Goods shelf layout adjusting method, system and device

Abstract

The invention provides a goods shelf layout adjusting method, system and device, and relates to the technical field of data processing. The method comprises the following steps: through an intelligent camera, obtaining a plurality of purchasing paths and a staying time thermodynamic diagram in a shop, and a spatial position and a commodity type of each shelf in the shop; calculating an effective exposure score of each goods shelf according to the plurality of purchase paths, and calculating a purchase willingness score of each goods shelf according to the staying duration thermodynamic diagram; according to the effective exposure score and the purchase willingness score of each goods shelf, calculating a layout score of the corresponding goods shelf, and according to the layout score of each goods shelf, sorting the full quantity of goods shelves; and adjusting the layout of at least one goods shelf according to the sorting result and the spatial position and the commodity type of each goods shelf in the shop to obtain a goods shelf adjustment layout, and pushing the goods shelf adjustment layout to the merchant. According to the method provided by the invention, through the layout score of each shelf, the adjustment suggestion of the shelf layout of the store is realized.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, system and device for adjusting shelf layout. Background Art

[0002] In the store management scenario, smart cameras use their advanced artificial intelligence (AI) detection capabilities to intelligently analyze the captured images and present the analysis results to merchants. This technology is mainly used for objective content detection of the environment and customer activities. A typical application is to count the number of people in the shelf area within a specific time period and generate a heat map of the length of stay for merchants to refer to and choose the purchase path.

[0003] However, when merchants actively adjust the layout of store shelves based on the dwell time heat map and the purchase path, they face the problem of being unable to accurately evaluate the actual impact of these layout changes on product sales. Therefore, how to implement the adjustment suggestions for the store shelf layout is an urgent problem to be solved in this application. Summary of the invention

[0004] The present application provides a shelf layout adjustment method, system and device, which implement adjustment suggestions for store shelf layout.

[0005] A first aspect of the present application provides a method for adjusting shelf layout, the method comprising:

[0006] Through smart cameras, multiple shopping routes and dwell time heat maps in the store are obtained, as well as the spatial location and product type of each shelf in the store;

[0007] According to multiple shopping paths, the effective exposure score of each shelf is calculated, and the purchase intention score of each shelf is calculated according to the dwell time heat map; wherein the effective exposure score is used to indicate the number of times the corresponding shelf is covered by at least one shopping path, and the purchase intention score is used to indicate the customer's purchase intention of the goods in the corresponding shelf;

[0008] According to the effective exposure score and purchase intention score of each shelf, the layout score of the corresponding shelf is calculated, and all shelves are sorted according to the layout score of each shelf;

[0009] According to the sorting result, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted to obtain an adjusted shelf layout, and the adjusted shelf layout is pushed to the merchant.

[0010] In one possible design, the target shelf is any one of the full quantity shelves;

[0011] For the target shelf, calculate the effective exposure score of each shelf based on multiple purchase paths, including:

[0012] According to the multiple shopping paths, a main path is determined, and multiple path points are determined from the main path;

[0013] Each path point within the effective exposure area of ​​the target shelf is determined as an exposure path point;

[0014] The effective exposure score of the target shelf is calculated based on the number of full exposure path points and the number of multiple path points.

[0015] In a possible design, a main path is determined based on multiple purchase paths, including:

[0016] Discretize multiple shopping routes according to preset distances to obtain multiple shopping sections;

[0017] Obtaining, by means of a smart camera, the number of customers passing through each shopping section for the first time within a first preset time period, and determining, from the plurality of shopping sections, a plurality of target shopping sections according to the number of customers passing through each shopping section for the first time;

[0018] A continuous path composed of multiple target shopping sections is determined as the main path;

[0019] Determine multiple waypoints from the main path, including:

[0020] One end point of each target shopping section in the main path is determined as a path point.

[0021] In a possible design, a continuous path composed of multiple target shopping sections is determined as the main path, including:

[0022] Multiple target shopping sections are spliced ​​together to form multiple continuous paths to be selected;

[0023] The smart camera is used to obtain the second number of customers passing through each to-be-selected continuous path within the second preset time period, and the to-be-selected continuous path with the largest number of customers passing through the second time is determined as the main path.

[0024] In a possible design, the shape of the effective exposure area is a rectangle, and the effective exposure area and the area where the target shelf is located share the same edge and do not intersect;

[0025] The target path point is any one of the multiple path points. For the target path point, each path point within the effective exposure area of ​​the target shelf is determined as an exposure path point, including:

[0026] Use the ray method to determine whether the target path point is within the effective exposure area;

[0027] When the target path point is within the effective exposure area, the target path point is determined as the exposure path point.

[0028] In one possible design, the target shelf is any one of the full quantity shelves;

[0029] For the target shelf, calculate the purchase intention score for each shelf based on the dwell time heat map, including:

[0030] Through smart cameras, obtain the customer stay time and customer contact frequency at the target shelf;

[0031] The purchase intention score of the target shelf is obtained by weighted calculation based on the customer stay time and customer contact frequency at the target shelf;

[0032] For the target shelf, the layout score of the corresponding shelf is calculated based on the effective exposure score and purchase intention score of each shelf, including:

[0033] Standardize the effective exposure scores and purchase intention scores of the target shelves;

[0034] According to the standardized effective exposure score and purchase intention score of the target shelf, the layout score of the target shelf is obtained by weighted calculation.

[0035] In a possible design, the weight of customer stay time is greater than the weight of customer contact frequency, and the weight of effective exposure score is less than the weight of purchase intention score.

[0036] In a possible design, according to the sorting result, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted to obtain an adjusted shelf layout, including:

[0037] According to the sorting results, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted through a preset database to obtain a shelf adjustment layout; wherein the database stores a plurality of shelf layout adjustment schemes.

[0038] A second aspect of the present application provides a shelf layout adjustment system, the system comprising:

[0039] At least one smart camera;

[0040] And a method for adjusting the shelf layout of any one of the first aspects, which is respectively connected to each smart camera for communication.

[0041] A third aspect of the present application provides a shelf layout adjustment device, the device comprising:

[0042] The data acquisition module is used to obtain multiple shopping routes and dwell time heat maps in the store, as well as the spatial location and product type of each shelf in the store through smart cameras;

[0043] A first calculation module is used to calculate the effective exposure score of each shelf according to multiple shopping paths, and calculate the purchase intention score of each shelf according to the dwell time heat map; wherein the effective exposure score is used to indicate the number of times the corresponding shelf is covered by at least one shopping path, and the purchase intention score is used to indicate the customer's purchase intention of the goods in the corresponding shelf;

[0044] The second calculation module is used to calculate the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sort all the shelves according to the layout score of each shelf;

[0045] The layout adjustment module is used to adjust the layout of at least one shelf according to the sorting results, the spatial position of each shelf in the store and the type of goods, obtain the shelf adjustment layout, and push the shelf adjustment layout to the merchant.

[0046] A fourth aspect of the present application provides an electronic device, comprising: a memory, and a memory communicatively connected to a processor;

[0047] Memory stores computer-executable instructions;

[0048] When the processor executes the computer-executable instructions stored in the memory, it is used to implement the shelf layout adjustment method of any one of the first aspects.

[0049] The fifth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the shelf layout adjustment method of any one of the first aspects.

[0050] The sixth aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the shelf layout adjustment method of any one of the first aspects.

[0051] The present application provides a method, system and device for adjusting shelf layout, which method includes: obtaining multiple shopping paths and dwell time heat maps in a store, as well as the spatial position and commodity type of each shelf in the store through a smart camera; calculating the effective exposure score of each shelf according to the multiple shopping paths, and calculating the purchase intention score of each shelf according to the dwell time heat map; calculating the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sorting all the shelves according to the layout score of each shelf; adjusting the layout of at least one shelf according to the sorting result, as well as the spatial position and commodity type of each shelf in the store, obtaining the shelf adjustment layout, and pushing the shelf adjustment layout to merchants. The following technical effects are achieved: based on the layout score of each shelf, the customer contact opportunities and customer purchasing intentions of the corresponding shelf are quantified, and then the actual impact of the shelf layout on product sales is evaluated. Afterwards, based on the layout score of each shelf, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted, and adjustment suggestions for the store shelf layout are implemented; based on the layout score of each shelf, all shelves are sorted, and shelves that do not need to be adjusted are identified, reducing the amount of data processing for adjusting the shelf layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A schematic diagram of a process for generating a residence time heat map provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of a scenario of a method for adjusting shelf layout provided in an embodiment of the present application;

[0055] Figure 3 Schematic diagram of the process of adjusting the shelf layout provided in the embodiment of the present application Figure 1 ;

[0056] Figure 4 Schematic diagram of the process of adjusting the shelf layout provided in the embodiment of the present application Figure 2 ;

[0057] Figure 5 A schematic diagram of the structure of a shelf layout adjustment device provided in an embodiment of the present application;

[0058] Figure 6A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0059] Reference numerals:

[0060] 210- shelf layout adjustment system; 211- smart camera; 212- data processing server; 213- data line; 220- shop; 221- shelf; 230- customer;

[0061] 510-data acquisition module; 520-first calculation module; 530-second calculation module; 540-layout adjustment module;

[0062] 610 - processor; 620 - memory; 630 - communication component; 640 - bus. DETAILED DESCRIPTION

[0063] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0064] In the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. It should be noted that in the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. In the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0065] It should be noted that the "at..." in this application can be the instant when a certain situation occurs, or it can be a period of time after a certain situation occurs, and this application does not make specific limitations on this. In addition, the method for adjusting the shelf layout provided in this application is only an example, and the method for adjusting the shelf layout can also include more or less content. The user information (including but not limited to user device information and user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data, etc.) involved in one or more embodiments of this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0066] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced in detail.

[0067] In the store management scenario, smart cameras use their advanced AI detection capabilities to intelligently analyze the captured images and present the analysis results to merchants. This technology is mainly used for objective content detection of the environment and customer activities, such as regional alerts, intelligent recognition or detection functions, etc. A typical application is to count the number of people in the shelf area within a specific time period, and generate a heat map of the length of stay and the purchase path for merchants to refer to.

[0068] Figure 1 A schematic diagram of a process for generating a residence time heat map provided in an embodiment of the present application. Figure 1 As shown, in the prior art, the process of generating a residence time heat map includes:

[0069] S101, the smart camera captures the video image.

[0070] S102: The data processing server obtains the video image. Specifically, the data processing server is in communication connection with the smart camera, and the data processing server obtains the video image from the smart camera.

[0071] S103, the data processing server processes the data. Specifically, the data processing server recognizes human faces from the video screen, and based on this, counts the number of people flowing into the shelf area within a specific time period, and based on this, generates a heat map of the length of stay and a shopping path.

[0072] S104: The data processing server outputs data. Specifically, the data processing server outputs and saves the number of people flow, the heat map of the stay time and the shopping path in a specific format.

[0073] S105, the data processing server pushes data. Specifically, the data processing server pushes the output data to the merchant so that the merchant can further process the data. For example, the merchant actively adjusts the layout of the store shelves based on the dwell time heat map.

[0074] According to the above process, merchants can only understand the number of people flow, dwell time heat map and purchase path, but cannot understand how to adjust the store shelf layout according to customer preferences to improve product efficiency. When merchants actively adjust the store shelf layout based on the dwell time heat map and purchase path, they will face the problem of not being able to accurately evaluate the actual impact of these layout changes on product sales. Therefore, how to implement the adjustment suggestions for the store shelf layout is an urgent problem to be solved in this application.

[0075] Therefore, in response to the above technical problems, the study found that the existing technology did not combine the AI ​​detection capabilities of smart cameras with the product sales of shops, and was not applied to the adjustment of shop shelf layouts, which had a weak impact on the empowerment of commercial activities. In order to solve this technical problem, on the basis of the AI ​​detection capability to generate the dwell time heat map and the customer's purchase path, further algorithm analysis was carried out, and the concepts of effective exposure score and purchase intention score of the shelf area were introduced. The two scores were superimposed and analyzed to obtain the scoring results of the shop shelf layout. The data processing server can adjust the shelf layout based on the result, optimize the customer's purchase path, and thus increase the product sales of the shop.

[0076] Based on the above creative findings, the technical solution of the present application is proposed.

[0077] The following introduces the application scenarios of the shelf layout adjustment method provided by this application.

[0078] Figure 2 A schematic diagram of a scenario of a method for adjusting shelf layout provided in an embodiment of the present application. It should be noted that: Figure 2 What is shown are merely examples of scenarios in which the present application can be applied, to help those skilled in the art understand the technical content of the present application, but it does not mean that the present application cannot be used in other devices, systems, environments or scenarios.

[0079] The application scenario includes: a shelf layout adjustment system 210 and a shop 220; wherein the shelf layout adjustment system 210 includes at least one smart camera 211, and a data processing server 212 respectively connected to each smart camera 211 for implementing a shelf layout adjustment method; a plurality of shelves 221 are arranged in the shop 220, and each smart camera 211 is arranged in the shop 220; the data processing server 212 is used to obtain a plurality of shopping routes and a heat map of the dwelling time in the shop 220, as well as the spatial position and commodity type of each shelf 221 in the shop 220 through at least one smart camera 211.

[0080] For example, if Figure 2 As shown, the shape of the shop 220 is rectangular, and there are four smart cameras 211, which are respectively arranged at the four corners of the shop 220. The four smart cameras 211 can fully cover every corner of the shop 220, avoiding blind spots and ensuring the clarity of the video screen. The four smart cameras 211 can jointly capture the facial image and movement trajectory of the customer 230 ( Figure 2 The motion trajectory is indicated by a dotted line), and the image of each shelf 221 ( Figure 2 The shelf 221 is indicated by a rectangular wireframe. The data processing server 212 and the four smart cameras 211 are connected to each other via a data line 213. The data processing server 212 generates multiple shopping routes and a heat map of the length of stay according to the facial image and movement trajectory of the customer 230, and generates the spatial position and commodity type of each shelf 221 according to the image of each shelf 221.

[0081] Smart camera 211 is a monitoring device that integrates image processing technology and network communication capabilities, and is used not only to capture high-definition video images, but also to perform intelligent analysis of video images through built-in algorithms, such as face recognition and motion tracking, etc. Smart camera 211 can be a spherical smart camera, a fixed-focus smart camera, or an infrared smart camera, etc.

[0082] The data processing server 212 is a computer device specially used for processing and analyzing large amounts of data. It is used to obtain the video screen images captured by the smart camera 211 through the data line 213, and process, output and push the video images. The data processing server 212 is usually equipped with a high-performance processor, a large-capacity storage device and a high-speed network interface, and can efficiently process data from data sources such as the smart camera 211. It can be deployed in the cloud or locally, and can be a distributed data processing server, a cloud computing data processing server or an edge computing data processing server.

[0083] The data line 213 is a physical connection medium for transmitting data, and is used to carry electrical signals or optical signals to realize data transmission and communication between the smart camera 211 and the data processing server 212. The data line 213 is usually composed of multiple wires, and can be a Universal Serial Bus (USB) data line, an Ethernet line, or an optical fiber data line.

[0084] The shelf 221 is a device for storing and displaying products. The shelf 221 is usually made of materials such as metal, wood or plastic, has a multi-layer structure, and can accommodate a variety of different types of products.

[0085] In other application scenarios, the smart camera 211 is pre-installed with a data processing device, which can directly generate multiple shopping routes and dwell time heat maps in the store, as well as the spatial location and product type of each shelf in the store, and send the generated information to the data processing server 212 via the data line 213.

[0086] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The present application will be described below in conjunction with the accompanying drawings.

[0087] Figure 3 Schematic diagram of the process of adjusting the shelf layout provided in the embodiment of the present application Figure 1 .like Figure 3 As shown, in the embodiment of the present application, the execution subject may be a shelf layout adjustment device, which may be located in an electronic device, which may be Figure 2 The data processing server in the embodiment of the present application comprises the following steps:

[0088] S301. Obtain multiple shopping routes and dwell time heat maps in the store, as well as the spatial location and product type of each shelf in the store, through smart cameras.

[0089] Specifically, smart cameras are placed in the store, and the data processing server uses preset image recognition and generation technology to analyze the facial image and movement trajectory of each customer, and marks the customer's location on the store's plane model, thereby establishing each customer's shopping path and the time they stay on each shelf, and then obtaining multiple shopping paths and stay time heat maps in the store. At the same time, the data processing server also needs to know the spatial location of each shelf in the store and the types of goods placed on them, which can be achieved by the data processing server analyzing the video screen, or by the merchant pre-building a plane model of the store and entering the product type. The above information together forms the basis for subsequent analysis and adjustment.

[0090] S302. Calculate the effective exposure score of each shelf based on multiple shopping paths, and calculate the purchase intention score of each shelf based on the dwell time heat map.

[0091] Specifically, the effective exposure score is used to indicate the number of times the corresponding shelf is covered by at least one shopping path. The effective exposure score defines the effective exposure of each shelf on one or more shopping paths, so as to quantify whether customers can see the corresponding shelf. There can be a positive correlation between the effective exposure score and the number of coverages, that is, if a shelf is often covered by shopping paths, then its effective exposure score will be higher; correspondingly, there can also be a negative correlation between the two.

[0092] The convergence area on the dwell time heat map indicates that customers are interested in or willing to buy the goods on the corresponding shelf, that is, the sales effect of the goods on the shelf is good. The purchase intention score of each shelf is calculated based on the dwell time heat map, and the purchase intention score is used to indicate the customer's purchase intention for the goods on the corresponding shelf. The purchase intention score defines the dwell time of customers near each shelf, so as to quantify the customer's purchase intention for the goods on the shelf. There can be a positive or negative correlation between the purchase intention score and the dwell time.

[0093] S303: Calculate the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sort all the shelves according to the layout score of each shelf.

[0094] Specifically, the effective exposure score and the purchase intention score are weighted or calculated comprehensively to obtain the layout score of the corresponding shelf. The layout score comprehensively quantifies the customer contact opportunities and customer purchase intention of the corresponding shelf. Afterwards, the data processing server sorts all shelves according to the layout score to identify which shelves perform better and can be adjusted later or not, and which shelves need to be adjusted first.

[0095] S304: According to the sorting result, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted to obtain an adjusted shelf layout, and the adjusted shelf layout is pushed to the merchant.

[0096] Specifically, based on the sorting results, as well as the spatial position and commodity type of each shelf in the store, the data processing server can make specific suggestions for adjusting the shelf layout, such as moving the positions of certain shelves, changing the commodity types on certain shelves, or rearranging the arrangement of shelves, etc. Among them, its implementation can be that the data processing server adjusts the layout of at least one shelf according to a pre-stored shelf layout adjustment plan. It can be understood that the store model corresponding to the adopted shelf layout adjustment plan is the same as or similar to the store; it can also be implemented by the operation and maintenance personnel combining their own industry experience and / or the preset large model, and feeding it back to the data processing server. The data processing server adjusts the layout of at least one shelf according to the feedback content. It can be understood that the large model has been continuously learned and analyzed in advance.

[0097] Afterwards, the data processing server pushes the shelf adjustment layout to the merchants through electronic reports, visual interfaces, video tutorials or on-site guidance demonstrations. Merchants can optimize the layout of shelves in the store based on the shelf adjustment layout or the suggestions in the shelf adjustment layout, and then optimize the customer's shopping path, thereby increasing the store's product sales.

[0098] In other embodiments, after a period of time has passed since the merchant adjusted the layout of the shelves, the layout score of each shelf is recalculated to verify the accuracy of the adjusted shelf layout.

[0099] An embodiment of the present application provides a method for adjusting shelf layout, the method comprising: obtaining, through a smart camera, multiple shopping paths and dwell time heat maps in a store, as well as the spatial position and commodity type of each shelf in the store; calculating the effective exposure score of each shelf according to the multiple shopping paths, and calculating the purchase intention score of each shelf according to the dwell time heat map; calculating the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sorting all shelves according to the layout score of each shelf; adjusting the layout of at least one shelf according to the sorting result, as well as the spatial position and commodity type of each shelf in the store, obtaining an adjusted shelf layout, and pushing the adjusted shelf layout to merchants. The following technical effects are achieved: based on the layout score of each shelf, the customer contact opportunities and customer purchasing intentions of the corresponding shelf are quantified, and then the actual impact of the shelf layout on product sales is evaluated. Afterwards, based on the layout score of each shelf, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted, and adjustment suggestions for the store shelf layout are implemented; based on the layout score of each shelf, all shelves are sorted, and shelves that do not need to be adjusted are identified, reducing the amount of data processing for adjusting the shelf layout.

[0100] Figure 4 Schematic diagram of the process of adjusting the shelf layout provided in the embodiment of the present application Figure 2 ,like Figure 4 As shown, the shelf layout adjustment method provided in the embodiment of the present application is Figure 3 Based on the shelf layout adjustment method provided in the embodiment, the target shelf is any one of the full quantity shelves, and for the target shelf, the shelf layout adjustment method provided in the embodiment includes the following steps.

[0101] S401. Obtain multiple shopping routes and dwell time heat maps in the store, as well as the spatial location and product type of each shelf in the store, through smart cameras.

[0102] Specifically, take the data processing server analyzing the video screen to obtain multiple shopping paths and dwell time heat maps in the store as an example. The data processing server needs to pre-process the video screen, that is, analyze it frame by frame, and then build a plane model of the store through graphic recognition capabilities. This is a prerequisite and provides a basic model for obtaining the above information. Building a plane model includes the following steps:

[0103] First, the weighted average method is used to reduce data complexity and grayscale processing is performed. The calculation formula of the weighted average method can be: Gray = 0.299R + 0.587B + 0.114C, where R, B and C are color channel values.

[0104] Secondly, a 3×3 Gaussian filter kernel is used to remove noise, and then Canny edge detection is used to set appropriate thresholds to extract the contours of shelves and channels as the basis for constructing the plane model.

[0105] Finally, take a corner of the store, for example, the upper left corner of the store as the origin, and determine the two-dimensional coordinates of the shelf according to the coordinates of the contour endpoints of the shelf. The coordinates of the contour endpoints of the upper left corner of the shelf are (x 1 ,y 1 ), the coordinates of the upper right corner contour endpoint are (x 2 ,y 2 ), then the two-dimensional coordinates of the shelf are |(x 1 ,y 1 ), (x 2 ,y 2 )|; The two-dimensional coordinates of the channel are determined by the region growing method, starting from the non-shelf pixels and expanding outward to the shelf boundary according to the four-connectivity rule to determine its boundary coordinates.

[0106] After executing S401, since the shopping paths of customers are complicated, it is necessary to select a standard path as a basis, that is, determine a main path based on multiple shopping paths, and determine multiple path points from the main path.

[0107] S402: Discretize the multiple shopping routes according to preset distances to obtain multiple shopping sections.

[0108] Specifically, the main path can be regarded as a path composed of a series of continuous path points. For the convenience of calculation, multiple shopping paths are discretized at a certain interval, for example, the preset distance is 0.1 meters, and a series of discrete points P = {p 1 , p 2 ,…,p n}, each discrete point p i With coordinates (x i ,y i ). According to the plane model of the shop, the line connecting two adjacent discrete points on the channel is defined as a shopping section.

[0109] S403, obtaining the first number of customers passing each shopping section within the first preset time period through a smart camera, and determining multiple target shopping sections from multiple shopping sections according to the first number of customers passing each shopping section.

[0110] Specifically, a path counting matrix is ​​set up, where the rows represent different shopping sections and the columns represent different statistical time periods. Within the first preset time period, whenever a customer is detected passing through a shopping section by the smart camera, the value of the corresponding matrix element is increased by one. After the first preset time period ends, the value of each shopping section is counted and recorded as the number of first times that customers pass through each shopping section within the first preset time period.

[0111] After accumulation of a first preset time period, multiple target shopping sections are determined from multiple shopping sections according to the number of first times customers pass through each shopping section, wherein this can be achieved by determining each shopping section where the number of first times customers pass through is greater than a preset threshold as a target shopping section; or by sorting all shopping sections in reverse order according to the number of first times customers pass through, and according to the sorting result, determining each shopping section ranked before a preset ranking threshold as a target shopping section.

[0112] After S403 is executed, the continuous path composed of multiple target shopping sections is determined as the main path. The main path is defined based on the number of first times the customer passes through, and is as close to the customer's actual shopping path as possible to ensure the accuracy of subsequent analysis.

[0113] In a possible design, multiple target shopping sections can be spliced ​​to form multiple continuous paths to be selected, and the overlapping parts of these continuous paths to be selected are equivalent to the trunk path, and their non-overlapping parts are equivalent to the branch paths of the trunk path. The main path can include the trunk path and all the branch paths, that is, the main path is composed of the splicing of all the continuous paths to be selected; the main path can also include only the main path and at least one branch path, that is, the main path is composed of the splicing of at least one continuous path to be selected.

[0114] When the main path includes the main path and a branch path, the main path is a certain continuous path to be selected, and the continuous path composed of multiple target purchase sections is determined as the main path, including:

[0115] S404, multiple target shopping sections are spliced ​​together to form multiple continuous paths to be selected.

[0116] S405. Obtain, through a smart camera, the second number of customers passing through each to-be-selected continuous path within a second preset time period, and determine the to-be-selected continuous path with the largest number of customers passing through the second time as the main path.

[0117] Specifically, within the second preset time period, whenever the smart camera detects that a customer passes through a certain continuous path to be selected, the value of the corresponding continuous path to be selected is increased by 1. After the second preset time period ends, the value of each continuous path to be selected is counted and recorded as the second number of customers passing through each continuous path to be selected within the second preset time period.

[0118] After the accumulation of the second preset time period, according to the second number of customers passing through each continuous path to be selected, the continuous path to be selected with the largest number of customers passing through the second time is determined as the main path. It can be understood that the second preset time period and the first preset time period can be the same time period, and the data processing server can simultaneously determine the first number of customers passing through each shopping section and the second number of customers passing through each continuous path to be selected; the two can also be different time periods, and the time relationship between the two is not limited in this embodiment. At the same time, the second number of customers passing through can be the same as the first number of customers passing through, or different, and the values ​​of the two can be set according to the store type.

[0119] It is understandable that the technical solution of determining a main path based on multiple shopping paths is not applicable to shops with simple shopping paths. A shop with a simple shopping path refers to a shop with only one shopping path and no obstructions between shelves, so that customers can see all the products at a glance.

[0120] After executing the continuous path composed of multiple target shopping sections and determining it as the main path, continue to execute S406.

[0121] S406: Determine one end point of each target shopping section in the main path as a path point.

[0122] Specifically, the main path is composed of multiple target shopping sections, and the two end points of each target shopping section in the main path are located on the main path. To ensure the accuracy of the path point coordinates, the end points of each target shopping section in the main path are determined as path points. The Pth path point on the main path has coordinates (x Pi ,y Pi ).

[0123] S407, each path point within the effective exposure area of ​​the target shelf is determined as an exposure path point.

[0124] In a possible design, the shape of the effective exposure area is a rectangle, and the effective exposure area and the area where the target shelf is located share the same edge and do not intersect.

[0125] The target path point is any one of the multiple path points. For the target path point, S407, each path point within the effective exposure area of ​​the target shelf is determined as an exposure path point, including:

[0126] Use the ray method to determine whether the target path point is within the effective exposure area;

[0127] When the target path point is within the effective exposure area, the target path point is determined as the exposure path point.

[0128] Specifically, a horizontal ray is emitted from the target path point in the positive direction (i.e., toward the target shelf), and the number of intersections between the ray and the four sides of the rectangle of the target shelf is calculated. If the number of intersections is an odd number, the target path point is within the effective exposure area, and the target path point is determined as an exposure path point; if the number of intersections is an even number, the target path point is outside the effective exposure area, and the target path point is not determined as an exposure path point.

[0129] It should be noted that the target path point is any one of the full path points. Figure 4 The technical solution of the method embodiment shown can also be applied to any path point other than the target path point among multiple path points, and its implementation principle and technical effect are the same as those of Figure 4 The method embodiments shown are similar and will not be described in detail in the embodiments of the present application.

[0130] After executing S407, continue to execute S408.

[0131] S408. Calculate the effective exposure score of the target shelf according to the number of full exposure path points and the number of multiple path points.

[0132] Specifically, the effective exposure score is positively correlated with the number of exposure path points, and the effective exposure score may be equal to the quotient of the number of full exposure path points and the number of multiple path points.

[0133] After S408 is executed, the layout score of the corresponding shelf is calculated according to the effective exposure score and the purchase intention score of each shelf.

[0134] S409. Obtain the customer stay time and customer contact frequency at the target shelf through the smart camera.

[0135] S410. According to the customer stay time and customer contact frequency at the target shelf, a weighted calculation is performed to obtain a purchase intention score for the target shelf.

[0136] Specifically, I is defined as the purchase intention score of the target shelf, T is the customer stay time of the target shelf, F is the customer contact frequency of the target shelf, and w is defined as T is the weight of the customer's stay time, w F is the weight of customer contact frequency, and w T +w F =1,w T and w F It can be adjusted according to the merchant's preferences, so the calculation formula for I is: I = w T ×T+w F ×F.

[0137] S411. Standardize the effective exposure score and purchase intention score of the target shelf.

[0138] Specifically, the purpose of standardization is to map the numerical range of the data set to between 0 and 1, and then standardize the effective exposure score and purchase intention score so that the numerical range of both is between 0 and 1, ensuring that the effective exposure scores and purchase intention scores of different shelves are comparable, which is convenient for subsequent calculations.

[0139] The calculation formula for standardization can be: std =(XX min ) / (X max -X), where X max is the maximum value of the corresponding score, X min is the minimum value of the corresponding score, X std is the standardized result of score X. Then the effective exposure score C of the target shelf is standardized to obtain the standardized result C std , standardize the purchase intention score I of the target shelf and obtain the standardized result I std .

[0140] S412. According to the standardized effective exposure score and purchase intention score of the target shelf, a layout score of the target shelf is obtained by weighted calculation.

[0141] Specifically, according to the business situation of the store, std and I std Assign weights. Define C std The weight is w C , I std The weight is w I , and w C +w I =1,w C and w I It can be adjusted according to the merchant's preference, and the calculation formula of the target shelf layout score S is: S = w C ×C std +w I ×I std .

[0142] In a possible design, the weight of customer stay time is greater than the weight of customer contact frequency, and the weight of effective exposure score is less than the weight of purchase intention score.

[0143] Specifically, the weight w of the customer's stay time is set T Greater than the weight w of customer contact frequency FThe reason is that when the frequency of customer contact is high, it may be due to the popularity of the product itself or the advantage of the placement; at the same time, the weight of the effective exposure score is set as w C The weight w is less than the purchase intention score I The reason is that the purchase intention score is more related to the actual sales volume of the product, while the effective exposure score represents better shelf exposure and is easier for customers to see.

[0144] S413. Sort all shelves according to the layout score of each shelf.

[0145] S414: According to the sorting result, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted to obtain an adjusted shelf layout, and the adjusted shelf layout is pushed to the merchant.

[0146] It should be noted that the target shelf is any one of the full shelves. Figure 4 The technical solution of the method embodiment shown can also be applied to any shelf other than the target shelf in the store, and its implementation principle and technical effect are the same as those of Figure 4 The method embodiments shown are similar and will not be described in detail in the embodiments of the present application.

[0147] In a possible design, S414 adjusts the layout of at least one shelf according to the sorting result, the spatial position of each shelf in the store, and the commodity type to obtain an adjusted shelf layout, including:

[0148] According to the sorting results, as well as the spatial position and commodity type of each shelf in the store, the layout of at least one shelf is adjusted through a preset database to obtain a shelf adjustment layout; wherein the database stores a plurality of shelf layout adjustment schemes.

[0149] For example, for a shelf with a large layout score, for example, a shelf with a layout score of (0.8, 1], based on the spatial position and product type of the shelf, it is decided to keep the shelf's position and arrange popular and high-profit products; for a shelf with a medium layout score, for example, a shelf with a layout score of (0.5, 0.8], based on the spatial position and product type of the shelf, it is decided to increase the openness of the display layout to allow customers to see more products; for a shelf with a low layout score, for example, a shelf with a layout score of (0, 0.5], based on the spatial position and product type of the shelf, it is decided to appropriately place the shelf at an appropriate position and angle to free up more aisle space, and to increase guidance and promotional products to make it easier for customers to reach and discover the products on the shelf.

[0150] The technical effects of the embodiments of the present application are as follows: by analyzing the frame sequence of the video screen, the shelf layout of the current store is detected and acquired, and a plane model of the store is established, thereby improving the accuracy of acquiring the shopping path; a main path is extracted through multiple shopping paths, and the effective exposure score of each shelf is calculated through the main path, thereby reducing the clutter of the shopping path and improving the accuracy of the calculation of the effective exposure score; the purchase intention score of the corresponding shelf is weightedly calculated through the customer's stay time and customer contact frequency on each shelf, thereby improving the accuracy of the calculation of the purchase intention score.

[0151] The present application also provides a shelf layout adjustment system. Figure 2 As shown, the shelf layout adjustment system 210 includes:

[0152] At least one smart camera 211;

[0153] And a data processing server 212 which is respectively connected to each smart camera 211 for implementing the shelf layout adjustment method of the above embodiment.

[0154] The shelf layout adjustment system provided in the embodiment of the present application can be executed Figure 3 to Figure 4 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 3 to Figure 4 The method embodiments shown are similar and will not be described in detail in the embodiments of the present application.

[0155] Figure 5 A schematic diagram of the structure of the shelf layout adjustment device provided in the embodiment of the present application, such as Figure 5 As shown, in the embodiment of the present application, the shelf layout adjustment device can be located in the electronic device. The shelf layout adjustment device includes:

[0156] The data acquisition module 510 is used to acquire multiple shopping routes and dwell time heat maps in the store, as well as the spatial location and product type of each shelf in the store through a smart camera;

[0157] The first calculation module 520 is used to calculate the effective exposure score of each shelf according to the multiple shopping paths, and calculate the purchase intention score of each shelf according to the dwell time heat map; wherein the effective exposure score is used to indicate the number of times the corresponding shelf is covered by at least one shopping path, and the purchase intention score is used to indicate the customer's purchase intention of the goods in the corresponding shelf;

[0158] The second calculation module 530 is used to calculate the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sort all the shelves according to the layout score of each shelf;

[0159] The layout adjustment module 540 is used to adjust the layout of at least one shelf according to the sorting result, the spatial position of each shelf in the store and the type of goods, obtain the shelf adjustment layout, and push the shelf adjustment layout to the merchant.

[0160] The shelf layout adjustment device provided in the embodiment of the present application can be executed Figure 3 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 3 The method embodiments shown are similar and will not be described in detail in the embodiments of the present application.

[0161] At the same time, the shelf layout adjustment device provided in the embodiment of the present application is further refined based on the shelf layout adjustment device provided in the embodiment of the previous application.

[0162] In one possible design, the target shelf is any one of the full quantity shelves;

[0163] For the target shelf, the first calculation module 520 includes:

[0164] A main path determination module is used to determine a main path according to multiple shopping paths, and determine multiple path points from the main path;

[0165] An exposure path point determination module is used to determine each path point within the effective exposure area of ​​the target shelf as an exposure path point;

[0166] The third calculation module is used to calculate the effective exposure score of the target shelf according to the number of full exposure path points and the number of multiple path points.

[0167] In a possible design, the main path determination module includes:

[0168] A first main path determination module is used to discretize multiple shopping paths according to a preset distance to obtain multiple shopping sections;

[0169] The second main path determination module is used to obtain the first number of customers passing through each shopping section within the first preset time period through a smart camera, and determine multiple target shopping sections from multiple shopping sections according to the first number of customers passing through each shopping section;

[0170] The third main path determination module is used to determine a continuous path composed of multiple target shopping sections as a main path;

[0171] The fourth determination module of the main path is used to determine the end points of one side of each target shopping section in the main path as path points.

[0172] In a possible design, the third main path determination module includes:

[0173] The fifth main path determination module is used to combine multiple target shopping sections to form multiple continuous paths to be selected;

[0174] The sixth main path determination module is used to obtain the second number of customers passing through each to-be-selected continuous path within the second preset time period through a smart camera, and determine the to-be-selected continuous path with the largest number of customers passing through the second time as the main path.

[0175] In a possible design, the shape of the effective exposure area is a rectangle, and the effective exposure area and the area where the target shelf is located share the same edge and do not intersect;

[0176] The target path point is any one of the multiple path points. For the target path point, the exposure path point determination module includes:

[0177] A first path point determination module is used to determine whether the target path point is located within the effective exposure area by using a ray method;

[0178] The second path point determination module is used to determine the target path point as an exposure path point when the target path point is within the effective exposure area.

[0179] In one possible design, the target shelf is any one of the full quantity shelves;

[0180] For the target shelf, the first calculation module 520 includes:

[0181] The duration and frequency determination module is used to obtain the customer stay duration and customer contact frequency of the target shelf through the smart camera;

[0182] A fourth calculation module is used to obtain a purchase intention score of the target shelf by weighted calculation based on the customer stay time and customer contact frequency of the target shelf;

[0183] For the target shelf, the second calculation module 530 includes:

[0184] A standardization processing module is used to standardize the effective exposure score and purchase intention score of the target shelf;

[0185] The fifth calculation module is used to obtain the layout score of the target shelf through weighted calculation according to the effective exposure score and the purchase intention score of the target shelf after the standardized processing.

[0186] In a possible design, the weight of customer stay time is greater than the weight of customer contact frequency, and the weight of effective exposure score is less than the weight of purchase intention score.

[0187] In one possible design, the layout adjustment module 540 is used to adjust the layout of at least one shelf according to the sorting results, as well as the spatial position and commodity type of each shelf in the store, through a preset database to obtain a shelf adjustment layout; wherein the database stores multiple shelf layout adjustment schemes.

[0188] The shelf layout adjustment device provided in the embodiment of the present application can be executed Figure 3 to Figure 4 The technical solution of the method embodiment shown in the figure has the same implementation principle and technical effect as Figure 3 to Figure 4 The method embodiments shown are similar and will not be described in detail in the embodiments of the present application.

[0189] The present application also provides an electronic device, Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device includes: at least one processor 610 and a memory 620. The electronic device also includes a communication component 630. The processor 610, the memory 620 and the communication component 630 are connected via a bus 640.

[0190] In a specific implementation process, at least one processor 610 executes the computer execution instructions stored in the memory 620, so that at least one processor 610 is used to implement the shelf layout adjustment method of the above embodiment.

[0191] The specific implementation process of the processor 610 can be found in the above-mentioned method embodiment, and its implementation principle and technical effect are similar, so the embodiments of the present application will not be repeated here.

[0192] In the above embodiment, it should be understood that the processor 610 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0193] The memory 620 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0194] The bus 640 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus 640 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus 640 in the drawings of the present application is not limited to only one bus or one type of bus.

[0195] The above functions implemented by the electronic device and the main control device introduce the scheme provided by the embodiment of the present application. It is understandable that in order to implement the above functions, the electronic device or the main control device includes a hardware structure and / or software module corresponding to each function. In combination with the units and algorithm steps of each example described in the embodiment disclosed in the embodiment of the present application, the embodiment of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present application.

[0196] The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the shelf layout adjustment method of the above embodiment. In the specific implementation of the above shelf layout adjustment method, each module can be implemented as a processor.

[0197] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0198] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a main control device.

[0199] The embodiment of the present application also provides a computer program product, including a computer program, which is used to implement the shelf layout adjustment method of the above embodiment when the computer program is executed by a processor.

[0200] The computer program is stored in a readable storage medium. At least one processor can read the computer program from the readable storage medium. At least one processor executes the computer program to execute the solution provided in any of the above embodiments.

[0201] A person skilled in the art can understand that all or part of the steps of implementing the above-mentioned application embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiment are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk, etc., various media that can store program codes.

[0202] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for adjusting shelf layout, characterized in that: The method comprises: Through smart cameras, multiple shopping routes and dwell time heat maps in the store are obtained, as well as the spatial location and product type of each shelf in the store; According to the multiple shopping paths, the effective exposure score of each shelf is calculated, and according to the dwell time heat map, the purchase intention score of each shelf is calculated; wherein the effective exposure score is used to indicate the number of times the corresponding shelf is covered by at least one shopping path, and the purchase intention score is used to indicate the customer's purchase intention of the goods in the corresponding shelf; Calculate the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sort all the shelves according to the layout score of each shelf; According to the sorting result, as well as the spatial position and commodity type of each shelf in the shop, the layout of at least one of the shelves is adjusted to obtain an adjusted shelf layout, and the adjusted shelf layout is pushed to the merchant.

2. The method for adjusting shelf layout according to claim 1, characterized in that: The target shelf is any one of the shelves in the total quantity; For the target shelf, calculating the effective exposure score of each shelf according to the multiple shopping paths includes: Determine a main path according to the multiple shopping paths, and determine multiple path points from the main path; Determine each of the path points located within the effective exposure area of ​​the target shelf as an exposure path point; The effective exposure score of the target shelf is calculated based on the total number of exposure path points and the number of the multiple path points.

3. The method for adjusting shelf layout according to claim 2, characterized in that: The step of determining a main path according to the plurality of shopping paths comprises: Discretizing the plurality of shopping routes according to a preset distance to obtain a plurality of shopping sections; The smart camera is used to obtain the first number of customers passing through each shopping section within a first preset time period, and multiple target shopping sections are determined from the multiple shopping sections according to the first number of customers passing through each shopping section; Determine a continuous path formed by splicing the plurality of target shopping sections as the main path; The determining a plurality of path points from the main path comprises: One end point of each target shopping section in the main path is determined as the path point.

4. The method for adjusting shelf layout according to claim 3, characterized in that: The continuous path composed of a plurality of target shopping sections is determined as the main path, including: A plurality of target shopping sections are spliced ​​together to form a plurality of continuous routes to be selected; The smart camera is used to obtain the second number of customers passing through each of the to-be-selected continuous paths within a second preset time period, and the to-be-selected continuous path with the largest number of customers passing through the second time is determined as the main path.

5. The method for adjusting shelf layout according to claim 2, characterized in that: The effective exposure area is in the shape of a rectangle, and the effective exposure area and the area where the target shelf is located share a common edge and do not intersect; The target path point is any one of the plurality of path points. For the target path point, each of the path points located within the effective exposure area of ​​the target shelf is determined as an exposure path point, including: Determining whether the target path point is within the effective exposure area by using a ray method; When the target path point is within the effective exposure area, the target path point is determined as the exposure path point.

6. The method for adjusting shelf layout according to claim 1, characterized in that: The target shelf is any one of the shelves in the total quantity; For the target shelf, calculating the purchase intention score of each shelf according to the dwell time heat map includes: Obtaining the customer stay time and customer contact frequency of the target shelf through the smart camera; According to the customer stay time and customer contact frequency of the target shelf, a purchase intention score of the target shelf is obtained by weighted calculation; For the target shelf, calculating the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf includes: Standardizing the effective exposure score and purchase intention score of the target shelf; According to the standardized effective exposure score and purchase intention score of the target shelf, a layout score of the target shelf is obtained by weighted calculation.

7. The method for adjusting shelf layout according to claim 6, characterized in that: The weight of the customer's stay time is greater than the weight of the customer's contact frequency, and the weight of the effective exposure score is less than the weight of the purchase intention score.

8. The method for adjusting shelf layout according to claim 1, characterized in that: The step of adjusting the layout of at least one shelf according to the sorting result, the spatial position of each shelf in the shop, and the type of goods to obtain an adjusted shelf layout includes: According to the sorting result, as well as the spatial position and commodity type of each shelf in the shop, the layout of at least one of the shelves is adjusted through a preset database to obtain the shelf adjustment layout; wherein the database stores a plurality of shelf layout adjustment schemes.

9. A shelf layout adjustment system, characterized in that: The system comprises: At least one smart camera; And a data processing server which is respectively connected to each of the smart cameras and is used to implement the shelf layout adjustment method as described in any one of claims 1 to 8.

10. A shelf layout adjustment device, characterized in that: The device comprises: A data acquisition module is used to obtain multiple shopping routes and dwell time heat maps in a store, as well as the spatial location and product type of each shelf in the store through a smart camera; A first calculation module is used to calculate the effective exposure score of each shelf according to the multiple shopping paths, and calculate the purchase intention score of each shelf according to the dwell time heat map; wherein the effective exposure score is used to indicate the number of times the corresponding shelf is covered by at least one shopping path, and the purchase intention score is used to indicate the customer's purchase intention of the goods in the corresponding shelf; A second calculation module is used to calculate the layout score of the corresponding shelf according to the effective exposure score and the purchase intention score of each shelf, and sort all the shelves according to the layout score of each shelf; The layout adjustment module is used to adjust the layout of at least one shelf according to the sorting result, the spatial position and commodity type of each shelf in the shop, obtain the shelf adjustment layout, and push the shelf adjustment layout to the merchant.

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