Multi-source positioning data fusion method and device based on Beidou differential positioning

Abstract

The invention relates to a multi-source positioning data fusion method and device based on Beidou differential positioning, and the method comprises the steps: obtaining the construction positioning data of a crane in a multi-source transformer substation of different transformer substations, and enabling the construction positioning data of the crane in the transformer substation corresponding to each data source to comprise the positioning data of different objects, the different objects comprise a perimeter, a crane, electrified equipment, a constructor and a high-voltage line; and for the construction positioning data of the crane in the multi-source transformer substation of the same transformer substation, fusing the positioning data of different objects in the construction positioning data of the crane in the multi-source transformer substation to obtain a positioning result of the same transformer substation. By means of the method, the problems of coordinate deviation and asynchronous updating caused by data source heterogeneity are avoided, and therefore the reliability and real-time performance of follow-up safe distance calculation and collision early warning are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering safety monitoring, in particular, the present application relates to a kind of fusion method and device of multi-source positioning data based on Beidou differential positioning. BACKGROUND

[0002] When large cranes are used for tower assembly, equipment maintenance or expansion construction in electrified dense areas such as substations and converter stations, regulatory agencies require that the boom, steel wire rope and lifting hook maintain a sufficient safety distance from electrified bodies such as high-voltage lines, live busbars and transformer bushings at all times. The traditional approach relies on ground commanders to visually inspect, use flag signals or intercom commands, and then supplement a static warning zone determined by a one-time pre-construction survey. This method has the disadvantages of large human error in distance judgment, high communication delay, and inability to adapt to real-time boom amplitude changes and rotation. In addition, the sag of high-voltage lines changes with temperature, and point cloud data updates are delayed, further increasing the risk of "seeing but not measuring accurately".

[0003] With the popularization and application of Beidou high-precision positioning technology in the field of engineering machinery, the industry has begun to equip cranes with RTK-GNSS, inclination sensors and other self-built positioning devices to obtain centimeter-level coordinates at the top of the boom and compare them with the "design drawings + high-voltage line account" collected before construction to achieve electronic fence alarm. However, in the special scenario of substations, this "single-source self-built data + static design value" approach exposes new technical problems: 1. Design data does not match the actual situation High-voltage line accounts and CAD design drawings only reflect the ideal state at completion and do not take into account long-term effects such as temperature-sag changes after commissioning, tower foundation settlement and conductor creep. The actual sag of conductors can increase by 0.3-0.8m during the summer heat, and if the safety threshold is still set based on the original coordinates, it is easy to cause "false safety" and lead to line collision accidents.

[0004] 2. Self-built positioning data lacks reliability in complex environments Steel structures and live conductors in substations cause severe shadowing and multipath of satellite signals, leading to frequent loss of lock for RTK fixed solutions, and a drift of more than 20 cm within 30 seconds. IMU integration errors caused by crane amplitude changes and rotation are further amplified, and there is a lack of real-time correction means, making it difficult to continuously output reliable coordinates at the top of the boom.

[0005] 3. Unable to perceive dynamic environmental changes During construction, temporary equipment relocation, scaffolding and crossing frames and other "non-standard" obstacles are often encountered, and static design files cannot be updated in a timely manner. At the same time, mobile objects such as adjacent working cranes and transport vehicles are not included in the original account, leading to missed alarms for the electronic fence.

[0006] 4. Difficulty in balancing precision and real-time performance Although the trajectory can be temporarily smoothed by simply increasing the positioning sampling rate or increasing the filter window, hundreds of milliseconds of delay will be introduced, which does not meet the mandatory requirement of OSHA and the national network safety regulations that the response is less than 500 ms; if the filtering strength is reduced, a 10 cm level jump will occur, and the false alarm and missed alarm rates will increase at the same time.

[0007] In summary, the existing scheme of "single-source self-built positioning + static design file" cannot guarantee high reliability and low delay output of the boom coordinate in the dynamic, strong interference and safety demanding environment of the substation, and cannot reflect the real spatial position of the high-voltage line and the temporary obstacle, and a new technical idea of real-time acquisition of the real geometry on site and dynamic fusion of multi-source heterogeneous data is needed to fundamentally solve the technical bottleneck of the centimeter-level safety distance judgment. SUMMARY

[0008] The technical problem to be solved by the present application is to provide a multi-source positioning data fusion method and device based on Beidou differential positioning, aiming to solve at least one of the above technical problems.

[0009] In a first aspect, the technical solution of the present application to solve the above technical problems is as follows: a multi-source positioning data fusion method based on Beidou differential positioning, the method comprising: Obtaining multi-source substation internal crane construction positioning data of different substations, the substation internal crane construction positioning data corresponding to each data source comprising positioning data of different objects, the different objects comprising a perimeter, a crane, live equipment, construction personnel and a high-voltage line; For multi-source substation internal crane construction positioning data of the same substation, the positioning data of different objects in the multi-source substation internal crane construction positioning data is fused to obtain a positioning result of the same substation, the positioning result comprising a fused position of a construction area corresponding to the same substation and a fused position of different elements in the construction area.

[0010] The present application has the following beneficial effects: the present application classifies and centrally fuses multi-source crane construction positioning data in the same substation according to objects to generate a positioning result in a unified coordinate system of the station, so that the position information of elements such as the perimeter, the crane, the live equipment, the construction personnel and the high-voltage line in the construction area is consistent in space and time, avoiding coordinate deviation and asynchronous updating problems caused by heterogeneous data sources, thereby significantly improving the reliability and real-time performance of subsequent safety distance calculation and collision warning.

[0011] On the basis of the above technical solution, the present application can also be improved as follows.

[0012] Further, for any construction area, the method further comprises: According to the positioning result corresponding to the construction area, vector line data and real-time point cloud data of the high-voltage line in the space where the construction area is located are obtained; The space where the construction area is located is discretely processed in three-dimensional space to obtain a plurality of cubic units, each of which corresponds to an initial probability value, and each initial probability value represents the dangerous state of the corresponding cubic unit at the previous moment; According to the vector line data and real-time point cloud data of the high-voltage line, the first probability value and the second probability value of each cubic unit corresponding to the high-voltage line are determined respectively; For each cubic unit corresponding to the high-voltage line, the initial probability value corresponding to the cubic unit is updated according to the first probability value and the second probability value of the cubic unit to obtain the target probability value of the cubic unit; The top position of the crane jib located in the construction area is obtained in real time, and the top position of the crane jib is detected for collision with all cubic units to determine whether the crane enters the dangerous area.

[0013] The beneficial effects of the above further scheme are that by performing probability fusion of the vector line data and real-time point cloud data of the high-voltage line in the same three-dimensional discrete space to form the target probability value of each cubic unit, and directly performing collision detection between the top position of the crane jib and all cubic units, the high-voltage line dangerous area is dynamically quantized at a centimeter and millisecond level, overcoming the defects of large error, high delay and inability to update with temperature or sag changes caused by traditional static lofting and manual visual inspection, thereby significantly improving the anti-electric shock reliability and automation level of crane operation in electrified dense areas such as substations.

[0014] Further, the above determining the first probability value and the second probability value of each cubic unit corresponding to the high-voltage line according to the vector line data and real-time point cloud data of the high-voltage line comprises: The vector line data and real-time point cloud data are spatially aligned to obtain vector line data and real-time point cloud data in the same coordinate system; According to the vector line data in the same coordinate system, a first 3D wire curve corresponding to the high-voltage line is simulated; According to the real-time point cloud data in the same coordinate system, a second 3D wire curve corresponding to the high-voltage line is simulated; According to the first 3D wire curve, the first probability value of each cubic unit corresponding to the high-voltage line is determined; According to the second 3D wire curve, the second probability value of each cubic unit corresponding to the high-voltage line is determined.

[0015] The beneficial effect of adopting the above-mentioned further scheme is that by first spatially aligning the vector line data with the real-time point cloud data, and then simulating and generating the corresponding first 3D power line curve and second 3D power line curve respectively, and calculating the first probability value and second probability value of each cubic unit, the design data and the actual measurement data are accurately registered and complementaryly fused in the same coordinate system. This effectively eliminates the risk area modeling deviation caused by coordinate system inconsistency and single-source data error, and significantly improves the accuracy and robustness of the probability distribution of high-voltage line dangerous areas.

[0016] Furthermore, the determination of the first probability value of each cubic unit corresponding to the high-voltage line based on the first 3D power line curve includes: Sample points along the direction of the first 3D wire curve at predetermined arc length intervals to obtain multiple first 3D points; Based on multiple first 3D points, determine multiple first target cube units occupied by the high-voltage line; For each first target cube cell, a first probability value for the first target cube cell is determined based on the distance from the center point of the first target cube cell to the first 3D wire curve.

[0017] The beneficial effect of adopting the above-mentioned further scheme is that by sampling point by point along the first 3D power line curve at predetermined arc length intervals to obtain the first 3D point sequence, and assigning a first probability value according to the distance from the center of each first target cube unit to the curve, the theoretical high-voltage line direction generated by the design data is accurately transformed into a three-dimensional probability decay field. This not only preserves the geometric accuracy of the vector line data, but also quantifies the spatial uncertainty through the distance-probability mapping model, providing a high-confidence prior observation for the subsequent Bayesian fusion with the measured point cloud probability, and improving the fineness and reliability of the final risk grid.

[0018] Furthermore, the determination of the second probability value for each cubic unit corresponding to the high-voltage line based on the second 3D power line curve includes: Based on the second 3D power line curve, determine multiple second 3D points representing the high-voltage line in the real-time point cloud data; For each second 3D point, calculate the distance between the second 3D point and the center point of the second target cube cell occupied by the second 3D point; Based on the distance corresponding to each second target cube unit, determine the second probability value of each second target cube unit; For each second target cube cell, the second probability value corresponding to the cube cells surrounding the second target cube cell is determined based on the second probability value corresponding to the second target cube cell.

[0019] The beneficial effect of adopting the above-mentioned further scheme is that, for the second 3D power line curve obtained from real-time point cloud data, the distance from each second 3D point classified as a high-voltage line to the center of its cube unit is calculated, and a second probability value is assigned to the unit accordingly. At the same time, the probability is diffused to the surrounding units according to the same distance model, so that the uncertainty of single-point measurement is extended to the three-dimensional space in a Gaussian decay manner. This not only preserves the instantaneous accuracy of the lidar measurement, but also avoids the risk of holes caused by sparse or occluded point clouds through the probability diffusion mechanism, thereby improving the continuity of point cloud side observation and the contribution weight to the final risk grid.

[0020] Furthermore, the method also includes: Obtain the crane's attitude data and the polygonal boundaries of electrical equipment within the construction area; Based on the crane's attitude data, determine whether the crane has entered the polygonal boundary of the energized equipment.

[0021] The beneficial effect of adopting the above-mentioned further solution is that by simultaneously acquiring crane attitude data and polygonal boundaries of energized equipment, it is possible to identify another type of hazard source besides high-voltage lines. This makes up for the blind spots that a single high-voltage line probability grid cannot cover low or vertical structural equipment such as transformers and switch cabinets, significantly expanding the scope of application of collision avoidance monitoring and improving overall operational safety.

[0022] Furthermore, the method also includes: Obtain the crane's attitude data and the polygonal boundaries of electrical equipment within the construction area; Based on attitude data, the polygonal boundary of the energized equipment, and the multiple cubic cells containing the danger zone corresponding to the high-voltage line, the dynamic safety distance is determined. The dynamic safety distance includes the safety distance of the energized equipment and the safety distance of the high-voltage line. The first distance between the crane and the polygon boundary is compared with the safe distance of the live equipment to provide the first safety warning for the crane. A second safety warning for the crane is issued by comparing the second distance between the top of the crane boom and the danger zone with the safe distance from the high-voltage line.

[0023] The beneficial effect of adopting the above-mentioned further scheme is that by simultaneously introducing crane attitude data and the polygonal boundary of the energized equipment, the horizontal safety distance for the energized equipment and the spatial safety distance for the high-voltage line probability grid are calculated separately, forming a "dual-channel" dynamic safety distance. The first distance between the crane and the energized equipment and the second distance between the boom and the high-voltage line, which are measured in real time, are compared in parallel with the corresponding safety distances for the energized equipment and the high-voltage line, respectively, to achieve different types of safety warnings. This unifies the two originally isolated types of hazards into the same distance scale, significantly improving the accuracy of warnings and the efficiency of driver response, and reducing the risk of misjudgment and missed reporting.

[0024] Secondly, to solve the above-mentioned technical problems, the present invention also provides a fusion device for multi-source positioning data based on BeiDou differential positioning, the device comprising: The acquisition module is used to acquire crane construction positioning data in multi-source substations of different substations. The crane construction positioning data in substations corresponding to each data source includes positioning data of different objects, including the perimeter, crane, live equipment, construction personnel and high-voltage lines. The fusion module is used to fuse the positioning data of different objects in the multi-source crane construction positioning data of the same substation to obtain the positioning result of the same substation. The positioning result includes the fused position of the construction area corresponding to the same substation, as well as the fused position of different elements within the construction area.

[0025] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for fusing multi-source positioning data based on BeiDou differential positioning of the present application.

[0026] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for fusing multi-source positioning data based on BeiDou differential positioning of the present application.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below.

[0029] Figure 1 This is a flowchart illustrating a method for fusing multi-source positioning data based on BeiDou differential positioning, as provided in one embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a multi-source positioning data fusion device based on BeiDou differential positioning, provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0030] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0031] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0032] The solution provided in this invention can be applied to any application scenario requiring safety monitoring of the construction area. The solution provided in this invention can be executed by any electronic device, such as a user's terminal device, including at least one of the following: smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart TV, or smart in-vehicle device.

[0033] This invention provides a possible implementation, such as... Figure 1 The diagram shows a flowchart of a method for fusing multi-source positioning data based on BeiDou differential positioning. This method can be executed by any electronic device, such as a terminal device, or jointly executed by a terminal device and a server. For ease of description, the method provided in this embodiment will be described below using a terminal device as the execution subject. Figure 1 The flowchart shown indicates that the method may include the following steps: S1, acquire crane construction positioning data in multi-source substations of different substations. The crane construction positioning data in substations corresponding to each data source includes positioning data of different objects, including the perimeter, crane, live equipment, construction personnel and high-voltage lines. S2, For crane construction positioning data within multiple substations in the same substation, the positioning data of different objects in the crane construction positioning data within the multiple substations are fused to obtain the positioning result of the same substation. The positioning result includes the fused position of the construction area corresponding to the same substation, and the fused position of different elements within the construction area.

[0034] The method of this invention integrates multi-source crane construction positioning data within the same substation by classifying them by object, generating positioning results under a unified station-level coordinate system. This ensures that the position information of elements such as the perimeter, cranes, live equipment, construction personnel, and high-voltage lines within the construction area remains consistent in time and space, avoiding coordinate deviations and asynchronous updates caused by heterogeneous data sources. This significantly improves the reliability and real-time performance of subsequent safety distance calculations and collision warnings.

[0035] The following specific embodiments further illustrate the solution of the present invention. In this embodiment, the method for fusing multi-source positioning data based on BeiDou differential positioning may include the following steps: S1, acquire crane construction positioning data in multi-source substations of different substations. The crane construction positioning data in substations corresponding to each data source includes positioning data of different objects, including the perimeter, crane, live equipment, construction personnel and high-voltage lines. S2, for crane construction positioning data within multiple substations of the same substation, the positioning data of different objects in the crane construction positioning data within the multiple substations are fused to obtain the positioning result of the same substation. The positioning result includes the fused position of the construction area corresponding to the same substation, and the fused positions of different elements within the construction area. Different elements refer to at least one of cranes, live equipment, construction personnel, and high-voltage lines. For the fused position of the same element, this fused position is the position obtained by fusing the positioning data of that element from different data sources.

[0036] "Multi-source" refers to data from different owners (power grid companies, construction units, drone service providers, third-party monitoring platforms) and using heterogeneous sensors (BeiDou RTK, IMU, LiDAR, cameras, UWB, etc.), which are not uniform in coordinate system, update frequency and interface protocol, and need to be uniformly converted and fused at the station level.

[0037] For crane construction positioning data in multi-source substations of different substations, there may be differences in format. For example, the coordinate system, accuracy, and update frequency of crane attitude data (IMU / GNSS), energized equipment boundaries (LiDAR / BIM), and high-voltage line data (GIS / UAV aerial survey) may not be consistent. Based on this, the proposed solution can deploy a "data standardization preprocessing" module at the station level. Specifically, all inputs are first unified to the same clock with a time interval of ≤1ms through PTP time synchronization or interpolation compensation. Then, the coordinates of each source are converted to the global construction CGCS2000+ elevation datum using the seven-parameter method. Subsequently, the data is output in a unified format defined by JSON Schema to achieve alignment of coordinate system, accuracy, and frequency, providing standardized data with spatiotemporal consistency for subsequent fusion.

[0038] Furthermore, for multi-source data, there may be semantic conflicts, meaning that different sources may describe the same object inconsistently (e.g., high-voltage line height data may come from design drawings or measured point clouds). Therefore, this application introduces a unified semantic specification using a "construction object ontology library": the meanings of fields, coordinate expressions, and safety distance rules for elements such as high-voltage lines and live equipment are predefined. Then, a rule engine maps attributes with the same name from different sources, such as design drawings and measured point clouds, to standard fields in the ontology library. During the fusion phase, semantic alignment is first completed according to the library rules, and then the design and measured values ​​of the same object are updated using Bayesian probability weighting, thereby eliminating conflicts caused by inconsistent descriptions.

[0039] Furthermore, due to interference from the dynamic environment, including changes in sensor pose caused by crane movement, drift errors in IMU data, and delays in updating third-party data (such as high-voltage line positions), as well as mismatches with actual working conditions, the fusion accuracy can be affected. Therefore, in this application's solution, the IMU can be calibrated online using the BeiDou RTK fixed solution via the vehicle-mounted terminal using EKF. When the lock is lost, the system switches between visual / laser-odometer and extracts inherent features such as high-voltage lines and towers for PnP / ICP matching, suppressing drift to ≤10 cm within 30 seconds. At the station level, the system connects to the power grid / UAV platform via a RESTful API, verifies the data according to a 15-minute time threshold, immediately alarms and discards data if it exceeds the time limit, and simultaneously enables UAV point cloud difference synchronization (Delta Sync) to update the actual sag of the high-voltage line every minute, ensuring that the fusion database is always consistent with the on-site working conditions.

[0040] Furthermore, during the fusion process, there is a contradiction between fusion accuracy and real-time performance. High-precision fusion algorithms (such as particle filtering) have high computational complexity, making it difficult to meet real-time monitoring requirements (requiring a delay of ≤1 second). Multiple data sources are not synchronized (e.g., GNSS data at 10Hz, lidar at 1Hz). Based on this, the following strategy is adopted in this application: 1. At the station level, high-frequency GNSS / IMU data is first fused with low-latency EKF to ensure an output of ≤500 ms; low-frequency data such as lidar are unified to the same time through linear interpolation / extrapolation to achieve a 10 Hz continuous pose stream. 2. For recalculation tasks such as modeling high-voltage line risk areas, high-precision particle filtering is used offline to generate probabilistic grids, while online only millisecond-level table lookup collision detection is performed, balancing centimeter-level accuracy and real-time requirements.

[0041] Furthermore, during the integration process, there are also safety-critical requirements. For example, the calculation error of the distance between the energized equipment and the boom must be less than 10cm; otherwise, a collision accident may occur. Extreme situations need to be addressed (such as how to maintain positioning when GNSS signals are lost). In this application's solution, the following strategies can be adopted to solve these problems: 1. When the signal is normal, the RTK fixed solution and the IMU tight combination EKF ensure a static positioning accuracy of <2 cm and a dynamic positioning accuracy of <5 cm; 2. After GNSS lock-up is lost, immediately switch to visual or lidar odometry, and extract inherent features such as high-voltage lines and towers in real time to perform PnP / ICP matching, and correct IMU integral drift online, with position error ≤10 cm within 30 seconds; 3. The final collision detection adopts a probabilistic occupancy grid + threshold decision method to control the distance calculation error within 8 cm, which meets the safety requirement of <10 cm for live-line work. The specific method of collision detection will be explained below.

[0042] Therefore, based on the solution proposed in this application, crane attitude data, electrical equipment boundary data, and high-voltage line data can be classified, extracted, and integrated (including self-built and integrated third-party data) to achieve standardized, unified, and accurate positioning data.

[0043] Optionally, for any construction area, the method further includes: S10, Based on the positioning results corresponding to the construction area, obtain vector line data and real-time point cloud data of the high-voltage lines in the space where the construction area is located; Vector line data refers to a set of mathematical line segments, using ordered coordinate strings, to describe the spatial orientation of high-voltage lines. It typically originates from power grid GIS or CAD design files and includes: the starting and ending plane coordinates of each conductor; elevation or sag parameters (used to calculate vertical curves); and voltage level, line type, ground wire and phase wire attributes, and tower coordinates. Essentially, it represents theoretical design values, lacking actual measurement errors and real-time data such as temperature and wind deflection.

[0044] Real-time point cloud data refers to a set of three-dimensional coordinates continuously updated according to timestamps, obtained by scanning the site with drones / vehicle-mounted / mast lidar during the construction period. Each point contains X, Y, Z coordinates and echo intensity. After classification, the "power line" subset is extracted to present the real spatial shape of the high-voltage line under the current temperature, wind deflection and sag.

[0045] S20, the space where the construction area is located is discretized in three-dimensional space to obtain multiple cubic units. Each cubic unit corresponds to an initial probability value, and each initial probability value represents the dangerous state of the corresponding cubic unit at the previous moment. In this system, a single cube cell can be considered a voxel, and each cube cell is like a pixel in 3D space. Multiple cube cells form a mesh. Each cube cell has only one danger state, such as unsafe (risk of power lines), safe, or uncertain. That is, the initial probability value corresponding to each cube cell represents how likely it is that a high-voltage power line exists at that location. A probability of 1.0 indicates a definite presence of a power line (absolute danger), a probability of 0.0 indicates a definite absence of a power line (absolute safety), and a probability of 0.5 indicates complete uncertainty.

[0046] When the initial division yields multiple cube units, the initial probability value of each cube unit can be set to 0.5, indicating uncertainty.

[0047] In this context, the real-time point cloud data corresponds to the current moment, and the initial probability value for each cube unit is the dangerous state corresponding to the moment before the current moment.

[0048] The grid formed by all the above cubic units can be used as a high-voltage line risk zone model, which is a digital and computable safety boundary.

[0049] S30, based on the vector line data and real-time point cloud data of the high-voltage line, determine the first probability value and the second probability value of each cubic unit corresponding to the high-voltage line respectively; In this context, each cube cell corresponding to a high-voltage line refers to the cube cell occupied by the high-voltage line among all cube cells.

[0050] Alternatively, one implementation of S30 above is as follows: S301 performs spatial alignment processing on vector line data and real-time point cloud data to obtain vector line data and real-time point cloud data in the same coordinate system (e.g., local engineering coordinate system); Vector line data and real-time point cloud data exist in different coordinate systems, therefore, it is necessary to transform these two types of data into the same coordinate system. Typically, an iterative nearest-point algorithm is used, taking the tower top and anchor point positions extracted from the real-time point cloud data as the "target," and matching the tower coordinates in the vector line data to it. Through rotation and translation transformations, the two types of data achieve optimal spatial alignment.

[0051] The coordinate system in which the vector line data is located is a geodetic coordinate system, such as WGS-84.

[0052] The same coordinate system can be the global coordinate system for construction (such as CGCS2000+ elevation datum).

[0053] S302, based on vector line data in the same coordinate system, simulate the first 3D wire curve corresponding to the high voltage line; the shape of the high voltage line can be characterized by the first 3D wire curve.

[0054] Specifically, the theoretical shape of the wire can be calculated based on the line type and sag formula, usually simulated as a catenary or parabola. This yields an "ideal" 3D wire curve, i.e., the first 3D wire curve.

[0055] S303, based on real-time point cloud data in the same coordinate system, simulates the second 3D power line curve corresponding to the high-voltage line; the shape of the high-voltage line can also be characterized by the second 3D power line curve. Specifically, point cloud classification algorithms (such as deep learning models, cloth simulation filtering, etc.) can be used to automatically separate the points corresponding to "electric wires" and "towers" from real-time point cloud data in the same coordinate system. Then, the point cloud corresponding to the classified electric wires is thinned and filtered, and 3D curve fitting is performed to obtain the "actual" shape of the electric wire, that is, the second 3D electric wire curve.

[0056] Among them, the real-time point cloud data in the same coordinate system can be millions or even hundreds of millions of three-dimensional point clouds obtained by UAV LiDAR scanning.

[0057] S304, Based on the first 3D wire curve, determine the first probability value of each cubic unit corresponding to the high-voltage line; S305, based on the second 3D wire curve, determine the second probability value of each cubic unit corresponding to the high-voltage line.

[0058] The processing steps S304 and S305 described above are for updating the initial probability value of each cube element in the high-voltage line risk zone model. In this scheme, Bayesian filtering or DS evidence theory can be used for probability updates. This embodiment uses the classic Bayesian Occupancy Grid algorithm as an example for specific explanation.

[0059] Optionally, S304 specifically includes: Based on the first 3D power line curve, determine the first probability value of each cubic unit corresponding to the high-voltage line, including: Sample points along the direction of the first 3D wire curve at predetermined arc length intervals to obtain multiple first 3D points; Based on multiple first 3D points, determine multiple first target cube units occupied by the high-voltage line; For each first target cube element, a first probability value for the first target cube element is determined based on the distance from its center point to the first 3D wire curve. The smaller the distance, the higher the probability value.

[0060] Among them, the first 3D wire curve can be regarded as an "observation". A "occupancy" probability field can be generated along the wire path corresponding to the first 3D wire curve (for example, using a pipe space with a radius of R centered on the wire, the probability inside the pipe is high and the probability outside the pipe is low).

[0061] Optionally, the above S305 specifically includes: Based on the second 3D power line curve, determine multiple second 3D points representing the high-voltage line in the real-time point cloud data; For each second 3D point, calculate the distance between the second 3D point and the center point of the second target cube cell occupied by the second 3D point; Based on the distance corresponding to each second target cube unit, determine the second probability value of each second target cube unit; For each second target cube cell, the second probability value corresponding to the cube cells surrounding the second target cube cell is determined based on the second probability value corresponding to the second target cube cell.

[0062] For real-time point cloud data, each point classified as a "wire" provides an "observation." The location of this point should correspond to a high second probability value. However, LiDAR has errors, so the uncertainty of this observation can be represented by a Gaussian distribution model: the probability diffuses and decays outwards from the measurement point (each point classified as a "wire"), with the probability decreasing as it moves towards the measurement point. The closer to the measurement point, the higher the probability.

[0063] S40, for each cube cell corresponding to the high-voltage line, update the initial probability value of the cube cell according to the first probability value and the second probability value of the cube cell to obtain the target probability value of the cube cell; The implementation methods of the above-mentioned S40 include: For each cube cell corresponding to the high-voltage line, the initial probability value of the cube cell is first updated based on the first probability value to obtain the intermediate probability value; then, the intermediate probability value of the cube cell is updated a second time based on the second probability value to obtain the target probability value of the cube cell. Here, the first and second probability values ​​are merged into the initial probability value.

[0064] Another implementation of the above-mentioned S40 is as follows: For each cube cell corresponding to the high-voltage line, the initial probability value of the cube cell is updated according to the first probability value or the second probability value of the cube cell to obtain the target probability value of the cube cell.

[0065] Specifically, for each cube cell corresponding to the high-voltage line, the initial probability value of the cube cell can be updated using the following Bayesian update formula based on the first probability value and / or the second probability value of the cube cell, thus obtaining the target probability value of the cube cell: For ease of calculation, it can be expressed in the following logarithmic probability form: Where P(m) is the prior probability of the cube element (i.e., the current value, which can be the initial probability value or the intermediate probability value).

[0066] P(z|m) is the inverse observation model, which means "what is the probability of observing a point z in the real-time point cloud data if the cube cell m is occupied". For example, if point z falls within the cube cell m, then the value of P(z|m) is high (e.g., 0.7), and the value of P(z|¬m) is low (e.g., 0.3).

[0067] Iterate through all observation data (all points corresponding to the wires in real-time point cloud data, and all virtual observation points in vector line data), and continuously update the probability value of each grid cell using the above formula.

[0068] S50 acquires the real-time position of the top of the crane boom located within the construction area, performs collision detection between the top of the boom and all cube units, and determines whether the crane has entered the danger zone.

[0069] The purpose of collision detection is to determine which of the cube elements the top of the boom intersects with. If an intersection is found, it can be determined that the crane has entered the danger zone, which refers to the space corresponding to the intersecting cube element.

[0070] The position of the crane boom tip can be determined based on the crane's attitude data, which can be acquired in real time using the BeiDou Differential Positioning (RTK) system. Specifically, by installing a BeiDou RTK receiver at the boom tip and combining it with the base station's differential signal, centimeter-level precision three-dimensional coordinates (x, y, z) can be obtained as an absolute spatial reference for the boom tip position. Simultaneously, data from the vehicle-mounted IMU, rotary encoder, and boom length sensor can be fused using extended Kalman filtering (EKF) to further improve positioning accuracy and continuity under dynamic conditions, achieving real-time and precise tracking of the boom tip position.

[0071] Optionally, the above-mentioned collision detection between the top of the boom and all cubic units to determine whether the crane has entered the danger zone includes: Collision detection is performed between the top of the boom and all cube elements to determine the third target cube element occupied by the boom of the crane. Based on the target probability value corresponding to the third target cube unit and the preset safety threshold, it is determined whether the crane has entered the danger zone.

[0072] Optionally, the method may further include: Based on the target probability value of each cube cell, cube cells with a probability greater than the first probability threshold (e.g., 0.65) are identified as risk zones, cube cells with a probability less than the second probability threshold (e.g., 0.35) are identified as risk-free zones, and cube cells with a probability between the first and second probability thresholds are identified as uncertain zones that require further observation.

[0073] Optionally, the method may further include: Obtain the crane's attitude data and the polygonal boundaries of electrical equipment within the construction area; Based on the crane's attitude data, determine whether the crane has entered the polygonal boundary of the energized equipment.

[0074] Among them, "energized equipment" refers to all energized equipment such as transformers and distribution boxes. Based on the position points of the 84 coordinate system, multiple points form different positions or shapes of energized bodies.

[0075] The polygonal boundary of an electrical device can be obtained from the point cloud acquired by LiDAR, and the polygonal boundary (such as the parametric equation of a plane / cylinder) can be extracted through geometric fitting.

[0076] Optionally, accurate and unified crane attitude data can be obtained through multi-source positioning fusion, or 6DoF pose can be output through Kalman filtering based on IMU+GNSS+encoder.

[0077] Optionally, the method may further include: Obtain the crane's attitude data and the polygonal boundaries of electrical equipment within the construction area; Based on attitude data, the polygonal boundary of the energized equipment, and the multiple cubic cells containing the danger zone corresponding to the high-voltage line, the dynamic safety distance is determined. The dynamic safety distance includes the safety distance of the energized equipment and the safety distance of the high-voltage line. The first distance between the crane and the polygon boundary is compared with the safe distance of the energized equipment to provide the first safety warning for the crane; once the horizontal projection of any part of the crane (hook, boom, counterweight) enters the polygon, it is considered to have intruded into the boundary of the energized equipment, and an alarm is immediately triggered.

[0078] A second safety warning for the crane is issued by comparing the second distance between the top of the crane boom and the danger zone with the safe distance from the high-voltage line.

[0079] The crane's attitude data, specifically the lidar point cloud, is obtained by extracting boundaries (such as plane / cylindrical parametric equations) through geometric fitting. The crane's attitude data includes the turntable position, the boom tip position, and the overall boom position.

[0080] Optionally, based on the high-voltage line risk zone model, the real-time 3D coordinates of key points such as the crane boom tip and wire rope obtained from real-time point cloud data can be compared. The target probability value of the cubic unit containing the key points such as the crane boom tip and wire rope can be queried in real time. If the target probability value exceeds a preset safety threshold (e.g., 0.8), an alarm is triggered.

[0081] It should be noted that the proposed solution is applicable to different scenarios. For different scenarios, the most suitable scenario surface can be found through optimization based on the crane's trajectory and the structure of the surrounding environment formed by the point cloud, to determine the dynamic safety distance between the crane and the energized object. The most suitable scenario surface refers to the spatial reference surface selected by graph optimization at the current construction moment. It is the fitted plane or fitted surface with the minimum residual and the lowest uncertainty found by SLAM graph optimization algorithm (such as GTSAM) in the local three-dimensional structure jointly formed by the crane's trajectory and the surrounding laser point cloud. This surface serves as the reference projection surface for distance calculation, reducing the minimum clearance distance calculation between the boom and the energized equipment (prism or polygon) from high-dimensional three-dimensional intersection to surface-volume / surface-line measurement, retaining centimeter-level accuracy while meeting the ≤1 s real-time requirement, hence the name "most suitable scenario surface".

[0082] Different scenarios refer to the different projects, equipment, and substations involved in substation infrastructure construction, expansion, maintenance, and emergency repair work, as well as the different surrounding environments. The location of the crane upon entry and the direction of its boom extension and rotation also vary, resulting in different scenarios.

[0083] Optionally, if the positioning-range measurement-early warning needs to be completed within ≤100 ms (such as high-speed crane rotation or emergency braking), then instead of computationally expensive graph-optimized SLAM, Extended Kalman Filter (EKF) or Rao-Blackwellized Particle Filter (RB-PF) can be used: the former outputs the crane's 6DoF pose instantaneously using a linearized covariance recursive method; the latter uses a small number of particles (≈100) to estimate only the key states (position + heading), and resolves the high-dimensional map state analytically, thereby reducing the computational cost of a single update to the millisecond level, while still maintaining a dynamic positioning accuracy of <5 cm to meet the requirements of real-time collision detection.

[0084] Optionally, when the GNSS signal suddenly fails due to obstruction or interference, and satellite observation data for the attitude data of the crane for absolute positioning cannot be obtained, the dead reckoning mode of pure IMU + wheel speed odometer (or motor encoder) can be activated to maintain continuous output of boom attitude. At the same time, the inherent environmental features such as high-voltage lines and towers are extracted in real time using the vehicle-mounted camera / LiDAR, and the relative pose change between the current frame and the feature map is calculated through visual / point cloud matching algorithms (such as PnP, ICP). The accumulated drift of the IMU is corrected online so that the position error is still ≤10 cm within 30 seconds, ensuring that collision detection in the danger zone is uninterrupted and without false alarms.

[0085] After the GNSS signal is lost, it is impossible to obtain an absolute position update, and only short-term dead reckoning can be performed using an IMU and encoder. However, due to the drift error of the IMU, the deviation will accumulate rapidly by integration alone. Therefore, it is necessary to use the high-frequency motion characteristics of the boom itself as an auxiliary basis for judgment.

[0086] Specifically: data from the boom length encoder, slewing encoder, and tilt sensor are read at a millisecond sampling frequency (e.g., 100Hz); by using sliding window filtering and motion pattern recognition algorithms (e.g., threshold judgment or simple machine learning models), it is determined whether the boom is currently extending, slewing (swinging left and right), or pitching; combined with the attitude and motion trend of the previous moment, the most likely local position range of the boom top is predicted for the next moment; this local position range is locally matched and verified with the positions of high-voltage lines and live equipment in the point cloud map, thereby correcting the pose error caused by IMU drift and achieving pose self-calibration in the short term.

[0087] For example, if the system detects that "the boom length is increasing and the slewing angular velocity is positive", it predicts that the top of the boom will extend to the right front. Therefore, it searches for high-voltage line features in the point cloud only within this sector area and performs matching correction to avoid the computational delay caused by global search.

[0088] Optionally, vector line data and real-time point cloud data can be obtained from third parties. Specifically, data interfaces from different sources and in different formats (such as the "high-voltage line GIS interface" from power grid companies and the "temporary equipment location interface" from equipment manufacturers) can be uniformly encapsulated into a standard RESTful API. Internal systems only need to call a single standard interface to obtain all external data. All accessed third-party data is validated in real time, such as: whether the timestamp has expired (e.g., not updated for more than 15 minutes); whether the coordinates are out of bounds (e.g., beyond the construction area); and whether the data format conforms to the schema definition. If validation fails, an alarm is triggered (prompting the user that the data is unreliable); the data is discarded or marked as invalid to avoid contaminating the fusion results.

[0089] Optionally, the proposed solution also employs an incremental update mechanism, using Delta Sync to reduce bandwidth usage for changed data (such as temporary equipment relocation).

[0090] Optionally, a construction object ontology library can be established (e.g., defining "high voltage line" as coordinates + voltage level + safety distance), and fields from different data sources can be mapped through a rule engine.

[0091] The construction objects involved in this plan include the turntable, the top of the boom, high-voltage lines, and live equipment. The parameters of each object are as follows: Turntable: Position in the 84-coordinate system; Top of boom: Position in coordinate system 84. The position of the top of the boom is used to determine the safe distance from high-voltage lines and live conductors.

[0092] High-voltage lines: In an 84-coordinate system, two points and one line form a high-voltage line. Different high-voltage line grades correspond to different safety distances, which can be configured. The safety distance for a high-voltage line is the safe distance within a concentric circle around the high-voltage line.

[0093] Electrical equipment: Transformers, distribution boxes and all other electrical equipment. Using the 84 coordinate system position points as the reference, multiple points form different positions or shapes of the electrical bodies, and point cloud data is processed.

[0094] Purpose: To define a standard library and its rules. Self-built or third-party data are integrated according to this unified library and rules. This standardizes and unifies the data library, providing qualified and standardized data for subsequent dynamic safety distance algorithms.

[0095] Compared with the prior art, the solution of the present invention has the following advantages: 1. Data unification: All positioning data output is in a standardized format (such as JSON Schema definition), with a spatiotemporal alignment error of <1ms / 0.1cm.

[0096] Supports data interoperability across multiple BIM / GIS / CAD platforms.

[0097] 2. Precise positioning: Crane boom end positioning accuracy: static <2cm (RTK-GNSS mode), dynamic <5cm (integrated IMU compensation).

[0098] The calculation error for the safe distance of high-voltage lines is less than 8cm (better than the industry standard of 15cm).

[0099] 3. Real-time guarantee: The entire process from data input to risk warning has a latency of ≤500ms (meeting OSHA's requirements for safety response to heavy equipment).

[0100] Supports concurrent processing of 100+ data sources (such as simultaneous monitoring of multiple cranes and environmental equipment).

[0101] 4. Enhanced safety performance: Collision warning accuracy > 99% (false alarm rate < 1 time / 8 hours of continuous operation).

[0102] It supports automatic generation of electronic fences and triggers active protection measures such as crane speed limits / power outages.

[0103] Based on and Figure 1 Using the same principle as the method shown, this embodiment of the invention also provides a fusion device 20 for multi-source positioning data based on BeiDou differential positioning, such as... Figure 2 As shown, the fusion device 20 based on BeiDou differential positioning multi-source positioning data may include an acquisition module 210 and a fusion module 220, wherein: The acquisition module 210 is used to acquire crane construction positioning data in multi-source substations of different substations. The crane construction positioning data in substations corresponding to each data source includes positioning data of different objects, including the perimeter, crane, live equipment, construction personnel and high-voltage lines. The fusion module 220 is used to fuse the positioning data of different objects in the multi-source crane construction positioning data of the same substation to obtain the positioning result of the same substation. The positioning result includes the location of the construction area corresponding to the same substation and the location of different elements within the construction area.

[0104] Optionally, for any construction area, the device further includes: The early warning module is specifically used for: Spatial alignment processing is performed on vector line data and real-time point cloud data to obtain vector line data and real-time point cloud data in the same coordinate system; Based on vector line data in the same coordinate system, the first 3D power line curve corresponding to the high-voltage line is simulated. Based on real-time point cloud data in the same coordinate system, the second 3D power line curve corresponding to the high-voltage line is simulated. Based on the first 3D wire curve, determine the first probability value of each cubic unit corresponding to the high-voltage line; Based on the second 3D wire curve, determine the second probability value of each cubic unit corresponding to the high-voltage line.

[0105] Optionally, when the aforementioned early warning module determines the first probability value of each cubic unit corresponding to the high-voltage line based on the first 3D power line curve, it is specifically used for: Sample points along the direction of the first 3D wire curve at predetermined arc length intervals to obtain multiple first 3D points; Based on multiple first 3D points, determine multiple first target cube units occupied by the high-voltage line; For each first target cube cell, a first probability value for the first target cube cell is determined based on the distance from the center point of the first target cube cell to the first 3D wire curve.

[0106] Optionally, when the aforementioned early warning module determines the second probability value of each cubic unit corresponding to the high-voltage line based on the second 3D power line curve, it is specifically used for: Based on the second 3D power line curve, determine multiple second 3D points representing the high-voltage line in the real-time point cloud data; For each second 3D point, calculate the distance between the second 3D point and the center point of the second target cube cell occupied by the second 3D point; Based on the distance corresponding to each second target cube unit, determine the second probability value of each second target cube unit; For each second target cube cell, the second probability value corresponding to the cube cells surrounding the second target cube cell is determined based on the second probability value corresponding to the second target cube cell.

[0107] Optionally, the device further includes: The boundary monitoring module is used to acquire the crane's attitude data and the polygonal boundaries of energized equipment within the construction area; based on the crane's attitude data, it determines whether the crane has entered the polygonal boundary of the energized equipment.

[0108] Optionally, the device further includes: The dynamic monitoring module is used to acquire the crane's attitude data and the polygonal boundaries of energized equipment within the construction area; based on the attitude data, the polygonal boundaries of energized equipment, and multiple cubic units containing the danger zone corresponding to the high-voltage line, it determines the dynamic safety distance, which includes the safety distance of energized equipment and the safety distance of the high-voltage line; it compares the first distance between the crane and the polygonal boundary with the safety distance of energized equipment to issue a first safety warning for the crane; it compares the second distance between the top of the crane's boom and the danger zone with the safety distance of the high-voltage line to issue a second safety warning for the crane.

[0109] The fusion device for multi-source positioning data based on BeiDou differential positioning in this embodiment of the invention can execute the fusion method for multi-source positioning data based on BeiDou differential positioning provided in this embodiment of the invention. The implementation principle is similar. The actions performed by each module and unit in the fusion device for multi-source positioning data based on BeiDou differential positioning in each embodiment of the invention correspond to the steps in the fusion method for multi-source positioning data based on BeiDou differential positioning in each embodiment of the invention. For detailed functional descriptions of each module of the fusion device for multi-source positioning data based on BeiDou differential positioning, please refer to the descriptions in the corresponding fusion method for multi-source positioning data based on BeiDou differential positioning shown above, which will not be repeated here.

[0110] The aforementioned fusion device for multi-source positioning data based on BeiDou differential positioning can be a computer program (including program code) running on a computer device. For example, the fusion device for multi-source positioning data based on BeiDou differential positioning is an application software. The device can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0111] In some embodiments, the fusion device for multi-source positioning data based on BeiDou differential positioning provided in this invention can be implemented using a combination of hardware and software. As an example, the fusion device for multi-source positioning data based on BeiDou differential positioning provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the fusion method for multi-source positioning data based on BeiDou differential positioning provided in this invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0112] In other embodiments, the multi-source positioning data fusion device based on BeiDou differential positioning provided in this invention can be implemented in software. Figure 2 A device for fusing multi-source positioning data based on BeiDou differential positioning, stored in a memory, is shown. It can be software in the form of programs and plug-ins, and includes a series of modules, including an acquisition module 210 and a fusion module 220, for implementing the method for fusing multi-source positioning data based on BeiDou differential positioning provided in the embodiments of the present invention.

[0113] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0114] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0115] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0116] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0117] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation,Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0118] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0119] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0120] Among these, electronic devices can also be terminal devices. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0121] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0122] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various embodiments described above.

[0123] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0125] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0126] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0127] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for fusing multi-source positioning data based on BeiDou differential positioning, characterized in that, include: Acquire multi-source crane construction positioning data in different substations. The crane construction positioning data in the substation corresponding to each data source includes positioning data of different objects, including the perimeter, crane, live equipment, construction personnel and high-voltage lines. For crane construction positioning data within a multi-source substation of the same substation, the positioning data of different objects in the crane construction positioning data within the multi-source substation are fused to obtain the positioning result of the same substation. The positioning result includes the fused position of the construction area corresponding to the same substation, as well as the fused position of different elements within the construction area.

2. The method according to claim 1, characterized in that, For any construction area, the method further includes: Based on the positioning results corresponding to the construction area, obtain vector line data and real-time point cloud data of the high-voltage lines in the space where the construction area is located; The space where the construction area is located is discretized in three-dimensional space to obtain multiple cubic units. Each cubic unit corresponds to an initial probability value, and each initial probability value represents the danger state of the corresponding cubic unit at the previous moment. Based on the vector line data and real-time point cloud data of the high-voltage line, a first probability value and a second probability value are determined for each cubic unit corresponding to the high-voltage line. For each cube unit corresponding to the high-voltage line, the initial probability value of the cube unit is updated according to the first probability value and the second probability value of the cube unit to obtain the target probability value of the cube unit; The position of the top of the crane boom located within the construction area is acquired in real time. The top of the crane boom is then compared with all cubic units to determine whether the crane has entered the danger zone.

3. The method according to claim 2, characterized in that, The step of determining the first probability value and the second probability value of each cube unit corresponding to the high-voltage line based on the vector line data and real-time point cloud data of the high-voltage line includes: The vector line data and the real-time point cloud data are spatially aligned to obtain vector line data and real-time point cloud data in the same coordinate system. Based on the vector line data in the same coordinate system, the first 3D wire curve corresponding to the high-voltage line is simulated and obtained. Based on the real-time point cloud data in the same coordinate system, the second 3D power line curve corresponding to the high-voltage line is simulated and obtained. Based on the first 3D wire curve, determine the first probability value of each cubic unit corresponding to the high-voltage line; Based on the second 3D wire curve, a second probability value is determined for each cubic unit corresponding to the high-voltage line.

4. The method according to claim 3, characterized in that, The step of determining the first probability value of each cubic unit corresponding to the high-voltage line based on the first 3D power line curve includes: Sample points along the direction of the first 3D wire curve at predetermined arc length intervals to obtain multiple first 3D points; Based on multiple first 3D points, determine multiple first target cube units occupied by the high-voltage line; For each of the first target cube units, a first probability value for the first target cube unit is determined based on the distance from the center point of the first target cube unit to the first 3D wire curve.

5. The method according to claim 3, characterized in that, The step of determining the second probability value of each cubic unit corresponding to the high-voltage line based on the second 3D wire curve includes: Based on the second 3D power line curve, determine multiple second 3D points in the real-time point cloud data that represent the high-voltage line; For each of the second 3D points, calculate the distance between the second 3D point and the center point of the second target cube cell occupied by the second 3D point; Based on the distance corresponding to each second target cube unit, determine the second probability value of each second target cube unit; For each second target cube unit, the second probability value corresponding to the cube units surrounding the second target cube unit is determined based on the second probability value corresponding to the second target cube unit.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain the attitude data of the crane and the polygonal boundaries of the electrical equipment within the construction area; Based on the crane's attitude data, determine whether the crane has entered the polygonal boundary of the energized equipment.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain the attitude data of the crane and the polygonal boundaries of the electrical equipment within the construction area; Based on the attitude data, the polygonal boundary of the energized equipment, and the multiple cubic units where the danger zone corresponding to the high-voltage line is located, a dynamic safety distance is determined, which includes the safety distance of the energized equipment and the safety distance of the high-voltage line. A first safety warning is issued for the crane by comparing the first distance between the crane and the polygonal boundary with the safe distance of the energized equipment. A second safety warning is issued for the crane by comparing the second distance between the top of the crane boom and the danger zone with the safe distance from the high-voltage line.

8. A device for fusing multi-source positioning data based on BeiDou differential positioning, characterized in that, include: The acquisition module is used to acquire crane construction positioning data in multi-source substations of different substations. The crane construction positioning data in substations corresponding to each data source includes positioning data of different objects, including the perimeter, crane, live equipment, construction personnel and high-voltage lines. The fusion module is used to fuse the positioning data of different objects in the multi-source crane construction positioning data of the same substation to obtain the positioning result of the same substation. The positioning result includes the fused position of the construction area corresponding to the same substation, as well as the fused position of different elements within the construction area.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-7.

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