Intelligent community retail management method and system

Abstract

The present application belongs to the technical field of smart community, and particularly relates to a smart community retail management method and system. The method comprises the following steps: establishing a neighborhood inventory sharing database and setting an initial trust degree of a user; deploying an intelligent express cabinet, a bulletin screen and an access control system, collecting package access data, bulletin screen interaction behavior, and the number and stay time of non-identity identification; dividing the community into a functional grid, statistically analyzing the demand intensity by time period, and generating a group demand signal when the demand intensity continuously exceeds a threshold; matching goods in the neighborhood inventory that are consistent in goods, close in location, and meet the trust degree of the supplier, and generating a neighborhood direct-pick recommendation; if the matching fails, group purchasing is initiated, and the goods are distributed to the express cabinet for distribution after the group purchasing meets the requirements; when the package is not picked up in time, a collection task is generated and pushed to nearby users; and the unified trust degree of the user is updated according to the evaluation, and the trust degree threshold is dynamically adjusted. The present application realizes the privacy protection and efficient cooperation of community retail demand through non-identity perception and a trust degree network.

Description

Technical Field

[0001] This invention belongs to the field of smart community technology, specifically, it relates to a smart community retail management method and system. Background Technology

[0002] In existing technologies, the demand for community retail typically relies on users' active searches or historical consumption, resulting in significant issues such as response lag and insufficient privacy protection. These issues manifest in several ways: Firstly, some community retail solutions use facial recognition or mobile phone location tracking to identify residents, which, while enabling accurate recommendations, carries legal risks of infringing on personal privacy. Secondly, a large amount of idle goods exist within communities (such as goods purchased in excess of purchases or not used by residents). These idle goods fail to achieve effective and scientific circulation, relying heavily on residents' spontaneous information postings. This not only leads to low matching efficiency but also makes it difficult to rationally assess users' ability to fulfill their obligations and their level of integrity, thus increasing overall trust costs. Summary of the Invention

[0003] The purpose of this invention is to provide a smart community retail management method to solve the technical problems existing in the prior art.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart community retail management method includes the following steps: Step S1: Establish a neighborhood inventory sharing database and set an initial trust level for each user. The neighborhood inventory sharing database includes the idle goods registered by users, with their location and expiration date marked. Step S2: Deploy smart parcel lockers, bulletin boards, and access control systems in the target community to collect data on parcel storage and retrieval time and dwell time, bulletin board interaction behavior, and the number of people and dwell time without identification. Step S3: Divide the community into several functional grids and calculate the demand intensity of each functional grid according to time periods. When the demand intensity of a certain grid exceeds the threshold for M consecutive days during a certain period, a collective demand signal is generated, and step S4 is executed. Step S4: Match products in the neighborhood inventory sharing database that match the product, are in close proximity, and have a supplier trust level higher than the preset trust level threshold. If the match is successful, generate a neighborhood direct recommendation. If the match is unsuccessful, proceed to step S5. Step S5: Initiate a group purchase, push the group purchase proposal to the residents covered by the grid, select a merchant to supply goods after the group purchase quantity reaches the target, and deliver them to the smart express cabinet for distribution, and notify the corresponding users to pick up the package; Step S6: If the express locker detects that the package has not been picked up within the preset time threshold, it generates a collection task and pushes it to the user's neighbors. The neighbors who accept the task complete the collection. Step S7: When a neighborhood direct pickup recommendation is completed or a collection on behalf of another party is completed, update the trust level of the corresponding user based on the evaluation of the service recipient, and adjust the preset trust level threshold according to the overall trust level distribution of the community.

[0005] Preferably, in step S2, the interactive behavior of the announcement screen includes the number of times a user clicks on product details through the announcement screen; the data on the number of people and the duration of stay without identification are collected collaboratively by the access control system and the announcement screen. The access control system collects the number of people and the duration of stay in the community's public area through an infrared sensor, and the announcement screen collects the number of people and the duration of stay in the area in front of the screen through a built-in infrared sensor. Both data sources only output the grid number, time period, number of people, and duration of stay statistics, without collecting or storing information that can identify the user.

[0006] Preferably, in step S3, the demand intensity Calculate using the following formula: ; In the formula, This represents the number of times grid g clicks on product details via the announcement screen within time period t. This refers to the number of people in grid g who stay for more than 30 seconds within time period t. The maximum historical demand count for grid g.

[0007] Preferably, in step S3, the number of consecutive days M is 3 days, and the preset threshold is 0.6.

[0008] Preferably, in step S4, proximity means that the distance between the building where the supplier is located and the center point of the demand grid is ≤200 meters; the neighborhood direct pickup recommendation includes the supplier's location, the location of the goods, and the temporary pickup code.

[0009] Preferably, in step S5, the target quantity Q for the group purchase is set as follows: ; In the formula, The average number of demands for this grid during this time period over the past 7 days, where 1 ≤ A < 1.5.

[0010] Preferably, in step S6, the collection task includes the location of the original package in the express locker and the collection code; the collection code is valid for a preset collection time. If the package is not picked up within the preset collection time, the current task is automatically canceled and reissued.

[0011] Preferably, in step S7, the trust level is updated according to the following formula: ; In the formula, s represents the rating of 1 to 5 points given by the service recipient, and T represents the current level of trust. This is the updated trust level.

[0012] Preferably, in step S7, the preset trust threshold is periodically adjusted based on the median or average trust level of all users in the community.

[0013] To achieve the above objectives, the present invention also provides a smart community retail management system, comprising: The database creation and initialization module is used to establish a neighborhood inventory sharing database and set an initial trust level for each user; the neighborhood inventory sharing database is used to store information on idle goods registered by users, including at least the product name, product location and shelf life; The data collection module is used to collect data on package storage and retrieval time and dwell time, interactive behavior on the announcement screen, and the number of people and dwell time without identification by deploying smart express cabinets, announcement screens and access control systems in the target community. The demand analysis module is used to divide the community into several functional grids and to statistically analyze the demand intensity of each functional grid by time period. When the demand intensity of a certain grid exceeds the threshold for M consecutive days during a certain period, a collective demand signal is generated. The neighborhood matching module is used to match products with the same product category, similar location and supplier trust level higher than the preset trust level threshold in the neighborhood inventory sharing database after receiving the group demand signal. If the matching is successful, a neighborhood direct recommendation is generated. If the matching is unsuccessful, the group purchase initiation module is triggered. The group-buying initiation module is used to initiate group-buying, push group-buying proposals to residents covered by the grid, select merchants to supply goods after the group-buying quantity reaches the target, deliver the goods to smart express cabinets for distribution, and notify the corresponding users to pick up the packages; The parcel collection task module is used to generate a parcel collection task and push it to the user's neighbors when the parcel locker detects that the parcel has not been picked up for more than a preset time threshold. The neighbor who accepts the task will then complete the collection. The trust update module is used to update the trust level of the corresponding user based on the evaluation of the service recipient when a neighborhood direct pickup recommendation or collection completion event occurs, and to adjust the preset trust level threshold according to the overall trust level distribution of the community.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention collects pedestrian flow data and dwell time through the collaborative collection of infrared sensors of access control and infrared sensors of notice screens. It only outputs anonymized statistical data such as grid number, time period, and number of people, without collecting any facial images or device identifiers that can identify the identity, thus fundamentally avoiding the risk of privacy leakage. At the same time, by combining the click behavior data of notice screens and quantifying the demand intensity of each grid and time period, it can realize the monitoring of group consumption demand without relying on individual identity.

[0015] (2) The present invention constructs a unified neighborhood inventory sharing database and user trust network. When a demand signal is triggered, the system automatically retrieves idle goods that meet the conditions of category, distance and trust, and generates direct pick-up recommendations containing specific location and temporary pick-up code. This breaks down the information barriers between supply and demand resources within the community and realizes the rapid circulation of idle goods.

[0016] (3) This invention establishes a unified trust score for each user. The shared or collected performance evaluations directly affect the user's credibility level in all community collaboration scenarios, solving the problem that the user's performance ability and integrity level cannot be rationally judged in the prior art. At the same time, the trust score is dynamically updated and maintained, so that the score tends to reflect long-term performance ability rather than occasional evaluations. The entry threshold is dynamically adjusted in combination with the overall trust score distribution of the community, so that the system can adaptively balance supply security and participation activity at different stages of community development. Attached Figure Description

[0017] Figure 1 This is a flowchart of Embodiment 1 of the present invention.

[0018] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to have a clearer understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described below are merely for illustrative purposes and to facilitate understanding. The technical solutions provided by the present invention are not limited to those provided in the following embodiments, nor should they limit the scope of protection of the present invention.

[0020] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex. Example

[0021] like Figure 1As shown, this embodiment provides a smart community retail management method, which achieves closed-loop management of the entire process of community retail demand through community spatial grid-based management and a unified trust network, coordinating neighborhood inventory sharing, community group buying, and mutual collection. The method mainly includes the following steps: 1. Step S1: Establish a neighborhood inventory sharing database and initialize trust levels The system backend establishes a neighborhood inventory sharing database, which runs on a community server or cloud platform to store information on idle goods voluntarily registered by users. Each registered user needs to fill in the product name, quantity, remaining shelf life in days, building number, etc. The sharing methods can include two types: door pickup and parcel locker transfer. The former means that the supplier places the goods at a designated location at their door, while the latter means that the supplier stores the goods in a dedicated shared parcel locker slot allocated by the system. Users can add, remove, or modify registered products at any time.

[0022] At the same time, the system establishes a unified trust account for each registered user, with an initial trust score of 5 points and a maximum score of 10 points. This trust account serves as the user's credibility as a supplier in the neighborhood sharing scenario and as a recipient in the collection and mutual assistance scenario, with the same score shared between the two scenarios.

[0023] 2. Step S2: Deploy nodes and collect data based on the deployed nodes. In this embodiment, the following three types of nodes are deployed within the community's physical space: (1) Smart parcel lockers are deployed at community entrances or building lobbies. Each parcel locker includes a main control unit and multiple storage compartments. Each compartment is equipped with an electric lock and a status sensor. When a courier deposits a parcel, the system records the parcel number, compartment number, and deposit time. When a user retrieves a parcel, the system records the retrieval time. The dwell time is calculated in real time by the backend based on the difference between the deposit time and the current time. In addition, some compartments in the parcel locker can be designated as shared dedicated compartments for suppliers to store goods in the neighborhood inventory sharing scenario.

[0024] (2) Announcement screens are deployed in densely populated areas such as community activity squares and building lobbies. The announcement screens are equipped with infrared thermopile sensors that can detect human thermal radiation within a 3-meter radius in front of the screen. The sensors process the data locally and only output structured data to the host computer. The format is as follows: {sensor ID, number of people in front of the screen, duration of each person's stay}. At the same time, the announcement screens serve as interactive terminals to display community group-buying product information and receive user touch clicks. When a user clicks on a product detail, the system records the timestamp of the click and the grid position of the announcement screen.

[0025] (3) Access control system, installed at the community gate and the entrance of each unit building. The access control host integrates infrared sensors facing the public area at the entrance. The infrared sensors collect the number of people in the entrance area at fixed intervals. When a person is detected to be in the same position for more than 30 seconds, it is determined to be a stay and recorded. The system outputs structured data to the host computer. The format example is as follows: {grid number, time period, number of people passing by, number of people staying for more than 30 seconds}. The data does not contain any access card number, mobile phone number or other identity identifiers.

[0026] Neither the data from the announcement screen nor the access control system collects or stores any identifiable information, thus ensuring user privacy and security from the data source.

[0027] 3. Step S3: Community grid division, demand analysis, and generation of group demand signals The system pre-divides the community space into multiple functional grids, including but not limited to: entrance and exit grids, activity square grids, unit building grids, and express delivery area grids; the time dimension is divided into 1-hour granularities, forming a two-dimensional statistical unit of {grid, time period}.

[0028] For each grid g and time period t, the system calculates the demand intensity. The calculation formula is as follows: ; In the formula, This represents the number of times grid g clicks on product details via the announcement screen within time period t. This refers to the number of people in grid g who stay for more than 30 seconds within time period t. The maximum historical demand count for grid g; repeated clicks on the same device on the announcement screen are only reported once within a 1-hour time window; the access control infrared sensor only reports one dwell record for the same continuous dwell event within a 1-hour time window.

[0029] Taking the activity plaza grid during the evening period of 19:00-20:00 as an example, the calculation process is as follows: Announcement screen interaction count Extract product detail click events reported by the announcement screen within the specified grid area during the specified time period. Multiple clicks on the same product by the same user within one hour are counted as only one instance, assuming deduplication. =12; Passenger dwell count Extract events reported by the access control system and the bulletin board sensor within the specified grid area that lasted for more than 30 seconds. Perform deduplication and merging on cross-sensor trajectories. If the same user stops multiple times within one hour, only one instance is counted. Assuming deduplication... =8; The highest number of times in history : Retrieve the calculated demand intensity values ​​for each time period over the past 90 days for this grid (continuous daily calculations but not necessarily triggering signals), and take the maximum value as the normalization benchmark, assuming = 20; Substitute the above values ​​into the demand intensity The calculation formula is as follows: .

[0030] The system presets a demand intensity threshold of 0.6 and a consecutive days M of 3 days. When the demand intensity of a certain grid exceeds 0.6 for 3 consecutive days, the system confirms the generation of a group demand signal for that grid and that time period. The demand signal carries the grid location, time period label, and information on the product categories that are frequently clicked within that time period, which serves as the triggering basis for subsequent matching and group buying processes.

[0031] 4. Neighborhood inventory matching and direct recommendation The system extracts frequently clicked product categories associated with demand signals, such as fruits, and then performs multi-condition matching retrieval in the neighborhood inventory shared database.

[0032] Matching conditions include: (1) the product name or label matches the required product category; (2) the remaining shelf life is ≥3 days (set value); (3) the straight-line distance between the supplier's building and the center point of the demand grid is within a preset range (set value, e.g., 200 meters); (4) the supplier's current trust level is ≥ the current preset trust level threshold (initial value is 5 points, dynamically adjusted). If the search finds at least one item, the system sorts all matching items in descending order of supplier trust level and generates a neighborhood direct pickup recommendation list. Each recommendation includes: the supplier's building number, the item's storage location (such as a self-pickup location at the entrance or a storage compartment in a parcel locker), and a one-time temporary pickup code generated by the system for this transaction. The system pushes the temporary pickup code to the requester via a mobile application. The code is valid for 2 hours after generation. The parcel locker's main control unit simultaneously receives the binding relationship between the pickup code and the corresponding compartment. After the requester enters the pickup code at the parcel locker terminal, the parcel locker verifies the code and opens the corresponding compartment. The pickup code expires immediately after use.

[0033] If there are no matching products in this category, or the matching results are empty, the system will automatically proceed to step S5 to initiate the group buying process. 5. Step S5: Initiate group purchase and delivery The system receives demand signals and corresponding product category information, first calculating the target quantity Q for group buying; then it retrieves the average number of demands for the same grid g within the time period t over the past 7 days. Multiply by a preset coefficient A (1 ≤ A < 1.5) to get Q = ×A, for example: = 15, A = 1.2, then Q = 18 parts.

[0034] The system pushes group-buying proposals to all registered users in the buildings covered by the demand grid through a mobile application. The proposals include: product name, specifications, estimated unit price, target quantity, and group-buying deadline. Users can view the proposals and choose to participate in the group-buying. The application displays the number of units already purchased and the remaining demand in real time. For short-shelf-life products such as fresh produce, the coefficient A is automatically adjusted to 1, and the group-buying deadline is half of the regular time. For example, if the regular group-buying deadline is 24 hours, then it is 12 hours for fresh produce.

[0035] When the cumulative number of group purchases reaches more than 80% of the target quantity, the system confirms the group purchase is successful. Subsequently, the system automatically sends price requests to partner merchants within a 3-kilometer radius of the community that have joined the platform. The request can specify the product category, quantity, and expected delivery time. After each merchant replies with the unit price and estimated delivery time, the system sorts them by comprehensive score by default. Comprehensive score = price score × 0.7 + delivery timeliness score × 0.3. In the case of equal comprehensive scores, the merchant with the highest historical score is given priority. Administrators can adjust the weighting ratio in the backend.

[0036] Suppliers deliver goods to community smart parcel lockers at the agreed time. The parcel locker system assigns an independent locker to each user based on the list of users participating in the group purchase and generates a pickup notification, which is pushed to the corresponding user via a mobile application. After receiving the notification, the user goes to the parcel locker to pick up the package using the pickup code, thus completing the group purchase transaction.

[0037] 6. Step S6: Parcel Collection Task Generation and Execution The smart parcel locker continuously monitors the status of each compartment. When it detects that a parcel in a compartment has not been retrieved for more than a preset time threshold (set value, for example: 4 hours) since it was deposited, the system initiates the parcel collection task judgment process.

[0038] In this step, the system can retrieve the most recent access control card swipe record of the recipient. If the record is in the direction of exit and the exit time is earlier than the current time, and no card swipe record of the user in the direction of entry has been detected so far, it is determined that the user is out of the house. If the user returns before the collection task is accepted (i.e., a card swipe record in the direction of entry is detected), the generated collection task will be automatically canceled.

[0039] The specific details of the package collection task include: the original package's locker number and specific compartment location, the temporary collection code generated by the system for this task (valid for 30 minutes), and the desired building number and room number for delivery.

[0040] After a task is generated, the system searches for potential users who meet the current criteria. The filtering criteria are: trust level ≥ 7 points (set value), and the straight-line distance between their location (building) and the parcel locker to be picked up is ≤ 100 meters (set value). The system sorts the users who meet the criteria from highest to lowest trust level and pushes task notifications to them sequentially through the application.

[0041] After a user accepts an order, they must pick up the package at the designated locker within 30 minutes using the pickup code. Once the locker verifies that the pickup code is valid and has not expired, it will open the corresponding compartment. At the same time, the access control system will temporarily grant the neighbor who accepted the order access to the target building. The access will be valid for one hour after the task is generated (set value), and the number of entries can be set to 1, 2, or other times to ensure that the neighbor can successfully enter the target building and deliver the package.

[0042] If no one accepts the order within 30 minutes or the package is not picked up after the order is accepted, the current task will be automatically canceled. The system will then generate a new collection code and reissue the task. The same package collection task can be issued a maximum of 3 times. If all 3 attempts fail, the system will stop issuing tasks.

[0043] 7. Step S7: Trust Level Update and Threshold Dynamic Adjustment After the neighborhood sharing transaction in step S4 is completed or the collection task in step S6 is completed, the system pushes an evaluation interface to the service recipient (i.e., the demander in the neighborhood sharing or the original recipient in the collection task), inviting them to rate the supplier or the neighbor who accepted the order. The rating range is 1 to 5 points. If no rating is given, the default rating is 4.

[0044] The system updates the uniform trust level of the evaluated party based on the rating. The update formula is as follows: ; In the formula, s represents the rating of 1 to 5 points given by the service recipient, and T represents the current level of trust. This is the updated trust level.

[0045] Taking a neighborhood sharing activity (step S4) as an example: the supplier's current trust level T=6.5, and the demander's rating s=4, then... =6.7, the supplier trust level has been updated from 6.5 to 6.7.

[0046] After each trust level update, the system statistically analyzes the trust level distribution of all users in the community according to a preset period (e.g., every Sunday morning) and calculates the median trust level of all users. If the median increases compared to the previous period, for example from 6.0 to 6.5, the preset trust level threshold is automatically adjusted from 6 points to 6.5 points; if the median decreases, the threshold is adjusted accordingly. Through this periodic adjustment, the system can dynamically adapt the participation threshold to the overall reputation level of the community, achieving a balance between ensuring supply security and maintaining participation activity. Example

[0047] like Figure 2 As shown, this embodiment provides a smart community retail management system for executing the method described in Embodiment 1. The system includes the following modules: The database establishment and initialization module is used to establish a neighborhood inventory sharing database and set an initial trust level for each user. The neighborhood inventory sharing database stores information on idle goods registered by users, including at least the product name, location, and remaining shelf life. This module also supports users to add, remove, and modify registered products. The initial trust level is uniformly set to 5 points, which serves as a unified credit benchmark for the user in subsequent sharing and collection scenarios.

[0048] The data acquisition module is used to collect data on package storage and retrieval time and dwell time, announcement screen interaction behavior, and non-identifiable personnel and dwell time through smart parcel lockers, bulletin board screens, and access control systems deployed in the target community. Specifically, the access control system uses infrared sensors to collect the number of people and dwell time in the community's public areas, while the bulletin board screen uses built-in infrared sensors to collect the number of people and dwell time in the area in front of the screen. Both data sources only output grid number, time period, number of people, and dwell time statistics, without collecting or storing identifiable information. Bulletin board interaction behavior includes the number of times users click on product details on the bulletin board screen, and the data reported by the smart parcel lockers includes the storage time, retrieval time, and dwell time of each compartment.

[0049] The demand analysis module is used to divide the community space into several functional grids (including at least entrance / exit grids, activity plaza grids, apartment building grids, and express delivery area grids), and to analyze the demand intensity of each functional grid in 1-hour increments. The demand intensity is calculated by weighting the number of clicks on the announcement screen, the number of people who stay for more than 30 seconds, and the historical maximum number of demand times. When the demand intensity of a certain grid exceeds the preset threshold of 0.6 for three consecutive days in a certain period, a group demand signal is generated and output to the neighborhood matching module.

[0050] The neighborhood matching module receives group demand signals and performs multi-condition matching searches in the neighborhood inventory sharing database. Matching conditions include: the product category matches the demand category, the distance between the supplier's building and the center point of the demand grid is ≤200 meters, and the supplier's trust level is ≥ the current preset trust level threshold. If a matching product is found, a neighborhood direct pickup recommendation list is generated in descending order of trust level. The recommendation information includes the supplier's location, the product's storage location, and a one-time temporary pickup code. If no matching product is found, a trigger signal is sent to the group purchase initiation module.

[0051] The group-buying initiation module is used to initiate the group-buying process after receiving a trigger signal. This module calculates the target quantity for the group-buying. ,in The average number of requests for this grid during this time period over the past 7 days is represented by A, which ranges from 1 to 1.5. The module pushes group-buying proposals to all users in the buildings covered by the demand grid, counts the number of orders in real time, and confirms the group purchase only after the target is reached. After the group purchase is completed, the module automatically inquires about prices from nearby merchants, selects suppliers based on a comprehensive comparison of prices and delivery time, and automatically uses more stringent parameter configurations for fresh or short-shelf-life products. After the goods are delivered to the smart parcel locker, the module notifies all participating users to pick up their packages.

[0052] The parcel collection task module is used to automatically generate a parcel collection task when the parcel locker detects that a fresh food parcel has been stored for more than 4 hours without being retrieved and the recipient is out. The task includes the location of the parcel locker, a dynamic parcel collection code with a validity period of 30 minutes, and the target address. The module selects neighbors with a trust score of ≥7 and a distance of ≤100 meters from the parcel locker as the target for push, and pushes them in descending order of trust score. The user who accepts the order picks up the parcel with the code and completes the delivery. This module is also responsible for automatically canceling the parcel if it is not picked up within the time limit and resending the task (up to 3 times).

[0053] The trust update module is used to receive a 1-5 point rating from the service recipient after a neighborhood sharing or collection task is completed, based on a formula. This module updates the unified trust level of the evaluated party; it is also responsible for statistically analyzing the trust level distribution of all users in the community at preset cycles, and dynamically adjusting the preset trust level threshold based on changes in the median, so that the entry threshold keeps pace with the overall reputation level of the community.

[0054] The data connections between the above modules are as follows: the data acquisition module outputs the perceived data to the demand analysis module; the demand analysis module outputs the group demand signal generated to the neighborhood matching module; the neighborhood matching module sends a trigger signal to the group purchase initiation module when matching fails; after completing the evaluation process, the trust update module synchronizes the updated trust threshold to the neighborhood matching module and the collection task module for use in the next round of matching and screening.

[0055] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A smart community retail management method, characterized in that, Includes the following steps: Step S1: Establish a neighborhood inventory sharing database and set an initial trust level for each user. The neighborhood inventory sharing database includes the idle goods registered by users, with their location and expiration date marked. Step S2: Deploy smart parcel lockers, bulletin boards, and access control systems in the target community to collect data on parcel storage and retrieval time and dwell time, bulletin board interaction behavior, and the number of people and dwell time without identification. Step S3: Divide the community into several functional grids and calculate the demand intensity of each functional grid according to time periods. When the demand intensity of a certain grid exceeds the threshold for M consecutive days during a certain period, a collective demand signal is generated, and step S4 is executed. Step S4: Match products in the neighborhood inventory sharing database that match the product, are in close proximity, and have a supplier trust level higher than the preset trust level threshold. If the match is successful, generate a neighborhood direct recommendation. If the match is unsuccessful, proceed to step S5. Step S5: Initiate a group purchase, push the group purchase proposal to the residents covered by the grid, select a merchant to supply goods after the group purchase quantity reaches the target, and deliver them to the smart express cabinet for distribution, and notify the corresponding users to pick up the package; Step S6: If the express locker detects that the package has not been picked up within the preset time threshold, it generates a collection task and pushes it to the user's neighbors. The neighbors who accept the task complete the collection. Step S7: When a neighborhood direct pickup recommendation is completed or a collection on behalf of another party is completed, update the trust level of the corresponding user based on the evaluation of the service recipient, and adjust the preset trust level threshold according to the overall trust level distribution of the community.

2. The smart community retail management method according to claim 1, characterized in that, In step S2, the interactive behavior of the announcement screen includes the number of times a user clicks on product details through the announcement screen; the data on the number of people and the duration of stay without identification are collected collaboratively by the access control system and the announcement screen. The access control system collects the number of people and the duration of stay in the community's public area through an infrared sensor, and the announcement screen collects the number of people and the duration of stay in the area in front of the screen through a built-in infrared sensor. Both data sources only output the grid number, time period, number of people, and duration of stay statistics, without collecting or storing information that can identify the user.

3. The smart community retail management method according to claim 1, characterized in that, In step S3, the demand intensity Calculate using the following formula: ; In the formula, This represents the number of times grid g clicks on product details via the announcement screen within time period t. This refers to the number of people in grid g who stay for more than 30 seconds within time period t. The maximum historical demand count for grid g.

4. The smart community retail management method according to claim 3, characterized in that, In step S3, the number of consecutive days M is 3 days, and the preset threshold is 0.

6.

5. The smart community retail management method according to claim 1, characterized in that, In step S4, proximity means that the distance between the building where the supplier is located and the center point of the demand grid is ≤200 meters; the neighborhood direct pickup recommendation includes the supplier's location, the location of the goods, and the temporary pickup code.

6. The smart community retail management method according to claim 1, characterized in that, In step S5, the target quantity Q for the group purchase is set as follows: ; In the formula, The average number of requests for this grid during this time period over the past 7 days, where 1 ≤ A < 1.

5.

7. The smart community retail management method according to claim 1, characterized in that, In step S6, the collection task includes the location of the original package in the express locker and the collection code; the collection code is valid for a preset collection time. If the package is not picked up within the preset collection time, the current task is automatically canceled and reissued.

8. The smart community retail management method according to claim 1, characterized in that, In step S7, the trust level is updated according to the following formula: ; In the formula, s represents the rating of 1 to 5 points given by the service recipient, and T represents the current level of trust. This is the updated trust level.

9. The smart community retail management method according to claim 8, characterized in that, In step S7, the preset trust threshold is periodically adjusted based on the median or average trust level of all users in the community.

10. A smart community retail management system implementing the method of any one of claims 1-9, characterized in that, include: The database creation and initialization module is used to create a neighborhood inventory sharing database and set the initial trust level for each user; The neighborhood inventory sharing database is used to store information on idle goods registered by users, including at least the product name, product location, and shelf life; The data collection module is used to collect data on package storage and retrieval time and dwell time, interactive behavior on the announcement screen, and the number of people and dwell time without identification by deploying smart express cabinets, announcement screens and access control systems in the target community. The demand analysis module is used to divide the community into several functional grids and to statistically analyze the demand intensity of each functional grid by time period. When the demand intensity of a certain grid exceeds the threshold for M consecutive days during a certain period, a collective demand signal is generated. The neighborhood matching module is used to match products with the same product category, similar location and supplier trust level higher than the preset trust level threshold in the neighborhood inventory sharing database after receiving the group demand signal. If the matching is successful, a neighborhood direct recommendation is generated. If the matching is unsuccessful, the group purchase initiation module is triggered. The group-buying initiation module is used to initiate group-buying, push group-buying proposals to residents covered by the grid, select merchants to supply goods after the group-buying quantity reaches the target, deliver the goods to smart express cabinets for distribution, and notify the corresponding users to pick up the packages; The parcel collection task module is used to generate a parcel collection task and push it to the user's neighbors when the parcel locker detects that the parcel has not been picked up for more than a preset time threshold. The neighbor who accepts the task will then complete the collection. The trust update module is used to update the trust level of the corresponding user based on the evaluation of the service recipient when a neighborhood direct pickup recommendation or collection completion event occurs, and to adjust the preset trust level threshold according to the overall trust level distribution of the community.

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