An industrial internet-based station intelligent operation and maintenance method, device and medium

Abstract

The application discloses a kind of based on industrial internet's yard intelligent operation and maintenance method, equipment and medium, it is related to industrial internet operation and maintenance technical field, comprising: based on standard yard operation and maintenance data, the association between yard equipment node and control interlocking node is established, and according to the parameter variation sequence and control interlocking state variation sequence of associated equipment under different operating conditions Generation yard operation dependence fingerprint, obtain fingerprinted yard equipment state atlas;Based on the standard yard operation and maintenance data is matched and dynamic baseline comparison based on fingerprinted yard equipment state atlas to operating condition, and the real-time parameter variation process is matched with yard operation dependence fingerprint, identifies abnormal parameter, eliminates the pseudo anomaly formed by short-time disturbance, obtains yard credible abnormal event;The application is by according to yard credible abnormal event and fingerprinted yard equipment state atlas to identify the time sequence association and control association between abnormal nodes, reduce the effect of false disposition probability.

Description

Technical Field

[0001] This invention relates to the field of industrial internet operation and maintenance technology, and in particular to a method, equipment and medium for intelligent operation and maintenance of sites based on industrial internet. Background Technology

[0002] As the application of the Industrial Internet in energy power plants, municipal power plants, and integrated station control scenarios deepens, existing intelligent operation and maintenance of power plants typically relies on data acquisition, communication protocol parsing, equipment status modeling, and threshold alarms to achieve operational monitoring. Specifically, it collects multi-source operational data from programmable controllers, smart meters, protection devices, and edge gateways, converts it into a unified data format through protocol adaptation, and then combines it with equipment ledgers, operating condition rules, and interlocking logic to display equipment status, aggregate alarms, and dispatch work orders, thereby improving the power plant's operational status perception, remote inspection, and daily operation and maintenance response capabilities.

[0003] When handling multi-device linkage anomalies, current site operation and maintenance typically relies on single-point threshold exceedances, alarm levels, and manual experience to determine the anomaly. While this can quickly detect parameter deviations and interlocking actions, under complex operating conditions such as start-stop switching, load fluctuations, bypass switching, and protection actions, there is a clear sequential dependency between changes in different equipment parameters and control interlocking states. Short-term disturbances, response delays, and subsequent linkage alarms are easily intertwined with the propagation of actual faults, making it difficult to accurately identify the source device, the affected area, and the point of blockage in the propagation of anomalies from ordinary alarm aggregation results. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a site intelligent operation and maintenance method based on the Industrial Internet to solve the problem of reliably locating the source and scope of impact of linkage anomalies under complex operating conditions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, this invention provides an intelligent operation and maintenance method for power stations based on the Industrial Internet, comprising: collecting power station operation-related data; performing protocol parsing and objectification processing on the power station operation-related data, and binding power station operation and maintenance association identifiers to obtain standard power station operation and maintenance data; establishing association relationships between power station equipment nodes and control interlocking nodes based on the standard power station operation and maintenance data, and generating power station operation dependency fingerprints according to the parameter change sequence of associated equipment and the control interlocking state change sequence under different operating conditions to obtain a fingerprinted power station equipment status map; performing operating condition matching and dynamic baseline comparison on the standard power station operation and maintenance data based on the fingerprinted power station equipment status map, and comparing the real-time parameter change process with the power station operation and maintenance data. The system relies on fingerprint matching to identify abnormal parameters, eliminates false anomalies caused by short-term disturbances, and obtains reliable anomaly events for the site. Based on the reliable anomaly events and the fingerprinted site equipment status map, it identifies the temporal and control correlations between anomaly nodes, establishes anomaly propagation relationships, generates anomaly propagation sequences, and traces back from subsequent anomalies according to the anomaly occurrence time and control interlock triggering order to screen anomaly source devices, determine the anomaly impact domain and anomaly propagation blocking point, and obtain the site operation and maintenance risk location results. Based on the site operation and maintenance risk location results, it determines the priority relationship of handling anomaly source devices, anomaly impact domains, and anomaly propagation blocking points, and generates site operation and maintenance handling strategies in conjunction with the fingerprinted site equipment status map.

[0007] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the specific steps of collecting power station operation-related data, performing protocol parsing and objectification processing on the power station operation-related data, and binding power station operation and maintenance association identifiers to obtain standard power station operation and maintenance data are as follows: Collect relevant data on the operation of the station, identify the data source and calibrate the collection time of the relevant data to obtain initial station operation data, perform protocol parsing on the initial station operation data, extract the equipment status content, operating condition content and control interlock content from the initial station operation data, and perform format unification and abnormal sampling and screening on the parsed data content to obtain standardized station operation data; The system matches standardized station operation data with station operation and maintenance association rules, identifies the station equipment identity, operating condition type, and control interlock relationship corresponding to the standardized station operation data, and configures station operation and maintenance association identifiers for the standardized station operation data. Standardized station operation data is objectified and encapsulated according to the station operation and maintenance association identifier. Standardized station operation data belonging to the same station equipment and the same operating condition are written into the same station operation and maintenance data object to obtain standard station operation and maintenance data.

[0008] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the specific steps for establishing the association between power station equipment nodes and control interlocking nodes based on standard power station operation and maintenance data are as follows: Based on the identification of the site operation and maintenance association identifier, the site equipment identity corresponding to the standard site operation and maintenance data is identified, and the site operation and maintenance data object corresponding to the same site equipment identity is configured as a site equipment node; Based on the control interlocking content corresponding to the station equipment nodes, identify the control constraint relationships corresponding to the station equipment nodes under different operating conditions, and configure the control constraint relationships as control interlocking nodes; Based on the control constraints and linkage response relationships between the station equipment nodes and the control interlocking nodes under the same operating conditions, the association relationship between the station equipment nodes and the control interlocking nodes is established.

[0009] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the specific steps for generating power station operation dependency fingerprints based on the parameter change sequence and control interlock status change sequence of associated equipment under different operating conditions to obtain a fingerprinted power station equipment status map are as follows: Based on the relationship between the equipment nodes and control interlocking nodes of the station, continuous operating data under the same operating condition is extracted from the standard station operation and maintenance data. The part of the continuous operating data that does not show abnormal markers and whose operating status remains stable is taken as the normal linkage analysis object to obtain the operating condition linkage sample. The timing alignment of the parameter change process and control interlock status change process of the associated equipment in the working condition linkage sample is performed, and the linkage response sequence of the associated equipment and control interlock node under the same operating condition is determined based on the time when the parameter starts to deviate, the direction of parameter change, the control interlock status switching time and the control interlock status holding result, so as to obtain the working condition linkage sequence record. Based on the working condition linkage sequence record, determine whether there is a repeatable sequential response relationship between the changes in the parameters of the associated equipment and the changes in the control interlock status. Then, take the sequential response relationship that occurs repeatedly under the same operating condition and whose response time is within a stable range as the normal linkage relationship, and obtain the station operation dependency fingerprint. The station operation dependency fingerprint is written into the association between the corresponding station equipment nodes and control interlocking nodes to obtain a fingerprinted station equipment status map.

[0010] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the step of matching the operating conditions and comparing the standard power station operation and maintenance data with the fingerprint-based power station equipment status map, matching the real-time parameter change process with the power station operation dependency fingerprint, identifying abnormal parameters, eliminating false anomalies caused by short-term disturbances, and obtaining reliable power station abnormal events are as follows: The current operating condition is determined based on standard station operation and maintenance data, and the corresponding station equipment nodes, control interlocking nodes and station operation dependency fingerprints are matched in the fingerprinted station equipment status map to obtain the station operation and maintenance data to be compared. Based on the operation and maintenance data of the site to be compared, the real-time parameter change process under the current operating conditions is extracted. According to the normal parameter fluctuation range and normal response time range in the fingerprinted site equipment status map, the real-time parameter change process is dynamically baseline compared to obtain the parameter deviation result. The real-time parameter change process corresponding to the parameter deviation result is matched with the station operation dependency fingerprint. Abnormal parameters are identified based on whether the real-time parameter change deviates from the normal linkage sequence, exceeds the normal response time range, and triggers the corresponding control interlock status change. Continuous verification of abnormal parameters is performed. When an abnormal parameter only appears within a short sampling interval and does not cause damage to the linkage relationship corresponding to the station operation dependency fingerprint, the deviation process corresponding to the abnormal parameter is discarded as a pseudo-anomaly. When an abnormal parameter persists and damages the linkage relationship corresponding to the station operation dependency fingerprint, the abnormal parameter and the corresponding station equipment node are regarded as reliable abnormal events of the station.

[0011] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the specific steps for identifying the temporal and control correlations between abnormal nodes, establishing abnormal transmission relationships, and generating abnormal propagation sequences based on trusted abnormal events and fingerprinted power station equipment status maps are as follows: The trusted anomaly events of the station are mapped to the station equipment nodes and control interlock nodes in the fingerprinted station equipment status map, and the anomaly nodes are determined according to the anomaly parameters, anomaly occurrence time and anomaly duration status corresponding to the trusted anomaly events of the station, thus obtaining the set of anomaly nodes; Based on the set of abnormal nodes, the station operation dependency fingerprint is extracted from the fingerprinted station equipment status map. Based on the normal linkage sequence recorded in the station operation dependency fingerprint, it is determined whether there is a temporal association between abnormal nodes caused by a previous abnormality, and the abnormal temporal association result is obtained. Based on the abnormal timing correlation results, the control interlock relationship between abnormal nodes is verified, and it is determined whether the change in control interlock state occurs after the change in abnormal parameters and whether it can affect subsequent abnormal nodes, so as to obtain the abnormal control correlation results. Anomaly propagation relationships are established for anomaly nodes that simultaneously satisfy both anomaly timing association results and anomaly control association results. The anomaly nodes are then arranged according to the order in which the anomalies appear, generating an anomaly propagation sequence.

[0012] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the specific steps of tracing back from subsequent anomalies according to the anomaly occurrence time and control interlock triggering sequence to screen the anomaly source equipment, determine the anomaly impact domain and anomaly propagation blocking point, and obtain the power station operation and maintenance risk location result are as follows: Select subsequent abnormal nodes based on the abnormal propagation sequence, and backtrack along the abnormal propagation relationship according to the abnormal occurrence time and the control interlock triggering order to determine whether the preceding abnormal nodes can explain the abnormal parameter changes and control interlock state changes of the subsequent abnormal nodes, and obtain the abnormal backtracking results. Based on the anomaly backtracking results, the earliest anomaly-causing and subsequently continuously changing station equipment nodes are selected. The selected station equipment nodes are marked as anomaly source devices. Based on the anomaly source devices and the anomaly propagation sequence, the range of subsequent anomaly nodes and related nodes affected by the anomaly source devices is delineated to obtain the anomaly impact domain. Based on the abnormal propagation sequence and the abnormal impact domain, the station equipment nodes located between the abnormal source device and the abnormal impact domain that can cut off the continued propagation of the abnormality are selected and marked as abnormal propagation blocking points. The abnormal source device, the abnormal impact domain and the abnormal propagation blocking points are associated to obtain the station operation and maintenance risk location results.

[0013] As a preferred embodiment of the intelligent operation and maintenance method for power stations based on the Industrial Internet described in this invention, the step of determining the priority relationship of handling anomaly source equipment, anomaly impact domain, and anomaly propagation blocking point based on the power station operation and maintenance risk location results, and generating a power station operation and maintenance handling strategy by combining the fingerprinted power station equipment status map, includes the following specific steps: Based on the risk location results of the site operation and maintenance, risk correlation analysis is performed on the abnormal source equipment, the abnormal impact domain, and the abnormal propagation blocking point. Based on the abnormal occurrence location of the abnormal source equipment, the diffusion range of the abnormal impact domain, and the blocking effect of the abnormal propagation blocking point on the abnormal transmission path, a priority relationship for handling is generated. Based on the priority of handling, the abnormal source equipment is taken as the fault investigation object, the abnormal propagation blocking point is taken as the risk control object, and the abnormal impact area is taken as the linkage monitoring object, forming a set of site operation and maintenance handling objects; The set of station operation and maintenance disposal objects is matched with the fingerprinted station equipment status map. Combined with the control interlocking relationship, control permission status and station operation dependency fingerprint of the corresponding equipment node, the type of operation and maintenance action applicable to different disposal objects is determined, and the disposal action matching result is obtained. Based on the matching results of the handling actions, a fault investigation strategy is configured for the abnormal source equipment, an interlocking control strategy is configured for the abnormal propagation blocking point, and a linkage monitoring strategy is configured for the abnormal impact domain. The fault investigation strategy, interlocking control strategy and linkage monitoring strategy are combined to generate the site operation and maintenance handling strategy.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the intelligent operation and maintenance method for site based on the Industrial Internet as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the intelligent operation and maintenance method for industrial internet-based sites as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by identifying the temporal and control correlations between abnormal nodes based on the reliable abnormal events and fingerprinted equipment status graphs of the station, and by tracing back from subsequent abnormalities according to the time of occurrence of the abnormality and the order of control interlocking triggers, it provides an interpretable risk location basis for generating priority relationships for handling, thereby improving the effect of making the station operation and maintenance handling strategy more in line with the real linkage relationship and reducing the probability of mishandling. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a site intelligent operation and maintenance method based on the Industrial Internet.

[0019] Figure 2 A flowchart for generating standard site operation and maintenance data.

[0020] Figure 3 A flowchart for constructing a fingerprint-based site equipment status map.

[0021] Figure 4 A flowchart for identifying credible abnormal events at a facility.

[0022] Figure 5 Comparison chart of the recognition effect of reliable abnormal events at the site. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figures 1-5 This is one embodiment of the present invention, which provides a method for intelligent operation and maintenance of power stations based on the Industrial Internet, including the following steps: S1. Collect station operation-related data, perform protocol parsing and objectification processing on the station operation-related data, and bind station operation and maintenance related identifiers to obtain standard station operation and maintenance data.

[0027] S1.1. Collect station operation-related data, identify the data source and calibrate the collection time of the station operation-related data to obtain initial station operation data, perform protocol parsing on the initial station operation data, extract the equipment status content, operating condition content and control interlock content from the initial station operation data, and perform format unification and abnormal sampling and screening on the parsed data content to obtain standardized station operation data.

[0028] Specifically, the system collects station operation-related data, which includes equipment data involved in operation control and maintenance judgment within the station, as well as control interlock data related to equipment operation. This data includes equipment status information generated during equipment operation, operating condition information characterizing the operating scenario of the station equipment, and control interlock information characterizing the controlled relationships of the station equipment. Specifically, equipment status information includes the start / stop status, operating parameters, alarm status, and feedback status of the station equipment; operating condition information includes the operating mode, load status, and operating condition switching status of the station equipment; and control interlock information includes the source of control commands, interlock triggering conditions, interlock feedback status, and control permission status. Based on the collection source information and collection time information carried by the station operation-related data, the system confirms the source attribution and calibrates the time sequence, ensuring that station operation-related data from different collection sources can enter subsequent processing in the same time sequence to obtain the initial station operation data. The initial site operation data is parsed using protocols to restore the message content under different communication protocols into identifiable equipment status, operating condition, and control interlock content. The field formats and semantic names of the equipment status, operating condition, and control interlock content are standardized to ensure consistency of operational content with the same meaning across different data sources. Based on the continuity of sampling time, field completeness, and the reasonableness of numerical changes, the parsed data is filtered for anomalies, removing abnormal sampling content corresponding to sampling interruptions, missing fields, and short-term mutations, thus obtaining standardized site operation data. S1.2. Match the standardized station operation data with the station operation and maintenance association rules, identify the station equipment identity, operating condition type and control interlock relationship corresponding to the standardized station operation data, and configure the station operation and maintenance association identifier for the standardized station operation data.

[0029] Specifically, the equipment status content in the standardized station operation data is used as the basis for matching station equipment identities. The equipment name, measurement point name, and acquisition address fields in the equipment status content are compared item by item with the station equipment identity information recorded in the station operation and maintenance association rules. When the field meanings are consistent and the acquisition address corresponds to the same station equipment, the corresponding station equipment identity is written into the standardized station operation data. The operating condition content in the standardized station operation data is used as the basis for matching operating condition types. The start / stop status, load status, and control status in the operating condition content are compared with the operating condition judgment conditions recorded in the station operation and maintenance association rules. When the start / stop status, load status, and control status correspond to the same operating scenario, the corresponding operating condition type is written into the standardized station operation data. Using the control interlocking content in the standardized station operation data as the basis for matching control interlocking relationships, the source of control commands, interlocking trigger conditions, and interlocking feedback status in the control interlocking content are compared with the control interlocking constraints recorded in the station operation and maintenance association rules. When the source of control commands, interlocking trigger conditions, and interlocking feedback status can correspond to the same control constraint, the corresponding control interlocking relationship is written into the standardized station operation data, forming a station operation and maintenance association identifier that can characterize the station equipment ownership, operating scenario, and control constraints.

[0030] It should be noted that the site operation and maintenance association rules are compiled based on the existing equipment location configuration, measurement point naming relationship, data acquisition address correspondence, operating condition judgment conditions, and control interlock constraints of the site. The site operation and maintenance association rules are used to record the correspondence between equipment status content and site equipment identity, the correspondence between operating condition content and operating condition type, and the correspondence between control interlock content and control interlock relationship. The equipment name, measurement point name, and data acquisition address corresponding to the same site equipment are compiled into site equipment identity matching conditions, the start / stop status, load status, and control status are compiled into operating condition type matching conditions, and the control command source, interlock triggering condition, and interlock feedback status are compiled into control interlock relationship matching conditions. The site equipment identity matching conditions, operating condition type matching conditions, and control interlock relationship matching conditions are then associated and saved to obtain the site operation and maintenance association rules.

[0031] S1.3. Based on the site operation and maintenance association identifier, the standard site operation data is objectified and encapsulated. The standard site operation data belonging to the same site equipment and the same operating condition are written into the same site operation and maintenance data object to obtain standard site operation and maintenance data.

[0032] Specifically, the standardized station operation data is objectified and encapsulated according to the station operation and maintenance association identifier. The station equipment identity and operating condition type in the station operation and maintenance association identifier are used as the collection basis. Standardized station operation data with the same station equipment identity and the same operating condition type are grouped into the same station operation and maintenance data object. The equipment status content, operating condition content and control interlock content in the standardized station operation data are written into the corresponding fields of the station operation and maintenance data object according to the collection time order. The data collection time sequence within the same station operation and maintenance data object is verified, and duplicate or misaligned entries are deleted. The integrity of fields within the same station operation and maintenance data object is verified, missing fields are added, and complete fields are retained. The correspondence of control interlock content within the same station operation and maintenance data object is verified, and control interlock content consistent with the station equipment identity and operating condition type is retained to obtain standard station operation and maintenance data.

[0033] S2. Based on standard station operation and maintenance data, establish the association between station equipment nodes and control interlocking nodes, and generate station operation dependency fingerprints according to the parameter change sequence of associated equipment and the control interlocking status change sequence under different operating conditions, thus obtaining a fingerprinted station equipment status map.

[0034] S2.1. Identify the identity of the station equipment corresponding to the standard station operation and maintenance data based on the station operation and maintenance association identifier, and configure the station operation and maintenance data object corresponding to the same station equipment identity as the station equipment node.

[0035] Specifically, taking the station operation and maintenance data objects in the standard station operation and maintenance data as the processing object, the content of the station operation and maintenance association identifier carried by each station operation and maintenance data object is checked, the equipment ownership content corresponding to the station equipment identity is extracted from the station operation and maintenance association identifier, and the consistency of the equipment ownership content with the equipment status content already written in the station operation and maintenance data object is verified. When the equipment ownership content and equipment status content point to the same station equipment, the corresponding station equipment identity is written into the station operation and maintenance data object; the station operation and maintenance data objects are collected according to the station equipment identity, and station operation and maintenance data objects with the same station equipment identity are grouped into the same node record, and the collection time order and operating condition type of the station operation and maintenance data objects in the same node record are retained, and the station operation and maintenance data objects corresponding to the same station equipment identity are configured as station equipment nodes.

[0036] S2.2. Based on the control interlocking content corresponding to the station equipment nodes, identify the control constraint relationships corresponding to the station equipment nodes under different operating conditions, and configure the control constraint relationships as control interlocking nodes.

[0037] Specifically, taking the already configured station equipment nodes as the processing objects, the operating condition type and control interlock content recorded in the station operation and maintenance data object within the station equipment node are checked one by one. The contents of the control interlock content that represent start and stop restrictions, interlock trigger conditions, control feedback status and manual permission status are verified to correspond with the equipment status content of the station equipment node under the same operating condition. When the control interlocking content can limit the start-stop status, action sequence, and control permission status of the station equipment nodes under the corresponding operating conditions, the control interlocking content is organized into control constraint relationships of the station equipment nodes under the corresponding operating conditions; the control constraint relationships are collected according to the type of operating conditions, and the control constraint relationships that constrain the same station equipment node under the same operating condition are configured as control interlocking nodes.

[0038] S2.3. Based on the control constraint relationship and linkage response relationship between the station equipment node and the control interlocking node under the same operating conditions, establish the association relationship between the station equipment node and the control interlocking node.

[0039] Specifically, an association is established based on the control constraint and linkage response relationships between the station equipment nodes and the control interlocking nodes under the same operating condition. Using the same operating condition as the merging condition, the equipment status content of the station equipment nodes is matched with the corresponding control constraint relationships of the control interlocking nodes to confirm that the control constraint relationships can limit the start-stop status, action sequence, and control permission status of the station equipment nodes under the current operating condition. Then, the parameter change process of the station equipment nodes is matched with the status change process of the control interlocking nodes to confirm whether the station equipment nodes show a corresponding linkage response after the status change of the control interlocking nodes, or whether the control interlocking nodes show a corresponding interlocking feedback after the operation of the station equipment nodes changes. When both the control constraint relationship and the linkage response relationship match the same operating condition, the corresponding station equipment node and control interlock node are connected to form an association relationship between the station equipment node and the control interlock node.

[0040] It should be noted that control constraint relationship refers to the control rule relationship formed after the equipment node of the station is limited by the content of control interlock under a certain operating condition. It mainly expresses what actions the equipment is allowed to take and what actions it is not allowed to take under the current operating condition. Linkage response relationship refers to the response correspondence between the equipment node of the station and the control interlock node after the state changes under the same operating condition. It mainly expresses whether the other equipment or control interlock will produce subsequent changes as expected after the change of one equipment or control interlock.

[0041] S2.4. Based on the relationship between the equipment nodes and control interlocking nodes of the station, extract continuous operating data under the same operating condition from the standard station operation and maintenance data, and take the part of the continuous operating data that does not show abnormal markers and whose operating status remains stable as the normal linkage analysis object to obtain the operating condition linkage sample.

[0042] Specifically, based on the relationship between station equipment nodes and control interlocking nodes, standard station operation and maintenance data is collected according to operating condition type. Station operation and maintenance data objects belonging to related station equipment nodes and control interlocking nodes under the same operating condition type are selected as the extraction scope. The equipment status content, operating condition content, and control interlocking content within the extraction scope are then organized according to the collection time sequence. Continuous operation data with continuous collection time, consistent operating condition type, and valid corresponding control interlocking content is retained. Anomalies and changes in operating status in the continuous operation data are verified, and data with abnormal markers or changes in operating status are deleted. Continuous operating data with abnormal sampling markers, interrupted control interlock content, or abrupt changes in operating condition type are retained. Continuous operating data without abnormal markers and whose equipment status content remains stable under the same operating condition are retained. The retained continuous operating data is used as the normal linkage analysis object to obtain the operating condition linkage sample. Among them, the stable operating status can be understood as meeting the requirements that the equipment parameters do not fluctuate significantly, the parameter change rate does not suddenly increase, the control interlock content is not interrupted, and the operating condition type does not change. Only continuous operating data segments that meet the requirements of "normal linkage analysis object" are used to enter the subsequent station operation dependency fingerprint generation.

[0043] S2.5. Perform time-series alignment on the parameter change process and control interlock status change process of the associated equipment in the working condition linkage sample, and determine the linkage response sequence of the associated equipment and control interlock node under the same operating condition based on the time when the parameter starts to deviate, the direction of parameter change, the control interlock status switching time and the control interlock status holding result, and obtain the working condition linkage sequence record.

[0044] Specifically, the time sequence of the parameter change process of the associated equipment and the control interlock status change process in the working condition linkage sample is aligned. Based on the acquisition time sequence of the working condition linkage sample, the parameter change process of the associated equipment and the control interlock status change process under the same operating condition are placed on the same time axis, and the time when the parameter starts to deviate, the direction of parameter change, the control interlock status switching time and the control interlock status holding result are marked. When the parameter change of the associated equipment deviates first and then stabilizes after the control interlock state switch, the parameter change process of the associated equipment is recorded as the preceding response content, and the control interlock state change process is recorded as the subsequent interlock content. When the control interlock state switch occurs first and drives the parameter change process of the associated equipment to stabilize, the control interlock state change process is recorded as the preceding interlock content, and the parameter change process of the associated equipment is recorded as the subsequent response content. The linkage response sequence of the associated equipment and the control interlock node under the same operating condition is organized according to the order of occurrence of the preceding and subsequent content to obtain the operating condition linkage sequence record.

[0045] S2.6. Based on the working condition linkage sequence record, determine whether there is a repeatable sequential response relationship between the changes in the parameters of the associated equipment and the changes in the control interlock status. Then, take the sequential response relationship that occurs repeatedly under the same operating condition and whose response time is within a stable range as the normal linkage relationship, and obtain the station operation dependency fingerprint.

[0046] Specifically, the preceding and following content, response time, and status maintenance status in the multi-segment linkage sequence records under the same operating condition are merged and organized. When the preceding and following content are consistent in the multi-segment linkage sequence records, and the chronological order of changes in associated equipment parameters and control interlock status changes is not reversed, the preceding and following content are marked as candidate sequential response relationships. The response time corresponding to the candidate sequential response relationship is further verified for concentration. When the response time is within the stable range formed by the normal linkage analysis object, and no abnormal status is marked in the status maintenance status, the candidate sequential response relationship is regarded as a normal linkage relationship. The operating condition type, preceding and following content, stable range of response time, and control interlock status maintenance status corresponding to the normal linkage relationship are written into the same fingerprint record to obtain the station operation dependency fingerprint. The station operation dependency fingerprint is a stable sequential linkage pattern that repeatedly occurs under the same operating condition, where changes in station equipment parameters and control interlock status changes occur.

[0047] It should be noted that when verifying the concentration of response time in the sequential response relationship of candidates, the response time between the preceding and subsequent content is calculated first. Then, the stable range of response time is determined based on the response time in the multi-segment working condition linkage sequence record. The expression for calculating the response time between the preceding and subsequent content is as follows: ; in, Indicates the first In the sequence record of the linkage between operating conditions, the preceding content To the subsequent content Response time between; Indicates the first In the sequence record of the linkage between operating conditions, the subsequent content... The time of its appearance; Indicates the first In the sequence record of the linkage between operating conditions, the preceding content The time of its appearance; Indicates preceding content; Indicates subsequent content; The sequence number indicates the sequence of operating conditions, used to distinguish different consecutive operating segments under the same operating condition.

[0048] The expression for calculating the stable range of response time is: ; ; in, Indicates preceding content To the subsequent content The lower limit of the stable range of response time; This indicates the response time in the multi-stage operating condition linkage sequence record. The 5th percentile; Indicates preceding content To the subsequent content The upper limit of the stable range of response time; This indicates the response time in the multi-stage operating condition linkage sequence record. The 95th percentile.

[0049] S2.7. Write the station operation dependency fingerprint into the association relationship between the corresponding station equipment node and the control interlock node to obtain the fingerprinted station equipment status map.

[0050] Specifically, the operating condition type in the station operation dependency fingerprint is compared item by item with the operating condition type in the association relationship between station equipment nodes and control interlocking nodes. When the operating condition types are consistent, the corresponding association relationship is retained as the writing object. Based on the operating condition type corresponding to the writing object, the equipment parameter values ​​that have not shown abnormal markers and whose operating status remains stable are extracted from the operating condition linkage sample. They are then merged according to the station equipment node and parameter type to calculate the normal parameter fluctuation range of the corresponding equipment parameters under the same operating condition. The consistency of the preceding content in the station operation dependency fingerprint with the preceding response content in the write object is checked, and the consistency of the subsequent content in the station operation dependency fingerprint with the subsequent response content in the write object is checked. If both the preceding and subsequent content are consistent, the write process is initiated. The write process writes the normal linkage sequence in the station operation dependency fingerprint into the linkage sequence field of the object, writes the stable response time range into the response time field of the object, writes the control interlock status maintenance status into the status maintenance field of the object, and writes the normal parameter fluctuation range into the parameter fluctuation field of the object. After the write process is completed, the station operation dependency fingerprint and normal parameter fluctuation range in the same write object are repeatedly verified. The write content with consistent operating condition type, consistent normal linkage sequence, consistent response time stability range and consistent normal parameter fluctuation range is retained, and the duplicate write content is deleted. After the repeated verification is completed, the association between the station equipment node and the control interlock node carries the normal linkage sequence, response time stability range, normal parameter fluctuation range and control interlock status under the corresponding operating condition, and the fingerprinted station equipment status map is obtained.

[0051] S3. Based on the fingerprint-based station equipment status map, the standard station operation and maintenance data is matched with the operating conditions and dynamic baselines. The real-time parameter change process is matched with the station operation dependency fingerprint to identify abnormal parameters, eliminate false anomalies caused by short-term disturbances, and obtain reliable abnormal events of the station.

[0052] S3.1. Determine the current operating condition based on standard station operation and maintenance data, and match the corresponding station equipment nodes, control interlocking nodes, and station operation dependency fingerprints in the fingerprinted station equipment status map to obtain the station operation and maintenance data to be compared.

[0053] Specifically, the equipment status, operating condition, and control interlocking information within the same site operation and maintenance data object are retrieved according to the collection time sequence in the standard site operation and maintenance data. The consistency of the operating condition type recorded in the site operation and maintenance association identifier with the start / stop status, load status in the equipment status content, and the interlocking status in the control interlocking content is verified. When the operating condition type corresponds to the start / stop status, load status, and interlocking status, the operating condition type is marked as the current operating condition. Once the current operating condition is established, the system searches for station equipment nodes with the same operating condition type and the same station equipment identity in the fingerprinted station equipment status map. It then compares the control interlock nodes associated with these station equipment nodes with the control interlock content in the standard station operation and maintenance data. If the control interlock nodes and control interlock content match, the system matches the station operation dependency fingerprints already written between the station equipment nodes and the control interlock nodes with the current operating condition, retaining station operation dependency fingerprints with the same operating condition type and whose normal linkage sequence can cover the changes in the current equipment status content. After matching, the standard station operation and maintenance data, the current operating condition, the station equipment nodes, the control interlock nodes, and the station operation dependency fingerprints are associated and encapsulated to obtain the station operation and maintenance data to be compared.

[0054] S3.2. Based on the operation and maintenance data of the site to be compared, extract the real-time parameter change process under the current operating conditions, and perform dynamic baseline comparison on the real-time parameter change process according to the normal parameter fluctuation range and normal response time range in the fingerprinted site equipment status map to obtain the parameter deviation result.

[0055] Specifically, the equipment status information corresponding to the same equipment node at the same station under the current operating condition is organized according to the collection time sequence. Continuously changing parameter values, parameter change directions, and parameter durations are extracted from the equipment status information to form a real-time parameter change process. After the real-time parameter change process is formed, the current operating condition and the corresponding station equipment node are matched with the fingerprinted station equipment status map to obtain the normal parameter fluctuation range and normal response time range under the same operating condition. The normal parameter fluctuation range is generated based on the operating condition linkage samples under the same operating condition. For the same equipment parameter of the same equipment node at the same station under the same operating condition, parameter values ​​without abnormal markers and with stable operating status are extracted. Abnormal sampling content corresponding to sampling interruptions, missing fields, and short-term mutations is removed to obtain a parameter value set. Based on the parameter value set, the lower limit and upper limit of the normal parameter fluctuation range are calculated. During the dynamic baseline comparison process, the parameter values ​​during the real-time parameter change process are compared with the normal parameter fluctuation range time by time. Parameter segments that exceed the normal parameter fluctuation range are marked, and the continuous parameter change time is verified to correspond with the normal response time range. Time segments with premature response, delayed response, or continuous abnormal response are marked. After the dynamic baseline comparison is completed, the parameter segments that exceed the normal parameter fluctuation range, the time segments with abnormal response time, and the corresponding field equipment nodes are associated to obtain the parameter deviation results.

[0056] S3.3. Match the real-time parameter change process corresponding to the parameter deviation result with the station operation dependency fingerprint, and identify abnormal parameters based on whether the real-time parameter change deviates from the normal linkage sequence, exceeds the normal response time range, and triggers the corresponding control interlock status change.

[0057] Specifically, the parameter segments marked in the parameter deviation results are matched segment by segment with the station operation dependency fingerprint in the station operation and maintenance data to be compared. The station equipment nodes corresponding to the parameter segments are mapped to the normal linkage sequence positions in the station operation dependency fingerprint. Then, the preceding content, following content, and control interlock status changes corresponding to the real-time parameter change process in the normal linkage sequence are checked. When a real-time parameter change occurs earlier than the preceding content, later than the following content, skips the control interlock status change in the normal linkage sequence, exceeds the normal response time range, or continuously deviates from the normal parameter fluctuation range when the control interlock status has not changed, the corresponding parameter segment is marked as a linkage deviation parameter. The linkage deviation parameter continues to be verified against the control interlock status change. If the linkage deviation parameter causes an abnormal change in the control interlock status, or if an abnormal change in the control interlock status causes the linkage deviation parameter to continuously deviate from the normal parameter fluctuation range, then the linkage deviation parameter is identified as an abnormal parameter.

[0058] S3.4. Continuously verify abnormal parameters. When an abnormal parameter only appears within a short sampling interval and does not cause damage to the linkage relationship corresponding to the station operation dependency fingerprint, the deviation process corresponding to the abnormal parameter is discarded as a pseudo-anomaly. When the abnormal parameter continues to exist and damages the linkage relationship corresponding to the station operation dependency fingerprint, the abnormal parameter and the corresponding station equipment node are regarded as reliable abnormal events of the station.

[0059] Specifically, the occurrence status of abnormal parameters in the short-term sampling interval and the continuous sampling interval is checked according to the collection time sequence, and the real-time parameter change process corresponding to the abnormal parameter is verified with the normal linkage sequence in the station operation dependency fingerprint. If the abnormal parameter only appears in the short-term sampling interval, and the real-time parameter change process corresponding to the abnormal parameter does not change the normal linkage sequence, does not exceed the normal response time range, and does not cause abnormal changes in the control interlock status, the deviation process corresponding to the abnormal parameter is marked as a pseudo-abnormality and removed from the abnormal parameters. If the abnormal parameter exists continuously in the continuous sampling interval, and the real-time parameter change process corresponding to the abnormal parameter causes deviation from the normal linkage sequence, mismatch of the normal response time range, or abnormal changes in the control interlock status, the abnormal parameter and the corresponding station equipment node are merged and recorded as a station reliable abnormal event.

[0060] It should be noted that, Figure 5 This is used to verify the effectiveness of the present invention in identifying credible abnormal events at the site under different abnormal duration groupings; The present invention shown in the figure refers to a solution for handling reliable abnormal events of a station based on a fingerprint-based station equipment status map, which involves matching operating conditions, comparing dynamic baselines, matching station operation dependency fingerprints, and continuously verifying standard station operation and maintenance data, and eliminating false anomalies caused by short-term disturbances. The control scheme refers to a conventional alarm scheme that judges anomalies solely based on single-point threshold exceeding and alarm persistence status. Depend on Figure 5 It is evident that the pseudo-anomaly elimination rate of the present invention is significantly higher than that of the control scheme in the short-term disturbance range, and the accuracy of identifying reliable anomaly events at the site remains at a high level as the duration of the anomaly increases. This indicates that the present invention can effectively distinguish between short-term disturbances and real linkage anomalies through site operation dependency fingerprints and continuous verification, thereby reducing the entry of pseudo-anomalies into the subsequent anomaly propagation analysis process.

[0061] S4. Based on the trusted anomaly events and fingerprinted station equipment status map, identify the temporal and control correlations between anomaly nodes, establish anomaly transmission relationships, generate anomaly propagation sequences, and backtrack from subsequent anomalies according to the anomaly occurrence time and control interlock triggering order to screen anomaly source equipment, determine the anomaly impact domain and anomaly propagation blocking point, and obtain the station operation and maintenance risk location results.

[0062] S4.1. Map the trusted abnormal events of the station to the station equipment nodes and control interlocking nodes in the fingerprinted station equipment status map, and determine the abnormal nodes according to the abnormal parameters, abnormal occurrence time and abnormal duration of the trusted abnormal events of the station, so as to obtain the abnormal node set.

[0063] Specifically, starting with the abnormal parameters and corresponding station equipment nodes in the trusted anomaly events at the station, the system searches for station equipment nodes with consistent station equipment identity and operating condition type in the fingerprinted station equipment status map. It then performs correlation verification between the control interlock status changes triggered or accompanied by the abnormal parameters and the control interlock nodes. This correlation verification is confirmed through consistency of station equipment identity, operating condition type, the time of anomaly occurrence and the range of linkage processes in the station's operational dependency fingerprint, and the consistency of control interlock status changes and the status content of the control interlock nodes. When a trusted anomaly event corresponds only to a station equipment node, the station equipment node is marked as an anomaly node. When a trusted anomaly event also involves changes in control interlock status, both the station equipment node and the control interlock node are marked as an anomaly node. After marking the anomaly nodes, the abnormal parameters, the time of anomaly occurrence, and the duration of the anomaly are written into the corresponding anomaly node, forming a set of anomaly nodes.

[0064] S4.2. Based on the set of abnormal nodes, extract the station operation dependency fingerprint from the fingerprinted station equipment status map, and determine whether there is a temporal association between abnormal nodes caused by a previous abnormality based on the normal linkage sequence recorded in the station operation dependency fingerprint, and obtain the abnormal temporal association result.

[0065] Specifically, according to the operating condition type corresponding to the abnormal node, the station operation dependency fingerprint under the same operating condition type is matched in the fingerprinted station equipment status map. The abnormal nodes in the abnormal node set are placed one by one into the normal linkage sequence of the station operation dependency fingerprint record for position matching. After the position matching is completed, the abnormal occurrence time of each abnormal node is checked with the order in the normal linkage sequence. If the abnormal node in the preceding position occurs first and the abnormal node in the following position occurs later, and the change direction of the abnormal parameter corresponding to the abnormal node in the following position is consistent with the subsequent response direction of the station operation dependency fingerprint record, then the abnormal node in the preceding position and the abnormal node in the following position are marked as candidate time-series associated nodes. The candidate time-series associated nodes continue to undergo response time verification. The abnormal interval between the abnormal node in the preceding position and the abnormal node in the following position is matched with the stable range of response time in the station operation dependency fingerprint. If the abnormal interval is within the stable range of response time, it is confirmed that the abnormal node in the preceding position can cause the abnormal node in the following position, and the abnormal time-series association result is obtained. The abnormal time-series association result is used to record whether there is a time-series relationship between abnormal nodes where the preceding abnormality causes the subsequent abnormality. The abnormal time-series association result includes the preceding abnormal node, the following abnormal node, the order of occurrence of abnormality, the abnormal interval, the consistency of the direction of change of abnormal parameters, and the status of the time-series association.

[0066] S4.3. Based on the abnormal timing correlation results, perform state verification on the control interlocking relationship between abnormal nodes, and determine whether the change in control interlocking state occurs after the change in abnormal parameters and whether it can affect subsequent abnormal nodes, thereby obtaining the abnormal control correlation results.

[0067] Specifically, the preceding and subsequent abnormal nodes in the abnormal time sequence association results are matched with the control interlocking relationships in the fingerprinted site equipment status map to confirm whether the preceding and subsequent abnormal nodes are within the same control interlocking relationship constraint range. After the matching is completed, the abnormal parameter change time, control interlocking status change time, and abnormal occurrence time of the preceding abnormal node are checked sequentially. If the control interlocking status change occurs after the abnormal parameter change of the preceding abnormal node and before the abnormal occurrence time of the subsequent abnormal node, the control interlocking status change is marked as a candidate control transmission content. The candidate control transmission content continues to be verified against the interlocking trigger conditions and interlocking feedback status in the control interlocking relationship. If the change in control interlocking status can change the start / stop status, action sequence, or control permission status of the corresponding station equipment node of the subsequent abnormal node, then it is confirmed that there is a control association between the preceding and subsequent abnormal nodes. The preceding abnormal node, the subsequent abnormal node, the order of control interlocking status changes, the interlocking trigger condition matching status, and the control association establishment status are written into the same association record to obtain the abnormal control association result. The abnormal control association result is used to record whether there is a control transmission relationship formed by the change in control interlocking status between the preceding and subsequent abnormal nodes. The abnormal control association result includes the preceding abnormal node, the subsequent abnormal node, the order of control interlocking status changes, the interlocking trigger condition matching status, the interlocking feedback status matching status, and the control association establishment status.

[0068] S4.4. Establish anomaly propagation relationships for anomaly nodes that simultaneously satisfy both anomaly timing association results and anomaly control association results, and arrange the anomaly nodes according to the order of anomaly occurrence corresponding to the anomaly propagation relationships to generate anomaly propagation sequences.

[0069] Specifically, the abnormal timing association results and abnormal control association results are verified to correspond. The same preceding and subsequent abnormal nodes are used as the pairing condition. Abnormal nodes whose timing association and control association are both established are retained as transmission candidate pairs. After the transmission candidate pairs enter the abnormal transmission relationship establishment process, the preceding abnormal node is used as the abnormal transmission start point and the subsequent abnormal node is used as the abnormal transmission end point. The order of abnormal occurrence, the order of control interlock status changes, and the status of control association establishment are written into the same abnormal transmission relationship. After multiple anomaly propagation relationships are formed, they are arranged in order according to the time of occurrence of the anomaly at the starting point and the time of occurrence of the anomaly at the ending point. Anomaly propagation relationships with common anomaly nodes are merged according to their sequential connection, and anomaly propagation relationships with parallel anomaly nodes are sorted according to their time of occurrence. This yields an anomaly propagation sequence that can characterize the process of anomaly propagation from the preceding anomaly node to the following anomaly node.

[0070] S4.5. Select subsequent abnormal nodes based on the abnormal propagation sequence, and backtrack along the abnormal propagation relationship according to the abnormal occurrence time and control interlock triggering order to determine whether the preceding abnormal nodes can explain the abnormal parameter changes and control interlock state changes of the subsequent abnormal nodes, and obtain the abnormal backtracking result.

[0071] Specifically, the anomaly propagation sequence enters the backtracking process. The subsequent anomaly node in the anomaly propagation sequence that is located in the later part and has a preceding anomaly propagation relationship is selected. The anomaly parameter change process and control interlock state change process corresponding to the subsequent anomaly node are used as the backtracking verification objects. The preceding anomaly node is traced back along the anomaly propagation sequence that has been established. The anomaly occurrence time is checked to see if the preceding anomaly node occurred earlier than the subsequent anomaly node. The control interlock state change corresponding to the preceding anomaly node can be propagated to the subsequent anomaly node according to the control interlock triggering order. If the direction of change of abnormal parameters of a preceding abnormal node is linked to the direction of change of abnormal parameters of a subsequent abnormal node, and the change of control interlock state corresponding to the preceding abnormal node can trigger the change of control interlock state of the subsequent abnormal node, the preceding abnormal node is marked as a candidate backtracking node that can explain the subsequent abnormal node. After completing the backtracking verification, the subsequent abnormal node, candidate backtracking node, the order of occurrence of the abnormality, the control interlock triggering order, and the explanation status are written into the same backtracking record to obtain the abnormal backtracking result. The abnormal backtracking result is used to record which preceding abnormal nodes can explain the subsequent abnormal node. The abnormal backtracking result includes the subsequent abnormal node, candidate backtracking node, the order of occurrence of the abnormality, the control interlock triggering order, the correlation status of abnormal parameter changes, and the explanation status.

[0072] S4.6. Based on the anomaly backtracking results, screen the station equipment nodes that first showed anomalies and subsequently showed continuous anomalies. Mark the screened station equipment nodes as anomaly source devices. Based on the anomaly source devices and the anomaly propagation sequence, delineate the range of subsequent anomaly nodes and related nodes affected by the anomaly source devices to obtain the anomaly influence domain.

[0073] Specifically, the anomaly backtracking results are used to screen the anomaly source devices. The anomaly occurrence time and explanation validity status of each candidate backtracking node are summarized in the anomaly backtracking results. It is also verified whether the candidate backtracking nodes can continuously explain the abnormal parameter changes and control interlock status changes of multiple subsequent anomaly nodes in the anomaly propagation sequence. When the station equipment node corresponding to a candidate backtracking node experiences an anomaly earlier than other candidate backtracking nodes, and the interpretation confirms that the state covers subsequent continuous anomaly changes, the station equipment node corresponding to the candidate backtracking node is marked as the anomaly source device. After the anomaly source device is marked, the subsequent anomaly nodes that have established anomaly propagation relationships are sorted out from the anomaly source device backward along the anomaly propagation sequence, and the station equipment nodes and control interlock nodes corresponding to the subsequent anomaly nodes are included in the affected range. The affected range is further verified against the correlation in the fingerprinted station equipment state map, retaining the range of associated nodes that have anomaly propagation relationships or control interlock constraints with the anomaly source device, and eliminating the range of nodes that only have time overlap but no anomaly propagation relationship, thus obtaining the anomaly influence domain.

[0074] S4.7. Based on the abnormal propagation sequence and the abnormal impact domain, screen the station equipment nodes located between the abnormal source device and the abnormal impact domain that can cut off the continued propagation of the abnormality, and mark them as abnormal propagation blocking points. Associate the abnormal source device, the abnormal impact domain and the abnormal propagation blocking points to obtain the station operation and maintenance risk location results.

[0075] Specifically, the abnormal propagation sequence and abnormal impact domain enter the abnormal propagation blocking point screening. The abnormal transmission relationship from the abnormal source device to the abnormal impact domain in the abnormal propagation sequence is verified segment by segment. It is verified whether each field station equipment node is located between the abnormal source device and the abnormal impact domain, whether it inherits the abnormal transmission relationship of the previous abnormal node, whether it continues to point to the subsequent abnormal node, and in combination with the control interlock relationship in the fingerprinted field station equipment status map, it is verified whether the field station equipment node can prevent the abnormal from continuing to propagate through control permission status change, action sequence suspension, or interlock feedback interruption. When a station equipment node simultaneously meets the conditions of intermediate location, transmission acceptance, and blocking, the station equipment node is marked as an abnormal propagation blocking point. After marking the abnormal propagation blocking point, the abnormal occurrence location corresponding to the abnormal source equipment, the range of affected nodes corresponding to the abnormal impact domain, the blocking location corresponding to the abnormal propagation blocking point, and the abnormal transmission relationship are written into the same risk location record to obtain the station operation and maintenance risk location result.

[0076] S5. Based on the results of the site operation and maintenance risk location, determine the priority relationship of handling the abnormal source equipment, the abnormal impact domain and the abnormal propagation blocking point, and generate the site operation and maintenance handling strategy by combining the fingerprinted site equipment status map.

[0077] S5.1. Based on the site operation and maintenance risk location results, conduct risk correlation analysis on the abnormal source equipment, abnormal impact domain and abnormal propagation blocking point, and generate the disposal priority relationship according to the abnormal occurrence location of the abnormal source equipment, the diffusion range of the abnormal impact domain and the blocking effect of the abnormal propagation blocking point on the abnormal transmission path.

[0078] Specifically, risk correlation analysis is performed based on the site operation and maintenance risk location results. This involves reading the abnormal source equipment, abnormal impact domain, abnormal propagation blocking point, and abnormal transmission relationship from the site operation and maintenance risk location results, and establishing a risk correlation record based on the abnormal transmission relationship. The risk correlation record records the source relationship between the abnormal source equipment and subsequent abnormal nodes, the diffusion relationship between the abnormal source equipment and the abnormal impact domain, and the blocking relationship between the abnormal propagation blocking point and the abnormal transmission path. When analyzing the source relationship of the abnormal source equipment, it is verified whether the abnormal occurrence location of the abnormal source equipment is at the beginning of the abnormal propagation sequence, whether the abnormal occurrence time of the abnormal source equipment is earlier than the abnormal occurrence time of subsequent abnormal nodes within the abnormal impact domain, and whether the abnormal parameter changes of the abnormal source equipment can explain the abnormal parameter changes and control interlock status changes of subsequent abnormal nodes. When the abnormal source equipment simultaneously meets the conditions for the starting location, the initial abnormality, and the explanation of subsequent abnormalities, a source relationship is established between the abnormal source equipment and subsequent abnormal nodes. A diffusion relationship analysis is performed on the anomaly impact domain. The station equipment nodes and control interlocking nodes affected by the anomaly propagation sequence are retrieved from the anomaly source equipment backward. The range of affected nodes is compared with the range of associated nodes in the fingerprinted station equipment status map. When there is an anomaly propagation relationship, control interlocking constraint relationship or normal linkage relationship between the affected node and the anomaly source equipment, the affected node is included in the anomaly impact domain, and a diffusion relationship is established between the anomaly source equipment and the anomaly impact domain.

[0079] Anomaly propagation blocking point analysis is performed to verify whether it is located on the anomaly transmission path between the anomaly source device and the anomaly impact domain, whether it receives the preceding anomaly node and points to the subsequent anomaly node, and whether the anomaly propagation blocking point can cut off the continued transmission of the anomaly by changing the control permission status, stopping the action sequence, or interrupting the interlock feedback, based on the control permission status, interlock triggering conditions, and interlock feedback status. When the anomaly propagation blocking point simultaneously meets the conditions of being in the middle of the path, receiving anomaly transmission, and cutting off anomaly transmission, a blocking relationship is established between the anomaly propagation blocking point and the anomaly transmission path. The anomaly source device, the anomaly impact domain, the anomaly propagation blocking point, the anomaly transmission path, the source relationship, the diffusion relationship, and the blocking relationship are written into the same risk association record. Based on the risk association record, it is determined whether the anomaly propagation blocking point can preferentially cut off the anomaly diffusion, whether the anomaly source device needs to be investigated for root causes, and whether the anomaly impact domain needs to be continuously monitored, and a priority relationship for handling is generated accordingly.

[0080] S5.2. Based on the priority of handling, the abnormal source equipment is taken as the fault investigation object, the abnormal propagation blocking point is taken as the risk control object, and the abnormal impact area is taken as the linkage monitoring object, forming a set of site operation and maintenance handling objects.

[0081] Specifically, the abnormal source equipment, abnormal propagation blocking point, and abnormal impact area are divided into handling objects according to the handling priority relationship. The abnormal source equipment ranked in the fault investigation position in the handling priority relationship is marked as the fault investigation object, and the abnormal occurrence location, abnormal parameters, and abnormal occurrence time of the abnormal source equipment are written into the fault investigation object; the abnormal propagation blocking point ranked in the priority handling position in the handling priority relationship is marked as the risk control object, and the blocking position, abnormal transmission relationship, and control interlock relationship of the abnormal propagation blocking point are written into the risk control object. Mark the abnormal impact domains that are ranked in the linkage monitoring position in the priority relationship as linkage monitoring objects, and write the affected node range, subsequent abnormal nodes and related node ranges corresponding to the abnormal impact domains into the linkage monitoring objects; after completing the object division and content writing, combine the fault investigation objects, risk control objects and linkage monitoring objects according to the priority relationship to form a set of site operation and maintenance disposal objects.

[0082] S5.3. Match the set of station operation and maintenance disposal objects with the fingerprinted station equipment status map, and combine the control interlocking relationship, control permission status and station operation dependency fingerprint of the corresponding equipment node to determine the operation and maintenance action type applicable to different disposal objects and obtain the disposal action matching result.

[0083] Specifically, when matching the set of station operation and maintenance objects with the fingerprinted station equipment status map, the fault investigation objects, risk control objects, and linkage monitoring objects are respectively mapped to the station equipment nodes and control interlock nodes in the fingerprinted station equipment status map, and the control interlock relationships, control permission states, and station operation dependency fingerprints of the corresponding nodes are compared item by item; when the station equipment node corresponding to the fault investigation object has continuous abnormal parameters and the control permission state does not restrict on-site verification, the fault investigation action is taken as the applicable operation and maintenance action type; When the anomaly propagation blocking point corresponding to the risk control object is located in the middle of the anomaly transmission path, and the control interlock relationship can change the direction of the anomaly's continued transmission, the interlock control action is taken as the applicable operation and maintenance action type. When the anomaly impact domain corresponding to the linkage monitoring object includes the range of subsequent anomaly nodes or related nodes, the linkage monitoring action is taken as the applicable operation and maintenance action type. After completing the mapping between different disposal objects and operation and maintenance action types, the disposal object name, corresponding site equipment node, corresponding control interlock node, control permission status, and applicable operation and maintenance action type are written into the same matching record to obtain the disposal action matching result.

[0084] S5.4. Based on the matching results of the handling actions, configure fault investigation strategies for the abnormal source equipment, interlock control strategies for the abnormal propagation blocking points, and linkage monitoring strategies for the abnormal impact domain. Combine the fault investigation strategies, interlock control strategies, and linkage monitoring strategies to generate the site operation and maintenance handling strategies.

[0085] Specifically, after the handling action matching results are entered into the strategy configuration, the matching records of the corresponding abnormal source equipment in the handling action matching results are transferred into the fault investigation strategy configuration process. According to the abnormal parameters of the abnormal source equipment, the time of abnormal occurrence, the corresponding station equipment node and the applicable fault investigation actions, the on-site verification content, parameter review content and fault cause investigation content are configured to form a fault investigation strategy. The matching records of the corresponding abnormal propagation blocking points in the matching results of the handling actions are transferred into the interlocking control strategy configuration process. According to the blocking position of the abnormal propagation blocking point, the corresponding control interlocking node, the control permission status and the applicable interlocking control actions, the control interlocking status review content, the action sequence suspension content and the interlocking feedback confirmation content are configured to form the interlocking control strategy. The matching records of the corresponding abnormal impact domain in the matching results of the handling actions are transferred into the linkage monitoring strategy configuration process. According to the affected node range of the abnormal impact domain, subsequent abnormal nodes and applicable linkage monitoring actions, the status tracking content of related nodes, the parameter change verification content and the abnormal spread observation content are configured to form a linkage monitoring strategy. After completing the strategy configuration, the interlocking control strategy is arranged in the risk control position, the fault investigation strategy is arranged in the fault investigation position, and the linkage monitoring strategy is arranged in the linkage monitoring position according to the priority of handling. The fault investigation strategy, interlocking control strategy and linkage monitoring strategy are then combined to generate the site operation and maintenance handling strategy.

[0086] This embodiment also provides a computer device applicable to the intelligent operation and maintenance method for industrial internet-based sites, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the intelligent operation and maintenance method for industrial internet-based sites proposed in the above embodiment.

[0087] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0088] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the intelligent operation and maintenance method for industrial internet-based sites as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0089] In summary, this invention identifies the temporal and control correlations between abnormal nodes based on trusted abnormal events and fingerprinted station equipment status maps, and traces back from subsequent abnormalities according to the time of abnormal occurrence and the order of control interlock triggering. This provides an interpretable risk location basis for generating priority relationships for handling, improving the effectiveness of station operation and maintenance handling strategies to better reflect real-world linkages and reducing the probability of mishandling.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent operation and maintenance of power stations based on the Industrial Internet, characterized in that, include: Collect station operation-related data, perform protocol parsing and objectification processing on the station operation-related data, and bind station operation and maintenance related identifiers to obtain standard station operation and maintenance data; Based on standard station operation and maintenance data, the association between station equipment nodes and control interlocking nodes is established, and station operation dependency fingerprints are generated according to the parameter change sequence of associated equipment and the control interlocking status change sequence under different operating conditions, resulting in a fingerprinted station equipment status map. The process of generating a station operation dependency fingerprint based on the parameter change sequence and control interlock status change sequence of associated equipment under different operating conditions, and obtaining a fingerprinted station equipment status map, is as follows: Based on the relationship between the equipment nodes and control interlocking nodes of the station, continuous operating data under the same operating condition is extracted from the standard station operation and maintenance data. The part of the continuous operating data that does not show abnormal markers and whose operating status remains stable is taken as the normal linkage analysis object to obtain the operating condition linkage sample. The timing alignment of the parameter change process and control interlock status change process of the associated equipment in the working condition linkage sample is performed, and the linkage response sequence of the associated equipment and control interlock node under the same operating condition is determined based on the time when the parameter starts to deviate, the direction of parameter change, the control interlock status switching time and the control interlock status holding result, so as to obtain the working condition linkage sequence record. Based on the working condition linkage sequence record, determine whether there is a repeatable sequential response relationship between the changes in the parameters of the associated equipment and the changes in the control interlock status. Then, take the sequential response relationship that occurs repeatedly under the same operating condition and whose response time is within a stable range as the normal linkage relationship, and obtain the station operation dependency fingerprint. The station operation dependency fingerprint is written into the association relationship between the corresponding station equipment node and the control interlock node to obtain a fingerprinted station equipment status map; Based on the fingerprint-based station equipment status map, the standard station operation and maintenance data is matched with the operating conditions and dynamic baselines. The real-time parameter change process is matched with the station operation dependency fingerprint to identify abnormal parameters, eliminate false anomalies caused by short-term disturbances, and obtain reliable abnormal events of the station. Based on the trusted anomaly events and fingerprinted equipment status map of the site, the temporal and control correlations between anomaly nodes are identified, anomaly transmission relationships are established, anomaly propagation sequences are generated, and the anomaly source equipment is screened by tracing back from the subsequent anomalies according to the anomaly occurrence time and control interlock triggering order, anomaly impact domain and anomaly propagation blocking point are determined, and the site operation and maintenance risk location results are obtained. Based on the results of the site operation and maintenance risk location, the priority relationship of handling the abnormal source equipment, the abnormal impact domain and the abnormal propagation blocking point is determined, and the site operation and maintenance handling strategy is generated by combining the fingerprinted site equipment status map.

2. The intelligent operation and maintenance method for power stations based on the Industrial Internet as described in claim 1, characterized in that, The process involves collecting station operation-related data, parsing and objectifying this data, and binding it with station operation and maintenance association identifiers to obtain standard station operation and maintenance data. The specific steps are as follows: Collect relevant data on the operation of the station, identify the data source and calibrate the collection time of the relevant data to obtain initial station operation data, perform protocol parsing on the initial station operation data, extract the equipment status content, operating condition content and control interlock content from the initial station operation data, and perform format unification and abnormal sampling and screening on the parsed data content to obtain standardized station operation data; The system matches standardized station operation data with station operation and maintenance association rules, identifies the station equipment identity, operating condition type, and control interlock relationship corresponding to the standardized station operation data, and configures station operation and maintenance association identifiers for the standardized station operation data. Standardized station operation data is objectified and encapsulated according to the station operation and maintenance association identifier. Standardized station operation data belonging to the same station equipment and the same operating condition are written into the same station operation and maintenance data object to obtain standard station operation and maintenance data.

3. The intelligent operation and maintenance method for power stations based on the Industrial Internet as described in claim 1, characterized in that, The specific steps for establishing the association between station equipment nodes and control interlocking nodes based on standard station operation and maintenance data are as follows: Based on the identification of the site operation and maintenance association identifier, the site equipment identity corresponding to the standard site operation and maintenance data is identified, and the site operation and maintenance data object corresponding to the same site equipment identity is configured as a site equipment node; Based on the control interlocking content corresponding to the station equipment nodes, identify the control constraint relationships corresponding to the station equipment nodes under different operating conditions, and configure the control constraint relationships as control interlocking nodes; Based on the control constraints and linkage response relationships between the station equipment nodes and the control interlocking nodes under the same operating conditions, the association relationship between the station equipment nodes and the control interlocking nodes is established.

4. The intelligent operation and maintenance method for power stations based on the Industrial Internet as described in claim 1, characterized in that, The process involves matching standard station operation and maintenance data with operating conditions and comparing dynamic baselines based on fingerprint-based station equipment status maps. It also matches real-time parameter changes with the station's operational fingerprints to identify abnormal parameters, eliminate false anomalies caused by short-term disturbances, and obtain reliable station anomaly events. The specific steps are as follows: The current operating condition is determined based on standard station operation and maintenance data, and the corresponding station equipment nodes, control interlocking nodes and station operation dependency fingerprints are matched in the fingerprinted station equipment status map to obtain the station operation and maintenance data to be compared. Based on the operation and maintenance data of the site to be compared, the real-time parameter change process under the current operating conditions is extracted. According to the normal parameter fluctuation range and normal response time range in the fingerprinted site equipment status map, the real-time parameter change process is dynamically baseline compared to obtain the parameter deviation result. The real-time parameter change process corresponding to the parameter deviation result is matched with the station operation dependency fingerprint. Abnormal parameters are identified based on whether the real-time parameter change deviates from the normal linkage sequence, exceeds the normal response time range, and triggers the corresponding control interlock status change. Continuous verification of abnormal parameters is performed. When an abnormal parameter only appears within a short sampling interval and does not cause damage to the linkage relationship corresponding to the station operation dependency fingerprint, the deviation process corresponding to the abnormal parameter is discarded as a pseudo-anomaly. When an abnormal parameter persists and damages the linkage relationship corresponding to the station operation dependency fingerprint, the abnormal parameter and the corresponding station equipment node are regarded as reliable abnormal events of the station.

5. The intelligent operation and maintenance method for power stations based on the Industrial Internet as described in claim 1, characterized in that, Based on the reliable anomaly events at the site and the fingerprinted site equipment status map, the temporal and control correlations between anomaly nodes are identified, anomaly propagation relationships are established, and anomaly propagation sequences are generated. The specific steps are as follows: The trusted anomaly events of the station are mapped to the station equipment nodes and control interlock nodes in the fingerprinted station equipment status map, and the anomaly nodes are determined according to the anomaly parameters, anomaly occurrence time and anomaly duration status corresponding to the trusted anomaly events of the station, thus obtaining the set of anomaly nodes; Based on the set of abnormal nodes, the station operation dependency fingerprint is extracted from the fingerprinted station equipment status map. Based on the normal linkage sequence recorded in the station operation dependency fingerprint, it is determined whether there is a temporal association between abnormal nodes caused by a previous abnormality, and the abnormal temporal association result is obtained. Based on the abnormal timing correlation results, the control interlock relationship between abnormal nodes is verified, and it is determined whether the change in control interlock state occurs after the change in abnormal parameters and whether it can affect subsequent abnormal nodes, so as to obtain the abnormal control correlation results. Anomaly propagation relationships are established for anomaly nodes that simultaneously satisfy both anomaly timing association results and anomaly control association results. The anomaly nodes are then arranged according to the order in which the anomalies appear, generating an anomaly propagation sequence.

6. The intelligent operation and maintenance method for power stations based on the Industrial Internet as described in claim 5, characterized in that, The process involves tracing back from subsequent anomalies according to their occurrence time and control interlock trigger sequence, filtering the anomaly source equipment, determining the anomaly impact domain and anomaly propagation blocking point, and obtaining the site operation and maintenance risk location results. The specific steps are as follows: Select subsequent abnormal nodes based on the abnormal propagation sequence, and backtrack along the abnormal propagation relationship according to the abnormal occurrence time and the control interlock triggering order to determine whether the preceding abnormal nodes can explain the abnormal parameter changes and control interlock state changes of the subsequent abnormal nodes, and obtain the abnormal backtracking results. Based on the anomaly backtracking results, the earliest anomaly-causing and subsequently continuously changing station equipment nodes are selected. The selected station equipment nodes are marked as anomaly source devices. Based on the anomaly source devices and the anomaly propagation sequence, the range of subsequent anomaly nodes and related nodes affected by the anomaly source devices is delineated to obtain the anomaly impact domain. Based on the abnormal propagation sequence and the abnormal impact domain, the station equipment nodes located between the abnormal source device and the abnormal impact domain that can cut off the continued propagation of the abnormality are selected and marked as abnormal propagation blocking points. The abnormal source device, the abnormal impact domain and the abnormal propagation blocking points are associated to obtain the station operation and maintenance risk location results.

7. The intelligent operation and maintenance method for power stations based on the Industrial Internet as described in claim 6, characterized in that, The process involves determining the priority relationships for handling anomaly source equipment, anomaly impact domain, and anomaly propagation blocking points based on the site operation and maintenance risk location results, and generating a site operation and maintenance handling strategy by combining the fingerprinted site equipment status map. The specific steps are as follows: Based on the risk location results of the site operation and maintenance, risk correlation analysis is performed on the abnormal source equipment, the abnormal impact domain, and the abnormal propagation blocking point. Based on the abnormal occurrence location of the abnormal source equipment, the diffusion range of the abnormal impact domain, and the blocking effect of the abnormal propagation blocking point on the abnormal transmission path, a priority relationship for handling is generated. Based on the priority of handling, the abnormal source equipment is taken as the fault investigation object, the abnormal propagation blocking point is taken as the risk control object, and the abnormal impact area is taken as the linkage monitoring object, forming a set of site operation and maintenance handling objects; The set of station operation and maintenance disposal objects is matched with the fingerprinted station equipment status map. Combined with the control interlocking relationship, control permission status and station operation dependency fingerprint of the corresponding equipment node, the type of operation and maintenance action applicable to different disposal objects is determined, and the disposal action matching result is obtained. Based on the matching results of the handling actions, a fault investigation strategy is configured for the abnormal source equipment, an interlocking control strategy is configured for the abnormal propagation blocking point, and a linkage monitoring strategy is configured for the abnormal impact domain. The fault investigation strategy, interlocking control strategy and linkage monitoring strategy are combined to generate the site operation and maintenance handling strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent operation and maintenance method for industrial internet-based sites as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent operation and maintenance method for site based on the Industrial Internet as described in any one of claims 1 to 7.

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