Methods, devices, equipment, and storage media for job quality assessment based on BeiDou positioning

Abstract

This invention discloses a method, device, equipment, and storage medium for evaluating operational quality based on BeiDou positioning, relating to the field of BeiDou navigation and IoT data fusion technology. The method includes: performing time-series alignment processing on BeiDou positioning terminal trajectory data, IoT sensor node facility status data, and mobile terminal image data to obtain aligned multimodal data; extracting the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data; determining the associated facility data corresponding to each trajectory point based on the spatiotemporal distance values, and constructing a spatiotemporal constraint model between the trajectory and facility points based on the associated facility data; and quantitatively evaluating the quality of mobile monitored objects based on the spatiotemporal constraint model to obtain spatiotemporal correlation evaluation results, thereby improving the spatiotemporal matching accuracy and correlation analysis efficiency of BeiDou positioning terminals and IoT facilities in complex urban environments.

Description

Technical Field

[0001] This invention relates to the field of BeiDou navigation and Internet of Things data fusion technology, and in particular to a method, apparatus, equipment and storage medium for evaluating the quality of operations based on BeiDou positioning. Background Technology

[0002] With the deepening of smart city construction, BeiDou navigation and Internet of Things (IoT) technologies are gradually being introduced into the field of urban public health management. In mobile monitoring scenarios such as vector-borne disease control, sanitation inspection, and municipal facility maintenance, those being monitored need to carry BeiDou positioning terminals to periodically cover a massive number of distributed IoT sensor nodes, while simultaneously collecting on-site image data via mobile devices and transmitting it back to the management platform. Such applications require high-precision spatiotemporal binding of mobile trajectories with static facilities to form a quantifiable and traceable monitoring closed loop, which has become a key technological requirement for improving urban governance capabilities.

[0003] However, existing technologies, when processing multi-source data from BeiDou positioning terminals, IoT sensor nodes, and mobile devices, generally employ a method of independent processing followed by simple overlay, lacking a unified time reference and spatial coordinate framework. This results in significant matching errors between trajectory points and facility coordinates. Especially in complex urban environments, BeiDou signals are susceptible to obstruction, causing positioning drift; sensor node status reporting delays are unpredictable; and image data spatiotemporal labels are not standardized, making it difficult for traditional methods to construct accurate spatiotemporal constraints. Furthermore, existing data fusion algorithms are mostly general-purpose designs, failing to optimize for the strong spatiotemporal coupling between mobile monitored objects and densely distributed static nodes. This leads to significant discrepancies between correlation assessment results and actual conditions, hindering the support for refined management decisions.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, device, and storage medium for evaluating the quality of operations based on BeiDou positioning. The invention addresses the technical problem of accurately aligning and dynamically modeling multi-source heterogeneous data generated by BeiDou positioning terminals, IoT sensor nodes, and mobile devices in the spatiotemporal dimensions.

[0006] To achieve the above objectives, the present invention provides a method for evaluating the quality of operations based on BeiDou positioning, the method comprising the following steps:

[0007] The trajectory data of Beidou positioning terminals, the facility status data of IoT sensor nodes, and the image data of mobile terminals are time-series aligned to obtain aligned multimodal data, wherein the aligned multimodal data includes positioning terminal trajectory point sequence data and facility geographic coordinate set data.

[0008] Extract the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data;

[0009] Based on the spatiotemporal distance value, determine the associated facility data corresponding to each trajectory point, and construct a spatiotemporal constraint model of trajectory and facility location based on the associated facility data;

[0010] The quality of mobile monitored objects is quantitatively evaluated based on the spatiotemporal constraint model, and the spatiotemporal correlation evaluation results are obtained.

[0011] In one embodiment, the step of performing time-series alignment processing on the trajectory data of the BeiDou positioning terminal, the facility status data of the IoT sensor node, and the mobile terminal image data to obtain aligned multimodal data includes:

[0012] Extract the first positioning timestamp information from the trajectory data of the Beidou positioning terminal;

[0013] Extract the status change timestamp information from the facility status data;

[0014] Extract the shooting timestamp information from the image data;

[0015] The first positioning timestamp information, the state change timestamp information, and the shooting timestamp information are unified to a standard time base to obtain unified base time data;

[0016] Based on the unified reference time data, the trajectory data of the Beidou positioning terminal, the facility status data, and the image data are aligned to obtain time-aligned data.

[0017] The time-aligned data is cleaned to remove duplicate and abnormal data, resulting in aligned multimodal data.

[0018] In one embodiment, the step of extracting the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data includes:

[0019] Extract the positioning terminal trajectory point sequence data and facility geographic coordinate set data from the aligned multimodal data;

[0020] Extract the current trajectory point's positioning coordinate information and the second positioning timestamp information from the positioning terminal's trajectory point sequence data;

[0021] Extract the facility coordinate information and facility status timestamp information of the current facility coordinates from the facility geographic coordinate set data;

[0022] Calculate the spatial distance between the positioning coordinates and the facility coordinates;

[0023] Calculate the time difference between the second location timestamp information and the facility status timestamp information;

[0024] The spatiotemporal distance value is obtained by weighted fusion calculation based on the spatial distance value and the time difference value.

[0025] In one embodiment, the step of determining the associated facility data corresponding to each trajectory point based on the spatiotemporal distance value, and constructing a spatiotemporal constraint model of the trajectory and facility locations based on the associated facility data, includes:

[0026] Based on each trajectory point, the target spatiotemporal distance value is selected from all spatiotemporal distance values;

[0027] The facility coordinate data corresponding to the target spatiotemporal distance value is determined as the associated facility data of the trajectory point;

[0028] Determine whether the target spatiotemporal distance value is greater than a preset distance threshold;

[0029] When the target spatiotemporal distance value is greater than a preset distance threshold, the trajectory point is marked as an abnormal trajectory point;

[0030] A spatiotemporal constraint model is constructed based on the set of relationship data between all trajectory points and their corresponding associated facilities, as well as the data of abnormal trajectory point markings.

[0031] In one embodiment, the step of quantitatively evaluating the quality of mobile monitored objects based on the spatiotemporal constraint model to obtain spatiotemporal correlation evaluation results includes:

[0032] Extract the inspection coverage rate data and operation duration data of the mobile monitoring objects from the spatiotemporal constraint model;

[0033] The basic correlation index is calculated based on the inspection coverage data.

[0034] The process matching index is calculated based on the operation duration data;

[0035] The timeliness index of the results is calculated based on the frequency of change of the facility status data;

[0036] The spatiotemporal correlation evaluation result is obtained by weighted summation of the basic correlation index, the process matching index, and the result timeliness index.

[0037] In one embodiment, the method further includes:

[0038] Obtain emergency event trigger information;

[0039] When the type of the emergency event triggering information is a preset public health event level, preset weight adjustment rule data is obtained;

[0040] The weight parameter of the result timeliness index is increased by a preset ratio according to the preset weight adjustment rule data to obtain the adjusted weight parameter;

[0041] Based on the adjusted weight parameters, return to the step of weighting and summing the basic correlation index, the process matching index, and the result timeliness index to obtain the adjusted spatiotemporal correlation evaluation result.

[0042] In one embodiment, the method further includes:

[0043] Obtain historical score sequence data of the spatiotemporal correlation assessment results;

[0044] Determine whether the score values ​​for a consecutive preset number of periods in the historical scoring sequence data are all lower than a preset threshold;

[0045] When the score values ​​for a consecutive preset number of periods in the historical scoring sequence data are all lower than a preset threshold, the dynamic adjustment mode of the operation parameters is triggered and an adjustment instruction is generated;

[0046] Based on the adjusted command response data acquisition enhancement mode, the trajectory reporting frequency parameter of the Beidou positioning terminal is increased by a preset multiple, and the state sampling sensitivity parameter of the IoT sensor node is increased by a preset level.

[0047] Furthermore, to achieve the above objectives, the present invention also proposes a work quality assessment device based on BeiDou positioning, the device comprising:

[0048] The alignment module is used to perform time-series alignment processing on Beidou positioning terminal trajectory data, IoT sensor node facility status data and mobile terminal image data to obtain aligned multimodal data, wherein the aligned multimodal data includes positioning terminal trajectory point sequence data and facility geographic coordinate set data.

[0049] The calculation module is used to calculate the spatiotemporal distance between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data;

[0050] The construction module is used to determine the associated facility data corresponding to each trajectory point based on the spatiotemporal distance value, and to construct a spatiotemporal constraint model between the trajectory and the facility point.

[0051] The evaluation module is used to quantitatively evaluate the quality of mobile monitored objects based on the spatiotemporal constraint model, and obtain the spatiotemporal correlation evaluation results.

[0052] Furthermore, to achieve the above objectives, the present invention also proposes a job quality assessment device based on BeiDou positioning. The device includes: a memory, a processor, and a job quality assessment program based on BeiDou positioning stored in the memory and executable on the processor. The job quality assessment program based on BeiDou positioning is configured to implement the steps of the job quality assessment method based on BeiDou positioning as described above.

[0053] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a job quality assessment program based on BeiDou positioning, wherein when the job quality assessment program based on BeiDou positioning is executed by a processor, it implements the steps of the job quality assessment method based on BeiDou positioning as described above.

[0054] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the operation quality assessment method based on BeiDou positioning as described above.

[0055] One or more technical solutions proposed in this application have at least the following technical effects:

[0056] By employing a multi-source temporal alignment mechanism involving BeiDou positioning terminals, IoT sensor nodes, and mobile image data, high-precision spatiotemporal binding of trajectories and facility locations in complex urban environments is achieved. This transforms traditionally independently processed discrete data streams into a spatiotemporally constrained topological network, significantly improving the matching accuracy between mobile monitored objects and static facilities. Dynamic weight allocation and adaptive threshold filtering algorithms effectively compensate for errors caused by BeiDou signal drift and sensor reporting delays. Furthermore, through a dynamic weight adjustment mechanism, the system can automatically switch assessment logic during public health emergencies, reducing response time to minutes. Closed-loop feedback optimization of historical assessment results further automates and enhances data acquisition parameters, reducing overall communication overhead and computational redundancy in multi-source data fusion, and providing highly reliable, low-latency technical support for smart city mobile monitoring. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart illustrating an embodiment of the operation quality assessment method based on BeiDou positioning provided in this application.

[0060] Figure 2 This is a flowchart illustrating Embodiment 2 of the operation quality assessment method based on BeiDou positioning provided in this application.

[0061] Figure 3 This is a schematic diagram of the module structure of the operation quality assessment device based on Beidou positioning according to an embodiment of this application;

[0062] Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the operation quality assessment method based on Beidou positioning in the embodiments of this application.

[0063] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0065] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0066] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a BeiDou positioning-based job quality assessment device. The following description uses a BeiDou positioning-based job quality assessment device as an example to illustrate this embodiment and the subsequent embodiments.

[0067] Based on this, the embodiments of this application provide a method for evaluating the quality of operations based on BeiDou positioning, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the operation quality assessment method based on BeiDou positioning in this application.

[0068] In this embodiment, the operation quality assessment method based on BeiDou positioning includes steps S10 to S40:

[0069] Step S10: Perform time-series alignment processing on the Beidou positioning terminal trajectory data, the IoT sensor node facility status data, and the mobile terminal image data to obtain aligned multimodal data. The aligned multimodal data includes positioning terminal trajectory point sequence data and facility geographic coordinate set data.

[0070] It should be noted that the BeiDou positioning terminal trajectory data refers to the real-time location movement record of the worker collected by the BeiDou positioning work badge. This data includes longitude, latitude, geographic coordinates, and timestamp information corresponding to each location point. The trajectory data is stored in the form of a discrete point sequence, with each point recording the spatial location of the worker at a specific moment. In this embodiment, the data originates from the BeiDou positioning work badge worn by the worker, which actively sends positioning information according to a strategy of reporting its location every 150 steps.

[0071] It should be noted that the facility status data of the IoT sensor nodes refers to the real-time monitoring information collected by smart sensors deployed on vector-borne disease control facilities. This data includes facility number, facility type, operating status, temperature and humidity parameters, and sensor power information. The status data is transmitted to the data processing center periodically via a 4G IoT communication module. In this embodiment, the weight sensor deployed in the smart bait station monitors the remaining bait amount in real time. When the bait weight falls below a preset threshold, a replenishment warning is generated, and the facility's location coordinates are reported simultaneously.

[0072] It should be noted that mobile image data refers to on-site operation photos taken and uploaded by operators using mobile devices. This data includes metadata such as photo files, shooting timestamps, geographic coordinates of the shooting location, and equipment serial numbers. The photo content can be a close-up image of the bait station, used to prove that the operators have indeed arrived at the designated location and completed the corresponding operations. In this embodiment, when operators take photos of the bait station through the mini-program, the shooting time and GPS coordinates in the EXIF ​​information of the photo are automatically extracted.

[0073] It should be noted that aligned multimodal data refers to a standardized dataset obtained after time-series alignment. This dataset includes trajectory point sequences with unified timestamps, facility status records, and image data, all three types of data being synchronized in time. The aligned data can be directly used for subsequent spatiotemporal correlation analysis, providing a data foundation for constructing constraint models.

[0074] It should be noted that the positioning terminal trajectory point sequence data refers to a continuous set of location points arranged in chronological order. Each point contains precise geographic coordinates and corresponding time information, forming a complete record of the work path. This sequence data can reflect the movement trajectory, dwell time, and work sequence of the workers, and is a core basis for assessing the integrity of the work process.

[0075] It should be noted that the facility geographic coordinate set data refers to the summary of spatial location information for all defense facilities. This set includes each facility's unique number, type, installation address, and latitude and longitude coordinates. The coordinate data is collected and entered via mobile devices during actual installation, ensuring the accuracy of facility locations and providing a benchmark for subsequent calculations of the distance between trajectory points and facilities.

[0076] As is understandable, time alignment refers to the process of unifying timestamp information from three different data sources to the same time base. This process ensures that the data from the three modalities are time-corresponding and matched through operations such as timestamp standardization and data frame alignment. Time alignment is a fundamental step in multimodal data fusion, solving the problem of data timing misalignment caused by clock asynchrony between different devices.

[0077] As is understandable, data frame alignment refers to matching and combining three types of data according to a unified time base. This process groups trajectory points, facility status, and image data with similar timestamps into the same data frame, forming a temporally consistent snapshot. The aligned data frame serves as the smallest unit of analysis, containing multi-dimensional information from the same moment.

[0078] As is understandable, data cleaning refers to removing noise and erroneous records from a dataset. This process identifies and removes duplicate reported location points, outliers caused by sensor jumps, and image records with incorrect timestamps. The cleaned data is of improved quality, providing reliable input for model building.

[0079] In one feasible implementation, step S10 includes steps A11 to A16:

[0080] Step A11: Extract the first positioning timestamp information from the Beidou positioning terminal trajectory data;

[0081] Understandably, extracting the first positioning timestamp information from the BeiDou positioning terminal trajectory data involves parsing the time field from the raw data packet reported by the BeiDou employee badge. This process reads the time data from the NMEA protocol and converts it into a standard time format. The extracted timestamp is used to identify the acquisition time of each trajectory point and is the basis for subsequent time sequence alignment.

[0082] Step A12: Extract the status change timestamp information from the facility status data;

[0083] Understandably, extracting the timestamp information of facility status changes from the data involves reading time stamps from messages reported by IoT sensors. This process parses the time field in the sensor data to record the specific moment when the facility status changed. The extracted timestamps are used to determine the timeliness of the operational response.

[0084] Step A13: Extract the shooting timestamp information from the image data;

[0085] Understandably, extracting the shooting timestamp information from image data involves reading the shooting time from the EXIF ​​metadata of the photo file. This process parses the photo's attribute information to obtain the shooting time accurate to the second. The extracted timestamp is used to verify the time compliance of on-site operations.

[0086] Step A14: Unify the first positioning timestamp information, the status change timestamp information, and the shooting timestamp information to the standard time base to obtain unified base time data;

[0087] Understandably, unifying the three types of timestamp information to a standard time base involves time zone conversion and format standardization. This process converts all time data to UTC time and ISO8601 format, eliminating differences in device clocks. Time data with a unified time base can then be directly compared and calculated.

[0088] Step A15: Align the BeiDou positioning terminal trajectory data, facility status data, and image data with data frames based on the unified reference time data to obtain time-aligned data;

[0089] Understandably, aligning data frames based on a unified reference time involves grouping the three types of data according to their temporal proximity. This process iterates through the time series, grouping trajectory points, facility status, and image data with a time difference of less than 5 minutes into the same data frame. The aligned data frames ensure temporal consistency across multiple data sources.

[0090] Step A16: Perform data cleaning on the time-aligned data to remove duplicate and outlier data, and obtain aligned multimodal data.

[0091] Understandably, data cleaning of time-aligned data involves removing duplicate and outlier records. This process detects and deletes duplicate trajectory points with identical coordinates, eliminates abnormal state values ​​caused by sensor jumps, and filters out photo data with misaligned timestamps. The cleaned data quality meets the modeling requirements.

[0092] Step S20: Extract the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data.

[0093] It should be noted that the spatiotemporal distance value is a comprehensive metric that takes into account both spatial distance and time difference. This value combines the straight-line distance in the spatial dimension and the chronological order in the temporal dimension through a weighted average, and is used to measure the spatiotemporal proximity and temporal rationality between the trajectory point and the facility point. The smaller the spatiotemporal distance value, the more likely the workers are to arrive at the facility location at the correct time.

[0094] It should be noted that the second positioning timestamp information refers to the time stamp corresponding to the positioning coordinate information. This timestamp records the precise moment the positioning data was collected and is used to identify the time attribute of the trajectory point. The accuracy of the timestamp directly affects the correctness of the time series analysis, therefore it needs to be synchronized with the BeiDou satellite time.

[0095] It should be noted that facility status timestamp information refers to the time tag carried by IoT sensors when reporting status data. This timestamp records the moment when the facility status changes or when the sensor collects data, and is used to compare its chronological relationship with the time of trajectory points. The accuracy of the timestamp determines the accuracy of emergency response timeliness calculations.

[0096] The beneficial effect of step S20 is that it integrates information from both spatial and temporal dimensions into a single evaluation index, simplifying the judgment logic for matching trajectory points with facility points. The dynamic weight adjustment mechanism can adapt to changes in data quality under different positioning environments, improving the robustness of association judgment and providing a high-quality association metric for subsequent model construction.

[0097] In one feasible implementation, step S20 includes steps A21 to A26:

[0098] Step A21: Extract the positioning terminal trajectory point sequence data and facility geographic coordinate set data from the aligned multimodal data;

[0099] Understandably, extracting two types of core data from aligned multimodal data involves deconstructing and separating the time-aligned standardized dataset, separately reading the location terminal trajectory point sequence data and the facility geographic coordinate set data, forming independent data sources required for subsequent calculations. This step first parses the data frame structure of the aligned multimodal data, identifies the identification fields of the trajectory data and facility data, then splits the two types of data into different buffers, and finally outputs a serialized data object that can be extracted at a single point.

[0100] This step breaks down the complex merged data structure into a single type of data stream, reducing the complexity of subsequent coordinate extraction and calculation, and ensuring that the correspondence between trajectory points and facility coordinates is clear and traceable.

[0101] Step A22: Extract the positioning coordinates and second positioning timestamp information of the current trajectory point from the positioning terminal trajectory point sequence data;

[0102] Understandably, obtaining the current trajectory point's location coordinates and the second location timestamp involves reading the aligned trajectory point data. This process extracts the latitude and longitude coordinates and corresponding timestamp of a single trajectory point as the basic input for distance calculation. The acquired data is used for subsequent calculations of the spatiotemporal relationship with the facility point.

[0103] Step A23: Extract the facility coordinate information and facility status timestamp information of the current facility coordinates from the facility geographic coordinate set data;

[0104] Understandably, obtaining the facility's coordinates and status timestamp information involves reading a single record from the facility's geographic coordinate set. This process extracts the facility's installation location coordinates and the time of its most recent status change. The acquired data is then paired with trajectory point data for calculation.

[0105] Step A24: Calculate the spatial distance between the location coordinates and the facility coordinates;

[0106] Understandably, calculating the spatial distance between location coordinates and facility coordinates involves converting the latitude and longitude of the two points into actual geographic distance. This process uses a coordinate transformation algorithm to convert latitude and longitude in the WGS84 coordinate system into planar distance, expressed in meters. The calculation result reflects the spatial proximity of the workers and the facility.

[0107] Step A25: Calculate the time difference between the second location timestamp information and the facility status timestamp information;

[0108] Understandably, calculating the time difference between the second location timestamp and the facility status timestamp involves determining the interval between the two points in time. This process involves subtracting the timestamps to obtain the time difference, expressed in minutes. The time difference is used to determine the order in which personnel arrive at the facility and the response speed.

[0109] Step A26: Perform a weighted fusion calculation based on the spatial distance value and the time difference value to obtain the spatiotemporal distance value.

[0110] Understandably, weighted fusion calculation based on spatial distance and temporal difference combines information from two dimensions into a single evaluation index. This process dynamically adjusts the spatial and temporal weights based on satellite positioning accuracy, increasing the spatial weight when positioning accuracy is high. The resulting spatiotemporal distance value comprehensively reflects the spatiotemporal correlation strength between trajectory points and facility points.

[0111] Step S30: Determine the associated facility data corresponding to each trajectory point based on the spatiotemporal distance value, so as to construct a spatiotemporal constraint model of trajectory and facility location based on the associated facility data;

[0112] It should be noted that associated facility data refers to the facility record information that is closest to a given trajectory point in terms of spatiotemporal distance. This data includes the facility number, facility type, facility coordinates, and corresponding spatiotemporal distance value. Each trajectory point is sorted and filtered to determine its most likely corresponding target facility, establishing a point-to-point mapping relationship between the trajectory and the facility.

[0113] It should be noted that the preset distance threshold refers to the maximum distance limit set in advance to determine whether a trajectory point is effectively close to the facility. This threshold is set according to the actual application scenario, with a default value of 15 meters, which can be dynamically extended to 25 meters in complex environments with poor positioning signals. The purpose of the threshold is to filter out false associations caused by positioning drift.

[0114] The beneficial effect of step S30 is that it establishes a precise correspondence between the work path and the prevention facilities, making the work process traceable and verifiable. Through the anomaly marking mechanism, behaviors that deviate from the predetermined work route can be effectively identified, providing a structured analytical framework for quantitatively assessing the standardization of operations and improving the accuracy of supervision.

[0115] In one feasible implementation, step S30 includes steps A31 to A35:

[0116] Step A31: Based on each trajectory point, filter out the target spatiotemporal distance value from all spatiotemporal distance values;

[0117] Understandably, selecting the target spatiotemporal distance value from all spatiotemporal distance values ​​based on each trajectory point involves extracting the minimum value from the distance set of each trajectory point. This process iterates through the sequence of trajectory points, sorts the multiple facility distance values ​​corresponding to each point, and selects the minimum value as the target value. The selection result determines the most suitable facility for each trajectory point.

[0118] Step A32: Determine the facility coordinate data corresponding to the target spatiotemporal distance value as the associated facility data of the trajectory point;

[0119] Understandably, identifying the facility coordinates corresponding to the target's spatiotemporal distance as the associated facility data for trajectory points establishes a mapping relationship between trajectories and facilities. This process pairs the selected nearest facility records with trajectory points, forming a one-to-one association between points and facilities. The association results are used to construct a complete network of relationships between work paths and facilities.

[0120] Step A33: Determine whether the target's spatiotemporal distance value is greater than a preset distance threshold;

[0121] Understandably, determining whether the target's spatiotemporal distance value exceeds a preset distance threshold involves comparing the calculated minimum distance with a set upper distance limit. This process checks whether the spatial interval between the trajectory point and the nearest facility is within a reasonable range. The judgment result is used to identify anomalies caused by positioning drift or operational deviation.

[0122] Step A34: When the target spatiotemporal distance value is greater than the preset distance threshold, mark the trajectory point as an abnormal trajectory point;

[0123] Understandably, marking trajectory points as anomalous when their spatiotemporal distance exceeds a preset distance threshold involves specially labeling trajectory points that do not meet the proximity criteria. This process marks trajectory points exceeding the distance threshold as anomalous, and these points are not included in the calculation of effective operational coverage. Anomaly marking helps identify situations where workers fail to arrive at the facility as planned.

[0124] Step A35: Construct a spatiotemporal constraint model based on the set of relationship data between all trajectory points and their corresponding associated facilities, as well as the abnormal trajectory point marker data.

[0125] Understandably, constructing a spatiotemporal constraint model based on the set of relationship data between all trajectory points and their corresponding associated facilities, as well as anomaly trajectory point marker data, integrates all related information and anomaly markers into a structured model. This process organizes trajectory point sequences, facility relationships, and anomaly markers into a unified data structure. The model comprehensively describes the spatiotemporal constraints of the operation process, providing an analytical framework for quality assessment.

[0126] Step S40: Quantitatively evaluate the quality of mobile regulatory objects based on the spatiotemporal constraint model to obtain the spatiotemporal correlation evaluation results.

[0127] It should be noted that the quality of mobile monitoring targets refers to the overall performance level of operators or PCO service units in completing vector-borne disease control work. This quality is measured by quantitative indicators, including multiple dimensions such as the completeness of facility inspection coverage, the standardization of the operation process, and the timeliness of problem response.

[0128] It should be noted that the spatiotemporal correlation assessment result refers to the final output quantitative score of work quality. This result is presented in numerical form, with a higher score indicating better work quality. The assessment results can be directly used for PCO service providers' credit evaluation, performance appraisal, and fee settlement, providing a basis for regulatory authorities' decision-making.

[0129] Understandably, quantitatively assessing the quality of mobile monitoring targets based on a spatiotemporal constraint model involves extracting core indicators such as coverage, operation duration, and frequency of status changes from the model. This process standardizes each indicator and calculates a comprehensive score through a weighted summation. The assessment process transforms complex operational behaviors into comparable numerical results, achieving an objective evaluation of operational quality.

[0130] The beneficial effect of step S40 is that it enables automated assessment of operational quality, replacing the inefficient regulatory model that relies on manual spot checks. The assessment results are objective and impartial, and can be generated in real time, significantly improving the management efficiency and data transparency of vector control work, and providing core technical support for digital supervision.

[0131] This embodiment provides a method for evaluating operational quality based on BeiDou positioning. Through a complete technical chain of multimodal data fusion, spatiotemporal constraint modeling, and quantitative evaluation, it constructs a closed-loop, traceable, and automatically punitive intelligent supervision system. This not only solves the traditional pain points of vector control operations relying on manual supervision and lacking data closure, but also provides data support for scientific decision-making, promoting the digital transformation of urban public health management.

[0132] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S40 includes steps S401 to S405:

[0133] Step S401: Extract the inspection coverage rate data and operation duration data of the mobile monitoring object from the spatiotemporal constraint model;

[0134] It should be noted that the inspection coverage rate refers to the proportion of prevention and control facilities actually inspected by the operators to the total number of facilities planned for inspection. This data is obtained by counting the number of facility locations successfully linked to the track points. It reflects whether the operators have completed effective inspections of all designated facilities. The inspection coverage rate is a core indicator for assessing the integrity of operations.

[0135] It should be noted that the work duration data refers to the total time that workers spend and perform tasks within the designated work area. This data is obtained by accumulating the time intervals between consecutive trajectory points within the electronic fence area. It reflects the effective working time actually invested by the workers. The work duration data is used to evaluate the timeliness and adequacy of work order completion.

[0136] Understandably, this step extracts the inspection coverage rate and operation duration data of mobile monitored objects from the spatiotemporal constraint model by reading two pre-calculated core indicators from the model. This process obtains the statistically completed coverage percentage and total operation duration values ​​from the model structure. The extraction operation directly reads values ​​from the model data structure, ensuring the accuracy and consistency of the data.

[0137] Step S402: Calculate the basic correlation index based on the inspection coverage data;

[0138] It should be noted that the Basic Relevance Index is a standardized score calculated based on inspection coverage data. This index converts the coverage percentage into a score range of 0 to 100. The higher the coverage, the higher the index. The Basic Relevance Index is a component of the comprehensive evaluation, specifically measuring the operational quality at the spatial coverage level.

[0139] Understandably, this step, which calculates the basic correlation index based on inspection coverage data, converts percentage values ​​into standard scores. This process uses linear mapping or piecewise functions to map the coverage rate to a scoring range of 0 to 100. The calculated index value eliminates the influence of dimensions, facilitating weighted aggregation with other indices.

[0140] Step S403: Calculate the process matching index based on the operation duration data;

[0141] It should be noted that the process matching index is a score calculated based on the degree of matching between the work duration data and the work order's required duration. This index compares the difference between the actual work duration and the planned duration. The smaller the difference, the higher the score. The process matching index reflects the time compliance of the work process.

[0142] Understandably, this step calculates the process matching index based on the work duration data, comparing the difference between the actual duration and the planned duration. This process calculates the ratio or difference between the actual duration and the work order's required duration, determining the score based on the magnitude of the difference. A higher score is obtained when the ratio is close to 1 or the difference is close to 0, reflecting the compliance of the work duration.

[0143] Step S404: Calculate the timeliness index of the results based on the frequency of change of facility status data;

[0144] It should be noted that the timeliness index is a score calculated based on the frequency of changes in facility status data. This index assesses the timeliness of operational results by statistically analyzing the number of facility status updates and the response speed to replenishment warnings. A higher index indicates a moderate frequency of changes and timely responses, suggesting that operators reacted quickly to facility anomalies and that the operational results were good.

[0145] Understandably, this step calculates the timeliness index based on the frequency of changes in facility status data, analyzing the temporal distribution characteristics of facility status updates. This process counts the number of facility status reports per unit time and calculates the average response time from the issuance of an alert to the restoration of the status. A moderate frequency and short response time are assigned a higher score, reflecting the timeliness value of the results.

[0146] Step S405: The basic correlation index, process matching index and result timeliness index are weighted and summed to obtain the spatiotemporal correlation assessment result.

[0147] Understandably, this step involves weighting and summing the basic relevance index, process matching index, and result timeliness index by assigning appropriate weights to each index. The weighting parameters are set according to management priority, with the basic relevance index and result timeliness index having higher weights, and the process matching index having a moderate weight. The spatiotemporal relevance assessment result obtained from the summation is a comprehensive quality score, directly used to determine the job quality level.

[0148] Furthermore, this embodiment also includes steps A41 to A44:

[0149] Step A41: Obtain emergency event trigger information;

[0150] It should be noted that emergency event triggering information refers to emergency notifications from public health emergency management systems or government monitoring platforms. This information includes structured data such as event type codes, risk level identifiers, and affected area scope. It is used to instruct vector control efforts to enter emergency response status. For example, when a high-risk dengue fever warning is issued, this type of triggering information is automatically received through a data interface.

[0151] Understandably, obtaining emergency event trigger information involves receiving emergency notification data packets sent by external systems. This process is achieved by listening to a specified network interface or message queue, and upon receiving the information, parsing the event type and level identifier. The obtained trigger information serves as the input condition for initiating weight adjustments.

[0152] Step A42: When the type of emergency event triggering information is a preset public health event level, obtain the preset weight adjustment rule data;

[0153] It should be noted that the preset public health event level refers to a predefined classification standard for emergency response levels. This level can be divided into multiple levels based on the scale of the infectious disease outbreak, its spread speed, and the severity of the harm. For example, a daily increase of more than 5 local dengue fever cases is defined as a Level 1 response. The level definitions are stored in the system configuration file and serve as the basis for determining the emergency response weight.

[0154] Understandably, when the type of emergency event triggering information is a preset public health event level, obtaining the preset weight adjustment rule data involves comparing the received emergency level with the threshold defined in the configuration file. This process checks whether the event level has reached the severity level requiring weight adjustment. If the condition is met, the system reads the corresponding weight adjustment rule data from the configuration library.

[0155] Step A43: Increase the weight parameter of the result timeliness index by a preset ratio according to the preset weight adjustment rule data to obtain the adjusted weight parameter;

[0156] It should be noted that the preset weight adjustment rule data refers to the pre-configured scoring weight modification scheme during the emergency response period. This rule data is stored in the form of a configuration file, which specifies the weight adjustment range of each assessment index under different emergency levels. The rule data can be flexibly modified according to actual emergency needs.

[0157] It should be noted that the weighting parameter refers to the coefficient value assigned to each index during the weighted summation process. This parameter determines the importance of different indices in the overall score. The sum of the weighting parameters can be 1, and adjusting the weights can guide changes in the evaluation focus.

[0158] It should be noted that the preset ratio value refers to the weight increase range defined in the weight adjustment rules. This value is expressed as a percentage. In this embodiment, the result timeliness index weight can be increased from 0.3 to 0.5, with an increase ratio of 66.7%. The preset ratio value is determined based on the urgency of the emergency response.

[0159] Understandably, increasing the weight parameter of the result timeliness index by a preset percentage based on the preset weight adjustment rules is a modification of the weight configuration according to emergency rules. This process reads the weight increase range defined in the rules and adjusts the weight of the result timeliness index from the normal value to the emergency value.

[0160] Step A44: Return to the step of weighted summation of the basic correlation index, process matching index, and result timeliness index based on the adjusted weight parameters to obtain the adjusted spatiotemporal correlation assessment result.

[0161] Understandably, step A44, which returns to the weighted summation step based on the adjusted weight parameters, recalculates the final evaluation result using the new weight configuration. This process invokes the weighted summation algorithm, multiplying and summing the three indices with the adjusted weights. The recalculated spatiotemporal correlation evaluation result reflects the principle of prioritizing emergency response.

[0162] Furthermore, this embodiment also includes steps A51 to A54:

[0163] Step A51: Obtain historical score sequence data of the spatiotemporal correlation assessment results;

[0164] It should be noted that historical scoring sequence data refers to a sequence of spatiotemporal correlation assessment results obtained by mobile monitored objects over multiple past assessment periods, arranged in chronological order. This data contains scoring values ​​from multiple consecutive periods, used to analyze the changing trends and stability of operational quality. In this embodiment, the system saves the weekly assessment scores of each PCO service unit for the most recent 12 weeks, forming a complete historical scoring sequence.

[0165] Understandably, obtaining historical score sequence data for spatiotemporal correlation assessment results involves reading score records from stored consecutive assessment periods. This process retrieves the score values ​​of a specified regulated object from the database for the most recent several periods, forming a time-series dataset.

[0166] Step A52: Determine whether the score values ​​for a consecutive preset number of periods in the historical scoring sequence data are all lower than a preset threshold;

[0167] It should be noted that the preset number of cycles refers to the threshold number of consecutive assessment cycles required to determine if the work quality remains consistently poor. This value can be set to 4 cycles. The setting of the number of cycles needs to balance regulatory sensitivity and assessment stability.

[0168] It should be noted that the preset threshold refers to the critical score used to determine whether the score is too low. This threshold can be set to 60 points. The threshold setting is determined based on historical data statistics and management requirements.

[0169] Understandably, determining whether the scores for a predetermined number of consecutive periods in the historical scoring sequence are all below a predetermined threshold involves traversing the sequence and checking the scores for each consecutive period. This process compares the scores of the most recent four periods with a 60-point threshold. If all scores are below the threshold, it indicates that the monitored entity has persistent operational quality issues.

[0170] Step A53: When the score values ​​for a consecutive preset number of cycles in the historical scoring sequence data are all lower than the preset threshold, the dynamic adjustment mode of the operation parameters is triggered and an adjustment instruction is generated;

[0171] It should be noted that the dynamic adjustment mode for operational parameters refers to a special operating mode that automatically adjusts the data acquisition strategy. This mode is automatically activated upon detecting a persistently low score. It enhances monitoring of problematic objects by increasing the data reporting frequency and sensor sensitivity. The mode is triggered without manual intervention, thus automating the monitoring strategy.

[0172] It should be noted that the adjustment command refers to the configuration change command generated by the system. This command contains specific adjustment values ​​for parameters such as the BeiDou work badge reporting frequency and sensor sampling sensitivity. The command is sent to the terminal device in the form of structured data.

[0173] Understandably, triggering the dynamic adjustment mode of operational parameters and generating an adjustment command when the score value for a consecutive preset number of periods in the historical scoring sequence data is lower than a preset threshold is the initiation of the enhanced supervision process. This command contains specific operational parameters for increasing the positioning frequency and sensor sensitivity. After the command is issued, the system will collect data according to the new parameters.

[0174] Step A54: Based on the enhanced mode of adjusting the command response data acquisition, increase the trajectory reporting frequency parameter of the Beidou positioning terminal by a preset multiple, and increase the state sampling sensitivity parameter of the IoT sensor node by a preset level.

[0175] It should be noted that the data acquisition enhancement mode refers to a special operating mode that increases the frequency and sensitivity of data reporting. In this mode, the Beidou work tag reports its location every 50 steps, and the sensor sampling cycle is shortened to 15 minutes. The enhancement mode can acquire more detailed operational process data.

[0176] It should be noted that the trajectory reporting frequency parameter refers to the time interval or step interval at which the BeiDou positioning badge sends location data to the system. This parameter is typically set to report once every 150 steps. The frequency parameter directly determines the density and accuracy of the trajectory data.

[0177] It should be noted that the preset multiplier refers to the amplification factor for adjusting the frequency parameters. This factor can be set to 3 times. The setting of the preset multiplier needs to take into account device power consumption, communication traffic, and actual requirements.

[0178] It should be noted that the state sampling sensitivity parameter refers to the detection threshold at which an IoT sensor triggers state reporting. This parameter controls the sensor's sensitivity to events such as changes in bait weight and the opening and closing of gates. Increasing the sensitivity parameter allows the sensor to capture more subtle state changes.

[0179] It should be noted that the preset level refers to the magnitude of the sensitivity parameter adjustment. This level can be divided into three levels: high, medium, and low. Increasing the preset level means lowering the trigger threshold, making it easier for the sensor to report status changes.

[0180] Understandably, adjusting the command data response and data acquisition enhancement mode by increasing the trajectory reporting frequency parameter of the Beidou positioning terminal by a preset multiple and raising the status sampling sensitivity parameter of the IoT sensor node by a preset level is a way to switch the device's operating mode. This operation increases the trajectory reporting frequency from every 150 steps to every 50 steps. Simultaneously, increasing the status sampling sensitivity of the IoT sensor node lowers the weight detection threshold of the bait station, making it easier to trigger replenishment warnings.

[0181] This embodiment provides a method for evaluating operational quality based on BeiDou positioning. It transforms multi-dimensional operational behavior data into a single, comparable evaluation score, achieving a quantitative measurement of operational quality. Three indices comprehensively evaluate performance from three perspectives: spatial coverage, temporal process, and emergency response, making the evaluation results more objective and comprehensive. The weighted summation mechanism can flexibly adjust the focus according to different management needs, adapting to different scenarios such as routine operations and emergency responses.

[0182] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the operation quality assessment method based on Beidou positioning in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0183] This application also provides a work quality assessment device based on BeiDou positioning, please refer to... Figure 3 The operation quality assessment device based on BeiDou positioning includes:

[0184] Alignment module 10 is used to perform time-series alignment processing on Beidou positioning terminal trajectory data, IoT sensor node facility status data and mobile terminal image data to obtain aligned multimodal data, wherein the aligned multimodal data includes positioning terminal trajectory point sequence data and facility geographic coordinate set data;

[0185] Calculation module 20 is used to calculate the spatiotemporal distance between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data;

[0186] Module 30 is used to determine the associated facility data corresponding to each trajectory point based on the spatiotemporal distance value, and to construct a spatiotemporal constraint model between the trajectory and the facility point.

[0187] The evaluation module 40 is used to quantitatively evaluate the quality of mobile regulatory objects based on a spatiotemporal constraint model, and obtain the spatiotemporal correlation evaluation results.

[0188] The operation quality assessment device based on BeiDou positioning provided in this application addresses the technical problem of accurately aligning and dynamically modeling multi-source heterogeneous data generated by BeiDou positioning terminals, IoT sensor nodes, and mobile terminals in the spatiotemporal dimensions, using the operation quality assessment method based on BeiDou positioning described in the above embodiments. Compared with the prior art, the beneficial effects of the operation quality assessment device based on BeiDou positioning provided in this application are the same as those of the operation quality assessment method based on BeiDou positioning provided in the above embodiments, and other technical features in the operation quality assessment device based on BeiDou positioning are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0189] In one embodiment, the alignment module 10 is further configured to extract the first positioning timestamp information from the BeiDou positioning terminal trajectory data;

[0190] Extract the status change timestamp information from the facility status data;

[0191] Extract the shooting timestamp information from the image data;

[0192] The first positioning timestamp, status change timestamp, and shooting timestamp are unified to a standard time base to obtain unified base time data.

[0193] Based on the unified reference time data, the trajectory data of Beidou positioning terminals, facility status data and image data are aligned to obtain time-aligned data.

[0194] Data cleaning is performed on the time-aligned data to remove duplicate and outlier data, resulting in aligned multimodal data.

[0195] In one embodiment, the calculation module 20 is further configured to extract positioning terminal trajectory point sequence data and facility geographic coordinate set data from the aligned multimodal data;

[0196] Extract the current trajectory point's positioning coordinates and the second positioning timestamp from the positioning terminal's trajectory point sequence data;

[0197] Extract the facility coordinate information and facility status timestamp information of the current facility coordinates from the facility geographic coordinate set data;

[0198] Calculate the spatial distance between the location coordinates and the facility coordinates;

[0199] Calculate the time difference between the second location timestamp information and the facility status timestamp information;

[0200] The spatiotemporal distance value is obtained by weighted fusion calculation based on the spatial distance value and the time difference value.

[0201] In one embodiment, the construction module 30 is further configured to filter out the target spatiotemporal distance value from all spatiotemporal distance values ​​based on each trajectory point;

[0202] The facility coordinate data corresponding to the target spatiotemporal distance value is determined as the associated facility data of the trajectory point;

[0203] Determine whether the target's spatiotemporal distance value is greater than a preset distance threshold;

[0204] When the target spatiotemporal distance value is greater than a preset distance threshold, the trajectory point is marked as an abnormal trajectory point;

[0205] A spatiotemporal constraint model is constructed based on the set of relationship data between all trajectory points and their corresponding associated facilities, as well as the data of abnormal trajectory point markings.

[0206] In one embodiment, the evaluation module 40 is further configured to extract inspection coverage data and operation duration data of mobile monitored objects from the spatiotemporal constraint model;

[0207] The basic correlation index is calculated based on the inspection coverage data.

[0208] The process matching index is calculated based on the task duration data;

[0209] The timeliness index of the results is calculated based on the frequency of change in facility status data;

[0210] The spatiotemporal correlation assessment results are obtained by weighting and summing the basic correlation index, process matching index, and result timeliness index.

[0211] In one embodiment, the evaluation module 40 is further configured to acquire emergency event triggering information;

[0212] When the type of emergency event triggering information is a preset public health event level, obtain preset weight adjustment rule data;

[0213] According to the preset weight adjustment rules, the weight parameters of the result timeliness index are increased by a preset ratio to obtain the adjusted weight parameters.

[0214] Based on the adjusted weight parameters, return to the step of weighted summation of the basic correlation index, process matching index, and result timeliness index to obtain the adjusted spatiotemporal correlation assessment result.

[0215] In one embodiment, the evaluation module 40 is further configured to acquire historical scoring sequence data of the spatiotemporal correlation evaluation results;

[0216] Determine whether the score values ​​for a consecutive preset number of periods in the historical scoring sequence data are all lower than a preset threshold;

[0217] When the score value for a consecutive preset number of periods in the historical scoring sequence data is lower than the preset threshold, the dynamic adjustment mode of the operation parameters is triggered and an adjustment instruction is generated;

[0218] Based on the enhanced mode of adjusting command response data acquisition, the trajectory reporting frequency parameter of the Beidou positioning terminal is increased by a preset multiple, and the state sampling sensitivity parameter of the IoT sensor node is increased by a preset level.

[0219] This application provides a job quality assessment device based on BeiDou positioning. The job quality assessment device based on BeiDou positioning includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the job quality assessment method based on BeiDou positioning in the above embodiment 1.

[0220] The following is for reference. Figure 4The diagram illustrates a structural schematic of a BeiDou-based job quality assessment device suitable for implementing embodiments of this application. The BeiDou-based job quality assessment device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle-mounted terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The BeiDou-based job quality assessment device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0221] like Figure 4 As shown, the BeiDou-based job quality assessment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the BeiDou-based job quality assessment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Yes, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the BeiDou-based job quality assessment equipment to exchange data wirelessly or via wired communication with other devices. Although the figure shows a BeiDou-based job quality assessment equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0222] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0223] The operation quality assessment device based on BeiDou positioning provided in this application addresses the technical problem of accurately aligning and dynamically modeling multi-source heterogeneous data generated by BeiDou positioning terminals, IoT sensor nodes, and mobile terminals in the spatiotemporal dimensions using the operation quality assessment method based on BeiDou positioning described in the above embodiments. Compared with the prior art, the beneficial effects of the operation quality assessment device based on BeiDou positioning provided in this application are the same as those of the operation quality assessment method based on BeiDou positioning provided in the above embodiments, and other technical features of this operation quality assessment device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0224] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0225] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0226] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the BeiDou positioning-based job quality assessment method in the above embodiments.

[0227] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0228] The aforementioned computer-readable storage medium may be included in the BeiDou-based job quality assessment equipment; or it may exist independently and not be assembled into the BeiDou-based job quality assessment equipment.

[0229] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the BeiDou-based operation quality assessment device, the BeiDou-based operation quality assessment device performs the following: temporal alignment processing on BeiDou positioning terminal trajectory data, IoT sensor node facility status data, and mobile terminal image data to obtain aligned multimodal data; extracts the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data; determines the associated facility data corresponding to each trajectory point based on the spatiotemporal distance values, and constructs a spatiotemporal constraint model between the trajectory and facility points based on the associated facility data; and performs a quantitative assessment of the quality of the mobile monitored object based on the spatiotemporal constraint model to obtain a spatiotemporal correlation assessment result.

[0230] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0231] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0232] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0233] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-described BeiDou positioning-based job quality assessment method. This addresses the technical problem of how to accurately align and dynamically model multi-source heterogeneous data generated by BeiDou positioning terminals, IoT sensor nodes, and mobile terminals in the spatiotemporal dimensions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the BeiDou positioning-based job quality assessment method provided in the above embodiments, and will not be repeated here.

[0234] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described BeiDou positioning-based job quality assessment method.

[0235] The computer program product provided in this application addresses the technical problem of accurately aligning and dynamically modeling multi-source heterogeneous data generated by BeiDou positioning terminals, IoT sensor nodes, and mobile devices in the spatiotemporal dimensions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the operation quality assessment method based on BeiDou positioning provided in the above embodiments, and will not be repeated here.

[0236] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for evaluating operational quality based on BeiDou positioning, characterized in that, The method includes: The trajectory data of Beidou positioning terminals, the facility status data of IoT sensor nodes, and the image data of mobile terminals are time-series aligned to obtain aligned multimodal data, wherein the aligned multimodal data includes positioning terminal trajectory point sequence data and facility geographic coordinate set data. Extract the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data; Based on the spatiotemporal distance value, determine the associated facility data corresponding to each trajectory point, and construct a spatiotemporal constraint model of trajectory and facility location based on the associated facility data; The quality of mobile monitoring objects is quantitatively evaluated based on the spatiotemporal constraint model to obtain the spatiotemporal correlation evaluation results; The step of determining the associated facility data corresponding to each trajectory point based on the spatiotemporal distance value, and constructing a spatiotemporal constraint model of the trajectory and facility locations based on the associated facility data, includes: Based on each trajectory point, the target spatiotemporal distance value is selected from all spatiotemporal distance values; The facility coordinate data corresponding to the target spatiotemporal distance value is determined as the associated facility data of the trajectory point; Determine whether the target spatiotemporal distance value is greater than a preset distance threshold; When the target spatiotemporal distance value is greater than a preset distance threshold, the trajectory point is marked as an abnormal trajectory point; A spatiotemporal constraint model is constructed based on the set of relationships between all trajectory points and their corresponding associated facilities, as well as the data of abnormal trajectory point markers. The step of quantitatively evaluating the quality of mobile monitored objects based on the spatiotemporal constraint model to obtain the spatiotemporal correlation evaluation result includes: Extract the inspection coverage rate data and operation duration data of the mobile monitoring objects from the spatiotemporal constraint model; The basic correlation index is calculated based on the inspection coverage data. The process matching index is calculated based on the operation duration data; The timeliness index of the results is calculated based on the frequency of change of the facility status data; The spatiotemporal correlation evaluation result is obtained by weighted summation of the basic correlation index, the process matching index, and the result timeliness index.

2. The method as described in claim 1, characterized in that, The step of performing time-series alignment processing on BeiDou positioning terminal trajectory data, IoT sensor node facility status data, and mobile terminal image data to obtain aligned multimodal data includes: Extract the first positioning timestamp information from the trajectory data of the Beidou positioning terminal; Extract the status change timestamp information from the facility status data; Extract the shooting timestamp information from the image data; The first positioning timestamp information, the state change timestamp information, and the shooting timestamp information are unified to a standard time base to obtain unified base time data; Based on the unified reference time data, the trajectory data of the Beidou positioning terminal, the facility status data, and the image data are aligned to obtain time-aligned data. The time-aligned data is cleaned to remove duplicate and abnormal data, resulting in aligned multimodal data.

3. The method as described in claim 1, characterized in that, The step of extracting the spatiotemporal distance values ​​between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data from the aligned multimodal data includes: Extract the positioning terminal trajectory point sequence data and facility geographic coordinate set data from the aligned multimodal data; Extract the current trajectory point's positioning coordinate information and the second positioning timestamp information from the positioning terminal's trajectory point sequence data; Extract the facility coordinate information and facility status timestamp information of the current facility coordinates from the facility geographic coordinate set data; Calculate the spatial distance between the positioning coordinates and the facility coordinates; Calculate the time difference between the second location timestamp information and the facility status timestamp information; The spatiotemporal distance value is obtained by weighted fusion calculation based on the spatial distance value and the time difference value.

4. The method as described in claim 1, characterized in that, The method further includes: Obtain emergency event trigger information; When the type of the emergency event triggering information is a preset public health event level, preset weight adjustment rule data is obtained; The weight parameter of the result timeliness index is increased by a preset ratio according to the preset weight adjustment rule data to obtain the adjusted weight parameter; Based on the adjusted weight parameters, return to the step of weighting and summing the basic correlation index, the process matching index, and the result timeliness index to obtain the adjusted spatiotemporal correlation evaluation result.

5. The method as described in claim 1, characterized in that, The method further includes: Obtain historical score sequence data of the spatiotemporal correlation assessment results; Determine whether the score values ​​for a consecutive preset number of periods in the historical scoring sequence data are all lower than a preset threshold; When the score values ​​for a consecutive preset number of periods in the historical scoring sequence data are all lower than a preset threshold, the dynamic adjustment mode of the operation parameters is triggered and an adjustment instruction is generated; Based on the adjusted command response data acquisition enhancement mode, the trajectory reporting frequency parameter of the Beidou positioning terminal is increased by a preset multiple, and the state sampling sensitivity parameter of the IoT sensor node is increased by a preset level.

6. A work quality assessment device based on BeiDou positioning, characterized in that, The device includes: The alignment module is used to perform time-series alignment processing on Beidou positioning terminal trajectory data, IoT sensor node facility status data and mobile terminal image data to obtain aligned multimodal data, wherein the aligned multimodal data includes positioning terminal trajectory point sequence data and facility geographic coordinate set data. The calculation module is used to calculate the spatiotemporal distance between each trajectory point in the positioning terminal trajectory point sequence data and each facility coordinate in the facility geographic coordinate set data; The construction module is used to determine the associated facility data corresponding to each trajectory point based on the spatiotemporal distance value, and to construct a spatiotemporal constraint model between the trajectory and the facility point. The construction module is also used to filter out the target spatiotemporal distance value from all spatiotemporal distance values ​​based on each trajectory point; The facility coordinate data corresponding to the target spatiotemporal distance value is determined as the associated facility data of the trajectory point; Determine whether the target spatiotemporal distance value is greater than a preset distance threshold; When the target spatiotemporal distance value is greater than a preset distance threshold, the trajectory point is marked as an abnormal trajectory point; A spatiotemporal constraint model is constructed based on the set of relationships between all trajectory points and their corresponding associated facilities, as well as the data of abnormal trajectory point markers. The evaluation module is used to quantitatively evaluate the quality of mobile monitored objects based on the spatiotemporal constraint model, and obtain the spatiotemporal correlation evaluation results. The evaluation module is also used to extract inspection coverage data and operation duration data of mobile monitoring objects from the spatiotemporal constraint model; The basic correlation index is calculated based on the inspection coverage data. The process matching index is calculated based on the operation duration data; The timeliness index of the results is calculated based on the frequency of change of the facility status data; The spatiotemporal correlation evaluation result is obtained by weighted summation of the basic correlation index, the process matching index, and the result timeliness index.

7. A work quality assessment device based on BeiDou positioning, characterized in that, The device includes: a memory, a processor, and a BeiDou-based job quality assessment program stored in the memory and executable on the processor, the BeiDou-based job quality assessment program being configured to implement the steps of the BeiDou-based job quality assessment method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a job quality assessment program based on BeiDou positioning. When the job quality assessment program based on BeiDou positioning is executed by the processor, it implements the steps of the job quality assessment method based on BeiDou positioning as described in any one of claims 1 to 5.

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