CN-122024385-A - Substation maintenance operation positioning and safety early warning method based on big data management

CN122024385ACN 122024385 ACN122024385 ACN 122024385ACN-122024385-A

Abstract

The application relates to the technical field of data processing, in particular to a substation maintenance operation positioning and safety early warning method based on big data management, which fuses unstructured operation task books, real-time SCADA equipment state data and GIS space data, and a safe operation area and a dangerous warning area which are completely matched with the on-site electrical state are automatically and dynamically constructed for each operation, so that the alarm is accurate and contextualized from the root. On the basis, in order to overcome the hysteresis of the traditional alarm, the scheme abstracts the dynamic geofence into an environmental potential field for providing attraction and repulsion, and carries out high-precision nonlinear prediction on future tracks by fusing the motion inertia of personnel and the environmental potential field force. The mechanism enables the system to pre-judge the impending dangerous approaching behavior in advance, and realizes the fundamental transition from post-response to pre-early warning, thereby actively solving the safety risk.

Inventors

LIU QIFENG
CHENG JIANJUN
WANG GUODONG
WEN BO
Huo Lianming
MA LIANGLIANG
HOU LIPING

Assignees

国网山西省电力有限公司吕梁供电分公司

Dates

Publication Date: 20260512
Application Date: 20260330

Claims (7)

1. The utility model provides a transformer overhauls operation location and safety precaution method based on big data management which characterized in that includes: based on the binding data of the personnel-positioning tag, carrying out operation task semantic analysis and key information extraction on the operation task book document to obtain structured task data; based on the equipment asset space database, carrying out equipment real-time state and space data association on the structured task data and the SCADA real-time data stream to obtain task space context data; processing the task space context data based on the security rule base to obtain a dynamic geofence set; Based on the dynamic geofence set and the structured task data, performing personnel real-time positioning and predictive risk assessment on the UWB real-time positioning data stream to obtain a risk assessment event; Based on the personnel-positioning label binding data, carrying out safety early warning on the risk assessment event to obtain an alarm action.
2. The method for positioning and pre-warning safety of transformer overhaul operation based on big data management according to claim 1, wherein the step of performing job task semantic analysis and key information extraction on the job task book document based on the personnel-positioning tag binding data to obtain structured task data comprises the steps of: carrying out named entity recognition on the job task book document to obtain an original extracted entity; Based on the personnel-positioning label binding data and the equipment alias knowledge base, carrying out entity normalization and data association on the original extraction entity to obtain a normalized binding entity; and carrying out structured task data aggregation and verification on the normalized binding entity to obtain structured task data.
3. The method for positioning and pre-warning safety of transformer overhaul operation based on big data management according to claim 2, wherein the structured task data comprises a task ID, a positioning tag ID list, a target operation equipment ID list and an operation type.
4. The method for positioning and pre-warning safety of power transformation maintenance operation based on big data management according to claim 1, wherein performing device real-time status and space data association on structured task data and SCADA real-time data stream based on device asset space database to obtain task space context data comprises: querying an equipment asset space database with each target operation equipment ID in a target operation equipment ID list in the structured task data to obtain a target equipment space list and a neighboring equipment space list; performing data association based on the device ID on the target device space list and the adjacent device space list and the SCADA real-time data stream to obtain a device state mapping table; and performing task space context aggregation on the structured task data, the target device space list, the adjacent device space list and the device state mapping table to obtain task space context data.
5. The method for positioning and pre-warning safety of power transformation maintenance operation based on big data management according to claim 1, wherein the processing of task space context data based on a safety rule base to obtain a dynamic geofence set comprises: based on the safety rule base, generating a safety operation area polygon for a target device list in the task space context data to obtain a safety polygon list; based on the safety rule base, generating a dangerous warning area polygon for a neighboring device list in the task space context data to obtain a dangerous polygon list; And respectively performing geometric union operation on the safe polygon list and the dangerous polygon list to obtain a dynamic geofence set.
6. The big data management based substation maintenance operation positioning and safety precaution method according to claim 5, wherein performing personnel real-time positioning and predictive risk assessment on UWB real-time positioning data stream based on dynamic geofence set and structured task data to obtain risk assessment event comprises: Acquiring the current real-time position of a person from UWB real-time positioning data streams; Calculating an environmental force vector based on the set of dynamic geofences and the current real-time location of the person; Based on the environmental force vector and the current real-time position of the personnel, carrying out velocity smoothing and acceleration correction based on Kalman filtering on the previous time state to obtain the estimated velocity and corrected acceleration vector at the current time; carrying out nonlinear track prediction based on the corrected acceleration vector, the current real-time position of the person and the estimated speed at the current moment to obtain a predicted position; Based on the predicted location, a risk level assessment is determined to obtain a risk assessment event.
7. The big data management based substation maintenance operation positioning and safety pre-warning method according to claim 1, wherein calculating the environmental force vector based on the dynamic geofence set and the current real-time position of the person comprises: Calculating a repulsive force vector based on the dangerous polygon list and the current real-time position of the person; calculating an attraction vector based on the safe polygon list and the current real-time position of the person; And vector synthesis is carried out on the repulsive force vector and the attractive force vector to obtain the environment force vector.

Description

Substation maintenance operation positioning and safety early warning method based on big data management Technical Field The application relates to the technical field of data processing, in particular to a substation overhaul operation positioning and safety early warning method based on big data management. Background The transformer substation is used as a core hub of the power system, and has the advantages of dense internal equipment, high voltage level, complex electromagnetic environment and extremely high safety risk in overhaul operation. For a long time, the security of the transformer overhaul operation is mainly dependent on strict regulations, and is aided with the traditional management means of setting physical security fences, preparing special person guardianship and the like. However, these approaches are highly dependent on the responsibility of the operator, experience, and the continued concentration of the on-site guardian, and it is difficult to completely avoid safety accidents caused by inattention, fatigue, or misjudgment of the person. The traditional management mode is a passive and lagging safety measure in nature, and active and real-time sensing and early warning of the risk in the operation process cannot be realized. In order to solve the problems, technologies such as video monitoring and infrared correlation electronic fence are tried to be introduced in the industry, but the technologies have obvious limitations that the video monitoring still needs to be manually attended in real time and cannot realize automatic warning, and the perimeter protection technologies such as infrared correlation are limited in coverage range and are easily interfered by environment to generate a large number of false alarms, so that the accurate positions and behaviors of people in an operation area cannot be effectively managed. With the development of high-precision indoor positioning technologies such as UWB (ultra wide band), a safety early warning scheme combining the real-time position of personnel with a preset static electronic fence appears. While such schemes enable alerting personnel to enter a pre-defined hazardous area, they have a central disadvantage in the static and indiscriminate alerting logic. On the one hand, once the static fences are defined, the static fences are fixed and cannot be dynamically adjusted according to the real-time change of the running state (such as electrification or grounding) of equipment in a transformer substation, so that a large number of invalid alarms are generated in some safe working areas, alarm fatigue is caused, and the practicability and the reliability of the system are reduced. On the other hand, the system only knows the coordinates of the personnel, but cannot understand what kind of operation task is being executed by the personnel, so that whether the behavior of the personnel approaching to the specific equipment is legal operation behavior or dangerous illegal behavior cannot be judged, and the lack of the ability of sensing the task context is a fundamental bottleneck that the prior art cannot realize accurate and effective early warning. Therefore, an optimized substation maintenance operation positioning and safety precaution scheme is desired. Disclosure of Invention The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a substation maintenance operation positioning and safety early warning method based on big data management. According to one aspect of the application, a method for positioning and pre-warning safety of transformer overhaul operation based on big data management is provided, which comprises the following steps: based on the binding data of the personnel-positioning tag, carrying out operation task semantic analysis and key information extraction on the operation task book document to obtain structured task data; based on the equipment asset space database, carrying out equipment real-time state and space data association on the structured task data and the SCADA real-time data stream to obtain task space context data; processing the task space context data based on the security rule base to obtain a dynamic geofence set; Based on the dynamic geofence set and the structured task data, performing personnel real-time positioning and predictive risk assessment on the UWB real-time positioning data stream to obtain a risk assessment event; Based on the personnel-positioning label binding data, carrying out safety early warning on the risk assessment event to obtain an alarm action. Compared with the prior art, the substation overhaul operation positioning and safety early warning method based on big data management provided by the application automatically constructs a safety operation area and a danger warning area which are completely matched with the on-site electrical state for each operation dynamically by fusing unstructured operat