Road dust accumulation condition monitoring method, system, equipment and medium

Abstract

The invention provides a road dust accumulation condition monitoring method, system and device and a medium, and relates to the technical field of road dust accumulation monitoring, and the method comprises the steps: dividing each piece of original GPS point location data into different monitoring road sections based on a road information base; for each monitored road section, determining a dust retention characteristic value based on the original road dust retention data corresponding to each piece of original GPS point location data in the monitored road section; determining a self-adaptive threshold value based on the dust retention characteristic value; adopting a Douglas-Peucker algorithm, and screening key GPS point location data from the original GPS point location data based on a self-adaptive threshold value; and determining and displaying the road dust accumulation condition based on the original road dust accumulation data corresponding to the key GPS point location data. The key GPS point location data is screened by using the adaptive threshold determined based on the dust retention characteristic value, and the adaptive threshold can adapt to the dust retention characteristic of the monitored road section, so that the calculated amount can be reduced and the screening efficiency can be improved while accurate key point screening is realized.

Description

Technical Field

[0001] The present application relates to the technical field of road dust monitoring, and in particular, to a method, system, device and medium for monitoring road dust conditions. Background Art

[0002] Road dust refers to a series of services and technical measures for monitoring, analyzing and controlling dust on urban or highway roads. The main purpose of this service is to reduce environmental pollution caused by road dust, improve air quality, and provide a cleaner living environment for residents.

[0003] Currently, the matching of road dust and road information mainly relies on a road dust condition monitoring system integrated with functions such as online monitoring, data calculation, map matching, video monitoring, data transmission and automatic statistics. Such systems are usually installed on vehicles. During vehicle driving, road dust data and original trajectory data are collected in real time and analyzed to match the road dust condition with the road position information, so as to realize the dynamic monitoring of the dust condition of the monitored road section.

[0004] However, in the prior art, the original trajectory data often contains a large number of redundant points. The dynamic monitoring of the dust condition of the monitored road section based on the original trajectory data not only consumes resources, but also may affect the accuracy and efficiency of data analysis, resulting in a poor matching effect between the road dust condition and the road position information, and further affecting the accuracy of dust condition monitoring. Summary of the Invention

[0005] The purpose of the present application is to provide a method, system, device and medium for monitoring road dust conditions to alleviate the above technical problems existing in the prior art. The technical solutions provided by the present application are as follows:

[0006] In a first aspect, the present application provides a method for monitoring road dust conditions, including:

[0007] Receiving each original GPS point data collected by a road dust monitoring device and the original road dust data corresponding to each original GPS point data;

[0008] Based on a road information database, dividing each original GPS point data into different monitored road sections; wherein, the road information database includes GPS point data corresponding to each road section;

[0009] For each monitored road section, based on the original road dust data corresponding to each original GPS point data included in the monitored road section, determining the dust characteristic value of the monitored road section; based on the dust characteristic value, determining the adaptive threshold of the monitored road section; using the Douglas-Peucker algorithm, based on the adaptive threshold, screening out key GPS point data from each original GPS point data included in the monitored road section;

[0010] Determine the road dust condition corresponding to each monitoring section based on the original road dust data corresponding to the key GPS point data included in each monitoring section;

[0011] Visually display the road dust condition corresponding to each monitoring section on the map.

[0012] Optionally, based on the original road dust data corresponding to each original GPS point data included in the monitoring section, determine the dust characteristic value of the monitoring section, including:

[0013] Extract the dust characteristic values corresponding to each original GPS point data included in the monitoring section from the original road dust data corresponding to each original GPS point data included in the monitoring section; wherein, the dust characteristic value includes at least one of dust thickness, dust deposition rate, dust thickness gradient, dust composition, and particle size distribution;

[0014] Determine the dust characteristic value of the monitoring section based on the average value of the dust characteristic values corresponding to each original GPS point data included in the monitoring section.

[0015] Optionally, based on the dust characteristic value, determine the adaptive threshold of the monitoring section, including:

[0016] Input the dust characteristic value into the threshold prediction model to obtain the adaptive threshold of the monitoring section; wherein, the threshold prediction model is a machine learning model trained based on the historical dust characteristic data and historical adaptive thresholds of different sections and satisfying the screening effect index of the key GPS point data.

[0017] Optionally, when using the Douglas-Peucker algorithm to screen the key GPS point data from each original GPS point data included in the monitoring section based on the adaptive threshold, it further includes:

[0018] Based on the dust characteristic values corresponding to each original GPS point data included in the monitoring section, divide the pairs of original GPS point data with similar dust characteristic values into the same monitoring area;

[0019] Determine the dust characteristic value of each monitoring area based on the average value of the dust characteristic values of each original GPS point data included in each monitoring area;

[0020] Based on the dust characteristic values of each monitoring area, determine the monitoring area with dust mutation as the key monitoring area from each monitoring area;

[0021] Give priority to screening the key GPS point data from each original GPS point data included in the key monitoring area.

[0022] Optionally, the road dust condition monitoring method provided by this application further includes:

[0023] Based on the dust accumulation characteristic value of the key monitoring area, determine the local threshold of the key monitoring area, update the adaptive threshold to the local threshold, and based on the updated adaptive threshold, screen the key GPS point data from each original GPS point data included in the key monitoring area.

[0024] Optionally, when using the Douglas-Peucker algorithm to screen the key GPS point data from each original GPS point data included in the monitoring section based on the adaptive threshold, it further includes:

[0025] Based on the dust accumulation center migration trajectory, determine the high-complexity area in the monitoring section, and preferentially screen the key GPS point data from each original GPS point data included in the high-complexity area.

[0026] Optionally, when using the Douglas-Peucker algorithm to screen the key GPS point data from each original GPS point data included in the monitoring section based on the adaptive threshold, it further includes:

[0027] When it is determined that any original GPS point data included in the monitoring section is the common GPS point data of other monitoring sections, determine the original GPS point data as the key GPS point data.

[0028] In a second aspect, this application provides a road dust condition monitoring system, including:

[0029] A data receiving unit, configured to receive each original GPS point data collected by the road dust monitoring device and the original road dust data corresponding to each original GPS point data;

[0030] A section division unit, configured to divide each original GPS point data into different monitoring sections based on the road information database; wherein, the road information database includes the GPS point data corresponding to each section;

[0031] A data thinning unit, for each monitoring section, based on the original road dust data corresponding to each original GPS point data included in the monitoring section, determine the dust accumulation characteristic value of the monitoring section; based on the dust accumulation characteristic value, determine the adaptive threshold of the monitoring section; use the Douglas-Peucker algorithm, based on the adaptive threshold, screen the key GPS point data from each original GPS point data included in the monitoring section;

[0032] A dust accumulation analysis unit, configured to determine the road dust condition corresponding to each monitoring section based on the original road dust data corresponding to the key GPS point data included in each monitoring section;

[0033] A visualization unit for visually displaying the road dust accumulation status corresponding to each monitored section on a map.

[0034] In a third aspect, the present application provides an electronic device, including a processor and a memory. The memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the above-mentioned road dust accumulation status monitoring method.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, and when the computer-executable instructions are executed by a processor, the above-mentioned road dust accumulation status monitoring method is implemented.

[0036] The beneficial effects of the present application are as follows:

[0037] By using the adaptive threshold determined based on the dust accumulation eigenvalue to screen the key GPS point data, the present application can make the adaptive threshold adapt to the dust accumulation characteristics of the monitored section, so as to achieve accurate key point screening while reducing the calculation amount and improving the screening efficiency, and further improve the matching effect between the road dust accumulation status and the road position information and the accuracy of dust accumulation status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a general flowchart of a road dust accumulation status monitoring method in an embodiment of the present application;

[0040] Figure 2 It is a general flowchart of another road dust accumulation status monitoring method in an embodiment of the present application;

[0041] Figure 3 It is a schematic diagram of dust accumulation level configuration in an embodiment of the present application;

[0042] Figure 4 It is a schematic functional structure diagram of a road dust accumulation status monitoring system in an embodiment of the present application;

[0043] Figure 5 It is a schematic hardware structure diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. Components of the embodiments of this application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

[0045] Therefore, the detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application claimed, but is merely representative of selected embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts fall within the scope of protection of this application.

[0046] It should be noted that like reference numerals and letters denote like items in the following figures. Therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

[0047] The embodiments of this application provide a method for monitoring road dust conditions, which is applied to a road dust condition monitoring system. The road dust condition monitoring system can be installed on electronic devices such as computers, tablets, mobile phones, etc. Refer to Figure 1 As shown, the method for monitoring road dust conditions provided by the embodiments of this application mainly includes the following steps:

[0048] Step 110: Receive each piece of original GPS point data collected by a road dust monitoring device and the original road dust data corresponding to each piece of original GPS point data.

[0049] The road dust monitoring device is used to collect road dust data on the road and record GPS point data through a GPS positioning system. In one implementation, original road dust data on the road can be collected through dust collection devices such as dust sensors, camera devices, laser rangefinders, lidar (LiDAR), ultrasonic sensors, X-ray fluorescence spectrometers (XRF), and portable gas chromatographs (GC) provided on the road dust monitoring device. Original GPS point data is collected through a GPS positioning device provided on the road dust monitoring device. The dust collection device and the GPS positioning device collect data synchronously, that is, the original GPS point data and the original road dust data are associated one by one through time information such as timestamps. In addition, time information such as timestamps can also indicate the sorting of the original GPS point data and the original road dust data. When there is retransmission and duplication of the original GPS point data, data with duplicate timestamps can be sorted and deleted according to the timestamp.

[0050] Step 120: Based on the road information database, divide each piece of original GPS point data into different monitored road sections; among them, the road information database includes the GPS point data corresponding to each road section.

[0051] In the embodiment of the present application, the road information database contains basic information such as the province, city, district, street, and road grade to which each road section belongs, as well as the GPS point data of multiple key positions (i.e., positions that can uniquely identify the road section) included in each road section. By matching each piece of original GPS point data with the GPS point data of multiple key positions of different road sections, the road section to which each piece of original GPS point data belongs can be determined as the monitored road section.

[0052] Step 130: For each monitored road section, determine the dust accumulation characteristic value of the monitored road section based on the original road dust accumulation data corresponding to each piece of original GPS point data included in the monitored road section; based on the dust accumulation characteristic value, determine the adaptive threshold of the monitored road section; use the Douglas-Peucker algorithm, based on the adaptive threshold, screen out the key GPS point data from each piece of original GPS point data included in the monitored road section.

[0053] In the embodiment of the present application, for each monitored road section, when determining the dust accumulation characteristic value of the monitored road section based on the original road dust accumulation data corresponding to each piece of original GPS point data included in the monitored road section, the following methods can be used but are not limited to:

[0054] First, extract the dust accumulation characteristic values corresponding to each piece of original GPS point data included in the monitored road section from the original road dust accumulation data corresponding to each piece of original GPS point data included in the monitored road section; among them, the dust accumulation characteristic value includes at least one of dust accumulation thickness, dust accumulation rate, dust accumulation thickness gradient, dust accumulation composition, and particle size distribution. In specific implementation, dust accumulation collection devices such as camera devices, laser rangefinders, lidar (LiDAR), ultrasonic sensors, X-ray fluorescence spectrometers (XRF), and portable gas chromatographs (GC) are set on the road dust accumulation monitoring equipment. Based on this, for each piece of original GPS point data included in the monitored road section, the following methods can be used to extract dust accumulation characteristic values such as dust accumulation thickness, dust accumulation rate, dust accumulation thickness gradient, dust accumulation composition, and particle size distribution from the original road dust accumulation data corresponding to the original GPS point data:

[0055] 1. Dust accumulation thickness:

[0056] Direct measurement: Select several points on the road and use a laser rangefinder or ultrasonic sensor to measure the distance from the dust accumulation surface to the road surface.

[0057] Image analysis: Take photos of the road surface, and use image processing technology to identify the dust accumulation coverage area and quantify its thickness.

[0058] 2. Dust accumulation rate:

[0059] Regular monitoring: Regularly (such as weekly or monthly) measure the dust accumulation thickness at the same location, calculate the thickness difference between two time points, and obtain the dust accumulation rate.

[0060] Model prediction: Combine meteorological data (such as wind speed, precipitation) and traffic flow data (such as traffic volume, vehicle type), and use statistical models or machine learning algorithms to predict the dust accumulation rate.

[0061] 3. Dust accumulation thickness gradient:

[0062] Direct measurement: Select multiple positions on the same road section for measurement, generate a spatial distribution map of the dust accumulation thickness, and analyze its gradient change.

[0063] Mobile monitoring: Use lidar (LiDAR) or ultrasonic sensors to drive along the road and record the dust accumulation thickness data in real time to generate a continuous thickness gradient map.

[0064] 4. Dust accumulation composition:

[0065] Use X-ray fluorescence spectrometer (XRF) and portable gas chromatograph (GC) to quickly detect the main components in the dust accumulation on-site, such as heavy metals, organic pollutants, etc.

[0066] 5. Particle size distribution:

[0067] Take microscopic photos of the dust accumulation particles with a microscope, analyze the size of each particle through image processing technology, and summarize to obtain the particle size distribution.

[0068] Then, based on the average value of the dust accumulation characteristic values corresponding to each original GPS point data included in the monitored road section, determine the dust accumulation characteristic value of the monitored road section. In an optional implementation manner, the dust accumulation characteristic value of the monitored road section may be the weighted sum value of the average values of different dust accumulation characteristic values corresponding to each original GPS point data, and the weights of different dust accumulation characteristic values may be determined according to the importance of the dust accumulation characteristic value to the monitored road section, and the importance of the dust accumulation characteristic value to the monitored road section may be determined according to the IV value. In another optional implementation manner, the dust accumulation characteristic value of the monitored road section may also be the average value of different dust accumulation characteristic values, that is, corresponding to the dust accumulation characteristic values corresponding to the original GPS point data, the dust accumulation characteristic value of the monitored road section includes at least one of dust accumulation thickness, dust accumulation rate, dust accumulation thickness gradient, dust accumulation composition, and particle size distribution.

[0069] Furthermore, for each monitored road section, after determining the dust accumulation characteristic value of the monitored road section based on the original road dust accumulation data corresponding to each original GPS point data included in the monitored road section, an adaptive threshold of the monitored road section can be determined based on the dust accumulation characteristic value. Specifically, the following methods can be adopted but are not limited to:

[0070] Input the dust accumulation eigenvalue into the threshold prediction model to obtain the adaptive threshold of the monitored section; among them, the threshold prediction model is trained based on the historical dust accumulation characteristic data and historical adaptive thresholds of different sections, and is a machine learning model that meets the screening effect index of key GPS point data.

[0071] In the embodiment of the present application, the threshold prediction model is trained in the following manner:

[0072] 1. Data collection and preprocessing:

[0073] Historical dust accumulation characteristic data: including information such as dust accumulation thickness, particle size distribution, and component analysis of different sections at different time periods.

[0074] Historical adaptive threshold: including the thresholds dynamically adjusted according to local dust accumulation characteristics (such as dust accumulation mutation, common points, dust accumulation center migration, etc.) corresponding to different sections.

[0075] Key GPS point data: including the pre-calibrated specific GPS point data contained in different sections, which is used to verify and evaluate the accuracy of the model.

[0076] 2. Feature engineering:

[0077] Feature extraction: Extract useful features from the historical data, such as average dust accumulation thickness, maximum-minimum value difference, seasonal change trend, etc.

[0078] Weighted calculation: Assign weights to each feature based on the importance of local dust accumulation characteristics to ensure that the model can capture key information.

[0079] Data standardization: Normalize or standardize all features to eliminate the dimension difference and improve the model performance.

[0080] 3. Model selection and training:

[0081] Model selection: Select a suitable model from models such as linear regression, decision tree, random forest, support vector machine (SVM), neural network, etc.

[0082] Model training: Use the historical data set to train the selected model and adjust the hyperparameters to optimize the model performance.

[0083] Cross-validation: Evaluate the generalization ability of the model through methods such as K-fold cross-validation to prevent overfitting.

[0084] 4. Model evaluation:

[0085] Set evaluation indicators: Define the criteria for model success, such as accuracy, recall rate, F1 score, etc., especially for the screening effect of key GPS point data.

[0086] Test model: Evaluate the performance of the model using an independent test set to ensure its reliability on new data.

[0087] 5. Application and optimization:

[0088] Real-time update: As new observation data is continuously added, retrain the model regularly to keep it up-to-date.

[0089] Feedback mechanism: Establish a feedback system to adjust the model parameters according to the performance in actual applications and further improve the prediction accuracy.

[0090] Furthermore, after determining the adaptive threshold of the monitoring section based on the dust accumulation eigenvalue, the Douglas-Peucker algorithm can be used to screen out the key GPS point data from the original GPS point data included in the monitoring section based on the adaptive threshold. Specifically, the following methods can be used but are not limited to:

[0091] Step 1, Select the starting point and the ending point: Select the starting point and the ending point from the original curve represented by the monitoring section.

[0092] Step 2, Calculate the distance: Calculate the perpendicular distance from all other points on the original curve to the straight line segment determined by the starting point and the ending point.

[0093] Step 3, Judgment and simplification: Find the point corresponding to the maximum distance. If the distance is greater than the adaptive threshold, retain this point and divide the original curve into two parts, and recursively execute the above process for each part; otherwise, remove all intermediate points and only retain the starting point and the ending point.

[0094] Step 4, Recursive execution: Repeat the above process for each sub-curve until all points are processed.

[0095] It should be noted that in the embodiments of the present application, in order to improve the screening efficiency of the key GPS point data and avoid the loss of key data, during the process of screening out the key GPS point data from the original GPS point data included in the monitoring section based on the Douglas-Peucker algorithm and the adaptive threshold, it further includes:

[0096] Based on the dust accumulation eigenvalues corresponding to the original GPS point data included in the monitoring section, pair the original GPS point data with similar dust accumulation eigenvalues and divide them into the same monitoring area;

[0097] Based on the average value of the dust accumulation eigenvalues of the original GPS point data included in each monitoring area, determine the dust accumulation eigenvalue of each monitoring area;

[0098] Based on the dust accumulation eigenvalues of each monitoring area, determine the monitoring areas with dust accumulation mutations as key monitoring areas from each monitoring area;

[0099] Key GPS point data is preferentially screened from the respective original GPS point data included in the key monitoring areas.

[0100] Furthermore, in order to further strengthen the screening effect of the key GPS point data in the key monitoring areas where dust accumulation mutations occur, the local threshold of the key monitoring area can also be determined based on the dust accumulation characteristic value of the key monitoring area, the adaptive threshold is updated to the local threshold, and based on the updated adaptive threshold, key GPS point data is screened from the respective original GPS point data included in the key monitoring area. Among them, when determining the local threshold of the key monitoring area based on the dust accumulation characteristic value of the key monitoring area, the aforementioned threshold prediction model can be used.

[0101] In addition, in order to further improve the screening efficiency and screening accuracy of the key GPS point data, during the process of using the Douglas-Peucker algorithm to screen out the key GPS point data from the respective original GPS point data included in the monitoring section based on the adaptive threshold, the high-complexity areas in the monitoring section can also be determined based on the dust accumulation center migration trajectory, and key GPS point data is preferentially screened from the respective original GPS point data included in the high-complexity areas; among them, based on the respective historical GPS point data included in the monitoring section and the historical road dust accumulation data corresponding to the respective historical GPS point data within the set time range (such as one week, half a month, one month, etc.) between the current time, the dust accumulation center of the monitoring section and the migration trajectory of the dust accumulation center can be analyzed, and the area passed by the migration trajectory of the dust accumulation center is determined as the high-complexity area.

[0102] In addition, in order to further improve the screening efficiency and screening accuracy of the key GPS point data, during the process of using the Douglas-Peucker algorithm to screen out the key GPS point data from the respective original GPS point data included in the monitoring section based on the adaptive threshold, when it is determined that any original GPS point data included in the monitoring section is the common GPS point data of other monitoring sections, the original GPS point data is determined as the key GPS point data. By preferentially retaining the common GPS point data, the loss of key road boundary points can be effectively avoided, and the retention rate of key dust accumulation points can be improved.

[0103] Step 140: Determine the road dust accumulation conditions corresponding to each monitoring section based on the original road dust accumulation data corresponding to the key GPS point data included in each monitoring section.

[0104] In the embodiments of the present application, based on the original road dust accumulation data corresponding to the key GPS point data included in each monitoring section, the dust accumulation characteristic values corresponding to the key GPS point data included in each monitoring section are determined, and based on the dust accumulation characteristic values corresponding to the key GPS point data included in each monitoring section, the road dust accumulation conditions corresponding to each monitoring section are determined; wherein, the road dust accumulation conditions can be the quantified data such as the dust accumulation level and the dust accumulation amount determined based on the weighted result after weighting the dust accumulation characteristic values corresponding to each key GPS point data included in the monitoring section.

[0105] Step 150: Visualize the road dust accumulation conditions corresponding to each monitoring section on the map.

[0106] In the embodiments of the present application, different road dust accumulation conditions correspond to visualization colors. For example, the visualization color corresponding to the monitoring section with a high dust accumulation level is red, the visualization color corresponding to the monitoring section with a medium dust accumulation level is yellow, and the visualization color corresponding to the monitoring section with a low dust accumulation level is green. By rendering each monitoring section on the map into the visualization color corresponding to its road dust accumulation condition, the visualization display of the road dust accumulation conditions corresponding to each monitoring section is realized. In this way, by using the adaptive threshold determined based on the dust accumulation characteristic value to screen the key GPS point data, the adaptive threshold can be adapted to the dust accumulation characteristics of the monitoring section, so that accurate key point screening can be achieved while reducing the calculation amount and improving the screening efficiency, and further the matching effect between the road surface dust accumulation condition and the road position information and the accuracy of the dust accumulation condition monitoring can be improved. In addition, during the screening process of the key GPS point data, by locally dynamically adjusting and optimizing the adaptive threshold, the screening accuracy and efficiency of the key GPS point data can be further improved, and further the matching effect between the road surface dust accumulation condition and the road position information and the accuracy of the dust accumulation condition monitoring can be improved.

[0107] Based on the above embodiments, the embodiments of the present application provide another method for monitoring road dust accumulation conditions, which is applied to a road dust accumulation condition monitoring system. The road dust accumulation condition monitoring system can be installed on electronic devices such as computers, tablet computers, and mobile phones. Refer to Figure 2 As shown, the method for monitoring road dust accumulation conditions mainly includes the following steps:

[0108] Step 210: Obtain the GPS point longitude and latitude information and the dust accumulation value of the road dust accumulation monitoring device.

[0109] Step 220: Perform matching processing on the processed GPS point longitude and latitude information and the road information, and perform matching processing on the dust accumulation value and the dust accumulation rule information; wherein, the GPS is respectively assigned to the road information and the dust accumulation rule information.

[0110] In one embodiment, the longitude and latitude information of the GPS points and the road information can be pre-processed. That is, the longitude and latitude information of the GPS points can be data-cleaned according to the road trajectory information to remove abnormal points and / or incorrect data points. Also, the drawn road is stored in the database, and the drawn road is assigned to determine the attribution information and road level information of the road.

[0111] In addition, to facilitate subsequent matching processing, the dust accumulation level can also be configured. Specifically, the road dust accumulation rules can be configured according to the dust accumulation requirements. The dust accumulation rules include a dust accumulation value range field, a color representation field, and a level (excellent, good, passing, poor, etc.) field.

[0112] After the above preprocessing and configuration processing are completed, matching processing can be performed. Specifically, corresponding matching processing can be performed according to the business requirements. For example, the business requirements may include viewing all the collected data, only viewing the data of the section one is responsible for, only viewing the section one is responsible for can reduce the amount of data, and detailed information such as the name of the section one is responsible for needs to be seen on the map.

[0113] In one embodiment, when the processed longitude and latitude information of the GPS points is matched with the road information, the longitude and latitude information of the GPS points that match the road information carries the ID of the road information. By comparing the dust accumulation value of the collected longitude and latitude information of the GPS points with the dust accumulation rules, evaluations such as excellent, good, and poor are obtained, and different evaluations are displayed in different colors on the map.

[0114] In practical applications, the above-mentioned GPS assignment of road information and dust accumulation rule information means that: the road information is a polyline drawn through several longitude and latitude points. When the distance from the GPS point to the point on the road to the straight line meets a preset threshold (for example, it can be 50 meters), then this GPS point is determined to belong to this road. Then the next GPS point will preferentially match the previously matched road, which increases the probability of correct matching. The same applies to dust accumulation. The GPS has a dust accumulation value and a dust accumulation rule match. The next time, it will preferentially match the rule matched last time. The dust accumulation rule information corresponding to the road dust accumulation rules at least includes a dust accumulation value range field, a color representation field, and a level field.

[0115] Step 230: Thinning the GPS points after cutting through Douglas-Peucker, and calculating the point information, dust accumulation rule information, and road information corresponding to each segment of the GPS sub-points after cutting respectively.

[0116] In one implementation, the Douglas-Peucker algorithm is used to thin the GPS points after cutting. It can be a set sorted according to the GPS timestamps. A threshold is set and recursive partitioning is performed to obtain a straight line approximating a curve, thereby obtaining the result of the thinning process. The size of the threshold is modified according to the matching similarity and deviation with all the original GPS routes and the user's acceptance level. Special roads can have custom thresholds. The depth of recursive partitioning is also a factor affecting performance, and a reasonable depth is set to improve performance.

[0117] Furthermore, the point information, dust accumulation rule information, and road information corresponding to each segment of the GPS sub-points after cutting are calculated separately. It can be that GPS points with the same dust accumulation level and belonging to the same road are counted as one segment, and the maximum value, minimum value, average value, and number of each segment are calculated separately.

[0118] Step 240: Visualize each thinned GPS sub-trajectory on the map.

[0119] After the thinning process, each thinned line segment can be displayed on the map. The user can see the color, level, maximum value, minimum value, average value, the number of device upload data, and road information of each line segment. When performing the visualization, whether a road is a working area is distinguished by the thickness of the drawn line on the map. For example, the thickness of the line can be used to distinguish whether a road is a working area to achieve the visualization of the GPS sub-trajectory on the map.

[0120] In summary, by thinning the GPS points using the Douglas-Peucker algorithm and calculating the corresponding point information, dust accumulation rule information, and road information, the accuracy of processing the original GPS data can be improved, and thus the accuracy and processing efficiency of road information matching are enhanced.

[0121] For ease of understanding, the following provides a detailed description of this road dust accumulation condition monitoring method.

[0122] In one example, the preliminary cleaning of the GPS point longitude and latitude information to remove abnormal or incorrect data points may include the following processing steps:

[0123] 1. Data range check

[0124] Geographical boundary filtering: Set a reasonable longitude and latitude range according to the actual working area and exclude points that are clearly outside this range.

[0125] Reasonable value verification: Ensure that the latitude is between [-90, 90] and the longitude is between [-180, 180].

[0126] 2. Time consistency check

[0127] Timestamp Check: Confirm that each GPS record has a correct timestamp and is arranged in chronological order. If time jumps or unreasonable time intervals are found, it may indicate problems with the data.

[0128] Continuity Analysis: For data collected in a time series, check whether the time difference between adjacent points is reasonable, avoiding situations that are too short (possibly duplicate points) or too long (possibly re-acquired after signal loss).

[0129] 3. Spatial Rationality Assessment

[0130] Distance Threshold Screening: Calculate the straight-line distance between adjacent GPS points and set a maximum allowable moving distance. If the distance between two points exceeds the maximum value that the normal driving speed of the vehicle can reach, then that point is considered an abnormal point.

[0131] Speed Limit: Based on the vehicle type and road conditions, set a reasonable maximum speed limit. Identify speeding situations by calculating the speed between two points, thereby marking potential problem points.

[0132] 4. Application of Clustering Algorithms

[0133] Using density clustering algorithms such as DBSCAN can automatically find isolated points that are far from most points as abnormal points. This method is particularly suitable for dealing with distribution patterns without clear boundaries.

[0134] 5. Comparison with Historical Trajectories

[0135] For repeated routes, such as bus routes or cleaning vehicles on fixed routes, it is possible to detect whether there are deviations from the normal route in newly collected data by comparing with historical trajectories.

[0136] 6. Data Smoothing

[0137] Applying a low-pass filter or other forms of data smoothing techniques can help eliminate small fluctuations caused by unstable satellite signals, but be careful not to over-smooth and mask the true motion characteristics.

[0138] 7. Map Matching

[0139] Project the original GPS points onto the nearest road network to correct the offset caused by positioning errors. This requires the use of geographic information system (GIS) software and a detailed map database.

[0140] 8. Rule Engine

[0141] Develop a set of rules to define in which cases data points should be marked as anomalies. For example, when GPS accuracy metrics (such as HDOP, VDOP, etc.) are too high, indicating poor positioning quality, the relevant records should be marked for further review.

[0142] 9. Automated Tools and Scripts

[0143] Use programming languages (such as Python, R, etc.) to write automated scripts to execute the above cleaning process, improving efficiency and reducing the possibility of human errors.

[0144] After completing the above steps, the cleaned dataset should be reviewed again to ensure that all operations are reasonable and valid data is not accidentally deleted. In addition, in practical applications, it is best to customize the cleaning strategy by combining domain knowledge and specific business logic.

[0145] In another example, the roads stored in the database can be assigned information such as the province, city, district, and street to which the road belongs, as well as the road grade information. In specific implementation, a multi-table structure can be designed to achieve this. For example, it can be built based on a relational database (such as MySQL, PostgreSQL):

[0146] 1. Design the Database Schema

[0147] Table 1: provinces (Province Table)

[0148] id: INT, primary key

[0149] name: VARCHAR, province name

[0150] Table 2: cities (City Table)

[0151] id: INT, primary key

[0152] province_id: INT, foreign key, associated with provinces.id

[0153] name: VARCHAR, city name

[0154] Table 3: districts (District Table)

[0155] id: INT, primary key

[0156] city_id: INT, foreign key, associated with cities.id

[0157] name: VARCHAR, district name

[0158] Table 4: streets (Street Table)

[0159] id: INT, primary key

[0160] district_id: INT, foreign key, associated with districts.id

[0161] name: VARCHAR, street name

[0162] Table 5: roads (road table)

[0163] id: INT, primary key

[0164] street_id: INT, foreign key, associated with streets.id

[0165] name: VARCHAR, road name

[0166] grade: ENUM or VARCHAR, road grade (e.g., 'highway','main road','secondary road', 'branch road').

[0167] 2. Draw roads and store geographic information

[0168] To support spatial queries and visualization, you can use GIS (Geographic Information System) functions, such as the PostGIS extension (for PostgreSQL), which allows you to store and manipulate geometric objects, such as points, lines, and polygons.

[0169] For each road, its geometry (usually a linestring) needs to be recorded. This can be achieved in the following ways:

[0170] Add a field in the roads table to store the geometric data type (e.g., GEOMETRY(LINESTRING) in PostGIS).

[0171] Use GIS tools or libraries (such as QGIS, ArcGIS, GeoPandas, Shapely, etc.) to draw the roads, convert them into WKT (Well-Known Text) format or other compatible formats, and then insert them into the corresponding fields.

[0172] 3. Data input and maintenance

[0173] Manual input: Manually input new roads and their attributes through the user interface.

[0174] Batch import: Batch import road data from existing GIS files (such as Shapefile, GeoJSON, etc.).

[0175] API interface: Develop an API interface to enable external systems to submit new road information or update existing information.

[0176] 4. Query and Analysis

[0177] Once the data is correctly stored in the database, you can perform various types of queries to retrieve all roads within a specific area, filter roads by grade, calculate the shortest path between two locations, etc. This usually involves spatial indexing and complex SQL query statements, and sometimes also requires combining services provided by a GIS server for more advanced spatial analysis.

[0178] In an optional implementation, in order to implement subsequent thinning using Douglas-Peucker, the GPS continuous point data corresponding to the GPS point longitude and latitude information can be segmented in advance, and the GPS continuous point data with the same dust accumulation level and the same affiliated road is divided into the same set; if there is GPS continuous point data that cannot be matched to the corresponding road, it is matched after adding attribute differentiation.

[0179] After segmentation, calculate the point information, dust accumulation rule information, and road information corresponding to each GPS sub-point after cutting. In specific implementation, the GPS sub-points can be segmented according to the dust accumulation level and the affiliated road; the GPS calculation segments with the same dust accumulation level and the same affiliated road are determined, and the point information, dust accumulation rule information, and road information corresponding to each GPS sub-point after cutting are calculated respectively.

[0180] Figure 3 The execution process of a specific road dust accumulation condition monitoring method is shown, which mainly includes the following steps:

[0181] 1. Receive the GPS point longitude and latitude information, timestamp, and dust accumulation value of the dust accumulation device.

[0182] 2. Perform preliminary cleaning on the trajectory data to remove abnormal or incorrect data points.

[0183] 3. Road management: Draw the road (a polyline of multiple points) and store it in the database. The road can be assigned the province, city, district, street to which it belongs, and road grade information.

[0184] 4. Dust accumulation level configuration: Develop road dust accumulation rules according to dust accumulation requirements, which can be divided by fields. For example, the dust accumulation rules can include a dust accumulation value range field, a color field, and a grade (excellent, good, passing, poor, etc.) field, as shown in Figure 3 shown.

[0185] 5. Match the GPS point longitude and latitude information with the road information by setting a threshold, and match the dust accumulation value of the GPS point with the road section grade and dust accumulation level configuration of the road matched by the GPS point.

[0186] 6. Segment the continuous GPS point data. Points with the same continuous dust accumulation level and belonging to the same road are grouped into the same set. Those that cannot be matched to a road will be distinguished by added attributes (thin lines are used for distinction on the map).

[0187] 7. Use the Douglas-Peucker algorithm for thinning. Optimize the Douglas-Peucker algorithm for the segmented GPS points. Combining the business segment cutting will increase the calculation efficiency, and calculate the maximum value, minimum value, average value, number, and the color, level information of the dust accumulation rule, and road information for each segment.

[0188] 8. Display each thinned line segment on the map. Users can see the color, level, maximum value, minimum value, average value, the number of device-uploaded data, and road information of each line segment. The thickness of the line can distinguish whether the road is a working area.

[0189] Based on the above method embodiments, the embodiments of the present application also provide a road dust accumulation condition monitoring system. Refer to Figure 4 as shown. The road dust accumulation condition monitoring system mainly includes the following parts:

[0190] A data receiving unit 401, configured to receive each original GPS point data collected by a road dust accumulation monitoring device and the original road dust accumulation data corresponding to each original GPS point data;

[0191] A section division unit 402, configured to divide each original GPS point data into different monitoring sections based on a road information database; wherein, the road information database includes the GPS point data corresponding to each section;

[0192] A data thinning unit 403, configured to, for each monitoring section, determine the dust accumulation characteristic value of the monitoring section based on the original road dust accumulation data corresponding to each original GPS point data included in the monitoring section; determine the adaptive threshold of the monitoring section based on the dust accumulation characteristic value; and use the Douglas-Peucker algorithm to screen out the key GPS point data from each original GPS point data included in the monitoring section based on the adaptive threshold;

[0193] A dust accumulation analysis unit 404, configured to determine the road dust accumulation condition corresponding to each monitoring section based on the original road dust accumulation data corresponding to the key GPS point data included in each monitoring section;

[0194] A visualization unit 405, configured to visually display the road dust accumulation condition corresponding to each monitoring section on a map.

[0195] In a possible implementation, the data thinning unit 403 is configured to extract the dust accumulation characteristic values corresponding to each original GPS point data included in the monitored section from the original road dust accumulation data corresponding to each original GPS point data included in the monitored section; wherein, the dust accumulation characteristic values include at least one of dust accumulation thickness, dust accumulation rate, dust accumulation thickness gradient, dust accumulation composition, and particle size distribution; and determine the dust accumulation characteristic value of the monitored section based on the average value of the dust accumulation characteristic values corresponding to each original GPS point data included in the monitored section.

[0196] In a possible implementation, the data thinning unit 403 is configured to input the dust accumulation characteristic value into a threshold prediction model to obtain the adaptive threshold of the monitored section; wherein, the threshold prediction model is a machine learning model trained based on the historical dust accumulation characteristic data and historical adaptive thresholds of different sections and satisfying the screening effect index of key GPS point data.

[0197] In a possible implementation, the data thinning unit 403 is further configured to divide the pairs of original GPS point data with similar dust accumulation characteristic values into the same monitoring area based on the dust accumulation characteristic values corresponding to each original GPS point data included in the monitored section; determine the dust accumulation characteristic value of each monitoring area based on the average value of the dust accumulation characteristic values of each original GPS point data included in each monitoring area; determine the monitoring area with dust accumulation mutation as the key monitoring area from each monitoring area based on the dust accumulation characteristic value of each monitoring area; and preferentially screen the key GPS point data from the original GPS point data included in the key monitoring area.

[0198] In a possible implementation, the data thinning unit 403 is further configured to determine the local threshold of the key monitoring area based on the dust accumulation characteristic value of the key monitoring area, update the adaptive threshold to the local threshold, and screen the key GPS point data from the original GPS point data included in the key monitoring area based on the updated adaptive threshold.

[0199] In a possible implementation, the data thinning unit 403 is further configured to determine the high-complexity area in the monitored section based on the dust accumulation center migration trajectory, and preferentially screen the key GPS point data from the original GPS point data included in the high-complexity area.

[0200] In a possible implementation, when the data thinning unit 403 determines that any original GPS point data included in the monitored section is the common GPS point data of other monitored sections, it determines the original GPS point data as the key GPS point data.

[0201] The road dust condition monitoring system provided by the embodiments of the present application has the same implementation principle and technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the embodiments of the road dust condition monitoring system, reference may be made to the corresponding contents in the foregoing embodiments of the road dust condition monitoring method.

[0202] The embodiments of the present application also provide an electronic device, as Figure 5 shown, which is a schematic structural diagram of the electronic device. Among them, the electronic device 100 includes a processor 51 and a memory 50. The memory 50 stores computer-executable instructions that can be executed by the processor 51, and the processor 51 executes the computer-executable instructions to implement the above-mentioned road dust condition monitoring method.

[0203] In Figure 5 the illustrated embodiment, the electronic device further includes a bus 52 and a communication interface 53. Among them, the processor 51, the communication interface 53, and the memory 50 are connected through the bus 52.

[0204] Among them, the memory 50 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 53 (which can be wired or wireless), a communication connection is established between the system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used. The bus 52 may be an ISA (Industry Standard Architecture, industrial standard architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture, extended industrial standard structure) bus, etc. The bus 52 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5 only a bidirectional arrow is used in

[0205] The processor 51 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 51 or instructions in the form of software. The above-mentioned processor 51 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application-specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor 51 reads the information in the memory and combines its hardware to complete the steps of the above road dust condition monitoring method.

[0206] The embodiments of the present application also provide a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the above road dust condition monitoring method is implemented. For the specific implementation, reference can be made to the foregoing method embodiments, and details will not be repeated here.

[0207] The above road dust condition monitoring method provided by the embodiments of the present application can also be implemented as a computer program product. The program product includes program code. When the program code is executed by a processor, the above road dust condition monitoring method provided by the embodiments of the present application is implemented. For the specific implementation, reference can be made to the method embodiments, and details will not be repeated here.

[0208] Unless otherwise specifically stated, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0209] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0210] In the description of this application, it should also be noted that unless otherwise clearly specified and limited, the terms "connected" and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific situations.

[0211] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of various embodiments of this application.

Claims

1. A method for monitoring road dust conditions, characterized in that: include: Receive each original GPS point data collected by the road dust monitoring device and the original road dust data corresponding to each original GPS point data; Based on the road information database, each of the original GPS point data is divided into different monitoring sections; wherein the road information database includes the GPS point data corresponding to each section; For each of the monitored sections, based on the original road dust data corresponding to each of the original GPS point data contained in the monitored section, determine the dust characteristic value of the monitored section; based on the dust characteristic value, determine the adaptive threshold of the monitored section; and use the Douglas-Peucker algorithm to filter out key GPS point data from each of the original GPS point data contained in the monitored section based on the adaptive threshold; Determine the road dust conditions corresponding to each of the monitored road sections based on the original road dust data corresponding to the key GPS point data contained in each of the monitored road sections; The road dust conditions corresponding to each of the monitored road sections are visualized on the map.

2. The road dust condition monitoring method according to claim 1, characterized in that: Determining the dust accumulation characteristic value of the monitored road section based on the original road dust accumulation data corresponding to each of the original GPS point data contained in the monitored road section includes: Extracting dust accumulation characteristic values ​​corresponding to the original GPS point data contained in the monitored road section from the original road dust accumulation data corresponding to the original GPS point data contained in the monitored road section; wherein the dust accumulation characteristic values ​​include at least one of dust accumulation thickness, dust accumulation rate, dust accumulation thickness gradient, dust accumulation composition, and particle size distribution; The dust accumulation characteristic value of the monitored road section is determined based on the average value of the dust accumulation characteristic values ​​corresponding to the original GPS point data contained in the monitored road section.

3. The method for monitoring road dust conditions according to claim 1, characterized in that: Determining the adaptive threshold of the monitored road section based on the dust accumulation characteristic value includes: The dust accumulation characteristic value is input into the threshold prediction model to obtain the adaptive threshold of the monitored section; wherein the threshold prediction model is a machine learning model trained based on historical dust accumulation characteristic data and historical adaptive thresholds of different sections, which meets the screening effect indicators of key GPS point data.

4. The method for monitoring road dust conditions according to claim 1, characterized in that: The process of selecting key GPS point data from each of the original GPS point data contained in the monitoring section by using the Douglas-Peucker algorithm based on the adaptive threshold also includes: Based on the dust accumulation characteristic values ​​corresponding to the respective original GPS point data included in the monitoring section, the original GPS point data pairs with similar dust accumulation characteristic values ​​are divided into the same monitoring area; Determine the dust accumulation characteristic value of each monitoring area based on the average value of the dust accumulation characteristic value of each of the original GPS point data contained in each of the monitoring areas; Based on the dust accumulation characteristic values ​​of each of the monitoring areas, determining a monitoring area where a sudden dust accumulation mutation occurs from each of the monitoring areas as a key monitoring area; The key GPS point data are preferentially screened from the original GPS point data contained in the key monitoring area.

5. The method for monitoring road dust conditions according to claim 4, characterized in that: Also includes: Based on the dust accumulation characteristic value of the key monitoring area, the local threshold of the key monitoring area is determined, the adaptive threshold is updated to the local threshold, and based on the updated adaptive threshold, the key GPS point data is filtered from the original GPS point data contained in the key monitoring area.

6. The method for monitoring road dust conditions according to claim 1, characterized in that: The process of selecting key GPS point data from each of the original GPS point data contained in the monitoring section by using the Douglas-Peucker algorithm based on the adaptive threshold also includes: Based on the migration trajectory of the dust accumulation center, the high-complexity area in the monitored section is determined, and the key GPS point data is preferentially screened from the various original GPS point data contained in the high-complexity area.

7. The method for monitoring road dust conditions according to claim 1, characterized in that: The process of selecting key GPS point data from each of the original GPS point data contained in the monitoring section by using the Douglas-Peucker algorithm based on the adaptive threshold also includes: When it is determined that any of the original GPS point data included in the monitoring section is common GPS point data with other monitoring sections, the original GPS point data is determined as the key GPS point data.

8. A road dust condition monitoring system, characterized in that: include: A data receiving unit, used for receiving each original GPS point data collected by the road dust monitoring device and the original road dust data corresponding to each original GPS point data; A road segment division unit, used to divide each of the original GPS point data into different monitoring road segments based on a road information database; wherein the road information database includes the GPS point data corresponding to each road segment; A data thinning unit is used to determine, for each of the monitored sections, the dust accumulation characteristic value of the monitored section based on the original road dust data corresponding to each of the original GPS point data contained in the monitored section; determine the adaptive threshold of the monitored section based on the dust accumulation characteristic value; and use the Douglas-Peucker algorithm to filter out key GPS point data from each of the original GPS point data contained in the monitored section based on the adaptive threshold; A dust accumulation analysis unit, configured to determine the road dust accumulation condition corresponding to each of the monitored road sections based on the original road dust accumulation data corresponding to the key GPS point data contained in each of the monitored road sections; The visualization unit is used to visualize the road dust conditions corresponding to each of the monitored road sections in a map.

9. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and when the processor executes the computer executable instructions, the road dust condition monitoring method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the road dust condition monitoring method according to any one of claims 1 to 7 is implemented.

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