A coal mine intelligent management system based on the Internet of Things

Abstract

The present invention discloses a coal mine intelligent management system based on the Internet of Things, which relates to the technical field of coal mine management. The system includes: a layout array acquisition module for obtaining a sensor layout array; an Internet of Things gateway device acquisition module for obtaining K Internet of Things gateway devices; a layout subarray acquisition module for obtaining K associated sensor layout subarrays; a subarray stability factor acquisition module for obtaining K subarray stability factors; a subarray transmission volume factor acquisition module for obtaining K subarray transmission volume factors; and a management module for uploading the data collected by the K associated sensor layout subarrays to the coal mine intelligent processing component for management. The present invention solves the technical problems in the prior art that the accuracy of collected data is low during coal mine management, there is a delay in management, and it is impossible to dynamically adapt to the complexity of the coal mine environment and the real-time requirements of data transmission, and achieves the technical effect of improving the reliability of coal mine management.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine management, and in particular to an intelligent coal mine management system based on the Internet of Things. Background Art

[0002] At present, the Internet of Things technology is widely used in coal mine smart management systems to collect environmental data by deploying sensors. However, the sensors and Internet of Things gateway devices in traditional systems are mostly fixedly distributed, lacking flexible adaptation to dynamic changes and regional characteristics in the coal mine environment, resulting in poor stability of sensor data transmission. At the same time, the existing technology does not fully consider the optimization configuration of data transmission volume, which easily causes overload or waste of resources of some gateway devices, limiting the reliability and efficiency of the system.

[0003] The existing technology has technical problems such as low accuracy of collected data during coal mine management, management delays, and inability to dynamically adapt to the complexity of the coal mine environment and the real-time requirements of data transmission. Summary of the invention

[0004] The present application provides an Internet of Things-based intelligent coal mine management system, which is used to solve the technical problems in the prior art of coal mine management, such as low accuracy of collected data, management delays, and inability to dynamically adapt to the complexity of the coal mine environment and the real-time requirements of data transmission.

[0005] In view of the above problems, the present application provides a coal mine intelligent management system based on the Internet of Things, the system comprising: A deployment array acquisition module is used to deploy sensors based on various key locations of the target coal mine and obtain a sensor deployment array; An Internet of Things gateway device acquisition module, used to acquire K Internet of Things gateway devices deployed in the target coal mine, wherein the K Internet of Things gateway devices include K device deployment locations, and K is an integer greater than or equal to 1; A subarray acquisition module is used to identify the transmission areas of the K IoT gateway devices based on the K device deployment locations and the coal mine environment information of the target coal mine, and divide the sensor deployment array according to the recognition result to obtain K associated sensor deployment subarrays; A subarray stability factor obtaining module, used to traverse the K associated sensor layout subarrays to perform data stability analysis and obtain K subarray stability factors; A subarray transmission volume factor acquisition module is used to traverse the K associated sensor layout subarrays to perform transmission volume analysis and obtain K subarray transmission volume factors; A management module is used to configure resources for the K IoT gateway devices based on the K sub-array stability factors and the K sub-array transmission volume factors, and use the configured K IoT gateway devices to upload data collected by sensors in the K associated sensor deployment sub-arrays to the coal mine intelligent processing component for management.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The array deployment acquisition module is used to deploy sensors based on various key positions of the target coal mine to obtain the sensor deployment array; the Internet of Things gateway device acquisition module is used to obtain K Internet of Things gateway devices deployed in the target coal mine, wherein the K Internet of Things gateway devices include K device deployment positions, and K is an integer greater than or equal to 1; the sub-array deployment acquisition module is used to identify the transmission area of ​​the K Internet of Things gateway devices based on the K device deployment positions and the coal mine environment information of the target coal mine, and divide the sensor deployment array according to the identification result to obtain K associated sensor deployment sub-arrays; the sub-array stability factor The sub-acquisition module is used to traverse the K associated sensor layout sub-arrays for data stability analysis and obtain K sub-array stability factors; the sub-array transmission volume factor acquisition module is used to traverse the K associated sensor layout sub-arrays for transmission volume analysis and obtain K sub-array transmission volume factors; the management module is used to configure resources for K IoT gateway devices based on the K sub-array stability factors and the K sub-array transmission volume factors, and use the configured K physical network gateway devices to upload the data collected by the sensors in the K associated sensor layout sub-arrays to the coal mine intelligent processing component for management. The technical effect of optimizing the resource configuration of the gateway device, thereby improving the data transmission reliability and overall management quality of the coal mine intelligent management system, is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic diagram of the structure of a coal mine intelligent management system based on the Internet of Things provided in an embodiment of the present application.

[0009] Figure 2 A flowchart of the steps executed by a neutron array stability factor acquisition module for intelligent management of coal mines based on the Internet of Things is provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: array layout acquisition module 11, Internet of Things gateway device acquisition module 12, sub-array layout acquisition module 13, sub-array stability factor acquisition module 14, sub-array transmission volume factor acquisition module 15, management module 16. DETAILED DESCRIPTION

[0011] The present application provides an intelligent coal mine management system based on the Internet of Things, which is used to solve the technical problems in the prior art of low accuracy of collected data during coal mine management, management delays, and the inability to dynamically adapt to the complexity of the coal mine environment and the real-time requirements of data transmission.

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings 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.

[0013] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, system, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, systems, products or devices.

[0014] Examples, such as Figure 1 As shown, the present application provides a coal mine intelligent management system based on the Internet of Things, the system comprising: The array deployment acquisition module 11 is used to deploy sensors based on various key positions of the target coal mine to obtain a sensor deployment array; The Internet of Things gateway device acquisition module 12 is used to obtain K Internet of Things gateway devices deployed in the target coal mine, wherein the K Internet of Things gateway devices include K device deployment locations, and K is an integer greater than or equal to 1; In a possible embodiment, the target coal mine is any coal mine that needs to be managed. Sensors are deployed according to key locations of the target coal mine, a sensor deployment network covering the target coal mine is constructed, and the sensor deployment array is obtained. Furthermore, K IoT gateway devices are obtained based on the construction data of the target coal mine. Among them, the IoT gateway device is used to receive sensor data and upload it to the data processing component in the coal mine intelligent management system for analysis. K represents the number of IoT gateway devices, which is a positive integer greater than or equal to 1, indicating the number of gateway devices deployed in the target coal mine.

[0015] Preferably, by analyzing the key locations of the target coal mine (such as dangerous areas, production areas, personnel activity areas, etc.), sensors are planned and arranged. These sensors form a layout array responsible for collecting real-time data of the coal mine environment (such as gas concentration, temperature and humidity, etc.). This process ensures that environmental data in key areas of the coal mine can be collected comprehensively and accurately.

[0016] Furthermore, the location information of the gateway devices is collected from the coal mine site, paving the way for the subsequent analysis of the associated sensor deployment sub-arrays that establish logical connections with each gateway device.

[0017] A subarray acquisition module 13 is used to identify the transmission areas of the K IoT gateway devices based on the K device deployment locations and the coal mine environment information of the target coal mine, and divide the sensor deployment array according to the recognition result to obtain K associated sensor deployment subarrays; Furthermore, the subarray arrangement obtaining module 13 is used to execute the following method: Taking the K equipment layout locations as indexes, extracting environmental features of the coal mine environment information of the target coal mine to obtain K environmental feature sets; Using the K device deployment locations and the K environmental feature sets as indexes, identify the reliable transmission area of ​​the gateway device to obtain the reliable transmission area of ​​the K gateway devices; The sensor layout array is divided based on the areas of the K gateway devices' reliable transmission regions and the K device layout positions to obtain the K associated sensor layout sub-arrays.

[0018] Furthermore, the subarray arrangement obtaining module 13 is used to execute the following method: Obtaining a set of sample equipment layout locations and a set of sample environment characteristics; Based on the sample device layout location set and the sample environment feature set, a gateway device reliable transmission area analysis test is performed, and a sample gateway device reliable transmission area set is obtained according to the test results; The sample device layout location set, the sample environment feature set and the sample gateway device reliable transmission area set are used as training data, and a framework constructed based on a feedforward neural network is supervised and calculated until the training converges to obtain a trained area identifier; The area identifier is used to identify the K device layout locations and the K environmental feature sets to obtain the areas of the K gateway devices' reliable transmission regions.

[0019] In one embodiment, the actual deployment environment characteristics of the K Internet of Things gateway devices are determined based on the coal mine environment information of the target coal mine and the K device deployment locations, and then the actual deployment environment characteristics are analyzed to determine the area that the K Internet of Things gateway devices can effectively cover, and the sensor deployment array is divided to obtain the K associated sensor deployment sub-arrays.

[0020] Optionally, taking the K device deployment locations as indexes, extract the environmental feature set of each IoT gateway device from the coal mine environmental information of the target coal mine to obtain the K environmental feature sets. The coal mine environmental information is used to describe the physical environment of the target coal mine, such as terrain, obstacle distribution, gas concentration, wireless signal strength distribution, etc. These features are used to analyze the area of ​​reliable signal transmission of each gateway device, and combined with the gateway device deployment location, divide the sensor deployment array to form K associated sensor deployment sub-arrays.

[0021] Preferably, the sample device deployment location set represents a set of device deployment locations, which is used to simulate the deployment of gateway devices, and includes location coordinates and related deployment information. Optionally, the selection of sample device deployment locations in the sample device deployment location set can cover typical scenarios in coal mines, including complex underground terrain, areas with many obstacles, open areas, etc. The sample environment feature set is used to describe the deployment environment of the Internet of Things gateway deployed at the sample device deployment location set.

[0022] Based on the sample device layout location set and the sample environment feature set, a gateway device reliable transmission area analysis test is performed, and a sample gateway device reliable transmission area set is obtained according to the test results; Install a gateway device at each sample device deployment location, and set the signal transmission parameters (such as transmission power, frequency band). Under the influence of the corresponding environmental characteristics, measure the signal strength (RSSI value) and signal stability of the IoT gateway devices deployed at the sample device deployment location set at different locations. Determine the threshold of signal strength (for example, an RSSI value below -85dBm is considered an unreliable signal). Draw the outline of the transmission area within the range exceeding the threshold, and calculate the area of ​​the area to obtain the set of reliable transmission area areas of the sample gateway devices. By conducting analytical tests on the areas of reliable transmission areas of sample gateway devices, a representative data set is formed. This data set can not only truly reflect the transmission characteristics under the coal mine environment, but also provide high-quality input data for subsequent neural network training and area prediction.

[0023] Preferably, the sample device layout location set, sample environmental feature set and sample gateway device reliable transmission area area set are used as training data, and supervised calculation is performed on the framework constructed based on the feedforward neural network to learn the two-to-one correlation mapping relationship between the device layout location and environmental features and the sample gateway device reliable transmission area area until the training converges to obtain a trained area identifier. The area identifier is used to identify the K device layout locations and the K environmental feature sets to obtain the K gateway device reliable transmission area areas.

[0024] By combining actual samples and deep learning technology, efficient and reliable transmission area identification is achieved, achieving the technical effect of providing data support for sensor subarray division and gateway resource configuration.

[0025] A subarray stability factor obtaining module 14 is used to traverse the K associated sensor layout subarrays to perform data stability analysis and obtain K subarray stability factors; Further, such as Figure 2 As shown, the subarray stability factor obtaining module 14 is used to perform the following steps: Extracting the monitoring data of the K associated sensor deployment subarrays within a preset data analysis window to obtain K sensor monitoring data subarray sequences; Traversing the K sensor monitoring data subarray sequences to identify data fluctuation stability, and obtaining K first subarray stability factors; Performing data interaction recognition on the K sensor monitoring data subarray sequences respectively to generate K sensor monitoring data interaction subarrays; Perform difference analysis on the K sensor monitoring data interaction subarrays and K standard sensor monitoring data interaction subarrays to obtain K second subarray stability factors; A weighted analysis is performed on the K first sub-array stability factors and the K second sub-array stability factors to obtain the K sub-array stability factors.

[0026] In an embodiment of the present application, the stability of the sensor monitoring data within the K associated sensor deployment subarrays is analyzed to determine the K subarray stability factors. Each subarray stability factor reflects the degree of fluctuation of the monitoring data of an associated sensor deployment subarray. The larger the subarray stability factor, the smaller the degree of fluctuation. In other words, the larger the subarray stability factor, the higher the stability of the amount of data transmitted by the corresponding Internet of Things gateway device. By obtaining the K subarray stability factors, the technical effect of providing reliable data support for the subsequent resource configuration of the K Internet of Things gateway devices is achieved.

[0027] Preferably, the monitoring data of the K associated sensor deployment subarrays within a preset data analysis window are extracted to obtain K sensor monitoring data subarray sequences, wherein the preset data analysis window is a time period for data analysis pre-set by a person skilled in the art.

[0028] Each sensor monitoring data subarray sequence in the K sensor monitoring data subarray sequences is traversed to perform fluctuation variance calculation, and the inverse of the calculation result is used as the K first subarray stability factors. The K first subarray stability factors are used to describe the stability of the K sensor monitoring data subarray arrays in terms of data fluctuation degree.

[0029] Then, data interaction recognition is performed on the K sensor monitoring data subarray sequences respectively, and the interaction data at a later time point in the preset data analysis window under the development trend of the previous data is analyzed to obtain K sensor monitoring data interaction subarrays. The K sensor monitoring data interaction subarrays are data under the consideration of the overall data development trend of the K sensor monitoring data subarray sequences.

[0030] Obtain K standard sensor monitoring data interaction subarrays monitored by the K associated sensor deployment subarrays under normal conditions. Perform corresponding difference calculations on the K sensor monitoring data interaction subarrays and the K standard sensor monitoring data interaction subarrays respectively, and compare the difference calculation results with the corresponding standard sensor monitoring data interaction subarrays, and use the difference between the obtained ratio and 1 as the stability factors of the K second subarrays.

[0031] Preferably, the K first subarray stability factors and the K second subarray stability factors are weighted analyzed according to weights pre-set by those skilled in the art to obtain the K subarray stability factors.

[0032] By accurately evaluating the stability of the monitoring data of each associated sensor deployment sub-array, the technical effect of providing reliable data support for subsequent gateway resource configuration is achieved.

[0033] Furthermore, the subarray stability factor obtaining module 14 is used to perform the following steps: Extracting K first sensor monitoring data subarrays and K second sensor monitoring data subarrays from the K sensor monitoring data subarray sequences; Performing data interaction identification on the K first sensor monitoring data subarrays and the K second sensor monitoring data subarrays to obtain K first sensor monitoring data phase interaction subarrays; Extracting K third sensor monitoring data subarrays from the K sensor monitoring data subarray sequences, and performing data interaction analysis on the third sensor monitoring data subarrays with the K first sensor monitoring data phase interaction subarrays to obtain K second sensor monitoring data phase interaction subarrays; Based on the K second sensor monitoring data stage interaction subarrays, data interaction analysis is performed on the remaining subarrays of the K sensor monitoring data subarray sequence in sequence until the end of the K sensor monitoring data subarray sequence is reached to obtain the K sensor monitoring data interaction subarrays.

[0034] Furthermore, the subarray stability factor obtaining module 14 is used to perform the following steps: Using a cosine similarity calculation formula, inner product mapping calculations are performed on the K first sensor monitoring data subarrays and the K second sensor monitoring data subarrays to obtain K similarity sets; Normalizing the K similarity sets to obtain K similarity normalized value sets; Embedding the K similarity normalized values ​​into a matrix to obtain K similarity normalized matrices; A graph convolutional network is used to perform interactive calculations on the K similarity normalization matrices and the K second sensor monitoring data subarrays to obtain the K first sensor monitoring data stage interactive subarrays.

[0035] In a possible embodiment, K first sensor monitoring data subarrays and K second sensor monitoring data subarrays located at the first position and the second position in the K sensor monitoring data subarray sequence are respectively extracted, and data interaction recognition is performed on the K first sensor monitoring data subarrays and the K second sensor monitoring data subarrays to obtain K first sensor monitoring data stage interaction subarrays.

[0036] Furthermore, K third sensor monitoring data subarrays located at the third position in the K sensor monitoring data subarray sequence are extracted, and based on the same principle as that of obtaining the K first sensor monitoring data stage interaction subarrays, data interaction analysis is performed on them together with the K first sensor monitoring data stage interaction subarrays, and combined with the data development trends of the first two positions, K second sensor monitoring data stage interaction subarrays are obtained.

[0037] Based on the same principle as that of obtaining the K first sensor monitoring data stage interaction subarrays, data interaction analysis is performed on the remaining subarrays of the K sensor monitoring data subarray sequence in turn based on the K second sensor monitoring data stage interaction subarrays until the end of the K sensor monitoring data subarray sequence is reached, thereby obtaining the K sensor monitoring data interaction subarrays.

[0038] Preferably, the inner product mapping calculation is performed on the K first sensor monitoring data subarrays and the K second sensor monitoring data subarrays respectively using the cosine similarity calculation formula, and the monitoring data of a sensor in each first sensor monitoring data subarray is analyzed for similarity with the monitoring data of the same sensor in the corresponding second sensor monitoring data subarray to obtain K similarity sets. The K similarity sets are normalized using the softmax formula to determine K similarity normalized value sets. Furthermore, the K similarity normalized values ​​are embedded into an initially empty matrix to obtain K similarity normalized matrices; A graph convolutional network is used to perform interactive calculations on the K similarity normalization matrices and the K second sensor monitoring data subarrays to obtain the K first sensor monitoring data stage interactive subarrays.

[0039] Optionally, multiple sample similarity normalized matrices and multiple sample second sensor monitoring data subarrays, as well as multiple corresponding sample first sensor monitoring data stage interaction subarrays are obtained as interactive training data, and the framework constructed based on the graph convolutional network is trained until the training converges to obtain the trained graph convolutional network. Then, the graph convolutional network is used to interactively calculate the K similarity normalized matrices and the K second sensor monitoring data subarrays to obtain the K first sensor monitoring data stage interaction subarrays.

[0040] The data is processed through multi-stage sequential interactive analysis and cosine similarity calculation, and the depth and breadth of analysis are further improved using graph convolutional networks to obtain the K second subarray stability factors, which are then combined with the K first subarray stability factors for comprehensive analysis to obtain the K subarray stability factors. This achieves the technical effect of comprehensively evaluating the synergy of the associated sensor layout subarrays and the degree of data stability.

[0041] A subarray transmission volume factor obtaining module 15 is used to traverse the K associated sensor layout subarrays to perform transmission volume analysis and obtain K subarray transmission volume factors; Furthermore, the subarray transmission volume factor obtaining module 15 is used to perform the following steps: Extracting the transmission data volume of the K associated sensor layout subarrays within a preset analysis window to obtain the transmission volume of the K subarrays; The K sub-array transmission volumes are respectively divided by the sum of the K sub-array transmission volumes to obtain the K sub-array transmission volume factors.

[0042] In a possible embodiment, the preset analysis window is a time period for performing transmission volume analysis pre-set by a person skilled in the art. Within the preset analysis window, the total data volume (unit: byte) uploaded by the K associated sensor layout subarrays is extracted to obtain the transmission volume of the K subarrays. The transmission volume of the K subarrays reflects the transmission data volume of the K associated sensor layout subarrays. The larger the subarray transmission volume, the more resources the IoT gateway device corresponding to the associated sensor layout subarray needs to configure.

[0043] Preferably, the K subarray transmission volumes are divided by the sum of the K subarray transmission volumes to obtain the K subarray transmission volume factors, wherein the K subarray transmission volume factors reflect the weights when determining resource allocation for the K IoT gateway devices from the perspective of transmission volume.

[0044] The management module 16 is used to configure resources for the K IoT gateway devices based on the K sub-array stability factors and the K sub-array transmission volume factors, and use the configured K IoT gateway devices to upload the data collected by the sensors in the K associated sensor deployment sub-arrays to the coal mine intelligent processing component for management.

[0045] Furthermore, the management module 16 is used to perform the following steps: Performing weighted calculation on the K sub-array stability factors and the K sub-array transmission volume factors to obtain K resource configuration coefficients; Dividing the K resource allocation coefficients by the sum of the K resource allocation coefficients respectively to obtain K resource allocation weights; Resources are configured for the K Internet of Things gateway devices based on the K resource configuration weights.

[0046] In one embodiment, after obtaining the K subarray stability factors and the K subarray transmission volume factors, the K IoT gateway devices are configured with resources according to the size of the K subarray stability factors and the K subarray transmission volume factors. Then, the configured K physical network gateway devices are used to upload the data collected by the sensors in the K associated sensor deployment subarrays to the coal mine intelligent processing component for management. The coal mine intelligent processing component is used to integrate and analyze the data, as well as process the data.

[0047] Optionally, according to the weighted weights pre-set by a person skilled in the art, the K subarray stability factors and the K subarray transmission volume factors are weightedly calculated to obtain K resource allocation coefficients. The K resource allocation coefficients reflect the comprehensive demand for resource allocation of the Internet of Things gateway device corresponding to each associated sensor deployment subarray.

[0048] Then, the K resource configuration coefficients are respectively divided by the sum of the K resource configuration coefficients to obtain K resource configuration weights. Then, resource configuration is performed on the K IoT gateway devices based on the size of the K resource configuration weights to obtain K configured physical network gateway devices.

[0049] By comprehensively analyzing the stability and transmission volume and rationally allocating the resources of the gateway equipment, we can achieve the technical effect of ensuring the high efficiency of data transmission and the dynamic data transmission requirements of sub-arrays of different associated sensors, providing reliable support for data collection and upload of the coal mine intelligent management system.

[0050] In summary, the embodiments of the present application have at least the following technical effects: This application achieves the technical effect of improving the data transmission reliability and overall management quality of the coal mine intelligent management system by dividing the sensor layout array, analyzing data stability and transmission volume, determining the reliable transmission area of ​​the gateway device, and optimizing the resource allocation of the gateway device.

[0051] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0053] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A coal mine intelligent management system based on the Internet of Things, characterized in that: The system comprises: A deployment array acquisition module is used to deploy sensors based on various key locations of the target coal mine and obtain a sensor deployment array; An Internet of Things gateway device acquisition module, used to acquire K Internet of Things gateway devices deployed in the target coal mine, wherein the K Internet of Things gateway devices include K device deployment locations, and K is an integer greater than or equal to 1; A subarray acquisition module is used to identify the transmission areas of the K IoT gateway devices based on the K device deployment locations and the coal mine environment information of the target coal mine, and divide the sensor deployment array according to the identification result to obtain K associated sensor deployment subarrays; A subarray stability factor obtaining module, used to traverse the K associated sensor layout subarrays to perform data stability analysis and obtain K subarray stability factors; A subarray transmission volume factor acquisition module is used to traverse the K associated sensor layout subarrays to perform transmission volume analysis and obtain K subarray transmission volume factors; A management module is used to configure resources for the K IoT gateway devices based on the K sub-array stability factors and the K sub-array transmission volume factors, and use the configured K IoT gateway devices to upload data collected by sensors in the K associated sensor deployment sub-arrays to the coal mine intelligent processing component for management.

2. The coal mine intelligent management system based on the Internet of Things as claimed in claim 1, characterized in that: The subarray arrangement acquisition module is used to execute the following method: Taking the K equipment layout locations as indexes, extracting environmental features of the coal mine environment information of the target coal mine to obtain K environmental feature sets; Using the K device deployment locations and the K environmental feature sets as indexes, identify the reliable transmission area of ​​the gateway device to obtain the reliable transmission area of ​​the K gateway devices; The sensor layout array is divided based on the areas of the K gateway devices' reliable transmission regions and the K device layout positions to obtain the K associated sensor layout sub-arrays.

3. The coal mine intelligent management system based on the Internet of Things as claimed in claim 2, characterized in that: The subarray arrangement acquisition module is used to execute the following method: Obtaining a set of sample equipment layout locations and a set of sample environment characteristics; Based on the sample device layout location set and the sample environment feature set, a gateway device reliable transmission area analysis test is performed, and a sample gateway device reliable transmission area set is obtained according to the test results; The sample device layout location set, the sample environment feature set and the sample gateway device reliable transmission area set are used as training data, and a framework constructed based on a feedforward neural network is supervised and calculated until the training converges to obtain a trained area identifier; The area identifier is used to identify the K device layout locations and the K environmental feature sets to obtain the areas of the K gateway devices' reliable transmission regions.

4. The coal mine intelligent management system based on the Internet of Things as claimed in claim 1, characterized in that: The subarray stability factor obtaining module is used to perform the following steps: Extracting the monitoring data of the K associated sensor deployment subarrays within a preset data analysis window to obtain K sensor monitoring data subarray sequences; Traversing the K sensor monitoring data subarray sequences to identify data fluctuation stability, and obtaining K first subarray stability factors; Performing data interaction recognition on the K sensor monitoring data subarray sequences respectively to generate K sensor monitoring data interaction subarrays; Perform difference analysis on the K sensor monitoring data interaction subarrays and K standard sensor monitoring data interaction subarrays to obtain K second subarray stability factors; A weighted analysis is performed on the K first sub-array stability factors and the K second sub-array stability factors to obtain the K sub-array stability factors.

5. The coal mine intelligent management system based on the Internet of Things as claimed in claim 4, characterized in that: The subarray stability factor obtaining module is used to perform the following steps: Extracting K first sensor monitoring data subarrays and K second sensor monitoring data subarrays from the K sensor monitoring data subarray sequences; Performing data interaction identification on the K first sensor monitoring data subarrays and the K second sensor monitoring data subarrays to obtain K first sensor monitoring data phase interaction subarrays; Extracting K third sensor monitoring data subarrays from the K sensor monitoring data subarray sequences, and performing data interaction analysis on the third sensor monitoring data subarrays with the K first sensor monitoring data phase interaction subarrays to obtain K second sensor monitoring data phase interaction subarrays; Based on the K second sensor monitoring data stage interaction subarrays, data interaction analysis is performed on the remaining subarrays of the K sensor monitoring data subarray sequence in sequence until the end of the K sensor monitoring data subarray sequence is reached to obtain the K sensor monitoring data interaction subarrays.

6. The coal mine intelligent management system based on the Internet of Things as claimed in claim 5, characterized in that: The subarray stability factor obtaining module is used to perform the following steps: Using a cosine similarity calculation formula, inner product mapping calculations are performed on the K first sensor monitoring data subarrays and the K second sensor monitoring data subarrays to obtain K similarity sets; Normalizing the K similarity sets to obtain K similarity normalized value sets; Embedding the K similarity normalized values ​​into a matrix to obtain K similarity normalized matrices; A graph convolutional network is used to perform interactive calculations on the K similarity normalization matrices and the K second sensor monitoring data subarrays to obtain the K first sensor monitoring data stage interactive subarrays.

7. The coal mine intelligent management system based on the Internet of Things as claimed in claim 1, characterized in that: The subarray transmission volume factor acquisition module is used to perform the following steps: Extracting the transmission data volume of the K associated sensor layout subarrays within a preset analysis window to obtain the transmission volume of the K subarrays; The K sub-array transmission volumes are respectively divided by the sum of the K sub-array transmission volumes to obtain the K sub-array transmission volume factors.

8. The coal mine intelligent management system based on the Internet of Things as claimed in claim 1, characterized in that: The management module is used to perform the following steps: Performing weighted calculation on the K sub-array stability factors and the K sub-array transmission volume factors to obtain K resource configuration coefficients; Dividing the K resource allocation coefficients by the sum of the K resource allocation coefficients respectively to obtain K resource allocation weights; Resources are configured for the K Internet of Things gateway devices based on the K resource configuration weights.

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