WO2024012552A1 - 尾矿库安全生产风险分级预警方法 - Google Patents

尾矿库安全生产风险分级预警方法 Download PDF

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WO2024012552A1
WO2024012552A1 PCT/CN2023/107383 CN2023107383W WO2024012552A1 WO 2024012552 A1 WO2024012552 A1 WO 2024012552A1 CN 2023107383 W CN2023107383 W CN 2023107383W WO 2024012552 A1 WO2024012552 A1 WO 2024012552A1
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risk
data
tailings pond
meteorological
area
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PCT/CN2023/107383
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English (en)
French (fr)
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陈友良
李海港
付士根
王守印
陈涛
隋建政
李佳
郑小龙
郭庞锋
陈爽
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中国安全生产科学研究院
江西省应急管理科学研究院
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Priority to US18/505,150 priority Critical patent/US11954415B2/en
Publication of WO2024012552A1 publication Critical patent/WO2024012552A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G06Q50/265—Personal security, identity or safety
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the tailings pond safety production risk classification early warning method proposed by the present invention belongs to the technical field of safety production monitoring.
  • Tailings pond is one of the important engineering construction contents of metal mining projects, and it is a dangerous source of debris flow with high potential energy.
  • many companies have been equipped with online tailings pond safety monitoring systems, which mainly use automatic total stations combined with fiber grating sensing networks to visually monitor the main parts of the tailings ponds.
  • the monitoring indicators include the tailings pond infiltration line and the dam body.
  • Displacement, reservoir water level, dry beach length, rainfall and other key safety indicators provide enterprises with real data on the production and operation status of tailings ponds, provide tailings pond safety hazard data in the form of reports, and send early warning information to managers. It has a certain supporting role in corporate security management.
  • the current tailings pond online safety monitoring system has the following shortcomings:
  • the monitoring system mainly monitors the main indicators of the tailings reservoir such as dam displacement, soaking line, reservoir water level, etc.
  • Other attributes of the tailings pond safety indicator system such as tailings pond hazard levels, regional hydrometeorological conditions, geological conditions and other indicators, are not sufficiently integrated, and there is a lack of a comprehensive quantitative model for tailings pond safety monitoring.
  • the present invention provides a tailings pond safety production risk grading early warning method to solve the problem that the existing tailings pond risk early warning has weak risk warning capabilities due to insufficient data comprehensiveness.
  • the technical solutions adopted are as follows:
  • Tailings pond safety production risk grading early warning method includes:
  • real-time monitoring and collection of basic internal data of the tailings pond, and basic monitoring and early warning based on the internal basic data of the tailings pond including:
  • the geographical location, meteorological data and historical geological disaster data of the current area where the tailings pond is located are collected in real time, and the safety production risks of the current tailings pond are classified based on the meteorological data and historical geological disaster data to obtain Risk levels of low, medium and high risk include:
  • the risk levels of low risk, medium risk and high risk are obtained by combining the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score.
  • the geographical location risk assessment score is obtained using historical earthquake record data, historical typhoon record data and historical tsunami record data combined with the geographical location evaluation model.
  • the geographical location evaluation model is as follows:
  • F 1 represents the geographical location risk assessment score
  • P 1 , P 2 and P 3 represent the evaluation coefficients
  • n, m and k respectively represent the number of earthquakes, typhoons and tsunamis in the area where the current tailings pond is located
  • H d , H t and H h respectively represent the average level of earthquakes, typhoons and tsunamis in the area where the current tailings pond is located
  • J d , J t and J h respectively represent the places where the current tailings pond is located.
  • T i represents the time when the i-th earthquake occurs
  • T i+1 represents the time when the i+1-th earthquake occurs
  • T j represents the time when the j-th earthquake occurs
  • T j+1 represents The time when the j+1th earthquake occurs
  • T k represents the time when the kth earthquake occurs
  • T k+1 represents the time when the k+1th earthquake occurs.
  • the historical rainfall data and continuous rainfall data are combined with the meteorological evaluation model to obtain the meteorological risk evaluation score.
  • meteorological evaluation model is as follows:
  • F 2 represents the meteorological risk assessment score
  • C represents the number of rainfalls in the current year
  • Y represents the continuous rainfall of each rainfall in the current year
  • Y 0 represents the preset rainfall benchmark
  • Y ie represents the specified time period, The total rainfall corresponding to the i-th month
  • e represents the number of months included in the specified time period
  • D represents the total number of days in which the rainfall in a single day exceeds the rainfall reference amount within the specified period.
  • historical geological disaster data of the area where the tailings pond is currently located is collected, and the disaster risk evaluation score is obtained through the historical geological disaster data combined with the geological disaster evaluation model, including:
  • the disaster risk evaluation score is obtained based on the historical geological disaster data and the geological disaster evaluation model.
  • geological hazard evaluation model is as follows:
  • W represents the number of geological disasters other than floods, debris flows and landslides that occur within the calibration area;
  • S 1 , S 2 and S 3 respectively represent the three types of floods, debris flows and landslides.
  • L 1 , L 2 and L 3 respectively represent the shortest distance between the occurrence places of the three geological disasters: floods, debris flows and landslides and the current location of the tailings pond;
  • ⁇ 1 , ⁇ 2 and ⁇ 3 respectively represent the evaluation coefficients corresponding to the three preset geological disasters: floods, debris flows and landslides; among them, the value ranges of ⁇ 1 , ⁇ 2 and ⁇ 3 respectively correspond to: 0.22-0.39; 0.17-0.26; 0.15-0.46.
  • the specific parameter size setting is related to the address environment of the current area where the tailings pond is located. The closer it is to the geological environment of the corresponding geological disaster type, the larger the
  • the risk levels of low risk, medium risk and high risk are obtained by combining the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score, including:
  • the risk level of the current tailings pond is determined to be a low risk level
  • the risk level of the current tailings pond is determined to be a medium risk level
  • the risk level of the current tailings pond is determined to be a high risk level.
  • the execution system of the tailings pond safety production risk classification early warning method includes:
  • the basic data acquisition module is used for real-time monitoring and collection of internal basic data of the tailings pond, and for basic monitoring and early warning through the internal basic data of the tailings pond;
  • An external data collection module is used to collect the geographical location, meteorological data and historical geological disaster data of the current area where the tailings pond is located in real time, and conduct safety production risk assessment of the current tailings pond location based on the meteorological data and historical geological disaster data. Risk grading, obtaining risk levels of low risk, medium risk, and high risk;
  • the case collection module is used to collect domestic and foreign tailings pond accident case data and hazard level data, obtain accident solution strategies corresponding to the cases, and combine different risk levels for management monitoring and risk warning.
  • the basic data collection module includes:
  • the data collection module is used to collect basic internal data of the tailings pond in real time, where the basic internal data of the tailings pond includes the geological structure of the tailings pond, the damming method of the tailings pond, the operating status of the tailings pond, the flood discharge capacity, and the infiltration line. , dry beach, reservoir water level and external displacement;
  • a comparison module used to compare various data indicators in the internal basic data of the tailings pond with their corresponding threshold data indicators
  • the monitoring and early warning module is used to perform basic monitoring and early warning when the quantitative relationship between each data indicator in the internal basic data of the tailings pond and its corresponding threshold data indicator does not meet the preset safety monitoring relationship.
  • the external data collection module includes:
  • the geographical location risk assessment score acquisition module is used to monitor and collect the geographical location data of the current area where the tailings pond is located, and obtain the geographical location risk assessment score through the geographical location data combined with the geographical location evaluation model;
  • the meteorological risk assessment score acquisition module is used to monitor and collect meteorological data in the area where the tailings pond is currently located, and obtain the meteorological risk evaluation score through the meteorological data combined with the meteorological evaluation model;
  • a qualitative disaster risk evaluation score acquisition module is used to collect historical geological disaster data in the area where the tailings pond is currently located, and obtain disaster risk evaluation scores through the historical geological disaster data combined with the geological disaster evaluation model;
  • the risk level classification module is used to obtain low risk, medium risk and high risk risk levels by combining the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score.
  • the tailings pond safety production risk grading early warning method proposed by the present invention combines basic enterprise data, safety inspection and hidden danger management status, and online monitoring access data. Correlation analysis with the enterprise information of the company where the accident occurred, the cause of the accident and other information, combined with meteorological data, geological disaster data, force majeure and other factors, to derive the risk trend of tailings pond operation.
  • meteorological data, geological disaster data, force majeure and other factors are integrated into the risk classification process, which effectively improves the rationality, scientificity and classification accuracy of risk classification.
  • early warning based on different risk levels can maximize the effectiveness of early warning of safety production risks in tailings ponds.
  • Figure 1 is a flow chart of the method of the present invention
  • Figure 2 is a flow chart 2 of the method of the present invention.
  • Figure 3 is a system block diagram of an execution system corresponding to the method of the present invention.
  • the tailings pond safety production risk grading early warning method is shown in Figure 1.
  • the tailings pond safety production risk grading early warning method includes:
  • the working principle of the above technical solution is: first, real-time monitoring and collection of internal basic data of the tailings pond, and basic monitoring through the internal basic data of the tailings pond; then, real-time collection of the geographical location of the area where the current tailings pond is located , meteorological data and historical geological disaster data. Based on the meteorological data and historical geological disaster data, the current safety production risks of the tailings pond are risk graded, and the risk levels of low risk, medium risk and high risk are obtained; finally, domestic Collect external tailings pond accident case data and hazard level data, obtain the accident solution strategy corresponding to the case, conduct management and monitoring based on different risk levels, and set and tail the indicator parameters in the basic monitoring process according to different risk levels. Classified early warning of safety production risks in mines.
  • the tailings pond safety production risk grading early warning method proposed in this embodiment can achieve better results by combining enterprise basic data, safety inspections and hidden danger management.
  • meteorological data, geological disaster data, force majeure and other factors are integrated into the risk classification process, which effectively improves the rationality, scientificity and classification accuracy of risk classification.
  • early warning based on different risk levels can maximize the effectiveness of early warning of safety production risks in tailings ponds.
  • One embodiment of the present invention monitors and collects internal basic data of the tailings pond in real time, and performs basic monitoring and early warning through the internal basic data of the tailings pond, including:
  • the internal basic data of the tailings pond include the geological structure of the tailings pond, the damming method of the tailings pond, the operating status of the tailings pond, flood discharge capacity, soaking line, dry beach, Reservoir water level and external displacement;
  • the working principle of the above technical solution is: first, collect the internal basic data of the tailings pond in real time, where the basic internal data of the tailings pond includes the geological structure of the tailings pond, the damming method of the tailings pond, the operating status of the tailings pond, and flood discharge. capacity, infiltration line, dry beach, reservoir water level and external displacement; then, compare each data indicator in the internal basic data of the tailings reservoir with its corresponding threshold data indicator; finally, when the internal basic data of the tailings reservoir When the quantitative relationship between each data indicator and its corresponding threshold data indicator does not meet the preset security monitoring relationship, basic monitoring and early warning will be carried out.
  • the effect of the above technical solution is: through the above method, the basic data inside the tailings pond can be effectively collected, and independent safety monitoring and corresponding early warning can be carried out for the production part corresponding to the basic data. In this way, the basic early warning and graded risk can be combined. Independent analysis of early warnings can effectively reduce the data content of risk classification early warnings. Filtering out basic data and classifying high-risk risks can further improve the accuracy of high-risk risk classification.
  • One embodiment of the present invention collects the geographical location, meteorological data and historical geological disaster data of the area where the current tailings pond is located in real time, and analyzes the current tailings pond based on the meteorological data and historical geological disaster data.
  • the safety production risks are classified into risk levels of low risk, medium risk and high risk, including:
  • S203 Collect historical geological disaster data of the area where the tailings pond is currently located, and obtain disaster risk assessment scores through the historical geological disaster data combined with the geological disaster evaluation model;
  • the working principle of the above technical solution is: first, monitor and collect the current geographical location data of the area where the tailings pond is located, and obtain the geographical location risk assessment score through the geographical location data combined with the geographical location evaluation model; then, monitor and collect the current geographical location risk assessment score.
  • the meteorological data of the area where the tailings pond is located is used to obtain the meteorological risk assessment score through the meteorological data combined with the meteorological evaluation model; then, the historical geological disaster data of the area where the tailings pond is currently located is collected, and the historical geological disaster data is used
  • the disaster risk assessment score is obtained by combining the geological hazard assessment model; finally, the risk levels of low risk, medium risk and high risk are obtained by combining the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score.
  • the effect of the above technical solution is to integrate meteorological data, geological disaster data, force majeure and other factors into the risk classification process, effectively improving the rationality, scientificity and classification accuracy of risk classification. And early warning based on different risk levels can maximize the effectiveness of early warning of safety production risks in tailings ponds.
  • One embodiment of the present invention monitors and collects the geographical location data of the area where the tailings pond is currently located, and obtains the geographical location risk assessment score through the geographical location data combined with the geographical location evaluation model, including:
  • S2011 Determine through survey whether the area where the current tailings pond is located is in a seismic zone, and collect historical seismic record data of the area where the current tailings pond is located;
  • S2012 Determine through survey whether the area where the current tailings pond is located is in a typhoon area, and collect historical typhoon record data in the area where the current tailings pond is located;
  • S2013 Determine through survey whether the area where the current tailings pond is located is in a tsunami area, and collect the current tailings Historical tsunami record data in the area where the mine is located;
  • the geographical location evaluation model is as follows:
  • F 1 represents the geographical location risk assessment score
  • P 1 , P 2 and P 3 represent the evaluation coefficients
  • n, m and k respectively represent the number of earthquakes, typhoons and tsunamis in the area where the current tailings pond is located
  • H d , H t and H h respectively represent the average series value corresponding to the occurrence of earthquakes, typhoons and tsunamis in the area where the current tailings pond is located
  • J d , J t and J h respectively represent the duration corresponding to the occurrence of earthquakes, typhoons and tsunamis in the area where the current tailings pond is located.
  • the working principle of the above technical solution is: first, determine whether the area where the current tailings pond is located is in a seismic zone through surveying, and collect historical seismic record data in the area where the current tailings pond is located; then, determine the area where the current tailings pond is located through surveying.
  • the geographical location data such as earthquakes, typhoons and tsunamis are obtained, and the geographical location risk assessment score is obtained by combining the above geographical location evaluation model.
  • the geographical location data such as earthquakes, typhoons and tsunamis
  • the geographical location risk assessment score is obtained by combining the above geographical location evaluation model.
  • the geographical location risk evaluation score obtained through the above geographical location evaluation model can effectively improve the accuracy and rationality of the risk score due to the setting of the model structure and parameter settings.
  • this embodiment adds a variety of meteorological data, geological disaster data and force majeure data. The increase in data will inevitably lead to an increase in the amount of calculations, and through this The geographical location evaluation model proposed in the embodiment can effectively improve the accuracy and accuracy of evaluation, simplify the model structure when the amount of data increases, minimize the amount of calculation, and effectively improve the efficiency of risk classification evaluation.
  • the meteorological data of the area where the tailings pond is currently located is monitored and collected.
  • the above meteorological data is combined with the meteorological evaluation model to obtain meteorological risk assessment scores, including:
  • S2021. Collect historical rainfall data and continuous rainfall data within a specified time period; wherein the specified time period ranges from 12 to 18 months;
  • meteorological evaluation model is as follows:
  • F 2 represents the meteorological risk assessment score
  • C represents the number of rainfalls in the current year
  • Y represents the continuous rainfall of each rainfall in the current year
  • Y 0 represents the preset rainfall benchmark
  • Y ie represents the specified time period, The total rainfall corresponding to the i-th month
  • e represents the number of months included in the specified time period
  • D represents the total number of days in which the rainfall in a single day exceeds the rainfall reference amount within the specified period.
  • the working principle of the above technical solution is: first, collect historical rainfall data and continuous rainfall data within a specified time period; where the specified time period ranges from 12 to 18 months; then, use the historical rainfall data Combined with continuous rainfall data and meteorological evaluation model, meteorological risk evaluation scores are obtained.
  • the effect of the above technical solution is: to obtain historical rainfall data and continuous rainfall data through the above method, and to obtain meteorological risk assessment scores in combination with the above meteorological evaluation model.
  • By combining the data information of historical rainfall and continuous rainfall it can effectively Improve the matching between the meteorological risk assessment and the actual geographical location of the tailings pond and the corresponding meteorological environment, thereby effectively improving the rationality and accuracy of the risk assessment and subsequent classification.
  • combining the above meteorological data to obtain risk assessment scores can effectively match the actual building location and environment of the tailings pond.
  • Combining environmental data can take into account more risk factors, thereby improving the accuracy and practical application of risk assessment.
  • the meteorological risk evaluation scores obtained through the above-mentioned meteorological evaluation model can effectively improve the accuracy and rationality of the risk score due to the setting of the model structure and parameter settings.
  • this embodiment adds a variety of meteorological data, geological disaster data and force majeure data. The increase in data will inevitably lead to an increase in the amount of calculations, and through this The meteorological evaluation model structure proposed in the embodiment can effectively improve the accuracy and precision of the evaluation, simplify the model structure when the amount of data surges, minimize the amount of calculation, and effectively improve the efficiency of risk classification evaluation.
  • historical geological disaster data of the area where the tailings pond is currently located is collected, and the disaster risk evaluation score is obtained through the historical geological disaster data combined with the geological disaster evaluation model, including:
  • geological hazard evaluation model is as follows:
  • F 3 represents the disaster risk assessment score
  • W represents the number of geological disaster types other than floods, debris flows and landslides occurring within the calibration area
  • S 1 , S 2 and S 3 represent floods respectively.
  • L 1 , L 2 and L 3 respectively represent the occurrence places of the three geological disasters of floods, debris flows and landslides and the location of the current tailings pond.
  • the shortest distance between them; ⁇ 1 , ⁇ 2 and ⁇ 3 respectively represent the preset evaluation coefficients corresponding to the three geological disasters: floods, debris flows and landslides; among them, the value ranges of ⁇ 1 , ⁇ 2 and ⁇ 3
  • the corresponding values are: 0.22-0.39; 0.17-0.26; 0.15-0.46.
  • the specific parameter size setting is related to the address environment of the current area where the tailings pond is located. The closer it is to the geological environment of the corresponding geological disaster type, the larger the parameter value setting is.
  • the working principle of the above technical solution is: first, according to the current location of the tailings pond, set the calibration area for geological disaster data collection in the area where the tailings pond is currently located; then, collect the geological disaster data that occurs within the calibration area.
  • Historical geological disaster data where the geological disasters include floods, debris flows, landslides, etc.; finally, the disaster risk evaluation score is obtained based on the historical geological disaster data combined with the geological disaster evaluation model.
  • the historical geological disaster data is obtained through the above method
  • the disaster risk evaluation score is obtained by combining the above geological disaster evaluation model.
  • the disaster risk assessment scores obtained through the above historical geological disaster data can effectively improve the accuracy and rationality of the risk score due to the model structure and parameter settings.
  • this embodiment adds a variety of meteorological data, geological disaster data and force majeure data. The increase in data will inevitably lead to an increase in the amount of calculations, and through this The geographical hazard evaluation model proposed in the embodiment can effectively improve the accuracy and precision of geological hazard evaluation, simplify the model structure when the amount of data increases, minimize the amount of calculation, and effectively improve the efficiency of risk classification evaluation.
  • the risk levels of low risk, medium risk and high risk are obtained by combining the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score, including:
  • the working principle of the above technical solution is: first, combine the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score to obtain a comprehensive risk assessment score; then, combine the risk comprehensive assessment scores The score is compared with the first risk threshold and the second risk threshold; then, when the risk comprehensive evaluation score is lower than the first risk threshold, the risk level of the current tailings pond is determined to be a low risk level; subsequently, when the risk When the comprehensive evaluation score is higher than the first risk threshold and lower than the second risk threshold, the risk level of the current tailings pond is determined to be a medium risk level; finally, when the comprehensive risk evaluation score is higher than the second risk threshold, the current risk level is determined The risk level of tailings pond is high risk level.
  • the effect of the above technical solution is that the efficiency of risk classification can be effectively improved through the above method, and at the same time, the rationality of risk classification can be effectively improved.
  • the execution system of the tailings pond safety production risk classification early warning method includes:
  • the basic data acquisition module is used for real-time monitoring and collection of internal basic data of the tailings pond, and for basic monitoring and early warning through the internal basic data of the tailings pond;
  • An external data collection module is used to collect the geographical location, meteorological data and historical geological disaster data of the current area where the tailings pond is located in real time, and conduct safety production risk assessment of the current tailings pond location based on the meteorological data and historical geological disaster data. Risk grading, obtaining risk levels of low risk, medium risk, and high risk;
  • the case collection module is used to collect domestic and foreign tailings pond accident case data and hazard level data, obtain accident solution strategies corresponding to the cases, and combine different risk levels for management monitoring and risk warning.
  • the basic data collection module includes:
  • the data collection module is used to collect basic internal data of the tailings pond in real time, where the basic internal data of the tailings pond includes the geological structure of the tailings pond, the damming method of the tailings pond, the operating status of the tailings pond, the flood discharge capacity, and the infiltration line. , dry beach, reservoir water level and external displacement;
  • a comparison module used to compare various data indicators in the internal basic data of the tailings pond with their corresponding threshold data indicators
  • the monitoring and early warning module is used to perform basic monitoring and early warning when the quantitative relationship between each data indicator in the internal basic data of the tailings pond and its corresponding threshold data indicator does not meet the preset safety monitoring relationship.
  • the external data collection module includes:
  • the geographical location risk assessment score acquisition module is used to monitor and collect the geographical location data of the current area where the tailings pond is located, and obtain the geographical location risk assessment score through the geographical location data combined with the geographical location evaluation model;
  • the meteorological risk assessment score acquisition module is used to monitor and collect meteorological data in the area where the tailings pond is currently located, and obtain the meteorological risk evaluation score through the meteorological data combined with the meteorological evaluation model;
  • a qualitative disaster risk evaluation score acquisition module is used to collect historical geological disaster data in the area where the tailings pond is currently located, and obtain disaster risk evaluation scores through the historical geological disaster data combined with the geological disaster evaluation model;
  • the risk level classification module is used to obtain low risk, medium risk and high risk risk levels by combining the geographical location risk assessment score, meteorological risk assessment score and qualitative disaster risk assessment score.
  • the execution system of the tailings pond safety production risk classification early warning method proposed in this embodiment combines the enterprise's basic data, safety inspection and hidden danger management status, online monitoring access data, and the enterprise where the accident occurred.
  • Information correlation analysis such as information and accident causes, combined with meteorological data, geological disaster data, force majeure and other factors, can determine the risk trend of tailings pond operation.
  • meteorological data, geological disaster data, force majeure and other factors are integrated into the risk classification process, which effectively improves the rationality, scientificity and classification accuracy of risk classification.
  • early warning based on different risk levels can maximize the effectiveness of early warning of safety production risks in tailings ponds.

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Abstract

本发明提出了尾矿库安全生产风险分级预警方法。所述尾矿库安全生产风险分级预警方法包括：实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控；实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控，并根据风险等级的不同对基础监控过程中的指标参数进行针对性设置和尾矿库安全生产风险的分级预警。

Description

尾矿库安全生产风险分级预警方法 技术领域
本发明提出的尾矿库安全生产风险分级预警方法，属于安全生产监控技术领域。
背景技术
尾矿库是金属矿采选项目的重要工程建设内容之一，是一个具有高势能的泥石流危险源。我国目前有大小尾矿上万座，具有分布广，数量多，稳定性差等特点。一旦发生溃坝事故，将对下游区域的人民生命财产造成不可估量的损失，同时还会带来环境污染、区域和谐等诸多社会问题。目前，很多企业已经配备在线尾矿库安全监测***，主要是采用自动式全站仪结合光纤光栅传感网络，对尾矿库主要部位进行可视化监测，监测指标包括尾矿库浸润线、坝***移、库水位、干滩长度、降雨量等关键安全指标，这些为企业提供尾矿库生产运行状况的真实数据，通过报表的形式提供尾矿库安全隐患数据，并发送预警信息给管理人员，对企业安全管理有一定的辅助作用。但是，目前的尾矿库在线安全监测***存在以下不足：
(1)监测***主要监测尾矿库的主要指标如坝***移、浸润线、库水位等。尾矿库安全指标体系的其他属性如尾矿库危险等级、区域水文气象条件、地质状况等指标的结合不够，缺少全方位的尾矿库安全监测量化模型。
(2)监测***大多都是安装在本地的应用且针对单个尾矿库，不利于监管部门按照尾矿库风险等级进行监管；同时，对于尾矿库监测报警，宏观风险预警，不能采用分级预警机制的方式进行推送。
发明内容
本发明提供了尾矿库安全生产风险分级预警方法，用以解决现有尾矿库风险预警由于采用数据全面性不足，导致风险预警能力较弱的问题，所采取的技术方案如下：
尾矿库安全生产风险分级预警方法，所述尾矿库安全生产风险分级预警方法包括：
实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控；
实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；
采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控，并根据风险等级的不同对基础监控过程中的指标参数进行针对性设置和尾矿库安全生产风险的分级预警。
进一步地，实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控预警，包括：
实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质 构造、尾矿库筑坝方式、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；
将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；
当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
进一步地，实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级，包括：
监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；
监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；
采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；
结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级。
进一步地，监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数，包括：
通过勘测确定当前尾矿库所在区域是否处于地震带区域，并且，采集当前尾矿库所在区域历史地震记录数据；
通过勘测确定当前尾矿库所在区域是否处于台风区域，并且，采集当前尾矿库所在区域历史台风记录数据；
通过勘测确定当前尾矿库所在区域是否处于海啸区域，并且，采集当前尾矿库所在区域历史海啸记录数据；
利用历史地震记录数据、历史台风记录数据和历史海啸记录数据结合地理位置评价模型获取地理位置风险评价分数。
其中，所述地理位置评价模型如下：



其中，F1表示地理位置风险评价分数；P1、P2和P3表示评价系数；n、m和k分别表示当前尾矿库所在区域发生地震、台风和海啸的次数；Hd、Ht和Hh分别表示当前尾矿库所在区域发生地震、台风和海啸对应的级数平均值；Jd、Jt和Jh分别表示当前尾矿库所在区域发生地 震、台风和海啸对应的持续时间长度；Ti表示第i次地震发生时刻，Ti+1表示第i+1次地震发生时刻；Tj表示第j次地震发生时刻，Tj+1表示第j+1次地震发生时刻；Tk表示第k次地震发生时刻，Tk+1表示第k+1次地震发生时刻。
进一步地，监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数，包括：
采集指定时间段内的历史降雨量数据和持续降雨量数据；其中，所述指定时间段范围为12-18个月；
利用所述历史降雨量数据和持续降雨量数据结合气象评价模型获取气象风险评价分数。
其中，所述气象评价模型如下：
其中，F2表示气象风险评价分数；C表示当前年份的降雨次数；Y表示当前年份每次降雨的持续降雨量；Y0表示预先设置的降雨基准量；Yie表示所述指定时间段内，第i个月对应的总降雨量；e表示指定时间段内所包含的月份数量；D表示所述指定时间内，单天降雨量超过所述降雨基准量的总天数。
进一步地，采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数，包括：
根据当前所述尾矿库所在区域位置，设置当前所述尾矿库所在区域地质灾害数据采集的标定区域范围；
采集所述标定区域范围内发生的历史地质灾害数据，其中，所述地质灾害包括洪水、泥石流和山体滑坡等；
根据所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数。
其中，所述地质灾害评价模型如下：
其中，W表示所述标定区域范围内，发生的除洪水、泥石流和山体滑坡三类地质灾害以外的地质灾害种类数量；S1、S2和S3分别表示洪水、泥石流和山体滑坡这三种地质灾害所波及的区域面积范围；L1、L2和L3分别表示洪水、泥石流和山体滑坡这三种地质灾害发生地与当前所述尾矿库所在区域位置之间的最短距离；α1、α2和α3分别表示预先设定的洪水、泥石流和山体滑坡这三种地质灾害对应的评价系数；其中，α1、α2和α3的取值范围分别对应为：0.22-0.39；0.17-0.26；0.15-0.46。具体的参数大小设定与当前所述尾矿库所在区域位置所处地址环境相关，与相应可发生地质灾害类型所具备的地质环境越近，参数值设置越大。
进一步地，结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级，包括：
将所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数进行结合，获取风险综合评价分数；
将所述风险综合评价分数与第一风险阈值和第二风险阈值进行比较；
当所述风险综合评价分数低于第一风险阈值时，确定当前尾矿库的风险等级为低风险等级；
当所述风险综合评价分数高于第一风险阈值且低于第二风险阈值时，确定当前尾矿库的风险等级为中风险等级；
当所述风险综合评价分数高于第二风险阈值时，确定当前尾矿库的风险等级为高风险等级。
进一步地，所述尾矿库安全生产风险分级预警方法的执行***包括：
基础数据采集模块，用于实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控预警；
外部数据采集模块，用于实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；
案例采集模块，用于采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控和风险预警。
进一步地，所述基础数据采集模块，包括：
数据采集模块，用于实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质构造、尾矿库筑坝方式、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；
比较模块，用于将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；
监控预警模块，用于当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
进一步地，所述外部数据采集模块包括：
地理位置风险评价分数获取模块，用于监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；
气象风险评价分数获取模块，用于监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；
质灾害风险评价分数获取模块，用于采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；
风险等级划分模块，用于结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级。
本发明有益效果：
本发明提出的一种尾矿库安全生产风险分级预警方法相对现有的尾矿库安全生产风险预警方法相比，通过结合结合企业基础数据、安全检查与隐患治理情况、在线监测接入数据，与发生事故企业的企业信息、事故原因等信息关联分析，结合气象数据、地质灾害数据、不可抗拒等因素，得出尾矿库运行的风险趋势。尤其是将气象数据、地质灾害数据、不可抗拒等因素融合至风险分级过程中，有效提高了风险分级的合理性、科学性和分级准确性。并根据不同风险级别进行预警能够最大限度提高尾矿库安全生产风险的预警有效性。
附图说明
图1为本发明所述方法的流程图一；
图2为本发明所述方法的流程图二；
图3为本发明所述方法对应执行***的***框图。
具体实施方式
以下结合附图对本发明的优选实施例进行说明，应当理解，此处所描述的优选实施例仅用于说明和解释本发明，并不用于限定本发明。
尾矿库安全生产风险分级预警方法，如图1所示，所述尾矿库安全生产风险分级预警方法包括：
S1、实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控；
S2、实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；
S3、采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控，并根据风险等级的不同对基础监控过程中的指标参数进行针对性设置和尾矿库安全生产风险的分级预警。
上述技术方案的工作原理为：首先，实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控；然后，实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；最后，采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控，并根据风险等级的不同对基础监控过程中的指标参数进行针对性设置和尾矿库安全生产风险的分级预警。
上述技术方案的效果为：本实施例提出的一种尾矿库安全生产风险分级预警方法相对现有的尾矿库安全生产风险预警方法相比，通过结合结合企业基础数据、安全检查与隐患治理情况、在线监测接入数据，与发生事故企业的企业信息、事故原因等信息关联分析，结合气象数据、地质灾害数据、不可抗拒等因素，得出尾矿库运行的风险趋势。尤其是将气象数据、地质灾害数据、不可抗拒等因素融合至风险分级过程中，有效提高了风险分级的合理性、科学性和分级准确性。并根据不同风险级别进行预警能够最大限度提高尾矿库安全生产风险的预警有效性。
本发明的一个实施例，实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控预警，包括：
S101、实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质构造、尾矿库筑坝方式、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；
S102、将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；
S103、当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之 间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
上述技术方案的工作原理为：首先，实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质构造、尾矿库筑坝方式、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；然后，将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；最后，当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
上述技术方案的效果为：通过上述方式能够将尾矿库内部基础数据进行有效采集，并针对基础数据对应的生产部分进行独立的安全监控和对应预警，通过这种方式能够将基础预警和分级风险预警进行独立分析，能够有效降低风险分级预警的数据含量，滤除基础数据而对高危害性风险进行分级能够进一步提高高危害性风险分级的准确性。
本发明的一个实施例，如图2所示，实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级，包括：
S201、监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；
S202、监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；
S203、采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；
S204、结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级。
上述技术方案的工作原理为：首先，监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；然后，监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；之后，采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；最后，结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级。
上述技术方案的效果为：将气象数据、地质灾害数据、不可抗拒等因素融合至风险分级过程中，有效提高了风险分级的合理性、科学性和分级准确性。并根据不同风险级别进行预警能够最大限度提高尾矿库安全生产风险的预警有效性。
本发明的一个实施例，监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数，包括：
S2011、通过勘测确定当前尾矿库所在区域是否处于地震带区域，并且，采集当前尾矿库所在区域历史地震记录数据；
S2012、通过勘测确定当前尾矿库所在区域是否处于台风区域，并且，采集当前尾矿库所在区域历史台风记录数据；
S2013、通过勘测确定当前尾矿库所在区域是否处于海啸区域，并且，采集当前尾 矿库所在区域历史海啸记录数据；
S2014、利用历史地震记录数据、历史台风记录数据和历史海啸记录数据结合地理位置评价模型获取地理位置风险评价分数。
其中，所述地理位置评价模型如下：



其中，F1表示地理位置风险评价分数；P1、P2和P3表示评价系数；n、m和k分别表示当前尾矿库所在区域发生地震、台风和海啸的次数；Hd、Ht和Hh分别表示当前尾矿库所在区域发生地震、台风和海啸对应的级数平均值；Jd、Jt和Jh分别表示当前尾矿库所在区域发生地震、台风和海啸对应的持续时间长度；Ti表示第i次地震发生时刻，Ti+1表示第i+1次地震发生时刻；Tj表示第j次地震发生时刻，Tj+1表示第j+1次地震发生时刻；Tk表示第k次地震发生时刻，Tk+1表示第k+1次地震发生时刻。
上述技术方案的工作原理为：首先，通过勘测确定当前尾矿库所在区域是否处于地震带区域，并且，采集当前尾矿库所在区域历史地震记录数据；然后，通过勘测确定当前尾矿库所在区域是否处于台风区域，并且，采集当前尾矿库所在区域历史台风记录数据；之后，通过勘测确定当前尾矿库所在区域是否处于海啸区域，并且，采集当前尾矿库所在区域历史海啸记录数据；最后，利用历史地震记录数据、历史台风记录数据和历史海啸记录数据结合地理位置评价模型获取地理位置风险评价分数。
上述技术方案的效果为：通过上述方式进行地震、台风和海啸等地理位置数据的获取，并结合上述地理位置评价模型获取地理位置风险评价分数，通过结合了地震、海啸和台风的数据信息能够有效提高地理位置风险评价与实际尾矿库所处地理位置之间的匹配性，进而有效提高风险评价和后续分级的合理性和准确性。同时，结合上述地理位置数据获得风险评价分数能够与尾矿库实际建筑地理位置和环境进行有效匹配，结合环境数据能够兼顾更多的风险因素，进而提高风险评价的精确性和实际应用型。
同时，通过上述地理位置评价模型获取的地理位置风险评价分数，由于模型结构的设置和参数设置能够有效提高风险评分的准确性和合理性。同时，由于相较于传统尾矿库的风险评价方式，本实施例增加了多种气象数据、地质灾害数据和不可抗力数据的结合，由于数据的增加势必会导致运算量的加大，而通过本实施例提出的地理位置评价模型能够在有效提高评价准确性和精确性的同时，在数据量激增的情况下简化模型结构，最大限度减少运算量，有效提高风险分级评价效率。
本发明的一个实施例，监控和采集当前所述尾矿库所在区域的气象数据，通过所 述气象数据结合气象评价模型获取气象风险评价分数，包括：
S2021、采集指定时间段内的历史降雨量数据和持续降雨量数据；其中，所述指定时间段范围为12-18个月；
S2022、利用所述历史降雨量数据和持续降雨量数据结合气象评价模型获取气象风险评价分数。
其中，所述气象评价模型如下：
其中，F2表示气象风险评价分数；C表示当前年份的降雨次数；Y表示当前年份每次降雨的持续降雨量；Y0表示预先设置的降雨基准量；Yie表示所述指定时间段内，第i个月对应的总降雨量；e表示指定时间段内所包含的月份数量；D表示所述指定时间内，单天降雨量超过所述降雨基准量的总天数。
上述技术方案的工作原理为：首先，采集指定时间段内的历史降雨量数据和持续降雨量数据；其中，所述指定时间段范围为12-18个月；然后，利用所述历史降雨量数据和持续降雨量数据结合气象评价模型获取气象风险评价分数。
上述技术方案的效果为：通过上述方式进行历史降雨量数据和持续降雨量数据的获取，并结合上述气象评价模型获取气象风险评价分数，通过结合了历史降雨量和持续降雨量的数据信息能够有效提高气象风险评价与实际尾矿库所处地理位置和对应气象环境之间的匹配性，进而有效提高风险评价和后续分级的合理性和准确性。同时，结合上述气象数据获得风险评价分数能够与尾矿库实际建筑地理位置和环境进行有效匹配，结合环境数据能够兼顾更多的风险因素，进而提高风险评价的精确性和实际应用型。
同时，通过上述气象评价模型获取的气象风险评价分数，由于模型结构的设置和参数设置能够有效提高风险评分的准确性和合理性。同时，由于相较于传统尾矿库的风险评价方式，本实施例增加了多种气象数据、地质灾害数据和不可抗力数据的结合，由于数据的增加势必会导致运算量的加大，而通过本实施例提出的气象评价模型结构能够在有效提高评价准确性和精确性的同时，在数据量激增的情况下简化模型结构，最大限度减少运算量，有效提高风险分级评价效率。
本发明的一个实施例，采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数，包括：
S2031、根据当前所述尾矿库所在区域位置，设置当前所述尾矿库所在区域地质灾害数据采集的标定区域范围；
S2032、采集所述标定区域范围内发生的历史地质灾害数据，其中，所述地质灾害包括洪水、泥石流和山体滑坡等；
S2033、根据所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数。
其中，所述地质灾害评价模型如下：
其中，F3表示灾害风险评价分数；W表示所述标定区域范围内，发生的除洪水、泥石流和山体滑坡三类地质灾害以外的地质灾害种类数量；S1、S2和S3分别表示洪水、泥石流和山体滑坡这三种地质灾害所波及的区域面积范围；L1、L2和L3分别表示洪水、泥石流和山体滑坡这三种地质灾害发生地与当前所述尾矿库所在区域位置之间的最短距离；α1、α2和α3分别表示预先设定的洪水、泥石流和山体滑坡这三种地质灾害对应的评价系数；其中，α1、α2和α3的取值范围分别对应为：0.22-0.39；0.17-0.26；0.15-0.46。具体的参数大小设定与当前所述尾矿库所在区域位置所处地址环境相关，与相应可发生地质灾害类型所具备的地质环境越近，参数值设置越大。
上述技术方案的工作原理为：首先，根据当前所述尾矿库所在区域位置，设置当前所述尾矿库所在区域地质灾害数据采集的标定区域范围；然后，采集所述标定区域范围内发生的历史地质灾害数据，其中，所述地质灾害包括洪水、泥石流和山体滑坡等；最后，根据所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数。
上述技术方案的效果为：通过上述方式进行历史地质灾害数据的获取，并结合上述地质灾害评价模型获取灾害风险评价分数，通过结合了历史地质灾害数据的数据信息能够有效提高气象风险评价与实际尾矿库所处地理位置和对应气象环境之间的匹配性，进而有效提高风险评价和后续分级的合理性和准确性。同时，结合上述气象数据获得风险评价分数能够与尾矿库实际建筑地理位置和环境进行有效匹配，结合环境数据能够兼顾更多的风险因素，进而提高风险评价的精确性和实际应用型。
同时，通过上述历史地质灾害数据获取的灾害风险评价分数，由于模型结构的设置和参数设置能够有效提高风险评分的准确性和合理性。同时，由于相较于传统尾矿库的风险评价方式，本实施例增加了多种气象数据、地质灾害数据和不可抗力数据的结合，由于数据的增加势必会导致运算量的加大，而通过本实施例提出的地理灾害评价模型能够在有效提高地质灾害评价准确性和精确性的同时，在数据量激增的情况下简化模型结构，最大限度减少运算量，有效提高风险分级评价效率。
本发明的一个实施例，结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级，包括：
S2041、将所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数进行结合，获取风险综合评价分数；
S2042、将所述风险综合评价分数与第一风险阈值和第二风险阈值进行比较；其中，所述第一风险阈值和第二风险阈值需要根据尾矿库实际建设位置以及建设位置的实际地理环境和气象环境的隐含风险情况而定；
S2043、当所述风险综合评价分数低于第一风险阈值时，确定当前尾矿库的风险等级为低风险等级；
S2044、当所述风险综合评价分数高于第一风险阈值且低于第二风险阈值时，确定当前尾矿库的风险等级为中风险等级；
S2045、当所述风险综合评价分数高于第二风险阈值时，确定当前尾矿库的风险等级为高风险等级。
上述技术方案的工作原理为：首先，将所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数进行结合，获取风险综合评价分数；然后，将所述风险综合评价 分数与第一风险阈值和第二风险阈值进行比较；之后，当所述风险综合评价分数低于第一风险阈值时，确定当前尾矿库的风险等级为低风险等级；随后，当所述风险综合评价分数高于第一风险阈值且低于第二风险阈值时，确定当前尾矿库的风险等级为中风险等级；最后，当所述风险综合评价分数高于第二风险阈值时，确定当前尾矿库的风险等级为高风险等级。
上述技术方案的效果为：通过上述方式能够有效提高风险分级划分的效率，同时，能够有效提高风险划分的合理性。
本发明的一个实施例，如图3所示，所述尾矿库安全生产风险分级预警方法的执行***包括：
基础数据采集模块，用于实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控预警；
外部数据采集模块，用于实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；
案例采集模块，用于采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控和风险预警。
其中，所述基础数据采集模块，包括：
数据采集模块，用于实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质构造、尾矿库筑坝方式、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；
比较模块，用于将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；
监控预警模块，用于当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
其中，所述外部数据采集模块包括：
地理位置风险评价分数获取模块，用于监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；
气象风险评价分数获取模块，用于监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；
质灾害风险评价分数获取模块，用于采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；
风险等级划分模块，用于结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级。
上述技术方案的效果为：本实施例提出的尾矿库安全生产风险分级预警方法的执行***通过结合结合企业基础数据、安全检查与隐患治理情况、在线监测接入数据，与发生事故企业的企业信息、事故原因等信息关联分析，结合气象数据、地质灾害数据、不可抗拒等因素，得出尾矿库运行的风险趋势。尤其是将气象数据、地质灾害数据、不可抗拒等因素融合至风险分级过程中，有效提高了风险分级的合理性、科学性和分级准确性。并根据不同风险级别进行预警能够最大限度提高尾矿库安全生产风险的预警有效性。
显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。

Claims (8)

  1. 尾矿库安全生产风险分级预警方法，其特征在在于，所述尾矿库安全生产风险分级预警方法包括：
    实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控；
    实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；
    采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控，并根据风险等级的不同对基础监控过程中的指标参数进行针对性设置和尾矿库安全生产风险的分级预警；
    实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级，包括：
    监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；
    监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；
    采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；
    结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级；
    监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数，包括：
    通过勘测确定当前尾矿库所在区域是否处于地震带区域，并且，采集当前尾矿库所在区域历史地震记录数据；
    通过勘测确定当前尾矿库所在区域是否处于台风区域，并且，采集当前尾矿库所在区域历史台风记录数据；
    通过勘测确定当前尾矿库所在区域是否处于海啸区域，并且，采集当前尾矿库所在区域历史海啸记录数据；
    利用历史地震记录数据、历史台风记录数据和历史海啸记录数据结合地理位置评价模型获取地理位置风险评价分数；
    其中，所述地理位置评价模型如下：
    其中，F1表示地理位置风险评价分数；P1、P2和P3表示评价系数；n和m分别表示当前尾矿库所在区域发生地震和台风的次数；Hd、Ht和Hh分别表示当前尾矿库所在区域发生地震、台风和海啸对应的级数平均值；Jd、Jt和Jh分别表示当前尾矿库所在区域发生地震、台风和海啸对应的持续时间长度；Ti表示第i次地震发生时刻，Ti+1表示第i+1次地震发生时刻；Tj表示第j次地震发生时刻，Tj+1表示第j+1次地震发生时刻；Tk表示第k次地震发生时刻，Tk+1表示第k+1次地震发生时刻。
  2. 根据权利要求1所述尾矿库安全生产风险分级预警方法，其特征在于，实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控预警，包括：
    实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质构造、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；
    将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；
    当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
  3. 根据权利要求1所述尾矿库安全生产风险分级预警方法，其特征在于，监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数，包括：
    采集指定时间段内的历史降雨量数据和持续降雨量数据；其中，所述指定时间段范围为12-18个月；
    利用所述历史降雨量数据和持续降雨量数据结合气象评价模型获取气象风险评价分数。
  4. 根据权利要求1所述尾矿库安全生产风险分级预警方法，其特征在于，采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数，包括：
    根据当前所述尾矿库所在区域位置，设置当前所述尾矿库所在区域地质灾害数据采集的标定区域范围；
    采集所述标定区域范围内发生的历史地质灾害数据，其中，所述地质灾害包括洪水、泥石流和山体滑坡；
    根据所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数。
  5. 根据权利要求1所述尾矿库安全生产风险分级预警方法，其特征在于，结合所述地理 位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级，包括：
    将所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数进行结合，获取风险综合评价分数；
    将所述风险综合评价分数与第一风险阈值和第二风险阈值进行比较；
    当所述风险综合评价分数低于第一风险阈值时，确定当前尾矿库的风险等级为低风险等级；
    当所述风险综合评价分数高于第一风险阈值且低于第二风险阈值时，确定当前尾矿库的风险等级为中风险等级；
    当所述风险综合评价分数高于第二风险阈值时，确定当前尾矿库的风险等级为高风险等级。
  6. 根据权利要求1所述尾矿库安全生产风险分级预警方法，其特征在于，所述尾矿库安全生产风险分级预警方法的执行***包括：
    基础数据采集模块，用于实时监控和采集尾矿库内部基础数据，并通过所述尾矿库内部基础数据进行基础监控预警；
    外部数据采集模块，用于实时采集当前所述尾矿库所在区域的地理位置、气象数据和历史地质灾害数据，根据所述气象数据和历史地质灾害数据对当前尾矿库所处安全生产风险进行风险分级，获得低风险、中风险和高风险的风险等级；
    案例采集模块，用于采集国内外尾矿库事故案例数据及危害程度数据，获取案例对应的事故解决方案策略，结合不同的风险等级进行管理监控和风险预警。
  7. 根据权利要求6所述尾矿库安全生产风险分级预警方法，其特征在于，所述基础数据采集模块，包括：
    数据采集模块，用于实时采集尾矿库内部基础数据，其中，所述尾矿库内部基础数据包括尾矿库地质构造、尾矿库运行状况、泄洪能力、浸润线、干滩、库水位和外部位移；
    比较模块，用于将所述尾矿库内部基础数据中各项数据指标与其对应的阈值数据指标进行比较；
    监控预警模块，用于当所述尾矿库内部基础数据中各项数据指标与其所对应的阈值数据指标之间的数量关系不满足预先设定的安全监控关系时，进行基础监控预警。
  8. 根据权利要求1所述尾矿库安全生产风险分级预警方法，其特征在于，外部数据采集模块包括：
    地理位置风险评价分数获取模块，用于监控和采集当前所述尾矿库所在区域的地理位置数据，通过所述地理位置数据结合地理位置评价模型获取地理位置风险评价分数；
    气象风险评价分数获取模块，用于监控和采集当前所述尾矿库所在区域的气象数据，通过所述气象数据结合气象评价模型获取气象风险评价分数；
    质灾害风险评价分数获取模块，用于采集当前所述尾矿库所在区域的历史地质灾害数据，通过所述历史地质灾害数据结合地质灾害评价模型获取灾害风险评价分数；
    风险等级划分模块，用于结合所述地理位置风险评价分数、气象风险评价分数和质灾害风险评价分数获得低风险、中风险和高风险的风险等级。
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