Multi-dimensional risk early warning method and system for salt-cavern gas storage

Abstract

The invention discloses a salt-cavern gas storage multidimensional risk early warning method and system, and the method comprises the steps: determining a plurality of safety events of a salt-cavern gas storage and a triggering factor of each safety event, and determining a monitoring index based on the triggering factor of each safety event; collecting historical data of each monitoring index of each security event, and constructing an analysis inversion model of the security event according to the historical data and the monitoring logic of each security event; performing analysis and inversion based on the real-time index value of the monitoring index corresponding to the security event through the analysis and inversion model of each security event; and performing risk assessment according to analysis and inversion results, determining risk factors and risk levels according to assessment results, and performing early warning based on the risk factors and the risk levels. Comprehensive risk analysis and early warning can be carried out from a plurality of monitoring stages of each security event, and the practicability and analysis and early warning comprehensiveness are improved.

Description

Technical Field

[0001] The present invention relates to the field of salt cavern gas storage operation and maintenance technology, and in particular to a multi-dimensional risk early warning method and system for salt cavern gas storage. Background Art

[0002] Currently, compressed gas energy storage is considered one of the most promising large-scale energy storage technologies. Gas storage is a key component, with salt cavern storage being the most widely used. Salt cavern storage utilizes underground salt cavities to store natural gas, offering advantages such as large gas storage capacity, good sealing, and strong peak-shaving capabilities. However, its operation faces various safety risks, including structural instability of the salt caverns, gas leakage risks, corrosion and equipment aging, and microseismic activity. Currently, monitoring of domestic salt cavern storage primarily relies on single sensors (such as pressure and temperature monitoring) and lacks a multi-dimensional, intelligent risk warning system, making it difficult to achieve early risk identification and accurate prediction. Summary of the Invention

[0003] In response to the above-mentioned problems, the present invention provides a multi-dimensional risk warning method and system for salt cavern gas storage to solve the problems mentioned in the background technology, such as reliance on a single sensor (such as pressure and temperature monitoring), lack of a multi-dimensional and intelligent risk warning system, and difficulty in achieving early risk identification and accurate prediction.

[0004] A multi-dimensional risk early warning method for salt cavern gas storage comprises the following steps:

[0005] Identify multiple safety incidents at salt cavern gas storage facilities and the triggering factors for each safety incident, and determine monitoring indicators based on the triggering factors for each safety incident;

[0006] Collect historical data of each monitoring indicator of each security incident, and build an analytical inversion model for the security incident based on the historical data and the monitoring logic of each security incident;

[0007] Through the analysis and inversion model of each security event, analysis and inversion are performed based on the real-time indicator values ​​of the monitoring indicators corresponding to the security event;

[0008] Conduct risk assessment based on analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warnings based on risk factors and risk levels.

[0009] Preferably, the determining of multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and determining monitoring indicators based on the triggering factors of each safety event, include:

[0010] Determine the basic structure and equipment operating parameters of the salt cavern gas storage, and determine the operating mechanism and potential risks of the salt cavern gas storage based on the basic structure and equipment operating parameters;

[0011] Identify multiple safety incidents of salt cavern gas storage based on its operating mechanism and potential risks, and obtain multiple potential causes for each safety incident;

[0012] Determine the triggering factors of each security incident based on multiple potential causes, and determine the mapping target based on the triggering factors of each security incident;

[0013] The monitoring indicators for each security incident are determined based on the mapping target and the abnormal form of the mapping target.

[0014] Preferably, the collecting of historical data of each monitoring indicator of each security event and constructing an analysis inversion model of the security event based on the historical data and the monitoring logic of each security event include:

[0015] Determine the data source format for each monitoring indicator of each security incident, determine the detection method based on the data source format, and collect historical data for each monitoring indicator of each security incident based on the detection method;

[0016] Preprocess historical data, extract statistical features and derived variables from the preprocessed historical data, determine the event characteristics of each security incident based on the statistical features and derived variables, and select a macro-physical model based on the event characteristics;

[0017] Select the micro-physical model for each monitoring stage based on the monitoring logic of each security event, and train the macro-physical model and micro-physical model using the pre-processed historical data of each security event;

[0018] An analytical inversion model for each security incident is constructed based on the training results.

[0019] Preferably, the analysis and inversion of each security event based on the real-time indicator value of the monitoring indicator corresponding to the security event through the analysis and inversion model includes:

[0020] Collect the real-time indicator value of each monitoring indicator of each security incident, and determine whether the indicator is abnormal based on the real-time indicator value through the experience database;

[0021] Based on the judgment results, potential target safety events of salt cavern gas storage are screened out, and dynamic analysis is performed based on the target real-time indicator values ​​of target monitoring indicators with abnormal indicators using the analysis and inversion model of potential target safety events;

[0022] The abnormal stage of the potential target security event is determined based on the analysis results, and the abnormal stage is inverted based on the target real-time indicator value through the analysis inversion model.

[0023] Preferably, the risk assessment is performed based on the analysis and inversion results, risk factors and risk levels are determined based on the assessment results, and early warning is performed based on the risk factors and risk levels, including:

[0024] Determine the risk dimensions and quantitative parameters of each risk dimension based on the analysis and inversion results, and construct a risk matrix based on the quantitative parameters of each risk dimension;

[0025] Determine the target risk level based on the risk matrix and obtain the direct and indirect risk factors corresponding to the target risk level;

[0026] Determine the early warning trigger logic and hierarchical response logic based on direct and indirect risk factors, and determine the early warning mechanism based on the early warning trigger logic and hierarchical response logic;

[0027] Select the warning method according to the warning mechanism and conduct risk warning through the warning method.

[0028] Preferably, after determining multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, the method further includes:

[0029] Determine the triggering scenario data of each security incident based on the triggering factors of the security incident, and divide the triggering scenario data according to the level dimension;

[0030] Determine the scenario-level context data and state-level context data for each security incident based on the classification results;

[0031] Determine the fine-grained environmental risk factors for each security incident based on scenario-level contextual data, and determine the fine-grained operational risk factors for each security incident based on state-level contextual data;

[0032] Determine multiple monitoring objects for each security incident based on fine-grained environmental risk factors and fine-grained operational risk factors, and obtain the risk stage characteristics of each monitoring object;

[0033] Divide all monitored objects into groups based on risk stage characteristics, obtain multiple belonging groups, and determine the cluster information keywords of each belonging group;

[0034] Determine the information label of each belonging group based on the cluster information keyword, and determine the data description and statistical parameters of each belonging group based on the information label;

[0035] Determine the data mapping indicators for each belonging group based on the data description and statistical parameters, and use the data mapping indicators as the reference sample for each security event monitoring indicator.

[0036] Preferably, determining the scenario-level context data and state-level context data of each security event based on the division results includes:

[0037] Determine the reference parameters for the scenario data division of each security incident based on the division results, and determine the data boundary description parameters for the scenario data at the scene level and the scenario data at the state level based on the division reference parameters;

[0038] Determine the respective judgment strategies for scenario-level scenario data and state-level scenario data based on data boundary description parameters;

[0039] The scenario data of each security incident is divided and judged according to the judgment strategy, and the scenario-level scenario data and state-level scenario data of each security incident are determined according to the judgment results.

[0040] A multi-dimensional risk early warning system for salt cavern gas storage, the system comprising:

[0041] A determination module is used to determine multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and to determine monitoring indicators based on the triggering factors of each safety event;

[0042] A construction module is used to collect historical data of each monitoring indicator of each security event, and build an analysis and inversion model of the security event based on the historical data and the monitoring logic of each security event;

[0043] An analysis and inversion module, configured to perform analysis and inversion based on the real-time indicator values ​​of the monitoring indicators corresponding to each security event through an analysis and inversion model of the security event;

[0044] The early warning module is used to conduct risk assessment based on the analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warnings based on the risk factors and risk levels.

[0045] Preferably, the determining module includes:

[0046] The first determination submodule is used to determine the basic structure and equipment operating parameters of the salt cavern gas storage, and determine the operating mechanism and potential risks of the salt cavern gas storage based on the basic structure and equipment operating parameters;

[0047] The acquisition submodule is used to determine multiple safety events of the salt cavern gas storage based on the operation mechanism and potential risks of the salt cavern gas storage, and obtain multiple potential causes of each safety event;

[0048] a second determination submodule, configured to determine a triggering factor of each security event based on a plurality of potential causes, and determine a mapping target based on the triggering factor of each security event;

[0049] The third determination submodule is used to determine the monitoring indicators of each security event according to the mapping target and the abnormal form of the mapping target.

[0050] Preferably, the building blocks include:

[0051] A fourth determination submodule is configured to determine a data source format for each monitoring indicator of each security event, determine a detection form based on the data source format, and collect historical data for each monitoring indicator of each security event based on the detection form;

[0052] The selection submodule is used to preprocess historical data, extract statistical features and derived variables of the preprocessed historical data, determine the event characteristics of each security event based on the statistical features and derived variables, and select a macro-physical model based on the event characteristics;

[0053] The training submodule is used to select the micro-physical model for each monitoring stage according to the monitoring logic of each security event, and train the macro-physical model and the micro-physical model through the pre-processed historical data of each security event;

[0054] The first construction submodule is used to construct an analysis inversion model for each security event based on the training results.

[0055] Preferably, the analysis and inversion module includes:

[0056] The judgment submodule is used to collect the real-time indicator value of each monitoring indicator of each security event, and judge whether the indicator is abnormal based on the real-time indicator value through the experience database;

[0057] The analysis submodule is used to screen out potential target safety events of salt cavern gas storage based on the judgment results, and to perform dynamic analysis based on the target real-time indicator values ​​of target monitoring indicators with abnormal indicators using the analysis inversion model of potential target safety events;

[0058] The inversion submodule is used to determine the abnormal stage of the potential target security event according to the analysis results, and to invert the abnormal stage based on the target real-time indicator value by analyzing the inversion model.

[0059] Preferably, the early warning module includes:

[0060] The second construction submodule is used to determine the risk dimensions and the quantitative parameters of each risk dimension based on the analysis and inversion results, and to construct a risk matrix based on the quantitative parameters of each risk dimension;

[0061] A fifth determination submodule is configured to determine a target risk level based on the risk matrix and obtain direct risk factors and indirect risk factors corresponding to the target risk level;

[0062] A sixth determination submodule is used to determine the warning trigger logic and the hierarchical response logic according to the direct risk factors and the indirect risk factors, and determine the warning mechanism based on the warning trigger logic and the hierarchical response logic;

[0063] The early warning submodule is used to select an early warning method according to the early warning mechanism and to issue risk warnings through the early warning method.

[0064] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0065] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0067] Figure 1 This is a workflow diagram of a multi-dimensional risk early warning method for salt cavern gas storage provided by the present invention;

[0068] Figure 2 Another workflow diagram of the multi-dimensional risk early warning method for salt cavern gas storage provided by the present invention;

[0069] Figure 3 This is a structural diagram of a multi-dimensional risk warning system for salt cavern gas storage provided by the present invention;

[0070] Figure 4 This is a structural schematic diagram of a determination module in a multi-dimensional risk warning system for salt cavern gas storage provided by the present invention. DETAILED DESCRIPTION

[0071] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0072] At present, compressed gas energy storage is considered to be one of the most promising large-scale energy storage technologies, in which gas storage is an important component, and the most widely used is salt cavern gas storage. Salt cavern gas storage is an important facility that uses underground salt layer dissolution cavities to store natural gas. It has the advantages of large gas storage capacity, good sealing, and strong peak-shaving capacity. However, it faces a variety of safety risks during operation, including: salt cavity structural instability, gas leakage risk, corrosion and equipment aging, and micro-seismic activity. At present, the monitoring of domestic salt cavern gas storage mainly relies on a single sensor (such as pressure and temperature monitoring), lacks a multi-dimensional and intelligent risk warning system, and it is difficult to achieve early risk identification and accurate prediction. In order to solve the above problems, this embodiment discloses a multi-dimensional risk warning method for salt cavern gas storage.

[0073] A multi-dimensional risk warning method for salt cavern gas storage, such as Figure 1 As shown, the following steps are included:

[0074] Step S101: determining multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and determining monitoring indicators based on the triggering factors of each safety event;

[0075] Step S102: Collect historical data of each monitoring indicator of each security event, and construct an analysis and inversion model of the security event based on the historical data and the monitoring logic of each security event;

[0076] Step S103: Analyze and invert the real-time indicator value of the monitoring indicator corresponding to each security event through the analysis and inversion model of each security event;

[0077] Step S104: perform risk assessment based on the analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warning based on the risk factors and risk levels.

[0078] In this embodiment, the multiple safety incidents of salt cavern gas storage mainly include: Gas leakage: may cause explosion or environmental pollution. Salt cavern collapse: cause structural instability or surface subsidence. Surface subsidence: threatens ground facilities and ecological environment. Wellbore integrity failure: cause gas or brine leakage. Brine leakage: pollute groundwater or surface water. Corrosion: weaken equipment and wellbore structure. Seismic activity: may be induced by gas storage operation. Overpressure / underpressure: exceed the design pressure range to cause risks;

[0079] In this embodiment, the triggering factors are represented by external human operation triggering factors or equipment abnormality triggering factors or environmental abnormality triggering factors of various safety events. For example, the triggering factors of gas leakage include: salt cavern seal failure, wellbore casing cracks, valve failure, etc. The triggering factors of salt cavern collapse include: accelerated salt rock creep, sudden change in internal pressure, geological tectonic activity, etc. The triggering factors of surface subsidence include: salt cavern volume shrinkage, groundwater level drop, stratum compaction, etc. The triggering factors of wellbore integrity failure include: corrosion, mechanical damage, material fatigue, etc. The triggering factors of brine leakage include: casing breakage, improper injection and production operations, seal failure, etc. The triggering factors of seismic activity include: changes in formation stress caused by injection and production, etc. The triggering factors of overpressure / underpressure include: excessive gas injection rate, excessive gas production rate, temperature change, etc.

[0080] In this embodiment, monitoring indicators are used to map various triggering factors. For example, monitoring indicators for seal failure and casing cracks include abnormal pressure fluctuations within the cavern / wellhead, methane concentration in the surrounding air, and wellbore acoustic detection (leak location). Monitoring indicators for salt rock creep and sudden pressure changes include cavern deformation (strain gauges, fiber optic sensing), formation microseismic activity monitoring, and real-time internal pressure monitoring.

[0081] The working principle of the above technical solution is as follows: determine multiple safety events of salt cavern gas storage and the triggering factors of each safety event, and determine monitoring indicators based on the triggering factors of each safety event; collect historical data of each monitoring indicator of each safety event, and construct an analysis and inversion model of the safety event based on the historical data and the monitoring logic of each safety event; analyze and invert the real-time indicator value of the monitoring indicator corresponding to the safety event through the analysis and inversion model of each safety event; conduct risk assessment based on the analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warnings based on the risk factors and risk levels.

[0082] The beneficial effects of the above technical solution are: by constructing an analytical inversion model for security events, comprehensive risk analysis and early warning can be carried out from multiple monitoring stages of each security event, thereby improving practicality and comprehensiveness of analysis and early warning. Furthermore, by utilizing the model for inversion, advance security event inversion can be carried out based on real-time monitoring data to achieve early risk identification and accurate prediction, thereby improving practicality and reliability, and solving the problems mentioned in the prior art of relying on a single sensor (such as pressure and temperature monitoring), lacking a multi-dimensional and intelligent risk warning system, and making it difficult to achieve early risk identification and accurate prediction.

[0083] In one embodiment, Figure 2 As shown, the method of determining multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and determining monitoring indicators based on the triggering factors of each safety event, includes:

[0084] Step S201: Determine the basic structure and equipment operating parameters of the salt cavern gas storage, and determine the operating mechanism and potential risks of the salt cavern gas storage based on the basic structure and equipment operating parameters;

[0085] Step S202: determining multiple safety events of the salt cavern gas storage based on the operation mechanism and potential risks of the salt cavern gas storage, and obtaining multiple potential causes of each safety event;

[0086] Step S203: determining a triggering factor for each security event based on multiple potential causes, and determining a mapping target based on the triggering factor for each security event;

[0087] Step S204: Determine the monitoring indicators for each security event based on the mapping target and the abnormal form of the mapping target.

[0088] The beneficial effects of the above technical solution are: by conducting in-depth mining of each security incident to determine comprehensive monitoring indicators, a comprehensive hidden danger investigation can be achieved for each security incident, thereby improving the efficiency, stability and practicality of risk assessment.

[0089] In one embodiment, collecting historical data of each monitoring indicator of each security event and constructing an analysis inversion model for the security event based on the historical data and the monitoring logic of each security event includes:

[0090] Determine the data source format for each monitoring indicator of each security incident, determine the detection method based on the data source format, and collect historical data for each monitoring indicator of each security incident based on the detection method;

[0091] Preprocess historical data, extract statistical features and derived variables from the preprocessed historical data, determine the event characteristics of each security incident based on the statistical features and derived variables, and select a macro-physical model based on the event characteristics;

[0092] Select the micro-physical model for each monitoring stage based on the monitoring logic of each security event, and train the macro-physical model and micro-physical model using the pre-processed historical data of each security event;

[0093] An analytical inversion model for each security incident is constructed based on the training results.

[0094] The beneficial effect of the above technical solution is that by selecting macroscopic physical models and microscopic physical models, an analytical inversion model for each safety time can be comprehensively constructed according to the monitoring direction and monitoring and investigation items, thereby ensuring the working efficiency, business adaptability and stability of the model.

[0095] In one embodiment, the analysis and inversion of each security event based on the real-time indicator value of the monitoring indicator corresponding to the security event through the analysis and inversion model includes:

[0096] Collect the real-time indicator value of each monitoring indicator of each security incident, and determine whether the indicator is abnormal based on the real-time indicator value through the experience database;

[0097] Based on the judgment results, potential target safety events of salt cavern gas storage are screened out, and dynamic analysis is performed based on the target real-time indicator values ​​of target monitoring indicators with abnormal indicators using the analysis and inversion model of potential target safety events;

[0098] The abnormal stage of the potential target security event is determined based on the analysis results, and the abnormal stage is inverted based on the target real-time indicator value through the analysis inversion model.

[0099] In this embodiment, taking microseismic monitoring as an example, the model can mainly realize functions such as data loading, travel time calculation, first arrival picking, source location, focal mechanism inversion, and seismic wave field numerical simulation;

[0100] Before reading data, first establish the work area and generate a *.prj database folder to store data files related to the work area;

[0101] During the "New Work Area" and "Open Work Area" processes, use the "Background Velocity" module to load and read the velocity model of the target work area. You can choose the average velocity / root mean square velocity / layer velocity model, and the default is layer velocity. The velocity loading file format can choose the velocity spectrum file format (*.txt), single well velocity file format (*.las), and seismic velocity file format (*.sgy). The default format is *.sgy. Seismic wave velocity is usually divided into longitudinal wave and shear wave velocity. At least the longitudinal wave velocity file should be provided. The spatial position and spatial morphology of the cavity can be added as needed, and the corresponding position in the velocity model can be replaced with the average velocity of the salt cavity;

[0102] After loading the velocity file, the coordinates (x, y, z) of the monitoring points must be set according to their spatial locations in the work area. The file format is ASCII text. Finally, the grid parameters required for microseismic positioning must be set, including the grid spacing in the x, y, and z directions, as well as the range of the monitoring target area, for use in seismic wave travel time calculations.

[0103] When the seismic wave velocity model is given and the initial microseismic source position is provided, the travel time of the seismic wave from the source position to the monitoring point position can be calculated using the layered model ray tracing, fast sweep, and fast march methods respectively. Depending on the earthquake source location method, you can choose to calculate the longitudinal wave travel time / transverse wave travel time. For relatively simple geological models, the layered model ray tracing method can be used to calculate the seismic wave travel time. This method has fast calculation speed and high efficiency. For more complex geological models, the geological structure has obvious undulations / dramatic changes in longitudinal and transverse velocities, and it is necessary to use fast scanning and fast marching methods to calculate the seismic wave travel time.

[0104] Input data is received seismic wave data and can be read in *.sgy, *.sac, and *.mseed formats. *.mseed is the default format. Signal-to-noise separation uses amplitude and frequency-domain filtering methods for signal processing. Amplitude filtering methods include amplitude thresholding and signal-to-noise orthogonalization. Frequency-domain filtering methods are categorized into low-pass, band-pass, and high-pass filtering, depending on the filter type.

[0105] First arrival picking can choose whether to use shear wave first arrival according to the needs of the positioning method; the first arrival picking methods include K-means, AIC and long-short time window ratio method;

[0106] Source location is the core of microseismic monitoring. The previously obtained seismic wave travel time and first arrival time are used in calculations to ultimately determine the spatial location of the microseismic event.

[0107] The Geiger positioning method uses the absolute travel time of longitudinal and shear waves of seismic waves for positioning, and selects longitudinal wave positioning, shear wave positioning, and joint longitudinal and shear wave positioning methods; the longitudinal and shear wave travel time difference method uses the travel time difference of longitudinal and shear waves for positioning. The relative time difference better reflects the travel time difference information and can better obtain the spatial position of the earthquake source. The wave equation positioning imaging method uses the finite difference method to solve the three-dimensional elastic wave equation, obtains the image at the zero time of the earthquake based on the grouped cross-correlation imaging condition, and uses the threshold method to determine the spatial position of the microseismic event. The wave equation positioning method takes a long time to calculate and is not suitable for the rapid processing of real-time monitoring results. It is suitable for the precise positioning and verification of key microseismic events;

[0108] Focal mechanism inversion is an effective method for revealing earthquake mechanisms and explaining rock failure mechanisms from the perspective of signals and rock mechanics. It includes the inversion of Richter magnitude, b-value, average polarity, and focal moment tensor. The Richter magnitude represents the intensity of an earthquake at the measurement point, while the b-value and average polarity reveal the type of earthquake source. The focal moment tensor uses a 3×3 second-order tensor to represent the direction and intensity of the source.

[0109] After obtaining the spatial location of the microseismic event, the rupture type of the earthquake source can be predicted as needed, and the risk assessment of underground geological activities can be carried out based on the prediction results. Among them, the Richter magnitude is used to describe the energy of the earthquake event. When a large-scale earthquake event occurs, it indicates a higher risk; the b value and average polarity characterize the rupture type of the earthquake source, which can be divided into tension type, shear type and collapse type earthquake sources according to the numerical range. The source moment tensor is used to quantitatively describe the strength and direction of the earthquake source, and the source type is divided into the weighted sum of three parts: expansion source, shear dislocation source and compensating linear vector dipole. The part with the largest weight among the three parts is the type of the earthquake source;

[0110] The numerical simulation of the seismic wavefield uses the finite-difference method to solve the three-dimensional elastic wave equations underlying the focal mechanism. By default, the method uses the eighth-order spatial and second-order temporal finite-difference method. By setting parameters and determining whether stability and dispersion conditions are met, the program can be continued or terminated. This method is used for designing observation systems for the work area and for numerical simulation applications. Furthermore, this simulation method takes the focal mechanism into account and uses the focal moment tensor to represent different source types, allowing for analysis of the wavefield characteristics of different source types.

[0111] The beneficial effects of the above technical solution are: by conducting safety event investigation, the corresponding events of potential safety hazards of salt cavern gas storage can be effectively screened out, and then stage investigation and inversion of potential safety hazard events can be carried out to determine the consequences of the events and the source investigation, thereby improving safety efficiency and stability.

[0112] In one embodiment, performing risk assessment based on the analysis and inversion results, determining risk factors and risk levels based on the assessment results, and issuing early warnings based on the risk factors and risk levels include:

[0113] Determine the risk dimensions and quantitative parameters of each risk dimension based on the analysis and inversion results, and construct a risk matrix based on the quantitative parameters of each risk dimension;

[0114] Determine the target risk level based on the risk matrix and obtain the direct and indirect risk factors corresponding to the target risk level;

[0115] Determine the early warning trigger logic and hierarchical response logic based on direct and indirect risk factors, and determine the early warning mechanism based on the early warning trigger logic and hierarchical response logic;

[0116] Select the warning method according to the warning mechanism and conduct risk warning through the warning method.

[0117] The beneficial effects of the above technical solution are: by determining the early warning mechanism and selecting the early warning method for risk early warning, the safety management level and emergency response capability can be improved, and at the same time, corresponding early warnings can be made in a targeted manner to ensure the reliability of the early warning.

[0118] In one embodiment, after determining multiple safety events of the salt cavern gas storage and triggering factors of each safety event, the method further includes:

[0119] Determine the triggering scenario data of each security incident based on the triggering factors of the security incident, and divide the triggering scenario data according to the level dimension;

[0120] Determine the scenario-level context data and state-level context data for each security incident based on the classification results;

[0121] Determine the fine-grained environmental risk factors for each security incident based on scenario-level contextual data, and determine the fine-grained operational risk factors for each security incident based on state-level contextual data;

[0122] Determine multiple monitoring objects for each security incident based on fine-grained environmental risk factors and fine-grained operational risk factors, and obtain the risk stage characteristics of each monitoring object;

[0123] Divide all monitored objects into groups based on risk stage characteristics, obtain multiple belonging groups, and determine the cluster information keywords of each belonging group;

[0124] Determine the information label of each belonging group based on the cluster information keyword, and determine the data description and statistical parameters of each belonging group based on the information label;

[0125] Determine the data mapping indicators for each belonging group based on the data description and statistical parameters, and use the data mapping indicators as the reference sample for each security event monitoring indicator.

[0126] In this embodiment, the scenario-level context data is represented as the descriptive context data of the environmental scenario when each security incident occurs;

[0127] In this embodiment, the state-level scenario data is represented as the descriptive scenario data of the state of the environment, equipment, and manual operation when each security incident occurs;

[0128] In this embodiment, the fine-grained environmental risk factors are represented as statistical factors of the fine-grained environmental risk at the scene when each security incident occurs;

[0129] In this embodiment, the fine-grained operational risk factors are represented as statistical factors of the fine-grained personnel operational risk at the scene when each security incident occurs;

[0130] In this embodiment, the risk stage characteristics are represented by the stage characteristics of each monitored object during the occurrence of a security event.

[0131] The beneficial effects of the above technical solution are: by determining the data mapping indicators of the group to which each security incident belongs and then using them as reference samples to determine the monitoring indicators, the monitoring indicators can be accurately determined according to the data information labels of the monitoring objects corresponding to each stage in the process of each security incident, thereby ensuring the objectivity of the indicators and the reference and monitoring value for each security incident, thereby improving work efficiency and practicality.

[0132] In one embodiment, determining the scene-level context data and state-level context data for each security event based on the segmentation results includes:

[0133] Determine the reference parameters for the scenario data division of each security incident based on the division results, and determine the data boundary description parameters for the scenario data at the scene level and the scenario data at the state level based on the division reference parameters;

[0134] Determine the respective judgment strategies for scenario-level scenario data and state-level scenario data based on data boundary description parameters;

[0135] The scenario data of each security incident is divided and judged according to the judgment strategy, and the scenario-level scenario data and state-level scenario data of each security incident are determined according to the judgment results.

[0136] The beneficial effects of the above data scheme are: by determining the boundary description parameters and then formulating the judgment strategy to divide and judge the scene-level scenario data and the state-level scenario data, it is possible to accurately divide and count the scenario data, thereby ensuring the quality and reliability of the data.

[0137] In one embodiment, this embodiment also discloses a multi-dimensional risk warning system for salt cavern gas storage, such as Figure 3 As shown, the system includes:

[0138] A determination module 301 is configured to determine multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and determine monitoring indicators based on the triggering factors of each safety event;

[0139] A construction module 302 is configured to collect historical data of each monitoring indicator of each security event and construct an analysis and inversion model of the security event based on the historical data and the monitoring logic of each security event;

[0140] An analysis and inversion module 303 is configured to perform analysis and inversion based on the real-time indicator value of the monitoring indicator corresponding to each security event through an analysis and inversion model of each security event;

[0141] The early warning module 304 is used to perform risk assessment based on the analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warnings based on the risk factors and risk levels.

[0142] The working principle and beneficial effects of the above technical solution have been explained in the method embodiment and will not be repeated here.

[0143] In one embodiment, Figure 4 As shown, the determining module 301 includes:

[0144] The first determination submodule 3011 is used to determine the basic structure and equipment operating parameters of the salt cavern gas storage, and determine the operation mechanism and potential risks of the salt cavern gas storage based on the basic structure and equipment operating parameters;

[0145] The acquisition submodule 3012 is used to determine multiple safety events of the salt cavern gas storage based on the operation mechanism and potential risks of the salt cavern gas storage, and obtain multiple potential causes of each safety event;

[0146] A second determining submodule 3013 is configured to determine a triggering factor for each security event based on multiple potential causes, and determine a mapping target based on the triggering factor for each security event;

[0147] The third determining submodule 3014 is configured to determine the monitoring indicator of each security event according to the mapping target and the abnormal form of the mapping target.

[0148] In one embodiment, the building block comprises:

[0149] A fourth determination submodule is configured to determine a data source format for each monitoring indicator of each security event, determine a detection form based on the data source format, and collect historical data for each monitoring indicator of each security event based on the detection form;

[0150] The selection submodule is used to preprocess historical data, extract statistical features and derived variables of the preprocessed historical data, determine the event characteristics of each security event based on the statistical features and derived variables, and select a macro-physical model based on the event characteristics;

[0151] The training submodule is used to select the micro-physical model for each monitoring stage according to the monitoring logic of each security event, and train the macro-physical model and the micro-physical model through the pre-processed historical data of each security event;

[0152] The first construction submodule is used to construct an analysis inversion model for each security event based on the training results.

[0153] In one embodiment, the analysis and inversion module comprises:

[0154] The judgment submodule is used to collect the real-time indicator value of each monitoring indicator of each security event, and judge whether the indicator is abnormal based on the real-time indicator value through the experience database;

[0155] The analysis submodule is used to screen out potential target safety events of salt cavern gas storage based on the judgment results, and to perform dynamic analysis based on the target real-time indicator values ​​of target monitoring indicators with abnormal indicators using the analysis inversion model of potential target safety events;

[0156] The inversion submodule is used to determine the abnormal stage of the potential target security event according to the analysis results, and to invert the abnormal stage based on the target real-time indicator value by analyzing the inversion model.

[0157] In one embodiment, the early warning module includes:

[0158] The second construction submodule is used to determine the risk dimensions and the quantitative parameters of each risk dimension based on the analysis and inversion results, and to construct a risk matrix based on the quantitative parameters of each risk dimension;

[0159] A fifth determination submodule is configured to determine a target risk level based on the risk matrix and obtain direct risk factors and indirect risk factors corresponding to the target risk level;

[0160] A sixth determination submodule is used to determine the warning trigger logic and the hierarchical response logic according to the direct risk factors and the indirect risk factors, and determine the warning mechanism based on the warning trigger logic and the hierarchical response logic;

[0161] The early warning submodule is used to select an early warning method according to the early warning mechanism and to issue risk warnings through the early warning method.

[0162] Those skilled in the art should understand that the first and second in the present invention simply refer to different application stages.

[0163] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0164] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A multi-dimensional risk warning method for salt cavern gas storage, characterized in that: The following steps are involved: Identify multiple safety incidents at salt cavern gas storage facilities and the triggering factors for each safety incident, and determine monitoring indicators based on the triggering factors for each safety incident; Collect historical data of each monitoring indicator of each security incident, and build an analytical inversion model for the security incident based on the historical data and the monitoring logic of each security incident; Through the analysis and inversion model of each security event, analysis and inversion are performed based on the real-time indicator values ​​of the monitoring indicators corresponding to the security event; Conduct risk assessment based on analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warnings based on risk factors and risk levels.

2. The multi-dimensional risk early warning method for salt cavern gas storage according to claim 1 is characterized in that: The determination of multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and the determination of monitoring indicators based on the triggering factors of each safety event, include: Determine the basic structure and equipment operating parameters of the salt cavern gas storage, and determine the operating mechanism and potential risks of the salt cavern gas storage based on the basic structure and equipment operating parameters; Identify multiple safety incidents of salt cavern gas storage based on its operating mechanism and potential risks, and obtain multiple potential causes for each safety incident; Determine the triggering factors of each security incident based on multiple potential causes, and determine the mapping target based on the triggering factors of each security incident; The monitoring indicators for each security incident are determined based on the mapping target and the abnormal form of the mapping target.

3. The multi-dimensional risk early warning method for salt cavern gas storage according to claim 1 is characterized in that: The process of collecting historical data of each monitoring indicator of each security event and constructing an analysis and inversion model of the security event based on the historical data and the monitoring logic of each security event includes: Determine the data source format for each monitoring indicator of each security incident, determine the detection method based on the data source format, and collect historical data for each monitoring indicator of each security incident based on the detection method; Preprocess historical data, extract statistical features and derived variables from the preprocessed historical data, determine the event characteristics of each security incident based on the statistical features and derived variables, and select a macro-physical model based on the event characteristics; Select the micro-physical model for each monitoring stage based on the monitoring logic of each security event, and train the macro-physical model and micro-physical model using the pre-processed historical data of each security event; An analytical inversion model for each security incident is constructed based on the training results.

4. The multi-dimensional risk early warning method for salt cavern gas storage according to claim 1 is characterized in that: The analysis and inversion model for each security event is performed based on the real-time indicator value of the monitoring indicator corresponding to the security event, including: Collect the real-time indicator value of each monitoring indicator of each security incident, and determine whether the indicator is abnormal based on the real-time indicator value through the experience database; Based on the judgment results, potential target safety events of salt cavern gas storage are screened out, and dynamic analysis is performed based on the target real-time indicator values ​​of target monitoring indicators with abnormal indicators using the analysis and inversion model of potential target safety events; The abnormal stage of the potential target security event is determined based on the analysis results, and the abnormal stage is inverted based on the target real-time indicator value through the analysis inversion model.

5. The multi-dimensional risk early warning method for salt cavern gas storage according to claim 1 is characterized in that: The risk assessment is performed based on the analysis and inversion results, risk factors and risk levels are determined based on the assessment results, and early warning is issued based on the risk factors and risk levels, including: Determine the risk dimensions and quantitative parameters of each risk dimension based on the analysis and inversion results, and construct a risk matrix based on the quantitative parameters of each risk dimension; Determine the target risk level based on the risk matrix and obtain the direct and indirect risk factors corresponding to the target risk level; Determine the early warning trigger logic and hierarchical response logic based on direct and indirect risk factors, and determine the early warning mechanism based on the early warning trigger logic and hierarchical response logic; Select the warning method according to the warning mechanism and conduct risk warning through the warning method.

6. The multi-dimensional risk early warning method for salt cavern gas storage according to claim 1 is characterized in that: After determining multiple safety events of the salt cavern gas storage and triggering factors of each safety event, the method further includes: Determine the triggering scenario data of each security incident based on the triggering factors of the security incident, and divide the triggering scenario data according to the level dimension; Determine the scenario-level context data and state-level context data for each security incident based on the classification results; Determine the fine-grained environmental risk factors for each security incident based on scenario-level contextual data, and determine the fine-grained operational risk factors for each security incident based on state-level contextual data; Determine multiple monitoring objects for each security incident based on fine-grained environmental risk factors and fine-grained operational risk factors, and obtain the risk stage characteristics of each monitoring object; Divide all monitored objects into groups based on risk stage characteristics, obtain multiple belonging groups, and determine the cluster information keywords of each belonging group; Determine the information label of each belonging group based on the cluster information keyword, and determine the data description and statistical parameters of each belonging group based on the information label; Determine the data mapping indicators for each belonging group based on the data description and statistical parameters, and use the data mapping indicators as the reference sample for each security event monitoring indicator.

7. The multi-dimensional risk early warning method for salt cavern gas storage according to claim 6 is characterized in that: The scenario-level context data and state-level context data of each security incident are determined based on the division results, including: Determine the reference parameters for the scenario data division of each security incident based on the division results, and determine the data boundary description parameters for the scenario data at the scene level and the scenario data at the state level based on the division reference parameters; Determine the respective judgment strategies for scenario-level scenario data and state-level scenario data based on data boundary description parameters; The scenario data of each security incident is divided and judged according to the judgment strategy, and the scenario-level scenario data and state-level scenario data of each security incident are determined according to the judgment results.

8. A multi-dimensional risk warning system for salt cavern gas storage, characterized in that: The system includes: A determination module is used to determine multiple safety events of the salt cavern gas storage and the triggering factors of each safety event, and to determine monitoring indicators based on the triggering factors of each safety event; A construction module is used to collect historical data of each monitoring indicator of each security event, and build an analysis and inversion model of the security event based on the historical data and the monitoring logic of each security event; An analysis and inversion module, configured to perform analysis and inversion based on the real-time indicator values ​​of the monitoring indicators corresponding to each security event through an analysis and inversion model of the security event; The early warning module is used to conduct risk assessment based on the analysis and inversion results, determine risk factors and risk levels based on the assessment results, and issue early warnings based on the risk factors and risk levels.

9. The multi-dimensional risk warning system for salt cavern gas storage according to claim 8 is characterized in that: The determining module includes: The first determination submodule is used to determine the basic structure and equipment operating parameters of the salt cavern gas storage, and determine the operating mechanism and potential risks of the salt cavern gas storage based on the basic structure and equipment operating parameters; The acquisition submodule is used to determine multiple safety events of the salt cavern gas storage based on the operation mechanism and potential risks of the salt cavern gas storage, and obtain multiple potential causes of each safety event; a second determination submodule, configured to determine a triggering factor of each security event based on a plurality of potential causes, and determine a mapping target based on the triggering factor of each security event; A third determination submodule is used to determine the monitoring indicators of each security event based on the mapping target and the abnormal form of the mapping target; The building blocks include: A fourth determination submodule is configured to determine a data source format for each monitoring indicator of each security event, determine a detection form based on the data source format, and collect historical data for each monitoring indicator of each security event based on the detection form; The selection submodule is used to preprocess historical data, extract statistical features and derived variables of the preprocessed historical data, determine the event characteristics of each security event based on the statistical features and derived variables, and select a macro-physical model based on the event characteristics; The training submodule is used to select the micro-physical model for each monitoring stage according to the monitoring logic of each security event, and train the macro-physical model and the micro-physical model through the pre-processed historical data of each security event; The first construction submodule is used to construct an analysis inversion model for each security event based on the training results.

10. The multi-dimensional risk warning system for salt cavern gas storage according to claim 6, characterized in that: The analysis and inversion module includes: The judgment submodule is used to collect the real-time indicator value of each monitoring indicator of each security event, and judge whether the indicator is abnormal based on the real-time indicator value through the experience database; The analysis submodule is used to screen out potential target safety events of salt cavern gas storage based on the judgment results, and to perform dynamic analysis based on the target real-time indicator values ​​of target monitoring indicators with abnormal indicators using the analysis inversion model of potential target safety events; The inversion submodule is used to determine the abnormal stage of the potential target security event based on the analysis results, and to invert the abnormal stage based on the target real-time indicator value by analyzing the inversion model; The early warning module includes: The second construction submodule is used to determine the risk dimensions and the quantitative parameters of each risk dimension based on the analysis and inversion results, and to construct a risk matrix based on the quantitative parameters of each risk dimension; A fifth determination submodule is configured to determine a target risk level based on the risk matrix and obtain direct risk factors and indirect risk factors corresponding to the target risk level; A sixth determination submodule is used to determine the warning trigger logic and the hierarchical response logic according to the direct risk factors and the indirect risk factors, and determine the warning mechanism based on the warning trigger logic and the hierarchical response logic; The early warning submodule is used to select an early warning method according to the early warning mechanism and to issue risk warnings through the early warning method.

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