Integratable multi-source positioning method and device

Abstract

The invention relates to the technical field of positioning, and particularly provides an integratable multi-source positioning method and device, based on multi-source positioning access and positioning algorithm integration, the multi-source positioning access comprises Beidou positioning data access, UWB positioning data access, Bluetooth positioning data access and Wi-Fi positioning algorithm access; beidou positioning data access is communicated with a Beidou satellite navigation system to obtain accurate position information of the equipment; in the UWB positioning data access, a UWB positioning base station is responsible for communicating with a UWB tag and transmitting data of the tag back to the background; the Bluetooth positioning data access is used for acquiring position information of equipment by communicating with nearby Bluetooth equipment; wi-Fi positioning algorithm access needs to complete Wi-Fi signal receiving and analysis work, and useful positioning information is extracted; the positioning algorithm integration technology comprises the following steps: S1, construction of a positioning algorithm bin and algorithm integration; s2, managing and configuring a positioning algorithm; and S2, scheduling and arranging a positioning algorithm. Compared with the prior art, more convenient and efficient service experience can be provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and specifically provides an integratable multi-source positioning method and device. Background Art

[0002] The Beidou Satellite Navigation System (BDS) is a global satellite navigation system independently developed by China, with high-precision positioning, navigation, and timing capabilities. Its high-precision positioning ability enables the Beidou system to provide centimeter-level or even millimeter-level positioning accuracy in outdoor environments, providing a solid foundation for various applications. However, the performance of the Beidou system in indoor environments is insufficient. Due to the shielding of satellite signals by buildings, Beidou signals will experience significant attenuation when penetrating buildings, resulting in a significant decline in indoor positioning accuracy. In addition, the complex and variable indoor environment, such as factors like multipath effects and non-line-of-sight propagation, will further affect the positioning accuracy and stability of the Beidou system.

[0003] An indoor distribution system is a system that realizes the enhancement and coverage of wireless signals such as satellite navigation signals and mobile communication signals through signal enhancement devices and antenna networks arranged in indoor spaces. The indoor distribution system can effectively solve the problem of insufficient indoor signal coverage and improve the accuracy and stability of indoor positioning. However, the indoor distribution system also has some limitations. First, the construction and maintenance costs of the indoor distribution system are relatively high, requiring professional equipment and manpower investment. Second, the differences and complexities of different indoor environments make the design and implementation of the indoor distribution system difficult. In addition, while the indoor distribution system realizes signal enhancement, it may also introduce additional noise and interference, affecting the positioning accuracy.

[0004] The Beidou + indoor distribution fusion positioning technology combines the high-precision positioning ability of the Beidou system and the signal enhancement and coverage advantages of the indoor distribution system, aiming to achieve seamless indoor and outdoor positioning and improve the accuracy and stability of indoor positioning. Through optimizing algorithms, improving hardware devices, and enhancing system integration capabilities, the Beidou + indoor distribution fusion positioning technology has been able to provide stable and reliable positioning services in complex and variable indoor environments. This technology not only broadens the application scenarios of the Beidou system but also injects new vitality into the development of indoor positioning technology.

[0005] However, the Beidou + indoor distribution fusion positioning technology also faces some challenges and problems. First, the technical response accuracy is generally average. Especially in complex and variable indoor environments, how to ensure the stable reception and processing of Beidou signals indoors has become a technical barrier that needs to be overcome urgently. Summary of the Invention

[0006] The present invention aims at the above-mentioned deficiencies of the prior art and provides a practical integratable multi-source positioning method.

[0007] A further technical task of the present invention is to provide an integratable multi-source positioning device with reasonable design, safety and applicability.

[0008] The technical solution adopted by the present invention to solve its technical problems is:

[0009] An integratable multi-source positioning method, based on multi-source positioning access technology and positioning algorithm integration technology, the multi-source positioning access technology includes Beidou positioning data access, UWB positioning data access, Bluetooth positioning data access and Wi-Fi positioning algorithm access;

[0010] The Beidou positioning data access communicates with the Beidou satellite navigation system to obtain the accurate position information of the device;

[0011] In the UWB positioning data access, the UWB positioning base station is responsible for communicating with the UWB tag and transmitting the tag data back to the background;

[0012] The Bluetooth positioning data access obtains the position information of the device by communicating with nearby Bluetooth devices;

[0013] The Wi-Fi positioning algorithm access needs to complete the reception and parsing of Wi-Fi signals and extract useful positioning information;

[0014] The positioning algorithm integration technology includes:

[0015] S1. Construction and algorithm integration of the positioning algorithm library;

[0016] S2. Management and configuration of the positioning algorithm;

[0017] S2. Scheduling and orchestration of the positioning algorithm.

[0018] Further, in the Beidou positioning data access, the Beidou device positioning has the following steps:

[0019] First, use the observation data of the site for single-point positioning to obtain the approximate coordinates of the site; then, perform parameter estimation to obtain the floating-point solutions of the baseline vector and ambiguity parameters; then, perform residual analysis based on the obtained baseline vector and ambiguity parameters. If new gross errors are found, re-perform parameter estimation until there are no gross errors;

[0020] After that, extract the floating-point solution of the ambiguity and the variance-covariance matrix to perform ambiguity fixing; if the ambiguity fixing is successful, introduce the ambiguity fixed solution, re-perform parameter estimation to obtain the fixed solution of the baseline vector, and output the solution; if the ambiguity fixing is not successful, output the floating-point solution.

[0021] Furthermore, during the process of processing the positioning data of Beidou devices, it is also necessary to preprocess the observation data to detect cycle slips and gross errors, and generate double-difference pseudorange and phase observation data. Then, using these observation data, parameter estimation is carried out in the least squares filtering manner, and the residuals of the observation data are analyzed to eliminate gross errors, and finally the latest estimation results of baseline vectors and ambiguity parameters are obtained.

[0022] Furthermore, in the access of UWB positioning data, the UWB tag is worn on a person or an object, and precise positioning is achieved through communication with the base station.

[0023] Furthermore, in the access of Bluetooth positioning data, Bluetooth base stations and tags are deployed, and indoor positioning functions are realized through the communication between the Bluetooth base stations and the tags.

[0024] Furthermore, in the access of the Wi-Fi positioning algorithm, the Wi-Fi positioning algorithm includes a fingerprint positioning algorithm and a triangulation positioning algorithm. The positioning algorithm collects Wi-Fi signal characteristics at different positions to construct a fingerprint database, and then uses a matching algorithm to achieve positioning;

[0025] The triangulation positioning algorithm measures the distance or angle information between a wireless terminal and multiple APs, and calculates the position of the wireless terminal using geometric relationships.

[0026] Furthermore, in step S1, the positioning algorithm repository stores and manages various positioning algorithms, including an adaptive filtering algorithm, a fingerprint positioning algorithm, a triangulation positioning algorithm, and a TDOA positioning algorithm;

[0027] In the adaptive filtering algorithm, the implementation code and documentation of the Kalman filtering algorithm are collected, the algorithm code is encapsulated and interface-defined as necessary, the encapsulated algorithm package is uploaded to the algorithm repository, and tested and verified to ensure that it can run correctly and output results;

[0028] In the fingerprint positioning algorithm, the signal strength data in the target area is collected and sorted to construct a fingerprint database, the implementation code of the fingerprint positioning algorithm is developed, the algorithm is encapsulated and interface-defined, uploaded to the algorithm repository, and tested and verified;

[0029] In the triangulation positioning algorithm and the TDOA positioning algorithm, the implementation code and documentation of the triangulation positioning algorithm and the TDOA positioning algorithm are collected, the algorithms are encapsulated and interface-defined, uploaded to the algorithm repository, the deployment parameters of the base station and the tag are configured to ensure that the algorithms can run correctly, tested and verified, and the positioning accuracy and stability of the algorithms are evaluated.

[0030] Furthermore, in step S2, it includes:

[0031] S2-1. Algorithm classification and version management;

[0032] Classify and manage different versions of the same algorithm, select the appropriate algorithm version, and perform operations such as modification, deletion, or taking off the shelf on it;

[0033] S2-2. Algorithm attribute description and computing power model;

[0034] For each algorithm package, provide detailed attribute description information, use the computing power model to uniformly manage algorithm resources and platform resources, and only algorithms that meet the requirements can run properly on the specified device;

[0035] S2-3. Algorithm scheduling and task management;

[0036] Design an intelligent task scheduling service to manage computing resources and intelligent analysis tasks, and dispatch tasks to the most suitable intelligent analysis device.

[0037] Furthermore, in step S3, it includes:

[0038] S3-1. Algorithm scheduling service;

[0039] The algorithm scheduling service connects the algorithm repository and the intelligent basic service, and is responsible for scheduling appropriate algorithms to complete tasks according to user requirements and resource conditions;

[0040] S3-2. Algorithm orchestration technology;

[0041] The algorithm orchestration technology combines multiple single-function algorithms into a new composite-function algorithm, and provides a visual interface and a rich algorithm component library to support users in algorithm combination and configuration;

[0042] S3-3. Support for intelligent basic services;

[0043] The intelligent basic service is responsible for loading and executing the specified algorithm package and algorithm service, and designs the intelligent basic service in the form of a pluggable component.

[0044] An integrable multi-source positioning device includes: at least one memory and at least one processor;

[0045] The at least one memory is used to store machine-readable programs;

[0046] The at least one processor is used to call the machine-readable program and execute an integrable multi-source positioning method.

[0047] Compared with the prior art, an integrable multi-source positioning method and device of the present invention has the following outstanding beneficial effects:

[0048] The multi-source positioning method based on the integration of Beidou and indoor distribution proposed by the present invention shows significant beneficial effects in aspects such as seamless indoor and outdoor positioning, improved positioning accuracy, enhanced system adaptability, and promoting the innovation and development of positioning technology.

[0049] Achieve seamless indoor and outdoor positioning: By integrating the Beidou satellite navigation system and various indoor positioning technologies such as Bluetooth, UWB, and Wi-Fi, the present invention successfully breaks through the limitations of traditional positioning technologies in indoor and outdoor scenarios, realizing a continuous and seamless positioning experience from outdoor to indoor, and greatly expanding the application scope of positioning technology.

[0050] Significantly improve positioning accuracy: With the high-speed and anti-interference characteristics of UWB technology and the high-precision position information of the Beidou satellite navigation system, the present invention can provide users with more accurate position services. At the same time, through the fusion and complementarity of multi-source data, the accuracy and reliability of positioning are further improved, meeting the requirements of high-precision positioning scenarios.

[0051] Enhance the adaptability of the positioning system: The basic positioning platform constructed by the present invention supports the access of multiple positioning technologies and provides standardized API interfaces, enabling third-party positioning algorithms to be easily integrated. This design not only improves the flexibility and scalability of the system but also enables the positioning system to quickly adjust and optimize the positioning scheme according to different scenarios and requirements, enhancing the adaptability and practicality of the system.

[0052] Promote the innovation and development of positioning technology: The proposal of the present invention not only provides a new solution for seamless indoor and outdoor positioning but also provides new ideas and methods for the innovation and development of positioning technology. Through the combination of multi-source positioning access technology and positioning algorithm integration technology, it provides a broader space and possibility for future positioning technology research and application. Specific implementation manners

[0053] In order to enable those skilled in the art of this technology to better understand the solution of the present invention, the following further detailed description of the present invention is made in conjunction with specific implementation manners. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention.

[0054] The following gives a best embodiment:

[0055] A multi-source positioning method in this embodiment is based on multi-source positioning access technology and positioning algorithm integration technology. Among them, the multi-source positioning access technology includes Beidou positioning data access, UWB positioning data access, Bluetooth positioning data access, and Wi-Fi positioning algorithm access.

[0056] BeiDou positioning data is being accessed:

[0057] The access of BeiDou positioning data is an important part of this module. By communicating with the BeiDou satellite navigation system, we can obtain the precise location information of the device. To achieve this step, functions such as connecting to the BeiDou system, data transmission, and parsing need to be completed.

[0058] During the BeiDou positioning process, the BeiDou positioning terminal will receive signals from BeiDou satellites and calculate the positioning coordinates based on these signals. Subsequently, this positioning information and status information will be sent to the data server through the communication network. After receiving these data, the server side will perform a series of processing tasks, including parsing, denoising, multi-source fusion, and conversion, etc., to calculate the precise location and trajectory of the relevant device.

[0059] The positioning of BeiDou devices can be roughly divided into the following steps: First, use the observation data of the station for single-point positioning to obtain the approximate coordinates of the station; then, perform parameter estimation to obtain the floating-point solutions of the baseline vector and ambiguity parameters; next, perform residual analysis based on the obtained baseline vector and ambiguity parameters. If new gross errors are found, re-perform parameter estimation until there are no gross errors; after that, extract the floating-point solution of the ambiguity and the variance-covariance matrix to fix the ambiguity; if the ambiguity is successfully fixed, introduce the fixed solution of the ambiguity, re-perform parameter estimation to obtain the fixed solution of the baseline vector, and output this solution; if the ambiguity is not successfully fixed, output the floating-point solution.

[0060] During the data processing process, it is also necessary to preprocess the observation data to detect cycle slips and gross errors, and generate double-difference pseudorange and phase observation data. Then, use these observation data to perform parameter estimation in the least squares filtering manner, analyze the residuals of the observation data, and eliminate gross errors to finally obtain the latest estimation results of the baseline vector and ambiguity parameters.

[0061] In actual operation, we usually use RTS devices to collect BeiDou data. Install relevant RTS devices at the field reference station and the observation station respectively, and then send the collected raw data to the server through network transmission. The server will decode and perform baseline solution and other processing on these raw data, and finally obtain the baseline vector result.

[0062] In this project, we will access the BeiDou positioning data source by calling the BeiDou positioning data transmission interface of the platform through the applications carried by the hardware system (such as mobile phones, vehicle-mounted devices, etc.).

[0063] UWB positioning data is being accessed,

[0064] With its characteristics such as high data transmission rate, strong anti-multipath interference ability, low power consumption, low cost, and strong penetration ability, UWB technology occupies an important position in wireless personal area network communication technology.

[0065] UWB technology uses nanosecond-level non-sinusoidal narrow pulses to transmit data. Therefore, its spectrum range is very wide, and the data transmission rate can reach more than several hundred megabits per second. This technology is not only applicable to civilian products but also widely used in military radars, positioning, and low probability of intercept / low probability of detection communication systems.

[0066] In a UWB positioning system, the UWB positioning base station is responsible for communicating with the UWB tag and transmitting the tag's data back to the background. The UWB tag is worn on people or objects to achieve precise positioning through communication with the base station. In addition, the UWB positioning system may also include supporting devices such as audible and visual alarms, temperature and humidity sensors, etc., to expand the functions and application scope of the system.

[0067] In this project, the platform will achieve the access of the data source after preliminary correction of UWB single high-precision positioning by docking with the data interface of the hardware system. This means that we can use the high-precision positioning ability of UWB technology to provide more accurate location information for the platform.

[0068] In the access of Bluetooth positioning data:

[0069] With its characteristics such as low power consumption, low cost, and easy integration, Bluetooth technology has been widely used in smart devices. By communicating with nearby Bluetooth devices, we can obtain the location information of the devices.

[0070] In the process of realizing the access of Bluetooth positioning data, functions such as scanning, connecting, and data transmission with Bluetooth devices need to be completed. The realization of these functions depends on the core features of Bluetooth technology, such as broadcasting, scanning, connecting, and data transmission.

[0071] In practical applications, we can use these features of Bluetooth technology to build a Bluetooth positioning system. For example, in places such as shopping malls and museums, we can deploy Bluetooth base stations and tags to achieve indoor positioning functions through the communication between Bluetooth base stations and tags. This positioning method is not only low-cost but also easy to deploy and maintain.

[0072] In the access of Wi-Fi positioning algorithms:

[0073] Wi-Fi positioning technology is a positioning technology based on wireless local area network (WLAN). It uses the communication information between wireless access points (APs) and wireless terminals (such as smartphones, tablets, etc.) to achieve positioning functions.

[0074] Wi-Fi positioning algorithms usually include fingerprint positioning algorithms and triangulation positioning algorithms, etc. The fingerprint positioning algorithm constructs a fingerprint database by collecting Wi-Fi signal characteristics (such as signal strength, signal quality, etc.) at different positions, and then uses a matching algorithm to achieve the positioning function. The triangulation positioning algorithm calculates the position of the wireless terminal by measuring the distance or angle information between the wireless terminal and multiple APs and using geometric relationships.

[0075] In the process of implementing the access of the Wi-Fi positioning algorithm, it is necessary to complete the reception and parsing of Wi-Fi signals and extract useful positioning information. Then, using this positioning information and the corresponding algorithm model, the position information of the device is calculated.

[0076] In practical applications, Wi-Fi positioning technology can be applied to indoor positioning services in large public places such as shopping malls, airports, and hospitals. By deploying Wi-Fi APs and the corresponding positioning algorithm model, accurate indoor navigation and location services can be provided for users.

[0077] In the positioning algorithm integration technology, there are the following steps:

[0078] S1. Construction of the positioning algorithm repository and algorithm integration;

[0079] In the first step of building the positioning algorithm integration framework, a comprehensive positioning algorithm repository is established. This repository is the core area for storing and managing various positioning algorithms. It contains various positioning algorithms, such as adaptive filtering algorithms (especially Kalman filtering), fingerprint positioning algorithms, triangulation positioning algorithms, TDOA (Time Difference of Arrival) positioning algorithms, etc.

[0080] (1) Integration of the adaptive filtering algorithm (such as Kalman filtering):

[0081] First of all, we need to integrate the adaptive filtering algorithm, especially the Kalman filtering algorithm, into the algorithm repository. The Kalman filtering algorithm plays an irreplaceable role in high-precision positioning systems with its excellent multi-source information fusion ability and real-time dynamic adjustment characteristics.

[0082] Integration steps:

[0083] 1. Collect the implementation code and documentation of the Kalman filtering algorithm to ensure its integrity and accuracy.

[0084] 2. Perform necessary encapsulation and interface definition on the algorithm code to make it conform to the unified management specification of the algorithm repository.

[0085] 3. Upload the encapsulated algorithm package to the algorithm repository and conduct test verification to ensure that it can run correctly and output results.

[0086] Key management points:

[0087] 1. Conduct version management for the Kalman filter algorithm, and record the change content and release time of each version.

[0088] 2. Provide functions such as viewing, modifying, and deleting algorithm details to facilitate users to select appropriate algorithm versions according to actual needs.

[0089] 3. Regularly evaluate and optimize the Kalman filter algorithm to improve its positioning accuracy and robustness.

[0090] (2) Integration of fingerprint positioning algorithm

[0091] The fingerprint positioning algorithm realizes precise positioning by constructing a signal fingerprint database and matching the signal strength data collected by the mobile device in real time with the fingerprints in the database.

[0092] Integration steps:

[0093] 1. Collect and organize the signal strength data in the target area to construct a fingerprint database.

[0094] 2. Develop the implementation code of the fingerprint positioning algorithm, including functions such as fingerprint collection, real-time positioning, and fingerprint matching.

[0095] 3. Package the algorithm and define the interface, upload it to the algorithm repository, and conduct test verification.

[0096] Key management points:

[0097] 1. Regularly update and maintain the fingerprint database to adapt to environmental changes.

[0098] 2. Provide the parameter configuration function of the fingerprint positioning algorithm to facilitate users to adjust algorithm parameters according to actual needs.

[0099] 3. Monitor the running status of the fingerprint positioning algorithm, and promptly detect and handle abnormal situations.

[0100] (3) Integration of triangulation positioning algorithm and TDOA positioning algorithm

[0101] The triangulation positioning algorithm and the TDOA positioning algorithm are two common algorithms in indoor positioning. They calculate the target position by measuring parameters such as the arrival time, time difference of arrival, or signal strength of wireless signals and using geometric relationships.

[0102] Integration steps:

[0103] 1. Collect the implementation code and documentation of the triangulation positioning algorithm and the TDOA positioning algorithm.

[0104] 2. Package the algorithm and define the interface, then upload it to the algorithm repository.

[0105] 3. Configure the deployment parameters of the base station and tags to ensure the correct operation of the algorithm.

[0106] 4. Conduct test verification to evaluate the positioning accuracy and stability of the algorithm.

[0107] Management key points:

[0108] 1. Regularly maintain and calibrate the base station and tags to ensure the accuracy of measurement data.

[0109] 2. Provide the algorithm parameter configuration function to facilitate users to adjust algorithm parameters according to actual needs.

[0110] 3. Monitor the running status of the algorithm and promptly detect and handle abnormal situations.

[0111] (4) Integration of Feature - based SLAM Algorithm and RSSI Positioning Algorithm

[0112] Feature - based SLAM algorithm and RSSI positioning algorithm are also commonly used positioning algorithms. The SLAM algorithm can achieve real - time positioning and map construction in an unknown environment, while the RSSI positioning algorithm estimates the distance between the transmitter and the receiver by measuring the signal strength at the wireless signal receiving end.

[0113] Integration steps:

[0114] 1. Collect the implementation code and documentation of the SLAM algorithm and RSSI positioning algorithm.

[0115] 2. Package the algorithm and define the interface, then upload it to the algorithm repository.

[0116] 3. Configure hardware devices such as sensors and base stations to ensure the correct operation of the algorithm.

[0117] 4. Conduct test verification to evaluate the positioning accuracy and real - time performance of the algorithm.

[0118] Management key points:

[0119] 1. Regularly maintain and calibrate the sensors and base stations to ensure the accuracy of measurement data.

[0120] 2. Provide the algorithm parameter configuration function to facilitate users to adjust algorithm parameters according to actual needs.

[0121] 3. Monitor the running status of the algorithm and promptly detect and handle abnormal situations.

[0122] S2. Management and configuration of positioning algorithms;

[0123] In the positioning algorithm integration framework, the management and configuration of algorithms are crucial steps. Through effective management and configuration, we can ensure that the algorithms can run correctly and meet the actual application requirements.

[0124] Specifically, it includes:

[0125] S2-1. Algorithm classification and version management;

[0126] In algorithm management, we need to classify and manage different versions of the same algorithm. These versions may be divided based on different dimensions such as platforms (e.g., X86 architecture, embedded architecture, etc.), analysis objects (e.g., video algorithms, image algorithms, etc.), or bit widths (32-bit, 64-bit).

[0127] Through classification management, we can easily search for and select the required algorithm versions, and perform operations such as modification, deletion, or taking off the shelf.

[0128] S2-2. Algorithm attribute description and computing power model;

[0129] For each algorithm package, we need to provide detailed attribute description information, including computing power definition, platform, algorithm classification, bit width, manufacturer, version number, update log, etc. This information will help users better understand the performance and application scenarios of the algorithms.

[0130] At the same time, we also need to use the computing power model to uniformly manage algorithm resources and platform resources. Only algorithms that meet the requirements can run properly on the specified devices. This ensures the compatibility and stability of the algorithms.

[0131] S2-3. Algorithm scheduling and task management;

[0132] In terms of algorithm scheduling, an intelligent task scheduling service needs to be designed to manage computing resources (such as physical servers, containers, virtual machines, etc.) and intelligent analysis tasks. Through flexible scheduling strategies, tasks can be dispatched to the most suitable intelligent analysis devices to achieve maximum performance and optimal resource utilization.

[0133] In addition, we also need to provide a task management function to track and monitor the execution status of tasks. This includes all aspects such as task creation, assignment, execution, and result feedback.

[0134] S3. Positioning algorithm scheduling and orchestration

[0135] In the last step of the positioning algorithm integration framework, the algorithm scheduling and orchestration function needs to be implemented. This can help us break through the functional limitations of a single algorithm and combine multiple single-functional algorithms as needed to form new composite-functional algorithms.

[0136] Specifically, it includes:

[0137] S3-1, Algorithm Scheduling Service;

[0138] The algorithm scheduling service is a bridge connecting the algorithm repository and the intelligent basic service. It is responsible for scheduling appropriate algorithms according to user requirements and resource conditions to complete tasks. This includes various links such as algorithm loading, unloading, and parameter configuration.

[0139] During the scheduling process, factors such as algorithm real-time performance, accuracy, and resource consumption need to be considered. Through a reasonable scheduling strategy, it can be ensured that the algorithm can complete tasks in the shortest time and in the optimal way.

[0140] S3-2, Algorithm Orchestration Technology;

[0141] The algorithm orchestration technology allows us to combine multiple single-function algorithms into new composite-function algorithms. This can be achieved through an algorithm orchestration tool, which provides a visual interface and a rich algorithm component library to support users in algorithm combination and configuration.

[0142] When performing algorithm orchestration, appropriate algorithm components need to be selected according to actual application requirements, and their dependencies and parameters need to be set. This can ensure that the combined algorithm can run correctly and meet the requirements.

[0143] S3-3, Support for Intelligent Basic Services;

[0144] The intelligent basic service is the basic framework for intelligent analysis. It is responsible for loading and executing specified algorithm packages and algorithm services. During the algorithm scheduling and orchestration process, the intelligent basic service will provide necessary support to ensure that the algorithm can run correctly and output results.

[0145] To improve the scalability and flexibility of the system, we design the intelligent basic service as a pluggable component form. In this way, users can select and configure different intelligent basic service components according to actual needs to meet the requirements of different application scenarios.

[0146] Based on the above method, an integrable multi-source positioning device in this embodiment includes: at least one memory and at least one processor;

[0147] The at least one memory is used to store machine-readable programs;

[0148] The at least one processor is used to call the machine-readable program and execute an integrable multi-source positioning method.

[0149] The above specific embodiments are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above specific embodiments. Any technical solution that conforms to the above specific embodiments of the present invention and any appropriate changes or substitutions made by any person of ordinary skill in the relevant technical field shall fall within the patent protection scope of the present invention.

[0150] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for integrating multi-source positioning, characterized in that: Based on multi-source positioning access technology and positioning algorithm integration technology, the multi-source positioning access technology includes Beidou positioning data access, UWB positioning data access, Bluetooth positioning data access and Wi-Fi positioning algorithm access; The Beidou positioning data access communicates with the Beidou satellite navigation system to obtain the precise location information of the device; In the UWB positioning data access, the UWB positioning base station is responsible for communicating with the UWB tag and transmitting the tag data back to the background; The Bluetooth positioning data access acquires the location information of the device by communicating with nearby Bluetooth devices; The Wi-Fi positioning algorithm access needs to complete the reception and analysis of Wi-Fi signals and extract useful positioning information; The positioning algorithm integration technology includes: S1. Construction of positioning algorithm warehouse and algorithm integration; S2, management and configuration of positioning algorithms; S2. Positioning algorithm scheduling and orchestration.

2. The integrated multi-source positioning method according to claim 1, characterized in that: In Beidou positioning data access, Beidou device positioning has the following steps: First, single-point positioning is performed using the observation data of the station to obtain the approximate coordinates of the station; then, parameter estimation is performed to obtain the floating-point solution of the baseline vector and ambiguity parameters; then, residual analysis is performed based on the obtained baseline vector and ambiguity parameters. If new gross errors are found, parameter estimation is performed again until there are no gross errors. Afterwards, the ambiguity floating-point solution and variance-covariance matrix are extracted to fix the ambiguity; if the ambiguity is fixed successfully, the ambiguity fixed solution is introduced, the parameters are re-estimated, the baseline vector fixed solution is obtained, and the solution is output; if the ambiguity is not fixed successfully, the floating-point solution is output.

3. The integrated multi-source positioning method according to claim 2, characterized in that: In the process of processing the Beidou equipment positioning data, the observation data also needs to be preprocessed to detect cycle slips and gross errors, and generate double-difference pseudorange and phase observation data. Then, these observation data are used to estimate parameters using the least squares filtering method, and the observation data residuals are analyzed to eliminate gross errors, and finally the latest estimation results of the baseline vector and ambiguity parameters are obtained.

4. The integrated multi-source positioning method according to claim 3, characterized in that: In the UWB positioning data access, the UWB tag is worn on a person or an object and achieves precise positioning by communicating with a base station.

5. The integrated multi-source positioning method according to claim 4, characterized in that: In the Bluetooth positioning data access, Bluetooth base stations and tags are deployed, and the indoor positioning function is realized through communication between the Bluetooth base stations and the tags.

6. The integrated multi-source positioning method according to claim 5, characterized in that: In the Wi-Fi positioning algorithm access, the Wi-Fi positioning algorithm includes a fingerprint positioning algorithm and a triangulation positioning algorithm. The positioning algorithm collects Wi-Fi signal features at different locations, builds a fingerprint database, and then uses a matching algorithm to achieve positioning; The triangulation positioning algorithm measures the distance or angle information between the wireless terminal and multiple APs and calculates the position of the wireless terminal using geometric relationships.

7. The integrated multi-source positioning method according to claim 6, characterized in that: In step S1, the positioning algorithm warehouse stores and manages various positioning algorithms, including adaptive filtering algorithm, fingerprint positioning algorithm, triangulation positioning algorithm and TDOA positioning algorithm; The adaptive filtering algorithm collects the implementation code and documents of the Kalman filtering algorithm, performs necessary encapsulation and interface definition on the algorithm code, uploads the encapsulated algorithm package to the algorithm warehouse, and performs testing and verification to ensure that it can run correctly and output results; The fingerprint positioning algorithm collects and organizes the signal strength data in the target area, builds a fingerprint database, develops the implementation code of the fingerprint positioning algorithm, encapsulates the algorithm and defines the interface, uploads it to the algorithm warehouse, and performs testing and verification; In the triangulation positioning algorithm and TDOA positioning algorithm, the implementation code and documents of the triangulation positioning algorithm and TDOA positioning algorithm are collected, the algorithm is packaged and the interface is defined, uploaded to the algorithm warehouse, the deployment parameters of the base station and the tag are configured to ensure that the algorithm can run correctly, and test verification is performed to evaluate the positioning accuracy and stability of the algorithm.

8. The integrated multi-source positioning method according to claim 7, characterized in that: In step S2, it includes: S2-1, Algorithm classification and version management; Categorize and manage different versions of the same algorithm, select the appropriate algorithm version, and modify, delete or remove it; S2-2, Algorithm attribute description and computing power model; For each algorithm package, detailed attribute description information is provided, and the computing power model is used to uniformly manage algorithm resources and platform resources. Only algorithms that meet the requirements can run normally on the specified device. S2-3, Algorithm scheduling and task management; Design intelligent task scheduling services to manage computing resources and intelligent analysis tasks, and assign tasks to the most appropriate intelligent analysis devices.

9. The integrated multi-source positioning method according to claim 8, characterized in that: In step S3, it includes: S3-1, algorithm scheduling service; The algorithm scheduling service connects the algorithm warehouse and intelligent basic services, and is responsible for scheduling appropriate algorithms to complete tasks based on user needs and resource conditions; S3-2, algorithm arrangement technology; Algorithm orchestration technology combines multiple single-function algorithms into new composite-function algorithms, and provides a visual interface and a rich algorithm component library to support users in algorithm combination and configuration; S3-3, support for intelligent basic services; The intelligent basic service is responsible for loading and executing the specified algorithm package and algorithm service, and the intelligent basic service is designed as a pluggable component.

10. An integrated multi-source positioning device, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to call the machine-readable program to execute the method according to any one of claims 1 to 9.