CN-121998518-A - Beidou positioning-based operation quality assessment method, device, equipment and storage medium

CN121998518ACN 121998518 ACN121998518 ACN 121998518ACN-121998518-A

Abstract

The invention discloses a Beidou positioning-based operation quality assessment method, a Beidou positioning-based operation quality assessment device, beidou navigation-based operation quality assessment equipment and a storage medium, and relates to the technical field of Beidou navigation and Internet of things data fusion, wherein the Beidou positioning-based operation quality assessment method comprises the steps of performing time sequence alignment processing on Beidou positioning terminal track data, facility state data of an Internet of things sensor node and mobile terminal image data to obtain aligned multi-mode data; the method comprises the steps of obtaining multi-modal data, extracting space-time distance values of each track point in the track point position sequence data of the positioning terminal and each facility coordinate in the facility geographic coordinate set data after alignment, determining associated facility data corresponding to each track point according to the space-time distance values to construct a space-time constraint model of the track and the facility point according to the associated facility data, quantitatively evaluating the quality of a mobile supervision object based on the space-time constraint model to obtain a space-time association evaluation result, and improving space-time matching precision and associated analysis efficiency of the Beidou positioning terminal and the Internet of things facility in a complex urban environment.

Inventors

DONG GUANGMING
XIONG FANG
ZHANG RUNYUN
HU REN

Assignees

深圳聚瑞云控科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260409

Claims (10)

1. The working quality assessment method based on Beidou positioning is characterized by comprising the following steps: carrying out time sequence alignment processing on the Beidou positioning terminal track data, the facility state data of the sensor nodes of the Internet of things and the image data of the mobile terminal to obtain aligned multi-mode data, wherein the aligned multi-mode data comprises positioning terminal track point position sequence data and facility geographic coordinate set data; Extracting space-time distance values of each track point in the track point position sequence data of the positioning terminal and each facility coordinate in the facility geographic coordinate set data from the aligned multi-mode data; Determining associated facility data corresponding to each track point according to the space-time distance value, and constructing a space-time constraint model of the track and the facility point according to the associated facility data; And quantitatively evaluating the quality of the mobile supervision object based on the space-time constraint model to obtain a space-time relevance evaluation result.
2. The method of claim 1, wherein the step of performing time sequence alignment processing on the track data of the Beidou positioning terminal, the facility state data of the sensor node of the internet of things and the image data of the mobile terminal to obtain aligned multi-mode data comprises the following steps: extracting first positioning time stamp information in the Beidou positioning terminal track data; extracting state change timestamp information in the facility state data; Extracting shooting time stamp information in the image data; unifying the first positioning timestamp information, the state change timestamp information and the shooting timestamp information to a standard time reference to obtain unified reference time data; Carrying out data frame alignment on the Beidou positioning terminal track data, the facility state data and the image data according to the unified reference time data to obtain time alignment data; And performing data cleaning treatment on the time alignment data to remove repeated data and abnormal data, thereby obtaining aligned multi-mode data.
3. The method of claim 1, wherein the step of extracting, from the aligned multimodal data, a spatio-temporal distance value of each track point in the positioning terminal track point location sequence data and each facility coordinate in the facility geographic coordinate set data comprises: extracting positioning terminal track point position sequence data and facility geographic coordinate set data from the aligned multi-mode data; Extracting positioning coordinate information and second positioning timestamp information of a current track point from the positioning terminal track point position sequence data; Extracting facility coordinate information and facility state timestamp information of current facility coordinates from the facility geographic coordinate set data; calculating a spatial distance value between the positioning coordinate information and the facility coordinate information; Calculating a time difference between the second location timestamp information and the facility status timestamp information; and carrying out weighted fusion calculation according to the space distance value and the time difference value to obtain a space-time distance value.
4. The method of claim 1, wherein the step of determining associated facility data corresponding to each track point based on the spatio-temporal distance values to construct a spatio-temporal constraint model of track and facility points based on the associated facility data comprises: Screening out target space-time distance values from all space-time distance values based on each track point; determining facility coordinate data corresponding to the target space-time distance value as associated facility data of the track point; judging whether the target space-time distance value is larger than a preset distance threshold value or not; When the target space-time distance value is larger than a preset distance threshold value, marking the track point as an abnormal track point; and constructing a space-time constraint model according to the relation set data of all the track points and the corresponding associated facility data and the abnormal track point marking data.
5. The method of claim 1, wherein the step of quantitatively evaluating the quality of the mobile supervision object based on the space-time constraint model to obtain a space-time correlation evaluation result comprises: Extracting patrol coverage rate data and operation duration data of the mobile supervision object from the space-time constraint model; calculating to obtain a basic association index according to the inspection coverage rate data; Calculating to obtain a process matching degree index according to the operation duration data; calculating according to the change frequency of the facility state data to obtain a result timeliness index; and carrying out weighted summation on the basic relevance index, the process matching degree index and the result timeliness index to obtain a space-time relevance evaluation result.
6. The method of claim 5, wherein the method further comprises: acquiring emergency event triggering information; When the type of the emergency event triggering information is a preset public health event grade, acquiring preset weight adjustment rule data; According to the preset weight adjustment rule data, the weight parameter of the timeliness index of the result is increased by a preset proportion value, and the adjusted weight parameter is obtained; and returning the step of carrying out weighted summation on the basic relevance index, the process matching index and the result timeliness index again according to the adjusted weight parameters to obtain an adjusted space-time relevance evaluation result.
7. The method of claim 5, wherein the method further comprises: acquiring historical scoring sequence data of a space-time relevance evaluation result; judging whether the scoring values of the continuous preset cycle numbers in the historical scoring sequence data are lower than a preset threshold value or not; triggering a dynamic operation parameter adjustment mode and generating an adjustment instruction when the scoring values of the continuous preset cycles in the historical scoring sequence data are all lower than a preset threshold value; And based on the adjustment instruction response data acquisition enhancement mode, increasing the track reporting frequency parameter of the Beidou positioning terminal by a preset multiple, and increasing the state sampling sensitivity parameter of the sensor node of the Internet of things by a preset level.
8. Work quality evaluation device based on big dipper location, its characterized in that, the device includes: The alignment module is used for carrying out time sequence alignment processing on the Beidou positioning terminal track data, the facility state data of the sensor nodes of the Internet of things and the image data of the mobile terminal to obtain aligned multi-mode data, wherein the aligned multi-mode data comprises positioning terminal track point position sequence data and facility geographic coordinate set data; The calculation module is used for calculating the space-time distance value between each track point in the track point position sequence data of the positioning terminal and each facility coordinate in the facility geographic coordinate set data; The construction module is used for determining associated facility data corresponding to each track point according to the space-time distance value and constructing a space-time constraint model of the track and the facility point; And the evaluation module is used for quantitatively evaluating the quality of the mobile supervision object based on the space-time constraint model to obtain a space-time relevance evaluation result.
9. A Beidou positioning based job quality assessment device, characterized in that the device comprises a memory, a processor and a Beidou positioning based job quality assessment program stored on the memory and executable on the processor, wherein the Beidou positioning based job quality assessment program is configured to implement the steps of the Beidou positioning based job quality assessment method according to any one of claims 1 to 7.
10. A storage medium, wherein a Beidou positioning based job quality assessment program is stored on the storage medium, and the Beidou positioning based job quality assessment program realizes the steps of the Beidou positioning based job quality assessment method according to any one of claims 1 to 7 when being executed by a processor.

Description

Beidou positioning-based operation quality assessment method, device, equipment and storage medium Technical Field The invention relates to the technical field of Beidou navigation and Internet of things data fusion, in particular to a Beidou positioning-based operation quality assessment method, a Beidou positioning-based operation quality assessment device, beidou positioning-based operation quality assessment equipment and a Beidou positioning storage medium. Background With the deep advancement of smart city construction, the urban public health management field gradually introduces Beidou navigation and Internet of things technologies. In the mobile supervision scenes such as disease medium biological prevention, environmental sanitation inspection, municipal facility maintenance and the like, supervision objects need to carry Beidou positioning terminals to periodically cover mass distributed sensor nodes of the Internet of things, and meanwhile, field image data are collected through a mobile terminal and returned to a management platform. The application requires that the moving track and the static facilities are bound in a high-precision space-time manner, and a quantifiable and traceable supervision closed loop is formed, so that the method has become a key technical requirement for improving the urban governance capability. However, in the prior art, when multi-source data of a Beidou positioning terminal, an Internet of things sensor node and a mobile terminal are processed, a simple superposition mode is generally adopted after independent processing, and a unified time reference and a space coordinate frame are lacked, so that a matching error of a track point and a facility coordinate is larger. Particularly, in a complex urban environment, beidou signals are easy to be blocked to generate positioning drift, the state reporting delay of sensor nodes is unstable, and space-time labels of image data are not standard, so that a precise space-time constraint relation is difficult to construct by a traditional method. In addition, most of the existing data fusion algorithms are of general design, are not optimized for strong space-time coupling characteristics between mobile supervision objects and densely distributed static nodes, have obvious deviation between correlation evaluation results and actual conditions, and cannot support fine management decisions. The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art. Disclosure of Invention The invention mainly aims to provide a Beidou positioning-based operation quality assessment method, device, equipment and storage medium, and aims to solve the technical problem of how to accurately align space-time dimensions and dynamically correlate and model multi-source heterogeneous data generated by a Beidou positioning terminal, an Internet of things sensor node and a mobile terminal. In order to achieve the above purpose, the invention provides a working quality assessment method based on Beidou positioning, which comprises the following steps: carrying out time sequence alignment processing on the Beidou positioning terminal track data, the facility state data of the sensor nodes of the Internet of things and the image data of the mobile terminal to obtain aligned multi-mode data, wherein the aligned multi-mode data comprises positioning terminal track point position sequence data and facility geographic coordinate set data; Extracting space-time distance values of each track point in the track point position sequence data of the positioning terminal and each facility coordinate in the facility geographic coordinate set data from the aligned multi-mode data; Determining associated facility data corresponding to each track point according to the space-time distance value, and constructing a space-time constraint model of the track and the facility point according to the associated facility data; And quantitatively evaluating the quality of the mobile supervision object based on the space-time constraint model to obtain a space-time relevance evaluation result. In an embodiment, the step of performing time sequence alignment processing on the track data of the Beidou positioning terminal, the facility state data of the sensor node of the internet of things and the image data of the mobile terminal to obtain aligned multi-mode data includes: extracting first positioning time stamp information in the Beidou positioning terminal track data; extracting state change timestamp information in the facility state data; Extracting shooting time stamp information in the image data; unifying the first positioning timestamp information, the state change timestamp information and the shooting timestamp information to a standard time reference to obtain unified reference time data; Carrying out data frame alignment o