A sprinkler truck operating status identification method and intelligent atmospheric monitoring and management platform

Abstract

The present invention discloses a method for identifying the operating status of a sprinkler truck and an intelligent atmospheric monitoring and management platform. The road network information data in the target monitoring area is widened to obtain expanded road network information data with a buffer zone; the track data of the sprinkler truck is matched with the expanded road network information data, and stored in a vehicle-road preliminary matching result set in sequence; the data in the vehicle-road preliminary matching result set is sampled in sequence and stored as a group of sampling data groups, the average speed of each sampling data group is compared with a preset speed threshold, and each sampling data group is marked; the sampling data group is screened according to the marking type by a sliding window continuous search method, and the sampling data groups that meet the screening conditions are summarized and analyzed according to the target road section to obtain the operating status of the sprinkler truck in the target road section. The operating status of the sprinkler truck is monitored only by matching the road network through the track data of the sprinkler truck, which reduces the implementation cost and hardware dependence.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city sprinkler operation monitoring, and in particular to a sprinkler truck operation status identification method and an intelligent atmospheric monitoring and management platform. Background Art

[0002] In the process of atmospheric monitoring, for dust pollution generated by urban roads, sprinkler trucks are often controlled to spray water on polluted sections of the road to reduce dust. Therefore, the operating efficiency and standardization of sprinkler trucks are directly related to the quality of urban environment and the quality of life of residents. The existing traditional management methods of sprinkler operations mostly rely on manual inspections and experience judgments. This method has problems such as low operating efficiency, high cost, and difficult supervision. In recent years, with the rapid development of the Internet of Things, big data, and artificial intelligence technologies, new ideas and tools have been provided to solve the problem of sprinkler operation supervision. Most of the existing methods use video surveillance systems or modified sprinkler trucks for supervision, but the installation and maintenance costs are high, especially for the transformation and upgrading of old sprinkler trucks that have been put into use. The installation of video surveillance equipment faces both technical and economic challenges. Summary of the invention

[0003] The purpose of the present invention is to provide a method for identifying the operating status of a sprinkler truck and an intelligent atmospheric monitoring and management platform, which monitors the operating status of the sprinkler truck by analyzing the driving trajectory data of the sprinkler truck, matching the road network, and combining preset operating routes, speeds, time and other parameters.

[0004] To achieve the above objectives, this application adopts the following scheme:

[0005] On the one hand, the present invention provides a method for identifying the operating status of a sprinkler truck, which specifically comprises the following steps:

[0006] S1. Obtain the track data of the sprinkler truck, and obtain the road network information data in the target monitoring area through the OSM map, widen the road network information data, and obtain the expanded road network information data with a buffer zone;

[0007] S2, matching the track data of the sprinkler truck with the expanded road network information data, and storing the successfully matched data in the vehicle-road preliminary matching result set in chronological order;

[0008] S3, sampling the data in the vehicle-road preliminary matching result set according to a preset time length, storing the data sampled each time as a set of sampled data groups, and calculating the average speed of each set of sampled data groups;

[0009] S4, comparing the average speed with a preset speed threshold, and marking each group of sampled data according to the comparison result;

[0010] S5. Use a sliding window continuous search method to filter the sampling data group according to the tag type, summarize and analyze the sampling data group that meets the filtering conditions according to the target section, and obtain the operating status of the sprinkler truck in the target section.

[0011] In some optional implementations, the road network information data includes the road network topology and the basic road data corresponding to each road section in the road network topology, and the basic road data includes the road section ID, the road section name, the road section length, the longitude and latitude of the starting point of the road section, and the longitude and latitude of the end point. The specific process of obtaining the expanded road network information data with a buffer zone in step S1 is:

[0012] S11, filtering the road section names of the road basic data according to the preset target road sections, retaining the road basic data corresponding to the target road sections in the target detection area, and updating the road network topology;

[0013] S12, widening the width of each road section in the road network topology according to a preset width to obtain a road section buffer corresponding to each road section;

[0014] S13. According to the latitude and longitude of the starting point and the latitude and longitude of the ending point of each road section and the widened width, the basic road data corresponding to each road section is updated to obtain the expanded road network information data with a buffer zone.

[0015] In some optional implementations, the specific process of step S2 is:

[0016] S21, the track data of the sprinkler truck includes longitude lon, latitude lat and recording time t, and each track data is recorded as Ti (t i ,lat i ,lon i ), and store the trajectory data at each recording moment into the first data group T (T1, ..., Ti) in chronological order;

[0017] S22, recording the expanded road network information data of each road section buffer as Rn (road section id, road section name, road section length, road section longitude range, road section latitude range);

[0018] S23, using the longitude and latitude of each track data, sequentially traverse the longitude and latitude range of the road section of the expanded road network information data of each road section buffer zone to obtain the road section buffer zone where the track data is located;

[0019] S24, adding the expanded road network information data of the buffer zone of the road section where the trajectory data is located to the trajectory data, and obtaining the preliminary vehicle-road matching result Si (t i ,lat i ,lon i, section id, section name, section length), and store the preliminary vehicle-road matching results in the vehicle-road preliminary matching result set S (S1,…,Si) in chronological order.

[0020] In some optional implementations, step S24 further includes the steps of:

[0021] Classify the set of preliminary vehicle-road matching results according to the road section ID, and for the road section IDs with a small number in the classification results, record the corresponding preliminary vehicle-road matching results as drift point data;

[0022] Correct the drift point data, obtain the preliminary vehicle-road matching results before and after the drift point, and compare whether the road section IDs in the preliminary vehicle-road matching results before and after are consistent. If they are consistent, update the preliminary vehicle-road matching results of the drift point to the corresponding expanded road network information data when they are consistent.

[0023] In some optional implementations, the process of calculating the average speed of each set of sampled data is:

[0024] S31, continuously sampling in the vehicle-road preliminary matching result set in sequence according to the preset time length, and storing the data of each sampling as a set of sampling data groups Xn (Sk, ..., Sj), where the time difference t of each sampling data group is j –t k is the preset time length;

[0025] S32, calculate the distance traveled by the vehicle according to the latitude and longitude data of the first and last data in each set of sampled data, and calculate the average speed v of the set of sampled data within a preset time length according to the distance. n .

[0026] In some optional implementations, the process marked in step S4 is:

[0027] S41, comparing the average speed of each sampling data group with a preset speed threshold range;

[0028] S42: if the average speed is within the preset speed threshold interval, the group of sampled data is marked as having been watered; if the average speed is outside the preset speed threshold interval, the group of sampled data is marked as not having been watered.

[0029] In some optional implementations, the screening process in step S5 is:

[0030] S51, all sample data groups are arranged in time sequence, and the sliding window is grouped and queried in sequence according to the preset window size;

[0031] S52, query whether all the sampling data groups in the current sliding window are marked as having been watered; if all the sampling data groups in the current sliding window are marked as having been watered; then obtain the average speed of each sampling data group, and determine whether each average speed is greater than the speed interference threshold;

[0032] S53, if the average speed of any sample data group in the current sliding window is greater than the speed interference threshold, then each sample data group in the current sliding window is stored in a continuous data set, and the sliding window is moved to query again until all sample data groups are queried;

[0033] S54: If there are sampling data groups marked as not watered in the current sliding window, move the sliding window to search again until all sampling data groups are searched.

[0034] In some optional implementations, the process of obtaining the operating status of the sprinkler truck on the target road section is:

[0035] Summarize the sampling data groups in the continuous data set according to the target road section, merge the temporally continuous sampling data groups in the same target road section, and record the number of watering operations of the sprinkler truck in the target road section as one;

[0036] Obtain the longitude and latitude of the first and last two sampling data groups from all sampling data groups of the watering operation, and calculate the watering operation length of the watering truck on the target road section;

[0037] Obtain the recording time of the first and last two sampling data groups from all sampling data groups of the watering operation, and calculate the watering operation time of the watering truck on the target road section;

[0038] The watering operation coverage rate of the sprinkler truck on the target road section is calculated according to the ratio of the watering operation length to the road section length of the target road section.

[0039] In a second aspect, the present application provides an intelligent atmospheric monitoring and management platform, which applies a sprinkler truck operation status identification method described in the first aspect, including:

[0040] A data cleaning unit is used to obtain the track data of the sprinkler truck, and obtain the road network information data in the target monitoring area through the OSM map, and widen the road network information data to obtain the expanded road network information data with a buffer zone;

[0041] The vehicle-road matching unit is used to match the track data of the sprinkler truck with the expanded road network information data, and store the successfully matched data in a vehicle-road preliminary matching result set in sequence according to the time sequence;

[0042] A data processing unit is used to sample the data in the vehicle-road preliminary matching result set according to a preset time length, store the data sampled each time as a set of sampled data groups, calculate the average speed of each set of sampled data groups; compare the average speed with a preset speed threshold, and mark each set of sampled data groups according to the comparison result;

[0043] The watering status monitoring unit is used to filter the sampling data group according to the tag type by using a sliding window continuous search method, summarize and analyze the sampling data group that meets the filtering conditions according to the target section, and obtain the operating status of the sprinkler truck in the target section.

[0044] The inventive concept of this application is:

[0045] The existing method for identifying the operating status of sprinkler trucks relies on hardware modification of the sprinkler trucks and the installation of hardware such as video surveillance equipment to monitor the operating status of the sprinkler trucks. However, due to the difficulty and high cost of modifying existing sprinkler trucks, the operating status of most sprinkler trucks cannot be effectively monitored.

[0046] Therefore, the present application proposes a method that does not rely on hardware modification (such as video surveillance) and only uses the trajectory data of the sprinkler truck to make judgments, so as to effectively monitor the operating status of the sprinkler truck. Taking into account the GPS positioning accuracy and street width, the road network topology data is widened, and the driving road corresponding to the GPS point of the sprinkler truck is accurately identified according to the correlation between the buffer zone and the road network structure and the relative position of the GPS positioning point of the sprinkler truck in these buffer zones. The longitude and latitude range of the road has been expanded, which can be closer to the GPS positioning range affected by the width of the road in reality, so that the matching process can be completed faster under a large amount of GPS data. According to the operating characteristics of the sprinkler truck, the operating speed of the sprinkler truck is used to judge whether the sprinkler truck has completed the sprinkler operation. By counting the data of the completed sprinkler operation, the operating status of the sprinkler truck in the target sprinkler section is obtained, thereby realizing the monitoring of the operating status of the sprinkler truck.

[0047] The present invention has the beneficial effects:

[0048] The present invention evaluates the effect of watering operations by analyzing only the trajectory data of the sprinkler truck, automatically identifies the operation coverage, frequency and duration, and does not require the installation of additional complex monitoring equipment, thereby greatly reducing implementation costs and hardware dependence.

[0049] In addition, through comprehensive consideration of GPS positioning accuracy and street width, the correlation between buffer zones and road network structure and the relative positions of GPS positioning points within these buffer zones are analyzed to accurately identify the driving roads corresponding to the GPS points. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1A flow chart of a method for identifying the operating status of a sprinkler truck provided in an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a road network topology provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a road network topology with a buffer zone provided by an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of a sliding window continuous search process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0056] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0057] Additionally, descriptions of well-known structures, functions, and configurations may be omitted for clarity and conciseness.One of ordinary skill in the art will recognize that various changes and modifications may be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0058] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0059] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0060] Example 1

[0061] like Figure 1 As shown, this embodiment provides a method for identifying the operating status of a sprinkler truck, which specifically includes the following steps:

[0062] S1. Acquisition and processing of research data

[0063] (1) Obtain the track data of the sprinkler truck, which includes the license plate number, longitude lon, latitude lat and recording time t. Each track data is recorded as Ti (t i ,lat i ,lon i ), and store the trajectory data at each recording moment in the first data group T (T1, ..., Ti) in chronological order; in this embodiment, an example is taken for a single sprinkler truck, so the license plate number may not be used as a screening condition;

[0064] (2) Obtain the historical actual operation sample data of the sprinkler truck, including the operation section, time, location, etc. According to the operation characteristics of the sprinkler truck, for example, the speed of the sprinkler truck during operation needs to be maintained at 5~8km / h. Considering that the speed during the actual sprinkler operation is not strictly in accordance with the above speed, the historical actual operation sample data of the sprinkler truck is combined for analysis. According to the statistical laws of the sprinkler operation obtained from the real operation data sample (such as the duration range of the sprinkler operation, the average speed range during sprinkler operation, etc.), the average speed threshold during sprinkler operation can be set. Dynamic adjustment, such as setting the preset speed threshold range to 2~12km / h.

[0065] (3) Obtain road network information data within the target monitoring area through the OSM map, widen the road network information data, and obtain expanded road network information data with a buffer zone; the road network information data includes the road network topology (such as Figure 2 As shown in the figure, the basic road data corresponding to each road section in the road network topology includes the road section id, road section name, road section length, the latitude and longitude of the starting point of the road section, and the latitude and longitude of the end point. It is recorded as (link_id, road_name, length, geometry), eg: {11070,"Renmin South Road Section 4", 104.6, LINESTRING (103.4 30.4, 104.730.43)}. The road network information is preliminarily processed. For example, for many unknown dead-end roads with road names such as "unknown road" or "intersection turn", they are deleted on the premise that they are not within the scope of the sprinkler operation section assessment to prevent interference.

[0066] Considering that the existing mainstream road network matching methods, Hidden Markov Model (HMM) and Shortest Path Method (SPM), have their own limitations in practical applications: HMM assumes that the transition between states depends only on the previous state, ignoring longer historical dependencies, which may lead to deviations in matching results in complex and changeable urban road network environments; at the same time, the complexity of model parameter estimation is high, especially in large-scale road networks, which consumes huge computing resources and affects real-time performance. SPM simply pursues the shortest distance and ignores the actual conditions of the road, such as road conditions, turning restrictions, etc., which will also cause the matched routes to often have errors. This embodiment adopts a road network matching method based on road buffers.

[0067] Specifically, the specific process of obtaining the expanded road network information data with a buffer zone is as follows:

[0068] S11, filtering the road section names of the road basic data according to the preset target road sections, retaining the road basic data corresponding to the target road sections in the target detection area, and updating the road network topology;

[0069] S12, widening the width of each road section in the road network topology according to a preset width to obtain a road section buffer corresponding to each road section;

[0070] For example, taking the road network topology straight line of each road section as the center, select an appropriate distance parameter such as 2 meters to widen it on both sides of the center to form a road section buffer zone, such as Figure 3 As shown; the latitude and longitude range of the road section is re-determined according to the widened width. The method of converting the length into longitude and latitude is a common technical method and will not be repeated in this application.

[0071] S13. According to the latitude and longitude of the starting point and the latitude and longitude of the ending point of each road section and the widened width, the basic road data corresponding to each road section is updated to obtain the expanded road network information data with a buffer zone.

[0072] S2, matching the track data of the sprinkler truck with the expanded road network information data, and storing the successfully matched data in the vehicle-road preliminary matching result set in chronological order;

[0073] S21, the track data of the sprinkler truck includes longitude lon, latitude lat and recording time t, and each track data is recorded as Ti (t i ,lat i ,lon i ), and store the trajectory data at each recording moment into the first data group T (T1, ..., Ti) in chronological order;

[0074] S22, recording the expanded road network information data of each road section buffer as Rn (road section id, road section name, road section length, road section longitude range, road section latitude range);

[0075] S23, using the longitude and latitude of each track data, sequentially traverse the longitude and latitude range of the road section of the expanded road network information data of each road section buffer zone to obtain the road section buffer zone where the track data is located;

[0076] S24, adding the expanded road network information data of the buffer zone of the road section where the trajectory data is located to the trajectory data, and obtaining the preliminary vehicle-road matching result Si (t i ,lat i ,lon i , section id, section name, section length), and store the preliminary vehicle-road matching results in the vehicle-road preliminary matching result set S (S1,…,Si) in chronological order.

[0077] Due to the GPS positioning accuracy problem, the longitude and latitude in the positioning track of the sprinkler truck are prone to drift. Therefore, in order to avoid failure to match, the matching result is corrected. The specific process is as follows:

[0078] Classify the set of preliminary vehicle-road matching results according to the road section ID, and for the road section IDs with a small number in the classification results, record the corresponding preliminary vehicle-road matching results as drift point data;

[0079] Correct the drift point data, obtain the preliminary vehicle-road matching results before and after the drift point, and compare whether the road section IDs in the preliminary vehicle-road matching results before and after are consistent. If they are consistent, update the preliminary vehicle-road matching results of the drift point to the corresponding expanded road network information data when they are consistent.

[0080] Specifically, the latitude and longitude of each sprinkler track are sequentially traversed through the road section buffer, and the relationship between it and each road section buffer is determined. i ,lon i ) is located in buffer section 1, it means that the vehicle is located on road 1 at this moment, and the preliminary matching result is obtained. For individual drifting trajectory points, correction or filtering can be performed according to the matching of the previous and next points. For example, for a vehicle matching result set S, S i-1 Located on Section 1, S i Located on Section 2, S i+1 Located on section 1, then S i It is possible that it is a drift point, and its matching section can be corrected from section 2 to section 1, and other data of the expanded road network information data can be modified accordingly.

[0081] S3, sampling the data in the vehicle-road preliminary matching result set according to a preset time length, storing the data sampled each time as a set of sampled data groups, and calculating the average speed of each set of sampled data groups;

[0082] S31, continuously sampling in the vehicle-road preliminary matching result set in sequence according to the preset time length, and storing the data of each sampling as a set of sampling data groups Xn (Sk, ..., Sj), where the time difference t of each sampling data group is j –t k The preset time length can be set to 2 minutes;

[0083] S32, calculate the distance traveled by the vehicle according to the latitude and longitude data of the first and last data in each set of sampled data, and calculate the average speed v of the set of sampled data within a preset time length according to the distance. n .

[0084] S4. Compare the average speed with the preset speed threshold, and mark each group of sampled data according to the comparison result. The specific process is as follows:

[0085] S41, comparing the average speed of each set of sampled data with a preset speed threshold range (for example, 2-12 kM / h);

[0086] S42. If the average speed is within the preset speed threshold interval, the group of sampled data is marked as watered, for example, marked as 1; if the average speed is outside the preset speed threshold interval, the group of sampled data is marked as not watered, marked as 0.

[0087] In order to facilitate the use of sampled data for screening, the data in the sampled data group Xn (Sk, ..., Sj) can be processed and merged, and the preliminary matching results Si (t i ,lat i ,lon i , segment id, segment name, segment length) are merged, and only the time and longitude and latitude of the two preliminary matching results of the two lanes at the two-minute endpoints are recorded. The average speed and watering mark are also added to the merged sampling data group. The final sampling data group can be recorded as Xn(t k , t j lat k ,lon k ,lat j ,lon j , section id, section name, section length, average speed v, watering mark);

[0088] S5. Use a sliding window continuous search method to filter the sampled data group according to the tag type, and summarize and analyze the sampled data group that meets the filtering conditions according to the target road section to obtain the operating status of the sprinkler truck on the target road section. The screening process is:

[0089] S51, all sample data groups are arranged in time sequence, and the sliding window is grouped and queried in sequence according to the preset window size;

[0090] S52, query whether all the sampling data groups in the current sliding window are marked as having been watered; if all the sampling data groups in the current sliding window are marked as having been watered; then obtain the average speed of each sampling data group, and determine whether each average speed is greater than the speed interference threshold;

[0091] S53, if the average speed of any sample data group in the current sliding window is greater than the speed interference threshold, then each sample data group in the current sliding window is stored in a continuous data set, and the sliding window is moved to query again until all sample data groups are queried;

[0092] S54: If there are sampling data groups marked as not watered in the current sliding window, move the sliding window to search again until all sampling data groups are searched.

[0093] For example, Figure 4 As shown, if the sliding window size is 4, every 4 consecutive sampling data groups are recorded as a group (such as X1, X2, X3, X4). Assuming that the average speeds of these 4 sampling data groups in the sliding window are v1, v2, v3, v4 and the corresponding sprinkler flags are all 1, then the average speeds of a group of sampling data groups in the sliding window are (v1=8, v2=5, v3=3, v4=5). It can be seen that although this set of data all meets the requirement that the average speed is within the preset speed threshold range, the difference between the average speeds is too large. It is very likely that the vehicle is waiting for the traffic light or other factors such as traffic jams cause the vehicle's running speed to be disturbed and move forward at an unstable speed. Therefore, in order to avoid this situation, a speed interference threshold v0 (such as 10km / h) is set. If the average speed v of any of them meets the condition v≥v0, then the data group is a record that meets the condition and is retained. If there is no data that meets the condition, such as the above example (8,5,3,5), this set of data is not needed, and the sliding window continues to be moved, and the judgment of the next array (X2,X3,X4,X5) continues. Move the sliding window and continue to judge the next array (X2,X3,X4,X5), and so on, until all the sampling data groups with the sprinkler operation flag of 1 are found. According to the final continuous data group, the original data position is located, thereby obtaining the data record when the vehicle performs the sprinkler operation.

[0094] According to the data in the continuous data set, the continuous records of the same road section are merged into one record, and the watering operation of the vehicle on the road section is recorded once. The starting time is the first continuous record of the road section, and the end time is the last continuous record. In this way, the watering behavior of all vehicles on the road section during the study period is obtained. Based on the preset watering operation plan, it is accurately identified whether the watering operation is carried out according to the plan and the quality of the operation. Specifically, the process of obtaining the operating status of the sprinkler truck on the target road section is:

[0095] Summarize the sampling data groups in the continuous data set according to the target road section, merge the temporally continuous sampling data groups in the same target road section, and record the number of watering operations of the sprinkler truck in the target road section as one;

[0096] Obtain the longitude and latitude of the first and last two sampling data groups from all sampling data groups of the watering operation, and calculate the watering operation length of the watering truck on the target road section;

[0097] Obtain the recording time of the first and last two sampling data groups from all sampling data groups of the watering operation, and calculate the watering operation time of the watering truck on the target road section;

[0098] The watering operation coverage rate of the sprinkler truck on the target road section is calculated according to the ratio of the watering operation length to the road section length of the target road section.

[0099] For example, suppose the sampling data set corresponding to the sprinkler truck in a certain section 1 is X1(10:00, 10:02, lat1, lon1, lat4, lon4, section id, section 1, 100, 10, 1), X2(10:02, 10:04, lat4, lon4, lat8, lon8, ​​section id, section 1, 100, 8, 1), X3(10:04, 10:06, lat8, lon8, ​​lat 11 ,lon 11 , section id, section 1, 100, 9, 1), X4 (10:06, 10:08, lat 11 ,lon 11 , lat 14 ,lon 14 , section id, section 1, 100, 10, 1), then these four sets of data are merged to obtain a watering record. The watering operation length of this watering record is calculated using the starting longitude and latitude and the ending longitude and latitude of the first and last two data sets X1 and X4 (lat 14 ,lon 14 ) and (lat1,lon1) (assuming it is 74 meters), the length of the watering operation is 10:08-10:00=8 (minutes), and the coverage rate of the watering operation is 74 / 100=74%.

[0100] Example 2

[0101] This embodiment provides an intelligent atmospheric monitoring and management platform, and applies the sprinkler truck operation status identification method in Embodiment 1, including:

[0102] A data cleaning unit is used to obtain the track data of the sprinkler truck, and obtain the road network information data in the target monitoring area through the OSM map, and widen the road network information data to obtain the expanded road network information data with a buffer zone;

[0103] The vehicle-road matching unit is used to match the track data of the sprinkler truck with the expanded road network information data, and store the successfully matched data in a vehicle-road preliminary matching result set in sequence according to the time sequence;

[0104] A data processing unit is used to sample the data in the vehicle-road preliminary matching result set according to a preset time length, store the data sampled each time as a set of sampled data groups, calculate the average speed of each set of sampled data groups; compare the average speed with a preset speed threshold, and mark each set of sampled data groups according to the comparison result;

[0105] The watering status monitoring unit is used to filter the sampling data group according to the tag type by using a sliding window continuous search method, summarize and analyze the sampling data group that meets the filtering conditions according to the target section, and obtain the operating status of the sprinkler truck in the target section.

[0106] The intelligent air monitoring and governance platform in this application can monitor the air pollution in each area, automatically mark the polluted area as the target area, and dispatch a sprinkler truck to the area for operation. In this way, for the sprinkler truck operation area, the target area is the target monitoring area for monitoring, and the road sections in the area are sprinkled with water. This application is to monitor the sprinkler operation of each sprinkler truck on each target road section in the above target monitoring area.

[0107] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. According to the technical essence of the present invention, within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for identifying the operating status of a sprinkler truck, characterized in that: The specific steps include: S1. Obtain the track data of the sprinkler truck, and obtain the road network information data in the target monitoring area through the OSM map, widen the road network information data, and obtain the expanded road network information data with a buffer zone; The road network information data includes the road network topology and the basic road data corresponding to each road section in the road network topology. The basic road data includes the road section ID, the road section name, the road section length, the longitude and latitude of the starting point of the road section, and the longitude and latitude of the end point. The specific process of obtaining the expanded road network information data with a buffer zone in step S1 is as follows: S11, filtering the road section names of the road basic data according to the preset target road sections, retaining the road basic data corresponding to the target road sections in the target detection area, and updating the road network topology; S12, widening the width of each road section in the road network topology according to a preset width to obtain a road section buffer corresponding to each road section; S13, according to the latitude and longitude of the starting point and the end point of each road section and the widened width, and updating the road basic data corresponding to each road section, to obtain the expanded road network information data with a buffer zone; S2, matching the track data of the sprinkler truck with the expanded road network information data, and storing the successfully matched data in the vehicle-road preliminary matching result set in chronological order; S3, sampling the data in the vehicle-road preliminary matching result set according to a preset time length, storing the data sampled each time as a set of sampled data groups, and calculating the average speed of each set of sampled data groups; S4, comparing the average speed with a preset speed threshold, and marking each group of sampled data according to the comparison result; S5. Use a sliding window continuous search method to filter the sampling data group according to the tag type, summarize and analyze the sampling data group that meets the filtering conditions according to the target section, and obtain the operating status of the sprinkler truck in the target section.

2. A method for identifying the operating status of a sprinkler truck according to claim 1, characterized in that: The specific process of step S2 is: S21, the track data of the sprinkler truck includes longitude lon, latitude lat and recording time t, and each track data is recorded as Ti (t i ,lat i ,lon i ), and store the trajectory data at each recording moment into the first data group T (T1, ..., Ti) in chronological order; S22, recording the expanded road network information data of each road section buffer as Rn (road section id, road section name, road section length, road section longitude range, road section latitude range); S23, using the longitude and latitude of each track data, sequentially traverse the longitude and latitude range of the road section of the expanded road network information data of each road section buffer zone to obtain the road section buffer zone where the track data is located; S24, adding the expanded road network information data of the buffer zone of the road section where the trajectory data is located to the trajectory data, and obtaining the preliminary vehicle-road matching result Si (t i ,lat i ,lon i , section id, section name, section length), and store the preliminary vehicle-road matching results in the vehicle-road preliminary matching result set S (S1,…,Si) in chronological order.

3. A method for identifying the operating status of a sprinkler truck according to claim 2, characterized in that: Step S24 also includes the steps of: Classify the set of preliminary vehicle-road matching results according to the road section ID, and for the road section IDs with a small number in the classification results, record the corresponding preliminary vehicle-road matching results as drift point data; Correct the drift point data, obtain the preliminary vehicle-road matching results before and after the drift point, and compare whether the road section IDs in the preliminary vehicle-road matching results before and after are consistent. If they are consistent, update the preliminary vehicle-road matching results of the drift point to the corresponding expanded road network information data when they are consistent.

4. A method for identifying the operating status of a sprinkler truck according to claim 2, characterized in that: The process of calculating the average speed of each set of sampled data is: S31, continuously sampling in the vehicle-road preliminary matching result set in sequence according to the preset time length, and storing the data of each sampling as a set of sampling data groups Xn (Sk, ..., Sj), where the time difference t of each sampling data group is j –t k is the preset time length; S32, calculating the distance traveled by the vehicle according to the latitude and longitude data of the first and last data in each group of sampled data, and calculating the average speed vn of the group of sampled data within a preset time length according to the distance.

5. A method for identifying the operating status of a sprinkler truck according to claim 1, characterized in that: The process marked in step S4 is: S41, comparing the average speed of each sampling data group with a preset speed threshold range; S42: if the average speed is within the preset speed threshold interval, the group of sampled data is marked as having been watered; if the average speed is outside the preset speed threshold interval, the group of sampled data is marked as not having been watered.

6. A method for identifying the operating status of a sprinkler truck according to claim 5, characterized in that: The screening process in step S5 is: S51, all sample data groups are arranged in time sequence, and the sliding window is grouped and queried in sequence according to the preset window size; S52, query whether all the sampling data groups in the current sliding window are marked as having been watered; if all the sampling data groups in the current sliding window are marked as having been watered; then obtain the average speed of each sampling data group, and determine whether each average speed is greater than the speed interference threshold; S53, if the average speed of any sample data group in the current sliding window is greater than the speed interference threshold, then each sample data group in the current sliding window is stored in a continuous data set, and the sliding window is moved to query again until all sample data groups are queried; S54: If there are sampling data groups marked as not watered in the current sliding window, move the sliding window to search again until all sampling data groups are searched.

7. A method for identifying the operating status of a sprinkler truck according to claim 6, characterized in that: The process of obtaining the operating status of the sprinkler truck on the target road section is as follows: Summarize the sampling data groups in the continuous data set according to the target road section, merge the temporally continuous sampling data groups in the same target road section, and record the number of watering operations of the sprinkler truck in the target road section as one; Obtain the longitude and latitude of the first and last two sampling data groups from all sampling data groups of the watering operation, and calculate the watering operation length of the watering truck on the target road section; Obtain the recording time of the first and last two sampling data groups from all sampling data groups of the watering operation, and calculate the watering operation time of the watering truck on the target road section; The watering operation coverage rate of the sprinkler truck on the target road section is calculated according to the ratio of the watering operation length to the road section length of the target road section.

8. An intelligent atmospheric monitoring and management system, using a method for identifying the operating status of a sprinkler truck as described in any one of claims 1 to 7, characterized in that: include: A data cleaning unit is used to obtain the track data of the sprinkler truck, and obtain the road network information data in the target monitoring area through the OSM map, and widen the road network information data to obtain the expanded road network information data with a buffer zone; The vehicle-road matching unit is used to match the track data of the sprinkler truck with the expanded road network information data, and store the successfully matched data in a vehicle-road preliminary matching result set in sequence according to the time sequence; A data processing unit is used to sample the data in the vehicle-road preliminary matching result set according to a preset time length, store the data sampled each time as a set of sampled data groups, calculate the average speed of each set of sampled data groups; compare the average speed with a preset speed threshold, and mark each set of sampled data groups according to the comparison result; The watering status monitoring unit is used to filter the sampling data group according to the tag type by using a sliding window continuous search method, summarize and analyze the sampling data group that meets the filtering conditions according to the target section, and obtain the operating status of the sprinkler truck in the target section.

Images