Ultra-wideband positioning system and method, storage medium and electronic device

Abstract

The invention discloses an ultra wide band positioning system and method, a storage medium and an electronic device, the system comprises ultra wide band positioning base stations, the ultra wide band positioning base stations are arranged in a plant according to a preset mode, and the preset mode indicates that the ultra wide band positioning base stations are arranged right above, right below, left and right sides of each water turbine area in the plant respectively, the ultra-wideband positioning base stations are uniformly arranged in other areas except the water turbine area according to preset intervals; the area base station range defining module is used for selecting three ultra-wideband positioning base stations with the highest signal intensity at each positioning point, and positioning data of the three ultra-wideband positioning base stations are used for calculating reference positioning data of each positioning point; and the positioning data calibration module is used for carrying out secondary calibration of position errors on the reference positioning data of the three ultra-wideband positioning base stations to obtain target positioning data of each positioning point. According to the invention, the technical problem of low accuracy of ultra-wideband positioning in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of wireless positioning technology, and in particular to an ultra-wideband positioning system, method, storage medium, and electronic device. Background Art

[0002] In the plant environment of small and medium-sized hydropower stations, the positioning accuracy of the ultra-wideband positioning system (UWB positioning system) is affected by many factors, mainly including environmental interference, unreasonable base station layout and limitations of the positioning algorithm. Specifically, the complex metal structure inside the hydropower plant will cause multiple reflections, refractions and absorptions of the UWB signal, affecting the propagation characteristics of the signal, thereby reducing the positioning accuracy. At the same time, the existing UWB positioning base station layout may not be able to guarantee uniform signal coverage, especially in plants with multiple turbine areas. The unreasonable layout of the base station will lead to positioning blind spots or insufficient signal strength, which will affect the reliability of the positioning results, resulting in large positioning errors and difficulty in meeting high-precision positioning requirements.

[0003] Therefore, there is a problem in the related art that the accuracy of ultra-wideband positioning is low. Summary of the invention

[0004] The embodiments of the present application provide an ultra-wideband positioning system, method, storage medium, and electronic device to at least solve the technical problem of low accuracy of ultra-wideband positioning in the related art.

[0005] According to one aspect of an embodiment of the present application, an ultra-wideband positioning system is provided, including: an ultra-wideband positioning base station, the ultra-wideband positioning base station is set in a factory building according to a preset method, the preset method indicates that the ultra-wideband positioning base station is arranged directly above, directly below, and on the left and right sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged at preset intervals in other areas outside the turbine area; a regional base station range delineation module, used to select three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the benchmark positioning data of each positioning point; a positioning data calibration module, used to perform secondary calibration of position errors on the benchmark positioning data of the three ultra-wideband positioning base stations to obtain target positioning data of each positioning point.

[0006] According to another aspect of an embodiment of the present application, an ultra-wideband positioning method is also provided, including: setting N ultra-wideband positioning base stations in a factory building according to a preset method, wherein the preset method indicates that the ultra-wideband positioning base stations are arranged directly above, directly below, and on the left and right sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged at preset intervals in other areas outside the turbine area, where N is a positive integer; selecting three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the benchmark positioning data of each positioning point; performing a secondary calibration of the position error on the benchmark positioning data of each positioning point to obtain the target positioning data of each positioning point.

[0007] According to another aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the ultra-wideband positioning method as described above.

[0008] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the ultra-wideband positioning method through the computer program.

[0009] According to another aspect of the embodiments of the present application, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the above-mentioned ultra-wideband positioning method is executed when the program is run.

[0010] Through the embodiments provided by the present application, firstly, the ultra-wideband positioning base stations are arranged in a specific preset manner to ensure that each turbine area and other areas outside the turbine area can obtain uniform and sufficiently strong signal coverage. This optimized base station arrangement method effectively overcomes the problems of environmental interference and signal coverage blind spots. Secondly, the introduction of the regional base station range demarcation module enables the positioning system to intelligently select the three base stations with the highest signal strength at each positioning point for positioning, thereby improving the accuracy and stability of the positioning data. Finally, the positioning data calibration module uses the secondary calibration technology and the positioning error of the historical calibration point to correct the position error of the positioning point, thereby greatly improving the positioning accuracy. Especially in the case of complex environmental factors and large fluctuations in signal quality, the secondary calibration algorithm can effectively compensate for these adverse effects and ensure the high accuracy and reliability of the positioning data. In summary, the embodiments provided by the present application have achieved the technical effect of improving the accuracy of ultra-wideband positioning in all aspects and at multiple levels, and solved the technical problem of low accuracy of ultra-wideband positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] Figure 1 is a schematic diagram of an ultra-wideband positioning system according to an embodiment of the present application;

[0014] Figure 2 is a schematic diagram of an ultra-wideband positioning method according to an embodiment of the present application;

[0015] Figure 3 is a schematic diagram of a plant layout of a single turbine according to an embodiment of the present application;

[0016] Figure 4 is a schematic diagram of base station division for point positioning applications in different regions according to an embodiment of the present application;

[0017] Figure 5 It is a schematic diagram of selecting calibration points and inspection points during an accuracy calibration process according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] Optionally, as an optional implementation, as Figure 1 As shown, an ultra-wideband positioning system comprises:

[0021] An ultra-wideband positioning base station 102, wherein the ultra-wideband positioning base station 102 is arranged in a factory building according to a preset method, wherein the preset method indicates that the ultra-wideband positioning base station is arranged respectively above, below, and on both sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged in other areas outside the turbine area at preset intervals;

[0022] The regional base station range delimiting module 104 is used to select three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the reference positioning data of each positioning point;

[0023] The positioning data calibration module 106 is used to perform a secondary calibration of the position error on the reference positioning data of the three ultra-wideband positioning base stations to obtain the target positioning data of each positioning point.

[0024] Optionally, in this embodiment, the ultra-wideband positioning base station is a signal transmitting and receiving device in the ultra-wideband positioning system, which is used to capture the UWB signal sent by the target device and perform positioning.

[0025] Optionally, in this embodiment, the preset method refers to a base station deployment strategy pre-designed according to plant layout, equipment distribution and signal propagation characteristics, aiming to achieve uniform signal coverage and maximize the use of base station resources.

[0026] Optionally, in this embodiment, the reference positioning data is preliminary positioning information obtained based on three positioning base stations with the highest signal strengths, which is used to calculate the original coordinates of the positioning point.

[0027] Optionally, in this embodiment, the secondary calibration indicates a process of performing error correction on the preliminary positioning data by analyzing historical positioning data and environmental interference factors, aiming to further improve the accuracy of the positioning data.

[0028] Optionally, in this embodiment, the base stations are arranged in a preset manner according to the distribution of turbine areas in the plant and the signal propagation characteristics of other areas outside the turbine area. Specifically, at least one ultra-wideband positioning base station is arranged directly above, directly below, and on the left and right sides of each turbine area to ensure full coverage of the signal in the turbine area; for other areas outside the turbine area, ultra-wideband positioning base stations are evenly arranged at preset intervals (such as 20 to 30 meters) according to environmental characteristics and signal propagation distances, so as to optimize the signal coverage of the entire plant area and reduce signal blind spots.

[0029] Optionally, in this embodiment, the regional base station range delimitation module is responsible for selecting the three ultra-wideband positioning base stations with the highest signal strength at each point to be positioned, and the positioning data of these three base stations will be used to calculate the benchmark positioning data of the positioning point. By analyzing the signal strength and quality of all base stations through intelligent algorithms and selecting the best three base stations for positioning, the impact of signal interference on positioning accuracy can be effectively reduced, and the reliability and accuracy of positioning data can be improved.

[0030] Optionally, in this embodiment, the initially obtained reference positioning data is recalibrated to eliminate positioning errors and obtain target positioning data for each positioning point. The secondary calibration is based on historical positioning data and environmental interference characteristics, and uses advanced machine learning algorithms such as neural networks and support vector machines to analyze the distribution law of positioning errors, predict and correct the coordinate errors of positioning points, thereby improving positioning accuracy. This process not only corrects signal propagation errors, but also compensates for positioning errors caused by environmental factors such as multipath effects, signal shielding and reflection.

[0031] Through the embodiments provided by the present application, firstly, the ultra-wideband positioning base stations are arranged in a specific preset manner to ensure that each turbine area and other areas outside the turbine area can obtain uniform and sufficiently strong signal coverage. This optimized base station arrangement method effectively overcomes the problems of environmental interference and signal coverage blind spots. Secondly, the introduction of the regional base station range demarcation module enables the positioning system to intelligently select the three base stations with the highest signal strength at each positioning point for positioning, thereby improving the accuracy and stability of the positioning data. Finally, the positioning data calibration module uses secondary calibration technology and combines the positioning errors of historical calibration points to correct the position errors of the positioning points, thereby greatly improving the positioning accuracy. Especially in the case of complex environmental factors and large fluctuations in signal quality, the secondary calibration algorithm can effectively compensate for these adverse effects and ensure the high accuracy and reliability of the positioning data. In summary, the embodiments provided by the present application have achieved the technical effect of improving the accuracy of ultra-wideband positioning in all aspects and at multiple levels.

[0032] As an optional solution, the positioning data calibration module includes:

[0033] A first acquisition submodule, used to acquire first real coordinates of the calibration points in the other areas and second real coordinates of the inspection points in the other areas;

[0034] A second acquisition submodule, configured to acquire first plane coordinates of the calibration point and second plane coordinates of the inspection point, wherein the first plane coordinates are reference positioning data of the calibration point, and the second plane coordinates are reference positioning data of the inspection point;

[0035] an error calculation submodule, configured to calculate an error distribution between an ultra-wideband positioning result and a true measurement result based on the first real coordinate, the second real coordinate, the first plane coordinate, and the second plane coordinate;

[0036] The calibration submodule is used to perform secondary calibration of position errors on the positioning data of the three ultra-wideband positioning base stations based on the error distribution.

[0037] Optionally, in this embodiment, the first acquisition submodule is responsible for acquiring the first real coordinates of the calibration point and the second real coordinates of the check point. These coordinates are usually acquired by high-precision measurement equipment such as a total station, ensuring the accuracy and reliability of the coordinate data.

[0038] Optionally, in this embodiment, the second acquisition submodule is used to acquire preliminary plane coordinates of the calibration point and the check point, that is, the reference positioning data calculated based on three ultra-wideband positioning base stations with the highest signal strength.

[0039] Optionally, in this embodiment, the error calculation submodule is used to calculate the error between the ultra-wideband positioning result and the actual measurement result based on the first real coordinates, the second real coordinates and the preliminary plane coordinates, form an error distribution map, and provide data support for secondary calibration.

[0040] Optionally, in this embodiment, the calibration submodule is used to perform secondary calibration on the preliminary positioning data using the error distribution data to eliminate or compensate for the positioning error to achieve a higher precision positioning result.

[0041] Optionally, in this embodiment, the real coordinates of calibration points and check points in other areas are obtained. The selection of calibration points and check points follows certain rules, such as selecting points 3-5 meters apart as calibration points in each turbine area or area with severe signal interference to ensure the comprehensiveness of calibration coverage. At the same time, additional points are selected between the calibration points as check points to verify the validity of the secondary calibration. The real coordinates of these points are accurately measured and recorded by a total station or other high-precision measuring equipment.

[0042] Optionally, in this embodiment, the preliminary plane coordinates of the calibration point and the check point are obtained, that is, the reference positioning data calculated by the ultra-wideband positioning system based on the three positioning base stations with the highest signal strength. This process is the acquisition of preliminary positioning data, which provides basic data for subsequent error analysis and secondary calibration.

[0043] Optionally, in this embodiment, based on the acquired first real coordinates, second real coordinates, first plane coordinates, and second plane coordinates, the error distribution between the ultra-wideband positioning result and the real measurement result is calculated. The error calculation takes into account multiple dimensions, such as errors in the x and y directions, and generates a distribution map of the error in space, providing intuitive error distribution information for the subsequent secondary calibration algorithm.

[0044] Optionally, in this embodiment, based on the error distribution generated by the error calculation submodule, interpolation and machine learning algorithms, such as neural networks and decision trees, are used to predict and correct the position errors of other positioning points, thereby realizing secondary calibration of the preliminary positioning data. This process can effectively compensate for positioning errors caused by complex environmental factors such as signal multipath effects and structural reflections, and significantly improve the overall accuracy of the positioning system.

[0045] As an optional solution, the positioning data calibration module further includes:

[0046] an analysis submodule, configured to input the error distribution and the first real coordinate into an electronic map, and obtain a first error distribution relationship between the errors of the calibration point and the check point in space, wherein the first error distribution relationship is used to indicate a law of the errors in space;

[0047] The first selection submodule is used to select a target machine learning algorithm from at least one machine learning algorithm, wherein the at least one machine learning algorithm is used to use the first error distribution relationship to predict the second error distribution relationship of the check point, and the target machine learning algorithm is the machine learning algorithm with the best prediction effect among the at least one machine learning algorithm.

[0048] Optionally, in this embodiment, the error distribution specifies the regular distribution of the positioning error of the site in space, which is usually determined by analyzing the relationship between the positioning errors of a large number of calibration points and the true coordinates.

[0049] Optionally, in this embodiment, the first error distribution relationship refers to an error distribution model in space constructed by an analysis submodule based on the error distribution and true coordinates of the calibration points and the test points, which is used to indicate the spatial regularity of the positioning error.

[0050] Optionally, in this embodiment, at least one machine learning algorithm includes but is not limited to a variety of prediction models such as neural networks, support vector machines, decision trees, etc., which are used to analyze the error distribution law and predict the error distribution of the check points.

[0051] Optionally, in this embodiment, the target machine learning algorithm is an algorithm selected from at least one machine learning algorithm, which is used to predict the error distribution relationship of the inspection points and has the best prediction effect.

[0052] Optionally, in this embodiment, the calculated error distribution and the first real coordinates of the calibration point are input into the electronic map to generate a first error distribution relationship of the errors of each calibration point and the test point in space. By analyzing the association between the positioning error and the spatial position, the regularity of the error distribution is identified to provide a data basis for the subsequent secondary calibration. The analysis process may involve statistical analysis, graphic visualization and other methods to intuitively display the spatial characteristics of the error distribution.

[0053] Optionally, in this embodiment, a target machine learning algorithm with the best prediction effect is selected from a variety of available machine learning algorithms. This process is performed based on the first error distribution relationship generated by the analysis submodule. First, the first selection submodule takes the first error distribution relationship as input, applies a variety of machine learning algorithms for training, and predicts the second error distribution relationship of the checkpoint, that is, the positioning error distribution of the checkpoint. Then, by comparing the prediction results of different algorithms with the actual errors, the prediction effect of the algorithm is evaluated, and finally the machine learning algorithm with the smallest prediction error and the best prediction effect is selected as the target machine learning algorithm for subsequent positioning data calibration.

[0054] As an optional solution, the calibration submodule further includes:

[0055] A prediction unit, configured to use the target machine learning algorithm to perform error prediction on the positioning data of the three ultra-wideband positioning base stations to obtain a prediction error vector;

[0056] The calibration unit is used to use the prediction error vector to perform coordinate correction on the positioning data of the three ultra-wideband positioning base stations to obtain the target positioning data of each positioning point.

[0057] Optionally, in this embodiment, the target machine learning algorithm is first used to train according to the first error distribution relationship generated by the analysis submodule to learn the intrinsic relationship between the positioning error and the spatial position. Subsequently, the prediction unit performs error prediction on the positioning data of the three ultra-wideband positioning base stations to generate a prediction error vector. The prediction error vector reflects the expected error of the positioning data in the x and y directions, providing a basis for subsequent coordinate correction.

[0058] Optionally, in this embodiment, based on the prediction error vector obtained by the prediction unit, the calibration unit performs coordinate correction on the positioning data. Specifically, the calibration unit applies the prediction error vector to the preliminary positioning data, and compensates for the predicted positioning error by adjusting the coordinates of the positioning point, thereby obtaining the target positioning data. This process is essentially a secondary calibration of the preliminary positioning result, which significantly improves the positioning accuracy, especially in areas with severe signal interference or where the positioning error has obvious spatial regularity.

[0059] As an optional solution, the positioning data calibration module further includes:

[0060] The adaptive adjustment submodule is used to correct and adjust the parameters of the target machine learning algorithm according to the motion state information and surrounding environment information of each positioning point.

[0061] Optionally, in this embodiment, the adaptive adjustment submodule obtains the motion state information and surrounding environment information of each positioning point in real time through sensors or devices, such as obtaining the motion state through accelerometers and gyroscopes, and obtaining surrounding environment data through temperature and humidity sensors and environmental scanning devices.

[0062] Based on the acquired motion state information and surrounding environment information, the adaptive adjustment submodule can dynamically calculate and adjust the parameters of the target machine learning algorithm. For example, if the positioning point is in a high-speed moving state, the algorithm may need a higher learning rate and a faster response speed; if the environmental conditions change, such as temperature increase or device layout adjustment, the algorithm may need to adjust the weights and biases to better adapt to the changes in signal propagation characteristics.

[0063] The parameter adjustment of the adaptive adjustment submodule is continuous and dynamic, which can ensure that the positioning system maintains a high positioning accuracy when the positioning point moves and the environmental conditions change.

[0064] As an optional solution, the positioning data calibration module further includes:

[0065] The second selection submodule is used to select M calibration points and N check points in the other area, wherein the calibration points among the M calibration points are evenly distributed according to preset intervals, and the check points are distributed between two adjacent calibration points among the M calibration points, and M and N are positive integers.

[0066] Optionally, in this embodiment, the second selection submodule selects M calibration points in the other area, wherein each of the M calibration points is evenly distributed according to a preset interval. The design of the preset interval takes into account the effective propagation distance of the UWB signal and the complexity of the environment, ensuring that the calibration points can fully cover the entire area, reflecting the diversity of signal propagation.

[0067] Optionally, in this embodiment, N check points are selected, and these check points are distributed between two adjacent calibration points in the M calibration points. By setting check points between adjacent calibration points, the effect of positioning calibration can be verified more carefully, and the trend of positioning error can be evaluated, especially for areas with complex or sensitive signal propagation characteristics.

[0068] It should be noted that the selection of M calibration points and N check points ensures that the positioning data calibration module can accurately identify and calibrate positioning errors and improve positioning accuracy through comprehensive and detailed data analysis.

[0069] As an alternative, Figure 2 As shown, an ultra-wideband positioning method includes:

[0070] S202, setting N ultra-wideband positioning base stations in the factory building according to a preset method, wherein the preset method indicates that the ultra-wideband positioning base stations are respectively arranged directly above, directly below, and on the left and right sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged in other areas outside the turbine area at preset intervals, and N is a positive integer;

[0071] S204, selecting three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the reference positioning data of each positioning point;

[0072] S206, performing secondary calibration of position error on the reference positioning data of each positioning point to obtain target positioning data of each positioning point.

[0073] Optionally, in this embodiment, the ultra-wideband positioning base station is a signal transmitting and receiving device in the ultra-wideband positioning system, which is used to capture the UWB signal sent by the target device and perform positioning.

[0074] Optionally, in this embodiment, the preset method refers to a base station deployment strategy pre-designed according to plant layout, equipment distribution and signal propagation characteristics, aiming to achieve uniform signal coverage and maximize the use of base station resources.

[0075] Optionally, in this embodiment, the reference positioning data is preliminary positioning information obtained based on three positioning base stations with the highest signal strengths, which is used to calculate the original coordinates of the positioning point.

[0076] Optionally, in this embodiment, the secondary calibration indicates a process of performing error correction on the preliminary positioning data by analyzing historical positioning data and environmental interference factors, aiming to further improve the accuracy of the positioning data.

[0077] Optionally, in this embodiment, the base stations are arranged in a preset manner according to the distribution of turbine areas in the plant and the signal propagation characteristics of other areas outside the turbine area. Specifically, at least one ultra-wideband positioning base station is arranged directly above, directly below, and on the left and right sides of each turbine area to ensure full coverage of the signal in the turbine area; for other areas outside the turbine area, ultra-wideband positioning base stations are evenly arranged at preset intervals (such as 20 to 30 meters) according to environmental characteristics and signal propagation distances, so as to optimize the signal coverage of the entire plant area and reduce signal blind spots.

[0078] Optionally, in this embodiment, the regional base station range delimitation module is responsible for selecting the three ultra-wideband positioning base stations with the highest signal strength at each point to be positioned, and the positioning data of these three base stations will be used to calculate the benchmark positioning data of the positioning point. By analyzing the signal strength and quality of all base stations through intelligent algorithms and selecting the best three base stations for positioning, the impact of signal interference on positioning accuracy can be effectively reduced, and the reliability and accuracy of positioning data can be improved.

[0079] Optionally, in this embodiment, the initially obtained reference positioning data is recalibrated to eliminate positioning errors and obtain target positioning data for each positioning point. The secondary calibration is based on historical positioning data and environmental interference characteristics, and uses advanced machine learning algorithms such as neural networks and support vector machines to analyze the distribution law of positioning errors, predict and correct the coordinate errors of positioning points, thereby improving positioning accuracy. This process not only corrects signal propagation errors, but also compensates for positioning errors caused by environmental factors such as multipath effects, signal shielding and reflection.

[0080] Through the embodiments provided by the present application, firstly, the ultra-wideband positioning base stations are arranged in a specific preset manner to ensure that each turbine area and other areas outside the turbine area can obtain uniform and sufficiently strong signal coverage. This optimized base station arrangement method effectively overcomes the problems of environmental interference and signal coverage blind spots. Secondly, the introduction of the regional base station range demarcation module enables the positioning system to intelligently select the three base stations with the highest signal strength at each positioning point for positioning, thereby improving the accuracy and stability of the positioning data. Finally, the positioning data calibration module uses secondary calibration technology and combines the positioning errors of historical calibration points to correct the position errors of the positioning points, thereby greatly improving the positioning accuracy. Especially in the case of complex environmental factors and large fluctuations in signal quality, the secondary calibration algorithm can effectively compensate for these adverse effects and ensure the high accuracy and reliability of the positioning data. In summary, the embodiments provided by the present application have achieved the technical effect of improving the accuracy of ultra-wideband positioning in all aspects and at multiple levels.

[0081] As an optional solution, the above ultra-wideband positioning system is applied to the UWB positioning accuracy improvement scenario applicable to small and medium-sized hydropower station buildings. In this scenario,

[0082] In order to effectively improve the level of production safety management in enterprise plants, optimize management processes, and improve safety supervision efficiency, UWB positioning systems have been widely used in the intelligent security system of hydropower plant plants, and are a necessary means for hydropower plants to achieve modern management. At present, UWB (ultra-wideband) wireless positioning systems have the characteristics of high bandwidth, high time resolution, strong penetration, and anti-multipath interference. UWB positioning technology transmits and receives specific pulse signals and measures the time difference during signal propagation to achieve precise positioning. This technology is widely used in the field of indoor positioning and can achieve high-precision positioning in densely populated buildings or indoor environments. Under ideal conditions, its accuracy can reach 10cm, which is significantly higher than other wireless positioning technologies.

[0083] For hydropower plant buildings, due to the complex environment, the UWB positioning system will be interfered by various factors, thus affecting the positioning accuracy. The main influencing factors include the following aspects: Environmental interference: The internal structure of the hydropower station is complex and may contain a large number of metal equipment and structures, which will cause reflection, refraction and absorption of the propagation of UWB signals, thereby affecting the positioning accuracy. Base station layout: The propagation distance of UWB signals is limited. If the layout of base stations (anchor points) is unreasonable or the spacing is too large, it may lead to insufficient signal coverage and affect the positioning accuracy. Software algorithm: The accuracy of the positioning algorithm will also affect the final positioning results. Advanced algorithms can better process signals, reduce errors and improve positioning accuracy.

[0084] As a large-scale energy production base, the safe operation of a hydropower station is crucial to the life safety of employees, the integrity of equipment and stable production. This embodiment provides a UWB positioning accuracy improvement algorithm and system suitable for small and medium-sized hydropower station plants. In view of the problems of more interference in hydropower station plants, less advanced algorithms and greater impact on positioning accuracy, the layout mode and location algorithm of UWB positioning base stations in typical plants of small and medium-sized hydropower stations are improved, overcoming factors that affect positioning accuracy such as more obstructions and unstable signals.

[0085] The implementation steps of the system and method include three steps: (1) the location of UWB base stations: based on the layout of typical small and medium-sized hydropower plant buildings, the traditional layout method is improved to achieve signal coverage of the largest possible area with as few base stations as possible; (2) the delineation of base station ranges in different areas; and (3) setting a reasonable algorithm for secondary calibration to improve accuracy.

[0086] UWB base station layout: The effective distance of the common UWB positioning base station is 20 to 30 meters. Combined with the layout and geometric dimensions of typical small and medium-sized hydropower station buildings, the layout is as follows: Figure 3 As shown in the figure, a plant with a single turbine is used as an example. UWB positioning base stations are arranged directly above, directly below, and on both sides of the turbine area. Other base stations are evenly arranged at intervals of 20 to 30 meters on each side along the four walls of the plant, thereby ensuring uniform positioning of the plant area. For plants with multiple turbines, UWB base stations should be arranged symmetrically in the same way.

[0087] Division of base station positioning area: After the base stations are deployed, the three base stations with the highest signal strength should be selected as positioning base stations for each point, and other base stations with lower signal strength should not be used as positioning base stations. The division of the three base stations for point positioning applications in different areas is as follows: Figure 4 As shown, taking the lower left area as an example, the 123 area indicates that the positioning base stations used for the points in this area are 1, 2, and 3. Even if other base stations detect signals, they are not used as a basis for positioning.

[0088] Secondary calibration of base station accuracy: According to the above base station layout, each point is located within the coverage of the UWB base station and has good signal strength, that is, the location coordinates can be determined by the UWB device and conventional positioning algorithm. Due to various interference factors, the accuracy calibration should be performed separately for each point in the base station area. The accuracy calibration steps are as follows:

[0089] Take n uniformly distributed calibration points and check points in the same area of ​​each of the three positioning base stations, such as Figure 5 As shown by the black dots in the figure, the real coordinates of each point are determined by on-site measurement. Select calibration points (test points) and inspection points: Select several points with an interval of 3-5m in the factory as calibration points. The interval between each point should be consistent, such as Figure 5As shown by the black points in the figure. A group of points are selected between the two black points as test points. The plane coordinates (x, y) of the UWB positioning system of each calibration point are obtained, and then the real coordinates (x1, y1) of each calibration point are obtained by total station or other scanning, and the error (Δx, Δy) of each point of the UWB positioning system is calculated, where x1 = x + Δx, y1 = y + Δy.

[0090] The position error (Δx, Δy) and true coordinates (x, y) of each calibration point are input into the electronic map to obtain the distribution relationship of the error in space, as shown in the figure below. The z coordinate is the error of the point in the x or y direction, so two maps (x error distribution map, y error distribution map) can be generated for each area. The law of spatial distribution of position error (Δx, Δy) is identified through a variety of machine recognition methods and interpolation methods.

[0091] The actual coordinates (a, b) of each inspection point are obtained through the UWB system, and then the real coordinates (a1, b1) of each inspection point are obtained through the total station or other scanning. The distribution law of the position error of the inspection point (blue point) in space is predicted through interpolation and a variety of commonly used machine learning algorithms, and compared with the actual error (a1-a, b1-b), and the best set of algorithms is selected.

[0092] (4) A set of algorithms with the best actual error prediction effect is selected to predict the position error vectors (Δx, Δy) of other points in the area, which are used to correct the coordinates (x, y) directly measured by UWB. The calibrated positions of other points in the area can be obtained, achieving a secondary improvement in accuracy.

[0093] Through the embodiments provided in this application, based on the layout of typical small and medium-sized hydropower plant buildings, the traditional layout method is improved to achieve signal coverage of the largest possible area through as few base stations as possible; corresponding positioning base station demarcation methods are set for different areas to ensure signal strength and stability; and a reasonable algorithm is set to perform secondary calibration to improve accuracy.

[0094] It is understandable that in the specific implementation of this application, related data such as user information is involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0095] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0096] According to another aspect of an embodiment of the present application, an electronic device for implementing the above-mentioned composite diaphragm repair method is also provided, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the steps in any of the above-mentioned method embodiments through the computer program.

[0097] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0098] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0099] S1, setting N ultra-wideband positioning base stations in a factory building according to a preset method, wherein the preset method indicates that the ultra-wideband positioning base stations are respectively arranged directly above, directly below, and on the left and right sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged in other areas outside the turbine area at preset intervals, and N is a positive integer;

[0100] S2, selecting three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the reference positioning data of each positioning point;

[0101] S3, performing secondary calibration of position error on the reference positioning data of each positioning point to obtain target positioning data of each positioning point.

[0102] Optionally, it will be understood by those skilled in the art that the memory can be used to store software programs and modules, such as the ultra-wideband positioning method and the corresponding program instructions / modules in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned ultra-wideband positioning method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] Optionally, the transmission device is used to receive or send data via a network. Specific examples of the network may include wired networks and wireless networks. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0104] In addition, the electronic device further comprises: a display for displaying information such as the target positioning data; and a connection bus for connecting various module components in the electronic device.

[0105] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. Among them, the nodes may form a peer-to-peer (P2P) network, and any form of computing device, such as a server, terminal or other electronic device, may become a node in the blockchain system by joining the peer-to-peer network.

[0106] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0107] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0108] It should be noted that the computer system of the electronic device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0109] The computer system includes a central processing unit (CPU), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) or the program loaded from the storage part to the random access memory (RAM). In the random access memory, various programs and data required for system operation are also stored. The central processing unit, the read-only memory and the random access memory are connected to each other through a bus. The input / output interface (I / O interface) is also connected to the bus.

[0110] The following components are connected to the input / output interface: an input part including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a local area network card, a modem, etc. The communication part performs communication processing via a network such as the Internet. A drive is also connected to the input / output interface as needed. Removable media, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive as needed so that the computer program read therefrom is installed into the storage part as needed.

[0111] In particular, according to an embodiment of the present application, the process described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program contains a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions defined in the system of the present application are executed.

[0112] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0113] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0114] S1, setting N ultra-wideband positioning base stations in a factory building according to a preset method, wherein the preset method indicates that the ultra-wideband positioning base stations are respectively arranged directly above, directly below, and on the left and right sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged in other areas outside the turbine area at preset intervals, and N is a positive integer;

[0115] S2, selecting three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the reference positioning data of each positioning point;

[0116] S3, performing secondary calibration of position error on the reference positioning data of each positioning point to obtain target positioning data of each positioning point.

[0117] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0118] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0119] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to execute all or part of the steps of the methods of each embodiment of the present application.

[0120] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0124] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An ultra-wideband positioning system, characterized in that: include: An ultra-wideband positioning base station, wherein the ultra-wideband positioning base station is arranged in a factory building according to a preset method, wherein the preset method indicates that the ultra-wideband positioning base station is arranged respectively above, below, and on both sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged in other areas outside the turbine area at preset intervals; A regional base station range delimiting module is used to select three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the reference positioning data of each positioning point; The positioning data calibration module is used to perform secondary calibration of position errors on the reference positioning data of the three ultra-wideband positioning base stations to obtain target positioning data of each positioning point.

2. The system according to claim 1, characterized in that The positioning data calibration module comprises: A first acquisition submodule, used to acquire first real coordinates of the calibration points in the other areas and second real coordinates of the inspection points in the other areas; A second acquisition submodule, configured to acquire first plane coordinates of the calibration point and second plane coordinates of the inspection point, wherein the first plane coordinates are reference positioning data of the calibration point, and the second plane coordinates are reference positioning data of the inspection point; an error calculation submodule, configured to calculate an error distribution between an ultra-wideband positioning result and a true measurement result based on the first real coordinate, the second real coordinate, the first plane coordinate, and the second plane coordinate; The calibration submodule is used to perform secondary calibration of position errors on the positioning data of the three ultra-wideband positioning base stations based on the error distribution.

3. The system according to claim 2, characterized in that The positioning data calibration module further includes: an analysis submodule, configured to input the error distribution and the first real coordinate into an electronic map, and obtain a first error distribution relationship between the errors of the calibration point and the check point in space, wherein the first error distribution relationship is used to indicate a law of the errors in space; The first selection submodule is used to select a target machine learning algorithm from at least one machine learning algorithm, wherein the at least one machine learning algorithm is used to use the first error distribution relationship to predict the second error distribution relationship of the check point, and the target machine learning algorithm is the machine learning algorithm with the best prediction effect among the at least one machine learning algorithm.

4. The system according to claim 3, characterized in that The calibration submodule further includes: A prediction unit, configured to use the target machine learning algorithm to perform error prediction on the positioning data of the three ultra-wideband positioning base stations to obtain a prediction error vector; The calibration unit is used to use the prediction error vector to perform coordinate correction on the positioning data of the three ultra-wideband positioning base stations to obtain the target positioning data of each positioning point.

5. The system according to claim 3, characterized in that The positioning data calibration module further includes: The adaptive adjustment submodule is used to correct and adjust the parameters of the target machine learning algorithm according to the motion state information and surrounding environment information of each positioning point.

6. The system according to claim 2, characterized in that The positioning data calibration module further includes: The second selection submodule is used to select M calibration points and N check points in the other area, wherein the calibration points among the M calibration points are evenly distributed according to preset intervals, and the check points are distributed between two adjacent calibration points among the M calibration points, and M and N are positive integers.

7. An ultra-wideband positioning method, characterized in that: include: According to a preset method, N ultra-wideband positioning base stations are arranged in the factory building, wherein the preset method indicates that the ultra-wideband positioning base stations are arranged directly above, directly below, and on both sides of each turbine area in the factory building, and the ultra-wideband positioning base stations are evenly arranged in other areas outside the turbine area at preset intervals, and N is a positive integer; Select three ultra-wideband positioning base stations with the highest signal strength at each positioning point, wherein the positioning data of the three ultra-wideband positioning base stations are used to calculate the reference positioning data of each positioning point; A secondary calibration of position error is performed on the reference positioning data of each positioning point to obtain target positioning data of each positioning point.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method of claim 7 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method of claim 7 through the computer program.

10. A computer program product, characterized in that The method comprises a computer program, which implements the steps of the method according to claim 7 when the computer program is executed by a processor.

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