CN116993061B - Coal mine anti-theft intelligent management system based on artificial intelligence - Google Patents
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Abstract
The invention belongs to the field of coal mine anti-theft, relates to a data analysis technology, and is used for solving the problem that the traditional coal mine anti-theft intelligent management system cannot be combined with various data of a coal mine in a factory to carry out comprehensive analysis, in particular to an artificial intelligence-based coal mine anti-theft intelligent management system, which comprises an anti-theft monitoring module, a data analysis module, a period management module and a region analysis module, wherein the anti-theft monitoring module, the data analysis module, the period management module and the region analysis module are sequentially in communication connection; the anti-theft monitoring module is used for monitoring and analyzing the coal mine theft phenomenon of the coal mine processing workshop: generating a monitoring period, dividing a processing workshop of the coal mine into a plurality of subareas, acquiring the coal mine storage total amount of the subareas in the monitoring period, and marking the coal mine storage total amount as a storage value; the invention can monitor and analyze the coal mine theft phenomenon in the coal mine processing workshop, and carry out warehouse-out and release when the total amount monitoring and warehouse-out monitoring results are all passed.
Description
Technical Field
The invention belongs to the field of coal mine anti-theft, relates to a data analysis technology, and in particular relates to an artificial intelligence-based coal mine anti-theft intelligent management system.
Background
In order to prevent coal mine theft, various materials, tools, cables, equipment and the like in the underground transportation and production sites are subject to jurisdiction by related departments, and each gate, warehouse, goods yard and stock yard of the production area should establish a strict access inspection system, and the access materials should be provided with a list of written names, specifications and numbers, and a list and a phase Fu Fangke are released.
The existing intelligent anti-theft management system for the coal mine can only check vehicles and personnel entering and exiting through a delivery bill, but cannot be combined with various data of the coal mine in a factory to carry out comprehensive analysis, and cannot directly monitor abnormal links when data abnormality occurs, so that the data tracing efficiency is low.
Aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based coal mine anti-theft intelligent management system, which is used for solving the problem that the conventional coal mine anti-theft intelligent management system cannot be combined with various data of a coal mine in a factory to carry out comprehensive analysis;
the technical problems to be solved by the invention are as follows: how to provide an artificial intelligence based coal mine anti-theft intelligent management system which can be combined with various data of a coal mine in a factory to carry out comprehensive analysis.
The aim of the invention can be achieved by the following technical scheme:
the coal mine anti-theft intelligent management system based on the artificial intelligence comprises an anti-theft monitoring module, a data analysis module, a period management module and an area analysis module, wherein the anti-theft monitoring module, the data analysis module, the period management module and the area analysis module are sequentially in communication connection;
the anti-theft monitoring module is used for monitoring and analyzing the coal mine theft phenomenon of the coal mine processing workshop: generating a monitoring period, dividing a processing workshop of a coal mine into a plurality of subareas, acquiring the coal mine storage total quantity of the subareas in the monitoring period, marking the total quantity as a storage value, and carrying out ex-warehouse monitoring and total quantity monitoring on coal transportation vehicles when the coal mine is ex-warehouse;
the data analysis module is used for analyzing and processing the ex-warehouse monitoring result and the total monitoring result: if the ex-warehouse monitoring result and the total amount monitoring result are both passed, judging that the ex-warehouse behavior is safe, generating an ex-warehouse signal and sending the ex-warehouse signal to a mobile phone terminal of a manager; if the ex-warehouse monitoring result is passing and the total amount monitoring result is not passing, judging that the ex-warehouse behavior is unsafe, generating a monitoring enhancement signal and sending the monitoring enhancement signal to the period management module; if the ex-warehouse monitoring result is not passing and the total amount monitoring result is passing, judging that the ex-warehouse behavior is unsafe, generating a total amount statistical signal and sending the total amount statistical signal to the period management module; if the ex-warehouse monitoring result and the total amount monitoring result are not passed, judging that the ex-warehouse behavior is unsafe, generating a link optimization signal and sending the link optimization signal to a period management module;
the period management module is used for periodically managing and analyzing the coal mine theft phenomenon of the coal mine processing workshop;
the regional analysis module is used for monitoring and analyzing the hidden trouble of theft of each region in the coal mine processing workshop.
As a preferred embodiment of the present invention, the specific process of monitoring the coal-moving vehicle ex-warehouse comprises: the method comprises the steps that the ex-warehouse data are obtained through an ex-warehouse list, the ex-warehouse data comprise coal mine class, sub-region numbers, ex-warehouse quantity and ex-warehouse time, and the ex-warehouse vehicle information is compared with the ex-warehouse data: if the results are consistent, judging that the warehouse monitoring passes; if the library monitoring is inconsistent, judging that the library monitoring is not passed.
As a preferred embodiment of the invention, the concrete process of monitoring the total amount during the coal mine ex-warehouse comprises the following steps: the method comprises the steps of obtaining residual coal values of sub-areas with corresponding numbers, marking the residual coal values as residual values, marking the sum of the residual values and the ex-warehouse quantities as a list total value, marking the absolute value of the difference value between the list total value and a stored value as a deviation total value, and comparing the deviation total value with a preset deviation threshold value: if the total deviation value is smaller than the deviation threshold value, judging that the total quantity monitoring is passed; if the total deviation value is greater than or equal to the deviation threshold value, judging that the total monitoring is not passed; and sending the ex-warehouse monitoring result and the total monitoring result to a data analysis module.
As a preferred embodiment of the present invention, the period management module is configured to perform a period management analysis on a coal mine theft phenomenon in a coal mine processing workshop: the method comprises the steps of marking the times of a monitoring enhancement signal, a total amount statistical signal and a link optimization signal received by a period management module in a monitoring period as an enhancement value ZQ, a statistical value TJ and an optimization value YH respectively, obtaining a theft coefficient DQ of the monitoring period through a formula DQ=α1 xZQ+α2 xTJ+α3 x YH, and judging the coal mine theft risk of the monitoring period through the theft coefficient DQ.
As a preferred embodiment of the invention, the concrete process for judging the coal mine theft risk of the monitoring period comprises the following steps: comparing the theft coefficient DQ of the monitoring period with a preset theft threshold DQmax: if the theft coefficient DQ is smaller than the theft threshold DQmax, the enhancement value ZQ, the statistical value TJ, and the optimized value YH are numerically compared: if the value of the enhancement value ZQ is the largest, sending a monitoring enhancement signal to a mobile phone terminal of a manager; if the value of the statistic value TJ is the largest, sending the total amount statistic signal to a mobile phone terminal of a manager; if the value of the optimized value YH is the largest, a link optimizing signal is sent to a mobile phone terminal of a manager; if the theft coefficient DQ is equal to or greater than the theft threshold DQmax, the regional analysis signal is generated and sent to the mobile phone terminal of the manager.
As a preferred embodiment of the invention, the specific process of monitoring and analyzing the hidden trouble of theft of each area in the coal mine processing workshop by the area analysis module comprises the following steps: marking the corresponding subareas when the period management module receives the monitoring enhancement signals, the total amount statistical signals or the link optimization signals in the monitoring period, substituting the marking data of the subareas into a calculation formula of the theft coefficient DQ to calculate to obtain the subareas, forming a theft set by the subareas of all the subareas, calculating variance of the theft set to obtain a concentration coefficient, and comparing the concentration coefficient with a preset concentration threshold value: if the concentration coefficient is smaller than the concentration threshold value, generating a collective training signal and sending the collective training signal to a mobile phone terminal of a manager; if the concentration coefficient is greater than or equal to the concentration threshold, marking the L1 sub-areas with the largest sub-coefficient values as key areas, and sending the key areas to the mobile phone terminals of the manager.
As a preferred implementation mode of the invention, the working method of the coal mine anti-theft intelligent management system based on artificial intelligence comprises the following steps:
step one: monitoring and analyzing coal mine theft phenomena of a coal mine processing workshop: generating a monitoring period, dividing a processing workshop of the coal mine into a plurality of sub-areas, and carrying out ex-warehouse monitoring and total quantity monitoring on coal transportation vehicles when the coal mine is ex-warehouse;
step two: analyzing and processing the ex-warehouse monitoring result and the total amount monitoring result, judging whether the ex-warehouse behavior is safe or not according to the ex-warehouse monitoring result and the total amount monitoring result, and generating a monitoring enhancement signal, a total amount statistical signal or a link optimization signal when the ex-warehouse behavior is unsafe;
step three: periodically managing and analyzing the coal mine theft phenomenon of a coal mine processing workshop: the method comprises the steps of marking the monitoring enhancement signal, the total amount statistical signal and the number of times of link optimization signals received by a period management module in a monitoring period as an enhancement value ZQ, a statistical value TJ and an optimization value YH respectively, carrying out numerical calculation to obtain a theft coefficient DQ, and judging the coal mine safety of the monitoring period through the theft coefficient DQ;
step four: monitoring and analyzing the hidden trouble of theft in each area of the coal mine processing workshop to obtain a centralized coefficient, generating a collective training signal or a key area through the centralized coefficient, and sending the collective training signal or the key area to a mobile phone terminal of a manager.
The invention has the following beneficial effects:
the anti-theft monitoring module can monitor and analyze the coal mine theft phenomenon of the coal mine processing workshop, monitor the coal mine theft risk of the ex-warehouse vehicle through total amount monitoring and ex-warehouse monitoring, and carry out ex-warehouse release when the total amount monitoring and the ex-warehouse monitoring result are all passed;
the data analysis module can analyze and process the ex-warehouse monitoring result and the total amount monitoring result, and judges the link causing unsafe ex-warehouse behavior according to the ex-warehouse monitoring result and the total amount monitoring result, so that the accident treatment is carried out by adopting targeted treatment measures according to the generated treatment signals;
the period management module can be used for carrying out periodic management analysis on the coal mine theft phenomenon of the coal mine processing workshop, and the theft coefficient is obtained by carrying out comprehensive analysis and calculation on the times of each signal received by the period management module in the monitoring period, so that the whole coal mine theft risk degree in the monitoring period is fed back through the theft coefficient;
4. the regional analysis module can monitor and analyze the hidden trouble of theft of each region in the coal mine processing workshop, the sub-coefficients of each sub-region are calculated, then the centralized coefficients are obtained through the sub-coefficient distribution states of all the sub-regions, and the processing decision analysis is carried out through the centralized coefficients when the degree of risk of theft of the coal mine is high.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in FIG. 1, the coal mine anti-theft intelligent management system based on artificial intelligence comprises an anti-theft monitoring module, a data analysis module, a period management module and an area analysis module, wherein the anti-theft monitoring module, the data analysis module, the period management module and the area analysis module are sequentially in communication connection.
The anti-theft monitoring module is used for monitoring and analyzing the coal mine theft phenomenon of the coal mine processing workshop: generating a monitoring period, dividing a processing workshop of a coal mine into a plurality of subareas, acquiring the coal mine storage total amount of the subareas in the monitoring period, marking the coal mine storage total amount as a storage value, and carrying out ex-warehouse monitoring on coal transportation vehicles when the coal mine is ex-warehouse: the method comprises the steps that the ex-warehouse data are obtained through an ex-warehouse list, the ex-warehouse data comprise coal mine class, sub-region numbers, ex-warehouse quantity and ex-warehouse time, and the ex-warehouse vehicle information is compared with the ex-warehouse data: if the results are consistent, judging that the warehouse monitoring passes; if the library monitoring is inconsistent, judging that the library monitoring is not passed; total amount monitoring at coal mine ex-warehouse: the method comprises the steps of obtaining residual coal values of sub-areas with corresponding numbers, marking the residual coal values as residual values, marking the sum of the residual values and the ex-warehouse quantities as a list total value, marking the absolute value of the difference value between the list total value and a stored value as a deviation total value, and comparing the deviation total value with a preset deviation threshold value: if the total deviation value is smaller than the deviation threshold value, judging that the total quantity monitoring is passed; if the total deviation value is greater than or equal to the deviation threshold value, judging that the total monitoring is not passed; sending the ex-warehouse monitoring result and the total monitoring result to a data analysis module; monitoring and analyzing coal mine theft phenomena in a coal mine processing workshop, monitoring coal mine theft risks of the ex-warehouse vehicles through total amount monitoring and ex-warehouse monitoring, and carrying out ex-warehouse release when the total amount monitoring and the ex-warehouse monitoring result are all passed.
The data analysis module is used for analyzing and processing the warehouse-out monitoring result and the total monitoring result: if the ex-warehouse monitoring result and the total amount monitoring result are both passed, judging that the ex-warehouse behavior is safe, generating an ex-warehouse signal and sending the ex-warehouse signal to a mobile phone terminal of a manager; if the ex-warehouse monitoring result is passing and the total amount monitoring result is not passing, judging that the ex-warehouse behavior is unsafe, generating a monitoring enhancement signal and sending the monitoring enhancement signal to the period management module; if the ex-warehouse monitoring result is not passing and the total amount monitoring result is passing, judging that the ex-warehouse behavior is unsafe, generating a total amount statistical signal and sending the total amount statistical signal to the period management module; if the ex-warehouse monitoring result and the total amount monitoring result are not passed, judging that the ex-warehouse behavior is unsafe, generating a link optimization signal and sending the link optimization signal to a period management module; analyzing and processing the ex-warehouse monitoring result and the total amount monitoring result, judging links which cause unsafe ex-warehouse behaviors according to the ex-warehouse monitoring result and the total amount monitoring result, and accordingly performing accident handling by adopting targeted handling measures according to the generated handling signals.
The period management module is used for periodically managing and analyzing the coal mine theft phenomenon of the coal mine processing workshop: the method comprises the steps of marking the monitoring enhancement signal, the total amount statistical signal and the number of times of link optimization signals received by a period management module in a monitoring period as an enhancement value ZQ, a statistical value TJ and an optimization value YH respectively, and obtaining a theft coefficient DQ of the monitoring period through a formula DQ=α1 xZQ+α2xTJ+α3xYH, wherein α1, α2 and α3 are proportionality coefficients, and α1 > α2 > α3 > 1; comparing the theft coefficient DQ of the monitoring period with a preset theft threshold DQmax: if the theft coefficient DQ is smaller than the theft threshold DQmax, the enhancement value ZQ, the statistical value TJ, and the optimized value YH are numerically compared: if the value of the enhancement value ZQ is the largest, sending a monitoring enhancement signal to a mobile phone terminal of a manager; if the value of the statistic value TJ is the largest, sending the total amount statistic signal to a mobile phone terminal of a manager; if the value of the optimized value YH is the largest, a link optimizing signal is sent to a mobile phone terminal of a manager; if the theft coefficient DQ is greater than or equal to the theft threshold DQmax, generating a regional analysis signal and transmitting the regional analysis signal to a mobile phone terminal of a manager; and (3) periodically managing and analyzing the coal mine theft phenomenon of the coal mine processing workshop, comprehensively analyzing and calculating the times of receiving each signal in the monitoring period by the period management module to obtain a theft coefficient, and feeding back the whole coal mine theft risk degree in the monitoring period through the theft coefficient.
The regional analysis module is used for monitoring and analyzing the hidden trouble of theft of each region in the coal mine processing workshop: marking the corresponding subareas when the period management module receives the monitoring enhancement signals, the total amount statistical signals or the link optimization signals in the monitoring period, substituting the marking data of the subareas into a calculation formula of the theft coefficient DQ to calculate to obtain the subareas, forming a theft set by the subareas of all the subareas, calculating variance of the theft set to obtain a concentration coefficient, and comparing the concentration coefficient with a preset concentration threshold value: if the concentration coefficient is smaller than the concentration threshold value, generating a collective training signal and sending the collective training signal to a mobile phone terminal of a manager; if the concentration coefficient is greater than or equal to the concentration threshold, marking the L1 sub-areas with the largest sub-coefficient values as key areas, wherein L1 is a numerical constant, and the specific numerical value of L1 is set by a manager; the key areas are sent to a mobile phone terminal of a manager; monitoring and analyzing the hidden trouble of theft of each region in the coal mine processing workshop, calculating the sub-coefficients of each sub-region, acquiring the centralized coefficient by the sub-coefficient distribution state of all the sub-regions, and performing processing decision analysis when the coal mine theft risk degree is high through the centralized coefficient.
Embodiment two: as shown in fig. 2, the coal mine anti-theft intelligent management method based on artificial intelligence comprises the following steps:
step one: monitoring and analyzing coal mine theft phenomena of a coal mine processing workshop: generating a monitoring period, dividing a processing workshop of the coal mine into a plurality of sub-areas, and carrying out ex-warehouse monitoring and total quantity monitoring on coal transportation vehicles when the coal mine is ex-warehouse;
step two: analyzing and processing the ex-warehouse monitoring result and the total amount monitoring result, judging whether the ex-warehouse behavior is safe or not according to the ex-warehouse monitoring result and the total amount monitoring result, and generating a monitoring enhancement signal, a total amount statistical signal or a link optimization signal when the ex-warehouse behavior is unsafe;
step three: periodically managing and analyzing the coal mine theft phenomenon of a coal mine processing workshop: the method comprises the steps of marking the monitoring enhancement signal, the total amount statistical signal and the number of times of link optimization signals received by a period management module in a monitoring period as an enhancement value ZQ, a statistical value TJ and an optimization value YH respectively, carrying out numerical calculation to obtain a theft coefficient DQ, and judging the coal mine safety of the monitoring period through the theft coefficient DQ;
step four: monitoring and analyzing the hidden trouble of theft in each area of the coal mine processing workshop to obtain a centralized coefficient, generating a collective training signal or a key area through the centralized coefficient, and sending the collective training signal or the key area to a mobile phone terminal of a manager.
An artificial intelligence-based coal mine anti-theft intelligent management system generates a monitoring period during operation, divides a processing workshop of a coal mine into a plurality of sub-areas, and monitors the ex-warehouse and total quantity of coal transportation vehicles during ex-warehouse of the coal mine; analyzing and processing the ex-warehouse monitoring result and the total amount monitoring result, judging whether the ex-warehouse behavior is safe or not according to the ex-warehouse monitoring result and the total amount monitoring result, and generating a monitoring enhancement signal, a total amount statistical signal or a link optimization signal when the ex-warehouse behavior is unsafe; the method comprises the steps of marking the monitoring enhancement signal, the total amount statistical signal and the number of times of link optimization signals received by a period management module in a monitoring period as an enhancement value ZQ, a statistical value TJ and an optimization value YH respectively, carrying out numerical calculation to obtain a theft coefficient DQ, and judging the coal mine safety of the monitoring period through the theft coefficient DQ; monitoring and analyzing the hidden trouble of theft in each area of the coal mine processing workshop to obtain a centralized coefficient, generating a collective training signal or a key area through the centralized coefficient, and sending the collective training signal or the key area to a mobile phone terminal of a manager.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula dq=α1×zq+α2×tj+α3×yh; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding theft coefficient for each group of sample data; substituting the set theft coefficient and the collected sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 4.58, 3.74 and 3.13;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding theft coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship of the parameter and the quantized value is not affected, for example, the theft coefficient is proportional to the value of the enhancement value.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (2)
1. The coal mine anti-theft intelligent management system based on the artificial intelligence is characterized by comprising an anti-theft monitoring module, a data analysis module, a period management module and an area analysis module, wherein the anti-theft monitoring module, the data analysis module, the period management module and the area analysis module are sequentially in communication connection;
the anti-theft monitoring module is used for monitoring and analyzing the coal mine theft phenomenon of the coal mine processing workshop: generating a monitoring period, dividing a processing workshop of a coal mine into a plurality of subareas, acquiring the coal mine storage total quantity of the subareas in the monitoring period, marking the total quantity as a storage value, and carrying out ex-warehouse monitoring and total quantity monitoring on coal transportation vehicles when the coal mine is ex-warehouse;
the data analysis module is used for analyzing and processing the ex-warehouse monitoring result and the total monitoring result: if the ex-warehouse monitoring result and the total amount monitoring result are both passed, judging that the ex-warehouse behavior is safe, generating an ex-warehouse signal and sending the ex-warehouse signal to a mobile phone terminal of a manager; if the ex-warehouse monitoring result is passing and the total amount monitoring result is not passing, judging that the ex-warehouse behavior is unsafe, generating a monitoring enhancement signal and sending the monitoring enhancement signal to the period management module; if the ex-warehouse monitoring result is not passing and the total amount monitoring result is passing, judging that the ex-warehouse behavior is unsafe, generating a total amount statistical signal and sending the total amount statistical signal to the period management module; if the ex-warehouse monitoring result and the total amount monitoring result are not passed, judging that the ex-warehouse behavior is unsafe, generating a link optimization signal and sending the link optimization signal to a period management module;
the period management module is used for periodically managing and analyzing the coal mine theft phenomenon of the coal mine processing workshop;
the regional analysis module is used for monitoring and analyzing the hidden trouble of theft of each region in the coal mine processing workshop;
the specific process for carrying out ex-warehouse monitoring on the coal-carrying vehicle comprises the following steps: the method comprises the steps that the ex-warehouse data are obtained through an ex-warehouse list, the ex-warehouse data comprise coal mine class, sub-region numbers, ex-warehouse quantity and ex-warehouse time, and the ex-warehouse vehicle information is compared with the ex-warehouse data: if the results are consistent, judging that the warehouse monitoring passes; if the library monitoring is inconsistent, judging that the library monitoring is not passed;
the concrete process for monitoring the total amount during the ex-warehouse of the coal mine comprises the following steps: the method comprises the steps of obtaining residual coal values of sub-areas with corresponding numbers, marking the residual coal values as residual values, marking the sum of the residual values and the ex-warehouse quantities as a list total value, marking the absolute value of the difference value between the list total value and a stored value as a deviation total value, and comparing the deviation total value with a preset deviation threshold value: if the total deviation value is smaller than the deviation threshold value, judging that the total quantity monitoring is passed; if the total deviation value is greater than or equal to the deviation threshold value, judging that the total monitoring is not passed; sending the ex-warehouse monitoring result and the total monitoring result to a data analysis module;
the period management module is used for periodically managing and analyzing the coal mine theft phenomenon of the coal mine processing workshop: the method comprises the steps of marking the monitoring enhancement signal, the total amount statistical signal and the number of times of link optimization signals received by a period management module in a monitoring period as an enhancement value ZQ, a statistical value TJ and an optimization value YH respectively, obtaining a theft coefficient DQ of the monitoring period through a formula DQ=α1 xZQ+α2 xTJ+α3 xYH, and judging the coal mine theft risk of the monitoring period through the theft coefficient DQ;
the concrete process for judging the coal mine theft risk in the monitoring period comprises the following steps: comparing the theft coefficient DQ of the monitoring period with a preset theft threshold DQmax: if the theft coefficient DQ is smaller than the theft threshold DQmax, the enhancement value ZQ, the statistical value TJ, and the optimized value YH are numerically compared: if the value of the enhancement value ZQ is the largest, sending a monitoring enhancement signal to a mobile phone terminal of a manager; if the value of the statistic value TJ is the largest, sending the total amount statistic signal to a mobile phone terminal of a manager; if the value of the optimized value YH is the largest, a link optimizing signal is sent to a mobile phone terminal of a manager; if the theft coefficient DQ is equal to or greater than the theft threshold DQmax, the regional analysis signal is generated and sent to the mobile phone terminal of the manager.
2. The intelligent coal mine anti-theft management system based on artificial intelligence according to claim 1, wherein the specific process of monitoring and analyzing the hidden trouble of theft in each area in the coal mine processing workshop by the area analysis module comprises the following steps: marking the corresponding subareas when the period management module receives the monitoring enhancement signals, the total amount statistical signals or the link optimization signals in the monitoring period, substituting the marking data of the subareas into a calculation formula of the theft coefficient DQ to calculate to obtain the subareas, forming a theft set by the subareas of all the subareas, calculating variance of the theft set to obtain a concentration coefficient, and comparing the concentration coefficient with a preset concentration threshold value: if the concentration coefficient is smaller than the concentration threshold value, generating a collective training signal and sending the collective training signal to a mobile phone terminal of a manager; if the concentration coefficient is greater than or equal to the concentration threshold, marking the L1 sub-areas with the largest sub-coefficient values as key areas, and sending the key areas to the mobile phone terminals of the manager.
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