Sampling task multi-region path planning method and system based on time limit constraint segmentation

Abstract

The invention discloses a sampling task multi-region path planning method and system based on time limit constraint segmentation. The method comprises the following steps: acquiring a periodic sampling task set, sampling historical data and a next sampling date set; obtaining a to-be-planned sampling task set based on the next sampling date set; acquiring a sampling point position based on the to-be-planned sampling task set, and constructing a full-sampling region connected graph structure based on the sampling point position; obtaining the last sampling time based on the periodic sampling task set and the sampling historical data, and setting a constraint condition based on the next sampling date set; and performing path planning based on the full-sampling region connected graph structure and the constraint condition. Through the method, the problem of multi-region path planning segmented according to the time limit constraint can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning, and in particular relates to a method and system for multi-area path planning of sampling tasks based on time-limit constraint segmentation. Background Art

[0002] Sample collection in the fields of environmental testing and environmental monitoring is characterized by periodicity and regionalization. The tested locations often require sampling personnel to regularly go to the sampling points to collect samples and bring them back for testing and analysis. The testing cycle is one week, half a month, one month, one quarter, half a year, etc. The specific testing time plan is determined by the testing interval and the number of periodic testing. For example, the monthly sampling test at a certain place is at least 15 days apart, once a month, that is, a 15-day interval is ensured between two adjacent sampling tests, and once a month (about 30 days) is ensured to meet the sampling and testing requirements.

[0003] Testing agencies generally undertake a large number of testing and monitoring tasks around their areas. Within a certain sampling period, they need to collect samples in multiple sampling areas and sampling points. Because environmental protection and environmental monitoring work often involves multiple regions and large spans, the choice of path from the departure point to the sampling point greatly determines the cost and efficiency of collection. However, path selection requires a lot of planning and calculation. At the same time, path planning algorithms generally tend to plan all areas into a total path, and the sampling period is a task of multiple days. The sampling paths within a period of time need to be evenly, reasonably and effectively distributed to daily work tasks. Therefore, the planned strategy is a collection of multiple paths, which are evenly distributed to daily work tasks. Summary of the invention

[0004] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:

[0005] A multi-area path planning method for sampling tasks based on time-limit constraint segmentation includes the following steps:

[0006] Obtain periodic sampling task set, sampling history data and next sampling date set;

[0007] Acquire a set of sampling tasks to be planned based on the next sampling date set;

[0008] Acquire sampling point locations based on the sampling task set to be planned, and construct a connected graph structure of the entire sampling area based on the sampling point locations;

[0009] Acquire the last sampling time based on the periodic sampling task set and the sampling history data, and set constraint conditions based on the next sampling date set;

[0010] Path planning is performed based on the full sampling area connectivity graph structure and the constraint conditions.

[0011] Preferably, the periodic sampling task set includes:

[0012] S=(s1,s2,...s n )

[0013] s i =(l i , t i , c i , r i )

[0014] Among them, l i Indicates the location information contained in the sampling location, t i represents the sampling interval, c i represents the sampling period, r i Indicates the duration of the sampling process;

[0015] The sampling history data includes:

[0016] H=(h1,h2,...h n )

[0017] h i =(h i1 ,h i2 ,...h in )

[0018] Among them, h′ i =h(s i ) represents the sampling task s i All sampling history records;

[0019] The next sampling date set includes:

[0020]

[0021] in, Indicates the start date of the next sampling. Indicates the end date of the next sampling.

[0022] Preferably, the method for obtaining the sampling task set to be planned includes: obtaining a time interval of a valid working day (T b , T e ), analyze the next task time of all sampling tasks to obtain the sampling task set to be planned in the interval:

[0023]

[0024] Among them, T b and T e All represent effective working days.

[0025] Preferably, the method for constructing the full sampling area connectivity graph structure comprises:

[0026] Get the sampling point location information, and record the sampling point location as the vertex set V = (v0, v1, v2, ..., v n ), where v0 represents the sampling start and end points, v1, v2, ..., v n Indicates the sampling point location;

[0027] Based on the sampling point positions, a path connection set E between the sampling points is established. ij |1≤i≤n, 1≤j≤n}, when the sampling point position v i and the sampling point position v j When connected ij =1, otherwise e ij =0;

[0028] The connected graph structure of the entire sampling area is constructed based on the sampling point positions and the path connection set.

[0029] Preferably, the method for setting the constraint condition includes:

[0030] Get the last sampling time based on the sampling history data:

[0031]

[0032] in, Indicates the last sampling time;

[0033] The constraint condition is set based on the last sampling time, including:

[0034] Minimum sampling interval constraint:

[0035]

[0036] Sampling period constraints:

[0037]

[0038] Time constraints for a single path:

[0039]

[0040] Among them, t i Indicates the sampling requirement interval, c i Indicates the sampling requirement period, Indicates the redundant retention time of the path on the day. It indicates the time required to rendezvous samples after the sampling is completed on the day, p ik Indicates the sampling point position vi To the sampling point position v k The path, T W Indicates the split time limit value.

[0041] Preferably, the path planning method includes:

[0042] Step 1: Using a path planning algorithm, combined with the constraint conditions, a node search is performed on the connected graph structure of the full sampling area to obtain nodes that meet the constraint conditions;

[0043] Step 2: Add the obtained nodes to the path to form the planned path set p i =(p i1 , p i2 , ..., p im );

[0044] Step 3: Remove the obtained nodes from the connected graph structure of the full sampling area, and re-search from the remaining nodes, repeat steps 1 and 2, and obtain the path planning strategy set P = (p1, p2, ..., p n ).

[0045] The present invention also provides a sampling task multi-area path planning system based on time limit constraint segmentation, the system applies any of the above methods, including: a data acquisition module, a task set acquisition module, a connectivity graph construction module, a constraint setting module and a planning module;

[0046] The data acquisition module is used to obtain a periodic sampling task set, sampling history data and a next sampling date set;

[0047] The task set acquisition module acquires a sampling task set to be planned based on the next sampling date set;

[0048] The connectivity graph construction module obtains sampling point locations based on the sampling task set to be planned, and constructs a connectivity graph structure of the entire sampling area based on the sampling point locations;

[0049] The constraint setting module obtains the last sampling time based on the periodic sampling task set and the sampling history data, and sets constraint conditions based on the next sampling date set;

[0050] The planning module performs path planning based on the connectivity graph structure of the full sampling area and the constraint conditions.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention aims to divide a vertex path connected graph formed by multiple regions into several uniform and efficient paths according to constraints, the vertex set of all paths is exactly the vertex set of the connected graph, the constraint refers to the daily working time limit of the sampling personnel, and the divided sub-paths are the daily sampling task path planning within the sampling period, the vertex set of all divided paths is exactly the sampling point set within the sampling period, and the present invention can effectively solve the multi-region path planning problem divided according to time limit constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0054] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0055] 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, not all of the embodiments. 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.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Embodiment 1

[0058] In this embodiment, if Figure 1 As shown, a multi-area path planning method for sampling tasks based on time constraint segmentation includes the following steps:

[0059] S1. Obtain a set of periodic sampling tasks, sampling history data, and a set of next sampling dates.

[0060] In this embodiment, the periodic sampling task set includes:

[0061] S=(s1,s2,...s n )

[0062] s i =(l i , t i ,c i , r i )

[0063] Among them, l i Indicates the location information contained in the sampling location, t i Indicates the sampling interval: c i Indicates the sampling period: Indicates the duration of the sampling process (in hours), represents the set of real numbers, n t , n c ∈Z + , z i ∈Z(z1,z2,...z n ); The periodic sampling task needs to complete at least one sampling within a period c, and the time interval between two adjacent samplings is not less than t i ; Sampling history data includes:

[0064] H=(h1,h2,...h n )

[0065] h i =(h i1 ,h i2 ,...h in )

[0066] Among them, h′ i =h(s i ) represents the sampling task s i All sampling history records of the next sampling date set include:

[0067]

[0068]

[0069] in, Indicates the start date of the next sampling. Indicates the end date of the next sampling. If t cost (s i ) is the time required for the task, including travel time and sampling time, then:

[0070]

[0071] S2. Obtain a set of sampling tasks to be planned based on the next sampling date set.

[0072] The method for obtaining the sampling task set to be planned includes: obtaining the time interval of the valid working day (T b , T e ), analyze the next task time of all sampling tasks and obtain the set of sampling tasks to be planned in the interval:

[0073]

[0074] Among them, T b and T e All represent effective working days.

[0075] S3. Obtain the sampling point locations based on the sampling task set to be planned, and construct a connected graph structure of the entire sampling area based on the sampling point locations.

[0076] The method for constructing a connected graph structure of the entire sampling area includes: obtaining sampling point location information, recording the names of the sampling point locations as vertex sets V = (v0, v1, v2, ..., v n ), where v0 represents the sampling start and end points, v1, v2, ..., v n Represents the sampling point location; based on the sampling point location, establish the path connection set E between the sampling points = {e ij |1≤i≤n, 1≤j≤n}, when the sampling point position v i and the sampling point position v j When connected ij =1, otherwise e ij =0; Construct a connected graph structure of the entire sampling area based on the sampling point locations and path connection sets.

[0077] In this embodiment, map information is imported (through commercial map API or open source map information), and all sampling point locations are marked on the map; for the road network, the sampling locations are used as nodes, and the roads are used as edges connecting the nodes. The map topology structure is constructed through the vertex-edge model to obtain the connected graph structure of the entire sampling area. Then, the sampling point v is obtained by querying the map information API. i and sampling point v j The time required between and distance Get the starting and ending points v0 and the sampling point v k The time required between and distance

[0078] S4. Obtain the last sampling time based on the periodic sampling task set and sampling history data, and set constraints based on the next sampling date set.

[0079] Methods for setting constraints include: obtaining the last sampling time based on sampling history data:

[0080]

[0081] in, Indicates the last sampling time; based on the last sampling time, set constraints, including: minimum sampling interval constraint:

[0082]

[0083] Sampling period constraints:

[0084]

[0085] Time constraints for a single path:

[0086]

[0087] Among them, t i Indicates the sampling requirement interval, c i Indicates the sampling requirement period, Indicates the redundant retention time of the path on the day. It indicates the time required to rendezvous samples after the sampling is completed on the day, p ik Indicates the sampling point position v i To the sampling point position v k The path, T W Indicates the split time limit value, one version is the working hours of a working day.

[0088] S5. Perform path planning based on the connectivity graph structure and constraints of the full sampling area.

[0089] The path planning method includes: step 1, using the path planning algorithm, combined with the constraint conditions, to search for nodes in the connected graph structure of the entire sampling area, and obtain nodes that meet the constraint conditions; step 2, adding the obtained nodes to the path to form a planned path set p i =(p i1 , p i2 , ..., p im ), where p i represents the work path of the i-th day, p ij represents the jth sampling point to be visited on the i-th day; Step 3, remove the obtained nodes from the connected graph structure of the entire sampling area, and re-search from the remaining nodes, repeat steps 1 and 2, and obtain the path planning strategy set P = (p1, p2, ..., p n ).

[0090] In this embodiment, it is assumed that path p i The starting time of the i-th working day is:

[0091] t begin (p i )=T b +(i-1)day round (T w )

[0092] Let the jth sampling point p to be visited on the i-th day be ij The corresponding sampling task is The sampling time required for the task is Then we reach the sampling point p ij The journey time used is Therefore, at the sampling point p ij The time spent on the task is:

[0093]

[0094] Among them, t reserve represents the redundant retention time of a single task adjustment, and λ represents the adjustment coefficient; according to the above formula, the sampling point p is obtained ij The start time of the task is:

[0095]

[0096] Sampling point p ij The task ends at:

[0097]

[0098] This completes the starting time planning of the task path.

[0099] Embodiment 2

[0100] In this embodiment, a sampling task multi-area path planning system based on time limit constraint segmentation includes: a data acquisition module, a task set acquisition module, a connectivity graph construction module, a constraint setting module and a planning module.

[0101] The data acquisition module is used to obtain the periodic sampling task set, sampling history data and the next sampling date set.

[0102] The periodic sampling task set includes:

[0103] S=(s1,s2,...s n )

[0104] s i =(l i , t i , c i , r i )

[0105] Among them, l i Indicates the location information contained in the sampling location, t i represents the sampling interval, c i represents the sampling period, r i Indicates the duration of the sampling process; sampling history data includes:

[0106] H=(h1,h2,...h n )

[0107] h i =(h i1 ,h i2 ,...h in )

[0108] Among them, h′ i =h(s i ) represents the sampling task s i All sampling history records of the next sampling date set include:

[0109]

[0110]

[0111] in, Indicates the start date of the next sampling. Indicates the end date of the next sampling.

[0112] The task set acquisition module acquires the sampling task set to be planned based on the next sampling date set.

[0113] The method for obtaining the sampling task set to be planned includes: obtaining the time interval of the valid working day (T b , T e ), analyze the next task time of all sampling tasks and obtain the set of sampling tasks to be planned in the interval:

[0114]

[0115] Among them, T b and T e All represent effective working days.

[0116] The connectivity graph construction module obtains the sampling point locations based on the sampling task set to be planned, and constructs the connectivity graph structure of the entire sampling area based on the sampling point locations.

[0117] The method for constructing a connected graph structure of the entire sampling area includes: obtaining sampling point location information, recording the names of the sampling point locations as vertex sets V = (v0, v1, v2, ..., v n ), where v0 represents the sampling start and end points, v1, v2, ..., v n Represents the sampling point location; based on the sampling point location, establish the path connection set E between the sampling points = {e ij |1≤i≤n, 1≤j≤n}, when the sampling point position v i and the sampling point position v jWhen connected ij =1, otherwise e ij =0; Construct a connected graph structure of the entire sampling area based on the sampling point locations and path connection sets.

[0118] The constraint setting module obtains the last sampling time based on the periodic sampling task set and the sampling history data, and sets the constraint conditions based on the next sampling date set.

[0119] Methods for setting constraints include: obtaining the last sampling time based on sampling history data:

[0120]

[0121] in, Indicates the last sampling time. Based on the last sampling time, set constraints, including:

[0122] Minimum sampling interval constraint:

[0123]

[0124] Sampling period constraints:

[0125]

[0126] Time constraints for a single path:

[0127]

[0128] Among them, t i Indicates the sampling requirement interval, c i Indicates the sampling requirement period, Indicates the redundant retention time of the path on the day. It indicates the time required to rendezvous samples after the sampling is completed on the day, p ik Indicates the sampling point position v i To the sampling point position v k The path, T W Indicates the split time limit value, one version is the working hours of a working day.

[0129] The planning module performs path planning based on the connected graph structure of the full sampling area and constraints.

[0130] The path planning method includes: step 1, using the path planning algorithm, combined with the constraint conditions, to search for nodes in the connected graph structure of the entire sampling area, and obtain nodes that meet the constraint conditions; step 2, adding the obtained nodes to the path to form a planned path set p i =(p i1 , p i2 , ..., p im); Step 3, remove the obtained nodes from the connected graph structure of the full sampling area, and re-search from the remaining nodes, repeat steps 1 and 2, and obtain the path planning strategy set P = (p1, p2, ..., p n ).

[0131] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A multi-area path planning method for sampling tasks based on time-limit constraint segmentation, characterized in that: The following steps are involved: Obtain periodic sampling task set, sampling history data and next sampling date set; Acquire a set of sampling tasks to be planned based on the next sampling date set; Acquire sampling point locations based on the sampling task set to be planned, and construct a connected graph structure of the entire sampling area based on the sampling point locations; Acquire the last sampling time based on the periodic sampling task set and the sampling history data, and set constraint conditions based on the next sampling date set; Path planning is performed based on the full sampling area connectivity graph structure and the constraint conditions.

2. According to claim 1, a sampling task multi-area path planning method based on time constraint segmentation is characterized in that: The periodic sampling task set includes: S=(s1,s2,...s n ) s i =(l i ,t i ,c i ,r i ) Among them, l i Indicates the location information contained in the sampling location, t i represents the sampling interval, c i represents the sampling period, r i Indicates the duration of the sampling process; The sampling history data includes: H=(h1,h2,...h n ) h i =(h′ i1 ,h′ i2 ,...h′ in ) Among them, h′ i =h(s i ) represents the sampling task s i All sampling history records; The next sampling date set includes: in, Indicates the start date of the next sampling. Indicates the end date of the next sampling.

3. According to claim 2, a sampling task multi-area path planning method based on time limit constraint segmentation is characterized in that: The method for obtaining the sampling task set to be planned includes: obtaining the time interval of the valid working day (T b , T e ), analyze the next task time of all sampling tasks to obtain the sampling task set to be planned in the interval: Among them, T b and T e All represent effective working days.

4. According to claim 1, a sampling task multi-area path planning method based on time constraint segmentation is characterized in that: The method for constructing the fully sampled region connected graph structure comprises: Get the sampling point location information, and record the sampling point location as the vertex set V = (v0, v1, v2, ..., v n ), where v0 represents the sampling start and end points, v1, v2, ..., v n Indicates the sampling point location; Based on the sampling point positions, a path connection set E between the sampling points is established. ij |1≤i≤n, 1≤j≤n}, when the sampling point position v i and the sampling point position v j When connected ij =1, otherwise e ij =0; The connected graph structure of the entire sampling area is constructed based on the sampling point positions and the path connection set.

5. According to claim 2, a sampling task multi-area path planning method based on time constraint segmentation is characterized in that: The method of setting the constraint condition includes: Get the last sampling time based on the sampling history data: in, Indicates the last sampling time; The constraint condition is set based on the last sampling time, including: Minimum sampling interval constraint: Sampling period constraints: Time limit constraints for a single path: Among them, t i Indicates the sampling requirement interval, c i Indicates the sampling requirement period, Indicates the redundant retention time of the path on the day. It indicates the time required to rendezvous samples after the sampling is completed on the day, p ik Indicates the sampling point position v i To the sampling point position v k The path, T W Indicates the split time limit value.

6. According to claim 1, a sampling task multi-area path planning method based on time constraint segmentation is characterized in that: The path planning method comprises: Step 1: Using a path planning algorithm, combined with the constraint conditions, a node search is performed on the connected graph structure of the full sampling area to obtain nodes that meet the constraint conditions; Step 2: Add the obtained nodes to the path to form the planned path set p i =(p i1 , p i2 , ..., p im ); Step 3: Remove the obtained nodes from the connected graph structure of the full sampling area, and re-search from the remaining nodes, repeat steps 1 and 2, and obtain the path planning strategy set P = (p1, p2, ..., p n ).

7. A multi-area path planning system for sampling tasks based on time constraint segmentation, the system applying the method according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, task set acquisition module, connectivity graph construction module, constraint setting module and planning module; The data acquisition module is used to obtain a periodic sampling task set, sampling history data and a next sampling date set; The task set acquisition module acquires a sampling task set to be planned based on the next sampling date set; The connectivity graph construction module obtains sampling point locations based on the sampling task set to be planned, and constructs a connectivity graph structure of the entire sampling area based on the sampling point locations; The constraint setting module obtains the last sampling time based on the periodic sampling task set and the sampling history data, and sets constraint conditions based on the next sampling date set; The planning module performs path planning based on the connectivity graph structure of the full sampling area and the constraint conditions.

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