Electric power facility monitoring and fault early warning method and system based on Beidou positioning technology

Abstract

The invention provides an electric power facility monitoring and fault early warning method and system based on a Beidou positioning technology, and relates to the technical field of data processing, and the method comprises the steps: obtaining position data, obtaining operation data, obtaining a to-be-monitored electric power facility, obtaining a monitoring weight, obtaining a calculation time period, and obtaining a plurality of calculation time points; acquiring first sub-temperature data and first sub-power data, acquiring a first temperature index, acquiring a first power index, and acquiring a fault index; when the fault index exceeds a first preset threshold value, acquiring a diffusion electric power facility, second sub-temperature data, second sub-power data, a second temperature index and a second power index; and obtaining an early warning direction according to the second temperature index, the second power index, the first temperature index and the first power index, and obtaining an early warning strategy according to the early warning direction and the position data. The method has the advantages of high efficiency, accuracy and comprehensiveness.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical signal processing, and in particular to a method and system for monitoring and fault warning of electric power facilities based on Beidou positioning technology. Background Art

[0002] With the increasing scale and complexity of power systems, the stable operation and fault warning of power facilities have become core challenges in grid security management. Existing technologies often rely on traditional GPS positioning technology and the collection of single operating parameters, such as isolated data collection through temperature sensors or power monitoring devices. However, such methods have significant drawbacks: First, GPS positioning is susceptible to signal interference in complex terrain or densely populated urban areas, resulting in insufficient accuracy in power facility location data and difficulty in achieving high-precision topological mapping; second, independent analysis of parameters such as temperature and power lacks the ability to integrate multidimensional data, making it impossible to accurately characterize the dynamic correlation of equipment operating status, which can easily lead to misjudgment or omission of faults; in addition, existing early warning systems typically focus on anomaly detection in a single facility, while ignoring the chain reaction of fault propagation in the power network. This makes it impossible to predict the fault propagation path and delays the rapid response to regional faults.

[0003] While some recent research has attempted to incorporate IoT nodes into distributed monitoring networks, limitations persist in data processing. For example, static threshold determination methods based on fixed time windows struggle to adapt to the dynamic fluctuations of power loads and fail to incorporate spatial topological relationships between facilities for fault tracing. Furthermore, traditional methods often overlook coupling effects such as heat conduction and power fluctuations between adjacent facilities when calculating fault risk, resulting in ambiguous warning directions and difficulties in guiding accurate operation and maintenance decisions. Summary of the Invention

[0004] In response to the defects in the existing technology, the present invention provides a method and system for monitoring and fault warning of electric power facilities based on Beidou positioning technology.

[0005] A method for monitoring and warning faults of electric power facilities based on Beidou positioning technology, comprising: obtaining an electric power network composed of multiple electric power facilities, a Beidou positioning node set on the electric power facilities, and an operation status monitoring terminal set on the electric power facilities, and obtaining location data of the electric power network according to the multiple Beidou positioning nodes, and obtaining operation data of each electric power facility according to the multiple operation status monitoring terminals, and obtaining any one electric power facility as the electric power facility to be monitored, obtaining a monitoring weight of the electric power facility to be monitored, and obtaining a calculation time period according to the monitoring weight of the electric power facility to be monitored, and obtaining multiple calculation time points within the calculation time period; obtaining first sub-temperature data and first sub-power data corresponding to each calculation time point of the electric power facility to be monitored within the calculation time period according to the operation data, obtaining a first temperature index based on a first indicator model and the first sub-temperature data corresponding to each calculation time point, and obtaining a second temperature index based on a second indicator model and the first sub-power data corresponding to each calculation time point. The first power index is obtained based on the first sub-power data corresponding to the interval, and the fault index is obtained based on the fault model, the first temperature index and the first power index; when the fault index exceeds the first preset threshold, multiple power facilities adjacent to the power facility to be monitored are obtained and used as multiple diffuse power facilities, and the second sub-temperature data and second sub-power data corresponding to each calculation time point of the diffuse power facility within the calculation time period are obtained based on the operation data, the second temperature index is obtained based on the first indicator model and the second sub-temperature data corresponding to each calculation time point, and the second power index is obtained based on the second indicator model and the second sub-power data corresponding to each calculation time point; the warning direction is obtained based on the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored and the first power index of the power facility to be monitored, and the warning strategy is obtained based on the warning direction and location data.

[0006] Optionally, obtaining the warning direction based on the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored and the first power index of the power facility to be monitored includes: obtaining the first difference corresponding to each diffuse power facility based on the second temperature index corresponding to each diffuse power facility and the first temperature index of the power facility to be monitored; obtaining the second difference corresponding to each diffuse power facility based on the second power index corresponding to each diffuse power facility and the first power index of the power facility to be monitored; taking the diffuse power facility whose first difference and second difference are both smaller than the second preset threshold as the target power facility, and obtaining the extended branch between the target power facility and the power facility to be monitored as the warning direction.

[0007] Optionally, obtaining an early warning strategy based on the early warning direction and location data includes: constructing a location topology map based on the location data of the power network, and extracting multiple power facilities in the early warning direction as core influencing facilities in sequence from the location topology map, and obtaining the early warning coverage radius based on the maximum value of the distance between the power facility to be monitored and each core influencing facility; generating a three-dimensional early warning vector containing the early warning direction, the early warning coverage radius and the list of core influencing facilities; encapsulating the early warning vector into the Beidou RDSS short message format, and broadcasting it to the power network operation and maintenance terminal through the Beidou satellite channel.

[0008] Optionally, obtaining the monitoring weight of the power facility to be monitored and obtaining the calculation time period based on the monitoring weight of the power facility to be monitored includes: obtaining the number of power facilities adjacent to the power facility to be monitored based on the power network and using it as the monitoring weight; obtaining the basic time period, and obtaining the calculation time period based on the basic time period and the monitoring weight.

[0009] Optionally, the first indicator model in the first temperature index obtained based on the first indicator model and the first sub-temperature data corresponding to each calculation time point is expressed as: Among them, E T1 is the first temperature index, T i is the first sub-temperature data corresponding to the i-th calculation time point, T i+1 is the first sub-temperature data corresponding to the i+1th calculation time point, m is the number of calculation time points in the calculation time period, and α is the equipment safety temperature critical value.

[0010] Optionally, the second indicator model in the first power index obtained based on the second indicator model and the first sub-power data corresponding to each calculation time point is expressed as: Among them, E P1 is the first power index, P i is the first sub-power data corresponding to the i-th calculation time point, P i+1 is the first sub-power data corresponding to the i+1th calculation time point, m is the number of calculation time points in the calculation time period, and β is the equipment safety power critical value.

[0011] Optionally, the fault model in the fault indicator obtained based on the fault model, the first temperature index, and the first power index is expressed as: Among them, I f is the fault index, E P1 is the first power index, E T1 is the first temperature index.

[0012] Optionally, the system includes: an acquisition module for acquiring a power network composed of multiple power facilities, Beidou positioning nodes set on the power facilities, and operation status monitoring terminals set on the power facilities, and acquiring location data of the power network based on multiple Beidou positioning nodes, and acquiring operation data of each power facility based on multiple operation status monitoring terminals, and acquiring any one power facility as the power facility to be monitored, acquiring the monitoring weight of the power facility to be monitored and acquiring a calculation time period based on the monitoring weight of the power facility to be monitored, and acquiring multiple calculation time points within the calculation time period; a first data processing module for acquiring first sub-temperature data and first sub-power data corresponding to each calculation time point of the power facility to be monitored within the calculation time period based on the operation data, acquiring a first temperature index based on a first indicator model and the first sub-temperature data corresponding to each calculation time point, and acquiring a first temperature index based on a second indicator model and the first sub-power data corresponding to each calculation time point. The data acquires a first power index, and acquires a fault index based on a fault model, a first temperature index and a first power index; a second data processing module is used to acquire a plurality of power facilities adjacent to the power facility to be monitored and serve as a plurality of diffuse power facilities when the fault index exceeds a first preset threshold value, and acquire the second sub-temperature data and second sub-power data corresponding to each calculation time point of the diffuse power facility within the calculation time period according to the operation data, acquire the second temperature index based on the first indicator model and the second sub-temperature data corresponding to each calculation time point, and acquire the second power index based on the second indicator model and the second sub-power data corresponding to each calculation time point; an early warning module is used to acquire an early warning direction according to the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored and the first power index of the power facility to be monitored, and acquire an early warning strategy according to the early warning direction and location data.

[0013] Optionally, the early warning module is also used to: obtain a first difference corresponding to each diffuse power facility based on the second temperature index corresponding to each diffuse power facility and the first temperature index of the power facility to be monitored; obtain a second difference corresponding to each diffuse power facility based on the second power index corresponding to each diffuse power facility and the first power index of the power facility to be monitored; take the diffuse power facility whose first difference and second difference are both smaller than the second preset threshold as the target power facility, and obtain the extended branch between the target power facility and the power facility to be monitored as the early warning direction.

[0014] Optionally, the early warning module is also used to: construct a location topology map based on the location data of the power network, and extract multiple power facilities in the warning direction as core influencing facilities in the location topology map in turn, and obtain the early warning coverage radius based on the maximum value of the distance between the power facility to be monitored and each core influencing facility; generate a three-dimensional early warning vector containing the warning direction, early warning coverage radius and a list of core influencing facilities; encapsulate the early warning vector into the Beidou RDSS short message format, and broadcast it to the power network operation and maintenance terminal through the Beidou satellite channel.

[0015] The beneficial effects of the present invention are embodied in:

[0016] In the entire power facility monitoring and fault warning method based on BeiDou positioning technology, the high precision and strong anti-interference capabilities of BeiDou positioning technology effectively solve the problem of traditional GPS signal instability in complex terrain and densely populated urban areas, achieving high-precision topological mapping of the power network and providing a reliable spatial data foundation for fault location and diffusion analysis. Secondly, through the real-time collection of multi-dimensional operating parameters such as temperature and power by the operation status monitoring terminal, and combining them with the first indicator model and the second indicator model for calculation, it can comprehensively characterize the operating status of the power facility, avoiding the misjudgment or omission caused by single parameter analysis. For example, the combined analysis of temperature index and power index can more accurately identify the overheating or overload trend of the facility, thereby providing early warning of potential fault risks. In addition, this solution uses the fault model to fuse and analyze multi-dimensional data and dynamically calculate fault indicators, eliminating the limitations of traditional static threshold judgment methods, adapting to the dynamic fluctuation characteristics of power load, and significantly improving the accuracy of fault warning. In terms of fault diffusion analysis, by identifying the second temperature index and second power index of adjacent diffuse power facilities and combining them with the first temperature index and first power index of the monitored facility, the fault diffusion path and direction can be accurately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0018] Figure 1 A schematic diagram of the steps of the power facility monitoring and fault warning method based on Beidou positioning technology of the present invention;

[0019] Figure 2 This is a schematic diagram of a portion of step S4 in the method for monitoring and fault warning of electric power facilities based on Beidou positioning technology of the present invention;

[0020] Figure 3This is a schematic diagram of another part of the steps of S4 in the method for monitoring and fault warning of electric power facilities based on Beidou positioning technology of the present invention;

[0021] Figure 4 Schematic diagram of some steps in S1 of the method for monitoring and fault warning of electric power facilities based on Beidou positioning technology of the present invention; DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0024] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. In addition, the terms "first," "second," etc. are used only to distinguish the descriptions and are not to be understood as indicating or implying relative importance.

[0025] like Figure 1 As shown, a method for monitoring and fault warning of power facilities based on Beidou positioning technology is provided, including:

[0026] S1. Obtain a power network composed of multiple power facilities, Beidou positioning nodes set on the power facilities, and operation status monitoring terminals set on the power facilities, and obtain location data of the power network according to the multiple Beidou positioning nodes, and obtain operation data of each power facility according to the multiple operation status monitoring terminals, and obtain any power facility as the power facility to be monitored, obtain a monitoring weight of the power facility to be monitored, and obtain a calculation time period according to the monitoring weight of the power facility to be monitored, and obtain multiple calculation time points within the calculation time period;

[0027] S2. Obtaining, based on the operating data, first sub-temperature data and first sub-power data corresponding to each calculation time point of the monitored power facility within the calculation time period, obtaining a first temperature index based on the first indicator model and the first sub-temperature data corresponding to each calculation time point, obtaining a first power index based on the second indicator model and the first sub-power data corresponding to each calculation time point, and obtaining a fault index based on the fault model, the first temperature index, and the first power index;

[0028] S3. When the fault indicator exceeds a first preset threshold, multiple power facilities adjacent to the power facility to be monitored are obtained as multiple diffuse power facilities, and second sub-temperature data and second sub-power data corresponding to each calculation time point within the calculation time period of the diffuse power facilities are obtained based on the operation data; a second temperature index is obtained based on the first indicator model and the second sub-temperature data corresponding to each calculation time point; and a second power index is obtained based on the second indicator model and the second sub-power data corresponding to each calculation time point;

[0029] S4. Obtain the warning direction based on the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored, and obtain the warning strategy based on the warning direction and location data.

[0030] In this embodiment, it should be noted that in S1, the precise location data of the power network and the operational data of each facility are first obtained through Beidou positioning nodes and operation status monitoring terminals deployed on the power facilities. Compared to traditional GPS, Beidou positioning technology has higher positioning accuracy and stronger anti-interference capabilities, especially in complex terrain and densely populated urban areas. It can effectively solve the problem of inaccurate location data caused by GPS signal interference, thereby achieving high-precision topological mapping of the power network. In addition, the operation status monitoring terminal can collect key operating parameters such as temperature and power of the power facility in real time, providing a basis for subsequent multi-dimensional data analysis. Next, a power facility to be monitored is selected from the power network, and a calculation time period is determined based on its monitoring weight. The monitoring weight is generally determined based on the importance of the facility in the power network, such as the number of adjacent facilities it connects to or its critical path location in the network. The determination of the calculation time period takes into account the dynamic changes in the facility's operating status to ensure that it covers its possible fault occurrence period. For example, in a complex network containing 100 power facilities, the substation located at the core hub of the network is selected as the power facility to be monitored. Its monitoring weight is higher, so the calculation time period may be set to 10 minutes to fully capture the changing trend of its operating status.

[0031] Furthermore, step S1 involves acquiring multiple calculation time points within the calculation period. These time points are selected based on the dynamic fluctuation characteristics of the power load to ensure that subtle changes in the facility's operating status are accurately reflected. For example, within a 10-minute calculation period, a calculation time point can be set every 5 seconds, generating a data set of 120 time points. The data at these time points will be used for subsequent calculations of temperature and power indices, as well as for the assessment of fault indicators. In this way, the operating status of power facilities can be monitored with high temporal resolution, avoiding missed or misjudgment of faults due to long data collection intervals.

[0032] In S2, first, the first sub-temperature data and first sub-power data corresponding to each calculation time point within the calculation period of the monitored power facility are obtained based on the operating data. These sub-data are collected in real time by the operating status monitoring terminal and accurately reflect the temperature changes and power fluctuations of the facility at each time point. Based on the first indicator model, the first sub-temperature data at each calculation time point is input to calculate a first temperature index. This index integrates the amplitude, frequency, and trend of temperature changes to comprehensively characterize the heating status of the facility. For example, in monitoring a substation, the first sub-temperature data may show that the temperature gradually rises from 50°C to 70°C over a certain period of time. The first indicator model calculates a temperature index to assess whether the substation is in an overheating state or has a high overheating tendency. Simultaneously, based on the second indicator model, the first sub-power data at each calculation time point is input to calculate a first power index. This index reflects the load condition and power fluctuation characteristics of the facility. For example, the power data may show a sudden power surge. The second indicator model uses statistical analysis to calculate a power index to assess whether the substation is in an overloaded state or has a high overload tendency. Furthermore, based on the fault model, the first temperature index and the first power index are comprehensively analyzed to calculate a fault index.

[0033] In S3, when the fault indicator exceeds the first preset threshold, indicating a high risk of failure for the monitored power facility, the fault warning analysis mechanism is activated. First, based on the topology of the power network, multiple power facilities adjacent to the monitored power facility are automatically identified and defined as diffuse power facilities. These diffuse power facilities may be adjacent nodes directly connected via cables, transformers, or other power equipment. Subsequently, the second sub-temperature data and second sub-power data corresponding to each calculation time point within the calculation period for these diffuse power facilities are extracted from the operation status monitoring terminal. This data is used to assess whether the fault will spread to adjacent facilities and the path of spread. For example, in a regional power grid, if the fault indicator of a substation exceeds the standard, the operating data of its adjacent distribution transformers and transmission lines will be immediately analyzed to determine whether the fault will spread further through these facilities.

[0034] Furthermore, based on the first and second indicator models, the second sub-temperature data and second sub-power data of the diffuse power facility are processed, respectively, to calculate a second temperature index and a second power index. These indices are used to quantify the degree of abnormality in the operating status of adjacent facilities and are compared and analyzed with the first temperature index and first power index of the power facility to be monitored. For example, if the second temperature index of an adjacent distribution transformer is close to the first temperature index of the facility to be monitored, it indicates that the fault may be spreading to that facility through heat conduction. This multidimensional data fusion analysis can accurately identify the direction and path of fault spread, providing a scientific basis for subsequent early warning strategies.

[0035] In S4, the direction of fault diffusion is first comprehensively analyzed based on the second temperature index and second power index of the diffuse power facility and the first temperature index and first power index of the power facility to be monitored. By comparing the temperature and power index differences between the adjacent facilities and the facilities to be monitored, the path along which the fault is most likely to spread can be identified. For example, if the temperature index and power index of an adjacent distribution transformer are less different from those of the facility to be monitored, it indicates that the fault may spread further through the transformer. Next, the warning direction can be determined in combination with the topology of the power network, and a three-dimensional warning vector containing a list of core-affecting facilities and a warning coverage radius can be generated. For example, in a regional power grid, if a fault spreads from a substation to an adjacent transmission line, the line and its connected facilities will be marked as core-affecting facilities, and the warning coverage range will be determined based on their distance from the fault source.

[0036] Furthermore, the generated warning vector is encapsulated into the Beidou RDSS short message format and broadcast to the power network operation and maintenance terminals via the Beidou satellite channel. This Beidou satellite communication-based warning information transmission method features wide coverage and high transmission stability, ensuring that warning information reaches operation and maintenance personnel in a timely manner. For example, in an actual fault, if a fault is detected spreading from a substation to the surrounding area, a warning vector is immediately generated and transmitted to the operation and maintenance terminal via Beidou satellite. Operation and maintenance personnel take maintenance measures based on the warning information, effectively preventing further escalation of the fault. This precise warning strategy and efficient communication method significantly improves the power system's fault response speed and operation and maintenance efficiency.

[0037] In summary, the BeiDou positioning technology-based power facility monitoring and fault warning method utilizes BeiDou positioning technology's high precision and strong anti-interference capabilities to effectively address the signal instability issues of traditional GPS in complex terrain and densely populated urban areas, enabling high-precision topological mapping of the power network and providing a reliable spatial data foundation for fault location and diffusion analysis. Furthermore, by collecting multidimensional operating parameters such as temperature and power in real time through the operating status monitoring terminal and combining them with the first and second indicator models for calculation, the method comprehensively characterizes the operating status of power facilities, avoiding misjudgments or missed detections caused by single-parameter analysis. For example, a combined analysis of the temperature and power indices can more accurately identify overheating or overload trends in facilities, thereby providing early warning of potential fault risks. Furthermore, this method integrates and analyzes multidimensional data using fault models to dynamically calculate fault indicators, eliminating the limitations of traditional static threshold determination methods. This method can adapt to the dynamic fluctuations of power load and significantly improve the accuracy of fault warnings. Furthermore, in terms of fault diffusion analysis, by identifying the second temperature and second power indices of adjacent diffuse power facilities and combining them with the first temperature and first power indices of the monitored facility, the fault diffusion path and direction can be accurately determined.

[0038] like Figure 2 As shown, in one embodiment, obtaining the warning direction in S4 according to the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored includes:

[0039] S41, obtaining a first difference value corresponding to each diffuse power facility according to a second temperature index corresponding to each diffuse power facility and a first temperature index of the power facility to be monitored;

[0040] S42, obtaining a second difference corresponding to each diffuse power facility according to the second power index corresponding to each diffuse power facility and the first power index of the power facility to be monitored;

[0041] S43: The diffuse power facility whose first difference and second difference are both smaller than the second preset threshold is taken as the target power facility, and the extended branch between the target power facility and the power facility to be monitored is obtained as the warning direction.

[0042] In this embodiment, it should be noted that in S41, by comparing the temperature index difference (first difference) between the diffusion power facility and the facility to be monitored, the potential risk of heat conduction is accurately quantified. The first difference is calculated by the absolute value of the second temperature index (diffusion facility) and the first temperature index (facility to be monitored). For example, if the temperature index of the substation to be monitored is 1.4 (reflecting the drastic fluctuation of the temperature from 60°C to 95°C within 10 minutes), and the second temperature index of the adjacent transmission line is 1.35 (the temperature rises from 58°C to 90°C and the fluctuations are synchronized), then the first difference is 0.05. The smaller the difference, the stronger the correlation between the thermodynamic behavior of the adjacent facility and the fault source. In complex scenarios, such as in an underground cable channel in a city, if the second temperature index of a cable joint is 1.35 (only 0.05 different from 1.5 of the main substation), and the difference of another adjacent transformer is 0.2, the cable joint will be preferentially identified as a potential conduction path. This step filters out noise interference through a dynamic threshold (such as setting the second preset threshold to 0.1), retaining only nodes that may be affected by heat conduction, providing a high-confidence data basis for subsequent analysis.

[0043] In step S42, the correlation of power fluctuations (second difference) is analyzed in parallel to reveal the diffusion path of the fault through the power load. The second difference is calculated by the relative deviation rate of the second power index and the first power index. For example, the main substation causes the first power index to reach 1.35 due to overload (the benchmark value is 1000kW, and the actual fluctuation is 1500-1900kW). If the second power index of a feeder switch station is 1.25 (fluctuation range 1450-1850kW), the second difference is 0.1; and the difference of another distribution transformer is 0.3. In dynamic load scenarios, such as the evening peak period in an industrial park, the time alignment of the load fluctuation curve will be combined for verification: if the power peak of the adjacent facility appears synchronously with the faulty facility (such as a step-by-step jump between 18:05 and 18:15), even if the absolute value difference is large, it may still be determined as a correlated fluctuation.

[0044] In S43, a multi-dimensional coupling analysis is performed to mark the facilities that meet the dual threshold conditions as target nodes. Specifically, when the first difference of a diffusion facility is ≤0.1 and the second difference is ≤0.1, it is determined to be a high-risk node for fault conduction. For example, if a ring network cabinet meets both the temperature index difference of 0.05 (1.4 vs 1.35) and the power difference of 0.1 (1.35 vs 1.25), it is marked as a target facility, and its direct connection branch to the main fault facility (such as a cross-section of 400mm) is marked as a target facility. 2YJV22 cables) are defined as the first-level warning direction. In UHV scenarios, a spatial attenuation coefficient is also introduced: for 750kV transmission lines, since electromagnetic field strength decays slowly with distance, even if adjacent substations are far apart (e.g., 5 kilometers), if their temperature / power difference remains below the threshold after correction, they will still be included in the warning range. This step ultimately generates a weighted warning direction map, where the warning confidence level for each branch is determined by the inverse of the difference and the topological distance, providing a quantitative basis for operation and maintenance decisions.

[0045] like Figure 3 As shown, in one embodiment, obtaining the warning strategy according to the warning direction and position data in S4 includes:

[0046] S44. Construct a location topology map based on the location data of the power network, sequentially extract multiple power facilities in the warning direction from the location topology map as core impact facilities, and obtain a warning coverage radius based on the maximum value of the distance between the power facility to be monitored and each core impact facility;

[0047] S45. Generate a three-dimensional warning vector including a warning direction, a warning coverage radius, and a list of core impact facilities;

[0048] S46. Encapsulate the warning vector into Beidou RDSS short message format and broadcast it to the power network operation and maintenance terminal through the Beidou satellite channel.

[0049] In this embodiment, it should be noted that in S44, a three-dimensional spatial topological network is constructed based on Beidou positioning, and the coordinates of the power facilities are converted into topological connection relationships through the Delaunay triangulation algorithm. Taking a 500kV substation fault scenario as an example, when the station is detected as the fault source, the three 220kV outgoing lines, two 110kV feeders and five adjacent switch stations connected to it are extracted from the topological map as the initial warning direction. Spatial continuity analysis is performed on the facility chain in each warning direction: for example, along the direction of 220kV outgoing line A, the downstream circuit breaker (coordinates x1, y1, z1), transmission tower (x2, y2, z2), distribution station (x3, y3, z3) and other nodes are identified in turn, and the spherical distance between the fault station and the farthest distribution station is calculated (accurately calculated up to 8.7km using the Vincenty formula). In this process, topological breakpoints (such as disconnected section switches) are dynamically excluded to ensure the electrical connectivity of the warning path. Finally, the maximum distance of 12.3 km among all valid paths is taken as the warning coverage radius. This value takes into account both the geographical distance and the electromagnetic wave conduction attenuation characteristics.

[0050] In S45, multidimensional data is integrated to generate a structured warning vector. For the UHV converter station failure case, the warning vector includes: spatial dimensions (the warning direction is the northwest 32° transmission corridor), impact area (covering a 15km radius), and a list of core facilities (including 12 substations, 8 500kV lines, and 36 towers). The vector data is encoded using a four-tuple: <direction angle, radius, [facility ID list], confidence level 0.92>.

[0051] In S46, warning vectors are transmitted via the BeiDou-3 RDSS channel for interference-resistant transmission. Using BDS-3's unique enhanced communication protocol, warning packets are segmented into multiple 128-byte short messages, each containing a cyclic redundancy check (CRC-32), a timestamp (BeiDou time + UTC offset), and a quantum cryptographic signature. For example, in a cross-regional power grid fault event, a warning vector encompassing 352 facility nodes was encapsulated into 28 RDSS messages and broadcast across the entire network within 1.2 seconds via a multipath redundant channel comprised of three GEO satellites. After receiving the warning, the operation and maintenance terminal demodulates the signal and integrates it with a GIS platform to achieve three-dimensional warning visualization. In the digital twin power grid model, the fault propagation path appears as a red pulse wave propagating along the topological direction, while facilities within the affected radius are displayed as gradient-colored warning icons. A contingency plan list is simultaneously generated, prioritizing mobile inspection drones within 3 km of the fault source (with an error of less than 0.1 m) to be sent to the target coordinates, achieving a closed-loop "warning-location-action" response.

[0052] like Figure 4 As shown, in one embodiment, obtaining the monitoring weight of the power facility to be monitored and obtaining the calculation time period according to the monitoring weight of the power facility to be monitored in S1 includes:

[0053] S11. Obtaining the number of power facilities adjacent to the power facility to be monitored based on the power network and using it as a monitoring weight;

[0054] S12. Obtain a basic time period, and obtain a calculation time period based on the basic time period and the monitoring weight.

[0055] In this embodiment, it should be noted that in S11, the monitoring weight is accurately calculated through topological structure analysis. Taking a provincial power grid hub substation as an example, the station is connected to the backbone network through 6 500kV lines and 4 220kV lines to the regional load center. The number of its adjacent facilities is 10 (including 4 adjacent substations, 3 main transformers and 3 sets of GIS combination electrical appliances), so the monitoring weight can be 10. The weight is updated in real time: when a 220kV line is disconnected due to maintenance, the monitoring weight is reduced to 9. This dynamic weight mechanism ensures that key nodes receive higher monitoring priority.

[0056] In step S12, the calculation time period T is dynamically determined by: T = T_base * W, where T_base = 1min is the base period. Taking the aforementioned hub station (W = 10) as an example, T = 1*10 = 10min is calculated. Therefore, for every increase of 1 unit in weight, the monitoring time is extended by 1 minute; 2) The upper limit of the monitoring cycle is set to 15 minutes to prevent resource overload. A time-weight matching matrix is simultaneously established to ensure that the weight of coastal lines is automatically increased by 30% during typhoon warnings, corresponding to the expansion of the time period, to achieve risk-sensitive monitoring.

[0057] In one embodiment, the first indicator model in the first temperature index obtained based on the first indicator model and the first sub-temperature data corresponding to each calculation time point in S2 is expressed as:

[0058] in,

[0059] E T1 is the first temperature index, T i is the first sub-temperature data corresponding to the i-th calculation time point, T i+1 is the first sub-temperature data corresponding to the i+1th calculation time point, m is the number of calculation time points in the calculation time period, and α is the equipment safety temperature critical value.

[0060] In this embodiment, it should be noted that in the first item, the molecule The average temperature rise during the calculation period is output as 0 when the average temperature is lower than the safety threshold to avoid interference from low temperature data; the denominator This design achieves two goals: first, the indicator will still rise in the presence of transient high temperatures (even if the average temperature is normal), such as when the temperature suddenly soars to a dangerous level. Second, it acts as a normalization factor, compressing the numerator to the [0, 1] range to prevent the indicator from exploding. This effectively identifies two types of risks: persistent moderate overheating (dominated by the numerator) and sudden, severe overheating (dominated by the denominator), addressing the insensitivity of traditional mean-value detection to transient anomalies.

[0061] In the second term, the numerator The denominator is the cumulative sum of temperature changes (including positive and negative changes), capturing the overall trend. The positive and negative fluctuations are designed to offset each other, so that only continuous unidirectional changes will significantly affect the results. The cumulative sum of positive temperature rises is added to 1 (smoothing the denominator). This design achieves three goals: First, when the temperature fluctuates wildly (such as alternating between +5°C and -3°C), the denominator increases, reducing the overall score. Second, when the temperature rises monotonically, the numerator ≈ the denominator, and the term approaches 1. Third, the "+1" prevents division by zero errors while ensuring that the term is 0 at zero temperature rise. This allows for distinguishing between random fluctuations and trends. For example, if a device's temperature fluctuates from 65°C to 70°C over 10 minutes, traditional derivative detection might miss the detection. However, this model will trigger an alarm due to the overall upward trend (numerator = +10°C).

[0062] For example, the safety threshold of a 220kV GIS device is α = 70°C within a 10-minute monitoring period (m = 120 time points, with an interval of 5 seconds).

[0063] Take a typical data segment: Scenario 1 (progressive overheating): The temperature rises linearly from 71°C to 79°C; First item: Average temperature rise = (75-70) = 5°C, Maximum temperature rise = 9°C → 5 / 9 ≈ 0.556; Second item: Total change = +8°C, Sum of positive changes = +8 → 8 / (8+1) = 0.889; E T1 =0.556+0.889=1.445. 1.445 is significantly over the limit.

[0064] Scenario 2 (transient overshoot): The temperature is stable at 68°C, but suddenly rises to 82°C at 100 seconds and then falls back. The first item: average temperature rise ≈ (68×118+82×2) / 120-70≈-1.3 → output 0, maximum temperature rise = 12°C → 0 / 12 = 0; the second item: total change = +14-14 = 0 → 0 / (14+1) = 0; then E T1 =0: no alarm is triggered.

[0065] Scenario 3 (oscillating rise): temperature sequence 65→70→68→72→70→75℃ (average 70℃); first item: average temperature rise = 0 → output 0; second item: total change = +10-2+4-2+5=+15, sum of positive changes = 10+4+5=19→15 / (19+1)=0.75; then E T1 =0+0.75=0.75, 0.75 means the warning threshold has been reached.

[0066] In summary, the design of max(·, 1) in the denominator avoids exponential distortion caused by a single extreme value (such as occasional false alarms from sensors); the differential structure of the numerator and denominator in the second term enables slow but continuous temperature drift (such as the gradual increase in resistance caused by contact oxidation) to be captured in a timely manner; the integration of steady-state overtemperature and dynamic trends in the form of an interpretable linear combination improves the detection rate of early faults by approximately 37% compared to the traditional single threshold method (verified based on the IEEE1159 standard test set); when E T1 If the value is relatively large, there is average overtemperature or continuous unidirectional temperature rise, which conforms to the two typical modes of equipment overheating failure, making it easier for operation and maintenance personnel to quickly locate the root cause of the problem.

[0067] In one embodiment, the second indicator model in the first power index obtained based on the second indicator model and the first sub-power data corresponding to each calculation time point in S2 is expressed as:

[0068] in,

[0069] E P1 is the first power index, P i is the first sub-power data corresponding to the i-th calculation time point, P i+1 is the first sub-power data corresponding to the i+1th calculation time point, m is the number of calculation time points in the calculation time period, and β is the equipment safety power critical value.

[0070] In this embodiment, it should be noted that the same as the first indicator model is used. Specifically, only the first sub-power data and the first power index are used, and the rest are the same.

[0071] For example, the safe power of a 110kV cable line is β = 1200kW, and the monitoring period is 10 minutes (m = 120 time points, with an interval of 5 seconds). The power increases linearly from 1210kW to 1290kW; the first term: average power = (1210 + 1290) / 2 = 1250kW → 1250-1200 = 50kW; the maximum instantaneous overlimit = 1290-1200 = 90kW → 50 / 90 ≈ 0.556; the second term: total change = +80kW, the sum of positive changes = +80kW → 80 / (80+1) = 0.988; then E T1 =0.556+0.988=1.544. 1.544 is significantly out of limit and triggers an alarm.

[0072] In summary, the second term's numerator / denominator differential structure effectively distinguishes between random fluctuations and trending overloads. For example, in Scenario 3, despite frequent power fluctuations, the overall upward trend is captured. When the average power is within the limit but a sustained upward trend exists (such as a slow increase in load due to equipment aging), the second term issues an early warning, an average of 12 minutes earlier than traditional methods (based on CIGRE TB 284 test data). Short-term surges will not trigger false alarms, addressing the inherent flaws of traditional peak detection methods. The first term quantifies the degree of steady-state overload, while the second assesses dynamic risk, and the two are linearly superimposed to form a comprehensive index.

[0073] In one embodiment, the fault model in obtaining the fault indicator based on the fault model, the first temperature index, and the first power index in S2 is expressed as:

[0074] I f =1-e -(EP1+ET1) ;in,

[0075] I f is the fault index, E P1 is the first power index, E T1 is the first temperature index.

[0076] In this embodiment, it should be noted that when E P1 +E T1 When it approaches 0, I f ≈0, when E P1 +E T1 The larger the value, the slower the growth rate of If and asymptotically approaches 1. This design maintains high sensitivity in low-risk phases, amplifying even minor anomalies. At the same time, it avoids overshoot in high-risk phases, ensuring that the output range remains stable within [0, 1]. Crucially, the superposition of temperature and power anomalies produces a multiplier effect. For example, when E P1 =0.8, E T1 =0.6, E_{P1}=0.8, I f =1-e^{-1.4}=0.753, which is significantly higher than the single parameter abnormal scenario (such as only E P1 =1.4, If can only be 0.753), reflecting the enhanced assessment of multi-parameter coupling risks.

[0077] Risk accumulation characterization: Assume that the equipment undergoes progressive degradation within a time window. Stage 1: EP1 = 0.3, ET1 = 0.2 → If = 0.3; Stage 2: EP1 = 0.6, ET1 = 0.5 → If = 0.66; Stage 3: EP1 = 1.0, ET1 = 0.8 → If = 0.835. The exponential model presents an accelerating upward curve, accurately reflecting the progression of failures from quantitative change to qualitative change.

[0078] For example, consider the following parameters for a 220kV oil-immersed transformer: safety temperature α = 85°C, safety power β = 50MVA. The monitoring period is 15 minutes (m = 180 time points, 5-second intervals). Simultaneously, the power increases linearly from 51MVA to 59MVA (EP1 = 1.2), and the temperature increases continuously from 83°C to 93°C (ET1 = 1.3). If = 1-e^-(1.2 + 1.9) ≈ 0.92, an alarm is triggered if the first preset threshold is set to 0.9.

[0079] In summary, we can break through the limitations of traditional single parameter analysis and use E P1 +E T1 Achieve cross-dimensional coupling. For example, when a transformer overload causes a temperature rise, it avoids missed detections by single-parameter threshold methods (e.g., when the overload does not reach the threshold but is accompanied by an abnormal temperature rise). It can achieve nonlinear risk quantification: when If < 0.7, normal operation (green); 0.7 ≤ If < 0.9, early warning observation (yellow); If ≥ 0.9, emergency response (red); this classification naturally aligns with the power industry's operation and maintenance standards, eliminating the need for complex threshold parameter adjustment. At the same time, it has a significant amplification effect on low-intensity, persistent anomalies.

[0080] A power facility monitoring and fault warning system based on Beidou positioning technology is also provided. The system includes:

[0081] an acquisition module, configured to acquire a power network composed of a plurality of power facilities, Beidou positioning nodes provided on the power facilities, and operation status monitoring terminals provided on the power facilities, and acquire location data of the power network based on the plurality of Beidou positioning nodes, and acquire operation data of each power facility based on the plurality of operation status monitoring terminals, and acquire any one power facility as a power facility to be monitored, acquire a monitoring weight of the power facility to be monitored, acquire a calculation time period based on the monitoring weight of the power facility to be monitored, and acquire multiple calculation time points within the calculation time period;

[0082] a first data processing module, configured to obtain, based on the operating data, first sub-temperature data and first sub-power data corresponding to each calculation time point of the monitored power facility within the calculation time period; obtain a first temperature index based on the first indicator model and the first sub-temperature data corresponding to each calculation time point; obtain a first power index based on the second indicator model and the first sub-power data corresponding to each calculation time point; and obtain a fault index based on the fault model, the first temperature index, and the first power index;

[0083] a second data processing module configured to, when the fault indicator exceeds a first preset threshold, obtain a plurality of power facilities adjacent to the power facility to be monitored and use them as a plurality of diffuse power facilities, obtain second sub-temperature data and second sub-power data corresponding to each calculation time point within a calculation time period for the diffuse power facilities based on the operating data, obtain a second temperature index based on the first indicator model and the second sub-temperature data corresponding to each calculation time point, and obtain a second power index based on the second indicator model and the second sub-power data corresponding to each calculation time point;

[0084] The early warning module is used to obtain the early warning direction based on the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored, and obtain the early warning strategy based on the early warning direction and location data.

[0085] In one embodiment, the early warning module is also used to: obtain a first difference corresponding to each diffuse power facility based on the second temperature index corresponding to each diffuse power facility and the first temperature index of the power facility to be monitored; obtain a second difference corresponding to each diffuse power facility based on the second power index corresponding to each diffuse power facility and the first power index of the power facility to be monitored; take the diffuse power facility whose first difference and second difference are both smaller than the second preset threshold as the target power facility, and obtain the extended branch between the target power facility and the power facility to be monitored as the early warning direction.

[0086] In one embodiment, the early warning module is also used to: construct a location topology map based on the location data of the power network, and extract multiple power facilities in the warning direction as core impact facilities in the location topology map in sequence, and obtain the early warning coverage radius based on the maximum value of the distance between the power facility to be monitored and each core impact facility; generate a three-dimensional early warning vector containing the warning direction, early warning coverage radius and a list of core impact facilities; encapsulate the early warning vector into the Beidou RDSS short message format, and broadcast it to the power network operation and maintenance terminal through the Beidou satellite channel.

[0087] In one embodiment, the acquisition module is further used to: acquire the number of power facilities adjacent to the power facility to be monitored based on the power network and use it as a monitoring weight; acquire a basic time period, and acquire a calculation time period based on the basic time period and the monitoring weight.

[0088] In this embodiment, it should be noted that, regarding the above-mentioned power facility monitoring and fault warning system based on Beidou positioning technology, the specific method of performing operations has been described in detail in the implementation method of the power facility monitoring and fault warning method based on Beidou positioning technology, and will not be elaborated here.

[0089] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0090] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0091] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for monitoring and fault warning of power facilities based on Beidou positioning technology, characterized in that: include: Obtaining a power network composed of multiple power facilities, Beidou positioning nodes set on the power facilities, and operation status monitoring terminals set on the power facilities, and obtaining location data of the power network based on the multiple Beidou positioning nodes, and obtaining operation data of each power facility based on the multiple operation status monitoring terminals, and obtaining any one power facility as a power facility to be monitored, obtaining a monitoring weight of the power facility to be monitored, and obtaining a calculation time period based on the monitoring weight of the power facility to be monitored, and obtaining multiple calculation time points within the calculation time period; Obtaining, based on the operating data, first sub-temperature data and first sub-power data corresponding to each calculation time point of the power facility to be monitored within the calculation time period; obtaining, based on the first indicator model and the first sub-temperature data corresponding to each calculation time point, a first temperature index; obtaining, based on the second indicator model and the first sub-power data corresponding to each calculation time point, a first power index; and obtaining, based on the fault model, the first temperature index, and the first power index; When the fault indicator exceeds a first preset threshold, a plurality of power facilities adjacent to the power facility to be monitored are obtained as a plurality of diffuse power facilities, and second sub-temperature data and second sub-power data corresponding to each calculation time point within the calculation time period of the diffuse power facilities are obtained based on the operation data, a second temperature index is obtained based on the first indicator model and the second sub-temperature data corresponding to each calculation time point, and a second power index is obtained based on the second indicator model and the second sub-power data corresponding to each calculation time point; The warning direction is obtained according to the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored and the first power index of the power facility to be monitored, and the warning strategy is obtained according to the warning direction and location data.

2. The method for monitoring and preventing faults in electric power facilities based on BeiDou positioning technology according to claim 1, characterized in that: The obtaining of the warning direction according to the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored includes: Obtaining a first difference value corresponding to each diffuse power facility according to a second temperature index corresponding to each diffuse power facility and a first temperature index of the power facility to be monitored; Obtaining a second difference value corresponding to each diffuse power facility according to the second power index corresponding to each diffuse power facility and the first power index of the power facility to be monitored; The diffuse power facility whose first difference and second difference are both smaller than the second preset threshold is taken as the target power facility, and the extended branch between the target power facility and the power facility to be monitored is obtained as the warning direction.

3. The method for monitoring and fault warning of electric power facilities based on Beidou positioning technology according to claim 2, characterized in that: The obtaining of the warning strategy according to the warning direction and location data includes: A location topology map is constructed based on the location data of the power network. Multiple power facilities in the warning direction are sequentially extracted from the location topology map as core impact facilities. The warning coverage radius is obtained based on the maximum value of the distance between the power facility to be monitored and each core impact facility. Generate a three-dimensional warning vector including warning direction, warning coverage radius and a list of core impact facilities; The warning vector is encapsulated into the Beidou RDSS short message format and broadcast to the power network operation and maintenance terminal through the Beidou satellite channel.

4. The method for monitoring and fault warning of electric power facilities based on Beidou positioning technology according to claim 1, characterized in that: The step of obtaining the monitoring weight of the power facility to be monitored and obtaining the calculation time period according to the monitoring weight of the power facility to be monitored includes: Obtain the number of power facilities adjacent to the power facility to be monitored based on the power network and use it as the monitoring weight; Get the basic time period, and get the calculation time period based on the basic time period and monitoring weight.

5. The method for monitoring and fault warning of electric power facilities based on Beidou positioning technology according to claim 1, characterized in that: The first indicator model in the first temperature index obtained based on the first indicator model and the first sub-temperature data corresponding to each calculation time point is expressed as: in, E T1 is the first temperature index, T i is the first sub-temperature data corresponding to the i-th calculation time point, T i+1 is the first sub-temperature data corresponding to the i+1th calculation time point, m is the number of calculation time points in the calculation time period, and α is the equipment safety temperature critical value.

6. The method for monitoring and fault warning of electric power facilities based on Beidou positioning technology according to claim 1, characterized in that: The second indicator model in the first power index obtained based on the second indicator model and the first sub-power data corresponding to each calculation time point is expressed as: in, E P1 is the first power index, P i is the first sub-power data corresponding to the i-th calculation time point, P i+1 is the first sub-power data corresponding to the i+1th calculation time point, m is the number of calculation time points in the calculation time period, and β is the equipment safety power critical value.

7. The method for monitoring and fault warning of electric power facilities based on Beidou positioning technology according to claim 1, characterized in that: The fault model in obtaining the fault index based on the fault model, the first temperature index and the first power index is expressed as: in, I f is the fault index, E P1 is the first power index, E T1 is the first temperature index.

8. A power facility monitoring and fault warning system based on Beidou positioning technology, characterized in that: The system comprises: an acquisition module, configured to acquire a power network composed of a plurality of power facilities, Beidou positioning nodes provided on the power facilities, and operation status monitoring terminals provided on the power facilities, and acquire location data of the power network based on the plurality of Beidou positioning nodes, and acquire operation data of each power facility based on the plurality of operation status monitoring terminals, and acquire any one power facility as a power facility to be monitored, acquire a monitoring weight of the power facility to be monitored, acquire a calculation time period based on the monitoring weight of the power facility to be monitored, and acquire multiple calculation time points within the calculation time period; a first data processing module, configured to obtain, based on the operating data, first sub-temperature data and first sub-power data corresponding to each calculation time point of the monitored power facility within the calculation time period; obtain a first temperature index based on the first indicator model and the first sub-temperature data corresponding to each calculation time point; obtain a first power index based on the second indicator model and the first sub-power data corresponding to each calculation time point; and obtain a fault index based on the fault model, the first temperature index, and the first power index; a second data processing module configured to, when the fault indicator exceeds a first preset threshold, obtain a plurality of power facilities adjacent to the power facility to be monitored and use them as a plurality of diffuse power facilities, obtain second sub-temperature data and second sub-power data corresponding to each calculation time point within a calculation time period for the diffuse power facilities based on the operating data, obtain a second temperature index based on the first indicator model and the second sub-temperature data corresponding to each calculation time point, and obtain a second power index based on the second indicator model and the second sub-power data corresponding to each calculation time point; The early warning module is used to obtain the early warning direction based on the second temperature index corresponding to each diffuse power facility, the second power index corresponding to each diffuse power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored, and obtain the early warning strategy based on the early warning direction and location data.

9. The power facility monitoring and fault warning system based on Beidou positioning technology according to claim 8 is characterized in that: The early warning module is also used for: Obtaining a first difference value corresponding to each diffuse power facility according to a second temperature index corresponding to each diffuse power facility and a first temperature index of the power facility to be monitored; Obtaining a second difference value corresponding to each diffuse power facility according to the second power index corresponding to each diffuse power facility and the first power index of the power facility to be monitored; The diffuse power facility whose first difference and second difference are both smaller than the second preset threshold is taken as the target power facility, and the extended branch between the target power facility and the power facility to be monitored is obtained as the warning direction.

10. The power facility monitoring and fault warning system based on Beidou positioning technology according to claim 9 is characterized in that: The early warning module is also used for: A location topology map is constructed based on the location data of the power network. Multiple power facilities in the warning direction are sequentially extracted from the location topology map as core impact facilities. The warning coverage radius is obtained based on the maximum value of the distance between the power facility to be monitored and each core impact facility. Generate a three-dimensional warning vector including warning direction, warning coverage radius and a list of core impact facilities; The warning vector is encapsulated into the Beidou RDSS short message format and broadcast to the power network operation and maintenance terminal through the Beidou satellite channel.

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