CN116822772A - Warehouse location determination method, device, computer equipment and storage medium - Google Patents

Abstract

The present application relates to a warehouse location determination method, device, computer equipment and storage medium. The method includes: dividing the area of open shared bicycle service into multiple grids based on the urban road network, each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network; for each Grid, determine the score of the grid based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value amount of the grid. The target object represents an object with a fixed position, and the value amount Used to represent the resources that need to be transferred when setting up a warehouse in the grid; determine the location of the warehouse based on the location of the grid with a score greater than the preset threshold. This method can accurately determine the location of the warehouse.

Description

Warehouse location determination method, device, computer equipment and storage medium

Technical field

The present application relates to the field of information processing technology, and in particular to a warehouse location determination method, device, computer equipment and storage medium.

Background technique

As shared service open cities develop and expand, newly open service cities face the challenge of selecting suitable warehouses. The location of a warehouse is critical to providing efficient logistics and services. However, in newly opened cities, the lack of complete infrastructure and detailed data often results in poor accuracy of determined warehouse locations.

Contents of the invention

Based on this, it is necessary to provide a warehouse location determination method, device, computer equipment and storage medium to accurately determine the warehouse location in view of the above technical problems.

In a first aspect, this application provides a method for determining a warehouse location, which method includes:

Divide the area open to shared bicycle services into multiple grids based on the urban road network, each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network;

For each grid, the score of the grid is determined based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid. The target object represents an object with a fixed position, The value amount is used to represent the resources that need to be transferred when setting up a warehouse in the grid;

Determine the warehouse location based on the location of the grid with a score greater than a preset threshold.

In a possible implementation, determining the score of the grid based on the target object, the urban road corresponding to the grid, and the location of the grid in the area includes:

Determine the object score according to the type information of the target object corresponding to the grid;

Determine the road score according to the road information of the urban road corresponding to the grid;

Determine a position score based on the value of said grid;

Normalize the object score, the road score, and the location score;

The score of the grid is obtained by performing a weighted sum of the normalized object score, the normalized road score, and the normalized location score.

In a possible implementation, determining the object score based on the type information of the target object corresponding to the grid includes:

Determine the target object corresponding to the grid based on the geographic information system and the coordinate information of the grid, where the coordinate information of the grid includes the coordinates of each vertex of the polygon corresponding to the grid;

Determine the type information of the target object corresponding to the grid;

Find the score corresponding to the type information of each target object according to the first correspondence relationship, where the first correspondence relationship is used to indicate the correspondence relationship between the type information and the score;

The object score is obtained by adding the scores corresponding to the type information of each target object.

In a possible implementation, determining the type information of the target object corresponding to the grid includes:

If the target object is a warehousing facility, a transportation hub, a logistics channel or a supply chain node, then use the first type as the type information of the target object;

If the target object is not the warehousing facility, the transportation hub, the logistics transportation channel, or the supply chain node, then use the second type as the type information of the target object;

Wherein, in the first correspondence relationship, the score corresponding to the first type is greater than the score corresponding to the second type.

In a possible implementation, determining a road score based on road information of urban roads corresponding to the grid includes:

Obtain the road information from the urban road network, the road information including road width, traffic flow and number of connected grids;

According to the road width, the traffic volume and the number of connected grids, determine the score of each urban road corresponding to the grid;

The road score is obtained by adding the scores of each urban road corresponding to the grid.

In one possible implementation,

The road width is positively correlated with the score of each city road;

The traffic volume is positively correlated with the score of each city road;

The number of connected grids is positively correlated with the score of each urban road.

In a possible implementation, the value amount is determined based on the position of the grid in the area, and the value amount is negatively correlated with the position score.

In a second aspect, this application also provides a warehouse location determination device, which includes:

A division module, used to divide the open shared bicycle service area into multiple grids based on the urban road network. Each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network;

The first determination module is used to determine, for each grid, the score of the grid based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid, and the target The object represents an object with a fixed position, and the value is used to represent the resources that need to be transferred when setting up a warehouse on the grid;

The second determination module is used to determine the location of the warehouse based on the location of the grid with a score greater than the preset threshold.

In a possible implementation, the first determination module is also used to:

Determine the object score according to the type information of the target object corresponding to the grid;

Determine the road score according to the road information of the urban road corresponding to the grid;

Determine a position score based on the value of said grid;

Normalize the object score, the road score, and the location score;

The score of the grid is obtained by performing a weighted sum of the normalized object score, the normalized road score, and the normalized location score.

In a possible implementation, determining the object score based on the type information of the target object corresponding to the grid includes:

Determine the target object corresponding to the grid based on the geographic information system and the coordinate information of the grid, where the coordinate information of the grid includes the coordinates of each vertex of the polygon corresponding to the grid;

Determine the type information of the target object corresponding to the grid;

Find the score corresponding to the type information of each target object according to the first correspondence relationship, where the first correspondence relationship is used to indicate the correspondence relationship between the type information and the score;

The object score is obtained by adding the scores corresponding to the type information of each target object.

In a possible implementation, determining the type information of the target object corresponding to the grid includes:

If the target object is a warehousing facility, a transportation hub, a logistics channel or a supply chain node, then use the first type as the type information of the target object;

If the target object is not the warehousing facility, the transportation hub, the logistics transportation channel, or the supply chain node, then use the second type as the type information of the target object;

Wherein, in the first correspondence relationship, the score corresponding to the first type is greater than the score corresponding to the second type.

In a possible implementation, determining a road score based on road information of urban roads corresponding to the grid includes:

Obtain the road information from the urban road network, the road information including road width, traffic flow and number of connected grids;

According to the road width, the traffic volume and the number of connected grids, determine the score of each urban road corresponding to the grid;

The road score is obtained by adding the scores of each urban road corresponding to the grid.

In one possible implementation,

The road width is positively correlated with the score of each city road;

The traffic volume is positively correlated with the score of each city road;

The number of connected grids is positively correlated with the score of each urban road.

In a possible implementation, the value amount is determined based on the position of the grid in the area, and the value amount is negatively correlated with the position score.

In a third aspect, this application also provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

Divide the area open to shared bicycle services into multiple grids based on the urban road network, each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network;

For each grid, the score of the grid is determined based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid. The target object represents an object with a fixed position, The value amount is used to represent the resources that need to be transferred when setting up a warehouse in the grid;

Determine the warehouse location based on the location of the grid with a score greater than a preset threshold.

In a fourth aspect, this application also provides a computer-readable storage medium. The computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by the processor, the following steps are implemented:

Divide the area open to shared bicycle services into multiple grids based on the urban road network, each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network;

For each grid, the score of the grid is determined based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid. The target object represents an object with a fixed position, The value amount is used to represent the resources that need to be transferred when setting up a warehouse in the grid;

Determine the warehouse location based on the location of the grid with a score greater than a preset threshold.

In a fifth aspect, this application also provides a computer program product. The computer program product includes a computer program that implements the following steps when executed by a processor:

Divide the area open to shared bicycle services into multiple grids based on the urban road network, each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network;

For each grid, the score of the grid is determined based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid. The target object represents an object with a fixed position, The value amount is used to represent the resources that need to be transferred when setting up a warehouse in the grid;

Determine the warehouse location based on the location of the grid with a score greater than a preset threshold.

The above warehouse location determination method, device, computer equipment and storage medium first divide the grid based on the urban road network, then determine the score of the grid based on the target object corresponding to the grid, the urban road corresponding to the grid and the value of the grid, and finally The warehouse location is determined based on the location of the grid with a score greater than a preset threshold, by integrating the fixed-position objects distributed in the grid, the transportation convenience of the grid and the need to transfer when setting up the warehouse in the grid when determining the grid score. These factors improve the comprehensiveness and reliability of the grid score, allowing the warehouse location to be accurately determined.

Description of the drawings

Figure 1 is a schematic flow chart of a warehouse location determination method provided by an embodiment of the present application;

Figure 2 is an exemplary schematic diagram of a grid provided by an embodiment of the present application;

Figure 3 is a structural block diagram of the warehouse location determination module provided by the embodiment of the present application;

Figure 4 is an internal structure diagram of a computer device in one embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

In one embodiment, FIG. 1 is a schematic flowchart of a warehouse location determination method provided by an embodiment of the present application. In the embodiment of the present application, the method is applied to the server as an example. It can be understood that the method can also be applied to the terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the server can be implemented as an independent server or a server cluster composed of multiple servers. Terminals include but are not limited to personal computers, laptops, smartphones, tablets, etc. As shown in Figure 1, the method may include the following steps:

Step S101: Divide the area where shared bicycle services are open into multiple grids based on the urban road network.

Each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network.

The area where shared bicycle services are open may include one or more cities, or may include part of a city, such as one or more administrative districts or one or more streets or one or more business districts of a city. The embodiment of this application does not limit the area where the shared taxi service is opened.

Urban road network refers to a road network composed of urban roads. The urban road network includes multiple urban roads, which can be connected directly or indirectly. Based on the urban road network, the area open to shared bicycle services can be divided into multiple grids. Each grid can be composed of irregular polygons, and the sides of the polygons can be urban roads in the urban road network.

Figure 2 is an exemplary schematic diagram of a grid provided by an embodiment of the present application. As shown in Figure 2, the area where shared bicycles are open is divided into multiple irregular polygonal areas by urban roads, such as parks, residential areas, and office buildings. At this time, parks, residential areas, and office buildings can all serve as grids. Of course, as shown in Figure 2, the area open to shared bicycles also includes other grids.

Step S102: For each grid, determine the score of the grid based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid.

Wherein, the target object represents an object whose position can be fixed. The target object can also be called a Point of Interest (POI). In a geographic information system, a POI can be a house, a shop, a mailbox, a bus stop, etc. In the embodiment of this application, target objects include but are not limited to logistics centers, factories, shopping malls, restaurants, companies, real estate, life service centers, transportation facilities, beauty and hairdressing service centers, entrances and exits, automobile service centers, government agencies, education Training institutions, leisure and entertainment centers, hotels, financial institutions, cultural media centers, landmarks, tourist attractions, sports and fitness venues, natural features, etc. There are multiple target objects in each grid. The number and type of target objects corresponding to the grid can affect the flow of people in the grid, which in turn affects the riding routes of shared bicycles. Therefore, the target object corresponding to the grid will affect the score of the grid.

The grid is composed of irregular polygons, and the edges of the polygons are urban roads in the urban road network. Therefore, the urban roads corresponding to the grid refer to the urban roads represented by the edges of the polygons. That is, the grid is actually formed by being surrounded by urban roads. The width and traffic convenience of urban roads can affect the flow of people in the grid, which in turn affects the riding routes of shared bicycles. Therefore, the urban roads corresponding to the grid will affect the score of the grid.

A grid's value amount can be used to represent the resources that need to be transferred when the grid is set up to warehouse. For example, the value of the grid can be the rent or house price corresponding to the grid. The higher the value of the grid, the higher the cost for the service provider to set up warehouses in the grid. Therefore, the amount of value of a grid affects the grid's score.

The target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid will affect the score of the grid. Therefore, in this embodiment of the present application, the score of the grid can be determined based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid.

In a possible implementation, step S102 may include: determining an object score based on the type information of the target object corresponding to the grid; determining a road score based on the road information of the urban road corresponding to the grid; The value of the grid is determined to determine the position score; the object score, the road score, and the position score are normalized; the normalized object score and the normalized road score are and perform a weighted summation of the normalized position scores to obtain the score of the grid.

Because the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid are influencing factors in different dimensions. Therefore, in the embodiment of the present application, the object score, road score and location score can be determined respectively based on the type information of the target object corresponding to the grid, the road information of the urban road corresponding to the grid and the value of the grid, and then the The three are summed to obtain the score of the grid.

The process of obtaining the object score, road score and location score is first explained below.

In a possible implementation, determining the object score based on the type information of the target object corresponding to the grid may include: determining the object score corresponding to the grid based on the geographical information system and the coordinate information of the grid. Target object; determine the type information of the target object corresponding to the grid; find the score corresponding to the type information of each target object according to the first correspondence relationship, wherein the first correspondence relationship is used to indicate the relationship between the type information and the score. Correspondence: Add the scores corresponding to the type information of each target object to obtain the object score.

The geographical information system stores the coordinate information of all target objects in the area where shared bicycle services are open, such as the coordinates of each vertex of the target object (specifically, it can be longitude and latitude). The coordinate information of the grid includes the coordinates (specifically, the latitude and longitude) of each vertex of the polygon corresponding to the grid. Therefore, the target object corresponding to the grid can be determined based on the coordinate information of the geographic information system and the grid. In one example, the target object whose coordinates are in the grid can be determined as the target object corresponding to the grid. In another example, a target object whose distance from the grid is less than a certain range (such as 100 meters or 50 meters, etc.) can be determined as the target object corresponding to the grid.

Since the flow of people or transportation convenience near different types of target objects is different, after determining the target objects corresponding to the grid, the type information of these target objects can be determined, and then the score of each target object can be obtained. Specifically, the corresponding relationship between the type information and the score, that is, the first object relationship, can be set in advance. Then, based on the first correspondence relationship, the score corresponding to the type information of the target object is searched, and the found score is the score of the target object.

Optionally, in this embodiment of the present application, the target objects are divided into two types: a first type and a second type. Among them, the first type of target object can represent a target object with a large flow of people or a large traffic convenience, and the second type of target object can represent a target object other than the first type of target object. At this time, in the first correspondence relationship, the score corresponding to the first type is greater than the score corresponding to the second type.

In one example, determining the type information of the target object corresponding to the grid may include: if the target object is a warehousing facility, a transportation hub, a logistics transportation channel, or a supply chain node, then using the first type as the Type information of the target object; if the target object is not the warehousing facility, the transportation hub, the logistics transportation channel, or the supply chain node, use the second type as the type information of the target object.

Warehousing facilities (such as logistics centers and existing warehouses) are targets directly related to warehouse location selection and can provide logistics support and distribution services for shared bicycles. Transportation hubs (such as freight ports, freight airports, railway stations, and freight distribution centers) can provide convenient logistics and freight channels and help warehouse supply chain operations. Transportation hubs (such as major roads, highways and highway exits) can provide fast-reading logistics and transportation channels to facilitate the entry and exit of goods into warehouses and rapid distribution. Supply chain nodes, such as warehouses located near commercial or industrial areas, can better serve commercial and industrial customers and should be more closely connected to other relief in the supply chain. Therefore, these target objects have a very important impact on the location of warehouses in areas where shared bicycle services are opened, and their scores can be greater than other target objects. For example, the score of a first type of target object may be 10, and the score of a second type of target object may be 1.

It should be noted that the above is only an exemplary description of type information. The type information of the target object can also be divided in other ways. For example, if the target object is a warehousing facility, the first type will be used as the type information of the target object. ; If the target object is a transportation hub, use the second type as the type information of the target object; if the target object is a logistics transportation channel, use the third type as the type information of the target object; if If the target object is a supply chain node, the fourth type is used as the type information of the target object; if the target object is not the warehousing facility, the transportation hub, the logistics transportation channel or the supply chain node, Then the fifth type is used as the type information of the target object. Wherein, in the first correspondence relationship, the score corresponding to the first type, the score corresponding to the second type, the score corresponding to the third type, the score corresponding to the fourth type and the score corresponding to the fifth type decrease in sequence.

In a possible implementation, determining the road score based on the road information of the urban roads corresponding to the grid may include: obtaining the road information from the urban road network, where the road information includes road width. , traffic flow and the number of connected grids; determine the score of each urban road corresponding to the grid according to the road width, the traffic flow and the number of connected grids; assign each urban road corresponding to the grid The scores for urban roads are added together to obtain the road score.

Road width has an important impact on the smoothness and safety of vehicle communication. Wider roads can accommodate more vehicle traffic and provide better access conditions. Therefore, the score of urban roads can be determined based on the road width. The road width is positively correlated with the score of each urban road. The wider the road, the higher the urban road score, and the narrower the road, the lower the urban road score. In terms of distance, assuming that the width of urban roads is [10, 50], a road width of 50 meters corresponds to the highest score of 50, and the scores corresponding to other road widths decrease linearly year-on-year. Then the corresponding score of a road width of 40 meters is 1/(50/40 )=0.8.

Traffic flow reflects road importance. Therefore, the score of urban roads can be determined based on traffic flow. The traffic volume is positively correlated with the score of each city road. Specifically, roads can be assigned a score based on the average traffic flow on the road. For example, urban roads can be divided into high, medium and low levels according to road traffic, and the scores can be set to 1, 0.66 and 0.33 respectively.

The number of connected grids can represent the connectivity of urban roads, that is, the degree to which roads connect various parts of the city. The number of connected grids is positively correlated with the score of each urban road. The greater the number of connected grids, the better the connectivity, which means more convenient traffic and higher integrity of the transportation network. Specifically, roads can be assigned scores based on connected grid data. For example, urban roads are divided into main roads, secondary roads and branch roads according to the number of connected grids, with corresponding scores of 1, 0.66 and 0.33 respectively.

In a possible implementation, the value amount is determined based on the position of the grid in the area, and the value amount is negatively correlated with the position score. Taking the rent of the grid as the value of the grid as an example, the grid rent = the average rent of commercial factories in the grid (unit: yuan/day/square meter). Assume that the grid rent ranges from 1 to 20. The higher the rent, the lower the score. The highest score of 1 point corresponds to 1 yuan/day/square meter. It can be obtained by scaling down the ratio. The score for a rent of 20 yuan/day/square meter is 1. /20=0.05. If there is no factory building in the grid, the value of this score can be set to 0.

At this point, the object score, road score and location score are obtained. Afterwards, a multi-dimensional comprehensive evaluation of the grid can be performed based on the object score, road score and location score to obtain the score of the grid. Considering that the dimensions of the above object score, road score and location score are not agreed, directly adding them as the score of the grid will amplify the weight of the object score. Therefore, the dimensions of the three need to be unified. Specifically, the object score, the road score, and the location score can be normalized; the normalized object score, the normalized road score, and the normalized The final position scores are weighted and summed to obtain the score of the grid.

Optionally, max-min normalization can be used for normalization. Taking the normalization process of road scores as an example, the normalization process of object scores and location scores can refer to the normalization process of road scores, which will not be described again here. First, obtain the maximum and minimum values of the road scores of each grid, and then substitute the road score before normalization as the original value into the following: Road score after normalization = (original value - minimum value) /(maximum value minimum value).

After that, the score of the grid can be obtained through the following formula: Score of the grid = first weight * object score + second weight * road score + third weight * road score. Among them, the first weight value, the second value and the third weight value can be set as needed. The first weight value, the second weight value and the third weight value may be set to the same numerical value, or may be set to different data. For example, the first weight, the second weight and the third weight can be determined based on factors such as city management decisions and budget. For example, if the city has a logistics park or port, the first weight can be set larger; if the city's traffic conditions are poor, the second weight can be set larger; if the budget is smaller, the third weight can be set larger. The weight value is set larger.

Step S103: Determine the location of the warehouse based on the location of the grid whose score is greater than the preset threshold.

The preset threshold represents a preset threshold, and the preset threshold can be set as needed. In one example, the preset threshold can be determined based on the number of warehouses that need to be set. If the number of warehouses that need to be set is large, the preset threshold can be smaller. If the number of warehouses that need to be set is small, the preset threshold can be larger.

In one possible implementation, grids whose scores are greater than a preset threshold can be filtered out first, and then the warehouse location is determined in one or more of the filtered grids. Alternatively, you can first filter out the grids whose scores are greater than the preset threshold, then sort these filtered grids in order from high to low scores, and determine the warehouse locations in each grid in turn until you get the required number of warehouses. Location.

The above warehouse location determination method first divides the grid based on the urban road network, then determines the score of the grid based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid, and finally based on the network whose score is greater than the preset threshold. The location of the warehouse is determined based on the location of the grid. By considering the fixed-position objects distributed in the grid, the transportation convenience of the grid, and the resources that need to be transferred when setting up the warehouse on the grid when determining the grid score, the network is improved. The comprehensiveness and reliability of the scoring allows accurate determination of warehouse locations.

It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or stages. These steps or stages are not necessarily executed at the same time, but may be completed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

Based on the same inventive concept, embodiments of the present application also provide a warehouse location determination device for implementing the above-mentioned warehouse location determination method. The solution to the problem provided by this device is similar to the solution recorded in the above method. Therefore, the specific limitations in the embodiments of one or more warehouse location determination devices provided below can be found in the above description of the warehouse location determination method. Limitations will not be repeated here.

In one embodiment, FIG. 3 is a structural block diagram of a warehouse location determination module provided by an embodiment of the present application. As shown in Figure 3, the device 300 may include: a dividing module 301, a first determining module 302 and a second determining module 303, wherein:

A division module, used to divide the open shared bicycle service area into multiple grids based on the urban road network. Each grid is composed of irregular polygons, and the sides of the polygons are urban roads in the urban road network;

The first determination module is used to determine, for each grid, the score of the grid based on the target object corresponding to the grid, the urban road corresponding to the grid, and the value of the grid, and the target The object represents an object with a fixed position, and the value is used to represent the resources that need to be transferred when setting up a warehouse on the grid;

The second determination module is used to determine the location of the warehouse based on the location of the grid with a score greater than the preset threshold.

In a possible implementation, the first determination module is also used to:

Determine the object score according to the type information of the target object corresponding to the grid;

Determine the road score according to the road information of the urban road corresponding to the grid;

Determine a position score based on the value of said grid;

Normalize the object score, the road score, and the location score;

The score of the grid is obtained by performing a weighted sum of the normalized object score, the normalized road score, and the normalized location score.

In a possible implementation, determining the object score based on the type information of the target object corresponding to the grid includes:

Determine the target object corresponding to the grid based on the geographic information system and the coordinate information of the grid, where the coordinate information of the grid includes the coordinates of each vertex of the polygon corresponding to the grid;

Determine the type information of the target object corresponding to the grid;

Find the score corresponding to the type information of each target object according to the first correspondence relationship, where the first correspondence relationship is used to indicate the correspondence relationship between the type information and the score;

The object score is obtained by adding the scores corresponding to the type information of each target object.

In a possible implementation, determining the type information of the target object corresponding to the grid includes:

If the target object is a warehousing facility, a transportation hub, a logistics channel or a supply chain node, then use the first type as the type information of the target object;

If the target object is not the warehousing facility, the transportation hub, the logistics transportation channel, or the supply chain node, then use the second type as the type information of the target object;

Wherein, in the first correspondence relationship, the score corresponding to the first type is greater than the score corresponding to the second type.

In a possible implementation, determining a road score based on road information of urban roads corresponding to the grid includes:

Obtain the road information from the urban road network, the road information including road width, traffic flow and number of connected grids;

According to the road width, the traffic volume and the number of connected grids, determine the score of each urban road corresponding to the grid;

The road score is obtained by adding the scores of each urban road corresponding to the grid.

In one possible implementation,

The road width is positively correlated with the score of each city road;

The traffic volume is positively correlated with the score of each city road;

The number of connected grids is positively correlated with the score of each urban road.

In a possible implementation, the value amount is determined based on the position of the grid in the area, and the value amount is negatively correlated with the position score.

Each module in the above-mentioned warehouse location determination device can be realized in whole or in part by software, hardware and combinations thereof. Each of the above modules may be embedded in or independent of the processor of the computer device in the form of hardware, or may be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in Figure 4 . The computer device includes a processor, a memory, an input/output interface (Input/Output, referred to as I/O), and a communication interface. Among them, the processor, memory and input/output interface are connected through the system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media and internal memory. The non-volatile storage medium stores operating systems, computer programs and databases. This internal memory provides an environment for the execution of operating systems and computer programs in non-volatile storage media. The computer device's database is used to store scoring data. The input/output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a warehouse location determination method.

Those skilled in the art can understand that the structure shown in Figure 4 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied. Specific computer equipment can May include more or fewer parts than shown, or combine certain parts, or have a different arrangement of parts.

In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, it implements the steps in the above method embodiments.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage. In the media, when executed, the computer program may include the processes of the above method embodiments. Any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic variable memory (Magnetoresistive Random) Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration and not a limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include blockchain-based distributed databases, etc., but are not limited thereto. The processors involved in the various embodiments provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to this.

The technical features of the above embodiments can be combined in any way. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, all possible combinations should be used. It is considered to be within the scope of this manual.

The above-described embodiments only express several implementation modes of the present application, and their descriptions are relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all fall within the protection scope of the present application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims (10)

1. A warehouse location determination method, the method comprising:

dividing an area of open sharing bicycle service into a plurality of grids based on an urban road network, wherein each grid is formed by an irregular polygon, and the sides of the polygons are urban roads in the urban road network;

determining a score of each grid according to a target object corresponding to the grid, an urban road corresponding to the grid and a value quantity of the grid, wherein the target object represents an object with a fixed position, and the value quantity is used for representing resources to be transferred when the grid is provided with a warehouse;

and determining the warehouse position according to the positions of the grids with the scores larger than the preset threshold value.

2. The method of claim 1, wherein the determining the score of the grid based on the target object, the urban road to which the grid corresponds, and the location of the grid in the area comprises:

determining an object score according to the type information of the target object corresponding to the grid;

determining a road score according to the road information of the urban road corresponding to the grid;

determining a location score based on the value of the grid;

normalizing the object score, the road score and the position score;

and carrying out weighted summation on the normalized object score, the normalized road score and the normalized position score to obtain the score of the grid.

3. The method according to claim 2, wherein determining an object score according to the type information of the target object corresponding to the grid comprises:

determining a target object corresponding to the grid based on a geographic information system and coordinate information of the grid, wherein the coordinate information of the grid comprises coordinates of each vertex of a polygon corresponding to the grid;

determining type information of a target object corresponding to the grid;

searching a score corresponding to the type information of each target object according to a first corresponding relation, wherein the first corresponding relation is used for indicating the corresponding relation between the type information and the score;

and adding the scores corresponding to the type information of each target object to obtain the object score.

4. A method according to claim 3, wherein determining type information of the target object corresponding to the grid comprises:

if the target object is a warehouse facility, a transportation hub, a logistics transportation channel or a supply chain node, the first type is used as the type information of the target object;

if the target object is not the warehouse facility, the transportation hub, the logistics transportation channel or the supply chain node, taking a second type as type information of the target object;

wherein, in the first correspondence, the score corresponding to the first type is greater than the score corresponding to the second type.

5. The method according to claim 2, wherein determining the road score according to the road information of the urban road corresponding to the grid comprises:

acquiring the road information from the urban road network, wherein the road information comprises road width, traffic flow and the number of connected grids;

determining the score of each urban road corresponding to the grid according to the road width, the traffic flow and the number of the connected grids;

and adding the scores of the urban roads corresponding to the grids to obtain the road scores.

6. The method of claim 5, wherein the step of determining the position of the probe is performed,

the road width is positively correlated with the score of each urban road;

the traffic flow is positively correlated with the score of each urban road;

the number of connected grids is positively correlated with the score of each urban road.

7. The method of claim 2, wherein the value amount is determined from a location of the grid in the area, the value amount being inversely related to the location score.

8. A warehouse location determination apparatus, the apparatus comprising:

the division module is used for dividing the area of the open sharing bicycle service into a plurality of grids based on the urban road network, each grid is formed by an irregular polygon, and the sides of the polygons are urban roads in the urban road network;

the first determining module is used for determining the score of each grid according to a target object corresponding to the grid, an urban road corresponding to the grid and a value quantity of the grid, wherein the target object represents an object with a fixed position, and the value quantity is used for representing resources to be transferred when the grid is provided with a warehouse;

and the second determining module is used for determining the warehouse position according to the positions of the grids with the scores larger than the preset threshold value.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.