Mine early warning method and system based on multi-system cooperation and computer equipment

Abstract

The invention belongs to the technical field of mine monitoring and early warning, and particularly relates to a mine early warning method and system based on multi-system cooperation and computer equipment. The method comprises the following steps: acquiring monitoring data, acquired by each early warning monitoring system, of a certain measuring point of a mine; comparing the monitoring data acquired by each early warning monitoring system at the monitoring point with corresponding reference data, and obtaining a score of the monitoring data of each early warning monitoring system according to a comparison result; summing the obtained scores of the monitoring data of the early warning monitoring systems to obtain a total score of the measuring point, and if the total score is lower than a set risk score threshold value of the measuring point, determining that the certain measuring point is a risk point; the reference data is a threshold value used for judging whether the corresponding monitoring data is abnormal or not. Risk points can be found and alarmed in time, it is not needed to wait for a large degree of abnormity of a certain item of monitoring data to give an alarm, and workers can find and solve abnormal problems in time conveniently.

Description

Technical Field

[0001] The present invention belongs to the technical field of mine monitoring and early warning, and in particular relates to a mine early warning method, system and computer equipment based on multi-system collaboration. Background Art

[0002] In the prior art, the various monitoring systems of mines are relatively isolated from each other, and the various links of mine construction lack linkage. When early warning is carried out, only the data of a single system can be used for early warning. When multiple systems have small abnormalities at the same time, each system will not alarm, but at this time there are already signs of abnormal conditions or even safety accidents. Only when the abnormal situation develops to a certain extent and triggers the alarm of a certain system can it play the effect of prompting the staff to take measures to solve the abnormal situation. At this time, the abnormal situation has developed to a certain situation and may be difficult to solve. Therefore, the mine early warning method in the prior art is relatively delayed and cannot discover and solve the problem in the first time. Summary of the invention

[0003] The purpose of the present invention is to provide a mine early warning method, system and computer equipment based on multi-system collaboration, so as to solve the technical problem that the mine early warning method in the prior art is relatively delayed, resulting in abnormal situations that are difficult to solve when they develop to a certain situation.

[0004] In order to solve the above technical problems, the present invention provides a mine early warning method based on multi-system collaboration, which includes the following steps: Acquire the monitoring data of a certain measuring point in the mine collected by each early warning monitoring system; compare the monitoring data collected by each early warning monitoring system of the measuring point with the corresponding reference data, and obtain the score of the monitoring data of each early warning monitoring system according to the comparison result; sum up the scores of the monitoring data of each early warning monitoring system to obtain the total score of the measuring point; if the total score is lower than the set risk score threshold of the measuring point, the measuring point is determined to be a risk point; the reference data is a threshold used to determine whether the corresponding monitoring data is abnormal.

[0005] Furthermore, the process of obtaining the score of the monitoring data of each early warning monitoring system based on the comparison results includes: when the monitoring data of a certain early warning monitoring system is not within the normal range determined by the corresponding reference data, the score of the monitoring data is subtracted from the first set score on the basis of the score corresponding to the reference data every time the degree of deviation between the monitoring data and the reference data increases by a first set deviation degree threshold.

[0006] Furthermore, the process of obtaining the score of the monitoring data of each early warning monitoring system based on the comparison results includes: when the monitoring data of a certain early warning monitoring system is not within the normal range determined by the corresponding reference data, and the degree of deviation from the reference data of the measuring point is greater than a second set deviation degree threshold, the score of the monitoring data is subtracted from the second set score on the basis of the score corresponding to the reference data, and the second set score is a score that, after deduction, can directly make the total score of the measuring point lower than the set risk score threshold.

[0007] Furthermore, the method also includes the following steps: obtaining historical monitoring data collected by various early warning monitoring systems at a certain measuring point in the mine, calculating the change trend of each monitoring data using the historical monitoring data, predicting each monitoring data within a subsequent set time based on the change trend of each monitoring data, comparing the predicted monitoring data with the corresponding reference data, and obtaining a predicted score for each predicted monitoring data based on the comparison result; summing up each predicted score to obtain a total predicted score for the measuring point; if the total predicted score is lower than the set risk score threshold for the measuring point, the measuring point is determined to be a potential risk point.

[0008] Furthermore, the method also includes the following steps: displaying the monitoring data of each measuring point collected by each early warning monitoring system at a corresponding position of the constructed real-time three-dimensional model of the mine.

[0009] Furthermore, the method also includes the following steps: inputting the data of each early warning monitoring system of the risk point into the auxiliary decision-making model, and outputting the decision-making process for eliminating the risk of the measuring point, wherein the auxiliary decision-making model is obtained by training the artificial intelligence algorithm using training data, and the training data includes multiple risk point monitoring data and corresponding decision-making processes for eliminating risks.

[0010] Furthermore, when a measuring point is determined to be a risk point, an alarm is given priority to the staff near the risk point.

[0011] Furthermore, the monitoring data of a measuring point at a certain location in the mine includes: temperature monitoring data of the measuring point, water accumulation depth detection data, earthquake level monitoring data and gas concentration monitoring data.

[0012] The present invention is a pioneering invention, and its beneficial effects are as follows: the mine early warning method based on multi-system collaboration of the present invention can integrate the data of each monitoring system of the measuring point to calculate the score when conducting risk monitoring and early warning on a certain measuring point of the mine, and then use the integrated total score to determine whether the measuring point is a risk point. Specifically, the score of the monitoring data of each single monitoring system is first calculated, and then the scores of the monitoring data of each monitoring system are summed to obtain the total score of the measuring point, and finally the total score of the measuring point is compared with the set risk score threshold to determine whether it is a risk point. The abnormalities of the monitoring data of each monitoring system can be superimposed, and it can be comprehensively judged whether it is a risk point, and the risk point can be discovered in time and an alarm can be issued in time, without waiting until a certain monitoring data has a large degree of abnormality before an alarm is issued, which is convenient for staff to discover and solve abnormal problems in time.

[0013] To solve the above technical problems, the present invention further provides a computer device, including a processor, which implements the method steps described in the mine early warning method based on multi-system collaboration of the present invention when executing a computer program.

[0014] The present invention is a pioneering invention, and its beneficial effects are the same as those of the mine early warning method based on multi-system collaboration of the present invention.

[0015] In order to solve the above technical problems, the present invention also provides a mine early warning system based on multi-system collaboration, including an early warning monitoring system, a monitoring data interface module and a data collaborative processing module; the monitoring data interface module is used to receive the monitoring data of each early warning monitoring system, and send the received data to the data collaborative processing module, the data collaborative processing module includes a processor, and the processor implements the method steps described in the mine early warning method based on multi-system collaboration of the present invention when executing a computer program.

[0016] Furthermore, the early warning system also includes a handheld terminal, which is used to send positioning information to the data collaborative processing module and receive alarm prompts.

[0017] Furthermore, the early warning monitoring system includes: a temperature monitoring system, an earthquake monitoring system, a water accumulation depth monitoring system and a gas concentration detection system, and each early warning monitoring system adopts a unified API interface.

[0018] The present invention is a pioneering invention, and its beneficial effects are the same as those of the mine early warning method based on multi-system collaboration of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system structure of a system embodiment of the present invention. DETAILED DESCRIPTION

[0020] The mine early warning method, system and computer equipment based on multi-system collaboration of the present invention can integrate the data of each monitoring system of the measuring point to calculate the score when conducting risk monitoring and early warning on a certain measuring point of the mine, and then use the integrated total score to determine whether the measuring point is a risk point. Specifically, the score of the monitoring data of each single monitoring system is first calculated, and then the scores of the monitoring data of each monitoring system are summed to obtain the total score of the measuring point, and finally the total score of the measuring point is compared with the set risk score threshold to determine whether it is a risk point. The abnormalities of the monitoring data of each monitoring system can be superimposed, and it can be comprehensively determined whether it is a risk point. The risk point can be discovered in time and an alarm can be issued in time, without waiting until a certain monitoring data has a large degree of abnormality before an alarm is issued, which is convenient for staff to discover and solve abnormal problems in time.

[0021] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0022] System Example: The structure of the mine early warning system of the present invention is as follows Figure 1As shown, it includes a monitoring data access module, which integrates various systems of the mine to provide the integrated data to the collaborative data processing module for processing and early warning. The monitoring data access module supports multiple communication protocols, MQTT is used for IoT device communication, HTTP / RESTful APId, etc. are used for Web service interaction. In this embodiment, the monitoring data access module connects the mine production system, temperature monitoring system, earthquake monitoring system, infrared monitoring system, video system, drainage and ventilation system, and personnel and vehicle management system to the early warning system of the present invention, establishes a two-way communication mechanism, connects different devices and systems through the network, and realizes data transmission and information interaction. Integrate data from different sources to achieve data standardization, cleaning and storage. The production system is used to obtain the current work tasks and the approximate distribution of personnel. The infrared monitoring system is used to monitor the situation of personnel on site. The video system can analyze personnel and work behaviors, identify equipment defects or personnel violations in production, and generate deduction scores according to different violations and defect levels. The drainage and ventilation system obtains internal water accumulation and internal mine gas information, and performs scoring calculations according to different water accumulation depths. The concentrations of oxygen / CO2 / gas in the gas are compared with the gas composition and concentration of normal air to generate scores. The early warning monitoring system in this embodiment includes a temperature monitoring system, an earthquake monitoring system and a drainage and ventilation system, wherein the drainage and ventilation system is both a gas concentration detection system and a water depth monitoring system. Although the mine production system, infrared detection system, and personnel and vehicle management system are also connected to the monitoring data access module, these systems do not belong to the early warning monitoring system and do not need to be scored. As other implementation methods, the monitoring data access module should connect at least two of the mine early warning monitoring systems to the early warning system of the present invention. In this embodiment, each monitoring system adopts a unified API interface specification to ensure seamless connection between different systems and reduce integration complexity.

[0023] The mine early warning system of the present invention comprises a data collaborative processing module, and the collaborative data processing module comprises a processor. When the processor executes a computer program, the mine early warning method based on multi-system collaboration of the present invention is implemented.

[0024] In this embodiment, different steps of the mine early warning method based on multi-system collaboration of the present invention are formed into software units, and each step of the early warning method is described in the form of software units.

[0025] The collaborative data processing module includes a real-time processing unit. The real-time processing unit can process the real-time information sent by each subsystem in real time, and extract the monitoring data of the mine system operation, including: earthquake level monitoring data, gas concentration data monitoring data, water depth monitoring data and temperature monitoring data, etc., and further process the data to collect the key performance indicators and operation data of each system, and find out the key factors affecting the system through data analysis. Collect environmental data in real time and provide data visualization, alarm and analysis.

[0026] After the data is processed by the real-time processing unit, the big data analysis module converts the real-time value into a corresponding score based on historical data and expert experience. When the real-time score is lower than the set risk score threshold, the system will immediately issue an alarm.

[0027] The real-time processing unit is used to coordinate early warning based on the data of each monitoring system. The specific early warning methods are as follows: The real-time processing unit compares the data of different monitoring systems with the reference data respectively, so as to score and quantify the monitoring data of a certain measuring point, and then superimposes the scores of all monitoring data. After superposition, the score is compared with the set risk score threshold. If the superimposed score is less than the set risk score threshold, an alarm is issued to indicate that the certain measuring point is a risk point. The abnormal conditions of different systems can be superimposed to comprehensively judge whether an abnormal condition occurs. It avoids the problem of not being able to detect abnormal conditions in time when multiple monitoring data are abnormal but have not reached the alarm level. It can detect abnormal conditions in advance and issue an alarm in time to remind staff to deal with them as soon as possible to prevent the abnormal conditions from further developing and deteriorating.

[0028] In this embodiment, the method for scoring and quantifying a certain monitoring data includes: when the monitoring data of a certain early warning monitoring system is not within the normal range determined by the corresponding reference data, the score of the monitoring data is deducted from the score corresponding to the reference data by the first set deviation degree threshold value every time the deviation degree between the monitoring data and the reference data increases. For example, when the pressure at a certain location is not within the corresponding reference range and is higher than the upper limit of the reference range, the score corresponding to the pressure is deducted by 1 point on the basis of the corresponding reference score for every 1% above the upper limit of the reference range, and the score corresponding to the pressure is deducted by 1 point on the basis of the corresponding reference score for every 1% below the lower limit of the reference range. The reference data is a threshold used to determine whether the corresponding monitoring data is abnormal.

[0029] In this embodiment, the method for scoring and quantifying a certain monitoring data also includes: when the monitoring data of a certain early warning monitoring system is not within the normal range determined by the corresponding reference data, and the degree of deviation from the reference data of the measuring point is greater than the second set deviation degree threshold, the score of the monitoring data is deducted from the second set score on the basis of the score corresponding to the reference data, and the second set score is the score that can directly make the total score of the measuring point lower than the set risk score threshold after deduction. When the monitoring data exceeds the upper limit of the range of the reference data, the degree of deviation between the monitoring data and the reference data is the percentage of the monitoring data exceeding the upper limit of the range of the reference data; when the monitoring data is lower than the lower limit of the range of the reference data, the degree of deviation between the monitoring data and the reference data is the percentage of the monitoring data lower than the lower limit of the range of the reference data. The second set score is greater than the first set score, and the second set score is a score that can directly cause the prediction and early warning system to alarm. This method can ensure that when a certain monitoring data is abnormal and the degree of abnormality is large, it can directly monitor and prompt whether the monitoring data is abnormal or not. For example, when the deviation degree of a certain pressure is greater than 10% compared with the reference value of a certain pressure, 20 points are deducted at one time.

[0030] The data collaborative processing module of the present invention also includes a prediction and early warning unit, which sorts the historical monitoring data collected by the early warning monitoring system in chronological order, uses statistical methods to analyze the sequence data and calculate the change trend of each monitoring data, predicts each monitoring data within a subsequent set time according to the change trend of each monitoring data, compares the predicted monitoring data with the reference data, and obtains a prediction score for each predicted monitoring data according to the comparison result; sums up the various prediction scores to obtain a total predicted score for the measuring point; if the total predicted score is lower than the set risk score threshold of the measuring point, an alarm is issued to prompt that the certain measuring point is a potential risk point.

[0031] The data collaborative processing module of the present invention also includes an analysis and judgment module, which is used to integrate machine learning and artificial intelligence algorithms. In this embodiment, machine learning and artificial intelligence algorithms are trained to obtain an auxiliary decision model. The training data includes multiple risk point monitoring data and the corresponding decision-making process for eliminating the risk. When an alarm prompts, the data of each early warning monitoring system is input into the auxiliary decision model, and the decision-making process for eliminating the risk of the measuring point is output. In this embodiment, in order to improve the accuracy of the auxiliary decision model, a subject knowledge base, a data analysis model base and an event processing method base based on the decision process are constructed to store the training data of the auxiliary decision model, build and continuously improve the auxiliary decision system, and provide multi-faceted and multi-level decision support and visual knowledge services for decision-making. Continuously improve the expert knowledge experience base to optimize the accuracy of prediction and analysis. Among them, the subject knowledge base stores themes, knowledge points and associations; the subject can be understood as an event; such as accident types: fire accidents, water leakage accidents, equipment failures, etc.; the knowledge points are the monitoring data characteristics of the mine system; associations: the relationship between knowledge points and the subject. The data analysis model library stores the instantiated measuring points and the corresponding scoring conditions of the measuring points. The relationship between the subject (event) and the event handling method library in the subject knowledge base. The event handling method library stores the handling methods of each basic feature in the subject knowledge base. All three databases are used to train the machine learning and artificial intelligence algorithms of the analysis and judgment module.

[0032] The data collaborative processing module of the present invention also includes an emergency handling unit, which is used to issue a response control command according to the handling strategy, such as issuing a water pump start command when there is a water leak.

[0033] The data collaborative processing module of the present invention also includes a model management unit and a digital twin unit. The model management unit integrates, stores and manages sensor data, Internet data and historical data, which are stored and managed by the model management unit and can be displayed on the three-dimensional digital model established by the digital twin unit. The digital twin unit can accurately model the actual mine, so that mine managers can monitor, analyze, predict and optimize in a virtual environment, establish a data model of the mine system, represent the time-varying system as a set of mathematical models, simulate the system, and set the score of the model data of the system. The digital twin unit can model the data of the actual environment and important equipment, and display the mine and important equipment in a three-dimensional visual form, intuitively display the real-time status and analysis data. Digitize the data accessed by the sensors and other data acquisition devices connected to the data monitoring data access module. Real-time parameters such as pressure, temperature, gas, etc. that can be displayed in the three-dimensional mine model. Real-time data and risk points are displayed in real time in the form of a three-dimensional model, so that decision makers can understand the real-time status of the mine more intuitively.

[0034] In this embodiment, the data collaborative processing module also includes a personnel positioning unit, which can display the positioning information of the personnel in the three-dimensional model. If risk points and potential risk points are detected, alarm prompts are sent to the staff at the risk points and potential risk points in priority.

[0035] The mine early warning system based on multi-system collaboration of the present invention also includes an information release module, which is used to release information to the handheld terminal, and the released information includes the early warning signal and the decision-making process output by the analysis and judgment unit for eliminating the risk of the measuring point. The information release module aggregates multiple subsystem signals, alarms, real-time status, three-dimensional and other information into one picture for display.

[0036] The mine early warning system based on multi-system collaboration of the present invention also includes several handheld terminals. The staff carry the handheld terminals during work. The handheld terminals are used to display alarm signals and emergency strategies, so that the staff can detect abnormal situations in time, take measures, and eliminate risks in time.

[0037] Method and device embodiment: The mine early warning method based on multi-system collaboration of the present invention is as described in the system embodiment. The specific process, principle and beneficial effects of the method have been described in detail in the system embodiment and will not be repeated in this embodiment.

[0038] Computer equipment example: A computer device of the present invention includes a processor, and the processor implements the steps of the mine early warning method based on multi-system collaboration as described in the system embodiment when executing a computer program. The specific process, principle and beneficial effects of the method have been described in detail in the system embodiment, and will not be repeated in this embodiment.

[0039] The processor may be a microprocessor MCU, a programmable logic device FPGA or other processing device.

Claims

1. A mine early warning method based on multi-system collaboration, characterized in that: The method comprises the following steps: Acquire the monitoring data of a certain measuring point in the mine collected by each early warning monitoring system; compare the monitoring data collected by each early warning monitoring system of the measuring point with the corresponding reference data, and obtain the score of the monitoring data of each early warning monitoring system according to the comparison result; sum up the scores of the monitoring data of each early warning monitoring system to obtain the total score of the measuring point; if the total score is lower than the set risk score threshold of the measuring point, the measuring point is determined to be a risk point; the reference data is a threshold used to determine whether the corresponding monitoring data is abnormal.

2. The mine early warning method based on multi-system collaboration according to claim 1 is characterized in that: The process of obtaining the score of the monitoring data of each early warning monitoring system based on the comparison results includes: when the monitoring data of a certain early warning monitoring system is not within the normal range determined by the corresponding reference data, the score of the monitoring data is subtracted from the first set score on the basis of the score corresponding to the reference data every time the degree of deviation between the monitoring data and the reference data increases by a first set deviation degree threshold.

3. The mine early warning method based on multi-system collaboration according to claim 1 is characterized in that: The process of obtaining the score of the monitoring data of each early warning monitoring system according to the comparison results includes: when the monitoring data of a certain early warning monitoring system is not within the normal range determined by the corresponding reference data, and the degree of deviation from the reference data of the measuring point is greater than a second set deviation degree threshold, the score of the monitoring data is subtracted from the second set score on the basis of the score corresponding to the reference data, and the second set score is a score that can directly make the total score of the measuring point lower than the set risk score threshold after deduction.

4. The mine early warning method based on multi-system collaboration according to claim 1 is characterized in that: The method also includes the following steps: obtaining historical monitoring data collected by various early warning monitoring systems at a certain measuring point in the mine, calculating the change trend of each monitoring data using the historical monitoring data, predicting each monitoring data within a subsequent set time based on the change trend of each monitoring data, comparing each predicted monitoring data with the corresponding reference data, and obtaining a predicted score for each predicted monitoring data based on the comparison result; summing up each predicted score to obtain a total predicted score for the measuring point, and if the total predicted score is lower than the set risk score threshold for the measuring point, the measuring point is determined to be a potential risk point.

5. The mine early warning method based on multi-system collaboration according to claim 1 is characterized in that: The method also includes the following steps: displaying the monitoring data of each measuring point collected by each early warning monitoring system at the corresponding position of the constructed real-time three-dimensional model of the mine.

6. The mine early warning method based on multi-system collaboration according to claim 1 is characterized in that: The method also includes the following steps: inputting the data of each early warning monitoring system of the risk point into the auxiliary decision-making model, and outputting the decision-making process for eliminating the risk of the measuring point, wherein the auxiliary decision-making model is obtained by training an artificial intelligence algorithm using training data, and the training data includes multiple risk point monitoring data and corresponding decision-making processes for eliminating risks.

7. The mine early warning method based on multi-system collaboration according to any one of claims 1 to 6, characterized in that: The monitoring data of a measuring point at a certain location in the mine includes: temperature monitoring data, water accumulation depth monitoring data, earthquake level monitoring data and gas concentration monitoring data of the measuring point.

8. A computer device comprising a processor, characterized in that: The processor implements the steps of the mine early warning method based on multi-system collaboration as described in any one of claims 1 to 7 when executing the computer program.

9. A mine early warning system based on multi-system collaboration, characterized in that: It includes an early warning monitoring system, a monitoring data interface module and a data collaborative processing module; the monitoring data interface module is used to receive monitoring data from each early warning monitoring system and send the received data to the data collaborative processing module, the data collaborative processing module includes a processor, and the processor implements the steps of the mine early warning method based on multi-system collaboration as described in any one of claims 1-7 when executing a computer program.

10. The mine early warning system based on multi-system collaboration according to claim 9 is characterized in that: The early warning monitoring system includes: a temperature monitoring system, an earthquake monitoring system, a water accumulation depth monitoring system and a gas concentration detection system, and each early warning monitoring system adopts a unified API interface.

Images