RTK positioning engine for combined multi-point fusion space positioning

Abstract

The invention discloses an RTK positioning engine for combined multi-point fusion spatial positioning, and relates to the technical field of spatial positioning, and the positioning engine comprises a base construction module which activates a camera network to carry out multi-point image collection on a target scene, and obtains a global image to construct a global base. The positioning data acquisition module acquires GNSS positioning information of a target unit in real time based on GNSS equipment, and transmits the GNSS positioning information to the fusion manager embedded in the positioning engine. The real-time image acquisition module acquires real-time image information of a target unit through a camera network and stores the real-time image information as a real-time image set. And the target positioning and coordinate conversion module performs image positioning of the target unit according to the real-time image set to obtain image positioning information. And the positioning fusion module activates a fusion manager, carries out spatial fusion on the image positioning information and the GNSS positioning information, and obtains spatial positioning information. Therefore, the technical effects of providing a fusion positioning path and improving positioning stability are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of spatial positioning, and particularly relates to a combined multi-point fusion spatial positioning RTK positioning engine. Background Art

[0002] Real-Time Kinematic (RTK) technology is a high-precision positioning method in the Global Navigation Satellite System (GNSS). By using the carrier phase difference between a GNSS receiver and a reference station, the position of a user can be determined in real time within the centimeter-level accuracy range.

[0003] Existing RTK positioning technologies usually rely on a single reference station to provide differential correction information, and their positioning accuracy is limited by the distribution of reference stations and environmental interference factors. The greater the distance between the reference station and the mobile terminal, and the more complex the environmental conditions, the more obvious the decline in positioning accuracy. There are technical problems such as a single positioning path and vulnerability to interference in positioning. Summary of the Invention

[0004] The purpose of this application is to provide a combined multi-point fusion spatial positioning RTK positioning engine, aiming to solve the technical problems of a single positioning path and vulnerability to interference in existing technologies.

[0005] In view of the above technical problems, this application provides a combined multi-point fusion spatial positioning RTK positioning engine.

[0006] In a first aspect, this application provides a combined multi-point fusion spatial positioning RTK positioning engine, wherein the positioning engine includes:

[0007] A base construction module, which is used to activate a camera network to perform multi-point image acquisition on a target scene, obtain a global image, and construct a global base based on the global image;

[0008] A positioning data acquisition module, which is used to obtain the GNSS positioning information of a target unit in real time based on a GNSS device and transmit it to a fusion manager through a wireless transmission device, where the fusion manager is embedded in the positioning engine;

[0009] A real-time image acquisition module, which is used to obtain the real-time image information of a target unit through the camera network and store it as a real-time image set;

[0010] A target positioning and coordinate conversion module, which is used to perform image positioning of a target unit according to the real-time image set and obtain image positioning information;

[0011] A positioning and fusion module, which is used to activate a fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information to obtain spatial positioning information.

[0012] In a feasible implementation manner, based on the global image, a global base is constructed, and the execution steps include:

[0013] An interactive camera network is used to obtain a set of intrinsic parameter information of the camera, where the set of intrinsic parameter information includes internal parameter information and external parameter information; based on the external parameter information, image correction and rough registration are performed; feature points in the image are extracted, and fine registration is performed based on a feature point matching algorithm; according to the fine registration result, combined with the internal parameter information, coordinate transformation of the global image is performed, and the global image is mapped to the base space; an image fusion technology is used to eliminate the stitching gap to obtain the global base.

[0014] In a feasible implementation manner, according to the real-time image set, image positioning of the target unit is performed to obtain image positioning information, and the execution steps include:

[0015] Based on an object detection algorithm, object detection is performed on the real-time image set to obtain an image of the target unit; the position reference points of the target unit are identified and extracted; according to the mapping relationship between the position reference points and the camera, the internal parameter information and the external parameter information are called, and based on the internal parameter information and the external parameter information, the position reference points are projected onto the global base to obtain the image positioning information.

[0016] In a feasible implementation manner, according to the real-time image set, image positioning of the target unit is performed to obtain image positioning information, and the execution steps further include:

[0017] Based on the real-time image set, standard feature points are obtained; the position difference data between the position reference points and the standard feature points is calculated and obtained; according to the position difference data, the position reference points are mapped to the global base, and calibration positioning information is extracted; the calibration positioning information is compared with the image positioning information to perform feedback correction of the image positioning.

[0018] In a feasible implementation manner, when activating the fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information to obtain spatial positioning information, the execution steps further include:

[0019] Based on the acquisition parameters of the image positioning information and the GNSS positioning information, fusion weights are configured; real-time environment information is obtained, and correction coefficients of the image positioning information and the GNSS positioning information are configured based on the real-time environment information; based on the fusion weights and the correction coefficients, spatial fusion is performed to generate the spatial positioning information.

[0020] In a feasible implementation manner, the steps performed by the positioning engine further include:

[0021] Continuously tracking the target unit based on the GNSS device to obtain a set of trajectory points, where the set of trajectory points is a serialized set of coordinate points; continuously tracking the target unit based on the camera network to obtain image trajectory information; fitting the set of trajectory points and the image trajectory information to generate a target unit trajectory, where the target unit trajectory is associated with and stores unit feature information; and visually displaying the unit trajectory based on the unit feature information.

[0022] In a feasible implementation manner, the steps performed by the positioning engine further include:

[0023] An interactive client to obtain statistical window constraints; based on the statistical window constraints, call historical positioning records; parse the historical positioning records, and perform heat accumulation calculation based on the parsing results to obtain a positioning heat map.

[0024] In a feasible implementation manner, the steps performed by the positioning engine further include:

[0025] Based on the spatial positioning information, combine with an electronic fence for control and discrimination; if the control and discrimination result indicates a security risk, generate an alarm instruction for risk warning.

[0026] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0027] By constructing a base module, which is responsible for activating the camera network to perform multi-point image acquisition on the target scene to obtain global images, and constructing a global base based on these global images. A positioning data acquisition module, which obtains the GNSS positioning information of the target unit in real time through the GNSS device and transmits this information to the fusion manager through a wireless transmission device. The fusion manager is embedded in the positioning engine. A real-time image acquisition module, which obtains the image information of the target unit in real time through the camera network and stores this information as a set of real-time images. A target positioning and coordinate conversion module, which performs image positioning of the target unit based on the set of real-time images to obtain image positioning information. A positioning fusion module, which activates the fusion manager to perform spatial fusion on the image positioning information and the GNSS positioning information to obtain spatial positioning information. Thus, the technical effect of providing a fusion positioning path and improving positioning stability is achieved.

[0028] The above description is only an overview of the technical solution of this application. In order to more clearly clarify the technical means of this application, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0029] The embodiments of the present invention and the following brief description are described in conjunction with the drawings. The brief description of the drawings is as follows:

[0030] Figure 1 It is a schematic structural diagram of a combined multi-point fusion spatial positioning RTK positioning engine for this application;

[0031] Figure 2 It is a schematic flowchart of obtaining image positioning information in a combined multi-point fusion spatial positioning RTK positioning engine for this application;

[0032] Description of reference numerals: Base construction module 11, positioning data acquisition module 12, real-time image acquisition module 13, target positioning and coordinate conversion module 14, positioning fusion module 15. Detailed Description of the Invention

[0033] This application provides a combined multi-point fusion spatial positioning RTK positioning engine, which solves the technical problems faced by the prior art, such as a single positioning path and easy interference in positioning.

[0034] The overall idea adopted by the solution in the embodiments of this technology to solve the above problems is as follows:

[0035] First, the base construction module activates the camera network to perform multi-point image acquisition on the target scene, obtains the global image, and constructs a global base based on the global image.

[0036] Next, the positioning data acquisition module, based on the GNSS device, continuously obtains the GNSS positioning information of the target unit and transmits it to the fusion manager through a wireless transmission device, where the fusion manager is embedded in the positioning engine.

[0037] Then, the real-time image acquisition module obtains the real-time image information of the target unit through the camera network and stores it as a real-time image set.

[0038] Subsequently, the target positioning and coordinate conversion module performs image positioning on the target unit according to the real-time image set to obtain image positioning information.

[0039] Finally, the positioning fusion module activates the fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information to obtain spatial positioning information.

[0040] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments. It should be noted that the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0041] Embodiment 1

[0042] As Figure 1 shown, the present application provides a combined multi-point fusion spatial positioning RTK positioning engine, and the positioning engine includes:

[0043] A base construction module 11, configured to activate a camera network to perform multi-point image acquisition on a target scene, obtain a global image, and construct a global base based on the global image;

[0044] Optionally, the base construction module is communicatively connected to the camera network of the target scene. The camera network includes cameras arranged at multiple points and in multiple directions of the target scene, and is used to obtain the global image of the target scene. After activation, the camera network starts to perform multi-point image acquisition synchronously. Specifically, each camera in the camera network captures images of the target scene from different angles and positions based on preset acquisition parameters, generates a set of multi-point image data, and stores it as the global image. Among them, the multi-point image data has a high acquisition accuracy to ensure the accuracy and quality of the constructed global base.

[0045] Optionally, a target scene coordinate system is constructed, the multi-point image data is mapped to the target scene coordinate system, and is fused and processed to construct the global base. The global base is a reference layer of the digital visualization of the target scene, providing a basis for accurately reflecting the positions of various structures, buildings, and points, etc. in the target scene in the future. Among them, the fusion and processing process includes image stitching, correction, and optimization to ensure the clarity and accuracy of the global image.

[0046] The above steps ensure that every corner of the target scene is covered through the multi-point image acquisition of the camera network to obtain a complete global image. Through image processing technology, the multi-point image data can be fused into the global base with high precision, providing accurate basic data for subsequent operations, and helping to improve the accuracy and reliability of positioning fusion.

[0047] The positioning data acquisition module 12 is used to obtain the GNSS positioning information of the target entity in real time based on the GNSS device, and transmit it to the fusion manager through the wireless transmission device, where the fusion manager is embedded in the positioning engine;

[0048] Optionally, the GNSS device is a wirelessly connected global navigation satellite device, and the target entity carries the GNSS device to perform operations and activities in the target scenario. Among them, the GNSS device has the RTK (Real-Time Kinematic) function to improve the positioning accuracy.

[0049] Specifically, the GNSS device obtains the position information of the target entity in real time, and through the RTK technology, performs differential correction to improve the positioning accuracy to centimeter level. Then, the wireless transmission device is started, and the data transmission channel is ensured to be unobstructed, and the real-time obtained GNSS positioning information is sent to the fusion manager through the wireless transmission device.

[0050] Optionally, the fusion manager is embedded in the positioning engine and is used to effectively fuse and manage the received positioning data, providing reliable data support for subsequent operations.

[0051] The real-time image acquisition module 13 is used to obtain the real-time image information of the target entity through the camera network and store it as a real-time image set;

[0052] Among them, the real-time image acquisition module 13 obtains the real-time image information of the target entity through the camera network and stores this image information as a real-time image set. This module is the front end of the entire image processing and positioning engine, responsible for providing high-quality image data for subsequent processing.

[0053] Optionally, the real-time image acquisition module 13 obtains the real-time image information of the target entity through the camera network. The frequency and resolution of image acquisition by the camera are set according to application requirements. Exemplarily, for a target object that is a vehicle, the camera may need to acquire images at a high frame rate (such as 30 frames per second) to ensure that the rapidly changing vehicle position images can be captured.

[0054] Optionally, before storing the images, necessary preprocessing is performed on the real-time image information. Exemplarily, it includes denoising, distortion correction, brightness and contrast adjustment, etc., to improve the image quality. The preprocessed images are stored as a real-time image set. The real-time image set is a set containing multiple image frames, and the multiple image frames correspond to the target object images at multiple angles, and are used for subsequent target detection, feature point extraction and positioning, etc. Among them, the real-time image information can be stored in the memory cache or hard disk storage of the fusion manager, specifically depending on the design requirements of the target scenario (such as data security requirements or responsiveness requirements) and resource limitations (the performance and size of the memory cache or hard disk).

[0055] The target positioning and coordinate conversion module 14 is used to perform image positioning of the target unit according to the real-time image set and obtain image positioning information.

[0056] Optionally, the main function of the target positioning and coordinate conversion module 14 is to process the real-time image set to identify and locate the position of the target unit and convert it into image positioning information. Specifically, performing image positioning of the target unit includes steps such as detecting and identifying the target object, selecting positioning feature points, coordinate conversion and mapping of the pixels of the positioning feature points, and involves target detection algorithms (such as YOLO, SSD, Faster R-CNN, etc.), feature extraction and matching methods, coordinate conversion matrices, and pixel projection methods. Image positioning of the target object through real-time images from multiple angles helps to improve the accuracy of image positioning.

[0057] The positioning fusion module 15 is used to activate the fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information and obtain spatial positioning information.

[0058] Optionally, the positioning fusion module 15 fuses the image positioning information and the GNSS positioning information based on a preset fusion strategy to obtain more accurate spatial positioning information and improve the anti-interference ability of the positioning at the same time. Among them, the fusion manager is responsible for specifically coordinating and processing the positioning data from these two sources, including data synchronization, error analysis, and data fusion.

[0059] Optionally, based on the acquisition time stamps of the real-time image set and the GNSS positioning information, the image positioning information and the GNSS positioning information are time-synchronized to ensure spatial fusion at the same time node. Exemplarily, if the sampling frequencies of the real-time image set and the GNSS positioning information are different, the linear interpolation method can be used to perform time alignment on the data.

[0060] Furthermore, based on the global image, a global base is constructed. The execution steps of the base construction module 11 include:

[0061] An interactive camera network is used to obtain the set of intrinsic parameter information of the camera, where the set of intrinsic parameter information includes internal parameter information and external parameter information;

[0062] Based on the external parameter information, image correction and rough registration are performed;

[0063] Feature points in the image are extracted, and fine registration is performed based on the feature point matching algorithm;

[0064] According to the fine registration result, the global image is coordinate-converted in combination with the internal parameter information, and the global image is mapped to the base space;

[0065] Use image fusion technology to eliminate stitching gaps and obtain a global base.

[0066] Optionally, first, communicate with the camera network through a network protocol to obtain the intrinsic parameter information of multiple cameras. The intrinsic parameter information includes the internal parameter information and external parameter information of the cameras. Among them, the internal parameter information is represented as the internal parameter matrix of the camera, including focal length, optical center position, distortion coefficient, etc. The external parameter information is represented as the external parameter matrix of the camera, including the position and orientation of the camera in space.

[0067] Optionally, use the external parameter information of the camera to perform geometric correction and rough registration on the collected images. First, read the external parameter information of the camera, including the position information and orientation information. Then, based on the external parameter information, correct the direction of the image to eliminate the image direction deviation caused by the position and orientation of the camera, and perform rough registration to preliminarily align the spatial positions of multiple images.

[0068] Optionally, extract feature points from the coarsely registered images and use the feature point matching algorithm for fine registration. Among them, the feature points refer to the fixed feature positions in the image. Exemplarily, they include markings, signs, placards, structures, etc. in the target scene. Specifically, use a feature point detection algorithm (such as SIFT, SURF, etc.) to extract the key feature points in the image. Based on the feature point matching algorithm (such as FLANN, BFMatcher, 4PCS, etc.), match the feature points in different images, and calculate the precise transformation matrix between the images through the feature point matching results for fine registration.

[0069] Optionally, according to the fine registration result and the internal parameter information of the camera, perform coordinate transformation on the global image and map the image to the base space. First, read the internal parameter information of the camera, including the focal length, optical center position, etc. Then, according to the fine registration result, determine the orientation and position of the camera coordinate system of the image, and perform the spatial coordinate transformation calculation of the image to map the global image through the pixel coordinate system, imaging coordinate system, and camera coordinate system to the base space. Among them, the base space has a unified global image coordinate system, and this global image coordinate system can be a geodetic coordinate system or a scene relative coordinate system of the target scene.

[0070] Furthermore, adopt image fusion technology to eliminate the gaps and inconsistencies generated during the stitching process and generate a seamless global base image. Specifically, adopt an image fusion algorithm (such as multi-band fusion, Pyramid fusion, etc.) to smooth the stitching area, eliminate the image stitching gaps, and then adjust the brightness, contrast, etc. of the image so that there are no obvious traces at the stitching area, thereby obtaining a seamless global base to provide a basis for subsequent processing.

[0071] Furthermore, as Figure 2As shown, according to the real-time image set, image positioning of the target unit is performed to obtain image positioning information. The execution steps of the target positioning and coordinate conversion module 14 include:

[0072] Based on the target detection algorithm, perform target detection on the real-time image set to obtain the target unit image;

[0073] Identify and extract the position reference points of the target unit;

[0074] According to the mapping relationship between the position reference point and the camera, call the internal parameter information and the external parameter information, and based on the internal parameter information and the external parameter information, project the position reference point to the global base to obtain the image positioning information.

[0075] Optionally, first, based on the target detection algorithm, obtain the target unit image in the real-time image set. Among them, the target detection algorithms include YOLO, Faster R-CNN, SSD, etc. Or the target unit image refers to the image in the anchor box output by the target detection algorithm. In other words, the target unit image contains the image pixels of the target object and its bounding box coordinates. Then, extract the position reference points from the target unit image. The position reference point is a feature point used to represent the position of the target object. Exemplarily, the position reference point selects the center point of the target object or other significant feature points (such as the face, feet, helmet, etc. of a person, the license plate, wheels, etc. of a vehicle).

[0076] Optionally, according to the mapping relationship between the position reference point and the camera, use the internal parameter information and the external parameter information to project the position reference point from the image coordinate system to the global coordinate system. Among them, the internal parameter information is used to convert the position reference point from the pixel coordinate system to the camera coordinate system, and the external parameter information is used to determine the position relationship between the camera coordinate system and the above global image coordinate system, so as to convert the position reference point from the camera coordinate system to the global base, thereby obtaining the coordinates of the position reference point in the global coordinate system, that is, the image positioning information.

[0077] Furthermore, according to the real-time image set, image positioning of the target unit is performed to obtain image positioning information. The execution steps of the target positioning and coordinate conversion module 14 further include:

[0078] Based on the real-time image set, obtain the standard feature points;

[0079] Calculate and obtain the position difference data between the position reference point and the standard feature point;

[0080] According to the position difference data, map the position reference point to the global base and extract the verification positioning information;

[0081] Compare the verification positioning information with the image positioning information to perform feedback correction of the image positioning.

[0082] Optionally, first, obtain standard feature points from the real-time image set. Standard feature points are usually predefined and consistent feature points in all images, such as certain fixed marker points or structural features (such as road markings, fixed buildings or structures in the target scene, signs, etc.). In other words, the selection of feature points is based on the saliency and repeatability of the target object.

[0083] Optionally, calculate the position differential data between the position reference point and the standard feature points. This position differential data represents the offset of the position reference point relative to the standard feature points. Specifically, the position differential data can be the offset in the pixel plane coordinate system, image coordinate system, or camera coordinate system.

[0084] Optionally, based on the position differential data, using the internal and external parameter information of the camera, map the position reference point to the global base. By mapping the position reference point to the global base, the first extraction verification positioning information is obtained. The verification positioning information is the global coordinates calculated based on the standard feature points and the position differential data.

[0085] Furthermore, compare the verification positioning information with the previously obtained image positioning information. If the deviation between the verification positioning information and the above-mentioned image positioning information is greater than the preset deviation control threshold, feedback correction of the image positioning is required to ensure the accuracy of positioning. Exemplarily, the feedback correction of image positioning includes: adjusting the position and angle of the camera, calibrating the internal and external parameter information of the camera, updating the image processing algorithm to improve the accuracy of feature point recognition and positioning, etc. Through the above steps, precise positioning of the target object is achieved, and the positioning accuracy is continuously improved through feedback correction, thereby ensuring the stability and reliability of the positioning engine.

[0086] Furthermore, activate the fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information to obtain spatial positioning information. The execution steps further include:

[0087] Configure fusion weights based on the acquisition parameters of the image positioning information and the GNSS positioning information;

[0088] Obtain real-time environment information and configure correction coefficients for the image positioning information and the GNSS positioning information based on the real-time environment information;

[0089] Based on the fusion weights and the correction coefficients, perform spatial fusion to generate the spatial positioning information.

[0090] Optionally, before performing spatial fusion, it is necessary to configure the fusion weight based on the acquisition parameters (such as acquisition accuracy and acquisition frequency) of the image positioning information and GNSS positioning information. The fusion weight determines the influence degree of different data sources in the final positioning result.

[0091] Specifically, to configure the fusion weight, first obtain the acquisition parameters of the image positioning information, such as acquisition accuracy (image resolution, accuracy of the detection algorithm, etc.) and acquisition frequency (image frame rate). Obtain the acquisition parameters of the GNSS positioning information, such as acquisition accuracy (accuracy of the GNSS signal) and acquisition frequency (GNSS data update rate). Then, based on the acquisition parameters and according to the preset acquisition parameter baseline, configure the fusion weight of the image positioning information and GNSS positioning information. Among them, the acquisition parameter baseline is the general level of the predefined acquisition parameters. If the acquisition parameter is lower than this acquisition baseline, the fusion weight of the corresponding positioning information is reduced accordingly. Specifically, the fusion weight of the image positioning information mainly depends on the acquisition frequency, while the fusion weight of the GNSS positioning information mainly depends on its acquisition accuracy.

[0092] By reasonably configuring and dynamically adjusting the fusion weight, the fusion effect of the image positioning information and GNSS positioning information can be effectively improved, so as to achieve more accurate spatial positioning.

[0093] Optionally, obtain real-time environmental information, including weather conditions (such as sunny, cloudy, rainy, etc.), terrain features (such as flat ground, mountains, building occlusion, etc.), environmental light intensity, etc. Then, according to the real-time environmental information, configure the correction coefficient of the image positioning information and GNSS positioning information. The correction coefficient is determined based on the influence of environmental indicators on the positioning information.

[0094] Exemplarily, the correction coefficient is configured based on the following principle: if the environmental light is good, the correction coefficient of the image positioning information is larger; if the environmental light is poor or the signal occlusion is serious, the correction coefficients of both the image positioning information and GNSS positioning information are smaller.

[0095] Furthermore, use the fusion manager to perform spatial fusion on the image positioning information and GNSS positioning information through a fusion algorithm based on the fusion weight and correction coefficient to obtain more accurate and stable spatial positioning information. Exemplarily, based on the fusion weight and correction coefficient, spatial fusion is performed to generate spatial positioning information, and the weighted average method is used. Through the above steps, combining the advantages of the image positioning information and GNSS positioning information, more accurate spatial positioning information is generated.

[0096] Furthermore, the steps executed by the positioning engine further include:

[0097] Continuously track the target entity using a GNSS device to obtain a set of trajectory points, where the set of trajectory points is a serialized set of coordinate points;

[0098] Continuously track the target entity using a camera network to obtain image trajectory information;

[0099] Fit the set of trajectory points and the image trajectory information to generate a target entity trajectory, where the target entity trajectory is associated with and stores entity feature information;

[0100] Based on the entity feature information, call the entity trajectory for visual display.

[0101] Optionally, first, use a GNSS device to continuously track the target entity to obtain a set of trajectory points. The set of trajectory points is a set of GNSS coordinate points of the target entity continuously collected over a period of time, representing the movement trajectory of the target entity. These trajectory points are stored in a serialized manner according to the time sequence. Then, continuously track the target entity through a camera network to obtain image trajectory information. The image trajectory information includes the image positions of the target entity at different time points.

[0102] Furthermore, fit the set of trajectory points obtained by the GNSS device and the image trajectory information obtained by the camera network. Exemplarily, algorithms such as the least squares method are used in the fitting process to ensure the continuity and accuracy of the trajectory. In addition, the generated target entity trajectory will be associated with and store entity feature information, such as vehicle / personnel information, name, job category, implementing entity, etc.

[0103] Optionally, based on the entity feature information, call the entity trajectory for visual display. The visual display is achieved through a global base, a GIS system, and a map service platform, which helps users intuitively understand the movement trajectory of the target entity and its feature information.

[0104] Furthermore, the steps executed by the positioning engine further include:

[0105] An interactive client obtains statistical window constraints;

[0106] Based on the statistical window constraints, call historical positioning records;

[0107] Parse the historical positioning records and perform heat accumulation calculation based on the parsing results to obtain a positioning heat map.

[0108] Optionally, the client is an interface between the user and the positioning engine, and the user can input query parameters and obtain results through the client. Specifically, the client can be a mobile application, a web application, or a desktop application.

[0109] Optionally, the user inputs statistical window constraints through the client. The statistical window constraints include time windows (such as querying historical location data for the past hour, day, or week), spatial regions (such as specific geographical regions or coordinate ranges), job windows (such as specific user groups, device types), etc. Then, based on the statistical window constraints input by the user, the positioning engine calls relevant historical location records from the database or storage unit. The historical location records include timestamps, spatial coordinates, trajectories, unit characteristic information, and other relevant information.

[0110] Furthermore, parse the historical location records, convert the raw data into a format convenient for processing and analysis, and perform heat accumulation calculation based on the parsed historical location records. Specifically, the heat accumulation calculation includes: grid processing, dividing the query area into multiple grids, each grid representing a small geographical area; heat counting, counting the number of location records in each grid, and the more the number, the higher the heat of the area; smoothing processing, smoothing the heat data to eliminate noise and outliers, making the heat map smoother and more continuous. Among them, the heat map is a visualization tool, using different colors to represent the heat of the area, and the brighter the color, the higher the heat. Through the heat map, it can help users intuitively understand the distribution of location hotspots in a specific area.

[0111] Specifically, the above execution steps include interacting with the client, obtaining statistical window constraints, calling historical location records, parsing historical location records, and performing heat accumulation calculation based on the parsing results, and finally generating a positioning heat map. This helps users more intuitively understand the distribution of location hotspots in a specific area, facilitating further decision-making analysis.

[0112] Furthermore, the execution steps of the positioning engine further include:

[0113] Based on the spatial positioning information, combine with an electronic fence for control and discrimination;

[0114] If the control and discrimination result shows a security risk, generate an alarm instruction for risk warning.

[0115] Optionally, based on the obtained spatial positioning information, combine with an electronic fence for control and discrimination, including judging whether the spatial positioning information violates the constraints of the electronic fence. If the control and discrimination result shows that the spatial positioning information violates the constraints of the electronic fence, it can be considered that there is a security risk at the current location of the target unit. Then, based on the security level and security response measures corresponding to the violated electronic fence, generate corresponding alarm instructions for warning.

[0116] Optionally, for risk warning, the warning paths include light warning, sound warning, SMS warning, etc.

[0117] In summary, the combined multi-point fusion spatial positioning RTK positioning engine provided by the present invention has the following technical effects:

[0118] By constructing a base module, which is responsible for activating the camera network to perform multi-point image acquisition on the target scene to obtain global images and constructing a global base based on these global images. A positioning data acquisition module, which obtains the GNSS positioning information of the target unit in real time through GNSS devices and transmits this information to the fusion manager through wireless transmission devices. The fusion manager is embedded in the positioning engine. A real-time image acquisition module, which obtains the image information of the target unit in real time through the camera network and stores this information as a real-time image set. A target positioning and coordinate conversion module, which performs image positioning of the target unit based on the real-time image set to obtain image positioning information. A positioning fusion module, which activates the fusion manager to perform spatial fusion on the image positioning information and the GNSS positioning information to obtain spatial positioning information. Furthermore, the technical effects of providing a fusion positioning path and improving positioning stability are achieved.

[0119] It should be understood that the embodiments and the above descriptions disclosed in this application enable those skilled in the art to implement this application using this application. At the same time, this application is not limited to the part of the embodiments mentioned above. Obvious modifications, combinations, and substitutions made to the embodiments mentioned in this application also fall within the protection scope of this application.

Claims

1. A combined multi-point fusion spatial positioning RTK positioning engine, characterized in that: include: A base construction module, the base construction module is used to activate the camera network to collect multi-point images of the target scene, obtain a global image, and construct a global base based on the global image; A positioning data acquisition module, which is used to obtain the GNSS positioning information of the target unit in real time based on the GNSS device, and transmit it to the fusion manager through a wireless transmission device, wherein the fusion manager is embedded in the positioning engine; A real-time image acquisition module, the real-time image acquisition module is used to obtain real-time image information of the target unit through the camera network and store it as a real-time image set; A target positioning and coordinate conversion module, wherein the target positioning and coordinate conversion module is used to perform image positioning of the target unit according to the real-time image set and obtain image positioning information; A positioning fusion module, wherein the positioning fusion module is used to activate a fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information to obtain spatial positioning information.

2. The engine according to claim 1, characterized in that Building a global base based on the global image, the execution steps include: Interacting with the camera network, obtaining an intrinsic parameter information set of the camera, wherein the intrinsic parameter information set includes internal reference information and external reference information; Correcting and roughly registering the image based on the external parameter information; Extract feature points from the image and perform precise registration based on feature point matching algorithm; According to the precise registration result, the global image is transformed in coordinates in combination with the internal reference information, and the global image is mapped to the base space; Image fusion technology is used to eliminate stitching gaps and obtain the global base.

3. The engine according to claim 2, characterized in that According to the real-time image set, image positioning of the target unit is performed to obtain image positioning information, and the execution steps include: Based on the target detection algorithm, target detection is performed on the real-time image set to obtain a target unit image; Identify and extract the location reference point of the target unit; According to the mapping relationship between the position reference point and the camera, the internal reference information and the external reference information are called, and based on the internal reference information and the external reference information, the position reference point is projected to the global base to obtain the image positioning information.

4. The engine according to claim 3, characterized in that According to the real-time image set, image positioning of the target unit is performed to obtain image positioning information, and the execution step further includes: Based on the real-time image set, obtaining standard feature points; Calculating and acquiring position difference data between the position reference point and the standard feature point; According to the position differential data, mapping the position reference point to the global base, extracting verification positioning information; The verification positioning information is compared with the image positioning information to perform feedback correction of image positioning.

5. The engine according to claim 4, characterized in that Activating the fusion manager to perform spatial fusion of the image positioning information and the GNSS positioning information to obtain spatial positioning information, the execution step further includes: Based on the acquisition parameters of the image positioning information and the GNSS positioning information, configuring a fusion weight; Acquire real-time environmental information, and configure correction coefficients of the image positioning information and the GNSS positioning information based on the real-time environmental information; Based on the fusion weight and the correction coefficient, spatial fusion is performed to generate the spatial positioning information.

6. The engine according to claim 1, characterized in that The positioning engine execution step also includes: Continuously tracking the target unit based on a GNSS device to obtain a trajectory point set, wherein the trajectory point set is a serialized coordinate point set; Continuously tracking the target unit based on a camera network to obtain image trajectory information; Fitting the trajectory point set and the image trajectory information to generate a target unit trajectory, wherein the target unit trajectory is associated with and stores unit feature information; The unit trajectory is called based on the unit characteristic information for visual display.

7. The engine according to claim 1, characterized in that The positioning engine execution step also includes: Interactive client, obtains statistical window constraints; Based on the statistical window constraint, calling the historical positioning record; The historical positioning records are analyzed, and heat accumulation calculation is performed based on the analysis results to obtain a positioning heat map.

8. The engine according to claim 1, characterized in that The positioning engine execution step also includes: Based on the spatial positioning information, control and identification are performed in combination with the electronic fence; If the control judgment result is that there is a security risk, an alarm instruction will be generated to issue a risk alarm.

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