A risk management and control method and system for a CNG filling parent station

Abstract

The application relates to the technical field of risk management and control of air filling mother stations, and discloses a risk management and control method and system for a CNG air filling mother station, which comprises the following steps: obtaining operation monitoring data of each operation unit of the CNG air filling mother station; performing time synchronization processing; checking the corresponding relationship of the operation monitoring data of adjacent operation units based on working conditions, generating an abnormal state identifier when there is an abnormal corresponding relationship, and determining a risk area; matching each operation monitoring data in the risk area with a risk feature library; generating a risk type when the matching is successful; checking the associated operation data according to the risk type; generating a risk result when the risk propagation relationship is met; re-matching when the risk propagation relationship is not met; generating risk management and control information according to the risk result, and performing interlocking control. The application improves the safety and continuity of the operation of the CNG air filling mother station.

Description

Technical Field

[0001] This invention relates to the field of risk management technology for CNG (Compressed Natural Gas) mother stations, and more specifically, to a risk management method and system for CNG (Compressed Natural Gas) mother stations. Background Technology

[0002] CNG refueling mother stations are crucial infrastructure in the natural gas vehicle fuel supply system, primarily responsible for receiving, filtering, compressing, storing, metering, and filling natural gas. Due to the flammable, explosive, and high-pressure storage and transportation characteristics of natural gas, the operation of a refueling mother station involves multiple high-pressure equipment and process units. Any natural gas leaks, abnormal equipment pressure, metering anomalies, or control system malfunctions can not only disrupt the normal operation of the refueling mother station but also potentially trigger safety accidents such as fires and explosions. Therefore, real-time monitoring and timely management of various risks during the operation of refueling mother stations are essential.

[0003] Existing patent CN120819736A discloses an emergency handling system and method for leaks at a hydrogen refueling mother station, which monitors the hydrogen concentration in the on-site environment through multiple leak detection units. When a leak alarm is detected, on-site personnel typically confirm the leak first, and then take measures such as closing valves, isolating process sections, releasing pressure, and nitrogen purging based on the alarm situation. However, while the existing technology can achieve leak alarm, area identification, and purging to a certain extent, its risk management mainly relies on the simultaneous alarm results of multiple detection units and the trend judgment of a few key parameters. It lacks a comprehensive analysis of the corresponding relationships between operating unit conditions and risk propagation relationships, making it difficult to more finely distinguish and interlock different types of risks such as leaks, pressure anomalies, and control anomalies; it also lacks hierarchical management of risk types, risk locations, and propagation paths.

[0004] Therefore, it is necessary to design a risk management method and system for CNG refueling mother stations to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a risk management method and system for CNG refueling mother stations, aiming to solve the problem of lacking a comprehensive analysis of the corresponding relationships between operating unit conditions and risk propagation relationships, making it difficult to make more precise distinctions and interlocking control of different types of risks such as leakage, pressure abnormality, and control abnormality.

[0006] In one aspect, the present invention proposes a risk management method for CNG refueling mother stations, comprising:

[0007] Acquire operational monitoring data from each operating unit of the CNG refueling mother station and perform time synchronization processing;

[0008] Based on the operating procedure of the CNG refueling mother station, the corresponding relationship of the operation monitoring data of adjacent operating units is checked. When an abnormal correspondence exists, an abnormal status identifier is generated. Based on the abnormal status identifier and the operating procedure, the risk area is determined.

[0009] The change characteristics of each of the operational monitoring data within the risk area are matched with the risk characteristics in the risk characteristic library; when the change characteristics and risk characteristics are successfully matched, a risk type is generated;

[0010] Based on the risk type, the associated operational data is checked; when the associated operational data matches the risk propagation relationship, the existence of a risk is confirmed and a risk result is generated; when the associated operational data does not match the risk propagation relationship, the matching is performed again.

[0011] Risk management information is generated based on the risk results, and interlocking control is executed based on the risk management information.

[0012] Furthermore, the operating unit includes a natural gas intake unit, a filtration unit, a dehydration unit, a gas storage unit, a manifold unit, a metering unit, a gas filling column unit, and a monitoring unit.

[0013] Furthermore, when checking the correspondence between the operational monitoring data of adjacent operating units, the following steps are included:

[0014] Check whether the operation monitoring data of adjacent operating units show continuous changes, sudden changes, long-term invariance, inconsistencies between preceding and following processes, or inconsistencies between actuator actions and pressure / flow changes; if so, determine that there is an abnormal correspondence; and mark the adjacent operating units with the abnormal correspondence with the abnormal status identifier.

[0015] Furthermore, when determining risk areas based on the abnormal status identifier and the operating procedure, the following steps are included:

[0016] When the operating unit marked with the abnormal status identifier includes the monitoring unit, it is determined whether the action status of the actuator corresponds to the monitoring record; if they do not correspond, the monitoring unit is identified as a risk area; otherwise, the operating units with the abnormal correspondence are investigated step by step, and the operating unit that first shows an abnormality is identified as the risk area.

[0017] When the operating unit marked with the abnormal status identifier does not include the monitoring unit, the operating units with the abnormal correspondence are investigated step by step, and the upstream operating unit where the abnormal correspondence first appears is identified as the risk area.

[0018] Furthermore, when matching the change characteristics of each of the operational monitoring data within the risk area with the risk characteristics in the risk characteristic library, the process includes:

[0019] The process involves: acquiring time-series data of the operational monitoring data within a preset time window; extracting features from the time-series data to obtain an operational feature set; the operational feature set includes data change rate, fluctuation amplitude, and steady-state deviation; calculating the matching degree between the operational feature set and the feature sets of each risk type in the risk feature library; and determining successful matching when the matching degree calculation result meets the matching conditions, and outputting the risk type.

[0020] Furthermore, when calculating the matching degree between the set of operational features and the set of features for each risk type in the risk feature library, the following steps are included:

[0021] Calculate the sub-feature similarity between the data change rate, volatility amplitude, and steady-state deviation and the standard data change rate, standard volatility amplitude, and standard steady-state deviation in the risk type feature set; perform weighted fusion on each sub-feature similarity to obtain the total feature similarity; when the total feature similarity is greater than or equal to the similarity threshold, it is determined that the matching condition is met.

[0022] Furthermore, when matching the change characteristics of each of the operational monitoring data within the risk area with the risk characteristics in the risk characteristic library, the method further includes:

[0023] When the change feature fails to match the risk feature, a matching failure warning message is generated.

[0024] Furthermore, when verifying the associated operational data according to the aforementioned risk type, the following steps are included:

[0025] Based on the risk type, obtain associated operational data; determine whether the change status of the associated operational data conforms to the risk propagation relationship; when the change status of the associated operational data conforms to the risk propagation relationship, confirm the existence of a risk; when the change status of the associated operational data does not conform to the risk propagation relationship, recalculate the matching degree.

[0026] Furthermore, when generating risk management information based on the aforementioned risk results, it includes:

[0027] The risk outcome includes the risk location and the risk type;

[0028] When the risk type is leakage risk, emergency shutdown control information is generated; when the risk type is equipment pressure abnormal risk, local isolation control information is generated; when the risk type is metering abnormal risk, filling termination control information is generated; when the risk type is control abnormal risk, system switching control information is generated.

[0029] Compared with existing technologies, the advantages of this invention are as follows: Based on the operating flow of a CNG refueling mother station, the corresponding relationship between the operating monitoring data of adjacent operating units is checked, rather than relying solely on a single monitoring point or parameter for risk assessment. When an abnormal correspondence is detected between operating units, the risk area is further determined by combining the operating flow. This fully utilizes the process correlation between each operating unit, achieving precise location of the risk source and improving the accuracy of risk identification. After determining the risk area, the change characteristics of the operating monitoring data within the risk area are matched with a risk feature database. Multi-dimensional features such as the rate of change, fluctuation amplitude, and steady-state deviation of the operating data are used to identify the risk type, comprehensively reflecting the equipment operating status and parameter evolution patterns, thus improving the accuracy of risk identification under complex operating conditions. After completing the risk feature matching, associated operating data is obtained based on the risk type and a consistency check is performed using a preset risk propagation relationship. Risk is confirmed only when the associated operating data conforms to the risk propagation pattern; otherwise, risk matching is repeated, thereby constructing a dual risk confirmation mechanism of "feature identification + propagation verification". Based on the risk results, risk management information corresponding to the risk type and location is generated, and corresponding interlocking controls are executed. This enables different risks to be handled with differentiated strategies, which helps reduce the risk of accident spread and improves the safety and continuity of CNG refueling mother station operation.

[0030] On the other hand, this application also provides a risk management system for a CNG refueling mother station, used to apply the above-mentioned risk management method for CNG refueling mother stations, including:

[0031] The data acquisition module is configured to acquire operational monitoring data from each operating unit of the CNG refueling mother station and perform time synchronization processing.

[0032] The detection module is configured to check the correspondence between the operation monitoring data of adjacent operating units based on the operating process of the CNG refueling mother station. When an abnormal correspondence exists, an abnormal status identifier is generated. Based on the abnormal status identifier and the operating process, the risk area is determined.

[0033] The matching module is configured to match the change characteristics of each of the operational monitoring data within the risk area with the risk characteristics in the risk characteristic library; when the change characteristics and risk characteristics are successfully matched, a risk type is generated;

[0034] The verification module is configured to verify the associated operational data according to the risk type; when the associated operational data matches the risk propagation relationship, a risk is confirmed and a risk result is generated; when the associated operational data does not match the risk propagation relationship, the matching is performed again.

[0035] The control module is configured to generate risk control information based on the risk results and to execute interlocking control based on the risk control information.

[0036] It is understandable that the risk management methods and systems of the aforementioned CNG refueling mother stations have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 A flowchart illustrating the risk management method for CNG refueling mother stations provided in this embodiment of the invention;

[0039] Figure 2 A schematic diagram of a CNG refueling mother station provided in an embodiment of the present invention;

[0040] Figure 3 This is a functional block diagram of the risk management system for a CNG refueling mother station provided in an embodiment of the present invention.

[0041] The components include: 1. Natural gas intake unit; 2. Filtration unit; 3. Dehydration unit; 4. Gas storage unit; 5. Manifold unit; 6. Metering unit; and 7. Gas filling column unit. Detailed Implementation

[0042] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0043] In some embodiments of this application, see Figure 1 As shown, a risk management method for CNG refueling mother stations is proposed, including:

[0044] Acquire operational monitoring data from each operating unit of the CNG refueling mother station and perform time synchronization processing;

[0045] Based on the operating procedures of the CNG refueling mother station, the corresponding relationship of the operation monitoring data of adjacent operating units is checked. When an abnormal correspondence exists, an abnormal status identifier is generated. Based on the abnormal status identifier and the operating procedures, the risk area is determined.

[0046] The change characteristics of each operational monitoring data within the risk area are matched with the risk characteristics in the risk characteristic database; when the change characteristics and risk characteristics match successfully, a risk type is generated.

[0047] Based on the risk type, the associated operational data is checked; when the associated operational data matches the risk propagation relationship, the existence of a risk is confirmed and a risk result is generated; when the associated operational data does not match the risk propagation relationship, the matching is performed again.

[0048] Risk management information is generated based on the risk results, and interlocking controls are executed based on the risk management information.

[0049] In this embodiment, refer to Figure 2 As shown, the CNG refueling mother station includes, in sequence according to the natural gas flow direction, a natural gas intake unit 1, a filtration unit 2, a dehydration unit 3, a gas storage unit 4, a manifold unit 5, a metering unit 6, a refueling column unit 7, and a monitoring unit. Each operating unit is equipped with corresponding on-site detection devices and control equipment. Each operating unit is connected to the station control system through a PLC control system, an RTU acquisition terminal, or an industrial IoT gateway to achieve centralized collection of operating data.

[0050] Specifically, the operational monitoring data acquired by each operating unit includes: the natural gas inlet unit acquires natural gas inlet pressure, inlet temperature, inlet flow rate, inlet valve opening, and inlet shut-off valve status; the filtration unit acquires filter inlet pressure, filter outlet pressure, filter differential pressure, filter temperature, and filter blockage alarm status; the dehydration unit collects water dew point values ​​to simultaneously analyze the water content of natural gas and the associated risks; the gas storage unit acquires the pressure, temperature, liquid level (applicable to gas storage devices with buffers), safety valve status, and cylinder switching status of each gas storage cylinder group; the manifold unit acquires manifold pressure, temperature, flow rate, and manifold valve opening / closing status; the metering unit acquires instantaneous flow rate, cumulative flow rate, filling mass, metering pressure, metering temperature, and metering equipment operating status; the gas filling column unit acquires filling pressure, filling flow rate, nozzle valve status, gas dispenser operating status, vehicle connection status, and emergency stop button status; and the monitoring unit acquires natural gas concentration, ambient temperature, ambient humidity, equipment vibration, equipment operating current, equipment operating voltage, smoke alarm status, flame detection status, and combustible gas alarm status.

[0051] Furthermore, all operational monitoring data are encapsulated according to a unified data structure. Each piece of operational monitoring data includes at least: operational unit identifier, monitoring point identifier, monitoring parameter type, monitoring value, acquisition timestamp, equipment status identifier, and data quality identifier. Among them, the data quality identifier is used to indicate whether the current data contains communication anomalies, sensor malfunctions, data out of bounds, or missing data, so that abnormally collected data can be removed during subsequent analysis.

[0052] Because the data from each operating unit comes from different types of field devices, the data acquisition cycles of each device differ. For example, the sampling cycle of a pressure sensor can be 100ms, that of a flow meter can be 200ms, that of a combustible gas detector can be 500ms, while the refresh cycle of PLC status variables can be 50ms. Therefore, there are time discrepancies between different operational monitoring data. If subsequent operational condition correspondence analysis is performed directly, it is easy to cause the data from different operating units to not correspond correctly. Therefore, after acquiring the operational monitoring data, time synchronization processing is required first.

[0053] Specifically, using the station control server as a unified time source, all PLC controllers, RTU acquisition terminals, industrial gateways, and smart meters use a unified clock for time synchronization, ensuring that all acquisition devices maintain a consistent system time. Subsequently, based on the acquisition timestamps corresponding to each operational monitoring data point, the data within the same analysis period are uniformly aligned.

[0054] For data with the same sampling frequency, they are directly mapped according to the timestamp. For data with different sampling frequencies, a preset analysis period is used as a unified time window. Multiple sets of sampled data within the time window are resampled so that different monitoring parameters are mapped to the same time node. For example, when the preset analysis period is 1 second, the data of each monitoring parameter within the time window are uniformly mapped to the corresponding time node, forming a set of operating status data at the same moment.

[0055] Furthermore, when a certain monitoring parameter has multiple sets of sampled data within the current time window, the average value, maximum value, minimum value, or the last sampled value can be used as the corresponding running monitoring value within the time window; when a certain monitoring parameter does not collect valid data within the current time window, the most recent valid data from the previous time window is used for compensation, and the data quality identifier is set as the compensation data identifier at the same time.

[0056] After time synchronization is completed, a mapping relationship of operational monitoring data between various operating units is established with a unified time node, forming an operational status dataset for the corresponding time node. Each operational status dataset contains all operational monitoring data for the natural gas intake unit, filtration unit, dehydration unit, gas storage unit, manifold unit, metering unit, gas filling column unit, and monitoring unit at the same time node. Finally, the operational status datasets corresponding to each time node are arranged in chronological order to form a continuous operational monitoring data sequence.

[0057] Specifically, the operating flow of a CNG refueling mother station refers to the sequential transmission relationship of natural gas within the station, from intake, filtration, storage, manifold, metering, to refueling output, as well as the corresponding sequence of equipment operation, pressure change, and flow rate change. The operating flow includes at least the sequential relationships and upstream / downstream transmission relationships between the natural gas intake unit, filtration unit, dehydration unit, storage unit, manifold unit, metering unit, refueling column unit, and monitoring unit.

[0058] To provide a clear basis for subsequent risk identification, the operational monitoring data after time synchronization was first examined to check the correspondence between adjacent operating units according to the operating condition process. Adjacent operating units refer to two operating units directly adjacent in the natural gas flow direction, such as the combination relationships between the natural gas intake unit and the filtration unit, the filtration unit and the dehydration unit, the dehydration unit and the gas storage unit, the gas storage unit and the manifold unit, the manifold unit and the metering unit, and the metering unit and the refueling column unit. The monitoring unit forms a correlation monitoring relationship with each operating unit to assist in verifying environmental conditions, leakage conditions, and safety conditions.

[0059] Specifically, the correspondence check takes the operating status dataset under a unified time node as input, and synchronously compares the pressure, flow, temperature, valve opening, equipment start-up and shutdown status and alarm status between adjacent operating units, and judges whether there is an abnormal correspondence between adjacent operating units according to the preset correspondence rules.

[0060] In one implementation, the corresponding rules include at least the following categories:

[0061] Continuous change correspondence rule: When the pressure or flow rate of the upstream operating unit continuously rises or falls, the corresponding parameters of the downstream operating unit should show a trend of change in the same direction after a preset time delay; if the upstream and downstream parameters show inconsistent changes for a long period of time under the same operating condition, an abnormal correspondence is determined to exist. For example, when the inlet pressure of the natural gas intake unit continuously rises, the inlet and outlet pressures of the filter unit should usually change accordingly; if the relevant pressures of the filter unit do not respond for a long time, it indicates that the unit may be blocked, the valve may not be activated, or the sensor may be malfunctioning.

[0062] Sudden Change Response Rule: When the pressure, flow rate, or valve opening of a certain operating unit changes by more than a preset threshold within a short period of time, the downstream operating unit should show a corresponding linkage change within a preset correlation time. If the linkage change is missing or the direction of change is reversed, an abnormal correlation is determined to exist. For example, when a gas storage unit switches, the manifold pressure should fluctuate accordingly; if the gas storage unit switches but the manifold pressure does not change, an abnormal correlation can be determined.

[0063] The rule for maintaining a constant value for an extended period: If an upstream operating unit has performed a clear action, such as opening a valve, starting or stopping a compressor, or switching filling status, while the pressure, flow rate, or status parameters of the downstream operating unit remain unchanged for a preset period, an abnormal relationship is determined to exist. This situation is often used to identify pipeline blockages, stuck valves, malfunctioning flow meters, or communication anomalies.

[0064] Inconsistency rule for upstream and downstream processes: When the upstream process has completed the switchover, but the downstream process has not entered the expected state, an abnormal correspondence is determined. For example, the air intake unit has shown an air supply status, but the filter unit and air storage unit remain in a shutdown state; or the manifold unit has switched to the filling branch, but the metering unit has not shown a corresponding flow change, all of which can be identified as abnormal correspondences.

[0065] Actuator Action and Pressure / Flow Change Inconsistency Rule: When an actuator such as a valve, shut-off device, compressor, or switching mechanism issues an action command and reports completion, but the corresponding pressure or flow parameter does not change as expected, or changes in the opposite direction, it is considered an abnormal correspondence. This rule is used to identify abnormal situations where "the action has been performed but the process has not responded," and can effectively identify mechanical jamming, pipeline leakage, sensor distortion, or control failure.

[0066] In practical implementation, the aforementioned corresponding rules can be assigned corresponding status markers. When any corresponding rule meets the abnormal condition, it is considered that there is an abnormal correspondence between adjacent operating units, and an abnormal status identifier is combined and marked for the adjacent operating units. The abnormal status identifier can be a numerical code, color code, level code, or text code, such as "Abnormal A", "Abnormal B", "Level 1 Abnormal", "Level 2 Abnormal", etc., used to record the abnormal type, abnormal location, and abnormal occurrence time.

[0067] Furthermore, the abnormal status identifier not only serves to indicate anomalies but also as input for locating risk areas. Based on the abnormal status identifier and operating procedures, abnormal units are investigated step-by-step to determine the true risk areas. This step-by-step investigation refers to sequentially determining whether an abnormal unit is the source of the anomaly, following the natural gas flow direction and process chain from upstream to downstream. Specifically, during the investigation, not all abnormally associated units are directly considered risk areas. Instead, by combining monitoring unit feedback, actuator status, pressure change sequence, and flow change sequence, the operating unit that first exhibited an anomaly is selected as the risk source.

[0068] In this embodiment, when the operating unit marked with an abnormal status identifier includes a monitoring unit, the monitoring unit is prioritized as the key investigation target. At this time, it is determined whether the action status of the actuator corresponding to the monitoring unit corresponds to the monitoring record: if the action status of the actuator does not correspond to the monitoring record, it indicates that the monitoring unit itself or its corresponding area is abnormal, and the monitoring unit is directly identified as a risk area; if the action status of the actuator corresponds to the monitoring record, it indicates that the monitoring unit is only an abnormal response unit, and the operating units with abnormal correspondence are investigated step by step to find the operating unit that first exhibited an abnormality. For example, if the monitoring unit detects an increase in combustible gas concentration, but the associated shut-off valve has already activated and the pressure has decreased, it indicates that the monitoring unit may only reflect the risk consequences; if the monitoring unit alarm and shut-off action do not match, the monitored area can be directly identified as a risk area.

[0069] When the operating unit marked with an abnormal status does not include the monitoring unit, the operating units with corresponding abnormalities are also investigated step by step according to the operating condition process, and the upstream operating unit where the abnormality first appears is identified as the risk area. The so-called upstream operating unit refers to the unit that is further upstream in the natural gas transmission direction, and its abnormality is more likely to be the source of subsequent abnormalities.

[0070] During the step-by-step investigation, the operating unit that "first appears abnormal" can be determined as follows: compare the timestamps of the first abnormal status indicators of each abnormal unit, and prioritize the earliest one; if the timestamps are close, compare whether the upstream process has taken effective action, and prioritize the unit that has taken action but has not generated the expected feedback; if multiple units are abnormal at the same time, combine the pressure transmission direction and the flow change direction, and prioritize the upstream unit that deviated from the normal operating condition first; if it is still impossible to distinguish, merge multiple units into a joint risk area and proceed to the subsequent risk feature matching step.

[0071] In a preferred embodiment, the natural gas intake unit, filtration unit, dehydration unit, and gas storage unit are connected in sequence. If the outlet pressure of the filtration unit suddenly drops while the pressure of the gas storage unit does not change accordingly, and simultaneously the intake unit valve has activated but the filtration unit flow rate does not respond, an abnormal relationship is determined in the filtration unit. If it is further found that the monitoring unit does not alarm and the actuator feedback is normal, the filtration unit is identified as a risk area. If the monitoring unit alarms simultaneously and the alarm information is inconsistent with the operational status, the monitoring unit is identified as a risk area.

[0072] In another preferred embodiment, if during the switching process of the gas storage unit, the gas storage pressure decreases while the manifold flow rate does not change significantly, and the flow rate of the metering unit remains unchanged, it indicates that there is an abnormal correspondence between the gas storage unit and the manifold unit; based on the timestamp and the upstream action status, the gas storage unit is determined to be the first abnormal unit, thereby identifying the gas storage unit as a risk area.

[0073] Through the aforementioned correspondence checks and step-by-step investigations, this embodiment can accurately identify the abnormal propagation chain from multiple adjacent operating units, distinguishing between "abnormal response units" and "abnormal source units." This limits the areas truly requiring further identification and handling to risk areas, preventing downstream alarm units from being misjudged as risk origins and improving the accuracy of subsequent risk feature matching and interlocking control. Furthermore, a risk area can be a single operating unit or a combined area composed of multiple consecutive operating units. In cases where multiple operating units simultaneously malfunction and cannot be distinguished by a single rule, these operating units are grouped into a joint risk area.

[0074] Specifically, after the risk area is identified, further analysis is conducted on the operational monitoring data within the risk area to identify the specific risk type corresponding to the current abnormal state. Risk types include at least one or more of the following: leakage risk, abnormal equipment pressure risk, abnormal metering risk, and abnormal control risk.

[0075] To improve the accuracy of risk identification, this embodiment does not directly judge the risk based on a single point value at a certain moment. Instead, it extracts the dynamic change characteristics of the operation monitoring data within the risk area based on a preset time window, and matches the change characteristics with the standard risk characteristics in the risk feature library to determine the risk type corresponding to the current abnormal state.

[0076] Specifically, the risk feature library stores feature sets corresponding to different risk types. Each risk feature set includes at least several standard feature items, which characterize the typical variation patterns of that risk type under historical samples or preset rules. Standard feature items may include standard data change rate, standard fluctuation amplitude, standard steady-state deviation, and other auxiliary features. The feature sets of each risk type in the risk feature library can be classified and stored according to operating unit type, operating condition stage, equipment type, or risk level for subsequent rapid retrieval and matching.

[0077] In one implementation, time-series data of operational monitoring data within the risk area is first acquired within a preset time window. The preset time window reflects the local trends in the current risk evolution process, and its length can be set based on the response speed of the station's equipment, the data sampling frequency, and the characteristics of the risk evolution. The time-series data within the preset time window includes at least a timestamp, monitoring parameter values, and the corresponding operating unit identifier. For cases where multiple monitoring points participate in the analysis simultaneously, the data from each monitoring point within the same time window are uniformly organized into a multi-dimensional time-series data group.

[0078] Subsequently, feature extraction is performed on the time series data to obtain an operational feature set. The operational feature set is used to characterize the dynamic change trend of each monitoring parameter within the risk area within a time window. Preferably, the operational feature set includes at least the following three types of features: Data change rate: used to characterize how quickly the monitoring parameter changes within the time window, reflecting whether pressure, flow rate, temperature, or state quantity shows trends such as rapid rise or fall, continuous deviation, or gradual decay; Fluctuation amplitude: used to characterize the fluctuation range of the monitoring parameter within the time window, reflecting whether the parameter exhibits large oscillations, periodic fluctuations, or abnormal jumps; Steady-state deviation: used to characterize the degree of deviation of the monitoring parameter from the normal operating condition reference value or historical benchmark value, reflecting whether the current operating state continuously deviates from the normal range.

[0079] After feature extraction is completed, the matching degree of the running feature set is calculated with the feature sets of each risk type in the risk feature library to determine which type of risk feature the current running feature best matches. Specifically, the sub-feature similarity is calculated for each sub-feature in the running feature set and the standard sub-feature in the corresponding risk type feature set. Then, the sub-feature similarities are weighted and fused to obtain the total feature similarity.

[0080] Sub-feature similarity describes the degree of closeness along a single feature dimension, such as the similarity between rates of change, fluctuation amplitudes, and steady-state deviations. Sub-feature similarity can be calculated using normalized distance, cosine similarity, correlation coefficient similarity, fuzzy membership, or other methods that reflect feature closeness. To ensure comparability between features with different dimensions, each feature can be normalized before similarity calculation, unifying its values ​​to the same range.

[0081] Furthermore, when weighting and fusing the similarities of each sub-feature, corresponding weights can be set according to the importance of different features for risk identification. For example, in leakage risk identification, the weights of flow rate change rate and steady-state deviation can be higher than those of fluctuation amplitude; in control anomaly risk identification, the weights of execution state change rate and operating condition response deviation can be higher than those of simple pressure fluctuation features. Weights can be preset, obtained from historical risk samples, and dynamically adjusted during operation based on the identification results. Through weighted fusion, the total feature similarity between the operational feature set and the standard risk feature set can be obtained, thereby achieving a comprehensive judgment of multi-dimensional features.

[0082] When the total feature similarity is greater than or equal to a preset similarity threshold, the current running feature set is determined to have successfully matched the corresponding risk feature set, and the risk type is output. The output of the risk type is used to characterize the anomaly category corresponding to the current risk area and serves as the basis for subsequent risk propagation relationship verification and interlocking control strategy generation.

[0083] In practical applications, the risk feature database can be pre-established based on historical operational data. The establishment process includes: collecting operational monitoring data samples under normal and abnormal operating conditions, classifying and labeling the samples, extracting typical feature patterns corresponding to various risks, and organizing these feature patterns into a risk type feature set stored in the risk feature database. With long-term operation, newly identified risk samples can be written back and updated, allowing the risk feature database to be continuously corrected and improved, thereby adapting to risk changes under different sites, equipment conditions, and seasonal operating conditions.

[0084] In a preferred embodiment, when a set of time-series data within a risk area exhibits a continuous decrease in pressure, a synchronous decrease in flow rate, and a continuous increase in steady-state deviation, this set of operational features is compared with the feature sets of each risk type in the risk feature library. If the standard feature set corresponding to the leakage risk has the highest overall similarity in pressure change rate, flow rate change rate, and steady-state deviation, and the total feature similarity reaches a preset threshold, then the leakage risk type is output. If multiple monitoring parameters within the same risk area simultaneously satisfy some features of different risk types, they can be sorted from high to low according to the total feature similarity, the best-matching risk type is output, and the second-highest matching result is retained as auxiliary discrimination information.

[0085] In another preferred implementation, when significant pressure fluctuations and relatively stable flow rates are detected within a risk area, but steady-state deviations continue to accumulate, the risk is preferentially matched with the set of equipment pressure anomaly risk characteristics. When the correlation similarity reaches a threshold, the equipment pressure anomaly risk type is output instead of simply classifying it as leakage risk or metering anomaly risk, thereby improving the accuracy of risk classification.

[0086] Furthermore, when the change characteristics and risk characteristics fail to match, a matching failure warning is generated. This warning indicates that an anomaly exists in the current risk area, but a sufficiently corresponding risk type has not yet been found in the existing risk characteristic database. The warning may further include the anomaly time, risk area identifier, major characteristic deviations, and a suggested review. It also generates associated fault alerts.

[0087] Through the above methods, this embodiment can perform refined classification and identification of abnormal states of CNG refueling mother stations based on the multi-parameter, multi-dimensional, and time-series change characteristics within the risk area without relying on a single threshold alarm.

[0088] Specifically, after risk feature matching, the risk type corresponding to the current abnormal state has been obtained. The risk type is used to characterize the possible anomaly category corresponding to the current risk area. However, local features within the risk area alone are insufficient to definitively confirm whether the risk actually exists. Therefore, this embodiment further verifies the risk propagation process by correlating operational data to improve the reliability of risk confirmation.

[0089] The so-called associated operational data refers to operational monitoring data that is related to the current risk area in terms of upstream and downstream processes, time sequence, or control linkage. Associated operational data includes at least the data collected by monitoring units adjacent to the risk area, adjacent downstream units, and monitoring units that have a safety interlock relationship with the risk area. Specifically, it can include pressure, flow rate, temperature, valve status, shut-off status, equipment start-up and shutdown status, combustible gas concentration, alarm status, and control command feedback status, etc.

[0090] In one implementation, the scope of associated operating units to be verified is first determined based on the risk type. Different risk types correspond to different scopes of associated operating data verification. For example: when the risk type is leakage risk, priority is given to acquiring pressure, flow rate, valve status, and combustible gas concentration change data of the upstream and downstream of the risk area, as well as the monitoring unit, to determine whether the leakage has spread along the process link; when the risk type is equipment pressure anomaly risk, priority is given to acquiring upstream gas supply data, downstream receiving unit data, and the status of actuators such as safety valves and shut-off valves adjacent to the pressure anomaly unit; when the risk type is metering anomaly risk, priority is given to acquiring flow rate, pressure, metering cumulative value, and filling status data of the front and rear ends of the metering unit, to determine whether the anomaly has propagated along the filling link; when the risk type is control anomaly risk, priority is given to acquiring control command output, actuator feedback, equipment operating status, and interlock action status, to determine whether a mismatch has occurred in the control link.

[0091] After acquiring the associated operational data, it is further determined whether the changes in the associated operational data conform to the preset risk propagation relationship. The so-called risk propagation relationship refers to the direction, speed, sequence, and characteristics of the impact of a specific risk type in the process flow. The risk propagation relationship can be pre-established based on the process structure, equipment connection relationships, historical risk samples, and interlocking logic of the CNG refueling mother station.

[0092] Specifically, the risk transmission relationship can be judged from at least the following dimensions:

[0093] Consistency of Change Direction: Determine whether the direction of change in the associated operational data is consistent with the propagation direction corresponding to the current risk type. For example, when a leak risk occurs, the pressure near the leak area usually decreases, the flow rate may decrease or fluctuate abnormally, and the concentration of combustible gas increases; if the associated operational data shows a change in the opposite direction, it is considered not to conform to the risk propagation relationship.

[0094] Consistency of change sequence: Determine whether abnormal changes in each associated operating unit occur sequentially according to the process flow or control logic. For example, upstream pressure anomalies should occur before downstream pressure anomalies; if a significant anomaly occurs in the downstream unit first while the upstream unit does not respond, it may indicate that the current risk characteristic matching result is inaccurate and the matching degree needs to be recalculated.

[0095] Consistency of Change Magnitude: Determine whether the magnitude of changes in the associated operational data is consistent with the expected risk type. For example, local leaks typically cause a gradual decrease in local pressure, rather than a simultaneous and significant change in all units across the entire site within a short period. If the magnitude of the change exceeds the typical propagation range of this type of risk, it suggests that the current risk type may not be valid.

[0096] Interlocking Response Consistency: This assesses whether the actions of the actuators, their shut-off status, and the monitoring alarm status are consistent with the risk propagation relationship. For example, when a shut-off command is issued based on the risk type, the relevant valves should report their closed status within a preset time. If the reported status does not match the expectation, it indicates that the risk propagation relationship is not established or the interlocking link is abnormal.

[0097] During the verification process, each item of related operational data can be compared individually, or data from multiple related operational units can be comprehensively analyzed. For each related operational unit, corresponding propagation judgment items can be set, including but not limited to pressure change items, flow change items, temperature change items, alarm change items, and actuator response items, and these judgment items are comprehensively judged. When most judgment items meet the preset propagation rules, the related operational data can be considered to conform to the risk propagation relationship.

[0098] When the associated operational data matches the risk propagation relationship, the current anomaly is confirmed as a real risk, and a risk result is generated. The risk result includes at least the risk location and risk type, and preferably also includes the risk level, risk impact range, risk confirmation time, and corresponding handling priority. The risk location indicates the specific unit or area where the risk occurred, the risk type indicates the specific category of the risk, and the risk level indicates the severity of the risk, so as to generate differentiated risk management information subsequently.

[0099] In one implementation example, if the risk type is leakage risk, and the associated operational data shows that the pressure near the risk area is continuously decreasing, the flow rate is abnormally decreasing, and the concentration of combustible gas is increasing, and this change gradually spreads from upstream to downstream, then a leakage risk is confirmed, and the area where the leakage occurs is used as the risk location to generate a risk result.

[0100] In another implementation example, if the risk type is control anomaly risk, and the associated operational data shows that the control command has been issued but the status of the actuator has not changed, and the associated pressure, flow and alarm status have not been linked as expected, then the existence of control anomaly risk is confirmed, and the unit or control loop where the control link anomaly is located is used as the risk location to generate risk results.

[0101] When the associated operational data does not conform to the risk propagation relationship, it indicates a discrepancy between the current risk type and the actual risk status, or that the current anomaly is merely a transient disturbance, sensor error, local interference, or a pseudo-anomaly that has not achieved true propagation. In this case, instead of directly confirming the risk, the matching degree should be recalculated to re-determine the risk type or reassess the risk area. When recalculating the matching degree, updated time window data, an expanded range of associated operational units, or continuous monitoring data over a longer period can be used to avoid misjudgments due to short-term fluctuations.

[0102] If multiple rematches fail to establish a risk propagation relationship in the associated operational data, the current status can be marked as a risk pending confirmation or a status requiring manual review, and a prompt message can be generated to alert maintenance personnel to further check the status of field equipment, sensors, and control loops. This prevents erroneous interlocking actions and ensures operational safety and stability.

[0103] Specifically, after the risk results are confirmed, they not only include the currently confirmed risk locations and types, but also, to comprehensively assess the safety status of the CNG refueling mother station system, the risk results further include the risks that related faults might cause based on the current risks, as well as the cascading relationships of these faults propagating through the mother station's process flow. For example, when a risk of excessive moisture content is confirmed in the dehydration unit, the downstream gas storage or compression units that this risk might affect are analyzed in conjunction with the operating process, and potential secondary faults (such as pipeline ice blockage, compressor liquid slugging, etc.) are deduced, thus forming a complete fault propagation chain.

[0104] Based on the comprehensive and multi-dimensional risk results described above, risk management information is generated that corresponds to the risk location, risk type, associated fault risks, and cascading fault relationships. This risk management information not only includes direct handling instructions for the current risk location (such as emergency shutdown and local isolation), but more importantly, it also includes preventative and blocking control instructions for cascading fault relationships (such as early depressurization, switching to a backup dehydration unit, downgrading downstream equipment operation, or early shutdown). Subsequently, interlocking control is automatically executed according to the risk management information. Through this global perspective of interlocking control, not only can rapid and accurate handling of current risks at the CNG refueling mother station be achieved, but more importantly, by executing preventative and blocking instructions, the propagation path of faults between adjacent operating units can be cut off in advance, thereby effectively preventing a local single fault from evolving into a systemic cascading accident, minimizing the generation of cascading risks, and ensuring the overall safety and reliability of the CNG refueling mother station operation.

[0105] Risk management information refers to a combination of control commands, interlock commands, alarm commands, and handling prompts generated for specific risk types and locations. Risk management information can be sent to the station control system, local PLCs, remote monitoring platforms, or field actuators to drive corresponding safety actions.

[0106] In one implementation, the generation of risk control information includes at least the following steps: determining the target area to be addressed based on the risk location in the risk results; determining the corresponding handling strategy based on the risk type in the risk results; associating the target area with the handling strategy to generate corresponding interlock control instructions; issuing the interlock control instructions to the corresponding execution mechanism and simultaneously generating alarm prompts and status records.

[0107] The risk management information corresponding to different risk types can be set as follows:

[0108] Risk management information corresponding to leakage risks; when the risk type is leakage risk, emergency shut-off management information is generated. Emergency shut-off management information includes at least: closing the upstream shut-off valve of the risk area, closing the relevant process isolation valve group of the risk area, stopping relevant compression or filling operations, initiating pressure relief or venting procedures, activating combustible gas alarms and audible and visual alarms, and linking the monitoring unit to enter high-frequency sampling mode. In specific implementation, a closing command can be issued to the main shut-off valve upstream of the risk location first, and a stop command can be issued to the downstream filling circuit to prevent natural gas from continuing to enter the risk area; if a local isolation valve is located near the risk location, the local isolation valve can be further closed to reduce the scope of leakage impact; if a safety venting device is configured on site, directional pressure relief can be initiated under safe conditions to reduce local pressure and the risk of leakage spread.

[0109] Risk management information corresponding to abnormal equipment pressure risks; when the risk type is abnormal equipment pressure risk, local isolation management information is generated. Local isolation management information includes at least: implementing partial shut-off of the abnormal pressure area, restricting related units from continuing to increase pressure, adjusting the gas supply path, suspending the abnormal unit's operation, linking safety valve checks, and prompting maintenance personnel to inspect the abnormal pressure unit. In specific implementation, the handling method can be determined according to the direction of the pressure anomaly. If an overpressure risk occurs, pressure reduction, switching, and isolation actions can be performed first; if an abnormally low pressure risk occurs, downstream filling can be restricted, and the stability of the upstream gas supply can be checked. Through local isolation, the pressure anomaly can be prevented from spreading to other operating units.

[0110] Risk management information corresponding to metering anomaly risks; when the risk type is metering anomaly risk, filling termination control information is generated. Filling termination control information includes at least: stopping the current filling operation, freezing metering data, closing relevant valves in the metering loop, prohibiting continued output of natural gas to the vehicle or storage loop, and recording the abnormal metering period and related monitoring data. Metering anomalies typically affect filling accuracy and trade settlement accuracy; therefore, upon confirming such risks, the filling process should be suspended first to prevent incorrect, insufficient, or excessive filling caused by continued operation under metering anomaly conditions. If necessary, a metering verification process can also be triggered, uploading the abnormal data to the backend for auditing.

[0111] Risk management information corresponding to control anomaly risks is generated. When the risk type is a control anomaly risk, switchover management information is generated. Switchover management information includes at least: switching to a backup control loop, switching to a backup communication link, switching to manual control mode, suspending automatic interlock extended control, and prompting for control system maintenance. Control anomaly risks typically manifest as no response from the actuator after a command is issued, inconsistent feedback status, or control link interruption. Continuing to rely on the original control link in this situation may lead to interlock failure. Therefore, priority is given to maintaining a safe state through the backup system or local manual mode, and the abnormal link is isolated for maintenance.

[0112] When executing interlocking control, the preferred approach is "alarm first, then interlock, and finally feedback confirmation." Specifically, after issuing the interlocking command, the feedback status of the corresponding actuator is read in real time, and the feedback result is confirmed: if the feedback status is consistent with the preset interlocking status, the interlocking control is considered successful; if the feedback status is inconsistent, the command is reissued or the system is switched to a backup execution channel, and an interlocking failure warning message is generated.

[0113] In this embodiment, risk outcomes are mapped one-to-one with control actions, enabling different risk types to trigger interlocking controls of different levels, scopes, and forms. This not only quickly cuts off the risk spread path but also avoids unnecessary site-wide shutdowns, achieving targeted and highly executable risk management.

[0114] Based on another preferred embodiment described above, see [link to preferred embodiment]. Figure 3 As shown, this embodiment provides a risk management system for a CNG refueling mother station, used to apply the above-mentioned risk management method for CNG refueling mother stations, including:

[0115] The data acquisition module is configured to acquire operational monitoring data from each operating unit of the CNG refueling mother station and perform time synchronization processing.

[0116] The detection module is configured to check the correspondence between the operation monitoring data of adjacent operating units based on the operating process of the CNG refueling mother station. When an abnormal correspondence exists, an abnormal status identifier is generated. Based on the abnormal status identifier and the operating process, the risk area is determined.

[0117] The matching module is configured to match the change characteristics of each operational monitoring data within the risk area with the risk characteristics in the risk characteristic library; when the change characteristics and risk characteristics match successfully, a risk type is generated.

[0118] The verification module is configured to verify the associated operational data based on the risk type; when the associated operational data matches the risk propagation relationship, the existence of a risk is confirmed and a risk result is generated; when the associated operational data does not match the risk propagation relationship, the matching is performed again.

[0119] The control module is configured to generate risk control information based on risk outcomes and execute interlocking controls based on the risk control information.

[0120] In summary, based on the operating procedures of the CNG refueling mother station, the correlation between the operational monitoring data of adjacent operating units is checked, rather than relying solely on a single monitoring point or parameter for risk assessment. When abnormal correlations are detected between operating units, the risk area is further determined by combining the operating procedures. This fully utilizes the process correlations between operating units, achieving precise location of the risk source and improving the accuracy of risk identification. After determining the risk area, the change characteristics of the operational monitoring data within the risk area are matched with a risk feature database. Multi-dimensional features such as the rate of change, fluctuation amplitude, and steady-state deviation of the operational data are used to identify the risk type, comprehensively reflecting the equipment operating status and parameter evolution patterns, thus improving the accuracy of risk identification under complex operating conditions. After completing the risk feature matching, related operational data is obtained based on the risk type and compared with the preset risk propagation relationship for consistency verification. Risk is confirmed only when the related operational data conforms to the risk propagation pattern; otherwise, risk matching is repeated. This constructs a dual risk confirmation mechanism of "feature identification + propagation verification." Based on the risk results, risk management information corresponding to the risk type and location is generated, and corresponding interlocking controls are executed. This enables different risks to be handled with differentiated strategies, which helps reduce the risk of accident spread and improves the safety and continuity of CNG refueling mother station operation.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A risk management method for a CNG refueling mother station, characterized in that, include: Obtain operational monitoring data from each operating unit of the CNG refueling mother station; And perform time synchronization processing; Based on the operating process of the CNG refueling mother station, the corresponding relationship of the operation monitoring data of adjacent operating units is checked, and when an abnormal correspondence exists, an abnormal status identifier is generated. Based on the abnormal status identifier and the operating procedure, the risk area is determined; The change characteristics of each of the operational monitoring data within the risk area are matched with the risk characteristics in the risk characteristic library; when the change characteristics and risk characteristics are successfully matched, a risk type is generated; Based on the risk type, the associated operational data is verified; When the associated operational data matches the risk propagation relationship, a risk is confirmed to exist, and a risk result is generated; If the associated operational data does not match the risk propagation relationship, re-matching should be performed. Risk management information is generated based on the risk results, and interlocking control is executed based on the risk management information.

2. The risk management method for CNG refueling mother stations according to claim 1, characterized in that, The operating unit includes a natural gas intake unit, a filtration unit, a dehydration unit, a gas storage unit, a manifold unit, a metering unit, a gas filling column unit, and a monitoring unit.

3. The risk management method for CNG refueling mother stations according to claim 2, characterized in that, When checking the correspondence between operational monitoring data of adjacent operating units, the following is included: Check whether the operation monitoring data of adjacent operating units show continuous changes, sudden changes, long-term invariance, inconsistencies between preceding and following processes, or inconsistencies between actuator actions and pressure / flow changes; if so, determine that there is an abnormal correspondence; and mark the adjacent operating units with the abnormal correspondence with the abnormal status identifier.

4. The risk management method for CNG refueling mother stations according to claim 3, characterized in that, When determining risk areas based on the abnormal status identifier and the operating procedure, the following are included: When the operating unit marked with the abnormal status identifier includes the monitoring unit, it is determined whether the action status of the actuator corresponds to the monitoring record; if they do not correspond, the monitoring unit is identified as a risk area; otherwise, the operating units with the abnormal correspondence are investigated step by step, and the operating unit that first shows an abnormality is identified as the risk area. When the operating unit marked with the abnormal status identifier does not include the monitoring unit, the operating units with the abnormal correspondence are investigated step by step, and the upstream operating unit where the abnormal correspondence first appears is identified as the risk area.

5. The risk management method for CNG refueling mother stations according to claim 4, characterized in that, When matching the change characteristics of each of the operational monitoring data within the risk area with the risk characteristics in the risk characteristic library, the following steps are included: The process involves: acquiring time-series data of the operational monitoring data within a preset time window; extracting features from the time-series data to obtain an operational feature set; the operational feature set includes data change rate, fluctuation amplitude, and steady-state deviation; calculating the matching degree between the operational feature set and the feature sets of each risk type in the risk feature library; and determining successful matching when the matching degree calculation result meets the matching conditions, and outputting the risk type.

6. The risk management method for CNG refueling mother stations according to claim 5, characterized in that, When calculating the matching degree between the set of operational features and the set of features for each risk type in the risk feature library, the following steps are included: Calculate the sub-feature similarity between the data change rate, volatility amplitude, and steady-state deviation and the standard data change rate, standard volatility amplitude, and standard steady-state deviation in the risk type feature set; perform weighted fusion on each sub-feature similarity to obtain the total feature similarity; when the total feature similarity is greater than or equal to the similarity threshold, it is determined that the matching condition is met.

7. The risk management method for CNG refueling mother stations according to claim 6, characterized in that, When matching the change characteristics of each of the operational monitoring data within the risk area with the risk characteristics in the risk characteristic library, the method further includes: When the change feature fails to match the risk feature, a matching failure warning message is generated.

8. The risk management method for CNG refueling mother stations according to claim 7, characterized in that, When verifying the associated operational data according to the aforementioned risk type, the following should be included: Based on the risk type, obtain associated operational data; determine whether the change status of the associated operational data conforms to the risk propagation relationship; when the change status of the associated operational data conforms to the risk propagation relationship, confirm the existence of a risk; when the change status of the associated operational data does not conform to the risk propagation relationship, recalculate the matching degree.

9. The risk management method for CNG refueling mother stations according to claim 8, characterized in that, When generating risk management information based on the aforementioned risk results, the following are included: The risk outcome includes the risk location and the risk type; When the risk type is leakage risk, emergency shutdown control information is generated; when the risk type is equipment pressure abnormal risk, local isolation control information is generated; when the risk type is metering abnormal risk, filling termination control information is generated; when the risk type is control abnormal risk, system switching control information is generated.

10. A risk management system for a CNG refueling mother station, used to apply the risk management method for a CNG refueling mother station as described in any one of claims 1-9, characterized in that, include: The data acquisition module is configured to acquire operational monitoring data from each operating unit of the CNG refueling mother station; And perform time synchronization processing; The detection module is configured to check the correspondence between the operation monitoring data of adjacent operating units based on the operating process of the CNG refueling mother station. When an abnormal correspondence exists, an abnormal status identifier is generated. Based on the abnormal status identifier and the operating procedure, the risk area is determined; The matching module is configured to match the change characteristics of each of the operational monitoring data within the risk area with the risk characteristics in the risk characteristic library; when the change characteristics and risk characteristics are successfully matched, a risk type is generated; The verification module is configured to verify the associated operational data according to the risk type. When the associated operational data matches the risk propagation relationship, a risk is confirmed to exist, and a risk result is generated; If the associated operational data does not match the risk propagation relationship, re-matching should be performed. The control module is configured to generate risk control information based on the risk results and to execute interlocking control based on the risk control information.

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