Field investigation sample point task planning method and device and electronic equipment

Abstract

The application relates to the field investigation sampling technical field and provides a field investigation sampling point task planning method, device and electronic equipment. The method converts original coordinate data of M investigation sampling points to be investigated and N alternative stations and traffic road vector data into standard coordinate data in the same coordinate system; network data sets are constructed based on the standard coordinate data; the network data sets are used for recording topological connection relationships among the investigation sampling points, the alternative stations and the traffic roads; based on the network data sets, the alternative stations with the shortest total path distance from the M investigation sampling points are selected from the N alternative stations as the stations; a path search algorithm is used to determine the planning path for reaching the remaining investigation sampling points in the M investigation sampling points from the stations in sequence, and a first data table corresponding to the i day is output; and the sampling task and the sampling path of the day are determined based on the first data table corresponding to the i day. The technical scheme can improve the field investigation sampling efficiency.

Description

Technical Field

[0001] This invention relates to the field of field survey and sampling technology, and in particular to a method, apparatus and electronic device for planning field survey sampling points. Background Technology

[0002] Field sampling is fundamental work in many disciplines such as geology, soil science, ecology, and environmental science, providing raw physical samples for subsequent laboratory analysis, data modeling, and scientific conclusions. When faced with survey tasks involving a large number of field sampling points, the sheer number and dispersed nature of these points, even with a known layout, means that completing the survey of all field sampling points in a county-level area using traditional methods typically takes several days or even dozens of days. This low efficiency underscores the critical need to improve the efficiency of field sampling. Summary of the Invention

[0003] This application provides a method, apparatus, and electronic device for planning field survey sampling tasks. Under the premise that the field survey workload is large and the layout of the sampling points is known, it can reasonably allocate and plan field survey tasks and traversal paths, thereby improving the efficiency of field surveys.

[0004] This application provides a method for planning field survey sampling points. The method includes: converting the original coordinate data of M survey sampling points and N candidate sites, as well as the vector data of traffic roads in the survey area, into standard coordinate data in the same coordinate system; constructing a network dataset based on the standard coordinate data; the network dataset is used to record the topological connection relationships between the survey sampling points, the candidate sites, and the traffic roads; selecting the candidate site with the shortest total path distance from the M survey sampling points from the N candidate sites as the site based on the network dataset; using a path search algorithm to determine the planned path from the site to the remaining multiple survey sampling points among the M survey sampling points, and outputting a first data table corresponding to day i; i≥1; and determining the sampling task and sampling path for day i based on the first data table corresponding to day i, provided that preset constraints are met.

[0005] According to the field survey sample point task planning method provided in the embodiments of this application, a network dataset is constructed based on the standard coordinate data, including: if there are survey sample points or alternative sites that are not connected to traffic roads, a shortest distance connection line is established between the survey sample point or the alternative site and the nearest traffic road, so that all points in the M survey sample points and the N alternative sites are connected to traffic roads.

[0006] According to the field survey sampling point task planning method provided in the embodiments of this application, based on the network dataset, the method selects the candidate station with the shortest total path distance from the M survey sampling points from the N candidate stations as the station, including: using a shortest path algorithm to determine the shortest distance between the m-th survey sampling point and the n-th candidate station, and obtaining a distance cost matrix; 1≤n≤N; 1≤m≤M; according to the distance cost matrix, calculating the sum of the shortest distances between each candidate station and the M survey sampling points as the total distance; and selecting the candidate station with the shortest total path distance from the N candidate stations as the station.

[0007] According to the field survey sample point planning method provided in the embodiments of this application, a path search algorithm is used to determine the planned path from the base station to the remaining multiple survey sample points among the M survey sample points, and output the first data table corresponding to the i-th day. This includes: calling the path search algorithm to determine the sample point sequence after sequentially reaching all the remaining multiple survey sample points from the base station, and determining the shortest path length between two adjacent survey sample points in the sample point sequence; generating and outputting the first data table corresponding to the i-th day; the first data table includes multiple records corresponding to the remaining multiple survey sample points; the multiple records are arranged according to the sample point sequence; the m-th record among the multiple records includes the sequence number of the m-th survey sample point and the shortest path length between the m-th survey sample point and the (m+1)-th survey sample point; or, the m-th record also includes the name of the m-th survey sample point.

[0008] According to the field survey sampling point task planning method provided in the embodiments of this application, the constraints include: the sampling duration on day i is less than or equal to the total sampling duration of the day; 1 ≤ i; wherein, the sampling duration is determined based on the following variables: sampling duration of a single survey sampling point, the number of surveyed sampling points on day i, the distance traveled on day i, and the average vehicle speed; under the condition of satisfying the preset constraints, the sampling task and sampling path for day i are determined based on the data table, including: under the condition of satisfying the preset constraints, the following operations are executed cyclically until the termination condition is reached: if the first number corresponding to day i is... Read the m-th record corresponding to the m-th survey point from the table, increment the number of survey points by 1, and add the shortest path length recorded in the m-th record to the current traveled distance; if the next record of the m-th record exists, move to the next record; if the next record of the m-th record does not exist, end the loop and generate the second data table for day i; the data structure of the second data table may be the same as or different from the data structure of the first data table; in response to the sampling duration of day i being greater than the total sampling duration of a single day, end the loop and generate the second data table corresponding to day i.

[0009] According to the field survey sampling point task planning method provided in the embodiments of this application, after generating the second data table corresponding to the i-th day, the method further includes: deleting the Z1 survey sampling points included in the second data table corresponding to the i-th day from the M survey sampling points to obtain the remaining M-Z1 survey sampling points; using a path search algorithm to determine the planned path from the base station to the remaining M-Z1 survey sampling points in sequence, and outputting the first data table corresponding to the i+1-th day; and determining the sampling task for the i+1-th day based on the first data table corresponding to the i+1-th day under the condition of satisfying preset constraints.

[0010] According to the field survey sampling task planning method provided in the embodiments of this application, the standard coordinate data is the CGCS2000 plane projection coordinates obtained by converting the Gauss-Kruger projection and the national standard zoning.

[0011] According to the field survey sampling task planning method provided in the embodiments of this application, the graph search algorithm includes at least one of the following algorithms: Dijkstra, Bellman-Ford, Floyd, and SPFA.

[0012] This invention also provides a field survey sampling point task planning device, which includes: a coordinate transformation module for converting the original coordinate data of M survey sampling points and N candidate sites, as well as the vector data of traffic roads included in the survey area, into standard coordinate data in the same coordinate system; a topology connection module for constructing a network dataset based on the standard coordinate data; the network dataset for recording the topological connection relationships between the survey sampling points, the candidate sites, and the traffic roads; a site selection module for selecting the candidate site with the shortest total path distance from the M survey sampling points from the N candidate sites based on the network dataset; a total path planning module for using a path search algorithm to determine the planned path from the site to the remaining multiple survey sampling points among the M survey sampling points, and outputting a first data table corresponding to day i; i≥1; and a daily path planning module for determining the sampling task and sampling path for day i based on the first data table corresponding to day i, under the condition of satisfying preset constraints.

[0013] This application also provides an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the field survey sampling task planning method as described above.

[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the field survey sample point task planning method as described above.

[0015] This application also provides a computer program product, including a computer program that, when executed by a processor, implements a field survey sampling task planning method as described above.

[0016] The field survey sample point planning method, apparatus, and electronic equipment provided in this application first convert the original coordinate data of survey sample points and candidate sites, as well as the vector data of transportation roads, into standard coordinate data. This solves the problem of being unable to directly calculate distances and topological relationships in field surveys due to different sources and inconsistent coordinate systems of sample point data (GPS coordinates), site data (township center coordinates), and road data (vector map). By unifying the coordinate system, all subsequent distance calculations and path searches become mathematically comparable and computable. Then, based on the standard coordinate data, a network dataset is constructed to ensure that all point elements, such as survey sample points and candidate sites, are topologically connected to transportation roads. This solves the practical problem in field surveys where survey sample points or candidate sites are isolated from the transportation road network. For example, if a sample point is located in the middle of farmland with no direct road access, constructing a network dataset that records topological connections ensures that each sample point and site can be reached by the path search algorithm, avoiding algorithm failure due to node disconnection. Based on the topological connection relationships between survey sample points, candidate sites, and transportation roads recorded in the network dataset, N candidate sites are selected from the network dataset. The optimal base is selected from multiple candidate bases based on the shortest total path distance to all M survey points. This means that the optimal base is chosen quantitatively, rather than empirically, from among multiple candidate bases. This optimal base minimizes the total path length to all survey points, thereby minimizing the cumulative daily mileage to and from the base during subsequent multi-day surveys. This directly reduces fuel costs, time costs, and vehicle wear and tear, improving the rationality of base selection. Based on this, a path search algorithm is used to determine the planned paths from the selected base to the remaining survey points among the M survey points. The system outputs the first data table corresponding to day i. Under preset constraints, it determines the sampling tasks and sampling paths for day i based on this data table. For example, a single day is considered a single day. The abstract path planning is transformed into a data table (sampling tasks) that can be executed daily. The data table provides the order of accessing sample points. Constraints (such as daily working hours, single-point sampling time, vehicle speed, etc.) ensure that the sampling path for each day is feasible in actual time. The final output is a task schedule divided by day, directly applicable to field operations, rather than a long path plan that cannot be completed in one day. Thus, based on the above overall technical solution, through four progressive technical means, the traditional field survey task planning process, which relies on experience, manual labor, and trial and error, is transformed into an automated, quantitative, and optimized calculation process. This reduces invalid driving mileage, avoids redundant path calculations, ensures the feasibility of the plan, and enables automatic splitting of multi-day tasks, thereby systematically improving the efficiency of field survey tasks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the field survey sampling task planning method provided in the embodiments of this application; Figure 2 This is a flowchart illustrating a specific embodiment of the field survey sampling task planning method provided in this application. Figure 3 This is a distribution map of 150 field survey points in a county-level area, which is a specific embodiment of the field survey sampling point task planning method provided in this application. Figure 4 This is a diagram illustration of the distribution of survey points and alternative settlements based on standard coordinate data transformed to a unified coordinate system, as shown in a specific embodiment of the field survey sample point task planning method provided in this application. Figure 5 This is a schematic diagram of the optimal camp location selected in a specific embodiment of the field survey sampling task planning method provided in this application; Figure 6a This is a schematic diagram of the day 1 survey sampling task and route arrangement in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 6b This is a schematic diagram of the second day's survey sampling task and route arrangement in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 6c This is a schematic diagram of the third day's survey sampling task and route arrangement in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 6d This is a schematic diagram of the survey sampling task and route arrangement on the 4th day in a specific embodiment of the field survey sampling task planning method provided in this application; Figure 6e This is a schematic diagram of the sampling task and route arrangement on the 5th day in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 6f This is a schematic diagram of the survey sampling task and route arrangement on the 6th day in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 6gThis is a schematic diagram of the 7th day's survey sampling task and route arrangement in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 6h This is a schematic diagram of the survey sampling task and route arrangement on the 8th day in a specific embodiment of the field survey sampling point task planning method provided in this application; Figure 7 This is a schematic diagram of all survey and sampling tasks and route arrangements from day 1 to day 8 in a specific embodiment of the field survey sampling task planning method provided in this application. Figure 8 This is a schematic diagram of the software module structure of the field survey sampling point task planning device provided in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone, where A and B can be singular or plural. In the textual description of this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0021] Field sampling refers to the process by which researchers or survey teams delve into the field environment, systematically collect samples of soil, water, rocks, and organisms according to a pre-designed scientific plan, and record relevant environmental information. Field sampling, including soil sampling, is a crucial technical means for conducting resource surveys; it involves spatial sampling to investigate samples and statistically infer overall target quantities. Field sampling surveys are of great significance for accurately understanding the status of natural resources and ensuring their sustainable use, and are widely used in fields such as geography, soil science, ecology, and geology.

[0022] In related technologies, in order to balance the accuracy and cost of field surveys and sampling, a series of studies and practices on optimizing the layout of sampling points have been carried out, aiming to reduce the number of sampling points while ensuring that the layout of sampling points is typical and representative.

[0023] Nevertheless, field sampling surveys face new challenges. Soil surveys (such as the Third National Soil Census) and surface matrix surveys, which use counties as the basic survey unit and involve a large number of field sampling points, traditional methods generally rely on manual experience for sampling route planning. Due to the large number and dispersed nature of field sampling points, a county-level soil survey may take several days or even dozens of days to complete all sampling tasks, resulting in low field survey efficiency. Therefore, given the large volume of field sampling tasks and the known layout of sampling points, it is of significant practical importance to rationally allocate and plan field sampling tasks and traversal routes to improve field sampling efficiency, reduce the manpower, material, and time costs of field sampling, and ensure the timeliness and accuracy of field survey data acquisition.

[0024] In view of this, to solve the above problems, this application proposes a method, apparatus, and electronic device for planning field survey sampling tasks. In application scenarios where the number of field survey sampling tasks is large (cannot be completed in one day) and the sampling point layout is clear, a method for allocating field survey sampling tasks and traversing field sampling points daily is provided. By eliminating manual data preprocessing, avoiding path planning failures, minimizing round-trip mileage to the base station, optimizing movement paths between sampling points, and automating daily task splitting, the method transforms field survey sampling from an experience-driven trial-and-error process into an algorithm-driven optimal execution plan, improving the efficiency of field sampling surveys, reducing time, fuel, and labor costs, and providing technical support for large-scale field sampling surveys such as soil surveys and surface matrix surveys.

[0025] It should be noted that the survey sampling points in the embodiments of the present invention can be one or more of various surveyable sampling objects, such as soil sampling points, surface matrix sampling points, biological sampling points (vegetation sampling points (plant species, coverage), animal trace sampling points, microbial sampling points), and environmental media sampling points (atmospheric sampling points (aerosols, dustfall), sediment sampling points, pollution source sampling points).

[0026] like Figure 1 As shown in the embodiments of this application, the field survey sampling point planning method may include the following process: Step 101: Convert the original coordinate data of the M survey points and N alternative sites to be investigated, as well as the vector data of the roads included in the area to be investigated, into standard coordinate data in the same coordinate system.

[0027] For example, suppose a soil survey is conducted in a county. A single survey point out of M survey points is in GPS format, with GPS defaulting to WGS84 latitude and longitude coordinates, such as 124.32°E, 45.51°N. The acquired candidate site data is township site data obtained from relevant departments, using the Beijing 54 coordinate system, such as X=24,567,890.12 meters, Y=4,567,890.34 meters. The acquired road data is vector road data obtained from the surveying and mapping bureau, using CGCS2000 Gauss-Kruger projection coordinates (such as X=405,123.45 meters, Y=3,541,234.56 meters). These three types of data use completely different coordinate systems and cannot be directly overlaid for calculation.

[0028] The original coordinate data of the survey points, the original coordinate data of the candidate sites, and the traffic and road vector data are converted into standard coordinate data in the same coordinate system, such as plane projection coordinates in the CGCS2000 coordinate system. For example, the survey points (WGS84 latitude and longitude) are converted into CGCS2000 geographic coordinates (the difference between WGS84 and CGCS2000 is negligible at the centimeter level), and then converted into plane metric coordinates through Gauss-Kruger projection, resulting in plane metric coordinates (X=405234.56 meters, Y=3541567.89 meters). The candidate sites (Beijing 54 plane coordinates) can be converted into CGCS2000 plane metric coordinates through seven-parameter transformation or simplification, for example, the resulting plane metric coordinates are (X=405245.67 meters, Y=3541589.01 meters). The original road vector data is already CGCS2000 projected coordinate data, so no conversion is needed.

[0029] Step 102: Construct a network dataset based on standard coordinate data.

[0030] The network dataset is used to record the topological connections between survey points, alternative sites, and transportation routes.

[0031] A network dataset is a vector dataset composed of survey points, candidate sites, and transportation routes, recording the topological connectivity relationships between these elements. The network dataset includes these three elements and is a structured data set formed under the same coordinate system by establishing topological relationships between points and lines. This structured data set is used to represent the connectivity paths that can be taken from any survey point or candidate site along the transportation route network to other elements.

[0032] Step 103: Based on the network dataset, select the candidate station with the shortest total path distance from the M survey points from the N candidate stations as the station.

[0033] Based on the network dataset, the shortest path algorithm can be used to traverse the paths between each candidate site and each survey point, thereby determining the shortest path between each candidate site and each survey point. Based on the shortest path from each candidate site to each of the M survey points, the total distance from the candidate site to the M survey points can be obtained. The candidate site with the shortest total path distance is selected as the optimal site.

[0034] The shortest path algorithm includes one or more of the following algorithms: A Heuristic search algorithms, Dijkstra's algorithm, Bellman-Ford algorithm, Floyd-Warshall algorithm, SPFA (Shortest Path Faster Algorithm), and Genetic Algorithm (GA).

[0035] Step 104: Use a path search algorithm to determine the planned path from the base to the remaining multiple survey points among the M survey points in sequence, and output the first data table corresponding to the i-th day.

[0036] After determining the location, a path search algorithm is used to search for paths from the location to all remaining survey points out of the M survey points, based on the aforementioned network dataset. The first data table is then output as a global table, containing sampling path information for all remaining survey points to be sampled.

[0037] Path search algorithms include one or more of the following algorithms: Dijkstra's algorithm, Bellman-Ford algorithm, Floyd-Warshall algorithm, and SPFA (Shortest Path Faster Algorithm).

[0038] It should be noted that the method proposed in this embodiment of the invention outputs the sampling task for each time period, for example, a time period of one day or half a day. This embodiment of the invention only uses a time period of one day as an example for illustrative purposes.

[0039] It should be noted that the first data table corresponding to day i is a global data table. For example, the first data table corresponding to day 1 is a data table containing the shortest path for all M points. The first data table is updated daily.

[0040] Step 105: Under the condition of satisfying the preset constraints, determine the sampling task and sampling path for day i based on the first data table corresponding to day i.

[0041] After selecting a suitable field sampling site, it is necessary to estimate the average vehicle speed based on the terrain and road conditions of the survey area, determine the daily working hours and the sampling time required for each survey point according to industry rules, and then determine the constraints based on variables such as the average vehicle speed, the sampling time for each survey point, and the daily working hours. The determination of the constraints can be completed in advance before step 101.

[0042] Under the premise of satisfying the preset constraints, the sampling task and sampling path for day i are determined based on the first data table corresponding to day i output in step 104, i≥1.

[0043] For example, before executing the survey sampling task for day 1, the number of currently sampled survey points is 0, and the number of remaining survey points is M. After determining the location, a path search algorithm is used to search for paths from the location to each of the M survey points in sequence, based on the aforementioned network dataset, forming a sample point sequence. The shortest path length between two adjacent survey points in the sample point sequence is determined. The first data table includes M records, each recording the sequence number, name (optional), and shortest path length between two adjacent survey points. The first data table (corresponding to day 1) is then output. Next, it is determined whether the preset constraints are met. If the preset constraints are met, since the first data table corresponding to day 1 records the sampling order of all survey points, the sample points that need to be sampled on day 1 can be selected as the sampling task for day 1. The second data table corresponding to day 1 is then generated and output. The second data table is the daily task data table. The data structure of the second data table can be the same as the first data table. For example, assuming the number of survey points to be sampled on day 1 is x, the second data table could be the first x records taken from the first data table. Based on the second data table for day 1, the sampling personnel can sequentially complete the survey sampling tasks for each survey point.

[0044] After completing the survey sampling task for Day 1, assuming Z1 survey points were collected on Day 1, these Z1 sampled survey points are deleted from the M survey points. Returning to step 104, the first data table (global data table) for Day 2 is regenerated: a path search algorithm is used to determine the path from the base station to the remaining M-Z1 survey points sequentially, and the first data table for Day 2 is output. Then, step 105 is executed. Based on the first data table for Day 2, and under the condition of satisfying preset constraints, the sampling task for Day 2 is determined, and the second data table for Day 2 (single-day data table) is output.

[0045] After completing the survey sampling task on the second day, assuming that Z2 survey points were collected on the second day, delete the Z2 survey points that have been sampled from the M-Z1 survey points, return to step 104, and regenerate the first data table (global data table) corresponding to the third day: use the path search algorithm to determine the planned path from the base to the remaining M-Z1-Z2 survey points in sequence, and output the third data table; This process continues until all M survey points have been sampled.

[0046] Figure 1 The illustrated embodiment provides an optimal path planning technique for traversing sampling points in a field survey. This technique involves planning the optimal path for the sampling route at each sampling point. First, the candidate camp is selected based on the shortest distance to each sampling point. Simultaneously, constraints are determined based on daily working hours, sampling point times, and estimated travel speed. Under these constraints, a path search algorithm is used to determine the daily sampling tasks and the optimal path. This improves the effectiveness of field surveys, saves manpower, resources, and time, increases the efficiency of field surveys, and ensures the reliability and efficiency of the sampling tasks.

[0047] The solution proposed in this invention will be further described in detail below with reference to specific embodiments and accompanying drawings. The following specific embodiments are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0048] like Figure 2 As shown, in one specific embodiment, the field survey sampling point planning method may include the following process: Step 201: Obtain the raw data of survey points, alternative sites, and transportation routes, and perform standardized processing.

[0049] Obtain the following three types of data: (1) Distribution data of field survey points (or sampling points), i.e., the original coordinate data of the survey points; (2) Original coordinate data of candidate sites. Generally, places with accommodation and sample preservation conditions and good transportation conditions are selected within the county as candidate sites for field surveys. After determining the candidate sites, it is necessary to obtain the original coordinate data of the candidate sites; (3) Obtain vector data of various levels and types of roads in the survey area.

[0050] For example, in conjunction with practical application scenarios, such as Figure 3 As shown, taking Da'an City, Baicheng City, Jilin Province as the survey area and soil sampling points as the sampling objects, a total of 150 field soil sampling points were set up to understand the overall soil conditions in Da'an City. These points covered various land types, including cultivated land, grassland, forest land, wetland, saline-alkali land, saline-alkali cultivated land, saline-alkali grassland, and bare land, and were distributed across 18 townships in Da'an City. To improve the efficiency of field survey sampling, soil samples needed to be collected from all 150 sampling points as mentioned above.

[0051] Collect raw coordinate data for 150 field sampling points, as well as raw coordinate data for candidate sites. Within Da'an City, select locations with suitable accommodation and sample preservation facilities and good transportation access, choosing the town areas of 17 townships as candidate sites. Extract the town areas of the 17 townships from remote sensing imagery, and then select the coordinates of the town center points as the candidate site coordinate data. Combine data from various websites, maps, and high-resolution remote sensing data to extract vector data for all levels and types of roads in Da'an City.

[0052] Next, a unified coordinate system is established, converting the above three types of data into standard coordinate data. For example, the standard coordinate data is the CGCS2000 plane projection coordinates obtained by converting the Gauss-Kruger projection and the national standard zoning. That is, based on the Gauss-Kruger projection and the national standard zoning, the coordinates of the above three types of data are converted into CGCS2000 plane projection coordinates under the 2000 National Geodetic Coordinate System (CGCS2000), and the elevation system adopts the 1985 National Elevation Datum.

[0053] For example, a graph drawn based on standardized coordinate data. Figure 4 As shown, Figure 4 The distribution of survey sampling points and alternative sites is shown.

[0054] Step 202: Construct a network dataset based on standard coordinate data.

[0055] First, ensure that the coordinates of the survey points and candidate sites are connected to transportation routes. For those not connected, establish the shortest connection line between the survey points / candidate sites and the transportation routes, and consider this shortest connection line as the route from the survey points / candidate sites to the nearest transportation route. Then, based on the topological connections between each transportation route (including the shortest connection line), the survey points, and the candidate sites, construct a network dataset. This dataset records the topological connections between the survey points / candidate sites in data form, providing a data foundation for subsequent path search and shortest path algorithm automation. When constructing the network dataset, it is necessary to ensure that all point elements, including the survey points / candidate sites, are topologically connected to the transportation routes. In other words, if there are survey points or alternative sites that are not connected to roads, establish a shortest distance connection between the survey points or alternative sites and the nearest road, so that all points in the M survey points and N alternative sites are connected to roads.

[0056] The process of establishing the shortest connection between the coordinates of the survey points or candidate sites and the roads can be achieved as follows: First, use the Make XY Event Layer tool in ArcGIS to convert the survey point table or candidate sites containing coordinates into temporary point features on the map. Next, use the Append tool to merge at least one list containing the aforementioned temporary point features into a single complete point feature class for easier subsequent processing. Then, use the Near tool to find the nearest location on the roads for each survey point or candidate site and calculate the distance. Finally, use the Points To Line tool to connect each survey point, candidate site, and the nearest point on the road using the results from the Near tool, generating the shortest connection line.

[0057] After topology checking, all coordinate points of the candidate sites were found to be connected to roads. For sampled coordinate points not connected to roads, shortest connection lines were established between them and the roads. Then, a network dataset was constructed based on the roads (including the shortest connection lines between the surveyed points and the roads), the coordinates of the surveyed points, and the coordinates of the candidate sites, ensuring that all point elements, including the sampled coordinate points and site coordinate points, were topologically connected to the roads. The original road data and the generated shortest connection lines were then merged to obtain the complete road network.

[0058] The Create Network Dataset tool in ArcGIS is used to construct a network dataset based on standard coordinate data. This tool records the connectivity relationships between all elements (points and lines) in the complete road network, creating a network dataset that can be directly used by path search and shortest path algorithms, providing a data foundation for subsequent automated calculations.

[0059] Step 203: Based on the network dataset, select the candidate station with the shortest total path distance from the M survey points from the N candidate stations as the station.

[0060] For example, such as Figure 5 As shown, among the 17 candidate sites, the one with the most convenient transportation and the shortest total path distance to the 150 survey sampling points was selected as the sampling site. This was done through A... Heuristic search algorithms calculate the new Ping'an Town ( The location marked as the sampling point is the one with the shortest total path distance from all sampling points, therefore Xinping'an Town was chosen as the sampling location.

[0061] It should be noted that, Figure 5 The straight lines shown from the base to each survey point are only schematic lines and do not represent the actual planned path from the base to the survey point.

[0062] Specifically, the shortest path algorithm is used to determine the shortest distance between the m-th survey point and the n-th candidate site, obtaining a distance cost matrix; 1≤n≤N; 1≤m≤M. Then, based on the distance cost matrix, the sum of the shortest distances between each candidate site and the M survey points is calculated as the total distance. From the N candidate sites, the candidate site with the shortest total path distance is selected as the site (the optimal site).

[0063] For example, based on the established survey points, select one of the candidate sites as the field survey sampling site. Each day, sampling begins at the site and ends upon completion of the sampling task. The site is selected based on its shortest distance from all survey points. Detailed steps (steps 2031-2031) are as follows: Step 2031: Set up the survey sample point set and the alternative site set.

[0064] ; This represents the set of survey sample points, which includes... M One survey sample point, The coordinates of the m-th survey point; 1 ≤ m ≤ M.

[0065] Set up a set of points for alternative base locations: ; This represents the set of candidate bases, consisting of N points. Let N be the coordinates of the nth candidate location. 1 ≤ n ≤ N.

[0066] Step 2032: Construct the distance cost matrix.

[0067] Build 1 The two-dimensional array or distance cost matrix, where D is the set of survey sample points and S is the set of candidate settlements.

[0068] In this specific embodiment, A is used. A heuristic search algorithm is used to search and obtain the set of survey samples. Any survey point in the The shortest path distance between any of the alternative locations.

[0069] The distance cost matrix is ​​as follows: ; d in the distance cost matrix mn This represents the shortest path distance between the m-th survey point and the n-th alternative site.

[0070] Step 2033: Find the residence with the shortest total path distance from the survey sample points.

[0071] Calculate the total distance T from each candidate site to all survey points based on the constructed distance-cost matrix. n Based on this, the total distance T is calculated. n Minimum alternative base This is considered the best place to stay.

[0072] Taking the distance cost matrix above as an example, summing the row elements of a column in the distance cost matrix yields the total distance corresponding to the candidate locations in the current column. The distance cost matrix above is merely an example; alternatively, candidate locations can be used as row elements, and summing the row elements of a row will give the total distance corresponding to the candidate locations in that row.

[0073] Step 204: Determine the constraint variables and constraint conditions.

[0074] The constraints that need to be determined include the average vehicle speed (the average speed of travel on roads), daily working hours, and the time taken for each sample point survey.

[0075] After selecting the optimal location for field sampling, the average vehicle speed needs to be estimated based on the terrain and road conditions of the survey area. For example, if the survey area is flat with convenient transportation and good road conditions, the average vehicle speed can be appropriately increased; if the survey area is mountainous with rugged roads requiring frequent uphill or downhill driving, the average vehicle speed should be decreased to reflect the actual working environment. Furthermore, the daily working hours and the time allotted for each sampling point should be determined according to industry rules or standards and the type of sample (soil, water quality, etc.).

[0076] Daily working hours (T): The total available time (hours or minutes) from departure to the completion of sampling at the last sampling point of the day.

[0077] The sampling time for a single sample point (sp_Time): The time (hours) required to complete the sampling survey for each sample point, which is generally estimated based on workload, characteristics of the survey area, and experience.

[0078] Average vehicle speed (v): estimated based on the terrain and road conditions of the survey area (km / h).

[0079] Based on the above multiple constraint variables, the sampling duration for day i is calculated, and the constraint condition is determined to be that the sampling duration is less than or equal to the daily working duration (T).

[0080] For example, based on the road conditions and time constraints within the Da'an City area, the constraint variables for this survey are defined as follows: Daily working hours T <= 10 hours; The sampling time for a single sample point is sp_Time = 1 / 3 hour; The average vehicle speed is v = 60 km / h.

[0081] Step 205: Use a path search algorithm to determine the planned path from the base to the remaining multiple survey points among the M survey points, and output the first data table corresponding to the i-th day.

[0082] Construct a temporary database DatabaseTemp. Use Dijkstra's algorithm to calculate the sample point sorting sequence (or sample point sequence) formed after traversing all survey sample points (currently remaining) starting from the base station, and the shortest path length between two points according to this sorting. Store the above data in the temporary database DatabaseTemp.

[0083] The temporary database stores a table (the first data table), which consists of three fields: Sequence, pointName, and shortestpath_length. The data structure of the first data table is shown in Table 1 below.

[0084] Table 1

[0085] The data type of the sequence number is integer (int), the data type of the survey sample name is string, and the data type of the shortest path length is double-precision floating-point number (double).

[0086] Based on the above exemplary description, it can be seen that in some embodiments, the first data table corresponding to day i can be obtained in the following manner: call the path search algorithm to determine the sequence of sample points after arriving at all the remaining multiple survey sample points from the base, and determine the shortest path length between two adjacent survey sample points in the sample point sequence; generate the first data table corresponding to day i and output it.

[0087] As shown in Table 1 above, the first data table includes multiple records corresponding to the remaining multiple survey sample points; the multiple records are arranged according to the sample point sequence; the m-th record among the multiple records includes the serial number of the m-th survey sample point and the shortest path length between the m-th survey sample point and the (m+1)-th survey sample point; or, the m-th record also includes the name of the m-th survey sample point, that is, the name column shown in Table 1 is optional. In other embodiments, the first data table may only include the serial number and the shortest path length between two adjacent survey sample points.

[0088] Step 206: Under the condition of satisfying the preset constraints, determine the sampling task and sampling path for day i based on the first data table corresponding to day i.

[0089] Let the distance traveled on day i be s, and initially s=0; Set the number of sampling points on day i, pointNum, with initial pointNum=0; Based on the constraint variables determined in step 204, calculate the sampling duration t': t' = (s / v) + pointNum sp_Time; Where s represents the distance traveled, v represents the average speed of the vehicle (user-defined), pointNum represents the number of survey points sampled that day, and sp_Time represents the sampling time for a single survey point.

[0090] After calculating the sampling duration t', if t' <= T, then the constraint condition is considered satisfied, that is, when ((s / v) + pointNum) When sp_Time <= T, perform the following operations in a loop: Step 20611: Read the records of the current DatabaseTemp. When performing this operation for the first time, start from the first record. Increase the number of sample points by 1: pointNum += 1; Increase the shortest path distance for the corresponding sample point: s += shortestpath_length; Step 20612: If the next record exists in DatabaseTemp: Move to the next record; Step 20613: If the next record in DatabaseTemp does not exist: generate the database "Day"+i+"Mission".

[0091] It should be noted that i represents the day number of the sampling task, starting from 1 and accumulating until step 2062 is reached; Step 20614: When ((s / v) + pointNum) When sp_Time > T), the database "Day" + i + "Mission" is generated.

[0092] The database “Day”+i+”Mission” contains a second data table, which stores the number of survey points to be completed on day i and the corresponding path lengths. The data structure is the same as Table 1.

[0093] Remove the survey samples that have been included in the "Day"+i+"Mission" database from the temporary database DatabaseTemp, and transfer the remaining survey sample data to step 205 to continue generating the first data table corresponding to the next day.

[0094] This creates I databases for "Day" + i + "Mission" that require I days to complete. Each database stores the survey sample point tasks and the shortest path length that need to be completed on day i.

[0095] Following the iterative steps 206, which involve updating the temporary database, the daily survey sampling points and routes are calculated until all survey points are assigned to their respective survey tasks, thus forming a daily survey sampling task and route arrangement plan.

[0096] Based on the above exemplary description, under the condition that the preset constraints are met, the sampling task and sampling path for day i are determined based on the data table. Specifically, under the condition that the preset constraints are met, the following operations are executed cyclically until the termination condition is reached: If the m-th record corresponding to the m-th survey sample point is read from the first data table corresponding to the i-th day, the number of survey sample points is incremented by 1, and the shortest path length recorded in the m-th record is added to the current distance traveled. If a record following the m-th record exists, move to the next record; If the next record after the m-th record does not exist, the loop ends, and a second data table for the i-th day is generated; the data structure of the second data table may be the same as or different from the data structure of the first data table. If the sampling duration on day i is greater than the total sampling duration for a single day, the loop ends and the second data table corresponding to day i is generated.

[0097] Furthermore, after generating the second data table corresponding to day i, the following operations are performed: Remove the Z1 survey points included in the second data table corresponding to the i-th day from the M survey points to obtain the remaining M-Z1 survey points, i=i+1; return to step 205, use the path search algorithm to determine the planned path from the base to the remaining M-Z1 survey points in sequence, and output the first data table corresponding to the i+1-th day. After obtaining the first data table corresponding to the i+1-th day, continue to execute step 206. First, determine whether the current task completion status meets the preset constraint conditions (i.e., the current sampling duration does not exceed the single-day sampling duration T). Based on the first data table corresponding to the i+1-th day, determine the sampling task for the i+1-th day.

[0098] For example, continuing with the above practical application scenario example, by automatically planning daily field sampling survey routes and assigning daily field sampling survey tasks, Figures 6a to 6h This shows the sampling routes for the survey in Da'an City from day 1 to day 8. The daily planned routes from day 1 to day 8 are integrated into a single map for display. Figure 7 , Figure 7 The optimal route planning map for sampling in Da'an City over 8 days.

[0099] Using traditional sampling methods, it would take at least 10 days to complete the sampling task in Da'an City. However, using the solution proposed in this invention, the sampling task can be completed within 8 days, reducing the time to 80% of the original time and significantly improving the efficiency of field sampling tasks.

[0100] The method proposed in this invention improves the rationality of sampling site selection by rationally choosing the sampling site. The sampling site is the starting and ending point for daily sampling work; therefore, rational site selection is a key prerequisite for improving sampling efficiency and saving sampling time. First, candidate sites must be identified. Generally, locations within a county with accommodation, sample preservation facilities, and good transportation access are selected as candidate sites for field surveys. Township areas are generally suitable as candidate sites. Based on the identified candidate sites, this invention calculates and selects the final site. This invention uses A... The heuristic search algorithm calculates the distance of each candidate site from all sample points in turn. By constructing a distance-cost matrix, it compares the total distance of all candidate sites from all sample points and finally determines the candidate site with the shortest total path distance from all sample points as the final site.

[0101] Furthermore, the embodiments of the present invention set work constraints according to actual conditions: the daily working hours and the time for individual sample point surveys are determined based on the terrain and road conditions of the survey area and industry rules, and important constraint variables such as the estimated average vehicle speed are used as prerequisites for allocating daily sampling tasks and calculating daily survey sampling paths.

[0102] This invention implements a daily sampling task allocation and survey sampling path calculation: Based on the determined location and constraints, the optimal sampling path for each day is calculated. First, the actual distances of all sample point roads are obtained. A path search algorithm is used to group the sample points according to their regional location, determining the initial number of sample points in each group and the estimated maximum number of sample points that can be completed each day. The number of daily sample points is dynamically adjusted according to daily time constraints. Dijkstra's algorithm is used again to generate the shortest path for each daily sample point, ensuring that the daily sampling workload is maximized.

[0103] This invention provides an optimal path planning technique for traversing sampling points in field surveys. It plans the optimal route for the sampling route, calculates and selects the shortest distance from each sampling point among the backup sampling locations, and determines the total number of days required for the field survey, the daily sampling tasks, and the optimal route based on daily working hours, sampling point times, and estimated travel speed. This improves the effectiveness of field surveys, avoids manpower, material, and time costs, increases the execution efficiency of field sampling tasks, and ensures the reliability and efficiency of field survey work.

[0104] The field survey sample point task planning device provided in the embodiments of this application is described below. The system described below and the field survey sample point task planning method described above can be referred to each other and can achieve the same or corresponding technical effects.

[0105] This application provides a field survey sampling point task planning device, such as... Figure 8 As shown, the device includes the following modules: The coordinate transformation module 801 is used to convert the original coordinate data of the M survey points and N alternative sites to be investigated, as well as the vector data of the traffic roads included in the area to be investigated, into standard coordinate data in the same coordinate system.

[0106] The topology connection module 802 is used to construct a network dataset based on standard coordinate data; the network dataset is used to record the topology connection relationships between survey points, alternative sites, and transportation routes.

[0107] The site selection module 803 is used to select the candidate site with the shortest total path distance from M survey sample points from N candidate sites based on the network dataset.

[0108] The overall path planning module 804 is used to determine the planned path from the base to the remaining multiple survey points among the M survey points in sequence using a path search algorithm, and output the first data table corresponding to the i-th day; i≥1.

[0109] The daily path planning module 805 is used to determine the sampling task and sampling path for day i based on the first data table corresponding to day i, under the condition of satisfying preset constraints.

[0110] In one embodiment, the topology connection module 802 is specifically used to: determine the connection relationship between each survey sample point and the traffic road and the connection relationship between each candidate site and the traffic road based on standard coordinate data; if there are survey sample points or candidate sites that are not connected to the traffic road, establish a connection line with the shortest distance between the survey sample point or candidate site and the nearest traffic road, so that all points in the M survey sample points and N candidate sites are connected to the traffic road.

[0111] In one embodiment, the site selection module 803 is specifically used to: use a shortest path algorithm to determine the shortest distance between the m-th survey sample point and the n-th candidate site, and obtain a distance cost matrix; 1≤n≤N; 1≤m≤M; calculate the sum of the shortest distances between each candidate site and the M survey sample points according to the distance cost matrix, as the total distance; and select the candidate site with the shortest total path distance from the N candidate sites as the site.

[0112] In one embodiment, the overall path planning module 804 is specifically used to: invoke a path search algorithm to determine the sequence of sample points after arriving at all remaining survey sample points from the base, and to determine the shortest path length between two adjacent survey sample points in the sample point sequence; generate and output the first data table corresponding to the i-th day; the first data table includes multiple records corresponding to the remaining survey sample points; the multiple records are arranged according to the sample point sequence; the m-th record in the multiple records includes the sequence number of the m-th survey sample point and the shortest path length between the m-th survey sample point and the (m+1)-th survey sample point; or, the m-th record also includes the name of the m-th survey sample point.

[0113] In one embodiment, the constraints include that the sampling duration on day i is less than or equal to the total sampling duration of the day; 1 ≤ i; wherein the sampling duration is determined based on the following variables: the sampling duration of a single survey point, the number of survey points on day i, the distance traveled on day i, and the average vehicle speed. The total path planning module 804 is specifically used to: under the condition of satisfying the preset constraints, cyclically execute the following operations until a termination condition is reached: if the m-th record corresponding to the m-th survey point is read from the first data table corresponding to day i, increment the number of survey points by 1, and add the shortest path length recorded in the m-th record to the current distance traveled; if the next record of the m-th record exists, move to the next record; if the next record of the m-th record does not exist, end the loop and generate the second data table for day i; the data structure of the second data table may be the same as or different from the data structure of the first data table; in response to the sampling duration on day i being greater than the total sampling duration of the day, end the loop and generate the second data table corresponding to day i.

[0114] In one embodiment, after generating the second data table corresponding to day i, the overall path planning module 804 is further configured to: delete the Z1 survey points included in the second data table corresponding to day i from the M survey points, obtaining the remaining M-Z1 survey points; use a path search algorithm to determine the planned path from the base station to the remaining M-Z1 survey points in sequence, and output the first data table corresponding to day i+1. The daily path planning module 805 is further configured to: determine the sampling task for day i+1 based on the first data table corresponding to day i+1, provided that preset constraints are met.

[0115] Figure 9 A schematic diagram of the physical structure of an electronic device is shown. (For example...) Figure 9As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930, and a communication bus 990, wherein the processor 910, the communication interface 920, and the memory 930 communicate with each other via the communication bus 990. The processor 910 can call the computer program in the memory 930 to execute the steps of the field survey sample point task planning method, such as including: The original coordinate data of the M survey points and N candidate sites, as well as the vector data of the roads in the survey area, are converted into standard coordinate data in the same coordinate system. Based on the standard coordinate data, a network dataset is constructed. The network dataset is used to record the topological connections between the survey points, candidate sites, and roads. Based on the network dataset, the candidate site with the shortest total path distance from the M survey points is selected from the N candidate sites as the site. A path search algorithm is used to determine the planned path from the site to the remaining survey points among the M survey points, and the first data table corresponding to day i is output; i≥1. Under the condition of satisfying the preset constraints, the sampling task and sampling path for day i are determined based on the first data table corresponding to day i.

[0116] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the 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 cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the field survey sample point task planning method provided in the above embodiments, such as including: The original coordinate data of the M survey points and N candidate sites, as well as the vector data of the roads in the survey area, are converted into standard coordinate data in the same coordinate system. Based on the standard coordinate data, a network dataset is constructed. The network dataset is used to record the topological connections between the survey points, candidate sites, and roads. Based on the network dataset, the candidate site with the shortest total path distance from the M survey points is selected from the N candidate sites as the site. A path search algorithm is used to determine the planned path from the site to the remaining survey points among the M survey points, and the first data table corresponding to day i is output; i≥1. Under the condition of satisfying the preset constraints, the sampling task and sampling path for day i are determined based on the first data table corresponding to day i.

[0118] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the methods provided in the above embodiments, such as including: The original coordinate data of the M survey points and N candidate sites, as well as the vector data of the roads in the survey area, are converted into standard coordinate data in the same coordinate system. Based on the standard coordinate data, a network dataset is constructed. The network dataset is used to record the topological connections between the survey points, candidate sites, and roads. Based on the network dataset, the candidate site with the shortest total path distance from the M survey points is selected from the N candidate sites as the site. A path search algorithm is used to determine the planned path from the site to the remaining survey points among the M survey points, and the first data table corresponding to day i is output; i≥1. Under the condition of satisfying the preset constraints, the sampling task and sampling path for day i are determined based on the first data table corresponding to day i.

[0119] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0120] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for planning field survey sampling points, characterized in that, The method includes: The original coordinate data of the M survey points and N alternative sites to be investigated, as well as the vector data of the traffic roads included in the area to be investigated, are converted into standard coordinate data in the same coordinate system. Based on the standard coordinate data, a network dataset is constructed; the network dataset is used to record the topological connections between the survey sample points, the alternative sites, and the transportation roads. Based on the network dataset, select the candidate station with the shortest total path distance from the M survey sample points from the N candidate stations as the station; A path search algorithm is used to determine the planned path from the base to the remaining multiple survey points among the M survey points in sequence, and the first data table corresponding to the i-th day is output; i≥1; Under the premise of satisfying the preset constraints, the sampling task and sampling path for day i are determined based on the first data table corresponding to day i.

2. The method according to claim 1, characterized in that, Based on the aforementioned standard coordinate data, a network dataset is constructed, including: If there are survey points or alternative sites that are not connected to roads, establish a shortest distance connection between the survey point or alternative site and the nearest road, so that all points in the M survey points and the N alternative sites are connected to roads.

3. The method according to claim 1, characterized in that, Based on the network dataset, the candidate site with the shortest total path distance to the M survey sample points is selected from the N candidate sites as the site, including: The shortest path algorithm is used to determine the shortest distance between the m-th survey point and the n-th candidate site, and the distance cost matrix is ​​obtained; 1≤n≤N; 1≤m≤M; Based on the distance cost matrix, calculate the sum of the shortest distances between each candidate site and the M survey points, and use this sum as the total distance; From the N candidate locations, select the candidate location with the shortest total path distance as the base.

4. The method according to claim 1, characterized in that, A path search algorithm is used to determine the planned path from the base to the remaining multiple survey points among the M survey points, and the first data table corresponding to day i is output, including: The path search algorithm is invoked to determine the sequence of sample points that can be reached sequentially from the base to all the remaining multiple survey sample points, and to determine the shortest path length between two adjacent survey sample points in the sample point sequence. Generate and output the first data table corresponding to day i; the first data table includes multiple records corresponding to the remaining multiple survey sample points; the multiple records are arranged according to the sample point sequence; the m-th record among the multiple records includes the sequence number of the m-th survey sample point and the shortest path length between the m-th survey sample point and the (m+1)-th survey sample point; or, the m-th record also includes the name of the m-th survey sample point.

5. The method according to claim 4, characterized in that, The constraints include that the sampling duration on day i is less than or equal to the total sampling duration of the day; 1 ≤ i; wherein the sampling duration is determined based on the following variables: the sampling duration of a single survey point, the number of survey points on day i, the distance traveled on day i, and the average vehicle speed; Under the condition of satisfying preset constraints, the sampling task and sampling path for day i are determined based on the data table, including: Under the condition of satisfying the preset constraints, the following operations are executed in a loop until the termination condition is reached: if the m-th record corresponding to the m-th survey sample point is read from the first data table corresponding to the i-th day, the number of survey sample points is incremented by 1, and the shortest path length recorded in the m-th record is added to the current distance traveled. If a record following the m-th record exists, move to the next record; If the next record after the m-th record does not exist, the loop ends, and a second data table for the i-th day is generated; the data structure of the second data table may be the same as or different from the data structure of the first data table. If the sampling duration on day i is greater than the total sampling duration for a single day, the loop ends and the second data table corresponding to day i is generated.

6. The method according to claim 5, characterized in that, After generating the second data table corresponding to day i, the method further includes: The Z1 survey points included in the second data table corresponding to the i-th day are deleted from the M survey points to obtain the remaining M-Z1 survey points; A path search algorithm is used to determine the planned path from the base to the remaining M-Z1 survey points in sequence, and the first data table corresponding to the (i+1)th day is output. Under the condition of satisfying the preset constraints, the sampling task for day i+1 is determined based on the first data table corresponding to day i+1.

7. The method according to claim 1, characterized in that, The standard coordinate data is the CGCS2000 plane projection coordinates obtained by converting the Gauss-Kruger projection and the national standard zoning.

8. The method according to any one of claims 1-8, characterized in that, The graph search algorithm includes at least one of the following algorithms: Dijkstra, Bellman-Ford, Floyd, and SPFA.

9. A field survey sampling point task planning device, characterized in that, The device includes: The coordinate transformation module is used to convert the original coordinate data of the M survey points and N alternative sites to be investigated, as well as the vector data of the traffic roads included in the area to be investigated, into standard coordinate data in the same coordinate system. The topology connection module is used to construct a network dataset based on the standard coordinate data; the network dataset is used to record the topological connection relationships between the survey sample points, the alternative settlements, and the transportation roads; The site selection module is used to select, based on the network dataset, the candidate site with the shortest total path distance from the M survey sample points from the N candidate sites as the site. The overall path planning module is used to determine the planned path from the base to the remaining multiple survey points among the M survey points in sequence using a path search algorithm, and outputs the first data table corresponding to the i-th day; i≥1; The daily path planning module is used to determine the sampling task and sampling path for day i based on the first data table corresponding to day i, provided that preset constraints are met.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory storing a computer program, wherein the processor executes the computer program to implement the field survey sampling task planning method as described in any one of claims 1-8.

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