Beidou positioning inspection method and system

Abstract

The invention relates to the technical field of Beidou positioning, and discloses a Beidou positioning inspection method and system. The method comprises the following steps: acquiring geographic information and equipment state parameters of an inspection area, and generating a structured task queue according to dimensions such as equipment types and fault levels; constructing a Beidou positioning dynamic model containing a position axis, a time axis and an environment factor axis; determining an initial inspection constraint condition according to the equipment type and the fault level, and performing path planning simulation to generate an initial inspection route; detecting a route anomaly, and adjusting an abnormal node by using a hybrid optimization algorithm to generate a stable route; model parameters are updated in combination with real-time environment data, and a final inspection instruction set containing information such as equipment moving time sequence is generated. The system comprises a data processing module, a model construction module, a constraint determination module, a path planning module, an anomaly detection module, an optimization adjustment module and the like. The inspection efficiency and precision are improved, the stability and reliability are enhanced, and the inspection requirements of complex environments can be effectively met.

Description

Technical Field

[0001] The present invention relates to the technical field of Beidou positioning, and specifically to a Beidou positioning inspection method and system. Background Art

[0002] In many fields such as modern industrial production, infrastructure maintenance, and urban management, it is crucial to conduct regular inspections on various equipment and areas. Traditional inspection methods mainly rely on manual experience, with low efficiency and prone to omissions, making it difficult to meet the inspection requirements in large-scale and complex environments. With the continuous progress of technology, inspection systems based on satellite positioning technology have emerged, but there are still many problems with existing positioning inspection technologies.

[0003] In terms of positioning accuracy, traditional satellite positioning systems perform poorly in complex environments. For example, in urban areas with high-rise buildings or mountainous areas, signals are easily blocked, reflected, and refracted, resulting in signal drift of the positioning, and the position of the inspection equipment cannot be accurately obtained. In industrial plants, there are a large number of electromagnetic interference sources, further affecting the positioning accuracy, making it difficult for inspection personnel to accurately reach the target equipment, reducing the inspection efficiency, and may also miss the inspection of key equipment, posing potential safety hazards.

[0004] From the perspective of path planning, existing inspection path planning often lacks a comprehensive consideration of actual environmental factors. It simply visits each inspection point in a preset order, without fully considering factors such as the distribution of equipment, the risk level of faults, the inspection cycle, and the safety distance. This results in the inspection route being tortuous, increasing unnecessary time and cost consumption. Moreover, in the face of emergencies such as equipment failures or obstacles, the route cannot be adjusted in a timely manner, affecting the smooth progress of the inspection task.

[0005] In terms of communication, the communication stability of existing inspection systems is poor. In remote areas or areas with weak signal coverage, communication interruptions are likely to occur, resulting in the inability to transmit inspection data in a timely manner, and the management center cannot grasp the inspection progress and equipment status in real time. At the same time, the problem of communication delay is also prominent, making inspection personnel receive instructions untimely and affecting the response speed to emergencies.

[0006] In the face of different types of equipment and diverse fault levels, traditional inspection methods also cannot achieve targeted and efficient inspections. High-risk equipment with faults cannot be prioritized for inspection, and the special inspection requirements for different types of equipment are also difficult to meet, resulting in the unreasonable allocation of inspection resources and reducing the overall inspection effect. Given the various defects of the above traditional inspection technologies, it is urgent to develop a more efficient, accurate, and stable Beidou positioning inspection method and system. Summary of the Invention

[0007] The object of the present invention is to provide a Beidou positioning inspection method and system to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: A Beidou positioning inspection method, the method comprising:

[0009] Obtain the geographic information data and equipment status parameters of the inspection area, classify the inspection tasks according to preset dimensions to generate a structured task queue, the preset dimensions including equipment type, fault level, inspection period and safety distance threshold;

[0010] Construct a Beidou positioning dynamic model, define a multi-dimensional coordinate system based on the geographic information data, the coordinate system including a position axis, a time axis and an environmental factor axis;

[0011] Determine the initial inspection constraint conditions according to the equipment type and fault level, the constraint conditions including the positioning accuracy range, the path deviation tolerance and the communication delay upper limit;

[0012] Based on the Beidou positioning model, perform path planning simulation on the inspection tasks, simulate the movement trajectory of the inspection equipment in the area through a discrete state transition engine, and generate an initial inspection route;

[0013] Perform anomaly detection on the initial inspection route, identify abnormal nodes with positioning signal drift, path overlap or communication interruption, and mark the anomaly type and risk level;

[0014] Adopt a hybrid optimization algorithm to iteratively adjust the abnormal nodes, correct the positioning parameters or communication frequency band strategies of the inspection equipment, and generate a stable inspection route.

[0015] Preferably, the construction of the Beidou positioning dynamic model includes:

[0016] Divide the grid sub-regions of the position axis according to the regional terrain characteristics, and each sub-region is associated with signal coverage intensity, obstacle distribution and electromagnetic interference index;

[0017] Divide the time axis into dynamically adjustable time windows, and each window is associated with the urgency weight of the inspection task;

[0018] Integrate meteorological data, geomagnetic disturbance and multipath effect parameters based on the environmental factor axis, and define the dynamic correction rules of the positioning signal.

[0019] Preferably, the hybrid optimization algorithm adopts an adaptive particle swarm algorithm, including:

[0020] Map the positioning parameters of the abnormal nodes to the particle position vectors, and design a fitness function to evaluate signal stability, path smoothness and energy consumption cost;

[0021] Generate a new solution set through the speed update formula and the neighborhood search strategy, and adopt a dynamic weight mechanism to balance the global and local optimization capabilities.

[0022] Preferably, the path planning simulation includes:

[0023] Introduce a multi-source data fusion module to receive satellite differential correction data, inertial navigation feedback, and obstacle recognition results in real time;

[0024] Adjust the state transition probability of the simulation engine according to the fusion data to generate an anti-interference redundant inspection route.

[0025] Preferably, the anomaly detection includes: establishing a positioning signal error matrix, detecting the signal fluctuation amplitude within a continuous time window through a sliding window statistical method, and matching the preset anomaly determination conditions based on a fuzzy logic rule base to identify anomaly events that exceed the positioning accuracy threshold or communication delay limit.

[0026] Preferably, the method further includes:

[0027] Synchronize the stable inspection route with the real-time environmental data, and dynamically update the calibration parameters of the Beidou positioning model; according to the updated Beidou positioning model, generate a final inspection instruction set, and the instruction set includes device movement timing, obstacle avoidance path, and communication relay node information;

[0028] The obstacle avoidance path planning adopts an improved A* algorithm, including:

[0029] Model the topological structure of the inspection area as a three-dimensional grid map, and define the device movement direction as the node expansion direction;

[0030] Dynamically adjust the path search priority through a heuristic function to minimize the number of turns and the obstacle bypass distance.

[0031] Preferably, the definition of the environmental factor axis includes:

[0032] Integrate meteorological prediction data into the positioning correction strategy, and the data includes rainfall intensity, atmospheric refractive index, and ionospheric delay parameters;

[0033] Dynamically adjust the window size and signal confidence threshold of the positioning filtering algorithm according to the meteorological data.

[0034] Preferably, the method further includes:

[0035] Construct a device failure mode library based on historical inspection data, and extract high-frequency failure areas and typical abnormal sequences;

[0036] Predict potential risk nodes of new inspection tasks through a time series prediction model, and generate a fault-tolerant path in advance.

[0037] Preferably, the communication frequency band strategy includes: adopting a dynamic spectrum allocation mechanism to manage the device communication link, adjusting the frequency band combination according to the signal interference intensity and bandwidth requirements, and reserving an emergency communication channel for key relay nodes.

[0038] Preferably, the present invention further includes a Beidou positioning inspection system, and the system includes:

[0039] A data processing and task generation module, configured to obtain the geographical information data and device status parameters of the inspection area, and classify the inspection tasks according to preset dimensions, namely device type, fault level, inspection period, and safety distance threshold, to generate a structured task queue;

[0040] A Beidou positioning model construction module, configured to construct a Beidou positioning dynamic model, and define a multi-dimensional coordinate system including a position axis, a time axis, and an environmental factor axis based on the geographical information data;

[0041] A constraint condition determination module, configured to determine initial inspection constraint conditions according to the device type and fault level, and the constraint conditions include a positioning accuracy range, a path deviation tolerance, and a communication delay upper limit;

[0042] A path planning simulation module, configured to perform path planning simulation on the inspection task based on the Beidou positioning model, simulate the movement trajectory of the inspection device in the area through a discrete state transition engine, and generate an initial inspection route;

[0043] An anomaly detection module, configured to perform anomaly detection on the initial inspection route, identify anomaly nodes with positioning signal drift, path overlap, or communication interruption, and mark the anomaly type and risk level;

[0044] An optimization and adjustment module, configured to iteratively adjust the anomaly nodes by using a hybrid optimization algorithm, correct the positioning parameters or communication frequency band strategy of the inspection device, and generate a stable inspection route.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] In terms of inspection task management, by obtaining the geographical information data and device status parameters of the inspection area, and classifying and generating a structured task queue according to preset dimensions such as device type, fault level, inspection period, and safety distance threshold, the refined management of inspection tasks is realized. It can reasonably arrange the inspection order and resource allocation according to the importance and fault risk degree of the devices, ensure that high-fault-risk devices are inspected and maintained in time, improve the pertinence and efficiency of the inspection, and avoid the blindness and randomness of the traditional inspection method.

[0047] In terms of improving positioning accuracy, the constructed Beidou positioning dynamic model has contributed significantly. A multi-dimensional coordinate system including a position axis, a time axis, and an environmental factor axis is defined based on geographical information data, fully considering the influence of various factors on positioning, such as regional terrain features, signal coverage intensity, obstacle distribution, electromagnetic interference index, meteorological data, geomagnetic disturbance, and multipath effect. By dividing the grid sub-regions of the position axis and associating relevant parameters, dividing the time axis into dynamically adjustable time windows and associating the urgency weight of the inspection task, and integrating multi-faceted data based on the environmental factor axis to define the dynamic correction rules of the positioning signal, the positioning becomes more accurate, effectively reducing problems such as positioning signal drift, providing a strong guarantee for the accurate navigation of inspection equipment.

[0048] In terms of path planning, path planning simulation is performed based on the Beidou positioning model, and a multi-source data fusion module is introduced to receive satellite differential correction data, inertial navigation feedback, and obstacle recognition results in real time. According to the fusion data, the state transition probability of the simulation engine is adjusted to generate a redundant inspection route with anti-interference. This method not only fully considers various interference factors in the actual environment but also improves the reliability and flexibility of path planning by generating redundant routes. In case of emergencies, such as signal interference or obstacle blockage, the inspection equipment can quickly switch to the standby route to ensure the smooth progress of the inspection task, greatly improving the inspection efficiency and reducing the inspection time and cost.

[0049] The functions of anomaly detection and optimization adjustment are also very crucial. By establishing a positioning signal error matrix, using the sliding window statistical method to detect the signal fluctuation amplitude within consecutive time windows, and matching the preset anomaly determination conditions based on the fuzzy logic rule base, it is possible to accurately identify anomaly nodes such as positioning signal drift, path overlap, or communication interruption, and mark the anomaly type and risk level. Then, a hybrid optimization algorithm, such as the adaptive particle swarm algorithm, is used to iteratively adjust the anomaly nodes, correct the positioning parameters or communication frequency band strategies of the inspection equipment, and generate a stable inspection route. This process ensures the stability and reliability of the inspection process, timely eliminates potential risks, and improves the overall performance of the inspection system.

[0050] In addition, this patent also has the functions of dynamic update and intelligent prediction. Synchronize the stable inspection route with real-time environmental data, dynamically update the calibration parameters of the Beidou positioning model, and generate the final inspection instruction set including equipment movement timing, obstacle avoidance path, and communication relay node information, enabling the inspection system to adapt to environmental changes in real time. At the same time, based on historical inspection data, a device failure mode library is constructed, and potential risk nodes of new inspection tasks are predicted through the time series prediction model, and a fault-tolerant path is generated in advance, further improving the safety and reliability of the inspection and reducing the losses caused by equipment failures. Description of the Drawings

[0051] Figure 1This is the working principle diagram of the Beidou positioning inspection method of the present invention;

[0052] Figure 2 This is the flowchart of the hybrid optimization algorithm (adaptive particle swarm algorithm);

[0053] Figure 3 This is the working flowchart of the efficacy prediction result evaluation and model optimization anomaly detection;

[0054] Figure 4 This is the flowchart for generating the final inspection instruction set. Specific implementation manners

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0056] Please refer to Figures 1 - 4 , the present invention relates to a Beidou positioning inspection method and system, and its overall implementation scheme is as follows:

[0057] Task classification and queue generation: Obtain the geographical information data of the inspection area, which cover information such as the terrain and landform, building distribution, etc. of the area, and at the same time obtain the device status parameters, such as the operating temperature, voltage and other parameters of the device. Classify the inspection tasks according to the preset dimensions, and the preset dimensions include device type, fault level, inspection period and safety distance threshold. For example, different types of devices, such as power equipment and communication equipment, have different inspection requirements; devices with a high fault level need to be inspected preferentially. According to these dimensions, a structured task queue is generated to arrange the inspection tasks in an orderly manner for subsequent processing. [[ID=**27**]]

[0058] Construct a Beidou positioning dynamic model: Define a multi-dimensional coordinate system based on the geographical information data, and this coordinate system includes a position axis, a time axis and an environmental factor axis. The position axis is used to determine the specific position of the inspection device in the area, the time axis records the time sequence of the inspection, and the environmental factor axis comprehensively considers various environmental factors affecting the positioning. By constructing this model, a basic framework is provided for subsequent path planning and positioning.

[0059] Determine the initial inspection constraint conditions: Determine the initial inspection constraint conditions according to the device type and fault level. Different types of devices have different requirements for positioning accuracy. For example, high-precision medical devices have extremely high requirements for positioning accuracy; for devices with a high fault level, higher positioning accuracy, lower tolerance for path deviation, and smaller upper limit of communication delay are required. These constraint conditions will play a restrictive and regulatory role in subsequent path planning and inspection processes.

[0060] Path planning simulation: Conduct path planning simulation for the inspection task based on the Beidou positioning model. Use a discrete state transition engine to simulate the movement trajectory of the inspection equipment within the area. Considering various factors in the actual environment, such as obstacles and signal interference, generate an initial inspection route. This initial route is the basis for subsequent optimization.

[0061] Anomaly detection: Conduct anomaly detection on the initial inspection route to identify abnormal nodes such as positioning signal drift, path overlap, or communication interruption. By establishing relevant detection mechanisms, such as monitoring the fluctuation of the positioning signal and analyzing the repeatability of the path, mark the anomaly type and risk level. For example, if the positioning signal fluctuates too much in a short period of time, it may be determined as a signal drift anomaly, and the risk level is determined according to the degree of fluctuation.

[0062] Optimization and adjustment to generate a stable route: Use a hybrid optimization algorithm to iteratively adjust the abnormal nodes. By correcting the positioning parameters or communication frequency band strategy of the inspection equipment, eliminate or reduce the impact of anomalies on the inspection, and generate a stable inspection route to ensure the smooth completion of the inspection task.

[0063] The present invention will be further described below in conjunction with Embodiments 1 to 5:

[0064] Embodiment 1:

[0065] This embodiment mainly relates to the specific implementation of constructing a Beidou positioning dynamic model. When constructing the Beidou positioning dynamic model, the position axis is divided into grid sub-regions. According to the regional terrain characteristics, the entire area is divided into multiple small grid sub-regions. For example, in mountainous areas, it is divided according to the mountain range trend and valley distribution; in urban areas, it is divided according to the building layout. Each sub-region is associated with signal coverage intensity, obstacle distribution, and electromagnetic interference index. The signal coverage intensity directly affects the quality of the Beidou signal received by the inspection equipment, the obstacle distribution determines whether the movement path of the inspection equipment is blocked, and the electromagnetic interference index reflects the degree of signal interference in this area.

[0066] For the time axis, it is divided into dynamically adjustable time windows. The division of the time window is based on the emergency degree weight of the inspection task. For example, for the inspection task of equipment with a high failure level, the corresponding time window is set shorter to ensure that the inspection can be completed in time; for the inspection task of general equipment, the time window can be appropriately relaxed. The emergency degree weight associated with each time window will affect the execution priority of the inspection task during this time period.

[0067] In terms of environmental factor axes, meteorological data, geomagnetic disturbances, and multipath effect parameters are integrated to define the dynamic correction rules for positioning signals. Parameters such as rainfall intensity, atmospheric refractive index, and ionospheric delay in meteorological data will all affect the Beidou positioning signal. For example, when the rainfall intensity is high, the signal may be attenuated; the change in the atmospheric refractive index will cause the signal propagation path to bend. Geomagnetic disturbances will also interfere with the positioning signal, and the multipath effect is due to the signal encountering reflectors during propagation, resulting in multiple paths and causing positioning deviations. Based on these parameters, corresponding dynamic correction rules for positioning signals are formulated to improve the positioning accuracy.

[0068] In an actual application scenario, assume that in the power equipment inspection area of a city, the above method is used to construct a Beidou positioning dynamic model. There are many high-rise buildings in the city, and the signal coverage intensity varies greatly in different areas. Buildings, as obstacles, have a significant impact on the movement of inspection equipment and signal propagation. By dividing the grid-like sub-areas, the signal coverage intensity, obstacle distribution, and electromagnetic interference index of each sub-area are detailedly recorded to provide accurate data for subsequent path planning and positioning. At the same time, according to the urgency of power equipment failures, the time window is reasonably divided to ensure that important equipment can be inspected in a timely manner. Combining environmental factors such as meteorological data and geomagnetic disturbances, the positioning signal is dynamically corrected, enabling the inspection equipment to accurately perform inspection tasks in a complex urban environment.

[0069] Example 2:

[0070] This example focuses on the specific implementation of the adaptive particle swarm optimization algorithm around the described hybrid optimization algorithm.

[0071] When using the adaptive particle swarm optimization algorithm, first map the positioning parameters of abnormal nodes to the particle position vector. Assume that the positioning parameters of abnormal nodes include longitude x, latitude y, and altitude z, then the particle position vector Through this mapping, the positioning parameters are transformed into the positions of particles in the search space, facilitating subsequent optimization using the particle swarm optimization algorithm.

[0072] Design the fitness function To evaluate signal stability, path smoothness, and energy consumption cost. The fitness function can be expressed as:

[0073]

[0074] Among them, w1, w2, and w3 are weight coefficients, and w1 + w2 + w3 = 1. They are respectively used to adjust the importance of signal stability, path smoothness, and energy consumption cost in the evaluation. Represents the signal stability evaluation function, which is used to measure the stability of the positioning signal at the particle position ; Represents a path smoothness evaluation function, which is used to evaluate the smoothness of the path from the current position to the target position; Represents an energy consumption cost evaluation function, which is used to calculate the energy consumed by the inspection device moving to the particle position Consumed energy.

[0075] Generate a new solution set through the velocity update formula and the neighborhood search strategy. The velocity update formula is:

[0076]

[0077] Among them, Is the velocity vector of particle i at time t; w is the inertia weight, which is used to balance the global and local search capabilities of the particle; c1 and c2 are learning factors, usually positive numbers, which are used to control the step size of the particle moving towards its own historical best position and the global best position; r1 and r2 are random numbers between [0,1]; Is the historical best position vector of particle i; Is the global best position vector; Is the position vector of particle i at time t.

[0078] Adopt a dynamic weight mechanism to balance the global and local optimization capabilities. At the initial stage of the algorithm, a larger inertia weight w is beneficial for the particle to perform global search and explore a wider search space; as the algorithm iterates, gradually reduce the inertia weight w to enhance the local search ability of the particle, so that the algorithm can find the optimal solution more accurately. For example, at the beginning, set w = 0.9, and as the number of iterations increases, according to a certain decreasing rule, such as (t is the current iteration number, T is the maximum iteration number), gradually reduce the value of w.

[0079] In an actual inspection scenario, assume that there are multiple abnormal nodes that need to be optimized. Taking the equipment inspection of a large factory as an example, due to the existence of various electromagnetic interference sources in the factory, some inspection devices have abnormal situations of positioning signal drift. By mapping the positioning parameters of these abnormal nodes into particle position vectors, the above-mentioned adaptive particle swarm algorithm is used for optimization. According to the actual situation of the factory, reasonably set the weight coefficients w1, w2, w3. For example, since the factory has high requirements for the stability of equipment operation, w1 can be set to 0.5, w2 to 0.3, and w3 to 0.2. During the iteration process, continuously adjust the velocity and position of the particle according to the velocity update formula, balance the global and local search capabilities through the dynamic weight mechanism, and finally obtain the optimized positioning parameters to correct the positioning of the inspection device and ensure that the inspection task can be carried out stably and efficiently.

[0080] Example 3:

[0081] This embodiment elaborates on the specific implementation process of path planning simulation. During the path planning simulation, a multi-source data fusion module is introduced. This module receives satellite differential correction data, inertial navigation feedback, and obstacle recognition results in real time. The satellite differential correction data can improve the accuracy of Beidou positioning. By receiving the differential signal sent by the reference station, the positioning data of the inspection equipment is corrected to reduce positioning errors. The inertial navigation feedback provides information such as the attitude and acceleration of the inspection equipment, which can assist in determining the position and motion state of the equipment even when the satellite signal is temporarily lost. The obstacle recognition result is obtained through sensors installed on the inspection equipment, such as lidar and cameras, and is used to detect obstacles in front of the equipment to avoid collisions.

[0082] Adjust the state transition probability of the simulation engine according to the fusion data to generate a redundant inspection route with anti-interference ability. Assume that the state transition probability matrix of the simulation engine is P = [p ij , where i and j represent different states, and p ij represents the probability of transitioning from state i to state j. When the received satellite differential correction data shows an improvement in positioning accuracy, the probability of transitioning to a better position state can be appropriately increased; if the inertial navigation feedback shows a large change in the attitude of the equipment, it may be due to encountering interference or obstacles. At this time, adjust the state transition probability to make the equipment avoid the current interference area. At the same time, considering the obstacle recognition result, for the area with obstacles, reduce the probability of transitioning to that area.

[0083] For example, in a pipeline inspection scenario in a mountainous area, the terrain of the mountainous area is complex, and satellite signals are easily blocked and interfered. When the inspection equipment conducts path planning simulation, the multi-source data fusion module continuously receives satellite differential correction data, inertial navigation feedback, and obstacle recognition results. Due to the dense trees in the mountainous area, satellite signals are often blocked. The satellite differential correction data can, to a certain extent, compensate for the positioning errors caused by signal loss. The inertial navigation feedback continuously provides the motion information of the equipment during the satellite signal loss. When the lidar detects an obstacle formed by a landslide in front, according to the obstacle recognition result, adjust the state transition probability of the simulation engine to make the inspection equipment bypass the obstacle. In this way, multiple redundant inspection routes with anti-interference ability are generated. Even if one route is severely interfered and cannot be passed, it can quickly switch to other backup routes to ensure the smooth progress of the inspection task. In actual operation, multiple backup route generation strategies can be preset in advance, and appropriate strategies can be selected according to different interference situations to generate redundant routes, improving the reliability and adaptability of the system.

[0084] Example 4:

[0085] This embodiment focuses on the specific implementation method of anomaly detection and elaborates in detail on its application process in actual scenarios.

[0086] In the first step of anomaly detection, a positioning signal error matrix E is established. It is constructed based on the positioning data obtained by the inspection device at different times. Assume that the inspection device obtains positioning coordinate data (x1, y1), (x2, y2), …, (x n at n different times t1, t2, …, t n , y n ), and the corresponding true position coordinate data is (x true1 , y true1 ), (x true2 , y true2 ), …, (x truen , y truen ). The element e ij of the positioning signal error matrix E is calculated as follows: when j = 1, e ij represents the positioning position error, which is calculated by the Euclidean distance formula, that is

[0087]

[0088] This formula measures the spatial distance deviation between the actual positioning coordinates and the true coordinates. The larger the distance, the greater the positioning position error.

[0089] When j = 2, e ij represents the time interval between adjacent times. e i2 = t i - t i-1 (i > 1, e 12 is meaningless). This time interval is used to analyze the time continuity of the positioning data. If the time interval is abnormal, it may imply problems such as communication delay or data loss.

[0090] After establishing the positioning signal error matrix, a sliding window statistical method is used to detect the signal fluctuation amplitude within consecutive time windows. Set a time window with a fixed length of m and let it slide successively on the positioning signal error matrix E. In each sliding window [t k , t k+m-1 , analyze the positioning position error data. First, find the maximum value e max and the minimum value e min of the positioning position error within this window, and then calculate the signal fluctuation amplitude A through the formula A = e max - e min . This fluctuation amplitude can intuitively reflect the stability of the positioning signal within this time period. The larger the fluctuation amplitude, the more unstable the positioning signal and the more likely an anomaly occurs.

[0091] Match the preset abnormal determination conditions based on the fuzzy logic rule base, so as to identify abnormal events that exceed the positioning accuracy threshold or communication delay limit. The fuzzy logic rule base is a series of preset logical judgment rules used to judge complex and ambiguous situations. For example, for the positioning signal fluctuation amplitude A, according to experience and system requirements, it is divided into fuzzy sets such as "very small", "smaller", "moderate", "larger", "very large"; for the time interval e i2 , it is also divided into fuzzy sets such as "very short", "shorter", "normal", "longer", "very long". Map the actual A value and e i2 value to the corresponding fuzzy sets through the membership function to determine the degree to which it belongs to each fuzzy set.

[0092] The preset positioning accuracy threshold is set as δ, and the upper limit of communication delay is set as τ. When the calculated signal fluctuation amplitude A > δ or the time interval e i2 > τ, perform matching judgment according to the rules in the fuzzy logic rule base. For example, if the signal fluctuation amplitude A is judged as "very large", and at the same time the time interval e i2 is judged as "longer", combining these fuzzy judgment results, according to the rule base, it can be identified that this may be an abnormal positioning signal drift, and mark the abnormal type. According to the severity of the abnormality, further mark the risk level. For example, if A is much larger than δ and e i2 far exceeds τ, mark it as a high risk level; if it only slightly exceeds the threshold, mark it as a low risk level.

[0093] In the actual application scenario, take the inspection of power equipment in the smart grid as an example. Power equipment is widely distributed, some are located in remote areas, the environment is complex, and signals are easily interfered. When the inspection equipment conducts inspections along the power line, it continuously collects positioning data. Due to the extremely high requirement for stability in power transmission, the positioning accuracy threshold δ is set very strictly, and the upper limit of communication delay τ is also short. By establishing a positioning signal error matrix, the sliding window statistical method is used to monitor the signal fluctuation amplitude in real time. Once it is detected that the signal fluctuation amplitude exceeds the threshold or the communication delay exceeds the limit, immediately start the fuzzy logic rule base for judgment. For the identified abnormalities, the system will issue an alarm in time to notify the operation and maintenance personnel to handle them. The operation and maintenance personnel can quickly take corresponding measures according to the marked abnormal type and risk level, such as recalibrating the positioning device, checking the communication link, etc., to ensure the normal operation of power equipment and the stable power supply of the power system.

[0094] Example 5:

[0095] This embodiment describes aspects such as the synchronization of the fixed route and environmental data, obstacle avoidance path planning, further definition of the environmental factor axis, construction of the failure mode library, and communication frequency band strategy.

[0096] Synchronize the stable inspection route with real-time environmental data to dynamically update the calibration parameters of the Beidou positioning model. The real-time environmental data includes meteorological data, geomagnetic data, etc. For example, when the rainfall intensity in the meteorological data changes, according to the definition of the above environmental factor axis, integrate the meteorological prediction data into the positioning correction strategy. Data such as rainfall intensity R, atmospheric refractive index n, and ionospheric delay parameter d will affect the positioning signal. Dynamically adjust the window size W and signal confidence threshold C of the positioning filtering algorithm according to these data. When the rainfall intensity R increases, appropriately increase the window size W of the positioning filtering algorithm to better process signal fluctuations; at the same time, reduce the signal confidence threshold C because the reliability of the signal will decrease in bad weather. In this way, continuously update the calibration parameters of the Beidou positioning model according to real-time environmental data to make the positioning more accurate.

[0097] Generate the final inspection instruction set according to the updated Beidou positioning model. When generating the obstacle avoidance path, use the improved A* algorithm. Model the topological structure of the inspection area as a three-dimensional grid map, and define the device movement direction as the node expansion direction. Assume that in the three-dimensional grid map, the coordinates of node i are (x i , y i , z i ), and the coordinates of node j are (x j , y j , z j ), then the movement cost g(i, j) from node i to node j can be expressed as:

[0098]

[0099] Dynamically adjust the path search priority through the heuristic function h(n) to minimize the number of turns and the detour distance around obstacles. The heuristic function h(n) can be designed according to factors such as the distance and direction between the target node and the current node. For example:

[0100] h(n) = w1d(n, goal) + w2θ(n, goal)

[0101] Among them, w1 and w2 are weight coefficients, d(n, goal) represents the straight-line distance from the current node n to the target node, and θ(n, goal) represents the angle between the direction from the current node n to the target node and the current movement direction. By reasonably adjusting the weight coefficients w1 and w2, the algorithm can take into account both distance and the number of turns when searching for a path and find the optimal obstacle avoidance path.

[0102] Based on historical inspection data, a library of equipment failure patterns is constructed to extract high-frequency failure areas and typical abnormality sequences. For example, during power equipment inspections, analysis of historical data reveals that certain areas frequently experience equipment failures, representing high-frequency failure areas. A typical abnormality sequence might include a series of related abnormalities, such as an initial increase in equipment temperature followed by abnormal current fluctuations. A time series prediction model is used to predict potential risk nodes for newly added inspection tasks and generate fault-tolerant paths in advance. The time series prediction model employs a time series analysis algorithm to predict potential equipment failure nodes based on historical data. For these predicted potential risk nodes, fault-tolerant paths are planned in advance to ensure that inspection tasks can continue even if equipment failures occur.

[0103] In terms of communication frequency band strategy, a dynamic spectrum allocation mechanism is used to manage device communication links. The frequency band combination is adjusted based on the signal interference intensity I and the bandwidth requirement B. For example, when the signal interference intensity I increases, a frequency band with less interference is selected to ensure communication quality. At the same time, frequency band resources are rationally allocated based on the bandwidth requirement B of different devices. Emergency communication channels are reserved for key relay nodes to ensure normal communication in emergency situations. For example, in an inspection scenario within a large industrial park, multiple communication frequency bands exist, and interference conditions vary from area to area. By real-time monitoring of the signal interference intensity I and the bandwidth requirement B of each device, the frequency band combination is dynamically adjusted. For key relay nodes, such as those connecting important communication nodes in different areas, dedicated emergency communication channels are reserved to ensure stable communication across the entire inspection system and improve inspection efficiency and reliability.

[0104] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0105] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A Beidou positioning inspection method, characterized in that, Including: Obtain the geographical information data and equipment status parameters of the inspection area, classify the inspection tasks according to preset dimensions to generate a structured task queue, and the preset dimensions include equipment type, fault level, inspection period, and safety distance threshold; Construct a Beidou positioning dynamic model, define a multi-dimensional coordinate system based on the geographical information data, and the coordinate system includes a position axis, a time axis, and an environmental factor axis; Determine the initial inspection constraint conditions according to the equipment type and fault level, and the constraint conditions include positioning accuracy range, path deviation tolerance, and communication delay upper limit; Based on the Beidou positioning model, perform path planning simulation for the inspection tasks, simulate the movement trajectory of the inspection equipment in the area through a discrete state transition engine, and generate an initial inspection route; Perform anomaly detection on the initial inspection route, identify abnormal nodes with positioning signal drift, path overlap, or communication interruption, and mark the anomaly type and risk level; Adopt a hybrid optimization algorithm to iteratively adjust the abnormal nodes, correct the positioning parameters or communication frequency band strategies of the inspection equipment, and generate a stable inspection route.

2. The Beidou positioning inspection method according to claim 1, wherein The construction of the Beidou positioning dynamic model includes: Divide the grid sub-regions of the position axis according to the regional terrain characteristics, and each sub-region is associated with signal coverage intensity, obstacle distribution, and electromagnetic interference index; Divide the time axis into dynamically adjustable time windows, and each window is associated with the urgency weight of the inspection task; Based on the environmental factor axis, integrate meteorological data, geomagnetic disturbance, and multipath effect parameters, and define the dynamic correction rules of the positioning signal.

3. The Beidou positioning and inspection method according to claim 1, wherein The hybrid optimization algorithm adopts an adaptive particle swarm algorithm, including: Map the positioning parameters of the abnormal nodes to the particle position vector, and design a fitness function to evaluate signal stability, path smoothness, and energy consumption cost; Generate a new solution set through the velocity update formula and the neighborhood search strategy, and adopt a dynamic weight mechanism to balance the global and local optimization capabilities.

4. The Beidou positioning inspection method according to claim 1, wherein, The path planning simulation includes: Introduce a multi-source data fusion module to receive satellite differential correction data, inertial navigation feedback, and obstacle recognition results in real time; Adjust the state transition probability of the simulation engine according to the fusion data to generate an anti-interference redundant inspection route.

5. The Beidou positioning inspection method according to claim 1, characterized in that, The anomaly detection includes: establishing a positioning signal error matrix, detecting the signal fluctuation amplitude within consecutive time windows through a sliding window statistical method, and matching the preset anomaly determination conditions based on the fuzzy logic rule base to identify abnormal events that exceed the positioning accuracy threshold or communication delay limit.

6. The Beidou positioning and inspection method according to claim 1, wherein The method further includes: Synchronize the stable inspection route with the real-time environmental data, and dynamically update the calibration parameters of the Beidou positioning model; according to the updated Beidou positioning model, generate a final inspection instruction set, and the instruction set includes equipment movement timing, obstacle avoidance path, and communication relay node information; The obstacle avoidance path planning adopts an improved A* algorithm, including: Model the topological structure of the inspection area as a three-dimensional grid map, and define the equipment movement direction as the node expansion direction; Dynamically adjust the path search priority through a heuristic function to minimize the number of turns and the distance of detouring around obstacles.

7. The Beidou positioning and inspection method according to claim 2, characterized in that The definition of the environmental factor axis includes: Integrate meteorological prediction data into the positioning correction strategy, and the data includes rainfall intensity, atmospheric refractive index, and ionospheric delay parameters; Dynamically adjust the window size and signal confidence threshold of the positioning filtering algorithm according to meteorological data.

8. The Beidou positioning inspection method according to claim 1, wherein The method further includes: Construct a device fault mode library based on historical inspection data, and extract high-frequency fault areas and typical abnormal sequences; Predict potential risk nodes of new inspection tasks through a time series prediction model, and generate a fault-tolerant path in advance.

9. The Beidou positioning inspection method according to claim 1, characterized in that The communication frequency band strategy includes: adopting a dynamic spectrum allocation mechanism to manage the device communication link, adjusting the frequency band combination according to the signal interference intensity and bandwidth requirements, and reserving an emergency communication channel for key relay nodes.

10. A Beidou positioning inspection system, characterized in that, It includes: A data processing and task generation module, which is used to obtain the geographic information data and device status parameters of the inspection area, and classify the inspection tasks according to preset dimensions, namely device type, fault level, inspection cycle and safety distance threshold, to generate a structured task queue; A Beidou positioning model construction module, which is used to construct a Beidou positioning dynamic model and define a multi-dimensional coordinate system including a position axis, a time axis and an environmental factor axis based on the geographic information data; A constraint condition determination module, which is used to determine the initial inspection constraint conditions according to the device type and fault level, and the constraint conditions include the positioning accuracy range, the path deviation tolerance and the communication delay upper limit; A path planning simulation module, which is used to perform path planning simulation on the inspection task based on the Beidou positioning model, simulate the movement trajectory of the inspection device in the area through a discrete state transition engine, and generate an initial inspection route; An anomaly detection module, which is used to perform anomaly detection on the initial inspection route, identify abnormal nodes such as positioning signal drift, path overlap or communication interruption, and mark the anomaly type and risk level; An optimization and adjustment module, which is used to iteratively adjust the abnormal nodes by using a hybrid optimization algorithm, correct the positioning parameters or communication frequency band strategy of the inspection device, and generate a stable inspection route.

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