CN115344790B - Collection scheduling method of distribution resources, electronic device and storage medium - Google Patents
Collection scheduling method of distribution resources, electronic device and storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure relates to the technical field of geographic information processing, and discloses a method for acquiring and scheduling distribution resources, electronic equipment and a storage medium, wherein the method comprises the steps of acquiring map resource data in a target area; acquiring travel track data of each distribution resource in a target area in a historical time period; performing track matching based on the map resource data and the travel track data, and determining associated data of the distribution resources at the interest points of the target area path; and selecting a distribution resource combination meeting the interest point acquisition requirement from all the distribution resources based on the associated data to acquire the interest point data, wherein the distribution resource combination comprises at least one distribution resource. According to the technical scheme, high-frequency interest point collection can be performed by using distribution resources, and the change of the interest points in the real world is captured and updated in time, so that the interest points in the map are more accurate, the accuracy and the usability of map application are improved, and the method is mainly used for interest point collection.
Description
Technical Field
The disclosure relates to the technical field of geographic information processing, and in particular relates to a collection and scheduling method of distribution resources, electronic equipment and a storage medium.
Background
A Point of Interest (POI) generally refers to a geographic object that can be abstracted as a Point, and is used as a data base for map retrieval, map navigation and other applications, and the accuracy of the POI is crucial to the accuracy and usability of various map applications. At present, most of POI (point of interest) acquisition is actively acquired by using professional acquisition equipment, high image acquisition equipment and high maintenance cost are required, and the POI acquisition cannot be performed at high frequency to capture the POI change in the real world due to the limitation of acquisition cost, so that the accuracy of the POI is ensured. Therefore, a scheme for performing POI acquisition at high frequency is urgently needed.
Disclosure of Invention
In order to solve the problems in the related art, embodiments of the present disclosure provide a method for acquiring and scheduling delivery resources, an electronic device, and a storage medium.
In a first aspect, an embodiment of the present disclosure provides a method for acquiring and scheduling delivery resources.
Specifically, the method for acquiring and scheduling the delivery resources includes:
acquiring map resource data in a target area;
acquiring travel track data of each distribution resource in the target area in a historical time period;
performing track matching based on the map resource data and the travel track data, and determining associated data of interest points of the distribution resources in the target area path;
and selecting a distribution resource combination meeting the interest point acquisition requirement from all the distribution resources based on the associated data to acquire the interest point data, wherein the distribution resource combination comprises at least one distribution resource.
In a second aspect, an embodiment of the present disclosure provides an acquisition scheduling apparatus for a delivery resource.
Specifically, the apparatus for acquiring and scheduling delivery resources includes:
the first acquisition module is configured to acquire map resource data in a target area;
the second acquisition module is configured to acquire travel track data of each distribution resource in the target area in a historical time period;
the matching module is configured to perform track matching based on the map resource data and the travel track data, and determine associated data of interest points of the distribution resources in the target area;
and the selecting module is configured to select a distribution resource combination meeting the interest point collecting requirement from all the distribution resources based on the associated data to collect the interest point data, wherein the distribution resource combination comprises at least one distribution resource.
In a third aspect, the disclosed embodiments provide an electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method according to any one of the first aspect.
In a fifth aspect, the disclosed embodiments provide a computer program product comprising computer instructions which, when executed by a processor, implement the method steps according to any one of the first aspect.
According to the technical scheme provided by the embodiment of the disclosure, the map resource data in the target area can be acquired, so that when the travel track data of each distribution resource in the target area in a historical time period is acquired, track matching can be performed on the basis of the map resource data and the travel track data, the association data of the points of interest of the paths of each distribution resource when the distribution resource travels in the target area is determined, and further, the distribution resource combination meeting the point of interest acquisition requirements can be selected from each distribution resource for point of interest acquisition on the basis of the association data. Therefore, in the process of carrying out distribution service on distribution resources in the distribution resource combination, the carried acquisition equipment can acquire the interest points of the paths, so that the interest points can be acquired at high frequency, the change of the interest points in the real world can be captured and updated in time, the interest points in the map are more accurate, and the accuracy and the usability of the map application are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. The following is a description of the drawings.
Fig. 1 shows a flowchart of an acquisition scheduling method of delivery resources according to an embodiment of the present disclosure.
Fig. 2 shows a schematic diagram of a coverage curve and a number of coverage curve for a plurality of regions, in accordance with an embodiment of the present disclosure.
Fig. 3 shows an acquisition schematic diagram of a travel trajectory sequence according to an embodiment of the present disclosure.
Fig. 4 shows a block diagram of an acquisition scheduling apparatus for delivering resources according to an embodiment of the present disclosure.
Fig. 5 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
FIG. 6 shows a schematic block diagram of a computer system suitable for use in implementing a method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In the present disclosure, the acquisition of the user information or the user data is an operation that is authorized, confirmed, or actively selected by the user.
As mentioned above, a Point of Interest (POI) generally refers to a geographic object that can be abstracted as a Point, and serves as a data base for applications such as map retrieval and map navigation, and the accuracy of the POI is crucial to the accuracy and usability of various map applications. At present, most POI is collected actively by using professional collection equipment, expensive image collection equipment and maintenance cost are required to be invested, and the POI collection can not be carried out frequently to capture the POI change of the real world due to the limitation of the collection cost, so that the accuracy of the POI is ensured. Therefore, a scheme for performing POI acquisition at high frequency is urgently needed.
The method can select a distribution resource combination meeting the interest point acquisition requirement from distribution resources passing through a target area in a historical time period, and install acquisition equipment for each distribution resource in the distribution resource combination, so that in the process of carrying out distribution service on the distribution resources, the carried acquisition equipment can acquire the interest points in the way, the interest point acquisition can be carried out at high frequency, the change of the interest points in the real world can be captured and updated in time, the interest points in a map are more accurate, and the accuracy and the availability of map application are improved.
Fig. 1 shows a flowchart of an acquisition scheduling method of delivery resources according to an embodiment of the present disclosure. As shown in fig. 1, the method for collecting and scheduling delivery resources includes the following steps S101 to S104:
in step S101, map resource data in a target area is obtained, where the map resource data includes road network data and interest point data;
in step S102, travel trajectory data of each distributed resource in the target area within the historical time period is acquired;
in step S103, performing trajectory matching based on the map resource data and the travel trajectory data, and determining associated data of interest points of the routes of the distribution resources in the target area;
in step S104, based on the associated data of the points of interest, a distribution resource combination meeting the point of interest collection requirement is selected from the distribution resources to perform point of interest collection, where the distribution resource combination includes at least one distribution resource.
In a possible embodiment, the collection scheduling method for the delivery resource is applied to an electronic device which can execute collection scheduling of the delivery resource, and the electronic device may be a computer, a computing device, a server cluster, or the like.
In a possible embodiment, the target area refers to any area that needs to be subjected to the point of interest acquisition, such as a popular business district in a city.
In one possible implementation, the map resource data of the target area may be obtained from a map database, and the map resource data includes road network data and point of interest data, the road network data refers to data of each road segment (link) in the target area, such as identification information, position information, direction information, and the like, and the point of interest data refers to information of position coordinates and the like of each point of interest in the target area.
In one possible embodiment, the delivery resources include human resources (e.g., deliverers) and/or machine resources (e.g., vehicles, etc.) that may perform delivery services. The travel track data refers to track data of the distributed resources when traveling in the target area, and the track data can be track data formed by travel positioning points arranged according to travel time sequence. For example, the historical time period may be historical three months before the current time, and trajectory data of travel of each delivered resource in the target area within the historical three months may be acquired.
In a possible implementation manner, based on the Map resource data and the travel track data, track Matching (Map-Matching) may be performed, a travel track of the distribution resource is associated to a road network of the electronic Map, and further, associated data of the points of interest of the route when the distribution resource travels in the target area, such as a point of interest coverage of the distribution resource, and the like, are determined according to the point of interest data, where the point of interest coverage of the distribution resource refers to a ratio of the points of interest of the route of the distribution resource in the target area to the points of interest in the target area, and the point of interest coverage of the distribution resource refers to a number of the points of interest of the route of the distribution resource in the target area to the points of interest in the target area.
In a possible implementation manner, after obtaining the relevant data of the interest points of the route when the distribution resources travel in the target area, a distribution resource combination may be selected from the distribution resources to collect the interest points, and the selection method of the distribution resource combination may be various, for example, a greedy algorithm may be used to select a distribution resource with the largest number of covered interest points from the distribution resources each time, and the selection is stopped until the distribution resource combination meets the interest point collection requirement, where the interest point collection requirement may be that a ratio (that is, a coverage ratio) of the interest points of the route of the distribution resource combination to the interest points in the target area is greater than or equal to a preset ratio (for example, 90%) or that the number of the distribution resources reaches a preset number (for example, 100). Or, the distribution resources of all the route target areas are optimized as a whole by adopting an operation and research solving method, a preset number of distribution resources are selected from the distribution resources to serve as a distribution resource combination, and the finally selected distribution resource combination needs to meet the interest point acquisition requirement, such as the maximum interest point coverage rate of the distribution resource combination.
The method and the device can acquire map resource data in the target area, wherein the map resource data comprises road network data and interest point data, so that when travel track data of each distribution resource in the target area in a historical time period is acquired, relevant data of interest points of paths of each distribution resource in the target area during travel can be determined based on the map resource data and the travel track data, and then distribution resource combinations meeting interest point acquisition requirements can be selected from each distribution resource to acquire the interest points based on the relevant data. Therefore, in the process of distributing resources for distribution service, the carried acquisition equipment can acquire the interest points of the paths, so that the interest points can be acquired at high frequency, the change of the interest points in the real world can be captured and updated in time, the interest points in the map are more accurate, and the accuracy and the usability of map application are improved.
In a possible implementation manner, the selecting, based on the association data, a delivery resource combination meeting the point of interest collection requirement from the delivery resources to perform the point of interest data collection includes:
constructing a target optimization model for selecting the distribution resource combination, wherein the target optimization model comprises a target function, and the target function is used for maximizing the collection operation return rate and maximizing the interest point coverage rate;
and solving the target optimization model based on the associated data to obtain the distribution resource combination.
In this embodiment, in order to achieve the minimization of cost while maximizing the coverage of the interest points, a multi-objective optimization algorithm may be used to construct an objective optimization model, an objective function of which needs to achieve two objectives, one is to maximize a collection operation Return Rate (ROI), and the other is to maximize the coverage of the collected interest points, where the collection ROI is the effect of the operation cost invested in collection equipment On the collection value and is the quantification of the collection operation result, and the interest point coverage is the quantification of the coverage of the interest points of the target area by the interest points of the distribution resource combination approach. The decision variables, i.e. variables to be determined, of the target optimization model are the identifications of the distribution resources in the distribution resource combination, and after the target optimization model is constructed, the target optimization model can be solved by using the associated data of the distribution resources at the interest points of the target area path, so that the optimal decision variables, i.e. the identifications of the distribution resources in the distribution resource combination, can be obtained.
According to the embodiment, the distribution resource combination capable of achieving the two goals of maximizing the collection operation return rate and maximizing the interest point coverage rate is obtained by constructing the target optimization model, so that the collection cost and the coverage effect of collecting the interest points are self-adaptively balanced.
In a possible embodiment, the constructing a target optimization model for selecting the distribution resource combination includes:
constructing constraints of the target optimization model, wherein the constraints comprise at least one of a first constraint, a second constraint and a third constraint; the first constraint condition is used for constraining the number of the delivery resources in the delivery resource combination; the second constraint condition is used for constraining the sum of the ranking of each delivery resource in the delivery resource combination, wherein the ranking of the delivery resources is determined based on the associated data of the interest points of the delivery resources in the target area path; and the third constraint condition is used for constraining the collection timeliness of the collection interest points of the distribution resources in the distribution resource combination.
In this embodiment, the optimization problem refers to solving an optimal decision variable under a certain constraint condition so that the objective function obtains an expected extremum, and therefore the constraint condition needs to be constructed when constructing the objective optimization model.
In this embodiment, the constraint condition may be a first constraint condition that includes a constraint for constraining the number of distribution resources in the distribution resource combination, where the smaller the number of distribution resources, that is, the smaller the number of installed collection devices, the lower the cost, and the greater the collection operation return rate, and the constraint for constraining the number of distribution resources in the distribution resource combination may constrain the objective function model to select the smaller number of distribution resources, so as to reduce the cost.
In this embodiment, the constraint condition may further include a second constraint condition that constrains a total of the ranks of the delivery resources in the delivery resource combination, the delivery resources may be sorted based on the association data of the delivery resources at the points of interest of the target area route, and the higher the rank is, the more stable the points of interest of the delivery resource route are indicated, the constraint on the total of the ranks may constrain the objective function model to select the delivery resources with the highest rank, so as to improve the overall collection effect.
In this embodiment, the constraint condition may further include a third constraint condition that constrains acquisition timeliness of the distribution resource acquisition interest points in the distribution resource combination, where the acquisition timeliness refers to a frequency of distribution resource approach interest points, and the higher the frequency is, the better the acquisition timeliness is, and the constraint on the acquisition timeliness may constrain the distribution resource combination selected by the objective function model to achieve a certain condition on the acquisition frequency of the interest points covered by the distribution resource combination, and may ensure high-frequency acquisition of the interest points in the target area.
In a possible embodiment, the method may further comprise the steps of:
determining an interest point coverage index of a distribution resource according to the associated data of the interest point of the distribution resource in the target area path, wherein the interest point coverage index of the distribution resource comprises at least one of trip stability of the distribution resource, interest point coverage aging of the distribution resource and flow thickness of the distribution resource;
and determining the ranking of the delivery resources according to the interest point coverage indexes of the delivery resources.
In this embodiment, the data associated with the point of interest of the delivery resource in the target area route may be time when the delivery resource is in the point of interest of the target area route and information (such as location, name, etc.) of the point of interest.
In this embodiment, the travel stability of the distribution resource may be travel days or travel frequency of the distribution resource, the interest point coverage of the distribution resource refers to a ratio of the interest point of the distribution resource in the target area route to the interest point in the target area, the interest point coverage aging of the distribution resource refers to an average value of time intervals of the interest points in the target area of the distribution resource route, and the like, and the flow thickness of the distribution resource refers to an average value of times of the interest points in the target area of the distribution resource route in a historical time period, and the like.
In this embodiment, an evaluation model may be used, the interest point coverage indexes of the distributed resources are input to a preset evaluation model, so as to obtain the evaluation values of the distributed resources output by the ranking model, and ranking of the distributed resources is obtained by ranking according to the evaluation values of the distributed resources from large to small, where the better the interest point coverage indexes of the distributed resources are, the higher the evaluation value is, the higher the ranking is; of course, other sorting methods are possible, not just exemplified here.
In one possible embodiment, the objective function includes maximizing a difference between a total number of points of interest in the target area for each delivery resource path in the delivery resource combination and a total collection cost, the total collection cost including a product of the number of delivery resources in the delivery resource combination and a cost of installing a collection device for one delivery resource.
In this embodiment, the objective function may be defined as a maximum value of a difference between the total number of the points of interest in the target area and the total collection cost of each distribution resource route in the distribution resource combination, so that when the maximum value is calculated, the point of interest coverage is maximized and the cost is minimized, and the collection operation return rate is maximized and the point of interest coverage rate is maximized.
For example, the objective optimization problem in the present embodiment may be defined as a 0-1 mixed integer linear programming problem, assuming that the distribution resource sets of the approach objective area in the historical time period are as follows
To distribute resources
Whether it is selected as
The set of interest points in the target region is
Points of interest
Whether it is any selected resource distribution route, i.e. point of interest
Whether or not to be covered by any selected delivery resource is marked as
，
For delivering resources
The cost of installing the acquisition device, then an objective function can be constructed as shown below:
the 0-1 mixed integer linear programming requires that decision variables are integers and can only take values of 0 or 1, that is, the constraint condition may include:
if it is not
Then there must be at least one point of interest that can be covered
The delivery resource of (2) is selected, which can be expressed as:
，
，
representing resources for delivery
Whether to cover points of interest
If the value is 1, the coverage is performed, and if the value is 0, the coverage is not performed;
by way of example, assume that resources are dispatched
And points of interest
The constraint relationships of the 0-1 mixed integer linear program of (a) can be illustrated by table 1 below.
Table 1:
as shown in Table 1, assume that
Then, then
，
，
，
，
，
。
The first constraint may be:
(ii) a Namely, the number of the distribution resources in the distribution resource combination is restricted to be less than or equal to N, wherein N is a preset value.
The second constraint may be:
，
is the ranking of the delivered resources; namely, the sum of the ranks of the distribution resources in the constraint distribution resource combination is less than or equal to R, and the R is a preset value.
The third constraint may be:
，
representing points of interest
Whether the distribution resources are covered by any selected distribution resource within T +7 days or not is judged, if yes, the value is 1, otherwise, the value is 0, namely
(ii) a The constraint condition is used for constraining the sum of the interest points covered by any selected delivery resource within T +7 days to be more than or equal to
The product is
Is a preset value; at this time, correspondingly, if
Then there must be at least one point of interest that can be covered in T +7 days
The delivery resource is selected, and can be expressed as:
wherein, in the step (A),
means resources are delivered within T +7 days
Whether to cover points of interest
If yes, the value is 1, otherwise the value is 0.
In a possible embodiment, the method may further comprise the steps of:
acquiring a coverage rate curve between the quantity of the distributed resources and the coverage rate of the interest points in a plurality of areas and a coverage quantity curve between the quantity of the distributed resources and the coverage quantity of the interest points;
respectively carrying out difference on each coverage rate curve and each coverage quantity curve to obtain a difference result;
based on the difference result, the cost of installing the acquisition equipment for one distribution resource is obtained.
In this embodiment, the cost of installing a collection facility for one delivery resource
Is a preset fixed value, the
The determination needs to be applicable to different areas, and to ensure that a distribution resource for installing a new acquisition device needs to be increased by a certain coverage rate and coverage rate of interest points, the coverage rate and the coverage rate need to be researched to determine the cost
。
In this embodiment, a coverage curve between the number of delivered resources and the coverage of the interest point in each of the plurality of regions and a coverage curve between the number of delivered resources and the coverage of the interest point in each of the plurality of regions may be obtained, the coverage curve and the coverage curve in each of the plurality of regions may be differentiated to obtain a difference result, and a value that makes most of the region truncation points appropriate may be selected as the difference result
The cost of
For each time a distribution resource is added in the distribution resource combination, the number of the distribution resource combination covering the interest points needs to be increased, and therefore the cost of installing and collecting equipment for the distribution resources can be obtained
。
Fig. 2 is a schematic diagram showing a coverage curve and a coverage quantity curve of a plurality of areas according to an embodiment of the present disclosure, as shown in fig. 2, a graph a in fig. 2 is a point-of-interest coverage curve of an area a, an area b, an area c, and an area d, an abscissa in the graph a represents the quantity of delivered resources within an area,the ordinate represents the interest point coverage rate of the distributed resources in the area; a graph B in fig. 2 is a curve of the coverage numbers of interest points of the area a, the area B, the area c, and the area d, an abscissa in the graph B represents the number of the distributed resources in the area, and an ordinate represents the coverage numbers of interest points of the distributed resources in the area, and a conclusion can be obtained by performing differential analysis on two curves of a plurality of areas respectively: adding one delivery resource, the coverage rate of the interest points needing to be increased by 3% or the interest points covered by the delivery resource combination is increased by 500, namely
I.e. by
Take a value of
And the minimum of 500.
In a possible embodiment, the method may further include the steps of:
determining an interest point coverage index of a distribution resource according to the associated data of the interest point of the distribution resource in the target area path, wherein the interest point coverage index of the distribution resource comprises at least one of trip stability of the distribution resource, interest point coverage aging of the distribution resource and flow thickness of the distribution resource;
filtering the distribution resources with the interest point coverage index not reaching the preset index standard to obtain candidate distribution resources;
selecting a distribution resource combination meeting the interest point acquisition requirement from all the distribution resources based on the associated data to acquire the interest point data, wherein the method comprises the following steps:
and selecting a distribution resource combination meeting the interest point acquisition requirement from the candidate distribution resources to acquire the interest point data based on the associated data of the candidate distribution resources at the interest point of the target area path, wherein the distribution resource combination comprises at least one candidate distribution resource.
In this embodiment, the data related to the points of interest of the distributed resources in the target area route and the point of interest coverage index of the distributed resources are described in the above embodiments, and are not described in detail here.
In the embodiment, the delivery resources whose interest point coverage indexes do not meet the preset index standard can be filtered out to obtain candidate delivery resources, and then the delivery resource combination is selected from the candidate delivery resources, so that the number of the delivery resources to be selected is reduced, and the efficiency of selecting the delivery resource combination is improved.
For example, the interest point coverage index includes trip stability of the delivered resource, that is, trip days of the delivered resource, and the preset index standard may be that the trip days of the delivered resource exceed 5 days, so that the delivered resource with the trip days less than or equal to 5 may be filtered out.
Here, it should be noted that the third constraint condition is subsequently constructed, and when the ranking of each delivery resource is calculated, the ranking of each candidate delivery resource in all the candidate delivery resources is determined according to the interest point coverage index of each candidate delivery resource.
In a possible implementation manner, the obtaining travel trajectory data of each delivered resource in the target area in the historical time period includes:
acquiring an original travel positioning point of the distribution resource in the target area within preset time in a historical time period;
according to the interval information between adjacent positioning points in the original trip positioning points, stringing a plurality of original trip positioning points into a trip track sequence, and acquiring a plurality of trip track sequences of the distribution resources;
and filtering the travel track sequences meeting abnormal track conditions in the plurality of travel track sequences of the distribution resources to obtain the normal travel track sequences of the distribution resources.
In this embodiment, in order to ensure the quality of the point-of-interest data acquired by the finally selected delivery resources, the travel track data of the delivery resources needs to be screened so as to select a travel track capable of acquiring better point-of-interest data, and thus the better point-of-interest data can be acquired by the selected delivery resource combination.
In this embodiment, in a general case, light is good in daytime, the acquisition device may acquire better point-of-interest data, light is not good at night, and the quality of the acquired point-of-interest data is not good, so that for a distribution resource, an original travel positioning point of the distribution resource in the target area in daytime (e.g. 6-18; therefore, a plurality of original travel positioning points can be serially connected to form a travel track sequence, and then a plurality of travel track sequences are serially connected.
In this embodiment, the abnormal trajectory condition includes that the travel trajectory sequence has an abnormality such as crossing a cell or crossing a river, or the total mileage of the travel trajectory sequence is less than or equal to a preset mileage (for example, the distributed resource stays in a place for a long time), or the standard deviation of the distance or the angle difference between adjacent positioning points in the travel trajectory sequence is greater than a preset value (indicating that the distributed resource is not stably driven in the route). After the plurality of travel track sequences of the distributed resources are obtained, the travel track sequences meeting abnormal track conditions in the plurality of travel track sequences of the distributed resources can be filtered, and the travel track sequences with normal distributed resources are obtained.
For example, fig. 3 shows a schematic diagram of obtaining a travel trajectory sequence according to an embodiment of the present disclosure, as shown in a diagram a in fig. 3, original travel anchor points of the distribution resource in the target area are discrete anchor points 31 with time information and direction information; fig. 3B shows a plurality of original travel anchor points strung into a travel trajectory sequence 32.
In a possible embodiment, the method may further comprise the steps of:
selecting a target area from a plurality of areas based on distribution heat parameters of the plurality of areas, wherein the distribution heat parameters comprise at least one of the following parameters: the number of interest points, the expiration rate of the interest points, the newly increased rate of the interest points, the distribution heat, the search times, the click times and the navigation times.
In this embodiment, the interest point expiration rate refers to a ratio of expired interest points in an area in a preset time period to all interest points in the area, and the interest point addition rate refers to a ratio of newly added interest points in the area in the preset time period to all interest points in the area; the delivery heat refers to the frequency of delivery transactions conducted within the area; the search times refer to the number of times of distributing the interest points in the resource search area; the number of clicks refers to the number of times that the delivery resource clicks the interest point in the area, and the number of navigations refers to the number of navigations of the delivery resource in the area.
In this embodiment, the distribution popularity parameter of a plurality of areas may be acquired, the distribution popularity of each area may be calculated based on the distribution popularity parameter of each area, and an area in which the distribution popularity exceeds a preset popularity may be determined as the target area.
In the embodiment, because the method collects the point of interest data by installing the collection equipment on the distribution resources, the method is suitable for hot areas with more distribution services, and when the selected target area is the hot area, the effect of collecting the point of interest data by selecting the distribution resource combination is better.
Fig. 4 shows a block diagram of an acquisition scheduling apparatus for delivering resources according to an embodiment of the present disclosure. The apparatus may be implemented as part or all of an electronic device through software, hardware, or a combination of both. As shown in fig. 4, the collection scheduling apparatus for the distribution resources includes:
a first obtaining module 401 configured to obtain map resource data in a target area;
a second obtaining module 402, configured to obtain travel trajectory data of each delivered resource in the target area in a historical time period;
a matching module 403, configured to perform track matching based on the map resource data and the travel track data, and determine associated data of the delivery resource at the point of interest of the target area route;
a selecting module 404 configured to select, based on the correlation data, a distribution resource combination meeting the point of interest collection requirement from the distribution resources to perform point of interest data collection, where the distribution resource combination includes at least one distribution resource.
In one possible implementation, the selecting module 404 is configured to:
constructing a target optimization model for selecting the distribution resource combination, wherein the target optimization model comprises a target function, and the target function is used for maximizing the collection operation return rate and maximizing the interest point coverage rate;
and solving the target optimization model based on the associated data to obtain the distribution resource combination.
In a possible implementation manner, the part of the selecting module 404 for constructing the target optimization model for selecting the delivery resource combination is configured to:
constructing constraints of the target optimization model, wherein the constraints comprise at least one of a first constraint, a second constraint and a third constraint; the first constraint condition is used for constraining the number of the delivery resources in the delivery resource combination; the second constraint condition is used for constraining the sum of the ranking of each delivery resource in the delivery resource combination, wherein the ranking of the delivery resources is determined based on the associated data of the interest points of the delivery resources in the target area path; and the third constraint condition is used for constraining the collection timeliness of the collection interest points of the distribution resources in the distribution resource combination.
In one possible implementation, the method further includes:
the determining module is configured to determine an interest point coverage index of a distribution resource according to the correlation data of the interest point of the distribution resource in the target area path, wherein the interest point coverage index of the distribution resource comprises at least one of trip stability of the distribution resource, interest point coverage aging of the distribution resource and flow thickness of the distribution resource;
and the ranking module is configured to determine the ranking of each delivery resource according to the interest point coverage index of each delivery resource.
In one possible implementation, the objective function includes maximizing a difference between a total number of points of interest in the target area for each delivery resource route in the delivery resource combination and a total collection cost, the total collection cost including a product of the number of delivery resources in the delivery resource combination and a cost of installing a collection device for one delivery resource.
In one possible implementation, the second obtaining module 402 is configured to:
acquiring an original travel positioning point of the distribution resource in the target area within preset time in a historical time period;
according to the distance interval and the time interval between adjacent positioning points in the original trip positioning points, stringing a plurality of original trip positioning points into a trip track sequence, and acquiring a plurality of trip track sequences of the distribution resources;
and filtering the travel track sequences meeting abnormal track conditions in the plurality of travel track sequences of the distribution resources to obtain the normal travel track sequences of the distribution resources.
In one possible implementation, the apparatus further includes:
the system comprises an area selection module and a distribution module, wherein the area selection module is configured to select a target area from a plurality of areas based on distribution heat parameters of the plurality of areas, and the distribution heat parameters comprise at least one of the following parameters: the number of interest points, the expiration rate of the interest points, the newly increased rate of the interest points, the distribution heat, the search times, the click times and the navigation times.
In one possible implementation, the apparatus further includes:
a third obtaining module configured to obtain a coverage rate curve between the number of distribution resources in the plurality of regions and the coverage rate of the collected interest points, and a coverage quantity curve between the number of distribution resources and the coverage quantity of the collected interest points;
the difference module is configured to carry out difference on the coverage rate curve and the coverage quantity curve to obtain a difference result;
a fourth obtaining module configured to obtain a cost of distributing the resource installation collection device based on the difference result.
Technical terms and technical features mentioned in the embodiment of the device are the same or similar, and for the explanation and description of the technical terms and technical features mentioned in the embodiment of the device, the explanation of the embodiment of the method can be referred to, and the description is not repeated herein.
The present disclosure also discloses an electronic device, and fig. 5 shows a block diagram of the electronic device according to an embodiment of the present disclosure.
As shown in fig. 5, the electronic device 500 comprises a memory 501 and a processor 502, wherein the memory 501 is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 502 to implement a method according to an embodiment of the disclosure.
FIG. 6 shows a schematic block diagram of a computer system suitable for use in implementing a method according to an embodiment of the present disclosure.
As shown in fig. 6, the computer system 600 includes a processing unit 601 which can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the computer system 600 are also stored. The processing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that the computer program read out therefrom is mounted in the storage section 608 as necessary. The processing unit 601 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.
In particular, the above described methods may be implemented as computer software programs according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising computer instructions that, when executed by a processor, implement the method steps described above. In such embodiments, the computer program product may be downloaded and installed from a network through the communication section 609 and/or installed from the removable media 611.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or by programmable hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or the computer system in the above embodiments; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (9)
1. A collection scheduling method of distribution resources comprises the following steps:
acquiring map resource data in a target area;
acquiring travel track data of each distribution resource in the target area in a historical time period;
performing track matching based on the map resource data and the travel track data, and determining associated data of interest points of the distribution resources in the target area path;
based on the associated data, selecting a distribution resource combination meeting the interest point acquisition requirement from all the distribution resources to acquire the interest point data, wherein the distribution resource combination comprises at least one distribution resource;
selecting a distribution resource combination meeting the interest point acquisition requirement from all the distribution resources based on the associated data to acquire the interest point data, wherein the method comprises the following steps:
constructing a target optimization model for selecting the distribution resource combination, wherein the constraint condition of the target optimization model comprises at least one of a first constraint condition, a second constraint condition and a third constraint condition; the first constraint condition is used for constraining the number of the delivery resources in the delivery resource combination; the second constraint condition is used for constraining the sum of the ranking of each delivery resource in the delivery resource combination, wherein the ranking of the delivery resources is determined based on the associated data of the interest points of the delivery resources in the target area path; the third constraint condition is used for constraining the collection timeliness of the collection interest points of the distribution resources in the distribution resource combination;
and solving the target optimization model based on the associated data to obtain the distribution resource combination.
2. The method of claim 1, wherein the objective optimization model comprises an objective function for maximizing acquisition operational return rate and maximizing point of interest coverage.
3. The method of claim 2, wherein the objective function includes maximizing a difference between a total number of points of interest in the target area for each of the distribution resource assemblies and a total collection cost, the total collection cost including a product of the number of distribution resources in the distribution resource assembly and a cost of installing a collection device for one distribution resource.
4. The method of claim 1, wherein the method further comprises:
determining an interest point coverage index of a distribution resource according to the associated data of the interest point of the distribution resource in the target area path, wherein the interest point coverage index of the distribution resource comprises at least one of trip stability of the distribution resource, interest point coverage aging of the distribution resource and flow thickness of the distribution resource;
filtering the distribution resources with the interest point coverage index not reaching the preset index standard to obtain candidate distribution resources;
selecting a distribution resource combination meeting the interest point acquisition requirement from all the distribution resources based on the associated data to acquire the interest point data, wherein the method comprises the following steps:
and selecting a distribution resource combination meeting the interest point acquisition requirement from the candidate distribution resources to acquire the interest point data based on the associated data of the candidate distribution resources at the interest point of the target area path, wherein the distribution resource combination comprises at least one candidate distribution resource.
5. The method according to claim 1, wherein the obtaining travel trajectory data of each delivery resource in the target area in the historical time period comprises:
acquiring an original travel positioning point of the distribution resource in the target area within preset time in a historical time period;
according to the distance interval and the time interval between adjacent positioning points in the original trip positioning points, stringing a plurality of original trip positioning points into a trip track sequence, and acquiring a plurality of trip track sequences of the distribution resources;
and filtering the travel track sequences meeting abnormal track conditions in the plurality of travel track sequences of the distribution resources to obtain the normal travel track sequences of the distribution resources.
6. The method of claim 1, wherein the method further comprises:
selecting a target area from a plurality of areas based on distribution heat parameters of the plurality of areas, wherein the distribution heat parameters comprise at least one of the following parameters: the number of interest points, the expiration rate of the interest points, the newly increased rate of the interest points, the distribution heat, the search times, the click times and the navigation times.
7. The method of claim 4, wherein the method further comprises:
acquiring a coverage rate curve between the number of distribution resources in a plurality of areas and the coverage rate of the collected interest points and a coverage quantity curve between the number of the distribution resources and the coverage quantity of the collected interest points;
differentiating the coverage rate curve and the coverage quantity curve to obtain a differential result;
based on the difference result, the cost of installing the acquisition device for one distribution resource is obtained.
8. An electronic device comprising a memory and a processor; wherein the memory is to store one or more computer instructions that are executed by the processor to implement the method steps of any one of claims 1 to 7.
9. A computer readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the method of any of claims 1-7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010040405A1 (en) * | 2008-10-08 | 2010-04-15 | Tomtom International B.V. | Navigation apparatus, server apparatus and method of providing point of interest information |
| CN111242349A (en) * | 2019-12-30 | 2020-06-05 | 北京顺达同行科技有限公司 | Dispatcher scheduling method, dispatcher scheduling device, readable storage medium and computer equipment |
| CN112559663A (en) * | 2020-12-15 | 2021-03-26 | 北京百度网讯科技有限公司 | POI data processing method, device, equipment, storage medium and program product |
| CN114676344A (en) * | 2022-03-09 | 2022-06-28 | 北京三快在线科技有限公司 | Method and device for determining coordinates of interest points, storage medium and electronic equipment |
| CN115168711A (en) * | 2019-11-18 | 2022-10-11 | 百度在线网络技术（北京）有限公司 | Interest point selection method and device, electronic equipment and storage medium |
-
2022
- 2022-10-17 CN CN202211269346.2A patent/CN115344790B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010040405A1 (en) * | 2008-10-08 | 2010-04-15 | Tomtom International B.V. | Navigation apparatus, server apparatus and method of providing point of interest information |
| CN115168711A (en) * | 2019-11-18 | 2022-10-11 | 百度在线网络技术（北京）有限公司 | Interest point selection method and device, electronic equipment and storage medium |
| CN111242349A (en) * | 2019-12-30 | 2020-06-05 | 北京顺达同行科技有限公司 | Dispatcher scheduling method, dispatcher scheduling device, readable storage medium and computer equipment |
| CN112559663A (en) * | 2020-12-15 | 2021-03-26 | 北京百度网讯科技有限公司 | POI data processing method, device, equipment, storage medium and program product |
| CN114676344A (en) * | 2022-03-09 | 2022-06-28 | 北京三快在线科技有限公司 | Method and device for determining coordinates of interest points, storage medium and electronic equipment |
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