Integrated safety online monitoring system for salt cavern storage areas

Abstract

The present invention discloses an integrated online safety monitoring system for salt cavern storage areas, comprising: a data acquisition unit for collecting and converging online monitoring data of the safety of salt cavern storage areas, the monitoring data including safety production risk factor data, micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage; a data analysis unit for comprehensively analyzing the acquired monitoring data based on a gas storage reservoir risk assessment model to obtain analysis results; a monitoring unit for generating risk warning information in real time based on the analysis results, monitoring the dynamic risk status of the gas storage reservoir, and automatically displaying the generated risk warning information, the risk warning information including a safety risk spatial distribution map. Through the timely generation and monitoring of risk warning information, corresponding measures can be taken to prevent potential safety risks and ensure the safe operation of the gas storage reservoir.

Description

Technical Field

[0001] The invention relates to the technical field of safe online monitoring, and in particular to an integrated safe online monitoring system for a salt cavern storage area. Background Art

[0002] Salt cavern gas storage refers to a facility that uses underground cavities formed by water-soluble salt in underground rock formations or salt domes to store natural gas. At present, with the expansion of salt cavern gas storage, the operating pressure will cause surface subsidence, damage to the salt cavern roof, and excessive shrinkage of the cavity, which will cause many safety problems. In order to ensure the safety of salt cavern gas storage during construction and operation, a complete monitoring system must be established.

[0003] The invention with application number: CN201610227701.8 discloses an online monitoring system and method for salt cavern storage cavity, including a surface unit and a downhole unit, using ultra-large-scale programmable logic devices and embedded soft-core microprocessors to make downhole instruments highly integrated, and using probe sensors to overcome the bottleneck of narrow space, solve the problem of placing measurement devices in the annulus of small wells, and promote the development of cavity technology to a great extent. The device sets a group of probes of unequal lengths and uses a fixed reference length between capacitor plates to dynamically detect the dielectric constant of the brine where the probe is located. The defects of this prior art include: although the design takes narrow space into consideration, the downhole environment usually has high temperature, high pressure and corrosive substances, which may affect the long-term stability of the equipment; using a fixed reference length between capacitor plates to dynamically detect the dielectric constant may be affected by temperature, pressure or other environmental factors, thereby affecting the measurement accuracy.

[0004] Therefore, there is an urgent need for an integrated safety online monitoring system for salt cavern storage areas. Summary of the invention

[0005] The present invention provides an integrated safety online monitoring system for salt cavern storage areas to solve the above-mentioned problems existing in the prior art, such as surface settlement of salt cavern gas storage, damage to salt cavern roof, and excessive shrinkage of the cavern, which may cause many safety problems.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The integrated safety online monitoring system for salt cavern storage areas includes:

[0008] Data acquisition unit, used to collect online monitoring data of the safety of the convergent salt cavern storage area, including data on production safety risk factors, micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage;

[0009] The data analysis unit conducts a comprehensive analysis of the acquired monitoring data based on the gas storage risk assessment model to obtain analysis results;

[0010] The monitoring unit generates risk warning information in real time based on the analysis results, monitors the dynamic risk status of the gas storage facility, and automatically displays the generated risk warning information, which includes a safety risk spatial distribution map.

[0011] The data acquisition unit includes: a safety production risk factor acquisition module, a microseismic data acquisition module, a comprehensive leakage data acquisition module and a ground subsidence data acquisition module;

[0012] The safety production risk factor acquisition module is used to obtain risk factor data related to safety production in real time. The risk factor data includes environmental parameters such as temperature, pressure, and humidity, as well as equipment operation status information;

[0013] A microseismic data acquisition module is used to access the microseismic monitoring equipment to obtain relevant values ​​of microseismic activities in real time. The relevant values ​​of microseismic activities include magnitude, focal depth and focal location;

[0014] Comprehensive leakage data acquisition module, used to access the monitoring equipment of wellbore leakage and natural gas leakage, and obtain leakage-related values ​​in real time, including leakage amount, leakage location and leakage speed;

[0015] The ground subsidence data acquisition module is used to access the ground subsidence monitoring equipment and obtain relevant values ​​of ground subsidence in real time. The relevant values ​​of ground subsidence include subsidence depth and subsidence speed.

[0016] Among them, the data analysis unit includes: data optimization processing module, risk quantitative analysis module, risk dynamic prediction module and strategy formulation module;

[0017] The data optimization processing module is used to clean and standardize the raw data obtained from the monitoring equipment of micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage, and obtain optimized monitoring data;

[0018] The risk quantitative analysis module is used to perform pattern recognition and outlier analysis on the optimized monitoring data based on the gas storage risk assessment model, assign corresponding weights to various risk factors based on the outlier analysis results, and calculate a comprehensive risk assessment based on the weight allocation;

[0019] The risk dynamic prediction module is used to perform trend analysis on comprehensive risk assessment based on time series analysis and obtain risk dynamic prediction results;

[0020] The strategy formulation module is used to specify corresponding production safety strategies based on risk trend prediction results. Production safety strategy recommendations include adjusting monitoring frequency, optimizing monitoring parameters, proposing equipment maintenance or upgrades, and formulating emergency response plans.

[0021] Among them, the monitoring unit includes: a warning information generation module, a safety risk spatial distribution map generation module and a warning information display module;

[0022] The early warning information generation module is used to check whether the analysis results exceed the preset early warning threshold based on the analysis results, and automatically generate risk early warning information;

[0023] The safety risk spatial distribution map generation module is used to convert risk warning information into a visual safety risk spatial distribution map to display the risk status of the gas storage facility;

[0024] The early warning information display module is used to display the generated risk warning information and safety risk spatial distribution map in real time on the monitoring interface to ensure that relevant personnel understand and take corresponding measures in a timely manner.

[0025] Among them, the corresponding weights are assigned to each risk factor based on the outlier analysis results, and a comprehensive risk assessment is calculated based on the weight assignment, including:

[0026] Obtain a preset pattern recognition experience library, and randomly select a first pattern information item from the pattern recognition experience library; obtain a first pattern verification value corresponding to the first pattern information item, and if the first pattern verification value is greater than or equal to a preset first pattern verification threshold, use the corresponding first pattern information item as the second pattern information item;

[0027] Obtain a preset outlier analysis experience library, and randomly select a third mode information item from the outlier analysis experience library; obtain a second mode verification value corresponding to the second mode information item, and if the second mode verification value is greater than or equal to a preset second mode verification threshold, use the corresponding third mode information item as a fourth mode information item;

[0028] Based on the fourth model information item, weights are assigned to various risk factors, and according to the results of the weight assignment, weighted calculations are performed on various risk factors to obtain a comprehensive risk assessment.

[0029] Wherein, obtaining the first mode verification value corresponding to the first mode information item includes:

[0030] Acquire pattern recognition process information corresponding to the first pattern information item;

[0031] Splitting the pattern recognition process information into a number of first sub-processes;

[0032] Based on the feature extraction technology, extract the first pattern feature and the corresponding first feature value of the first sub-process;

[0033] Obtaining a preset pattern specification verification library, and matching the first pattern feature with the second pattern feature in the pattern specification verification library;

[0034] If the match is met, the first feature value corresponding to the first pattern feature is used as the second feature value, and the feature threshold corresponding to the second pattern feature is obtained;

[0035] If the second eigenvalue is less than the feature threshold, calculate the first difference between the second eigenvalue and the feature threshold; determine the subprocess position of the first subprocess in the pattern recognition process information, and obtain the position weight corresponding to the position; assign the first difference position weight, obtain the second difference, and associate it with the first subprocess;

[0036] Cumulatively calculate all associated second differences to obtain the difference sum; if the difference sum is greater than or equal to the preset difference sum threshold, use the value 0 as the first mode verification value corresponding to the first mode information item to complete the acquisition; otherwise, calculate the inverse of the difference sum as the first mode verification value corresponding to the first mode information item to complete the acquisition.

[0037] Among them, according to the risk trend prediction results, the corresponding safety production strategy is specified, including:

[0038] Collect the safety risk monitoring data at the time point at a fixed time interval, the safety risk monitoring data includes the equipment operation status and production environment parameters, process the collected safety risk monitoring data at different time points in chronological order, and obtain the difference of safety risk data in adjacent fixed time intervals;

[0039] According to the difference of safety risk monitoring data in adjacent fixed time intervals, a risk trend prediction model is established, and the dynamic safety risk monitoring data in the fixed time interval is input into the risk trend prediction model to determine the risk change trend under each difference;

[0040] According to the risk trend, specify the corresponding production safety strategy. If the risk trend increases, it is recommended to increase the monitoring frequency of the corresponding data or perform equipment maintenance; if the risk trend decreases, maintain the current monitoring frequency and equipment maintenance plan.

[0041] Among them, the risk warning information is automatically generated, including:

[0042] Acquire and analyze multi-dimensional data of the current operating environment, device status, and user behavior, and use a pre-set risk assessment model to calculate the risk level based on the multi-dimensional data to generate a risk assessment result, which includes the risk level and the corresponding risk factors;

[0043] Based on the risk assessment results, risk warning information is automatically generated. The risk warning information includes risk level, risk factors and recommended response measures. If the risk level exceeds the preset threshold, the warning information will be immediately transmitted to relevant personnel or systems.

[0044] The generated risk warning information and safety risk spatial distribution map are displayed in real time on the monitoring interface, including:

[0045] Process the analysis results to generate risk warning information. At the same time, generate a safety risk spatial distribution map based on the spatial attributes of the risk data;

[0046] Based on the preset interface update rules, the generated risk warning information and security risk spatial distribution map are integrated into the monitoring interface in real time. The risk warning information is displayed in a pop-up window, label or prominent way, and the security risk spatial distribution map is displayed in a specific area of ​​the interface.

[0047] When risk warning information appears, the system automatically triggers the preset response mechanism, which includes sound and light reminders to ensure that relevant personnel are aware of it in a timely manner. At the same time, the system sends the risk warning information to the communication equipment of relevant personnel to ensure that they take corresponding measures in a timely manner.

[0048] Among them, the risk status of the gas storage is displayed, including:

[0049] Based on the risk warning information database, obtain the first impact relationship database of meteorological parameters and risk levels related to the safety risk warning of the gas storage reservoir; obtain the second impact relationship database of the internal structural stability of the gas storage reservoir and external environmental factors;

[0050] Based on the first impact relationship database, a first matching correspondence relationship data group between meteorological parameter thresholds and warning levels of gas storage safety risk warning content items is obtained; based on the first matching correspondence relationship data group, a first warning display condition between meteorological parameters and warning levels is set, and a corresponding first warning display spatial distribution content is generated according to the first warning display condition;

[0051] Based on the second impact relationship database, using the preset gas storage reservoir safety prevention simulation model, obtain the simulation results of the internal structure stability and the degree of harm of external environmental factors; according to the simulation results, set the second warning display conditions of the internal structure stability and the degree of harm, and generate the corresponding second warning display space distribution content according to the second warning display conditions;

[0052] According to the spatial distribution content displayed by the first warning and the spatial distribution content displayed by the second warning, a visual safety risk spatial distribution map is summarized and generated, which intuitively displays the risk status of the gas storage.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] The integrated online safety monitoring system for salt cavern storage areas includes: a data acquisition unit, which is used to collect and aggregate online monitoring data of the safety of the salt cavern storage area, including data on production safety risk factors, micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage; a data analysis unit, which conducts a comprehensive analysis of the acquired monitoring data based on the gas storage risk assessment model and obtains the analysis results; a monitoring unit, which generates risk warning information in real time based on the analysis results, monitors the dynamic risk status of the gas storage, and automatically displays the generated risk warning information, including a safety risk spatial distribution map. Through the timely generation and monitoring of risk warning information, appropriate measures can be taken to prevent potential safety risks and ensure the safe operation of the gas storage.

[0055] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0056] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 It is a structural diagram of the integrated safety online monitoring system for the salt cavern storage area in an embodiment of the present invention;

[0059] Figure 2 This is a structural diagram of a data acquisition unit in an embodiment of the present invention;

[0060] Figure 3 4 is a structural diagram of a data analysis unit in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0062] The embodiment of the present invention provides an integrated safety online monitoring system for a salt cavern storage area, comprising:

[0063] Data acquisition unit, used to collect online monitoring data of the safety of the convergent salt cavern storage area, including data on production safety risk factors, micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage;

[0064] The data analysis unit conducts a comprehensive analysis of the acquired monitoring data based on the gas storage risk assessment model to obtain analysis results;

[0065] The monitoring unit generates risk warning information in real time based on the analysis results, monitors the dynamic risk status of the gas storage facility, and automatically displays the generated risk warning information, which includes a safety risk spatial distribution map.

[0066] The working principle of the above technical solution is as follows: the data acquisition unit is responsible for collecting the online monitoring data of the safety of the salt cavern storage area, including data on production safety risk factors, micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage; through various sensors and monitoring equipment, these monitoring data are collected in real time and transmitted to the data analysis unit for processing;

[0067] The data analysis unit conducts a comprehensive analysis of the acquired monitoring data based on the gas storage risk assessment model. The unit matches and compares the monitoring data with the risk assessment model, and obtains analysis results through algorithm and model calculations. These analysis results include risk levels, risk trends, abnormal conditions, etc., which are used to assess the risk status of the gas storage.

[0068] The monitoring unit generates risk warning information in real time based on the results of the data analysis unit and monitors the dynamic risk status of the gas storage facility. The monitoring unit determines whether the current risk status has reached the warning level based on the preset risk thresholds and rules, and automatically displays the generated risk warning information. The risk warning information may include a safety risk spatial distribution map to intuitively display the risk distribution of the gas storage facility.

[0069] The beneficial effects of the above technical scheme are as follows: by real-time collection and analysis of monitoring data, the risk status of the gas storage can be understood in a timely manner and abnormal situations can be discovered in a timely manner; by comprehensive analysis of different monitoring data, the risk status of the gas storage can be comprehensively evaluated, and the accuracy and reliability of risk assessment can be improved; by automated generation and display of risk warning information, manual intervention can be reduced and work efficiency can be improved; by visual methods such as safety risk spatial distribution maps, the risk distribution of the gas storage can be intuitively displayed to facilitate decision-making and management; by timely generation and monitoring of risk warning information, corresponding measures can be taken to prevent potential safety risks and ensure the safe operation of the gas storage.

[0070] In another embodiment, the data acquisition unit includes:

[0071] The safety production risk factor acquisition module is used to obtain risk factor data related to safety production in real time. The risk factor data includes environmental parameters such as temperature, pressure, and humidity, as well as equipment operation status information;

[0072] A microseismic data acquisition module is used to access the microseismic monitoring equipment to obtain relevant values ​​of microseismic activities in real time. The relevant values ​​of microseismic activities include magnitude, focal depth and focal location;

[0073] Comprehensive leakage data acquisition module, used to access the monitoring equipment of wellbore leakage and natural gas leakage, and obtain leakage-related values ​​in real time, including leakage amount, leakage location and leakage speed;

[0074] The ground subsidence data acquisition module is used to access the ground subsidence monitoring equipment and obtain relevant values ​​of ground subsidence in real time. The relevant values ​​of ground subsidence include subsidence depth and subsidence speed.

[0075] The working principle of the above technical solution is as follows: the safety production risk factor acquisition module is responsible for real-time acquisition of risk factor data related to safety production, including environmental parameters such as temperature, pressure, and humidity, as well as equipment operation status information. Through various sensors and monitoring equipment, these risk factor data are collected in real time and transmitted to the data analysis module for processing; the microseismic data acquisition module is used to access the microseismic monitoring equipment and obtain the relevant values ​​of microseismic activities in real time, including magnitude, focal depth, and focal location. By monitoring the microseismic activities, the changes in the underground rock formations can be understood, thereby determining whether there is a risk of geological disasters; the comprehensive leakage data acquisition module is used to access the monitoring equipment for wellbore leakage and natural gas leakage, and obtain the leakage-related values ​​in real time, including leakage volume, leakage location, and leakage speed. By monitoring the leakage situation, leakage events can be discovered in time, and corresponding measures can be taken to deal with them and reduce safety risks; the ground subsidence data acquisition module is used to access the ground subsidence monitoring equipment and obtain the relevant values ​​of ground subsidence in real time, including subsidence depth and subsidence speed. By monitoring the ground subsidence situation, the stability of the underground gas storage can be determined, and timely measures can be taken to prevent safety problems caused by ground subsidence.

[0076] The beneficial effects of the above technical solution are as follows: by collecting monitoring data in real time, the safety status of the gas storage can be understood in time and abnormal situations can be discovered in time; by obtaining different types of monitoring data, the risk factors of the gas storage can be fully understood, and the accuracy and comprehensiveness of risk assessment can be improved; by real-time acquisition of monitoring data, risk warning information can be generated in time to remind relevant personnel to take corresponding measures to reduce safety risks; by real-time monitoring and acquisition of data, potential safety risks can be discovered in time, preventive measures can be taken to ensure the safe operation of the gas storage; by obtaining rich monitoring data, a scientific basis can be provided for risk assessment, decision-making and management, and the safety and reliability of the gas storage can be improved.

[0077] In another embodiment, the data analysis unit comprises:

[0078] The data optimization processing module is used to clean and standardize the raw data obtained from the monitoring equipment of micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage, and obtain optimized monitoring data;

[0079] The risk quantitative analysis module is used to perform pattern recognition and outlier analysis on the optimized monitoring data based on the gas storage risk assessment model, assign corresponding weights to various risk factors based on the outlier analysis results, and calculate a comprehensive risk assessment based on the weight allocation;

[0080] The risk dynamic prediction module is used to perform trend analysis on comprehensive risk assessment based on time series analysis and obtain risk dynamic prediction results;

[0081] The strategy formulation module is used to specify corresponding production safety strategies based on risk trend prediction results. Production safety strategy recommendations include adjusting monitoring frequency, optimizing monitoring parameters, proposing equipment maintenance or upgrades, and formulating emergency response plans.

[0082] The working principle of the above technical solution is as follows: the data optimization processing module is responsible for cleaning and standardizing the original monitoring data to remove noise, outliers, data missing and other problems, and obtain optimized monitoring data, which can improve the quality and reliability of the data and provide an accurate data basis for subsequent risk assessment and prediction;

[0083] The risk quantitative analysis module performs pattern recognition and outlier analysis on the optimized monitoring data based on the gas storage risk assessment model. By analyzing the characteristics and change trends of the monitoring data, potential risk factors and abnormal situations are identified. According to the results of the outlier analysis, each risk factor is weighted and a comprehensive risk score is calculated to evaluate the risk status of the gas storage.

[0084] The risk dynamic prediction module conducts trend analysis on comprehensive risk assessment based on the time series analysis method. By analyzing the historical data of risk scores, it identifies the changing trend of risks and predicts the future development trend of risks. This can detect the changes and trends of risks in advance and provide a reference for safety management and decision-making.

[0085] The strategy formulation module specifies the corresponding production safety strategy based on the risk trend prediction results, and proposes corresponding measures and suggestions based on the risk trend and prediction results, including increasing the monitoring frequency of corresponding data, strengthening equipment maintenance and overhaul, etc., so that timely measures can be taken to prevent potential safety risks and ensure the safe operation of the gas storage facility.

[0086] Among them, obtaining the risk dynamic prediction results includes:

[0087] Obtain comprehensive risk assessments from the risk score data source at preset time intervals, arrange the scores in chronological order to form time series data, and perform model training on the collected comprehensive risk assessment time series data based on the preset time series analysis model; use the trained time series analysis model to predict the comprehensive risk assessment for a period of time in the future, compare the predicted results with the actual risk scores, and generate risk trend prediction results. If the predicted results show that the risk score is rising or falling, the risk trend will be marked on the preset risk management interface.

[0088] In another embodiment, the monitoring unit comprises:

[0089] The early warning information generation module is used to check whether the analysis results exceed the preset early warning threshold based on the analysis results, and automatically generate risk early warning information;

[0090] The safety risk spatial distribution map generation module is used to convert risk warning information into a visual safety risk spatial distribution map to display the risk status of the gas storage facility;

[0091] The early warning information display module is used to display the generated risk warning information and safety risk spatial distribution map in real time on the monitoring interface to ensure that relevant personnel understand and take corresponding measures in a timely manner.

[0092] The working principle of the above technical solution is as follows: the warning information generation module checks whether the analysis result exceeds the preset warning threshold based on the analysis result, and judges whether there is a risk exceeding the warning level by comparing with the preset threshold. When the analysis result exceeds the warning threshold, the warning information generation module will automatically generate corresponding risk warning information; the safety risk spatial distribution map generation module converts the risk warning information into a visual safety risk spatial distribution map, and generates an intuitive graphic display by associating the risk warning information with the spatial layout of the gas storage. This can intuitively display the risk status of the gas storage and help relevant personnel quickly understand the risk distribution and degree; the warning information display module displays the generated risk warning information and safety risk spatial distribution map in real time on the monitoring interface. By displaying the warning information and risk distribution map on the monitoring interface, it ensures that relevant personnel can understand the risk situation in a timely manner and take corresponding measures, which can improve the timeliness and accuracy of risk response.

[0093] The beneficial effects of the above technical solution are as follows: through the early warning information generation module, it is possible to timely detect whether the analysis results exceed the early warning threshold and provide timely risk early warning information; through the safety risk spatial distribution map generation module, the risk warning information is converted into a visual graphic display to intuitively display the risk status of the gas storage; through the early warning information display module, the generated early warning information and risk distribution map are displayed in real time on the monitoring interface to ensure that relevant personnel can understand the risk situation in a timely manner; through the intuitive risk information display, it helps relevant personnel to quickly understand the risk distribution and degree, and make corresponding decisions and measures; through timely early warning and visual display, it can help gas storage managers to promptly discover and deal with potential safety risks and improve the safety and reliability of the gas storage.

[0094] In another embodiment, a corresponding weight is assigned to each risk factor based on the outlier analysis result, and a comprehensive risk assessment is calculated according to the weight assignment, including:

[0095] Obtain a preset pattern recognition experience library, and randomly select a first pattern information item from the pattern recognition experience library; obtain a first pattern verification value corresponding to the first pattern information item, and if the first pattern verification value is greater than or equal to a preset first pattern verification threshold, use the corresponding first pattern information item as the second pattern information item;

[0096] Obtain a preset outlier analysis experience library, and randomly select a third mode information item from the outlier analysis experience library; obtain a second mode verification value corresponding to the second mode information item, and if the second mode verification value is greater than or equal to a preset second mode verification threshold, use the corresponding third mode information item as a fourth mode information item;

[0097] Based on the fourth model information item, weights are assigned to various risk factors, and according to the results of the weight assignment, weighted calculations are performed on various risk factors to obtain a comprehensive risk assessment.

[0098] The working principle of the above technical solution is: obtain a preset pattern recognition experience library (the pattern recognition experience library is constructed based on historical data and expert experience, and includes pattern information of various risk factors), randomly select a first pattern information item from the pattern recognition experience library (the first pattern information item is a randomly selected information item in the pattern recognition experience library, which is used to describe a specific risk factor), obtain a first pattern verification value corresponding to the first pattern information item (the first pattern verification value is the result obtained by comparing the actual monitoring data with the first pattern information item, which is used to evaluate the degree of the risk factor), if the first pattern verification value is greater than or equal to the preset first pattern verification threshold, then the corresponding first pattern information item is used as the second pattern information item (if the first pattern verification value exceeds the preset threshold, it means that the risk factor is abnormal and needs to be further analyzed);

[0099] Obtain a preset abnormal point analysis experience database (the abnormal point analysis experience database is constructed based on historical abnormal data and expert experience, and includes abnormal mode information of each risk factor), and randomly select a third mode information item from the abnormal point analysis experience database (the third mode information item is a randomly selected information item in the abnormal point analysis experience database, which is used to describe the abnormal situation of a specific risk factor); obtain a second mode verification value corresponding to the second mode information item (the second mode verification value is the result obtained by comparing the actual monitoring data with the second mode information item, which is used to evaluate the abnormal degree of the risk factor); if the second mode verification value is greater than or equal to the preset second mode verification threshold, the corresponding third mode information item is used as the fourth mode information item (if the second mode verification value exceeds the preset threshold, it means that the risk factor has an abnormal situation and needs further analysis);

[0100] Based on the fourth mode information items, weights are assigned to various risk factors (i.e., weights are assigned to various risk factors according to the characteristics and importance of the fourth mode information items), and based on the results of the weight assignments, weighted calculations are performed on various risk factors to arrive at a comprehensive risk assessment (i.e., weighted calculations are performed based on the weights of various risk factors and verification results to arrive at a comprehensive risk score for assessing the risk status of the gas storage facility).

[0101] Assume that the first pattern information item in the pattern recognition experience library is "ground subsidence velocity", the first pattern verification value is 0.5mm / day, and the preset first pattern verification threshold is 0.3mm / day. If the first pattern verification value is greater than or equal to the preset threshold, it means that there is an abnormality in the ground subsidence velocity; the third pattern information item in the abnormal point analysis experience library is "the rate of change of ground subsidence velocity", the second pattern verification value is 0.2mm / day2, and the preset second pattern verification threshold is 0.1mm / day2. If the second pattern verification value is greater than or equal to the preset threshold, it means that there is an abnormality in the rate of change of ground subsidence velocity; according to the fourth pattern information item "the rate of change of ground subsidence velocity", weights are assigned to each risk factor, and according to the result of the weight assignment, weighted calculation is performed on each risk factor to obtain a comprehensive risk score.

[0102] The beneficial effects of the above technical solution are as follows: through the methods of pattern recognition and outlier analysis, a comprehensive risk score is obtained by comprehensively considering the situations of multiple risk factors, and the risk status of the gas storage is evaluated more comprehensively; through automated means, the weights of various risk factors are assigned and calculated, which reduces the need for manual intervention and improves efficiency and accuracy; by setting early warning thresholds, situations where risks exceed the warning level can be discovered in a timely manner, and timely risk warning information can be provided; the comprehensive risk assessment obtained through weighted calculation can quantify the risk status for easy comparison and analysis; through comprehensive risk assessment, a decision-making basis can be provided for relevant personnel, helping them to formulate corresponding measures and strategies to reduce the possibility of risk occurrence.

[0103] In another embodiment, obtaining a first mode verification value corresponding to the first mode information item includes:

[0104] Acquire pattern recognition process information corresponding to the first pattern information item;

[0105] Splitting the pattern recognition process information into a number of first sub-processes;

[0106] Based on the feature extraction technology, extract the first pattern feature and the corresponding first feature value of the first sub-process;

[0107] Obtaining a preset pattern specification verification library, and matching the first pattern feature with the second pattern feature in the pattern specification verification library;

[0108] If the match is met, the first feature value corresponding to the first pattern feature is used as the second feature value, and the feature threshold corresponding to the second pattern feature is obtained;

[0109] If the second eigenvalue is less than the feature threshold, calculate the first difference between the second eigenvalue and the feature threshold; determine the subprocess position of the first subprocess in the pattern recognition process information, and obtain the position weight corresponding to the position; assign the first difference position weight, obtain the second difference, and associate it with the first subprocess;

[0110] Cumulatively calculate all associated second differences to obtain the difference sum; if the difference sum is greater than or equal to the preset difference sum threshold, use the value 0 as the first mode verification value corresponding to the first mode information item to complete the acquisition; otherwise, calculate the inverse of the difference sum as the first mode verification value corresponding to the first mode information item to complete the acquisition.

[0111] The working principle of the above technical solution is as follows: obtaining the pattern recognition process information corresponding to the first pattern information item (the pattern recognition process information refers to the detailed information describing the pattern recognition process of a specific risk factor); splitting the pattern recognition process information into a number of first sub-processes (splitting the pattern recognition process information according to certain rules to obtain a number of sub-processes, each of which describes a specific stage or link in the pattern recognition process); extracting the first pattern feature and the corresponding first feature value of the first sub-process based on feature extraction technology (using feature extraction technology to extract features related to the first pattern information item from the first sub-process and calculate the corresponding feature value); obtaining a preset pattern specification verification library (the pattern specification verification library is based on historical data and expert experience); Constructed, including the standard value of each pattern feature); matching the first pattern feature with the second pattern feature in the pattern specification verification library (i.e. comparing and matching the first pattern feature with the features in the pattern specification verification library to find the corresponding second pattern feature); if the match is met, it means that the first pattern feature corresponds to the second pattern feature, the first feature value is used as the second feature value, and the feature threshold corresponding to the second pattern feature is obtained; if the second feature value is less than the feature threshold, it means that the first pattern feature has an abnormality, and the difference between the second feature value and the feature threshold is calculated; according to the position of the first sub-process in the pattern recognition process information, the corresponding position weight is determined; according to the position weight, the first difference is associated with the first sub-process to obtain the second difference;

[0112] All associated second difference values ​​are accumulated and calculated to obtain the difference sum; if the difference sum is greater than or equal to the preset difference sum threshold, the value 0 is used as the first mode verification value corresponding to the first mode information item to complete the acquisition; otherwise, the reciprocal of the difference sum is calculated as the first mode verification value corresponding to the first mode information item to complete the acquisition (i.e., the first mode verification value corresponding to the first mode information item is determined based on the comparison result of the difference sum with the preset difference sum threshold).

[0113] Assume that the first pattern information item is "temperature change", the first sub-process in the pattern recognition process information is "temperature acquisition", the first pattern feature is "temperature fluctuation amplitude", the first eigenvalue is 2°C, the second pattern feature in the pattern specification verification library is "temperature stability", and the feature threshold is 1°C; through the feature extraction technology, the temperature fluctuation amplitude feature in the first sub-process "temperature acquisition" is extracted, and the first eigenvalue is calculated to be 2°C; the first pattern feature "temperature fluctuation amplitude" is matched with the second pattern feature "temperature stability" in the pattern specification verification library, and if the match is satisfactory, the first eigenvalue 2°C is used as the second eigenvalue, and the feature threshold 1°C is obtained; since the second eigenvalue 2°C is greater than the feature threshold 1°C, the difference between the second eigenvalue and the feature threshold is calculated to be 1°C. According to the position of the first sub-process "temperature acquisition" in the pattern recognition process information, the position weight is determined to be 0.8, and the position weight of the difference 1°C is assigned 0.8, and the second difference 0.8°C is obtained; all associated second differences are accumulated and calculated to obtain a difference sum of 0.8°C. Since the difference sum is less than the preset difference sum threshold, the inverse of the difference sum 1.25 is calculated as the first mode verification value corresponding to the first mode information item.

[0114] The beneficial effects of the above technical solution are: through the method of pattern recognition and feature extraction, a comprehensive pattern verification value is obtained by comprehensively considering multiple sub-processes in the pattern recognition process, and the risk factor situation is more comprehensively evaluated; the pattern recognition process information is split and feature extracted in an automated manner, which reduces the need for manual intervention and improves efficiency and accuracy; by matching and comparing with the features in the pattern specification verification library, abnormal situations of risk factors can be discovered; by calculating the difference and the reciprocal, the pattern verification value is quantified for easy comparison and analysis; through the pattern verification value, a decision-making basis can be provided for relevant personnel, helping them to formulate corresponding measures and strategies to reduce the possibility of risk occurrence.

[0115] In another embodiment, according to the risk trend prediction result, a corresponding safety production strategy is specified, including:

[0116] Collect the safety risk monitoring data at the time point at a fixed time interval, the safety risk monitoring data includes the equipment operation status and production environment parameters, process the collected safety risk monitoring data at different time points in chronological order, and obtain the difference of safety risk data in adjacent fixed time intervals;

[0117] According to the difference of safety risk monitoring data in adjacent fixed time intervals, a risk trend prediction model is established, and the dynamic safety risk monitoring data in the fixed time interval is input into the risk trend prediction model to determine the risk change trend under each difference;

[0118] According to the risk trend, specify the corresponding production safety strategy. If the risk trend increases, it is recommended to increase the monitoring frequency of the corresponding data or perform equipment maintenance; if the risk trend decreases, maintain the current monitoring frequency and equipment maintenance plan.

[0119] The working principle of the above technical solution is: at each fixed time point, collect safety risk monitoring data such as equipment operating status and production environment parameters; sort and process the collected safety risk monitoring data in chronological order to ensure the continuity and consistency of the data; calculate the difference in safety risk monitoring data in adjacent fixed time intervals to obtain the risk change situation in each time interval; based on the difference in risk data at adjacent time points, establish a risk trend prediction model, and statistical analysis, machine learning and other methods can be used for modeling; input the dynamic safety risk monitoring data in each fixed time interval into the risk trend prediction model to predict the risk change trend under each difference; based on the prediction results of the risk change trend, generate corresponding production safety recommendations. If the risk trend increases, it is recommended to increase the monitoring frequency of the corresponding data or perform equipment maintenance; if the risk trend decreases, maintain the current monitoring frequency and equipment maintenance plan.

[0120] The beneficial effects of the above technical solution are: by collecting data at fixed time intervals, it is possible to monitor changes in safety risks in real time and discover potential risk problems in a timely manner; by establishing a risk trend prediction model, it is possible to predict the changing trend of risks and make preparations in advance; the production safety suggestions generated based on the risk changing trend can provide specific response plans and guidance for relevant personnel, helping them to take timely measures to reduce risks; based on the predicted results of risk trends, monitoring resources and equipment maintenance plans can be reasonably allocated to improve resource utilization efficiency; by automating data collection, difference calculation and risk trend prediction, the need for manual intervention is reduced and efficiency and accuracy are improved.

[0121] In another embodiment, risk warning information is automatically generated, including:

[0122] Acquire and analyze multi-dimensional data of the current operating environment, device status, and user behavior, and use a pre-set risk assessment model to calculate the risk level based on the multi-dimensional data to generate a risk assessment result, which includes the risk level and the corresponding risk factors;

[0123] Based on the risk assessment results, risk warning information is automatically generated. The risk warning information includes risk level, risk factors and recommended response measures. If the risk level exceeds the preset threshold, the warning information will be immediately transmitted to relevant personnel or systems.

[0124] The working principle of the above technical solution is: collect various data related to the current operating environment, equipment status and user behavior, including environmental sensor data, equipment status data, and user behavior data; input the collected multi-dimensional data into a pre-set risk assessment model, and calculate the risk level according to the rules and algorithms defined in the model; generate a risk assessment result based on the calculation result of the risk level, including the classification of the risk level and the description of the corresponding risk factors; automatically generate risk warning information based on the risk assessment result, including risk level, risk factors and recommended response measures; determine whether the risk level exceeds the threshold according to the preset threshold, and if it exceeds the threshold, immediately transmit the warning information to the relevant personnel or system so that timely response measures can be taken.

[0125] The beneficial effects of the above technical solution are: through the analysis of multi-dimensional data, it is possible to comprehensively consider multiple factors such as the current operating environment, equipment status and user behavior, and evaluate the risk situation more comprehensively; by automatically generating risk warning information and judging whether it needs to be transmitted immediately based on the threshold, it is possible to timely discover and remind relevant personnel or systems of existing risks; the risk assessment results include corresponding risk factor descriptions, which can help relevant personnel understand the specific sources and causes of risks; the risk warning information includes recommended response measures, which can provide relevant personnel with specific response plans and guidance, helping them to take timely measures to reduce risks; data analysis and risk assessment are performed in an automated manner, reducing the need for manual intervention and improving efficiency and accuracy.

[0126] In another embodiment, the generated risk warning information and the safety risk spatial distribution map are displayed in real time on the monitoring interface, including:

[0127] Process the analysis results to generate risk warning information. At the same time, generate a safety risk spatial distribution map based on the spatial attributes of the risk data;

[0128] Based on the preset interface update rules, the generated risk warning information and security risk spatial distribution map are integrated into the monitoring interface in real time. The risk warning information is displayed in a pop-up window, label or prominent way, and the security risk spatial distribution map is displayed in a specific area of ​​the interface.

[0129] When risk warning information appears, the system automatically triggers the preset response mechanism, which includes sound and light reminders to ensure that relevant personnel are aware of it in a timely manner. At the same time, the system sends the risk warning information to the communication equipment of relevant personnel to ensure that they take corresponding measures in a timely manner.

[0130] The working principle of the above technical solution is as follows: based on the analysis results, corresponding risk warning information is generated, including risk level, risk factors and recommended response measures; based on the spatial attributes of risk data, the risk information is visualized in space to generate a safety risk spatial distribution map, so as to intuitively understand the distribution of risks; according to the preset interface update rules, the generated risk warning information and safety risk spatial distribution map are integrated into the monitoring interface in real time, the risk warning information can be displayed in a pop-up window, label or prominent manner, and the safety risk spatial distribution map is displayed in a specific area of ​​the interface; when risk warning information appears, the system will automatically trigger the preset response mechanism, which may include sound and light reminders to ensure that relevant personnel can understand the risk situation in a timely manner; at the same time, the system will send the risk warning information to the communication equipment of relevant personnel, such as mobile phones, e-mails, etc., to ensure that they can receive the risk warning information in a timely manner and take corresponding measures.

[0131] The beneficial effects of the above technical solution are: by integrating risk warning information and safety risk spatial distribution maps into the monitoring interface, risk information can be displayed in real time to help relevant personnel understand the risk situation in a timely manner; through the display of the safety risk spatial distribution map, the distribution of risks can be intuitively understood, which helps to quickly identify high-risk areas; through the preset response mechanism and sending risk warning information to communication equipment, it can ensure that relevant personnel can receive risk warning information in a timely manner and take corresponding measures; through conspicuous risk warning information and sound and light reminders, the safety awareness of relevant personnel can be improved and the degree of attention to risks can be enhanced; by integrating into the monitoring interface, relevant personnel can obtain risk warning information and safety risk spatial distribution maps on one interface, which is convenient for them to operate and make decisions.

[0132] In another embodiment, the risk status of a gas storage facility is displayed, including:

[0133] Based on the risk warning information database, obtain the first impact relationship database of meteorological parameters and risk levels related to the safety risk warning of the gas storage reservoir; obtain the second impact relationship database of the internal structural stability of the gas storage reservoir and external environmental factors;

[0134] Based on the first impact relationship database, a first matching correspondence relationship data group between meteorological parameter thresholds and warning levels of gas storage safety risk warning content items is obtained; based on the first matching correspondence relationship data group, a first warning display condition between meteorological parameters and warning levels is set, and a corresponding first warning display spatial distribution content is generated according to the first warning display condition;

[0135] Based on the second impact relationship database, using the preset gas storage reservoir safety prevention simulation model, obtain the simulation results of the internal structure stability and the degree of harm of external environmental factors; according to the simulation results, set the second warning display conditions of the internal structure stability and the degree of harm, and generate the corresponding second warning display space distribution content according to the second warning display conditions;

[0136] According to the spatial distribution content displayed by the first warning and the spatial distribution content displayed by the second warning, a visual safety risk spatial distribution map is summarized and generated, which intuitively displays the risk status of the gas storage.

[0137] The working principle of the above technical solution is as follows: First, based on the risk warning information database, the first impact relationship database of the meteorological parameters and risk levels of the gas storage safety risk warning is obtained, which means that the relationship database between meteorological parameters and risk levels is established by collecting historical data and risk events. For example, the meteorological conditions when the gas storage accident occurs, such as wind speed, temperature, humidity, etc., can be collected, and then the relationship between these meteorological parameters and risk levels can be analyzed to establish a database for risk warning.

[0138] Secondly, based on the first impact relationship database, the first matching correspondence relationship data group between the meteorological parameter threshold and the warning level of the gas storage safety risk warning content item is obtained, which means that according to the data in the first impact relationship database, a matching correspondence relationship data group between the meteorological parameter threshold and the risk warning content item is established. For example, when a certain meteorological parameter reaches a certain value, what is the corresponding risk warning level? Such a corresponding relationship data group can help determine the content and level of the risk warning.

[0139] Next, based on the second impact relationship database, the preset gas storage reservoir safety prevention simulation model is used to obtain simulation results of the internal structure stability and the degree of harm of external environmental factors. This means that the relationship between the internal structure stability and the external environmental factors is analyzed through the simulation model to obtain simulation results of the degree of harm. For example, the stability of the internal structure of the gas storage reservoir under different meteorological conditions and the degree of harm caused by external environmental factors can be analyzed through simulation.

[0140] Finally, based on the spatial distribution content of the first warning display and the spatial distribution content of the second warning display, a visual safety risk spatial distribution map is generated, which intuitively displays the risk status of the gas storage. This step is to summarize the content generated by the first and second warning display conditions and display the risk status of the gas storage in a visual way, so that people can intuitively understand the safety risk distribution of the gas storage.

[0141] The beneficial effect of the above technical solution is that by establishing a risk warning information database and a simulation model, the relationship between meteorological parameters and internal structural stability and external environmental factors can be comprehensively considered, so as to more comprehensively evaluate the safety risk of the gas storage. This method can provide a more intuitive and comprehensive spatial distribution map of safety risks, help relevant personnel to understand the risk status of the gas storage in a timely manner, take corresponding safety measures, thereby reducing the possibility of accidents and ensuring the safe operation of the gas storage.

[0142] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The integrated safety online monitoring system for salt cavern storage area is characterized by: include: Data acquisition unit, used to collect online monitoring data of the safety of the convergent salt cavern storage area, including data on production safety risk factors, micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage; The data analysis unit conducts a comprehensive analysis of the acquired monitoring data based on the gas storage risk assessment model to obtain analysis results; The monitoring unit generates risk warning information in real time based on the analysis results, monitors the dynamic risk status of the gas storage, and automatically displays the generated risk warning information, which includes a safety risk spatial distribution map; The data analysis unit includes: data optimization processing module, risk quantitative analysis module, risk dynamic prediction module and strategy formulation module; The data optimization processing module is used to clean and standardize the raw data obtained from the monitoring equipment of micro-earthquakes, wellbore leakage, ground subsidence and natural gas leakage, and obtain optimized monitoring data; The risk quantitative analysis module is used to perform pattern recognition and outlier analysis on the optimized monitoring data based on the gas storage risk assessment model, assign corresponding weights to various risk factors based on the outlier analysis results, and calculate a comprehensive risk assessment based on the weight allocation; The risk dynamic prediction module is used to perform trend analysis on comprehensive risk assessment based on time series analysis and obtain risk dynamic prediction results; The strategy formulation module is used to specify the corresponding safety production strategy based on the risk trend prediction results. The safety production strategy recommendations include adjusting the monitoring frequency, optimizing monitoring parameters, proposing equipment maintenance or upgrades, and formulating emergency response plans; Based on the outlier analysis results, the corresponding weights are assigned to each risk factor, and a comprehensive risk assessment is calculated based on the weight assignment, including: Obtain a preset pattern recognition experience library, and randomly select a first pattern information item from the pattern recognition experience library; obtain a first pattern verification value corresponding to the first pattern information item, and if the first pattern verification value is greater than or equal to a preset first pattern verification threshold, use the corresponding first pattern information item as the second pattern information item; Obtain a preset outlier analysis experience library, and randomly select a third mode information item from the outlier analysis experience library; obtain a second mode verification value corresponding to the second mode information item, and if the second mode verification value is greater than or equal to a preset second mode verification threshold, use the corresponding third mode information item as a fourth mode information item; Based on the fourth model information item, weights are assigned to various risk factors, and according to the results of the weight assignment, weighted calculations are performed on various risk factors to obtain a comprehensive risk assessment.

2. The integrated safety online monitoring system for salt cavern storage area according to claim 1 is characterized in that: The data acquisition unit includes: a safety production risk factor acquisition module, a microseismic data acquisition module, a comprehensive leakage data acquisition module and a ground subsidence data acquisition module; The safety production risk factor acquisition module is used to obtain risk factor data related to safety production in real time. The risk factor data includes environmental parameters such as temperature, pressure, and humidity, as well as equipment operation status information; A microseismic data acquisition module is used to access the microseismic monitoring equipment to obtain relevant values ​​of microseismic activities in real time. The relevant values ​​of microseismic activities include magnitude, focal depth and focal location; Comprehensive leakage data acquisition module, used to access the monitoring equipment of wellbore leakage and natural gas leakage, and obtain leakage-related values ​​in real time, including leakage amount, leakage location and leakage speed; The ground subsidence data acquisition module is used to access the ground subsidence monitoring equipment and obtain relevant values ​​of ground subsidence in real time. The relevant values ​​of ground subsidence include subsidence depth and subsidence speed.

3. The integrated safety online monitoring system for salt cavern storage area according to claim 1 is characterized in that: The monitoring unit includes: a warning information generation module, a safety risk spatial distribution map generation module and a warning information display module; The early warning information generation module is used to check whether the analysis results exceed the preset early warning threshold based on the analysis results, and automatically generate risk early warning information; The safety risk spatial distribution map generation module is used to convert risk warning information into a visual safety risk spatial distribution map to display the risk status of the gas storage facility; The early warning information display module is used to display the generated risk warning information and safety risk spatial distribution map in real time on the monitoring interface to ensure that relevant personnel understand and take corresponding measures in a timely manner.

4. The integrated safety online monitoring system for salt cavern storage area according to claim 1 is characterized in that: Obtaining a first mode verification value corresponding to the first mode information item includes: Acquire pattern recognition process information corresponding to the first pattern information item; Splitting the pattern recognition process information into a number of first sub-processes; Based on the feature extraction technology, extract the first pattern feature and the corresponding first feature value of the first sub-process; Obtaining a preset pattern specification verification library, and matching the first pattern feature with the second pattern feature in the pattern specification verification library; If the match is met, the first feature value corresponding to the first pattern feature is used as the second feature value, and the feature threshold corresponding to the second pattern feature is obtained; If the second eigenvalue is less than the feature threshold, calculate the first difference between the second eigenvalue and the feature threshold; determine the subprocess position of the first subprocess in the pattern recognition process information, and obtain the position weight corresponding to the position; assign the first difference position weight, obtain the second difference, and associate it with the first subprocess; Cumulatively calculate all associated second differences to obtain the difference sum; if the difference sum is greater than or equal to the preset difference sum threshold, use the value 0 as the first mode verification value corresponding to the first mode information item to complete the acquisition; otherwise, calculate the inverse of the difference sum as the first mode verification value corresponding to the first mode information item to complete the acquisition.

5. The integrated safety online monitoring system for salt cavern storage area according to claim 1 is characterized in that: According to the risk trend prediction results, specify the corresponding safety production strategy, including: Collect the safety risk monitoring data at the time point at a fixed time interval, the safety risk monitoring data includes the equipment operation status and production environment parameters, process the collected safety risk monitoring data at different time points in chronological order, and obtain the difference of safety risk data in adjacent fixed time intervals; According to the difference of safety risk monitoring data in adjacent fixed time intervals, a risk trend prediction model is established, and the dynamic safety risk monitoring data in the fixed time interval is input into the risk trend prediction model to determine the risk change trend under each difference; According to the risk trend, specify the corresponding production safety strategy. If the risk trend increases, it is recommended to increase the monitoring frequency of the corresponding data or perform equipment maintenance; if the risk trend decreases, maintain the current monitoring frequency and equipment maintenance plan.

6. The integrated safety online monitoring system for salt cavern storage area according to claim 3 is characterized in that: Automatically generate risk warning information, including: Acquire and analyze multi-dimensional data of the current operating environment, device status, and user behavior, and use a pre-set risk assessment model to calculate the risk level based on the multi-dimensional data to generate a risk assessment result, which includes the risk level and the corresponding risk factors; Based on the risk assessment results, risk warning information is automatically generated. The risk warning information includes risk level, risk factors and recommended response measures. If the risk level exceeds the preset threshold, the warning information will be immediately transmitted to relevant personnel or systems.

7. The integrated safety online monitoring system for salt cavern storage area according to claim 3 is characterized in that: The generated risk warning information and safety risk spatial distribution map are displayed in real time on the monitoring interface, including: Process the analysis results to generate risk warning information. At the same time, generate a safety risk spatial distribution map based on the spatial attributes of the risk data; Based on the preset interface update rules, the generated risk warning information and security risk spatial distribution map are integrated into the monitoring interface in real time. The risk warning information is displayed in a pop-up window, label or prominent way, and the security risk spatial distribution map is displayed in a specific area of ​​the interface. When risk warning information appears, the system automatically triggers the preset response mechanism, which includes sound and light reminders to ensure that relevant personnel are aware of it in a timely manner. At the same time, the system sends the risk warning information to the communication equipment of relevant personnel to ensure that they take corresponding measures in a timely manner.

8. The integrated safety online monitoring system for salt cavern storage area according to claim 3 is characterized in that: Demonstrate the risk profile of the gas storage facility, including: Based on the risk warning information database, obtain the first impact relationship database of meteorological parameters and risk levels related to the safety risk warning of the gas storage reservoir; obtain the second impact relationship database of the internal structural stability of the gas storage reservoir and external environmental factors; Based on the first impact relationship database, a first matching correspondence relationship data group between meteorological parameter thresholds and warning levels of gas storage safety risk warning content items is obtained; based on the first matching correspondence relationship data group, a first warning display condition between meteorological parameters and warning levels is set, and a corresponding first warning display spatial distribution content is generated according to the first warning display condition; Based on the second impact relationship database, using the preset gas storage reservoir safety prevention simulation model, obtain the simulation results of the internal structure stability and the degree of harm of external environmental factors; according to the simulation results, set the second warning display conditions of the internal structure stability and the degree of harm, and generate the corresponding second warning display space distribution content according to the second warning display conditions; According to the spatial distribution content displayed by the first warning and the spatial distribution content displayed by the second warning, a visual safety risk spatial distribution map is summarized and generated, which intuitively displays the risk status of the gas storage.

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