A power equipment operation and maintenance decision management system based on big data analysis

Abstract

The application relates to the technical field of power equipment operation and maintenance management and big data analysis, in particular to a power equipment operation and maintenance decision management system based on big data analysis, which comprises a data access unit, a state data arrangement unit, an equipment state change evaluation unit, an alarm merging and fault point determination unit and the like which are communicatively connected; wherein the data access unit accesses operation, asset, topology, operation resource, inventory, business consumption code and environment data and carries out unified mapping processing, and outputs standardized data objects containing equipment identification, function position identification, a time stamp and a main data version number; the state data arrangement unit is hung according to three-level objects of equipment, components and function positions, carries out missing value completion, abnormal jump elimination, event numbering and cycle cache area establishment, forms a state data set, thereby reducing misjudgment and providing a unified data basis for subsequent maintenance decision and on-site release judgment.

Description

Technical Field

[0001] This invention relates to the field of power equipment operation and maintenance management and big data analysis technology, specifically a power equipment operation and maintenance decision management system based on big data analysis. Background Technology

[0002] Power equipment operation and maintenance decision management based on big data analytics refers to the comprehensive processing of operation data, asset maintenance data, topology data, work resource data, inventory data, financial data, and environmental data, and the use of this data to conduct equipment status assessment, alarm merging, maintenance determination, resource allocation, and on-site operation collaboration. Current power equipment operation and maintenance management methods typically include status analysis based on real-time monitoring data and operation and maintenance management based on work orders, invoices, and inventory information. However, when managing the operation and maintenance of power equipment based on existing technologies, on the one hand, the equipment codes, functional locations, timestamps, and master data versions of different systems are often inconsistent, which can easily lead to inaccurate data aggregation, status identification deviations, and distorted alarm associations. On the other hand, existing methods lack unified linkage processing between equipment status changes, topology impacts, resource matching, and on-site safety interlocks. They also suffer from inaccurate work order priority determination, insufficient connection between resource allocation and procurement needs, and low reliability of on-site release, all of which reduce the accuracy of maintenance decisions and execution efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide a power equipment operation and maintenance decision management system based on big data analysis, addressing the following technical problems: Existing power equipment operation and maintenance management systems suffer from significant shortcomings in multi-source data merging distortion and the lack of unified linkage processing for status changes and on-site resource coordination. There is an urgent need for a system that can unify multi-source data mapping, accurately assess status alarms, and efficiently coordinate resource allocation and on-site interlocking release. This invention's objective can be achieved through the following technical solutions: A power equipment operation and maintenance decision management system based on big data analysis includes a data access unit for communication connection, a status data processing unit, an equipment status change assessment unit, an alarm aggregation and fault point determination unit, a maintenance judgment rule unit, a resource allocation and standard work package matching unit, a work order and work ticket operation ticket coordination unit, an on-site safety interlock execution unit, a material procurement demand generation unit, and a maintenance resource consumption collection and standard library update unit. The data access unit is used to access and perform unified mapping processing on operation, asset, topology, job resources, inventory, business consumption codes and environmental data; the status data processing unit is used to construct a set of equipment status data; the equipment status change assessment unit is used to generate equipment status values; the alarm merging and fault point determination unit is used to merge alarms and determine the severity of fault points. The maintenance judgment rule unit is used to generate status classification threshold rules, work order priority rules, and on-site interlocking conditions; the resource allocation and standard work package matching unit is used to perform resource matching and output resource matching results or resource shortage results; the work order and work ticket operation ticket coordination unit is used to generate work orders, work tickets, operation tickets, and isolation applications; the on-site safety interlocking execution unit is used to make release judgments based on the interlocking condition set; the material procurement demand generation unit is used to generate procurement demand information; and the maintenance resource consumption collection and standard library update unit is used to collect work order resource consumption and update the fault record library, interlocking record library, and related rule parameters.

[0004] Preferably, the data access unit includes a real-time operation data access module, an asset operation and maintenance data access module, a spatial topology data access module, an operational resource data access module, and an environmental data access module; the real-time operation data access module accesses real-time data from the data acquisition and monitoring control system and IoT measurement point data; the asset operation and maintenance data access module accesses equipment asset ledgers, defect standard libraries, fault standard libraries, and business consumption code data; the spatial topology data access module accesses topology and spatial location data; the operational resource data access module accesses standard operation packages, personnel role and shift data, and material lists and inventory ledgers; and the environmental data access module accesses meteorological and environmental data. The data access unit is also used to perform unified mapping processing of device identifier, functional location identifier, timestamp and master data version number for each access data, and output standardized data objects.

[0005] Preferably, the status data processing unit is used to construct a status data set for the device, including operating status data, asset maintenance data, and spatial environment data. Specifically, the status data processing unit is used to map the access data to three-level objects (device, component, and functional location) according to the unified device identifier, perform missing value completion and abnormal jump removal processing on the mapped data, process the action event quantity and status event quantity into event numbers, establish a periodic cache according to the measurement point category, and synchronize the edge measurement points with the cloud master data version number, and organize the cleaned data, event number data, and version number data into the status data set.

[0006] Preferably, the equipment status change assessment unit is used to generate an equipment status value based on the change rate of continuous measurement points in the status data set, the parameter deviation value of preset key operating parameters within a preset time period, the degree of conformity between the current equipment status and the defect patterns in the defect standard library, and the historical fault tendency value; wherein, the degree of conformity is the matching ratio between the current equipment status characteristics and the corresponding defect pattern characteristic items, the historical fault tendency value is determined based on the defect frequency, service life, and degree of non-performance of maintenance due within a recent set period, and the equipment status value is determined based on the change rate, parameter deviation value, degree of conformity, and historical fault tendency value.

[0007] Preferably, the alarm merging and fault point determination unit is used to calculate the alarm correlation degree based on the temporal proximity of alarms, the topological relationship of the geographic information system, and the relationship of measurement point categories, and to classify alarms with a correlation degree that meets a preset merging threshold into the same alarm group; wherein, the alarm correlation degree is determined based on temporal proximity, topological proximity, and measurement point category matching degree; the alarm merging and fault point determination unit is also used to determine the severity of the fault point according to the status value of the corresponding device in the alarm group, the size of the alarm group, and the topological influence range, and to output the main alarm and auxiliary alarm identification information; The severity of the fault point is determined by the weighted sum of the standardized state value, alarm combination size, and topology impact range, and is classified according to a low severity threshold and a high severity threshold, wherein the low severity threshold is less than the high severity threshold; when the severity value is less than the low severity threshold, it is marked as general; when the severity value is greater than or equal to the low severity threshold and less than the high severity threshold, it is marked as important; and when the severity value is greater than or equal to the high severity threshold, it is marked as urgent.

[0008] Preferably, the maintenance judgment rule unit includes a threshold generation module, a work order priority module, and a field interlocking condition generation module; the threshold generation module is used to generate a current state classification threshold based on the equipment status value and load offset, and to construct switching rules for normal state, attention state, and warning state, wherein the load offset is the deviation of the current load from the rated load or planned load; The switching rules include at least a focus threshold and a warning threshold, wherein the focus threshold is less than the warning threshold; when the device status value is less than the focus threshold, it is marked as normal; when the device status value is greater than or equal to the focus threshold and less than the warning threshold, it is marked as focus; when the device status value is greater than or equal to the warning threshold, it is marked as warning; the work order priority module is used to generate work order priorities based on the device status value, power supply impact, operational safety risk, deadline urgency, and resource scarcity of the work order. The work order priority is a weighted sum of the standardized values ​​of the equipment status, power supply impact, operational safety risk, deadline urgency, and resource scarcity. The on-site interlocking condition generation module is used to generate a set of on-site interlocking conditions based on the operation type, equipment topology location, isolation application template, and list of available edge sensors, and generate corresponding rules to send to the on-site safety interlocking execution unit.

[0009] Preferably, the resource allocation and standard operation package matching unit is used to perform resource matching on work orders that meet the resource matching conditions, wherein the resource matching conditions include the work order priority reaching a preset start threshold and the absence of unmet prerequisite operation dependencies; the resource allocation and standard operation package matching unit is specifically used to read the standard operation package corresponding to the work order, obtain the process list, job type, tools, material list, planned working hours, reference resource consumption and safety measure template, and calculate the resource availability rate based on the availability and demand of personnel, materials and tools; The resource availability rate is the minimum ratio of the available quantity to the required quantity of each resource category (personnel, materials, and tools). When the resource availability rate meets the preset requirements, the resource matching result is output. When the resource availability rate does not meet the preset requirements, the resource shortage result is output and sent back to update the resource locking range and subsequent work order start time.

[0010] Preferably, the work order priority module is further used to calculate the material gap ratio based on the demand and current availability of each material item in the material list required by the work order; wherein, the material gap ratio is the ratio of the sum of the gaps of material items whose demand is greater than the availability to the sum of the demand of each material item; when the work order priority value meets the preset locking threshold, a resource locking mark is generated for the work order and the locking interval is recorded. Within the locked interval, the locked resources are not allocated to other work orders with a priority lower than the locked threshold; for work orders with execution dependencies, if the preceding work order has not been completed or the locked resources have not been released, the subsequent work orders remain in a queue.

[0011] Preferably, the on-site safety interlocking execution unit is used to perform station-end operation release judgment based on the on-site interlocking condition set; the on-site interlocking condition set includes one or more of the following: remote signal disconnection duration threshold condition, mechanical position confirmation condition, induced voltage safety value condition, grounding switch position condition, geographic information system topology version validity condition, local sensor online condition, and network communication quality condition. The on-site safety interlocking execution unit is also used to enter a communication abnormal state when the packet loss rate or link delay of network communication exceeds a preset upper limit, and switch to the local security verification release mode in the communication abnormal state, output release judgment result information and record failure condition items, rule version, operator and judgment time.

[0012] Preferably, the material procurement demand generation unit is used to generate procurement demand information based on safety stock, the cumulative demand of locked work orders within the planning period, and the current available inventory; wherein, the procurement demand is determined by the difference between the sum of safety stock and cumulative demand and the current available inventory, and the corresponding procurement demand information is generated when the difference is greater than zero, and a zero procurement demand marker is generated when the difference is less than or equal to zero. The maintenance resource consumption collection and standard library update unit is used to calculate the resource consumption of work orders and collect the resource consumption of work orders according to the equipment level; the maintenance resource consumption collection and standard library update unit is also used to write back the fault phenomenon, fault cause, handling measures, fault measurement point data before the fault, the difference between the plan and consumption, and the false alarm or missed alarm mark after the work order is completed, so as to update the defect standard library, status reference value, threshold correction coefficient, alarm merging threshold and lockout template.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. This system outputs standardized data objects by uniformly mapping the access data to device identifiers, functional location identifiers, timestamps, and master data version numbers. This solution solves the problems of inaccurate data aggregation and status identification deviation caused by inconsistent data between different systems. It enables the system to calculate alarm correlation and aggregate alarms based on a unified benchmark, avoiding alarm correlation distortion and improving the accuracy of status assessment and fault determination. 2. The system generates work order priorities based on comprehensive equipment status values, power supply impact, and resource scarcity, and performs resource locking. Based on the locked work orders, it generates procurement requirements, solving the problems of inaccurate work order priority determination and insufficient coordination between allocation and procurement. On-site, the system performs release determination based on the set of locking conditions, and switches to local security verification release mode when communication is abnormal, realizing unified linkage processing of the business chain and improving the reliability of on-site release. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application and the prior art, the accompanying drawings used in the description of the embodiments and the prior art will be briefly introduced below.

[0015] Figure 1 This is an architecture diagram of a power equipment operation and maintenance decision management system based on big data analysis, as described in this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] like Figure 1As shown, a power equipment operation and maintenance decision management system based on big data analysis includes a data access unit for communication connection, a status data processing unit, an equipment status change assessment unit, an alarm aggregation and fault point determination unit, a maintenance judgment rule unit, a resource allocation and standard work package matching unit, a work order and work ticket operation ticket coordination unit, an on-site safety interlock execution unit, a material procurement demand generation unit, and a maintenance resource consumption collection and standard library update unit. The data access unit is used to access and perform unified mapping processing of operation, asset, topology, job resources, inventory, business and environmental data; The status data processing unit is used to construct a set of device status data; The equipment status change assessment unit is used to generate equipment status values; The alarm merging and fault point determination unit is used to merge alarms and determine the severity of fault points; The maintenance judgment rule unit is used to generate status classification threshold rules, work order priority rules, and on-site interlocking conditions; The resource allocation and standard operation package matching unit is used to perform resource matching and output resource matching results or resource shortage results; The work order and work ticket operation ticket collaboration unit is used to generate work orders, work tickets, operation tickets, and isolation requests; The on-site safety interlocking execution unit is used to make release decisions based on the set of interlocking conditions; The material procurement requirement generation unit is used to generate procurement requirement information; The maintenance resource consumption collection and standard library update unit is used to collect work order resource consumption and update the fault record library, lockout record library and related rule parameters.

[0018] The data access unit includes a real-time operation data access module, an asset operation and maintenance data access module, a spatial topology data access module, an operational resource data access module, and an environmental data access module; The real-time operation data access module accesses real-time data from the data acquisition and monitoring control system and IoT measurement point data; the asset operation and maintenance data access module accesses equipment asset ledgers, defect standard library, fault standard library and business consumption code data; the spatial topology data access module accesses geographic information system topology and spatial location data; the operation resource data access module accesses standard operation packages, personnel role and shift data, as well as material lists and inventory ledgers; and the environmental data access module accesses meteorological and environmental data. The data access unit is also used to perform unified mapping processing of device identifiers, functional location identifiers, timestamps and master data version numbers for each access data, and output standardized data objects.

[0019] The status data processing unit is used to build a status data set for the equipment, including operational status data, asset maintenance data, and spatial environment data. The status data processing unit is specifically used for: mapping access data to three-level objects—device, component, and functional location—based on a unified device identifier; performing missing value completion and abnormal jump removal on the mapped data; processing action event quantities and status event quantities by event numbering; establishing a periodic cache according to the measurement point category and synchronizing the edge measurement points with the cloud master data version number; and organizing the cleaned data, event number data, and version number data into a status data set.

[0020] This implementation method is applicable to equipment operation and maintenance management in power transmission, substation or distribution scenarios; the system consists of a cloud server, edge acquisition device, station communication equipment and field execution terminal; the cloud server is responsible for data access, status sorting, status assessment, alarm merging, maintenance determination, resource allocation, work order collaboration, procurement generation and cost collection and processing. Edge acquisition devices connect to field measurement points and save local version information; station-end communication equipment is used to establish a network transmission channel between edge acquisition devices, field execution terminals, and cloud servers; field execution terminals are used to display work orders, tickets, and interlocking judgment results; equipment identifiers are unique codes that correspond to a specific power equipment or its components; functional location identifiers are codes that characterize the interval, circuit, or functional node where the equipment is located; master data version numbers are the version numbers of equipment ledgers, topologies, and rule templates within the current period, used to ensure that the cloud and edge use the same version of basic data; When the system enters a processing cycle, the data access unit first receives real-time operation data, asset data, topology data, operational resource data, inventory data, business consumption data, and environmental data. Real-time operation data is sent in through the data acquisition and monitoring control system interface and the Internet of Things interface. The data acquisition and monitoring control system interface uploads continuous quantities and remote signaling quantities, while the Internet of Things interface uploads measurement point values ​​such as temperature, pressure, current, partial discharge, and micro-water. The asset operation and maintenance data access module reads equipment model, service life, and equipment level from the equipment asset ledger; it reads defect pattern feature items from the defect standard library and fault standard library; and it reads the collection codes for labor, materials, and outsourced resource consumption from the business consumption code data. The spatial topology data access module reads the topological connection relationship of the geographic information system and the spatial location relationship of the equipment. The operation resource data access module reads standard operation packages, personnel roles, shift data, material lists, and inventory ledgers. The environmental data access module reads meteorological data such as ambient temperature, humidity, wind speed, and rainfall. The data formats from the different sources are not consistent, so the data access unit performs unified mapping processing after receiving the data. Specifically, the cloud server converts the equipment name in the data acquisition and monitoring control system measurement points, the asset code in the IoT platform, the topology node number in the geographic information system, and the equipment object code in the work order system into the same equipment identifier according to the equipment master data dictionary. Then, it converts the work location in the work package, the interval location in the geographic information system, and the installation location in the ledger into the functional location identifier. Each data entry is appended with a timestamp corresponding to the time of data collection, and the asset ledger version, geographic information system topology version, and standard operation package version used at that time are written into the master data version number field. After this processing, a standardized data object is formed. This standardized data object includes at least the device identifier, functional location identifier, data category, original value, timestamp, and master data version number. This allows the same device in different systems to be uniformly merged in subsequent calculations, preventing the same device from being identified as multiple objects due to coding differences. After receiving standardized data objects, the status data processing unit constructs a status data set for each device. The status data set refers to all data related to status evaluation for the same device within the same processing cycle, including operational status data, asset maintenance data, and spatial environment data. Operational status data mainly corresponds to objective operational quantities such as current, voltage, temperature, pressure, switch position, and protection actions. Asset maintenance data mainly corresponds to service life, historical defect frequency, maintenance records, planned parameters, and related inventory materials. Spatial environment data mainly corresponds to topological regions, location relationships, and environmental conditions. During the data processing, the cloud server uses a unified device identifier to attach various types of data to three levels of objects: equipment, components, and functional locations. For example, the oil temperature and load current of the main equipment can be directly attached to the equipment object, the number of operations of a certain cooling component can be attached to the component object, and the topological location and geographic information system connectivity of a certain interval can be attached to the functional location object. After the connection is completed, the status data processing unit performs missing value completion and abnormal jump removal. Missing value completion is used to handle measurement points that have not been uploaded in a single period but whose data are continuous before and after. Priority is given to using the continuous relationship between the previous and next valid values ​​of the same measurement point for completion. If there is only one valid value in the current period, the valid value is retained and marked as missing. Abnormal jump rejection is used to handle sudden values ​​that clearly do not conform to the physical operating rules of the equipment, such as the same thermal inertia quantity in adjacent sampling periods. and The absolute value of the relative rate of change of the measured value with respect to the rated parameters of the equipment. ; in, and Sampling period and The measured value of thermal inertia at that time. The system's preset thermal variation threshold, This refers to the absolute temperature scale rated reference value for this measuring point; and the absolute value of the relative rate of change of the relevant load relative to the rated load within the same period. ; in, and Sampling period and Load measurement value at that time The system's preset load stability judgment threshold, When the device is at its rated load value, the server will sample periodically. Hotspots at certain points are identified as abnormal jumps and removed from the subsequent evaluation set; For discrete measurement points where the previous effective value is close to zero, a preset minimum positive number is used as the denominator protection term, while the original record is retained for traceability. Since the state assessment depends on continuous change, this process is used to avoid directly using missing points or erroneous abrupt changes, which would cause subsequent calculations to deviate from the actual operating conditions of the equipment state value. For action events and status events, the status data processing unit also performs event numbering. Action events can correspond to discrete actions such as circuit breaker opening and closing, protection actions, and cooling equipment activation and deactivation. Status events can correspond to status changes such as remote signaling from closed to open and from open to closed. The server generates event numbers based on device identifiers, event categories, and occurrence order, and associates and saves the event numbers with the timestamps of the events. Thus, in subsequent status assessment and alarm merging, the system can not only read continuous quantities, but also identify whether discrete actions related to the fault mode have occurred within a certain period. After completing data cleaning and event numbering, the status data processing unit establishes periodic buffers according to the measurement point category. The periodic buffer refers to the buffer storage area established for rapid disturbance measurement points, thermal inertia measurement points, and slow change measurement points respectively. The buffer length for rapid disturbance measurement points is set to multiple sampling periods to store continuous fluctuation data within a short period. The buffer length of thermal inertial measurement points can cover a longer change process and is used to store slow changes such as oil temperature or winding hot spots; slow change measurement points are used to store data with a low update frequency, such as service life and defect frequency; the edge acquisition device uploads the local master data version number while uploading the measurement point data, and the cloud server compares the version number with the current master data version number. When the two match, the data is directly written to the cache. When the two are inconsistent, the server retains the version difference marker and sends a forced version synchronization update instruction and the latest version incremental data packet to the edge acquisition device in the response message. This is to prevent the deadlock state of continuous version inconsistency and incorrect mapping in subsequent consecutive cycles. At the same time, it enables the automatic performance of version compatibility downgrade processing or waiting for update confirmation when using the data later. Finally, the cleaned data, event number data and version number data are organized into a complete status data set and provided to the device status change assessment unit. In one embodiment, when a power device simultaneously uploads oil temperature, load current, switch position and ambient temperature data during a continuous processing cycle, the data access unit first maps these data to the device identifier and functional position identifier of the device; the status data processing unit then puts the oil temperature and load current into the continuous quantity buffer, generates a status event number for the switch position change, and associates and saves the ambient temperature as an environmental feature with the device object. If the oil temperature is missing in a certain cycle but the oil temperature is continuous in adjacent cycles, the server will fill in the missing points; if the switch position signal fluctuates frequently in a very short cycle and does not form a stable state, the original record will be retained but will not be used as a valid state event for subsequent judgment; in this way, the subsequent units will obtain a set of state data that has been normalized by device, aligned by version, and organized by event number, which will facilitate the continued calculation of device state values ​​and alarm merging. With the above structure, the data relationships between the units are clear: the data access unit outputs standardized data objects, the status data processing unit outputs status data sets, and all subsequent units work around the same equipment identifier, the same functional location identifier, and the same version number; this can reduce misjudgments caused by inconsistent data encoding, timing, and version, and provide a unified data foundation for subsequent maintenance decisions and on-site release judgments. In this embodiment, the equipment status change assessment unit is used to generate equipment status values ​​based on the rate of change of continuous measurement points in the status data set, the parameter deviation values ​​of preset key operating parameters within a preset time period, the degree of conformity between the current equipment status and the defect patterns in the defect standard library, and historical fault tendency values. Among them, the degree of conformity is the matching ratio between the current state characteristics of the equipment and the corresponding defect mode characteristics; the historical fault tendency value is determined based on the frequency of defects, service life, and degree of non-performance of maintenance within the recent set period; and the equipment state value is determined based on the rate of change, parameter deviation value, degree of conformity, and historical fault tendency value.

[0021] The alarm merging and fault point determination unit is used to calculate the alarm correlation degree based on the alarm's temporal proximity, geographic information system topology, and measurement point category, and to group alarms whose correlation degree meets the preset merging threshold into the same alarm group. Among them, the alarm correlation degree is determined based on time proximity, topological proximity and measurement point category matching degree; The alarm merging and fault point determination unit is also used to determine the severity of the fault point based on the status value of the corresponding device in the alarm combination, the scale of the alarm combination and the topology impact range, and output the main alarm and auxiliary alarm identification information; The severity of the fault point is determined by the weighted sum of the standardized state value, alarm combination size, and topology impact range, and is classified into low severity threshold and high severity threshold, with the low severity threshold being lower than the high severity threshold. When the severity value is less than the low severity threshold, it is marked as normal; when the severity value is greater than or equal to the low severity threshold but less than the high severity threshold, it is marked as important; when the severity value is greater than or equal to the high severity threshold, it is marked as urgent.

[0022] The maintenance judgment rule unit includes a threshold generation module, a work order priority module, and an on-site interlocking condition generation module; The threshold generation module is used to generate current status classification thresholds based on equipment status values ​​and load offsets, and to construct switching rules for normal, watch, and warning states, where load offset is the deviation of the current load from the rated load or planned load. The switching rules include at least a attention threshold and an early warning threshold, with the attention threshold being less than the early warning threshold. When the device status value is less than the attention threshold, it is marked as normal; when the device status value is greater than or equal to the attention threshold and less than the early warning threshold, it is marked as attention; when the device status value is greater than or equal to the early warning threshold, it is marked as early warning. The work order priority module is used to generate work order priorities based on the corresponding equipment status value, power supply impact, operational safety risks, deadline urgency, and resource scarcity. Among them, the priority of the work order is the weighted sum of the standardized values ​​of equipment status, power supply impact, operational safety risk, deadline urgency and resource scarcity. The on-site interlocking condition generation module is used to generate a set of on-site interlocking conditions based on the operation type, equipment topology location, isolation application template, and list of available edge sensors, and generate corresponding rules to send to the on-site safety interlocking execution unit.

[0023] The execution process of equipment status change assessment, alarm merging, and maintenance judgment rule generation is as follows: The main execution entity is the cloud server, the input data comes from the aforementioned status data set, and the output results are, in order, equipment status value, alarm combination and fault severity, status classification threshold rule, work order priority, and on-site interlocking condition set. The rate of change refers to the relative degree of change of continuous measurement points within adjacent sampling periods, used to indicate whether the equipment operating quantity changes steadily or suddenly; the parameter deviation value refers to the degree of deviation of preset key operating parameters from the reference value within a preset time period. The preset time period is a sliding data statistical window set according to the equipment heating time constant or the historical typical fault evolution cycle. The reference value can be the rated operating value, the planned operating value, or the baseline value of a healthy sample of similar equipment. The degree of conformity refers to the matching ratio between the current equipment status characteristics and a certain defect pattern characteristic item in the defect standard library; the historical failure tendency value is the comprehensive value obtained after standardization of the equipment's defect frequency, service life, and degree of failure to perform maintenance within the recent set period; the equipment status value is the comprehensive evaluation result formed after uniformly processing the above four types of factors, which is used to characterize whether the equipment is currently in a stable state or a preset risk state. After receiving the status data set, the equipment status change assessment unit first calculates the rate of change for the continuous measurement points. Taking any continuous measurement point as an example, the server reads the current sample value and the previous sample value, calculates the difference between the two, and then compares the absolute magnitude of the difference with that of the previous sample value to obtain the relative degree of change. If the previous sample value is close to zero, a preset minimum positive number is used as the denominator protection term to avoid meaningless excessively large results. Since different measuring points have different dimensions, the rate of change needs to be standardized after the calculation is completed so that different quantities such as temperature, current, and pressure can be included in the same subsequent calculation system. Standardization can be achieved by mapping based on the health sample interval of similar equipment, converting the rate of change of each measuring point into a dimensionless fraction of 0 to 1 within a unified range. For example, the absolute value of the current rate of change is divided by the preset maximum safe rate of change threshold. If the result exceeds 1, it is truncated to 1 to reflect the abnormal change relationship that is first manifested by the latent deterioration of the equipment, rather than necessarily manifested as the absolute value exceeding the limit. For parameter deviation values, the server first extracts preset key operating parameters from the status data set; for example, for thermal status judgment, it can read oil temperature, hot spot temperature, and load current; for gas status judgment, it can read gas pressure and trace water content; for mechanical status judgment, it can read the number of actions and the holding status of the open / close position, etc.; within a preset time period, it calculates the deviation between the current parameter and its reference value, and then standardizes the deviation according to the reference interval corresponding to the equipment type; the specific standardization formula is: ; in, The standardized value of the parameter deviation. This represents the absolute deviation of the current parameter from the reference value. This represents the maximum permissible limit deviation for this type of equipment under the corresponding operating conditions. The theoretical minimum value of this normalized absolute deviation is zero; if… Then the truncation value is assigned to 1; If the deviation of a certain parameter is continuous The device status change assessment unit will maintain a monotonically increasing trend within each sampling period, even if its absolute value has not yet exceeded the traditional alarm threshold, according to the gain mapping function. Update the influence weight of this parameter; where, The updated parameters affect the weights. The number of consecutive sampling periods to keep the parameters monotonically increasing. To initially assign weights, The preset degradation acceleration penalty coefficient is used; and after the update, the weights of all feature items participating in the summation calculation will be normalized so that the sum of their weights remains 1, thereby reflecting the trend of equipment condition deterioration in advance. To determine the degree of compliance, the server reads multiple defect patterns corresponding to the current equipment type from the defect standard library. Each defect pattern corresponds to several feature items, such as load increase accompanied by slow oil temperature rise and frequent cooling action with increased number of actions and unstable mechanical position. The server compares the status features extracted from the current device within the current period with the feature items of each defect mode one by one, counts the number of feature items that meet the conditions, and then divides them by the total number of feature items of that defect mode to obtain the matching ratio. Among multiple defect modes, the one with the highest matching ratio corresponds to the defect type that is closest to the current device. This highest matching ratio is used as the degree of compliance in the subsequent calculation of the device status value. The matching ratio is combined with continuous quantities, event quantities and historical defect experience to make a joint judgment on the status. The process of generating historical fault tendency values ​​is as follows: The server reads the defect frequency, service life, and degree of non-execution of maintenance due dates for the equipment within the recent set period; the defect frequency comes from the statistical results in the defect record database; the service life comes from the asset ledger; the degree of non-execution of maintenance due dates can be calculated by converting the planned maintenance cycle with the actual non-execution situation; the three data are first standardized separately, and then weighted and summed according to preset weights; Weights can be pre-configured according to equipment type. For example, the proportion of service life can be appropriately increased for older equipment, and the proportion of defect frequency can be appropriately increased for equipment with frequent defects. The equipment status change assessment unit combines the current operating status with historical operational vulnerabilities to avoid judgment based solely on single-cycle measurement values. For example: Assuming that for defect frequency, each defect occurrence is assigned a standardized value of 0.2, with an upper limit of 1; for service life, the current service time is divided by the design life, and if it exceeds the design life, it is assigned a value of 1; for the degree of non-performance of maintenance due, the number of days overdue is divided by 60 days, and if it exceeds 60 days, it is assigned a value of 1; if a piece of equipment has reached half of its design life (0.5), has experienced 2 defects in the most recent cycle (0.4), and has exceeded the maintenance due date by 30 days (0.5), and the preset weights for the three factors are 0.3, 0.4, and 0.3 respectively, then its historical failure tendency value is... ; The calculation of the device status value is performed after the above three items are completed; the server standardizes the rate of change, parameter deviation value, compliance degree and historical fault tendency value, and performs a weighted sum according to the preset weight to obtain the device status value; For example, if the weights of the four dimensions mentioned above are set to 0.3, 0.2, 0.3, and 0.2 respectively, and a device has a standardized rate of change of 0.6, a parameter deviation of 0.5, a conformity of 0.8 (meaning it matches 80% of the defect features), and a historical failure tendency of 0.46, its device status value is... ; The larger the value, the more obvious the device's status changes, key parameter deviations, defect patterns, and historical vulnerability accumulation are within the current cycle. In practical applications, the cloud server can save a set of weight parameters according to the device type and adjust them according to false alarms or missed alarms after subsequent completion and write-back. However, the calculation path within each processing cycle remains fixed and can be traced. After obtaining the device status value, the alarm merging and fault point determination unit merges the concurrent alarms; the time proximity relationship referred to here refers to the degree of proximity of multiple alarms appearing in the same or adjacent processing windows; the geographic information system topology relationship refers to whether there is an electrical connection, upstream and downstream subordination, or same functional location relationship between the alarm-corresponding devices in the geographic information system topology. The measurement point category relationship refers to whether alarm types belong to a preset association category, such as whether load, temperature rise, and switch position categories have a common fault chain in the defect database; the server calculates the time proximity, topological proximity, and measurement point category matching degree for any two alarms, and then sums the three after standardization according to weight to obtain the alarm correlation degree; For example, if the time difference is within 10 minutes, the time proximity is recorded as 1, and if it exceeds 1 hour, it is recorded as 0, and the intermediate segment is linear interpolation; in the topology relationship, the topology proximity of devices in the same interval is recorded as 1, the adjacent interval is recorded as 0.5, and other cases such as cross-station are recorded as 0. If two alarms belong to the same related category (temperature and load), the measurement point category matching degree is recorded as 1; otherwise, it is 0. Assuming the weights of the three categories are 0.4, 0.4, and 0.2 respectively, when two alarms have a time difference of 10 minutes (standardized value 1), are within the same interval (standardized value 1), and belong to a strongly correlated category (standardized value 1), the alarm correlation degree is 1. ; If the correlation between two alarms reaches the preset merging threshold, they will be classified into the same alarm group. The merging threshold is determined by the results of historical cascading event playback. By configuring the preset merging threshold, multiple alarms triggered by the same root cause will be merged, and independent alarms will be separated. The method for calibrating the merging threshold is as follows: extract all cascading alarm event pairs that have been identified as being caused by the same root cause from the historical fault record database, calculate their alarm correlation degree and form a correlation degree distribution set, remove a preset proportion, such as the lowest 5% of abnormal outliers, and take the minimum value in the distribution set as the preset merging threshold. This quantitative historical playback method ensures that the set threshold can cover the vast majority of real cascading alarms, while preventing excessive merging due to individual extreme and accidental events causing the threshold to be too low. After merging, the server counts the number of devices, the number of alarms, and the topological impact range involved in each alarm combination. The topological impact range can be calculated based on the affected device level or the range of the connected area in the geographic information system. The server reads the status values ​​of relevant devices within the group, and combines the alarm group size and topology impact range to obtain the severity of the fault point by a standardized weighted summation method. To clarify the calculation rules, the standardized value of the alarm group size can be calculated by dividing the total number of alarms in the group by the preset baseline number of alarms. For example, if there are 5 alarms, the result is greater than 1 and is taken as 1. The standardized value of the topology impact range can be calculated by dividing the affected interval or the number of backbone nodes by the total number of nodes in the area under its jurisdiction. If the result is greater than 1, it is taken as 1. The status value of the above devices, the standardized value of the alarm combination scale, and the standardized value of the topology impact range are multiplied by the preset severity weights, such as 0.5, 0.3, and 0.2 respectively, and then summed to obtain the specific value of the severity of the fault point. After calculating the severity of the fault, the system then classifies it according to a low severity threshold and a high severity threshold. When the severity value is less than the low severity threshold, it is marked as moderate; when the severity value is greater than or equal to the low severity threshold but less than the high severity threshold, it is marked as important; when the severity value is greater than or equal to the high severity threshold, it is marked as urgent. Since the low severity threshold is less than the high severity threshold, the classification intervals are clear and do not overlap. In practice, the low and high severity thresholds are quantified based on power grid safety operation specifications and historical work order response rating experience. For example, the upper limit of the severity quantification for a fault that causes a local single-point device to operate with a fault but does not temporarily affect external power supply is quantified as the low severity threshold, such as 0.4; the lower limit of the severity quantification for a fault that causes the main nodes in the topology to shut down, poses a risk of propagation, or significantly affects external power supply is quantified as the high severity threshold, such as 0.75. The cloud server selects the alarm with the highest status value or the greatest overall impact within the same alarm combination as the primary alarm, and the remaining alarms that assist in fault diagnosis as secondary alarms, and outputs the primary alarm and secondary alarm identification information; this can reduce the situation where the same fault is repeatedly issued as multiple independent work orders; After receiving the equipment status value and the severity of the fault point, the maintenance judgment rule unit generates three types of rules. The first type is the status classification threshold rule. The threshold generation module reads the equipment status value and the load offset, where the load offset is the deviation of the current load from the rated load or the planned load. When generating the attention threshold and warning threshold, the server incorporates load offset for correction, specifically using the following calculation logic: The system pre-sets a basic attention threshold and a basic warning threshold; when the load offset, i.e., the relative ratio of the current load to the rated load, is greater than the preset load tolerance lower limit but less than the extreme overload threshold, the system calculates a threshold correction coefficient. This coefficient is the load offset multiplied by a preset relaxation coefficient, for example, 0.2. The basic attention threshold and the basic warning threshold are then multiplied by this coefficient. To obtain the current state classification threshold, thereby reducing false alarms caused by normal overload; When the load offset is greater than or equal to the extreme overload threshold, it indicates that the equipment is in a dangerous state of severe overload. At this time, the threshold correction coefficient is also recorded as zero, and a warning signal is forcibly triggered, without any threshold relaxation. After the threshold is generated, the system establishes a three-state switching rule: normal state, attention state, and warning state. That is, when the equipment status value is less than the attention threshold, it is in the normal state; when it is between the attention threshold and the warning threshold, it is in the attention state; and when it is greater than or equal to the warning threshold, it is in the warning state. The result of this state machine will directly affect whether to generate a work order candidate in the future. The second category is the work order priority rule; the work order priority module reads the status value of the associated equipment, power supply impact, operational safety risk, deadline urgency, and resource scarcity for each candidate work order; the standardization and quantification rules for the above factors are as follows: the power supply impact is assigned a ratio of 0 to 1 based on the expected load loss, such as 0.1 per megawatt, with an upper limit of 1; the operational safety risk is directly mapped to a numerical value according to the State Grid safety risk level table, such as 0.25 for level 4 risk and 1.0 for level 1 risk; The deadline urgency is calculated as the reciprocal of the current remaining period. If the deadline has passed or the current remaining period is zero, it is directly recorded as 1 to avoid arithmetic errors and system crashes caused by a zero or negative denominator. The resource scarcity is directly based on the material shortage ratio calculated from the material list required by the work order. The above five factors are standardized, multiplied by their preset weights, and summed to form the work order priority. Work orders with higher priority will enter the resource allocation process first, while work orders with lower priority will continue to queue or wait for conditions to mature. The third category is the set of on-site interlocking conditions; the on-site interlocking condition generation module generates the condition items to be used by the subsequent on-site safety interlocking execution unit based on the operation type, equipment topology location, isolation application template, and list of available edge sensors. For example, for high-risk operations that require isolation before they can be carried out, if the edge side has remote signaling, mechanical position, induced voltage, and grounding status sensors, the condition set will include the corresponding condition items; if a certain type of sensor does not exist in the edge list, the system will not generate the automatic release condition corresponding to that sensor, but will retain other existing conditions; the generated on-site interlocking condition set includes the applicable equipment identifier, functional position identifier, rule version number, and validity period, and is sent to the on-site safety interlocking execution unit. In one embodiment, a device exhibits a slow increase in load current, an abnormally large rate of oil temperature change, frequent cooling actions, and an expired maintenance plan during a continuous processing cycle. The device status change assessment unit first calculates the rate of change greater than a preset change threshold and the parameter deviation value greater than a preset deviation threshold. Then, it finds that the matching ratio of these parameters with the characteristic items of the cooling efficiency reduction defect mode is relatively high, and the historical fault tendency value is greater than a preset tendency threshold. Finally, the device status value is obtained that is greater than the preset status threshold. If multiple related temperature rise alarms and location alarms occur within the same topology area, the alarm merging and fault point determination unit will group these alarms into the same combination and determine whether they are important or urgent based on the device status value exceeding a preset threshold, the alarm combination size exceeding a preset scale threshold, and the topology impact range exceeding a preset range threshold. The maintenance judgment rule unit will then adjust the tolerance of the status classification threshold based on this result, increase the priority of related work orders, and generate a set of interlocking conditions for subsequent isolation operations. Based on this, the system will output a set of rules that can continue to be executed, providing a basis for subsequent resource matching and on-site release. In this embodiment, the resource allocation and standard operation package matching unit is used to perform resource matching on work orders that meet the resource matching conditions, wherein the resource matching conditions include the work order priority reaching a preset start threshold and the absence of unmet preceding operation dependencies. The resource allocation and standard work package matching unit is specifically used to read the standard work package corresponding to the work order, obtain the process list, job roles, tools, material list, planned working hours, reference resource consumption and safety measure templates, and calculate the resource availability rate based on the availability and demand of personnel, materials and tools. Among them, the resource availability rate is the minimum ratio of the available quantity to the demand quantity of each resource category, including personnel, materials, and tools. When the resource availability rate meets the preset requirements, the resource matching result is output; when the resource availability rate does not meet the preset requirements, the resource shortage result is output and sent back to update the resource lock range and the subsequent work order start time.

[0024] The work order priority module is also used to calculate the material shortage ratio based on the required quantity of each material item in the material list of the work order and the current available quantity. Among them, the material shortage ratio is the ratio of the sum of the material shortages where the demand exceeds the available quantity to the sum of the demand for each material item. When the priority value of a work order meets the preset locking threshold, a resource locking mark is generated for the work order and the locking interval is recorded; within the locking interval, the locked resource is not allocated to other work orders with a priority lower than the locking threshold; For work orders with prior execution dependencies, subsequent work orders remain in a queue until the previous work order is completed or the locked resources are released.

[0025] The on-site safety interlock execution unit is used to make station-end operation release decisions based on the on-site interlock condition set; The set of on-site interlocking conditions includes one or more of the following: remote signal disconnection duration threshold condition, mechanical position confirmation condition, induced voltage safety value condition, grounding switch position condition, geographic information system topology version validity condition, local sensor online condition, and network communication quality condition. The on-site safety interlocking execution unit is also used to enter a communication abnormal state when the packet loss rate or link delay of network communication exceeds the preset upper limit, and switch to the local security verification release mode in the communication abnormal state, output release judgment result information and record failure condition items, rule version, operator and judgment time.

[0026] The material procurement demand generation unit is used to generate procurement demand information based on safety stock, the cumulative demand of locked work orders within the planning cycle, and the current available inventory; The procurement demand is determined by the difference between the sum of safety stock and cumulative demand and the current available stock. When the difference is greater than zero, the corresponding procurement demand information is generated, and when the difference is less than or equal to zero, a zero procurement demand marker is generated. The maintenance resource consumption collection and standard library update unit is used to calculate work order resource consumption and collect work order resource consumption according to equipment level; The maintenance resource consumption collection and standard library update unit is also used to write back the fault phenomena, fault causes, handling measures, pre-fault measurement point data, planned and consumed differences, and false alarm or missed alarm marks after the work order is completed, so as to update the defect standard library, status reference value, threshold correction coefficient, alarm merging threshold and lockout template.

[0027] The process from work order entry into resource matching to completion and write-back is as follows: The execution entities include cloud servers, resource management databases, edge execution nodes, and on-site operation terminals; After the maintenance judgment rule unit outputs the work order priority, the resource allocation and standard work package matching unit does not immediately match resources for all work orders, but first checks whether the resource matching conditions are met; the work order priority reaches the preset start threshold, which means that the work order priority value is not lower than the start threshold set by the system for the current processing cycle. The absence of unmet prerequisite dependencies means that the work order in the priority tree or dependency chain requires the prior work order to be executed to have been completed, cancelled, or its locked resources released. If any of these conditions are not met, the work order remains in a pending state and does not enter the resource matching calculation. This is to prevent low-priority work orders from occupying critical resources and to prevent subsequent work orders from being prematurely transferred before the preceding work order has met the execution conditions. For work orders that meet the resource matching conditions, the resource allocation and standard operation package matching unit first reads the standard operation package corresponding to the work order. The standard operation package is a structured template corresponding to the operation type and equipment type, which at least includes a list of procedures, job roles, tools, material list, planned working hours, reference resource consumption, and safety measures template. The cloud server retrieves the operation package according to the equipment identifier, fault type, or maintenance category in the work order. After obtaining the corresponding procedure list, it reads the available number of job roles from the personnel scheduling database, the available quantity of materials from the inventory ledger, and the available quantity of tools from the tool library. The system calculates the resource availability rate. Specifically, it calculates the ratio of available personnel to demand, the ratio of available materials to demand, and the ratio of available tools to demand, and takes the minimum value among the three ratios as the resource availability rate. When calculating the ratio, the system has a built-in zero demand protection judgment: if the demand for a certain type of resource, such as materials or tools, corresponding to the current work order is zero, it means that there is no physical dependency for that item. In this case, the ratio of that category is directly set to a preset saturation value, such as 100% or 1, so that it does not interfere with the determination of the minimum availability rate. The minimum value is taken to reflect the situation where a shortage of any type of critical resource in demand prevents the work order from being fully executed according to the standard work package; for example, if personnel and tools are sufficient, but a certain critical seal is insufficient, the work order still cannot be started as planned. In this case, the material comparison value is the minimum, which directly reflects the execution bottleneck. If the resource availability rate reaches the preset requirement, the system outputs the resource matching result; here, the preset requirement is usually set to a resource availability rate of 100%, that is, a ratio of 1, which means that the available quantity of personnel, materials and tools is not less than the demand of the current work order. In specific situations where partial deficiencies are allowed, this preset requirement can also be set to a pre-approved safety allowance baseline, such as 80%. However, in this case, the system will include a resource shortage warning flag in the output results. The resource matching results should include at least the matched work package identifier, the planned execution team, the personnel resources used, the material list, the tool list, the planned working hours, and the reference resource consumption. The work order status changes from pending resource confirmation to the status of generating an executable document. If the resource availability rate does not meet the preset requirements, the system outputs a resource shortage result. This result specifies the type of resource shortage, the quantity of the shortage, and the affected processes. At the same time, the resource shortage result is sent back to the work order sorting and resource locking logic to update the resource locking range and the start time of subsequent work orders. When a high-priority work order cannot start temporarily due to a shortage of a certain type of resource, the system combines the locking rules to control whether low-priority work orders can use the prepared resources. In this embodiment, the material shortage ratio is calculated synchronously at this stage. Since different materials have different physical measurement units, directly adding the absolute quantities will cause a conflict of dimensions. Therefore, the server compares the required quantity of each material item in the material list required by the work order with the current available quantity item by item. For material items whose required quantity is greater than the available quantity, the basic shortage quantity of each item is calculated first, and a preset keyness weight factor and unit equivalent coefficient are assigned to each material item. For core materials that are on the critical path in the standard operating procedure package and cannot be replaced on-site, the system triggers a bottleneck veto mechanism, forcibly amplifying their equivalent gap to infinity or a preset maximum penalty value; for non-core auxiliary materials, they are converted into a unified dimensionless equivalent gap by multiplying them by a unit equivalent coefficient based on resource consumption pricing codes, and then the equivalent gaps corresponding to each material item are summed and divided by the sum of the equivalent demand of all material items after conversion by the same unit equivalent coefficient to obtain the material gap ratio; To illustrate with a concrete quantitative example: Suppose a work order's bill of materials includes material A (required 10 units, available 8 units, equivalent coefficient 1), material B (required 5 units, available 10 units, equivalent coefficient 2), and material C (required 3 units, available 0 units, equivalent coefficient 4). The system comparison reveals that the basic demand for materials A and C exceeds their available quantity. The basic shortage for A is 2 units, corresponding to an equivalent shortage of... The basic gap for C is 3 units, and the corresponding equivalent gap is... The sum of the equivalent gaps of the two is 14. The sum of the equivalent requirements of all material items after equivalent conversion is Therefore, the material shortage ratio is calculated as follows: To prevent anomalies caused by purely manual inspection work orders or operation tickets with no material requirements resulting in a zero denominator, if the system detects that the sum of the requirements for all material items is zero, it will directly trigger the anomaly protection logic and assign the material shortage ratio to zero. The larger this ratio is, the more significant the material supply amount or critical supply restriction the work order is. The work order priority module can use this result as a component of resource scarcity in the work order priority calculation, or it can be directly referenced when generating subsequent procurement requirements. When the priority value of a work order meets the preset locking threshold, the system generates a resource locking tag for the work order and records the locking interval. The preset locking threshold is not a fixed constant, but a quantile dynamically determined by the cloud server based on the overall priority distribution of candidate work orders in the current period. The specific setting rules are as follows: sort the priority values ​​of all work orders to be matched in the current processing cycle from largest to smallest, select the top 20% of the work orders as the preset locking threshold for this cycle, or calculate the average of the priorities of all work orders and add a standard deviation as the preset locking threshold. This ensures that only truly urgent and critical work orders can obtain the right to lock resources first, and prevents the overall resource turnover rate from decreasing due to inefficient large-scale locking. The locking interval here refers to a processing interval between the resource preparation phase before the work order is started and the scheduled release phase. Within this interval, key personnel, tools, and materials reserved for this work order will not be allocated to other work orders with a priority lower than the locking threshold. If multiple work orders compete for the same resource, the system will retain the needs of high-priority work orders that have generated locking tags, and the remaining resources will be available for other work orders to match. For work orders with execution dependencies, if the preceding work order has not been completed or has not released the locked resources, the subsequent work orders will remain in a queue to avoid conflicts in station isolation, personnel arrangement, and material allocation. After a work order resource is successfully matched, the work order and work ticket operation ticket collaboration unit can generate a business object. The on-site safety interlock execution unit then begins to determine the station-end operation release according to the on-site interlock condition set. The release determination refers to deciding whether a certain work ticket or operation ticket is allowed to proceed from pending confirmation to executable status. The edge execution node periodically receives station-end remote signaling, machine position, induced voltage, grounding status, local sensor online status, network communication quality, and geographic information system topology version information. It then calculates each condition item in the interlock condition set. For example, if the set of conditions for a high-risk isolation operation includes remote signal disconnection duration threshold conditions, mechanical position confirmation conditions, induced voltage safety value conditions, grounding switch position conditions, geographic information system topology version validity conditions, local sensor online conditions, and network communication quality conditions, then the edge execution node will sequentially determine: whether the remote signal disconnection state has lasted for a preset threshold; whether the mechanical position sensor has confirmed that the equipment is in the specified position and remains stable; Check if the induced voltage is below the safety value; if the grounding switch is in the correct position; if the current GIS topology version is still valid; if the local sensor is online; if the packet loss rate and link latency are within the allowable range; if all required conditions are met, output the release decision information as "allowed to release," and write the rule version, operator, and decision time into the audit log; if any condition is not met, output "not allowed to release," and record the failure condition; the on-site operation terminal controls the ticket interface to be in an operable or frozen state accordingly. When the packet loss rate or link delay exceeds the preset limit, the on-site safety interlocking execution unit enters a communication abnormal state. The preset limit is established by the specific network communication protocol specification and historical communication quality benchmark. For example, the judgment condition is set as an average packet loss rate greater than 10% or an average link delay greater than 500 milliseconds within three consecutive sampling periods, so as to define the hard boundary of unreliability of cloud monitoring signals with quantitative indicators. In this state, the system no longer automatically allows passage solely based on remote signaling results synchronized from the cloud, but switches to a local security verification and passage mode. This local security verification and passage mode means that the edge execution node prioritizes using locally acquired data such as mechanical position, induced voltage, grounding status, and manual verification results for judgment. If all local hard conditions are not met, passage remains denied. Only after the preset local security conditions are met will a permission result be returned. This mitigates the risk of inconsistencies between the on-site and system states caused by cloud status delays during network anomalies. In terms of generating material procurement requirements, the material procurement requirements generation unit calculates the cumulative requirements based on the work orders locked within the planning period; here, safety stock is the minimum inventory level reserved in inventory management to ensure continuous operation, and current available inventory is the inventory level that can be actually issued after deducting the occupied and frozen inventory; the server performs the following calculation for each material item: add the safety stock to the cumulative requirements, and then subtract the current available inventory to obtain the difference; If the difference is greater than zero, the corresponding procurement demand information is generated; if the difference is less than or equal to zero, a zero procurement demand mark is generated; the procurement demand information can include material code, suggested quantity, associated work order and demand urgency; since the cumulative demand is taken from the locked work order, the procurement request corresponds to the operation demand that meets the preset execution conditions, reducing inventory allocation deviation; Once a work order is completed, the maintenance resource consumption collection and standard library update unit begins its work; it calculates the work order resource consumption; the work order resource consumption consists of labor consumption, material consumption, outsourced service consumption, and other consumption; labor consumption can be calculated based on actual working hours and business consumption codes, material consumption records are taken from actual requisition, outsourced service consumption is taken from outsourced settlement records, and other expenses may include auxiliary expenditures directly corresponding to the work order; after calculating the resource consumption of a single work order, the server collects it according to the equipment level, that is, it accumulates the component-level work order resource consumption to its corresponding upper-level equipment, thereby forming the equipment-level maintenance resource consumption statistics; The system also writes back the fault phenomenon, fault cause, handling measures, pre-fault measurement data, planned and consumed differences, and false alarm or missed alarm markers to the knowledge base after the work order is completed; among them, the pre-fault measurement data comes from the status data cache of multiple processing cycles before the fault occurs, which can be used to correct the defect standard library; the planned and consumed differences can be used to correct the time and material reference values ​​in the standard work package; False alarm or missed alarm flags can be used to correct reference values, threshold correction coefficients, alarm merging thresholds, and interlock templates. For example, if a certain type of alarm is frequently merged but still proves not to be caused by the same fault, the corresponding merging threshold can be appropriately increased. If a certain type of operation is frequently interrupted on-site due to the jitter of a certain sensor signal, the duration condition in the interlock template can be corrected according to the audit records. In one embodiment, a high-priority equipment maintenance work order enters resource matching after being judged by rules. The standard work package requires two types of personnel, a set of tools and equipment, and several spare parts. The system finds that the personnel and tools are sufficient, but the inventory of a certain key spare part is insufficient. Therefore, the resource availability rate is determined by the material item and is lower than the preset requirement. The server outputs the resource shortage result and retains the key personnel resource lock mark for the work order. The material procurement demand generation unit calculates the procurement demand and generates procurement information based on safety stock, the cumulative demand of this work order and other locked work orders, and current available inventory. After the materials are replenished, the work order enters the document generation and on-site interlocking determination stage. If the station network quality is normal and the mechanical position, induced voltage, grounding status, etc., all meet the conditions, the edge execution node releases the order. If the network quality is abnormal, it switches to the local safety verification release mode, allowing execution only when local conditions are met. After the work order is completed, the actual fault cause and consumption data are written back to correct the resource requirements and judgment parameters for the next round of similar work orders. Through the above implementation methods, resource allocation, on-site release, procurement generation, and cost write-back are linked into a continuous data processing process; thereby achieving the following: the human, material, and tool resources required for high-priority work orders are not easily occupied by low-priority work orders; when the network is abnormal, the release of tickets no longer depends on the potentially lagging cloud status; procurement needs are directly associated with locked work orders, reducing inventory judgment errors; and the data written back upon completion can in turn correct subsequent status judgments, alarm merging, and locking rules.

[0028] 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.

Claims

1. A power equipment operation and maintenance decision management system based on big data analysis, characterized in that, It includes a data access unit for communication connection, a status data processing unit, a device status change assessment unit, an alarm aggregation and fault point determination unit, a maintenance judgment rule unit, a resource allocation and standard work package matching unit, a work order and work ticket operation ticket coordination unit, an on-site safety interlock execution unit, a material procurement demand generation unit, and a maintenance resource consumption collection and standard library update unit. The data access unit is used to access operation, asset, topology, job resources, inventory, business and environmental data and perform unified mapping processing; The status data processing unit is used to construct a device status data set; The equipment status change assessment unit is used to generate equipment status values; The alarm merging and fault point determination unit is used to merge alarms and determine the severity of fault points; The maintenance judgment rule unit is used to generate status classification threshold rules, work order priority rules, and on-site interlocking conditions. The resource allocation and standard operation package matching unit is used to perform resource matching and output resource matching results or resource shortage results; The work order and work ticket operation ticket coordination unit is used to generate work orders, work tickets, operation tickets, and isolation applications; The on-site safety interlocking execution unit is used to make a release decision based on the interlocking condition set; The material procurement demand generation unit is used to generate procurement demand information; The maintenance resource consumption collection and standard library update unit is used to collect work order resource consumption and update the fault record library, lockout record library and related rule parameters.

2. The power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The data access unit includes a real-time operation data access module, an asset operation and maintenance data access module, a spatial topology data access module, an operation resource data access module, and an environmental data access module. The real-time operation data access module accesses real-time data from the data acquisition and monitoring control system and IoT measurement point data; the asset operation and maintenance data access module accesses equipment asset ledgers, defect standard library, fault standard library and business consumption code data; the spatial topology data access module accesses geographic information system topology and spatial location data; the operation resource data access module accesses standard operation packages, personnel role and shift data, and material list and inventory ledger; the environmental data module accesses meteorological environmental data. The data access unit is also used to perform unified mapping processing of device identifier, functional location identifier, timestamp and master data version number for each access data, and output standardized data objects.

3. The power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The status data processing unit is used to construct a status data set for the equipment, including operating status data, asset maintenance data, and spatial environment data. The status data processing unit is specifically used for: mapping access data to three-level objects—device, component, and functional location—based on a unified device identifier; performing missing value completion and abnormal jump removal processing on the mapped data; processing action event quantities and status event quantities by event numbering; establishing a periodic cache area according to the measurement point category and synchronizing the edge measurement points with the cloud master data version number; and organizing the cleaned data, event number data, and version number data into the status data set.

4. The power equipment operation and maintenance decision management system based on big data analysis according to claim 3, characterized in that, The equipment status change assessment unit is used to generate equipment status values ​​based on the rate of change of continuous measurement points in the status data set, the parameter deviation values ​​of preset key operating parameters within a preset time period, the degree of conformity between the current equipment status and the defect patterns in the defect standard library, and historical fault tendency values. Wherein, the degree of conformity is the matching ratio between the current state characteristics of the equipment and the corresponding defect mode characteristics, the historical fault tendency value is determined based on the defect frequency, service life and the degree of non-performance of maintenance due within the recent set period, and the equipment state value is determined based on the rate of change, parameter deviation value, degree of conformity and historical fault tendency value.

5. A power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The alarm merging and fault point determination unit is used to calculate the alarm correlation degree based on the alarm's temporal proximity, geographic information system topology, and measurement point category, and to group alarms whose correlation degree meets the preset merging threshold into the same alarm group. The alarm correlation degree is determined based on time proximity, topological proximity, and measurement point category matching degree. The alarm merging and fault point determination unit is also used to determine the severity of the fault point based on the status value of the corresponding device in the alarm combination, the size of the alarm combination and the topology influence range, and output the main alarm and auxiliary alarm identification information. The severity of the fault point is determined by the weighted sum of the standardized state value, alarm combination size, and topology impact range, and is classified according to a low severity threshold and a high severity threshold, wherein the low severity threshold is less than the high severity threshold. When the severity value is less than the low severity threshold, it is marked as normal; when the severity value is greater than or equal to the low severity threshold but less than the high severity threshold, it is marked as important; when the severity value is greater than or equal to the high severity threshold, it is marked as urgent.

6. The power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The maintenance judgment rule unit includes a threshold generation module, a work order priority order module, and a field interlocking condition generation module. The threshold generation module is used to generate current status classification thresholds based on the equipment status value and load offset, and to construct switching rules for normal state, watch state and warning state, wherein the load offset is the deviation of the current load from the rated load or planned load; The switching rules include at least a attention threshold and an early warning threshold, and the attention threshold is less than the early warning threshold; when the device status value is less than the attention threshold, it is marked as normal state; when the device status value is greater than or equal to the attention threshold and less than the early warning threshold, it is marked as attention state; when the device status value is greater than or equal to the early warning threshold, it is marked as early warning state. The work order priority module is used to generate work order priorities based on the equipment status value, power supply impact, operational safety risks, deadline urgency, and resource scarcity corresponding to the work order. The priority of the work order is the weighted sum of the standardized values ​​of the equipment status, power supply impact, operational safety risk, deadline urgency, and resource scarcity. The on-site interlocking condition generation module is used to generate a set of on-site interlocking conditions based on the operation type, equipment topology location, isolation application template and list of available edge sensors, and generate corresponding rules to send to the on-site safety interlocking execution unit.

7. The power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The resource allocation and standard operation package matching unit is used to perform resource matching on work orders that meet the resource matching conditions, wherein the resource matching conditions include the work order priority reaching a preset start threshold and the absence of unmet preceding operation dependencies. The resource allocation and standard work package matching unit is specifically used to read the standard work package corresponding to the work order, obtain the process list, job type, tools, material list, planned working hours, reference resource consumption and safety measure template, and calculate the resource availability rate based on the availability and demand of personnel, materials and tools. The resource availability rate is the minimum ratio of the available quantity to the required quantity of each resource category, including personnel, materials, and tools. When the resource availability rate meets the preset requirements, the resource matching result is output; when the resource availability rate does not meet the preset requirements, the resource shortage result is output and sent back to update the resource lock range and the subsequent work order start time.

8. A power equipment operation and maintenance decision management system based on big data analysis according to claim 6, characterized in that, The work order priority module is also used to calculate the material shortage ratio based on the required quantity of each material item in the material list of the work order and the current available quantity. Wherein, the material shortage ratio is the ratio of the sum of the shortages of material items whose demand exceeds the available quantity to the sum of the demand for each material item. When the priority value of a work order meets the preset locking threshold, a resource locking mark is generated for the work order and the locking interval is recorded; within the locking interval, the locked resource is not allocated to other work orders with a priority lower than the locking threshold; For work orders with prior execution dependencies, subsequent work orders remain in a queue until the previous work order is completed or the locked resources are released.

9. A power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The on-site safety interlocking execution unit is used to perform station-end operation release determination based on the on-site interlocking condition set; The set of on-site interlocking conditions includes one or more of the following: remote signal disconnection duration threshold condition, mechanical position confirmation condition, induced voltage safety value condition, grounding switch position condition, geographic information system topology version validity condition, local sensor online condition, and network communication quality condition. The on-site safety interlocking execution unit is also used to enter a communication abnormal state when the packet loss rate or link delay of network communication exceeds a preset upper limit, and switch to the local security verification release mode in the communication abnormal state, output release judgment result information and record failure condition items, rule version, operator and judgment time.

10. A power equipment operation and maintenance decision management system based on big data analysis according to claim 1, characterized in that, The material procurement demand generation unit is used to generate procurement demand information based on safety stock, the cumulative demand of locked work orders within the planning period, and the current available inventory. The procurement demand is determined by the difference between the sum of safety stock and cumulative demand and the current available stock. When the difference is greater than zero, corresponding procurement demand information is generated. When the difference is less than or equal to zero, a zero procurement demand marker is generated. The maintenance resource consumption collection and standard library update unit is used to calculate work order resource consumption and collect work order resource consumption according to equipment level. The maintenance resource consumption collection and standard library update unit is also used to write back the fault phenomenon, fault cause, handling measures, pre-fault measurement point data, planned and consumed differences, and false alarm or missed alarm marks after the work order is completed, so as to update the defect standard library, status reference value, threshold correction coefficient, alarm merging threshold and lockout template.

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