Pipe network facility data error correction method and system based on augmented reality

Abstract

The invention provides a pipe network facility data error correction method and system based on augmented reality, and relates to the field of urban infrastructure informatization, and the method comprises the steps: constructing a pipe network three-dimensional model based on pipe network GIS data; obtaining a pipe network live-action image through a camera, loading a pipe network three-dimensional model to AR equipment for superposition display based on a feature point recognition algorithm and a model display calibration algorithm, and generating an augmented reality scene; identifying each live-action pipe network facility in the pipe network live-action image, and performing comparative analysis on data of the live-action pipe network facilities in the augmented reality scene and data of the corresponding pipe network three-dimensional model to obtain an equipment error identification result; and updating the pipe network GIS data by using an equipment error identification result. According to the invention, the error condition of the pipe network GIS data is automatically obtained through the data comparison of the live-action pipe network facility and the virtual pipe network facility, and the pipe network GIS data is updated in real time through the comparison result, so that the error correction efficiency and convenience of the pipe network facility data are improved, and the accuracy and reliability of the pipe network facility data are improved.

Description

Technical Field

[0001] The present invention relates to the field of urban infrastructure informatization, and in particular to a method and system for error correction of pipe network facility data based on augmented reality. Background Art

[0002] With the acceleration of urbanization, urban infrastructure, especially underground pipeline facilities, has been rapidly constructed, and its scale and coverage have been continuously increasing, forming a huge pipeline facility GIS data. As the core of the city's lifeline, the safe and efficient operation of urban underground pipelines such as gas, water supply, and drainage is an important basis for ensuring urban development. The accuracy and completeness of underground pipeline facility data is an important means to improve the efficiency of pipeline network management and ensure pipeline network safety.

[0003] Data errors not only reduce the quality of pipeline network data, affect the pipeline network data management capabilities and operational efficiency, but also easily cause pipeline damage and safety accidents when constructing around pipelines based on erroneous data. Pipeline network data correction involves identifying and correcting erroneous, missing or inconsistent information in pipeline network facilities, thereby improving data accuracy. The conventional method of pipeline network facility data correction is to conduct regular manual inspections and maintenance of pipeline network facilities. Due to the high coverage and complexity of the pipeline network, this method is not only inefficient, but also prone to false detection and missed detection. In the existing pipeline network inspection technology, the positioning method mainly relies on GPS positioning technology, which usually cannot achieve high-precision positioning, and the error range can reach more than ten meters, resulting in poor pipeline network data accuracy. In addition, since underground pipelines are invisible and many pipeline network facilities are located in areas such as flower beds, semi-enclosed fences, and the middle of roads, it is usually necessary to compare field data and drawings to judge the accuracy of facility data. It is difficult to verify directly through GNSS equipment, which limits operational efficiency and increases the risk of errors. Although there are currently means of using mobile electronic devices for auxiliary inspections, it is still impossible to intuitively view the current status of the underground pipeline network, and manual comparison of data accuracy is still required, which has limitations. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a pipeline facility data error correction method and system based on AR real scene, which is used to solve the problems of low efficiency, easy omission and lack of visualization of existing pipeline data error correction methods.

[0005] The present invention provides a method for correcting pipe network facility data based on augmented reality, comprising the steps of:

[0006] S1: Construct a 3D model of the pipe network based on the pipe network GIS data and generate a 3D model file of the pipe network;

[0007] S2: Obtain the real-life image of the pipeline network through the camera, load the 3D model file of the pipeline network, and load the 3D model of the pipeline network into the AR device for overlay display based on the feature point recognition algorithm and the model display calibration algorithm to generate an augmented reality scene;

[0008] S3: Identify each real-life pipeline network facility in the real-life pipeline network image, compare and analyze the data of the real-life pipeline network facilities in the augmented reality scene with the data of the corresponding pipeline network three-dimensional model, and obtain a device error recognition result;

[0009] S4: Use the equipment error identification results to update the pipeline network GIS data.

[0010] Preferably, step S1 specifically comprises:

[0011] S11: Obtain the location, type, color and specifications of each pipeline network facility through the pipeline network GIS data;

[0012] S12: Construct a three-dimensional model of the pipeline network according to the location, type, color and specifications of the pipeline network facilities;

[0013] S13: Convert the three-dimensional pipe network model into a Glb format for storage, and obtain a three-dimensional pipe network model file.

[0014] Preferably, step S2 specifically comprises:

[0015] S21: Initialize the AR device and the GNSS device, enable the AR device to communicate with the GNSS device, and start the camera in the AR device to obtain a real-life image of the pipe network;

[0016] S22: Load the three-dimensional model file of the pipe network, and obtain all feature points in the three-dimensional model through a feature point recognition algorithm;

[0017] S23: iteratively updating each feature point through a model display calibration algorithm to generate a final three-dimensional pipe network model;

[0018] S24: Adaptively superimpose the pipeline network three-dimensional model and the pipeline network real-scene image to generate an augmented reality scene and display it on a display of the AR device.

[0019] Preferably, step S3 specifically comprises:

[0020] S31: Obtain the bottom pixel coordinates (P1x, P1y) of the real-life pipe network facility i and the bottom pixel coordinates (P2x, P2y) of the corresponding pipe network three-dimensional model I; if Abs(P1x-P2x)>Tx or Abs(P1y-P2y)>Ty, then determine that the position of the pipe network three-dimensional model I is wrong, otherwise determine that the position of the pipe network three-dimensional model I is correct; wherein, Abs() represents taking the absolute value, Tx and Ty represent the abscissa threshold and the ordinate threshold, respectively;

[0021] S32: Obtain the type F1 of the real-life pipe network facility i and the type F2 of the corresponding pipe network 3D model I; if the values ​​of F1 and F2 are not equal, it is determined that the type of the pipe network 3D model I is incorrect; otherwise, it is determined that the type of the pipe network 3D model I is correct;

[0022] S33: Obtain the returned color value C1 of the real-life pipe network facility i and the returned color value C2 of the corresponding pipe network 3D model I; if the values ​​of C1 and C2 are not equal, it is determined that the color of the pipe network 3D model I is wrong; otherwise, it is determined that the color of the pipe network 3D model I is correct;

[0023] S34: Obtain the display range A1 of the real-scene pipeline network facility i, and convert A1 from screen coordinates to space coordinates to obtain the coordinate range S1 of the real-scene pipeline network facility i; subtract the maximum value from the minimum value of the height of S1 to obtain the height difference H1 of the real-scene pipeline network facility i; subtract the maximum value from the minimum value of the width of S1 to obtain the width difference W1 of the real-scene pipeline network facility i;

[0024] S35: Obtain the display range A2 of the pipe network three-dimensional model I, convert A2 from screen coordinates to space coordinates, and obtain the coordinate range S2 of the pipe network three-dimensional model I; subtract the maximum value and the minimum value of the height of S2 to obtain the height difference H2 of the pipe network three-dimensional model I; subtract the maximum value and the minimum value of the width of S2 to obtain the width difference W2 of the pipe network three-dimensional model I;

[0025] S36: If Abs(H1-H2)>TH or Abs(W1-W2)>TW, it is determined that the specification of the pipe network three-dimensional model I is wrong, otherwise it is determined that the specification of the pipe network three-dimensional model I is correct, where TH and TW represent the height difference threshold and the width difference threshold respectively;

[0026] S37: Repeat steps S31-S36 to compare the positions, types, colors and specifications of all real-life pipe network facilities with the corresponding three-dimensional pipe network models to obtain equipment error recognition results.

[0027] Preferred:

[0028] The device error identification results include: location identification results and attribute identification results;

[0029] The position recognition result is the position determination result of all pipe network three-dimensional models;

[0030] The attribute recognition results include the type judgment results, color judgment results and specification judgment results of all pipe network three-dimensional models.

[0031] Preferably, step S4 is specifically:

[0032] S41: obtaining the position judgment result, type judgment result, color judgment result and specification judgment result of each pipe network three-dimensional model through the device error recognition result;

[0033] S42: The positions, types, colors and specifications of the three-dimensional models of each pipe network that are judged to be erroneous are modified to the positions, types, colors and specifications of the corresponding real-life pipe network facilities, so as to update the pipe network GIS data.

[0034] A pipe network facility data error correction system based on augmented reality, used to implement the pipe network facility data error correction method based on augmented reality, comprising:

[0035] AR equipment, GNSS equipment, GIS database, model generation module, AR display module, comparison analysis module and data update module;

[0036] The AR device is used to obtain the real-life image of the pipe network and to overlay and display the real-life image of the pipe network and the three-dimensional model of the pipe network;

[0037] GNSS devices are used to obtain real-time positioning data of AR devices;

[0038] The GIS database is used to store the original pipe network GIS data and the pipe network GIS data after error correction and update;

[0039] The model generation module is used to construct a three-dimensional model of the pipeline network based on the pipeline network GIS data and generate a three-dimensional model file of the pipeline network;

[0040] The AR display module is used to load the three-dimensional model of the pipeline network into the AR device for superimposed display to generate an augmented reality scene;

[0041] The comparison and analysis module is used to compare and analyze the data of the real-life pipe network facilities in the augmented reality scene with the data of the corresponding three-dimensional model of the pipe network to obtain the equipment error recognition result;

[0042] The data update module is used to update the pipeline network GIS data using the equipment error identification results.

[0043] A pipeline network facility data error correction device based on augmented reality includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement the pipeline network facility data error correction method based on augmented reality.

[0044] The present invention has the following beneficial effects:

[0045] The inspection method based on AR equipment will overlay the real-life image obtained by the camera with the three-dimensional model of the pipeline network in the AR equipment, and inspect the pipeline network facilities in a visual way, thereby improving the efficiency of pipeline network facility inspections and avoiding false inspections and missed inspections. By comparing the data of real-life pipeline network facilities with virtual pipeline network facilities, the error conditions of the pipeline network GIS data are automatically obtained, and the pipeline network GIS data is updated in real time based on the comparison results, improving the efficiency and convenience of error correction of pipeline network facility data, and improving the accuracy and reliability of pipeline network facility data. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a method according to an embodiment of the present invention;

[0047] Figure 2 It is an augmented reality scene graph;

[0048] Figure 3 A schematic diagram of the principle of obtaining device error recognition results;

[0049] Figure 4 This is a structural diagram of the device according to an embodiment of the present invention;

[0050] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0052] Reference Figure 1 The present invention provides a method for correcting pipe network facility data based on augmented reality, comprising the steps of:

[0053] S1: Construct a 3D model of the pipe network based on the pipe network GIS data and generate a 3D model file of the pipe network;

[0054] Furthermore, step S1 is specifically as follows:

[0055] S11: Obtain the location, type, color and specifications of each pipeline network facility through the pipeline network GIS data;

[0056] S12: Construct a three-dimensional model of the pipeline network according to the location, type, color and specifications of the pipeline network facilities;

[0057] S13: Convert the three-dimensional pipe network model into a Glb format for storage, and obtain a three-dimensional pipe network model file.

[0058] Specifically, after starting up, the AR device downloads the model file through the wifi / 4G / 5G network, or directly copies the model file to the storage space of the AR device through a USB cable.

[0059] The storage format of the 3D model file is Glb (G-buffer Layered Binary), which is a 3D model format widely used in virtual reality (VR) and augmented reality (AR) applications. The storage location is: the storage space (disk) of the AR device.

[0060] S2: Obtain the real-life image of the pipeline network through the camera, load the 3D model file of the pipeline network, and load the 3D model of the pipeline network into the AR device for overlay display based on the feature point recognition algorithm and the model display calibration algorithm to generate an augmented reality scene;

[0061] Furthermore, step S2 is specifically as follows:

[0062] S21: Initialize the AR device and the GNSS device, enable the AR device to communicate with the GNSS device, and start the camera in the AR device to obtain a real-life image of the pipe network;

[0063] S22: Load the three-dimensional model file of the pipe network, and obtain all feature points in the three-dimensional model through a feature point recognition algorithm;

[0064] Specifically, the feature point recognition algorithm is: ORB (Oriented FAST and Rotated BRIEF) algorithm, which is provided by the open cv library (a cross-platform computer vision and machine learning software library released based on the Apache2.0 license (open source) that implements multiple general algorithms related to image processing and computer vision).

[0065] S23: iteratively updating each feature point through a model display calibration algorithm to generate a final three-dimensional pipe network model;

[0066] Specifically, the model display calibration algorithm is: based on the recognition of feature points in the picture and the auxiliary calculation and updating of feature points by GNSS high-precision equipment, an algorithm for continuous correction of the display position and posture of the AR model of the picture is performed.

[0067] S24: Adaptively superimpose the pipeline network three-dimensional model and the pipeline network real-scene image to generate an augmented reality scene and display it on a display of the AR device.

[0068] Specifically, augmented reality scenarios such as Figure 2 As shown, the three-dimensional model of the pipeline network is displayed in the real-life image of the pipeline network.

[0069] S3: Identify each real-life pipeline network facility in the real-life pipeline network image, compare and analyze the data of the real-life pipeline network facilities in the augmented reality scene with the data of the corresponding pipeline network three-dimensional model, and obtain a device error recognition result;

[0070] Furthermore, the device in the screen is identified and compared and analyzed to obtain the device error identification result; the screen is captured in a loop and the device in the screen is identified through the AI ​​service, and the identified device type attributes and the device coordinates on the screen are compared and analyzed with the AR model type and the coordinates of the model displayed on the screen. If there is a difference in the comparison results, it is considered that the device error is identified.

[0071] Step S3 is specifically as follows:

[0072] S31: Obtain the bottom pixel coordinates (P 1x ,P 1y ) and the corresponding bottom pixel coordinates of the pipe network 3D model I (P 2x ,P 2y ); if Abs(P 1x -P 2x )>T x or Abs(P 1y -P 2y )>T y , then the position of the three-dimensional model I of the pipe network is judged to be wrong, otherwise the position of the three-dimensional model I of the pipe network is judged to be correct; wherein Abs() represents taking the absolute value, T x and T y Represent the abscissa threshold and ordinate threshold respectively;

[0073] Preferably, T x The value is 40, T y The value is 30;

[0074] S32: Obtain the type F1 of the real-life pipe network facility i and the type F2 of the corresponding pipe network 3D model I; if the values ​​of F1 and F2 are not equal, it is determined that the type of the pipe network 3D model I is incorrect; otherwise, it is determined that the type of the pipe network 3D model I is correct;

[0075] S33: Obtain the returned color value C1 of the real-life pipe network facility i and the returned color value C2 of the corresponding pipe network 3D model I; if the values ​​of C1 and C2 are not equal, it is determined that the color of the pipe network 3D model I is wrong; otherwise, it is determined that the color of the pipe network 3D model I is correct;

[0076] S34: Obtain the display range A1 of the real-scene pipeline network facility i, and convert A1 from screen coordinates to space coordinates to obtain the coordinate range S1 of the real-scene pipeline network facility i; subtract the maximum value from the minimum value of the height of S1 to obtain the height difference H1 of the real-scene pipeline network facility i; subtract the maximum value from the minimum value of the width of S1 to obtain the width difference W1 of the real-scene pipeline network facility i;

[0077] S35: Obtain the display range A2 of the pipe network three-dimensional model I, convert A2 from screen coordinates to space coordinates, and obtain the coordinate range S2 of the pipe network three-dimensional model I; subtract the maximum value and the minimum value of the height of S2 to obtain the height difference H2 of the pipe network three-dimensional model I; subtract the maximum value and the minimum value of the width of S2 to obtain the width difference W2 of the pipe network three-dimensional model I;

[0078] S36: If Abs(H1-H2)>T H or Abs(W1-W2)>T W , then the specification of the three-dimensional pipe network model I is judged to be wrong, otherwise the specification of the three-dimensional pipe network model I is judged to be correct, where T H and T W Represent the height difference threshold and width difference threshold respectively;

[0079] Preferably, T H The value is 40, T W The value is 40;

[0080] S37: Repeat steps S31-S36 to compare the positions, types, colors and specifications of all real-life pipe network facilities with the corresponding three-dimensional pipe network models to obtain equipment error recognition results.

[0081] Specifically, the schematic diagram of the principle of obtaining the device error recognition result is as follows: Figure 3 As shown, Figure 3 The comparison of the position error, the type error + specification error, and the color error + specification error are shown in Figure 2.

[0082] Further, the device error identification result includes: a location identification result and an attribute identification result;

[0083] The position recognition result is the position determination result of all pipe network three-dimensional models;

[0084] The attribute recognition results include the type judgment results, color judgment results and specification judgment results of all pipe network three-dimensional models.

[0085] S4: Use the equipment error identification results to update the pipeline network GIS data.

[0086] Furthermore, step S4 is specifically as follows:

[0087] S41: obtaining the position judgment result, type judgment result, color judgment result and specification judgment result of each pipe network three-dimensional model through the device error recognition result;

[0088] S42: The positions, types, colors and specifications of the three-dimensional models of each pipe network that are judged to be erroneous are modified to the positions, types, colors and specifications of the corresponding real-life pipe network facilities, so as to update the pipe network GIS data.

[0089] Specifically, if a device location error or device attribute error is identified, and the error information of the device has not been reported, the device setting error or device attribute error form will be displayed on the screen and the error details will be automatically filled in. Then, it will be reported manually or automatically to the GIS server, and marked as the current device has been reported. The automatically filled error details are as follows:

[0090] If the device position is wrong, the device position error will be automatically filled in the device error type item in the form, and the screenshot item will be automatically filled in with the screenshot used for AI recognition. t , fill in the device location details item.

[0091] If the device type is incorrect, the device attribute error will be automatically filled in the device error type item in the form, and the screenshot used for AI recognition will be automatically attached to the screenshot item. The identified device type F1, color C1, identified device display range A1, and spatial range S1 calculated by A1 will be filled in the device type error details item.

[0092] Conduct data review on the reported erroneous data. Once the review is passed, update it to the GIS system to complete the error correction of the erroneous data.

[0093] Methods for handling erroneous data: Use the GIS editing system to review the erroneous data reported in the system, view screenshots, error attributes, attributes of other surrounding equipment, etc., or conduct data review through manual offline review; when the data review is passed, the pipe network data is revised through the editing function, and the GIS data is updated in real time after the revision is completed; if the data review fails, the task is terminated and no data processing is required.

[0094] The present invention provides a pipe network facility data error correction system based on augmented reality, which is used to implement the pipe network facility data error correction method based on augmented reality, comprising:

[0095] AR equipment, GNSS equipment, GIS database, model generation module, AR display module, comparison analysis module and data update module;

[0096] The AR device is used to obtain the real-life image of the pipe network and to overlay and display the real-life image of the pipe network and the three-dimensional model of the pipe network;

[0097] GNSS devices are used to obtain real-time positioning data of AR devices;

[0098] The GIS database is used to store the original pipe network GIS data and the pipe network GIS data after error correction and update;

[0099] The model generation module is used to construct a three-dimensional model of the pipeline network based on the pipeline network GIS data and generate a three-dimensional model file of the pipeline network;

[0100] The AR display module is used to load the three-dimensional model of the pipeline network into the AR device for superimposed display to generate an augmented reality scene;

[0101] The comparison and analysis module is used to compare and analyze the data of the real-life pipe network facilities in the augmented reality scene with the data of the corresponding three-dimensional model of the pipe network to obtain the equipment error recognition result;

[0102] The data update module is used to update the pipeline network GIS data using the equipment error identification results.

[0103] See also Figure 4 , Figure 4 It is a schematic diagram of the working of the hardware equipment of an embodiment of the present invention, and the hardware equipment specifically includes: a pipeline facility data error correction device 401 based on AR real scene, a processor 402 and a storage medium 403.

[0104] A pipeline network facility data error correction device 401 based on AR real scene: The pipeline network facility data error correction device 401 based on AR real scene implements the pipeline network facility data error correction method based on AR real scene.

[0105] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the pipeline facility data error correction method based on AR real scene.

[0106] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the pipeline facility data error correction method based on AR real scene.

[0107] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0108] The serial numbers of the embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order and these words may be interpreted as identifiers.

[0109] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A pipeline facility data error correction method based on augmented reality, characterized in that: Includes steps: S1: Construct a 3D model of the pipe network based on the pipe network GIS data and generate a 3D model file of the pipe network; S2: Obtain the real-life image of the pipeline network through the camera, load the 3D model file of the pipeline network, and load the 3D model of the pipeline network into the AR device for overlay display based on the feature point recognition algorithm and the model display calibration algorithm to generate an augmented reality scene; S3: Identify each real-life pipeline network facility in the real-life pipeline network image, compare and analyze the data of the real-life pipeline network facilities in the augmented reality scene with the data of the corresponding pipeline network three-dimensional model, and obtain a device error recognition result; S4: Use the equipment error identification results to update the pipeline network GIS data.

2. The method for correcting pipe network facility data based on augmented reality according to claim 1, characterized in that: Step S1 is specifically as follows: S11: Obtain the location, type, color and specifications of each pipeline network facility through the pipeline network GIS data; S12: Construct a three-dimensional model of the pipeline network according to the location, type, color and specifications of the pipeline network facilities; S13: Convert the three-dimensional pipe network model into a Glb format for storage, and obtain a three-dimensional pipe network model file.

3. The method for correcting pipe network facility data based on augmented reality according to claim 1, characterized in that: Step S2 is specifically as follows: S21: Initialize the AR device and the GNSS device, enable the AR device to communicate with the GNSS device, and start the camera in the AR device to obtain a real-life image of the pipe network; S22: Load the three-dimensional model file of the pipe network, and obtain all feature points in the three-dimensional model through a feature point recognition algorithm; S23: iteratively updating each feature point through a model display calibration algorithm to generate a final three-dimensional pipe network model; S24: Adaptively superimpose the pipeline network three-dimensional model and the pipeline network real-scene image to generate an augmented reality scene and display it on a display of the AR device.

4. The method for correcting pipe network facility data based on augmented reality according to claim 1, characterized in that: Step S3 is specifically as follows: S31: Obtain the bottom pixel coordinates (P 1x ,P 1y ) and the corresponding bottom pixel coordinates of the pipe network 3D model I (P 2x ,P 2y ); if Abs(P 1x -P 2x )>T x or Abs(P 1y -P 2y )>T y , then the position of the three-dimensional model I of the pipe network is judged to be wrong, otherwise the position of the three-dimensional model I of the pipe network is judged to be correct; wherein Abs() represents taking the absolute value, T x and T y Represent the abscissa threshold and ordinate threshold respectively; S32: Obtain the type F1 of the real-life pipe network facility i and the type F2 of the corresponding pipe network 3D model I; if the values ​​of F1 and F2 are not equal, it is determined that the type of the pipe network 3D model I is incorrect; otherwise, it is determined that the type of the pipe network 3D model I is correct; S33: Obtain the returned color value C1 of the real-life pipe network facility i and the returned color value C2 of the corresponding pipe network 3D model I; if the values ​​of C1 and C2 are not equal, it is determined that the color of the pipe network 3D model I is wrong; otherwise, it is determined that the color of the pipe network 3D model I is correct; S34: Obtain the display range A1 of the real-scene pipeline network facility i, and convert A1 from screen coordinates to space coordinates to obtain the coordinate range S1 of the real-scene pipeline network facility i; subtract the maximum value from the minimum value of the height of S1 to obtain the height difference H1 of the real-scene pipeline network facility i; subtract the maximum value from the minimum value of the width of S1 to obtain the width difference W1 of the real-scene pipeline network facility i; S35: Obtain the display range A2 of the pipe network three-dimensional model I, convert A2 from screen coordinates to space coordinates, and obtain the coordinate range S2 of the pipe network three-dimensional model I; subtract the maximum value and the minimum value of the height of S2 to obtain the height difference H2 of the pipe network three-dimensional model I; subtract the maximum value and the minimum value of the width of S2 to obtain the width difference W2 of the pipe network three-dimensional model I; S36: If Abs(H1-H2)>T H or Abs(W1-W2)>T W , then the specification of the three-dimensional pipe network model I is judged to be wrong, otherwise the specification of the three-dimensional pipe network model I is judged to be correct, where T H and T W Represent the height difference threshold and width difference threshold respectively; S37: Repeat steps S31-S36 to compare the positions, types, colors and specifications of all real-life pipe network facilities with the corresponding three-dimensional pipe network models to obtain equipment error recognition results.

5. The method for correcting pipe network facility data based on augmented reality according to claim 4, characterized in that: The device error identification results include: location identification results and attribute identification results; The position recognition result is the position determination result of all pipe network three-dimensional models; The attribute recognition results include the type judgment results, color judgment results and specification judgment results of all pipe network three-dimensional models.

6. The method for correcting pipe network facility data based on augmented reality according to claim 1, characterized in that: Step S4 is specifically as follows: S41: obtaining the position judgment result, type judgment result, color judgment result and specification judgment result of each pipe network three-dimensional model through the device error recognition result; S42: The positions, types, colors and specifications of the three-dimensional models of each pipe network that are judged to be erroneous are modified to the positions, types, colors and specifications of the corresponding real-life pipe network facilities, so as to update the pipe network GIS data.

7. A pipe network facility data error correction system based on augmented reality, used to implement the pipe network facility data error correction method based on augmented reality according to any one of claims 1 to 6, characterized in that: include: AR equipment, GNSS equipment, GIS database, model generation module, AR display module, comparison analysis module and data update module; The AR device is used to obtain the real-life image of the pipe network and to overlay and display the real-life image of the pipe network and the three-dimensional model of the pipe network; GNSS devices are used to obtain real-time positioning data of AR devices; The GIS database is used to store the original pipe network GIS data and the pipe network GIS data after error correction and update; The model generation module is used to construct a three-dimensional model of the pipeline network based on the pipeline network GIS data and generate a three-dimensional model file of the pipeline network; The AR display module is used to load the three-dimensional model of the pipeline network into the AR device for superimposed display to generate an augmented reality scene; The comparison and analysis module is used to compare and analyze the data of the real-life pipe network facilities in the augmented reality scene with the data of the corresponding three-dimensional model of the pipe network to obtain the equipment error recognition result; The data update module is used to update the pipeline network GIS data using the equipment error identification results.

8. A pipeline facility data error correction device based on augmented reality, characterized in that: include: Processor and storage medium; the processor loads and executes instructions and data in the storage medium to implement the augmented reality-based pipeline facility data error correction method described in any one of claims 1 to 6.

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