Bedroom information recommendation method and device, equipment and medium

Abstract

The invention relates to the technical field of data processing, and provides a bunk information recommendation method, device, equipment and medium, which can determine a business district reference point according to bunk demand information, and determine a target business district according to the business district reference point, thereby preliminarily dividing the business districts according to user demands; performing quadrant division on the target business district according to the traffic information and the population distribution information so as to further refine the business district; each available bunk is subjected to grid division to obtain a plurality of bunk grids, and the shop type and grade of each bunk grid are marked according to the grid information of each bunk grid, so that the characteristics of each bunk grid are given more accurately; the candidate bunk grids are determined according to the bunk demand information, the user portrait and the store type and the grade of each bunk grid, so that the bunk can be reasonably matched and recommended to the user by integrating the multi-dimensional information of the user, the bunk and the store type, and the customer conversion rate is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a berth information recommendation method, device, equipment and medium. Background Art

[0002] Currently, many convenience store chains in the retail industry are undergoing site selection and evaluation for expansion. The process typically involves finding a location, evaluating it, and then opening it. After this series of evaluations, the client is presented with the selected store.

[0003] The above-mentioned single-store location evaluation and store opening strategy will lead to problems such as high store rent, excessively high comprehensive store opening costs, and too low a return on investment. Summary of the Invention

[0004] In view of the above, it is necessary to provide a berth information recommendation method, device, equipment and medium, aiming to solve the problem of single and unreasonable berth information recommendation.

[0005] A method for recommending berth information, comprising:

[0006] In response to a berth information recommendation instruction triggered by a target user, parsing the berth information recommendation instruction to obtain berth demand information;

[0007] Determining a business district benchmark point based on the shop space demand information, and determining a target business district based on the business district benchmark point;

[0008] Obtaining traffic information and population distribution information of the target business district, and dividing the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants;

[0009] Obtain available berths in each quadrant, and divide each available berth into grids to obtain a plurality of berth grids;

[0010] Obtaining grid information of each shop grid, and marking the store type and level of each shop grid according to the grid information of each shop grid;

[0011] Obtaining a user profile of the target user, and determining candidate shop grids based on the shop demand information, the user profile, and the store type and grade of each shop grid;

[0012] The berth information corresponding to the candidate berth grid is sent to the target user.

[0013] According to a preferred embodiment of the present invention, determining the target business district according to the business district reference point includes:

[0014] Get the configuration radius and map information;

[0015] Taking the business district reference point as the center, draw a circle in the map information according to the configured radius to delineate the target business district;

[0016] The target business district includes recommended area passenger flow information, business information, competitor information, and sales forecast information.

[0017] According to a preferred embodiment of the present invention, the target business district is divided into quadrants according to the traffic information and the population distribution information, and the obtained multiple quadrants include:

[0018] Generating a regional thermal distribution of the target business district according to the traffic information and the population distribution information;

[0019] Acquire, from the target business district according to the traffic information, intersections whose distance from the business district reference point is less than or equal to a configured distance as candidate intersections;

[0020] selecting a target intersection from the candidate intersections according to the regional thermal distribution;

[0021] Determine the intersection type of the target intersection; wherein the intersection type includes a crossroads and a T-junction;

[0022] Dividing the target business district into quadrants according to the intersection type of the target intersection to obtain the multiple quadrants;

[0023] The traffic information includes daily entry and exit direction information determined based on subway entrances and bus stops, as well as physical route information and intersection information;

[0024] When the intersection type of the target intersection is the crossroads, the target business district is divided into four quadrants; when the intersection type of the target intersection is the T-junction, the target business district is divided into three quadrants.

[0025] According to a preferred embodiment of the present invention, dividing each available berth into a grid to obtain a plurality of berth grids comprises:

[0026] The service range of each available berth is delineated with a preset step length as the edge, and multiple berth grids are obtained.

[0027] According to a preferred embodiment of the present invention, marking the store type and level of each shop grid according to the grid information of each shop grid includes:

[0028] Obtaining the shop area, store construction cost and product structure of each shop grid corresponding to the shop from the grid information;

[0029] Determine the store type of each shop based on the shop area, store construction cost and product structure of each shop grid;

[0030] The store type of each shop is determined as the store type of the corresponding shop grid.

[0031] According to a preferred embodiment of the present invention, marking the store type and level of each shop grid according to the grid information of each shop grid further includes:

[0032] Determine, based on the grid information, whether the shop corresponding to each shop grid is a corner shop, whether it is the first-block shop at the pedestrian entrance of a residential area, whether it is a subway entrance, and the pedestrian direction, store entry rate, shop rent, and predicted sales of the shop corresponding to each shop grid, and use these as at least one indicator;

[0033] Get the weight of each indicator and the indicator value of each indicator;

[0034] Calculate the score of each berth grid based on the weight of each indicator and the indicator value of each indicator;

[0035] The grade of each bunk grid is determined based on the score of each bunk grid.

[0036] According to a preferred embodiment of the present invention, determining candidate shop grids based on the shop demand information, the user profile, and the store type and level of each shop grid includes:

[0037] Obtain a pre-trained candidate bunk prediction model;

[0038] Inputting the store demand information, the user profile, the store type and grade of each store grid into the candidate store prediction model to obtain a sales estimate for each store grid;

[0039] The shop grids whose estimated sales value is greater than or equal to the configured value are obtained from each shop grid as the candidate shop grids.

[0040] A berth information recommendation device, comprising:

[0041] a parsing unit, configured to respond to a berth information recommendation instruction triggered by a target user and parse the berth information recommendation instruction to obtain berth demand information;

[0042] a determining unit, configured to determine a commercial district benchmark point according to the shop demand information, and determine a target commercial district according to the commercial district benchmark point;

[0043] a division unit, configured to obtain traffic information and population distribution information of the target business district, and divide the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants;

[0044] The division unit is further configured to obtain available berths in each quadrant and perform grid division on each available berth to obtain a plurality of berth grids;

[0045] a marking unit, for obtaining grid information of each shop grid, and marking the store type and grade of each shop grid according to the grid information of each shop grid;

[0046] The determining unit is further configured to obtain a user profile of the target user, and determine a candidate shop grid based on the shop demand information, the user profile, and the store type and grade of each shop grid;

[0047] A sending unit is used to send the berth information corresponding to the candidate berth grid to the target user.

[0048] A computer device, comprising:

[0049] a memory storing at least one instruction; and

[0050] A processor executes instructions stored in the memory to implement the bunk information recommendation method.

[0051] A computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the bunk information recommendation method.

[0052] It can be seen from the above technical solutions that the present invention can determine the business district benchmark points based on the shop demand information, and determine the target business district based on the business district benchmark points, so as to preliminarily divide the business district according to user demand; divide the target business district into quadrants according to traffic information and population distribution information to further refine the business district; grid-divide each available shop to obtain multiple shop grids, and mark the store type and level of each shop grid according to the grid information of each shop grid, so as to more accurately give the characteristics of each shop grid; determine the candidate shop grid according to the shop demand information, user portrait, store type and level of each shop grid, so as to reasonably match and recommend shops to users based on the multi-dimensional information of users, shops and store types, thereby effectively improving the customer conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a preferred embodiment of the berth information recommendation method of the present invention.

[0054] Figure 2 It is a schematic diagram of the target business district of the present invention.

[0055] Figure 3 It is a schematic diagram of multiple quadrants of the present invention.

[0056] Figure 4 Schematic diagram of store type markings of the shop grid of the present invention.

[0057] Figure 5 Schematic diagram of the grade markings of the bunk grid of the present invention.

[0058] Figure 6 It is a functional module diagram of a preferred embodiment of the berth information recommendation device of the present invention.

[0059] Figure 7 It is a structural diagram of a computer device according to a preferred embodiment of the present invention for implementing the method for recommending berth information. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of the berth information recommendation method of the present invention. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted.

[0062] The berth information recommendation method is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0063] The computer device can be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0064] The computer device may also include a network device and / or a user device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0065] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0066] Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0067] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0068] The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0069] S10 , in response to a berth information recommendation instruction triggered by a target user, parsing the berth information recommendation instruction to obtain berth demand information.

[0070] In this embodiment, the target user may be a user who is selecting a store, for example, a staff member of a convenience store chain company.

[0071] In this embodiment, the berth information recommendation instruction may be automatically triggered when it is detected that the berth demand information is uploaded to a designated platform.

[0072] In this embodiment, the store demand information may include, but is not limited to, one or a combination of the following information: store type, store location, rent, etc.

[0073] S11, determining a business district benchmark point according to the shop demand information, and determining a target business district according to the business district benchmark point.

[0074] In this embodiment, the commercial district reference point may be a point selected by the target user, the commercial district reference point may be the residence of the target user, or may be a location initially selected by the target user.

[0075] In this embodiment, determining the target business district according to the business district reference point includes:

[0076] Get the configuration radius and map information;

[0077] Taking the business district reference point as the center, draw a circle in the map information according to the configured radius to delineate the target business district;

[0078] The target business district includes recommended area passenger flow information, business information, competitor information, and sales forecast information.

[0079] The configuration radius may be an optimal radius selected based on experiments, such as 1 kilometer.

[0080] For example: See Figure 2 , which is a schematic diagram of the target business district of the present invention. Figure 2 The cross mark in represents the business district reference point, and the circle represents the target business district.

[0081] Through the above embodiments, business districts can be preliminarily divided according to user needs.

[0082] S12: Obtain traffic information and population distribution information of the target business district, and divide the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants.

[0083] In this embodiment, the target business district is divided into quadrants according to the traffic information and the population distribution information, and the obtained multiple quadrants include:

[0084] Generating a regional thermal distribution of the target business district according to the traffic information and the population distribution information;

[0085] Acquire, from the target business district according to the traffic information, intersections whose distance from the business district reference point is less than or equal to a configured distance as candidate intersections;

[0086] selecting a target intersection from the candidate intersections according to the regional thermal distribution;

[0087] Determine the intersection type of the target intersection; wherein the intersection type includes a crossroads and a T-junction;

[0088] Dividing the target business district into quadrants according to the intersection type of the target intersection to obtain the multiple quadrants;

[0089] The traffic information includes daily entry and exit direction information determined based on subway entrances and bus stops, as well as physical route information and intersection information;

[0090] When the intersection type of the target intersection is the crossroads, the target business district is divided into four quadrants; when the intersection type of the target intersection is the T-junction, the target business district is divided into three quadrants.

[0091] The configuration distance may be an optimal distance selected based on experiments, such as 100 meters.

[0092] For example: See Figure 3 , which is a schematic diagram of multiple quadrants of the present invention. Figure 3 The target business district is divided into four quadrants: A, B, C, and D, using crossroads as the dividing line.

[0093] Through the above embodiments, the target business district can be further refined, thereby assisting in making more reasonable store recommendations later.

[0094] S13, obtaining available berths in each quadrant, and dividing each available berth into a grid to obtain a plurality of berth grids.

[0095] In this embodiment, the available shops may be shops to be rented out or vacant shops, etc.

[0096] In this embodiment, the step of dividing each available berth into a grid to obtain a plurality of berth grids includes:

[0097] The service range of each available berth is delineated with a preset step length as the edge, and multiple berth grids are obtained.

[0098] The preset step length may be an optimal step length determined through experiments, such as 200 meters.

[0099] Through the above embodiment, the bunks can be gridded, so as to depict the characteristics of each bunk in more detail.

[0100] S14: Obtain grid information of each shop grid, and mark the store type and grade of each shop grid according to the grid information of each shop grid.

[0101] In this embodiment, the grid information of each shop grid may include, but is not limited to, one or a combination of the following information: shop area, store construction cost, product structure, etc.

[0102] In this embodiment, marking the store type and level of each shop grid according to the grid information of each shop grid includes:

[0103] Obtaining the shop area, store construction cost and product structure of each shop grid corresponding to the shop from the grid information;

[0104] Determine the store type of each shop based on the shop area, store construction cost and product structure of each shop grid;

[0105] The store type of each shop is determined as the store type of the corresponding shop grid.

[0106] For example: See Figure 4 , a schematic diagram illustrating store type markings in the store grid of the present invention. The store types marked in the figure include Plus and Pro. Plus represents an enhanced or upgraded store, while Pro represents a specialized store. Of course, depending on the store area, store construction costs, and product mix, MINI stores and STD stores can also be included. MINI stores represent mini stores, while STD stores represent standard stores. The specific store type classification can be configured based on actual user needs.

[0107] In this embodiment, marking the store type and level of each shop grid according to the grid information of each shop grid further includes:

[0108] Determine, based on the grid information, whether the shop corresponding to each shop grid is a corner shop, whether it is the first-block shop at the pedestrian entrance of a residential area, whether it is a subway entrance, and the pedestrian direction, store entry rate, shop rent, and predicted sales of the shop corresponding to each shop grid, and use these as at least one indicator;

[0109] Get the weight of each indicator and the indicator value of each indicator;

[0110] Calculate the score of each berth grid based on the weight of each indicator and the indicator value of each indicator;

[0111] The grade of each bunk grid is determined based on the score of each bunk grid.

[0112] For example: See Figure 5 , a schematic diagram illustrating the grade markings of the shop grid according to the present invention. The figure uses different star ratings as markings, such as 2-star shops, 4-star shops, and 5-star shops. A higher rating corresponds to a higher star rating, and the shop's operating costs and return on investment are also higher.

[0113] S15, obtaining a user portrait of the target user, and determining candidate shop grids according to the shop demand information, the user portrait, and the store type and level of each shop grid.

[0114] In this embodiment, the user portrait can be generated based on indicators such as user preferences, financial capabilities, risk assessment, etc., to comprehensively reflect the attributes of the target user, so as to facilitate adaptation to the shop location and store type.

[0115] In this embodiment, determining candidate shop grids according to the shop demand information, the user profile, and the store type and level of each shop grid includes:

[0116] Obtain a pre-trained candidate bunk prediction model;

[0117] Inputting the store demand information, the user profile, the store type and grade of each store grid into the candidate store prediction model to obtain a sales estimate for each store grid;

[0118] The shop grids whose estimated sales value is greater than or equal to the configured value are obtained from each shop grid as the candidate shop grids.

[0119] The candidate berth prediction model may be a pre-trained LightGBM (Light Gradient Boosting Machine).

[0120] Through the above embodiments, the candidate berth grid can be comprehensively predicted by combining artificial intelligence means and multi-dimensional indicators.

[0121] S16: Send the store information corresponding to the candidate store grid to the target user.

[0122] Through the above-mentioned embodiments, we can integrate multiple dimensions of information, including user attributes, store location, and store type, to rationally match and recommend stores to users. We then refine the data by circles, regions, grids, and outlets, facilitating the allocation of store scouting tasks. Furthermore, we categorize stores by functional area, product mix, and store construction costs, ensuring a better match between store type and location in the business district. Finally, by combining customer profiles, we can match store owners with more desirable store types, thereby recommending more suitable store locations to customers, effectively improving customer conversion rates.

[0123] It can be seen from the above technical solutions that the present invention can determine the business district benchmark points based on the shop demand information, and determine the target business district based on the business district benchmark points, so as to preliminarily divide the business district according to user demand; divide the target business district into quadrants according to traffic information and population distribution information to further refine the business district; grid-divide each available shop to obtain multiple shop grids, and mark the store type and level of each shop grid according to the grid information of each shop grid, so as to more accurately give the characteristics of each shop grid; determine the candidate shop grid according to the shop demand information, user portrait, store type and level of each shop grid, so as to reasonably match and recommend shops to users based on the multi-dimensional information of users, shops and store types, thereby effectively improving the customer conversion rate.

[0124] like Figure 6Figure 1 shows a functional block diagram of a preferred embodiment of a berth information recommendation device according to the present invention. The berth information recommendation device 11 includes a parsing unit 110, a determination unit 111, a division unit 112, a marking unit 113, and a sending unit 114. As used herein, a module or unit refers to a series of computer program segments that can be executed by a processor and perform fixed functions, and are stored in a memory. The functions of each module or unit in this embodiment will be described in detail in subsequent embodiments.

[0125] The parsing unit 110 is configured to, in response to a berth information recommendation instruction triggered by a target user, parse the berth information recommendation instruction to obtain berth demand information;

[0126] The determining unit 111 is configured to determine a business district benchmark point according to the shop demand information, and determine a target business district according to the business district benchmark point;

[0127] The division unit 112 is configured to obtain traffic information and population distribution information of the target business district, and divide the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants;

[0128] The division unit 112 is further configured to obtain available berths in each quadrant and perform grid division on each available berth to obtain a plurality of berth grids;

[0129] The marking unit 113 is used to obtain grid information of each shop grid and mark the store type and level of each shop grid according to the grid information of each shop grid;

[0130] The determining unit 111 is further configured to obtain a user profile of the target user and determine candidate shop grids based on the shop demand information, the user profile, and the store type and grade of each shop grid;

[0131] The sending unit 114 is configured to send the berth information corresponding to the candidate berth grid to the target user.

[0132] It can be seen from the above technical solutions that the present invention can determine the business district benchmark points based on the shop demand information, and determine the target business district based on the business district benchmark points, so as to preliminarily divide the business district according to user demand; divide the target business district into quadrants according to traffic information and population distribution information to further refine the business district; grid-divide each available shop to obtain multiple shop grids, and mark the store type and level of each shop grid according to the grid information of each shop grid, so as to more accurately give the characteristics of each shop grid; determine the candidate shop grid according to the shop demand information, user portrait, store type and level of each shop grid, so as to reasonably match and recommend shops to users based on the multi-dimensional information of users, shops and store types, thereby effectively improving the customer conversion rate.

[0133] like Figure 7 FIG. 1 is a schematic diagram of the structure of a computer device for implementing a preferred embodiment of the method for recommending berth information according to the present invention.

[0134] The computer device 1 may include a memory 12, a processor 13 and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a berth information recommendation program.

[0135] Those skilled in the art will understand that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 may have either a bus structure or a star structure. The computer device 1 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components. For example, the computer device 1 may also include input and output devices, network access devices, etc.

[0136] It should be noted that the computer device 1 is only an example. Other existing or future electronic products that are suitable for the present invention should also be included in the scope of protection of the present invention and included here by reference.

[0137] The memory 12 includes at least one type of readable storage medium, including a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 12 may be an internal storage unit of the computer device 1, such as a mobile hard disk of the computer device 1. In other embodiments, the memory 12 may also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 1. Furthermore, the memory 12 may include both an internal storage unit of the computer device 1 and an external storage device. The memory 12 may be used not only to store application software and various types of data installed in the computer device 1, such as the code of the berth information recommendation program, but also to temporarily store data that has been output or is about to be output.

[0138] In some embodiments, the processor 13 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a combination of multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control core (Control Unit) of the computer device 1, connecting the various components of the entire computer device 1 using various interfaces and circuits. It executes programs or modules stored in the memory 12 (e.g., executing a berth recommendation program) and accessing data stored in the memory 12 to perform various functions and process data.

[0139] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above-mentioned embodiments of the berth information recommendation method, for example Figure 1 Steps shown.

[0140] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a parsing unit 110, a determining unit 111, a dividing unit 112, a marking unit 113, and a sending unit 114.

[0141] The integrated unit implemented in the form of a software functional module can be stored in a computer-readable storage medium. The software functional module stored in a storage medium includes a number of instructions for causing a computer device (which can be a personal computer, computer device, or network device, etc.) or a processor to execute the portion of the berth information recommendation method described in various embodiments of the present invention.

[0142] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments.

[0143] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory, etc.

[0144] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0145] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0146] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The figure shows that only one straight line is used, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13.

[0147] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 via a power management device, thereby implementing functions such as charging management, discharging management, and power consumption management through the power management device. The power supply may also include one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be detailed here.

[0148] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device 1 and other computer devices.

[0149] Optionally, the computer device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the computer device 1 and to display a visual user interface.

[0150] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0151] It will be understood by those skilled in the art that Figure 7 The structure shown does not constitute a limitation on the computer device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0152] Combine Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement a berth information recommendation method, and the processor 13 can execute the plurality of instructions to implement:

[0153] In response to a berth information recommendation instruction triggered by a target user, parsing the berth information recommendation instruction to obtain berth demand information;

[0154] Determining a business district benchmark point based on the shop space demand information, and determining a target business district based on the business district benchmark point;

[0155] Obtaining traffic information and population distribution information of the target business district, and dividing the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants;

[0156] Obtain available berths in each quadrant, and divide each available berth into grids to obtain a plurality of berth grids;

[0157] Obtaining grid information of each shop grid, and marking the store type and level of each shop grid according to the grid information of each shop grid;

[0158] Obtaining a user profile of the target user, and determining candidate shop grids based on the shop demand information, the user profile, and the store type and grade of each shop grid;

[0159] The berth information corresponding to the candidate berth grid is sent to the target user.

[0160] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0161] It should be noted that the data involved in this case were all obtained legally. The software tools or components not produced by our company that appear in the embodiments of this application are merely examples and do not represent actual use.

[0162] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division, and actual implementation may employ other division methods.

[0163] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0164] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0165] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0166] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0167] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0168] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the present invention may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for recommending berth information, characterized in that: The berth information recommendation method includes: In response to a berth information recommendation instruction triggered by a target user, parsing the berth information recommendation instruction to obtain berth demand information; Determining a business district benchmark point based on the shop space demand information, and determining a target business district based on the business district benchmark point; Obtaining traffic information and population distribution information of the target business district, and dividing the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants; Obtain available berths in each quadrant, and divide each available berth into grids to obtain a plurality of berth grids; Obtaining grid information of each shop grid, and marking the store type and level of each shop grid according to the grid information of each shop grid; Obtaining a user profile of the target user, and determining candidate shop grids based on the shop demand information, the user profile, and the store type and grade of each shop grid; The berth information corresponding to the candidate berth grid is sent to the target user.

2. The method for recommending store information according to claim 1, wherein: Determining the target business district according to the business district benchmark point includes: Get the configuration radius and map information; Taking the business district reference point as the center, draw a circle in the map information according to the configured radius to delineate the target business district; The target business district includes recommended area passenger flow information, business information, competitor information, and sales forecast information.

3. The method for recommending berth information according to claim 1, wherein: The target business district is divided into quadrants according to the traffic information and the population distribution information, and the obtained multiple quadrants include: Generating a regional thermal distribution of the target business district according to the traffic information and the population distribution information; Acquire, from the target business district according to the traffic information, intersections whose distance from the business district reference point is less than or equal to a configured distance as candidate intersections; selecting a target intersection from the candidate intersections according to the regional thermal distribution; Determine the intersection type of the target intersection; wherein the intersection type includes a crossroads and a T-junction; Dividing the target business district into quadrants according to the intersection type of the target intersection to obtain the multiple quadrants; The traffic information includes daily entry and exit direction information determined based on subway entrances and bus stops, as well as physical route information and intersection information; When the intersection type of the target intersection is the crossroads, the target business district is divided into four quadrants; when the intersection type of the target intersection is the T-junction, the target business district is divided into three quadrants.

4. The method for recommending berth information according to claim 1, wherein: The gridding of each available berth to obtain a plurality of berth grids comprises: The service range of each available berth is delineated with a preset step length as the edge, and multiple berth grids are obtained.

5. The method for recommending berth information according to claim 1, wherein: The marking of the store type and level of each shop grid according to the grid information of each shop grid includes: Obtaining the shop area, store construction cost and product structure of each shop grid corresponding to the shop from the grid information; Determine the store type of each shop based on the shop area, store construction cost and product structure of each shop grid; The store type of each shop is determined as the store type of the corresponding shop grid.

6. The method for recommending berth information according to claim 3, wherein: The step of marking the store type and grade of each shop grid according to the grid information of each shop grid further includes: Determine, based on the grid information, whether the shop corresponding to each shop grid is a corner shop, whether it is the first-block shop at the pedestrian entrance of a residential area, whether it is a subway entrance, and the pedestrian direction, store entry rate, shop rent, and predicted sales of the shop corresponding to each shop grid, and use these as at least one indicator; Get the weight of each indicator and the indicator value of each indicator; Calculate the score of each berth grid based on the weight of each indicator and the indicator value of each indicator; The grade of each bunk grid is determined based on the score of each bunk grid.

7. The method for recommending berth information according to claim 1, wherein: Determining candidate shop grids according to the shop demand information, the user portrait, and the store type and level of each shop grid includes: Obtain a pre-trained candidate bunk prediction model; Inputting the store demand information, the user profile, the store type and grade of each store grid into the candidate store prediction model to obtain a sales estimate for each store grid; The shop grids whose estimated sales value is greater than or equal to the configured value are obtained from each shop grid as the candidate shop grids.

8. A berth information recommendation device, characterized in that: The berth information recommendation device includes: a parsing unit, configured to respond to a berth information recommendation instruction triggered by a target user and parse the berth information recommendation instruction to obtain berth demand information; a determining unit, configured to determine a commercial district benchmark point according to the shop demand information, and determine a target commercial district according to the commercial district benchmark point; a division unit, configured to obtain traffic information and population distribution information of the target business district, and divide the target business district into quadrants according to the traffic information and the population distribution information to obtain a plurality of quadrants; The division unit is further configured to obtain available berths in each quadrant and perform grid division on each available berth to obtain a plurality of berth grids; a marking unit, for obtaining grid information of each shop grid, and marking the store type and grade of each shop grid according to the grid information of each shop grid; The determining unit is further configured to obtain a user profile of the target user, and determine a candidate shop grid based on the shop demand information, the user profile, and the store type and grade of each shop grid; A sending unit is used to send the berth information corresponding to the candidate berth grid to the target user.

9. A computer device, characterized in that: The computer device comprises: a memory storing at least one instruction; and A processor is configured to execute instructions stored in the memory to implement the bunk information recommendation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the bunk information recommendation method according to any one of claims 1 to 7.

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