lengths differ from the shortest path in the escape path group by no more than 5%, sort them from shortest to longest by path length, and aggregate them into a set of paths to be evaluated. Perform a path hazard assessment on these paths, and select the final escape path based on the assessment results.

[0032] Furthermore, the process of constructing a dynamic downhole undirected weighted graph for the downhole undirected weighted graph according to the deduction result in step S2 includes the following steps:

[0033] Step S21: Add a time attribute to the underground undirected weighted graph, and add a gas concentration attribute, a smoke concentration attribute, and a spread attribute to each element in the underground undirected weighted graph. The gas concentration attribute and the smoke concentration attribute are floating-point types with a default value of 0. The spread attribute is a string type with a default value of not spread. The time attribute is a datetime type with a default value of 00:00:00.

[0034] Step S22: defining a dynamic update function, which extracts information from the deduction results according to the changes in the attribute values ​​of the deduction results over time to update the values ​​of the gas concentration attribute and the smoke concentration attribute;

[0035] Step S23: Collecting gas lethal concentration and smoke lethal concentration from the network, defining a spread range function. This function updates the spread attribute value of elements whose value of at least one of the gas concentration attribute and the smoke concentration attribute reaches the corresponding lethal concentration to be affected.

[0036] Step S24: Obtain a dynamic downhole undirected weighted graph.

[0037] Furthermore, the process of step S5 includes the following steps:

[0038] Step S51: creating a particle for each path in the K shortest path group;

[0039] Step S52: Verification start: extract the fire occurrence time from the deduction results as the time attribute value of the dynamic underground undirected weighted graph, and each particle moves along the path at a speed of 10 kilometers per hour;

[0040] Step S53: updating the dynamic downhole undirected weighted graph: using the timedelta function to add 1 second to the time attribute of the dynamic downhole undirected weighted graph, and calling the dynamic update function and the spread range function to update the attributes of each element of the dynamic downhole undirected weighted graph;

[0041] Step S54: Update the particle state and perform screening: Update the current position of each particle, record the smoke concentration attribute value of the element at each particle's position as the instantaneous smoke exposure, record the gas concentration attribute value as the instantaneous gas exposure, and delete the path corresponding to the affected particle from the K shortest path group by setting the spread attribute value of the element at the particle's position as the affected path, and terminate the particle's movement;

[0042] Step S55: Terminate the movement of the particle that has reached the endpoint and record the data: Terminate the movement of the particle whose position is the endpoint, sum the recorded instantaneous smoke exposure of the particle to form the cumulative smoke exposure, and sum the recorded instantaneous gas exposure of the particle to form the cumulative gas exposure;

[0043] Step S56: repeating steps S53 to S55 until all particles created in step S51 have finished moving;

[0044] Step S57: The undeleted portion of the K shortest path group is used as the remaining K shortest path group.

[0045] Furthermore, the process of path risk assessment in step S7 specifically includes the following steps:

[0046] Step S71: Preprocessing the path set to be evaluated: For all paths in the path set to be evaluated, extract the slope of each lane in the path from the GIS 3D simulation model, take the absolute value and sum it to form the cumulative path slope. Perform Min-Max normalization on the cumulative path slopes of all paths in the path set to be evaluated and the cumulative smoke exposure and cumulative gas exposure of the corresponding particles, respectively, to generate the standard path slope, standard smoke exposure, and standard gas exposure corresponding to each path in the path set to be evaluated.

[0047] Step S72: Select a path from the set of paths to be evaluated in order;

[0048] Step S73: Calculate the risk index of this path using a risk assessment formula, which is as follows:

[0049] Where D is the hazard index, I is the standard path slope, S is the standard smoke exposure, and G is the standard gas exposure;

[0050] Step S74: repeating steps S72 to S73 until all paths in the set of paths to be evaluated have a corresponding risk index;

[0051] Step S75: Sorting and final escape path selection: sort all paths in the path set to be evaluated from small to large according to the danger index, and select the path with the lowest danger index and the end point being the mine exit as the final escape path. If such a path does not exist, select the path with the lowest danger index as the final escape path.

[0052] The beneficial effects of the present invention are as follows:

[0053] (1) The present invention constructs a visibility compensation component based on the empirical formula of visibility and smoke, and realizes compensating the change of visibility when adjusting the luminous power without the need for additional light sensors and motion sensors. In addition, a simulated K shortest path algorithm is proposed, which uses the Yen algorithm to generate K shortest path groups, and uses the particle swarm simulation path verification algorithm and hazard assessment to perform two rounds of screening to generate the final escape path. In this process, the refuge chamber is also regarded as a potential end point. The simulated K shortest path algorithm organically combines the Yen algorithm, fire simulation, particle swarm algorithm and hazard assessment to realize dynamic and comprehensive intelligent escape path planning. Finally, the central processing module controls the light-emitting units along the escape path to flash in sequence from far to near according to the distance relative to the end point of the escape path to indicate the escape path.

[0054] (2) The present invention includes refuge chambers in the destination list when using the Yen algorithm to generate K shortest path groups, overcoming the industry's long-standing technical bias of ignoring refuge chambers when planning escape routes, increasing the robustness of escape route planning, and injecting more possibilities into escape route planning.

[0055] (3) The particle swarm simulation path verification algorithm proposed in the present invention realizes the organic combination of dynamic simulation, graph theory and particle swarm algorithm, defines the dynamic update function and the spread range function to reflect the deduction results in the dynamic underground undirected weighted graph, rather than directly simulating in the GIS three-dimensional simulation model. While retaining the characteristics of the dynamic evolution of the fire situation over time, it compresses the computing power expenditure and realizes accurate and fast escape path planning.

[0056] (4) The hazard assessment formula proposed in the present invention measures the ruggedness of the path by the sum of the absolute values ​​of the slope, and sets the weights according to the toxic concentrations of gas and smoke. The formula is standardized before being introduced to eliminate the differences caused by the dimensions, so as to better measure the impact of different factors on the hazard of the path and ultimately improve the reliability of the hazard assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1This is a module diagram of an adaptive intelligent mining explosion-proof lighting system proposed by the present invention;

[0058] Figure 2 This is a flow chart of the particle swarm simulation path verification algorithm proposed in the present invention;

[0059] Figure 3 A flow chart of the path hazard assessment proposed in the present invention;

[0060] Figure 4 This is a schematic diagram of the sector area in Example 4.

[0061] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0063] Example 1, see Figure 1 ,The present invention provides an adaptive intelligent mining explosion-proof lighting system, including: a positioning base station, a lighting network, a personnel identification card, a three-dimensional GIS module, an environmental monitoring module and a central processing module;

[0064] The three-dimensional GIS module is electrically connected to the central processing module, the environmental monitoring module is electrically connected to the central processing module, the positioning base station is communicatively connected to the three-dimensional GIS module, the lighting network is communicatively connected to the central processing module, the personnel identification card is communicatively connected to the positioning base station, and the lighting network is communicatively connected to the positioning base station;

[0065] The lighting network is installed in the mine and includes multiple lighting groups. Each lane in the mine corresponds to a lighting group. The lighting group includes a first camera, a second camera, and multiple lighting modules. The lighting modules are fixedly installed on the ceiling of the lane at equal intervals. The lighting modules include device identification cards and light-emitting units. The first camera and the second camera are respectively located at opposite ends of the lane, arranged diagonally. The device identification cards report the device location to the central processing module. In this embodiment, the lighting modules are spaced 4 meters apart.

[0066] The first camera and the second camera collect videos in the lane and upload them to the central processing module and the environmental monitoring module;

[0067] The environmental monitoring module collects and processes the gas concentration, smoke concentration and tunnel video in real time to generate an underground environmental report and transmits it to the central processing module, and issues a fire warning to the central processing module based on the underground environmental report;

[0068] The central processing module controls the switch of the light-emitting unit and automatically adjusts the power of the light-emitting unit according to changes in brightness and smoke concentration;

[0069] After receiving the fire warning, the central processing module plans the escape route by using the simulated K shortest path algorithm, and controls the light-emitting units along the escape route to flash in sequence from far to near according to the distance relative to the end point of the escape route.

[0070] Embodiment 2: This embodiment is based on the above embodiment, and the personnel identification card reports the personnel location to the 3D GIS module in real time;

[0071] The central processing module uploads the personnel location and equipment location to the three-dimensional GIS module;

[0072] The three-dimensional GIS module collects the mine design drawing, and constructs a GIS three-dimensional simulation model based on the mine design drawing, personnel positions and equipment positions, and inputs the model into the central processing module.

[0073] Example three. This example is based on Example two. The central processing module corrects the illumination of the light-emitting unit to comply with the "Mine Power Design Standards", records the power of the light-emitting unit at this time as the benchmark power, and the average brightness of the video in the tunnel of the lighting group where the light-emitting unit is located is the benchmark brightness. The smoke concentration in this tunnel at this time is the benchmark smoke concentration.

[0074] Example 4, see Figure 4 This embodiment is based on the third embodiment, and the process of controlling the switch of the light-emitting unit includes the following steps:

[0075] Step E1: The central processing module calculates the displacement vector of the personnel identification card according to the GIS three-dimensional simulation model;

[0076] Step E2: The central processing module uses the displacement vector of the personnel identification card as the angle bisector to demarcate a sector area with an angle of 188° and a radius of 100 meters, and turns on the light-emitting units within this sector area.

[0077] Example 5: This example is based on Example 4. The process of automatically adjusting the power of the light-emitting unit according to the brightness change and the smoke concentration change includes the following steps:

[0078] Step T1: The central processing module receives the video of the tunnel of the lighting group where the light-emitting units in the sector area are located, calculates the average brightness of the tunnel at that time, and extracts the smoke concentration of the tunnel at that time from the underground environment report;

[0079] Step T2: Adjust the power of the light-emitting unit according to the changes in brightness and smoke concentration. The power adjustment formula is as follows:

[0080]

[0081]

[0082] Wherein W is the adjusted light-emitting unit power, W' is the reference power, L' is the reference brightness, L is the average brightness at this time, C is the visibility compensation component, ρ' is the reference smoke concentration, and ρ is the smoke concentration at this time. In this embodiment, the reference power is 25W, the reference brightness is 7 nits, and the reference smoke concentration is 200ppm. At this time, the average brightness is 5 nits, and the smoke concentration is 300ppm. The adjusted light-emitting unit power is 41W.

[0083] Example 6: This example is based on Example 5. The process of using the simulated K shortest path algorithm to plan an escape path includes the following steps:

[0084] Step S1: The central processing module constructs an underground undirected weighted graph based on the GIS three-dimensional simulation model, extracts lanes from the GIS three-dimensional simulation model as edges of the underground undirected weighted graph, and extracts the intersections between lanes, mine exits, and refuge chambers as vertices of the underground undirected weighted graph. The distances between lanes extracted from the GIS three-dimensional simulation model are used as the weights of the edges of the underground undirected weighted graph.

[0085] Step S2: The central processing module uses Pyrosim to deduce the fire situation based on the underground environmental report and the GIS three-dimensional simulation model to generate a deduction result. Based on the deduction result, a dynamic underground undirected weighted graph is constructed for the underground undirected weighted graph. The elements of the dynamic underground undirected weighted graph include vertices and edges. Each element has a spread attribute, whose value is either spread or not spread. The value of the spread attribute changes over time according to the deduction result.

[0086] Step S3: The central processing module marks the location of the personnel identification card in the underground undirected weighted graph based on the GIS three-dimensional simulation model, extracts the locations of the mine exits and refuge chambers as the end point list, and sorts them according to their distance from the location of the personnel identification card;

[0087] Step S4: Select a location from the destination list in order as the path destination, use the location of the personnel identification card as the path starting point, and use the Yen algorithm to generate K shortest path groups;

[0088] Step S5: Use the particle swarm simulation path verification algorithm to screen the K shortest path groups and generate the remaining K shortest path groups;

[0089] Step S6: Execute steps S4 to S5 for each position in the destination list, and aggregate all remaining K shortest path groups into an escape path group;

[0090] Step S7: Select the paths in the escape path group whose path lengths differ from the shortest path in the escape path group by no more than 5%, sort them from shortest to longest by path length, and aggregate them into a set of paths to be evaluated. Perform a path hazard assessment on these paths, and select the final escape path based on the assessment results.

[0091] Embodiment 7: This embodiment is based on embodiment 6. The process of constructing a dynamic downhole undirected weighted graph for the downhole undirected weighted graph according to the deduction result in step S2 includes the following steps:

[0092] Step S21: Add a time attribute to the underground undirected weighted graph, and add a gas concentration attribute, a smoke concentration attribute, and a spread attribute to each element in the underground undirected weighted graph. The gas concentration attribute and the smoke concentration attribute are floating-point types with a default value of 0. The spread attribute is a string type with a default value of not spread. The time attribute is a datetime type with a default value of 00:00:00.

[0093] Step S22: defining a dynamic update function, which extracts information from the deduction results according to the changes in the attribute values ​​of the deduction results over time to update the values ​​of the gas concentration attribute and the smoke concentration attribute;

[0094] Step S23: Collecting gas lethal concentration and smoke lethal concentration from the network, defining a spread range function. This function updates the spread attribute value of elements whose value of at least one of the gas concentration attribute and the smoke concentration attribute reaches the corresponding lethal concentration to be affected.

[0095] Step S24: Obtain a dynamic downhole undirected weighted graph.

[0096] Example 8, see Figure 2 This embodiment is based on the seventh embodiment, and the process of step S5 includes the following steps:

[0097] Step S51: creating a particle for each path in the K shortest path group;

[0098] Step S52: Verification start: extract the fire occurrence time from the deduction results as the time attribute value of the dynamic underground undirected weighted graph, and each particle moves along the path at a speed of 10 kilometers per hour;

[0099] Step S53: updating the dynamic downhole undirected weighted graph: using the timedelta function to add 1 second to the time attribute of the dynamic downhole undirected weighted graph, and calling the dynamic update function and the spread range function to update the attributes of each element of the dynamic downhole undirected weighted graph;

[0100] Step S54: Update the particle state and perform screening: Update the current position of each particle, record the smoke concentration attribute value of the element at each particle's position as the instantaneous smoke exposure, record the gas concentration attribute value as the instantaneous gas exposure, and delete the path corresponding to the affected particle from the K shortest path group by setting the spread attribute value of the element at the particle's position as the affected path, and terminate the particle's movement;

[0101] Step S55: Terminate the movement of the particle that has reached the endpoint and record the data: Terminate the movement of the particle whose position is the endpoint, sum the recorded instantaneous smoke exposure of the particle to form the cumulative smoke exposure, and sum the recorded instantaneous gas exposure of the particle to form the cumulative gas exposure;

[0102] Step S56: repeating steps S53 to S55 until all particles created in step S51 have finished moving;

[0103] Step S57: The undeleted portion of the K shortest path group is used as the remaining K shortest path group.

[0104] Example 9, see Figure 3 This embodiment is based on the eighth embodiment, and the process of path risk assessment in step S7 specifically includes the following steps:

[0105] Step S71: Preprocessing the path set to be evaluated: For all paths in the path set to be evaluated, extract the slope of each lane in the path from the GIS 3D simulation model, take the absolute value and sum it to form the cumulative path slope. Perform Min-Max normalization on the cumulative path slopes of all paths in the path set to be evaluated and the cumulative smoke exposure and cumulative gas exposure of the corresponding particles, respectively, to generate the standard path slope, standard smoke exposure, and standard gas exposure corresponding to each path in the path set to be evaluated.

[0106] Step S72: Select a path from the set of paths to be evaluated in order;

[0107] Step S73: Calculate the risk index of this path using a risk assessment formula, which is as follows:

[0108] Where D is the hazard index, I is the standard path slope, S is the standard smoke exposure, and G is the standard gas exposure. In this embodiment, the standard path slope is 0.8, the standard smoke exposure is 0.21, and the standard gas exposure is 0.23, resulting in a hazard index of 0.026.

[0109] Step S74: repeating steps S72 to S73 until all paths in the set of paths to be evaluated have a corresponding risk index;

[0110] Step S75: Sorting and final escape path selection: sort all paths in the path set to be evaluated from small to large according to the danger index, and select the path with the lowest danger index and the end point being the mine exit as the final escape path. If such a path does not exist, select the path with the lowest danger index as the final escape path.

[0111] Example 10, based on Example 9, runs in the Windows operating system environment, relies on the Anacond3 environment, and uses NetworkX to construct an underground undirected weighted graph and a dynamic underground undirected weighted graph as the framework of the particle swarm simulation path verification algorithm. The Pandas library is used in combination with the Numpy tool to complete data processing, and the sklearn library is used to complete Min-Max standardization. The three-dimensional GIS module is implemented using KJ2236J, the environmental monitoring module is implemented using KJ2427, the positioning base station is implemented using KJ2236-F1, the personnel identification card is implemented using KJ2236-K1, and the central processing module is implemented by SIMATI C industrial PC is used for implementation, the first camera and the second camera are implemented by KBA-127, the light-emitting unit is implemented by DGS51 / 127L(A), and the equipment identification card is implemented by KJ2236-K2. The units of the smoke concentration, gas concentration, instantaneous gas exposure and instantaneous smoke exposure are ppm, the units of the cumulative smoke exposure and cumulative gas exposure are ppm·s, the units of the slope and the cumulative path slope are degrees, the standard path slope, standard smoke exposure and standard gas exposure are dimensionless quantities due to Min-Max standardization, and the average brightness and reference brightness are obtained by processing the video using the cv2.mean() function in the OpenCV library.

[0112] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0113] The present invention and its implementation methods are described above. This description is not restrictive. What is shown in the accompanying drawings is only one of the implementation methods of the present invention. The actual structure is not limited to this. In short, if ordinary technicians in this field are inspired by the present invention and do not depart from the purpose of the invention, they can creatively design structural methods and embodiments similar to the technical solution, which should all fall within the scope of protection of the present invention.

Claims

1. An adaptive intelligent explosion-proof lighting system for mines, characterized by: The adaptive intelligent mining explosion-proof lighting system includes: a positioning base station, a lighting network, a personnel identification card, a three-dimensional GIS module, an environmental monitoring module and a central processing module; The three-dimensional GIS module is electrically connected to the central processing module, the environmental monitoring module is electrically connected to the central processing module, the positioning base station is communicatively connected to the three-dimensional GIS module, the lighting network is communicatively connected to the central processing module, the personnel identification card is communicatively connected to the positioning base station, and the lighting network is communicatively connected to the positioning base station; a three-dimensional GIS simulation model is constructed through the three-dimensional GIS module; The lighting network includes a plurality of lighting groups, each lighting group includes a plurality of lighting modules, each lighting module includes a device identification card and a light-emitting unit, and the device identification card reports the location of the lighting module to the central processing module; The lighting group collects videos in the lanes and uploads them to the central processing module and the environmental monitoring module; The environmental monitoring module collects and processes the gas concentration, smoke concentration and tunnel video in real time to generate an underground environmental report and transmits it to the central processing module, and issues a fire warning to the central processing module based on the underground environmental report; The central processing module controls the switch of the light-emitting unit and automatically adjusts the power of the light-emitting unit according to changes in brightness and smoke concentration; The central processing module plans an escape route using a simulated K shortest path algorithm after receiving a fire warning; The process of the central processing module planning the escape route includes the following steps: Step S1: The central processing module constructs an underground undirected weighted graph based on the GIS three-dimensional simulation model, extracts lanes from the GIS three-dimensional simulation model as edges of the underground undirected weighted graph, and extracts the intersections between lanes, mine exits, and refuge chambers as vertices of the underground undirected weighted graph. The distances between lanes extracted from the GIS three-dimensional simulation model are used as the weights of the edges of the underground undirected weighted graph. Step S2: The central processing module uses Pyrosim to deduce the fire situation based on the underground environmental report and the GIS three-dimensional simulation model to generate a deduction result. Based on the deduction result, a dynamic underground undirected weighted graph is constructed for the underground undirected weighted graph. The elements of the dynamic underground undirected weighted graph include vertices and edges. Each element has a spread attribute, whose value is either spread or not spread. The value of the spread attribute changes over time according to the deduction result. Step S3: The central processing module marks the location of the personnel identification card in the underground undirected weighted graph based on the GIS three-dimensional simulation model, extracts the locations of the mine exits and refuge chambers as the end point list, and sorts them according to their distance from the location of the personnel identification card; Step S4: Select a location from the destination list in order as the path destination, use the location of the personnel identification card as the path starting point, and use the Yen algorithm to generate K shortest path groups; Step S5: Use the particle swarm simulation path verification algorithm to screen the K shortest path groups and generate the remaining K shortest path groups; Step S6: Execute steps S4 to S5 for each position in the destination list, and aggregate all remaining K shortest path groups into an escape path group; Step S7: Select the paths in the escape path group whose path length differs by no more than 5% from the shortest path in the escape path group, sort them from shortest to longest by path length, and aggregate them into a set of paths to be evaluated. Perform a path hazard assessment on these paths, and select the final escape path based on the assessment results.

2. The adaptive intelligent explosion-proof lighting system for mines according to claim 1, characterized in that: The personnel identification card reports the personnel location to the 3D GIS module in real time; The central processing module uploads the positions of the personnel and the lighting module to the three-dimensional GIS module; The three-dimensional GIS module collects the mine design drawing, and constructs a GIS three-dimensional simulation model according to the mine design drawing, personnel positions and the position of the lighting module and inputs it into the central processing module.

3. The adaptive intelligent explosion-proof lighting system for mines according to claim 2, characterized in that: The central processing module corrects the illumination of the light-emitting unit to meet the standard, records the power of the light-emitting unit at this time as the reference power, the average brightness of the video in the lane of the lighting group where the light-emitting unit is located at this time as the reference brightness, and the smoke concentration in this lane at this time as the reference smoke concentration.

4. The adaptive intelligent explosion-proof lighting system for mines according to claim 3, characterized in that: The process of controlling the switch of the light emitting unit includes the following steps: Step E1: The central processing module calculates the displacement vector of the personnel identification card according to the GIS three-dimensional simulation model; Step E2: The central processing module uses the displacement vector of the personnel identification card as the angle bisector to demarcate a sector area with an angle of 188° and a radius of 100 meters, and turns on the light-emitting units within this sector area.

5. The adaptive intelligent explosion-proof lighting system for mines according to claim 4, characterized in that: The process of automatically adjusting the power of the light-emitting unit according to the brightness change and the smoke concentration change includes the following steps: Step T1: The central processing module receives the video of the tunnel of the lighting group where the light-emitting units in the sector area are located, calculates the average brightness of the tunnel at that time, and extracts the smoke concentration of the tunnel at that time from the underground environment report; Step T2: Adjust the power of the light-emitting unit according to the changes in brightness and smoke concentration. The power adjustment formula is as follows: ； ； in is the adjusted light-emitting unit power, is the reference power, is the reference brightness, For the average brightness at this time, is the visibility compensation component, is the baseline smoke concentration, This is the smoke concentration at this time.

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