Cloud store long-cloud on-duty rider take-out goods taking management system

Abstract

The invention, which belongs to the technical field of cloud e-commerce, discloses a cloud store long-cloud-on-duty rider take-out goods taking management system comprising at least one remote server for providing uninterrupted operation support all the year round; an intelligent terminal device; a plurality of aggregate payment interfaces; the intelligent LED colored lamp prompt module is used for accurately indicating the positions of commodities needing to be picked up by a take-out rider in combination with order information and assisting in completing the self-service commodity picking process; a customer service module; a sales data interconnection system; and the AI intelligent prediction scheduling module analyzes historical order data, weather conditions and holiday effect multi-dimensional information by using a machine learning algorithm, predicts a peak period in advance, and automatically adjusts in-store resource allocation. According to the invention, the peak period can be predicted in advance, and in-store resource allocation can be automatically adjusted. According to the prediction and adjustment mechanism, the shop can make preparation before the peak period of demand, and delay or disorder caused by temporary resource allocation is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud e-commerce, and in particular, to a cloud store manager cloud-attended rider takeout pick-up management system. Background Art

[0002] Cloud store manager cloud-attended is an intelligent management solution designed specifically for the catering and retail industries. It aims to help merchants achieve digital transformation, improve operational efficiency, enhance customer satisfaction, and ultimately drive business growth. It is applicable to various commercial entities such as chain restaurants, cafes, convenience stores, etc. that require efficient management and customer service. It performs particularly well in dealing with peak-hour customer flows and unified management of multiple stores, significantly shortening customer waiting times and improving overall service quality.

[0003] Traditional takeout management systems mainly rely on manual experience and historical data for resource allocation and lack the ability to analyze real-time and multi-dimensional data. For example, a store manager may judge that weekends and evenings are peak hours based on past experience, without considering factors such as weather changes, temporary events (such as an event being held nearby) or social media trends. Therefore, when these factors cause a sudden surge in demand, the system cannot respond in a timely manner.

[0004] Due to the failure to prepare sufficient human and material resources in advance, the order processing speed slows down, which may result in customers waiting for a long time and even cases of delayed delivery. Frequent order delays and poor service experiences will cause customers to switch to other platforms or merchants, resulting in customer loss. For example: Suppose a restaurant usually has a high order volume from 7 pm to 9 pm on Friday nights, but due to a large concert ending on the same day, a large number of concert-goers flocked to the surrounding area in search of food, resulting in a much higher order volume than expected. Since the system failed to predict this situation, the restaurant did not increase staff or adjust inventory, ultimately leading to many order delays, some customers canceling their orders, and even complaints about poor service quality, affecting the restaurant's reputation. Summary of the Invention

[0005] The purpose of the present invention is to provide a cloud store manager cloud-attended rider takeout pick-up management system to solve the problems raised in the background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A cloud store manager cloud-attended rider takeout pick-up management system, comprising:

[0007] At least one remote server for providing year-round uninterrupted operation support, with support at least covering access control management, video surveillance, payment processing, and takeout rider pick-up management;

[0008] An intelligent terminal device connected to the remote server via an Ethernet cable and adapted for access by an Android system client;

[0009] Multiple integrated payment interfaces, integrating third-party payment platforms to achieve multi-channel payment functions;

[0010] Intelligent LED color light prompt module, combined with order information to accurately indicate the locations of the goods that the food delivery riders need to pick up, assisting in completing the self-service pick-up process;

[0011] Customer service module, providing online services to help users solve questions and guide operations;

[0012] Sales data interconnection system, used to collect sales data, personnel access records, and store operation status monitoring information in real time, supporting data analysis and business decision-making;

[0013] AI intelligent prediction and scheduling module, using machine learning algorithms to analyze multi-dimensional information such as historical order data, weather conditions, and holiday effects, predicting peak hours in advance, and automatically adjusting the allocation of in-store resources.

[0014] Preferably, the system further includes an augmented reality-assisted pick-up navigation function, which uses AR technology to display real-time path guidance in a three-dimensional space on the rider's mobile device. The AR navigation system uses image recognition technology and environmental perception algorithms.

[0015] Preferably, the system integrates an unmanned delivery vehicle or drone delivery docking system to achieve automated cargo transportation from the store to a specific location. The unmanned delivery system has the capabilities of autonomous obstacle avoidance, path planning, and task scheduling to ensure safe and efficient delivery services.

[0016] Preferably, intelligent shelves and intelligent temperature control modules are established in the system. The intelligent shelves can sense changes in the weight of goods and instantly update the inventory status. The intelligent temperature control module ensures the freshness of fresh food, and there is an intelligent trash can that notifies the cleaning requirement according to the filling level.

[0017] Preferably, a blockchain trust module is introduced into the system to record transaction details and ensure the transparency and immutability of the information of each order.

[0018] Preferably, the system supports personalized user experience customization. According to the user's preference settings and past behavior habits, it provides personalized interface layouts, recommendation lists, and promotional activities. The user personalized recommendation model is based on the collaborative filtering algorithm CF. By analyzing the user's historical purchase records R and the behaviors S of similar users, a recommendation matrix M is constructed: M = g(R, S), where: R represents the user's historical purchase records; S represents the behaviors of similar users; g is a recommendation generation function that comprehensively considers the above factors.

[0019] Preferably, in this solution, a virtual assistant application is developed in the system, allowing customers and riders to complete a series of operations such as querying, placing orders, and paying using voice commands. The natural language processing technology is utilized to understand instructions in complex contexts. The virtual assistant is equipped with an emotion recognition module, which can adjust the response tone according to the conversation content to provide a user-friendly interaction experience.

[0020] Preferably, in this solution, renewable energy is used for power supply in the system, and it is also equipped with a power management system.

[0021] The system energy consumption model E can be expressed as:

[0022] E = h(P, T, C)

[0023] Where: P represents the power consumption of the system; T represents the running time; C represents the environmental conditions; h is an energy consumption estimation function that comprehensively considers the above factors.

[0024] Preferably, in this solution, the system uses high-precision indoor positioning technology to accurately track the movement trajectory of riders in the store, ensuring that they can be accurately guided to the target location even in complex environments. The high-precision positioning module has a centimeter-level positioning accuracy and can maintain stable positioning performance in a crowded environment.

[0025] Preferably, a social interaction function is added to the system to promote communication and interaction among users, forming a good word-of-mouth communication effect. The social interaction platform supports a UGC review mechanism to ensure the quality and security of the published content and maintain a healthy and positive community atmosphere.

[0026] Compared with the prior art, the technical effects and advantages of the present invention:

[0027] This cloud store manager and cloud on-duty rider food delivery pick-up management system

[0028] By analyzing multi-dimensional information such as historical order data, weather conditions, and holiday effects through machine learning algorithms, the system can predict peak hours in advance and automatically adjust the allocation of in-store resources. This prediction and adjustment mechanism enables the store to be prepared before the peak demand period arrives, avoiding delays or chaos caused by ad-hoc resource allocation. For example, before the peak period from 7 pm to 9 pm on Friday, the system will increase the inventory of popular products in advance according to the prediction results, adjust the staff shifts, or notify the riders of the expected arrival time, thus significantly improving efficiency and service quality.

[0029] Since the system can dynamically arrange the rider's arrival time, it reduces the waiting time of the rider in the store, and at the same time ensures that customers can receive the ordered goods in the shortest time. This not only improves the customer's shopping experience, but also enhances their trust and loyalty to the platform. For example, when the system predicts that it is about to enter the peak period, it will send a notice of the estimated arrival time to the rider, and the rider can arrive on time according to the prompt, avoiding long queues.

[0030] Intelligent prediction and scheduling technology helps the store make more effective use of human and material resources, reducing unnecessary overtime costs and the risk of inventory backlog. In addition, accurate resource allocation also helps to improve the sales conversion rate, thereby increasing the store's revenue. For example, by optimizing shift scheduling and inventory management, the store can reduce waste of human resources and ensure that each item is sold in a timely manner, improving the overall operational efficiency and economic benefits.

[0031] Based on AI intelligent prediction and scheduling, the system can not only predict the peak period, but also make dynamic adjustments according to real-time data. For example, when there is a sudden surge in temporary demand caused by certain special events (such as breaking news, social media hotspots), the system can quickly respond and adjust resource allocation. When the system detects a sudden increase in the number of orders within a short period of time, it will automatically trigger an emergency plan, such as temporarily increasing staff, adjusting inventory priorities, etc., to ensure that the service is not interrupted.

[0032] Augmented reality (AR) assisted pick-up navigation combined with the use of high-precision indoor positioning technology makes AR navigation rely on the accurate location information provided by high-precision positioning technology. The combination of the two makes the guidance more accurate and reliable. When the rider enters the store, the mobile phone receives the signals of multiple BLE beacons to determine the current location; then, the application calculates the centimeter-level positioning accuracy by combining UWB technology, dynamically adjusts the indicator light color or emits a sound prompt to guide the rider along the optimal path to the target commodity location. Brief Description of the Drawings

[0033] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is the flowchart of the cloud store manager cloud guard rider takeout pick-up management system of the present invention;

[0035] Figure 2 It is the operation flowchart of the AI intelligent prediction and scheduling module of the present invention;

[0036] Figure 3 The operation flowchart customized for the personalized user experience of the present invention. Detailed implementation manners

[0037] In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, some technical features well known to the art are not described to avoid confusion with the present invention.

[0038] Unless otherwise defined, the up, down, left, right, front, back, inside and outside directions involved in this article are based on the up, down, left, right, front, back, inside and outside directions in the figures shown in the present invention, which are hereby explained together.

[0039] This embodiment provides a cloud store manager cloud on-duty rider food delivery pick-up management system as Figures 1 - 3 shown, including:

[0040] At least one remote server, which is used to provide 24-hour uninterrupted operation support throughout the year, and the support at least covers access control management, video monitoring, payment processing, and food delivery rider pick-up management;

[0041] Intelligent terminal devices, which communicate with the remote server through network cables, Wi-Fi, 2G, 3G, 4G communication networks, and are adapted to the access of Android system clients. The minimum configuration requirement of the intelligent terminal devices is to support the Android operating system version above 5.0;

[0042] Multiple aggregated payment interfaces, integrating including but not limited to WeChat Pay and Alipay third-party payment platforms to achieve multi-channel payment functions;

[0043] Intelligent LED color light prompt module, which accurately indicates the location of the goods to be picked up by the food delivery rider in combination with the order information to assist in completing the self-service pick-up process;

[0044] Customer service module, which provides online services to help users solve questions and guide operations;

[0045] Sales data interconnection system, which is used to collect sales data, personnel access records, and store operation status monitoring information in real time, and support data analysis and business decision-making;

[0046] AI intelligent prediction and scheduling module, which uses machine learning algorithms to analyze multi-dimensional information such as historical order data, weather conditions, and holiday effects, predicts peak hours in advance, and automatically adjusts the allocation of in-store resources.

[0047] The system can calculate the probability P of the peak period according to the following formula:

[0048] P = f(T, W, H, D)

[0049] Where: T represents the time factor (such as different time periods in a day); W represents the weather condition; H represents the holiday impact; D represents the dynamic inventory level; f is a non-linear function that comprehensively considers the above factors, enabling the system to more accurately optimize the product placement to shorten the picking time, or dynamically arrange the rider's arrival time, thereby significantly improving efficiency and service quality. The system collects a large amount of data from multiple sources (such as historical order databases, weather forecast APIs, holiday calendars, etc.), including but not limited to the time factor T, weather condition W, holiday impact H, and dynamic inventory level D. The raw data is converted into feature vectors that can be used for machine learning model training. For example, the date and time are converted into different time period encodings in a day; the weather conditions are classified as sunny, cloudy, rainy, etc., and corresponding numerical representations are given. Using supervised learning algorithms (such as random forests, gradient boosting machines, or deep neural networks), training is carried out based on the labeled historical order data, with the goal of predicting the order demand within a specific time period. The probability P = f(T, W, H, D) during the peak period is calculated through the following formula, where f is a non-linear function that comprehensively considers the above factors and can be implemented through regression analysis or deep learning frameworks. The value output by this function is the probability value of an order peak occurring within a certain time period. According to the prediction results, the system automatically adjusts the in-store resource allocation, such as preparing the inventory of popular products in advance, adjusting the staff shifts, or notifying the rider of the expected arrival time to ensure efficient order processing when the peak period arrives.

[0050] In this embodiment, the system also includes an augmented reality (AR) assisted picking navigation function. Through AR technology, real-time path guidance in a three-dimensional space is displayed on the rider's mobile device, helping the rider quickly find the location of the specified product, reducing the search time and the probability of misoperation. The AR navigation system uses image recognition technology and environmental perception algorithms to ensure accurate guidance for the rider even in complex environments. Image recognition uses the camera on the mobile device to capture the internal environment of the store, and through computer vision technology, it identifies the shelf positions, product labels, and other key elements. Combining the store floor plan and real-time image information, a three-dimensional model of the store interior is constructed, enabling the system to understand the spatial structure. Based on the three-dimensional model and the order details, the shortest and obstacle-free walking path from the current location to the target product is calculated, and guidance information such as virtual arrows and digital labels is superimposed on the rider's mobile device screen. These information will be dynamically updated as the rider moves, ensuring that the guidance is always accurate. Integrated sensors (such as accelerometers and gyroscopes) monitor the device's attitude changes to ensure that the AR display is synchronized with the actual environment and remains stable even in fast movement or complex environments. AR technology helps the rider quickly find the location of the specified product by displaying real-time path guidance in a three-dimensional space, reducing the search time and the probability of misoperation. This not only speeds up the picking speed but also reduces the risks of complaints and returns caused by incorrect picking. For example, after the rider opens the application, the camera captures the internal environment of the store, the model identifies the products on the shelves and marks their positions, and the guidance information on the screen is dynamically updated as the rider moves, always keeping the guidance correct.

[0051] In this embodiment, the system integrates an unmanned delivery vehicle or drone delivery docking system to achieve automated cargo transportation from the store to a specific location, which is especially suitable for short and fixed delivery routes, improving the delivery efficiency and service scope. The unmanned delivery system has the capabilities of autonomous obstacle avoidance, path planning, and task scheduling to ensure safe and efficient delivery services. A small unmanned delivery vehicle or drone with autonomous navigation capabilities is developed, equipped with various sensors such as GPS modules, lidar, and cameras for positioning, obstacle avoidance, and environmental perception. The system selects the optimal delivery method (unmanned vehicle or drone) according to factors such as the distance to the order destination and traffic conditions, and sends delivery instructions to the selected device through the cloud platform. Using geographic information system (GIS) and real-time traffic data, the best driving route is planned for the unmanned delivery device to ensure safe and efficient arrival at the specified location. The unmanned delivery device is equipped with an automated loading and unloading mechanism, which can automatically load packages after receiving an order and unload them to the recipient after arriving at the destination. Through 4G / 5G network connection, managers can monitor the status of the unmanned delivery device in real time in the background, including location, battery level, load condition, etc., and intervene when necessary.

[0052] In this embodiment, an intelligent shelf and an intelligent temperature control module are established in the system. The intelligent shelf can sense changes in the weight of goods and instantly update the inventory status. The intelligent temperature control module ensures the freshness of fresh food. Additionally, there is an intelligent trash can that notifies the cleaning requirement according to the filling level. Each shelf is equipped with a weight sensor. When a product is taken out or put back, the sensor detects the weight change and reports it to the server, thus realizing real-time inventory management. For the area where fresh food is stored, intelligent temperature control equipment is installed to continuously monitor the temperature and automatically adjust it according to the preset range to ensure food safety. The trash can is built-in with an overflow sensor that triggers a cleaning notice when the garbage reaches a certain capacity, reducing odors and sanitation problems. All IoT devices are connected to the cloud through a low-power wide area network (such as LoRa, NB-IoT), ensuring the stability and reliability of data transmission, while reducing energy consumption. The collected data is uploaded to the cloud for centralized processing, generating reports for managers to refer to and helping to optimize operation strategies.

[0053] In this embodiment, a blockchain trust module is introduced into the system to record transaction details, ensuring the transparency and immutability of the information of each order, enhancing consumers' trust in the platform, and at the same time providing a reliable basis for resolving disputes. Blockchain technology is applied in multiple aspects such as supply chain finance, anti-counterfeiting and traceability, providing a more secure and transparent service experience for merchants and customers. A distributed ledger is constructed using blockchain technology to record the detailed information of each transaction, including the order creation time, payment status, logistics tracking, etc. Public and private keys are used to encrypt and sign the transaction data to ensure the security and immutability of the information. Through the Proof of Work (PoW), Proof of Stake (PoS) or other consensus algorithms, the validity of the transaction is verified and added to the blockchain. Smart contracts are written to automatically execute certain business logics, such as automatically confirming payments, issuing coupons, etc., to improve transaction efficiency. Customers can query their order history, view information such as the origin of the product and the transportation process, enhancing trust.

[0054] In this embodiment, the system supports personalized user experience customization. According to the user's preference settings and past behavior habits, personalized interface layouts, recommendation lists, and promotional activities are provided, increasing user stickiness and satisfaction. The user personalized recommendation model is based on the collaborative filtering algorithm CF. By analyzing the user's historical purchase records R and the behaviors S of similar users, a recommendation matrix M is constructed: M = g(R, S), where: R represents the user's historical purchase records; S represents the behaviors of similar users; g is a recommendation generation function that comprehensively considers the above factors. Data such as the user's purchase behavior, preference settings, and social interactions are collected to establish a detailed user profile. The application interface layout is adjusted according to the user profile to highlight the products or activities that the user may be interested in, providing a more personalized shopping experience. Customized promotional information, such as limited-time discounts and member-exclusive offers, is sent to specific user groups to increase user participation.

[0055] In this embodiment, a virtual assistant application is developed in the system, allowing customers and riders to complete a series of operations such as querying, placing orders, and making payments using voice commands. By leveraging advanced natural language processing (NLP) technology, it can understand instructions in complex contexts, improving communication efficiency. The virtual assistant is equipped with an emotion recognition module that can adjust the response tone according to the conversation content, providing a more user-friendly interaction experience. An advanced speech recognition engine is integrated to convert the user's voice commands into text input, supporting multiple languages and dialects. Natural language processing technology is used to analyze the text content and extract the user's intentions, such as querying product information, placing orders, and consulting customer service. Through audio feature analysis, the user's emotional state (such as happy, angry, confused) is judged to adjust the response tone and content. A dialogue flow is designed to ensure that the virtual assistant can smoothly guide the user to complete the required operations, such as confirming order details and answering questions. It supports complex multi-round dialogue scenarios, remembers context information, and provides a coherent service experience.

[0056] In this embodiment, renewable energy is used to supply power to the system. At the same time, with the cooperation of a power management system, carbon emissions are reduced while ensuring service continuity, in response to the call for green environmental protection.

[0057] The system energy consumption model E can be expressed as:

[0058] E = h(P, T, C)

[0059] Where: P represents the power consumption of the system; T represents the running time; C represents the environmental conditions (such as light intensity, temperature, etc.); h is an energy consumption estimation function that comprehensively considers the above factors. Solar panels are installed as one of the main power sources of the system, collecting solar energy during the day and converting it into electrical energy for storage and use at night. A highly efficient power management unit is equipped to dynamically adjust the working states of various components according to actual needs, such as sleep mode, low-power mode, etc., to further save energy.

[0060] In this embodiment, the system uses high-precision indoor positioning technologies such as Bluetooth Low Energy (BLE) and Ultra-Wideband (UWB) to accurately track the movement trajectory of riders in the store, ensuring that they can be accurately guided to the target location even in complex environments. The high-precision positioning module has centimeter-level positioning accuracy and can maintain stable positioning performance in a dense crowd environment. Multiple BLE beacons are deployed inside the store to regularly broadcast signals for determining the position of mobile devices. The UWB technology is used to accurately measure the signal flight time and calculate centimeter-level positioning accuracy, which is suitable for precise positioning in high-density crowd environments. By combining the data of multiple beacons, the specific position of the rider in the store is calculated through triangulation or polygon positioning algorithms. According to the positioning information, the system adjusts the indicator light color or emits a sound prompt in real time to guide the rider to the target product location along the optimal path.

[0061] In this embodiment, a social interaction function is added to the system. For example, customers are allowed to share shopping experiences, evaluate product quality, or participate in community discussions, thereby promoting communication and interaction among users and forming a good word-of-mouth spread effect. The social interaction platform supports a UGC (User-Generated Content) review mechanism to ensure the quality and security of the published content and maintain a healthy and positive community atmosphere. Create a user-generated content (UGC) platform that allows customers to share shopping experiences, evaluate product quality, make suggestions, etc. Set up a dedicated content review team or algorithm to ensure that the published UGC complies with community norms and maintains a healthy and positive communication atmosphere. Introduce mechanisms such as point rewards and level promotions to encourage users to actively participate in social interactions and form a good word-of-mouth spread effect. Collect UGC data for analysis to understand customer needs and opinions and provide a basis for product improvement and service optimization.

[0062] In this embodiment, refer to Figure 1 , start: The user places an order through the UI.

[0063] Order reception: The order information is received by the order management module, and the inventory status is checked.

[0064] Inventory verification: If the product is in stock, the order continues; if not, the user is notified and alternative options are tried to be provided.

[0065] Payment confirmation: Once the user confirms the order, the system guides to the payment processing module to complete the transaction.

[0066] Rider assignment: After successful payment, the rider dispatch module assigns the most suitable rider based on the rider's location and availability.

[0067] Notification sending: The notification and communication module sends the order details and estimated arrival time to the rider and the user.

[0068] Rider pick-up: The rider arrives at the store and confirms the pick-up through the system.

[0069] Delivery process: The rider delivers the takeaway to the user-specified location and updates the delivery status in the system.

[0070] Delivery completed: The user receives the takeaway and can confirm the receipt or give a review through the UI.

[0071] Data collection: The data of each transaction is recorded for subsequent analysis.

[0072] Feedback loop: Optimize the service based on the results of user feedback and data analysis.

[0073] It should be noted that in this text, relational terms such as "one" and "two" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0074] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cloud store manager and cloud duty rider takeaway pickup management system, characterized by: At least one remote server to provide year-round uninterrupted operational support, including at least access control management, video surveillance, payment processing, and food delivery rider pickup management; A smart terminal device is connected to the remote server via a network cable and is adapted to Android system client access; Multiple aggregated payment interfaces, integrating third-party payment platforms, and realizing multi-channel payment functions; The intelligent LED colored light prompt module combines order information to accurately indicate the location of the goods that the takeaway rider needs to pick up, assisting in completing the self-service pickup process; Customer service module, which provides online services to help users solve questions and guide operations; Sales data interconnection system, used to collect sales data, personnel entry and exit records and store operation status monitoring information in real time, supporting data analysis and business decision-making; The AI ​​intelligent prediction and scheduling module uses machine learning algorithms to analyze multi-dimensional information such as historical order data, weather conditions, and holiday effects, predict peak hours in advance, and automatically adjust store resource allocation.

2. According to claim 1, a cloud store manager cloud duty rider takeaway pickup management system is characterized by: The system also includes an augmented reality-assisted pickup navigation function, which uses AR technology to display path guidance in three-dimensional space in real time on the rider's mobile device. The AR navigation system uses image recognition technology and environmental perception algorithms.

3. A cloud store manager cloud duty rider takeaway pickup management system according to claim 2, characterized in that: The system integrates unmanned delivery vehicles or drone delivery docking systems to realize automated cargo transportation from stores to specific locations. The unmanned delivery system has autonomous obstacle avoidance, path planning and task scheduling capabilities to ensure safe and efficient delivery services.

4. A cloud store manager cloud duty rider takeaway pickup management system according to claim 3, characterized in that: The system establishes smart shelves and smart temperature control modules. The smart shelves can sense changes in the weight of goods and update the inventory status in real time. The smart temperature control module ensures the freshness of fresh food and has smart trash cans that notify cleaning needs based on the filling level.

5. A cloud store manager cloud duty rider takeaway pickup management system according to claim 4, characterized in that: Introduce a blockchain trust module into the system to record transaction details and ensure that the information of each order is transparent and cannot be tampered with.

6. A cloud store manager cloud duty rider takeaway pickup management system according to claim 5, characterized in that: The system supports personalized user experience customization, and provides personalized interface layout, recommendation lists and promotional activities based on user preferences and past behavior habits. The user personalized recommendation model is based on the collaborative filtering algorithm CF, which constructs the recommendation matrix M by analyzing the user's historical purchase records R and the behavior S of similar users: M=g(R,S), where: R represents the user's historical purchase record; S represents the behavior of similar users; g is a recommendation generation function that comprehensively considers the above factors.

7. A cloud store manager cloud duty rider takeaway pickup management system according to claim 6, characterized in that: A virtual assistant application is developed in the system, allowing customers and riders to use voice commands to complete a series of operations such as inquiry, ordering, and payment, and using natural language processing technology to understand instructions in complex contexts. The virtual assistant is equipped with an emotion recognition module that can adjust the response tone according to the content of the conversation, providing a humanized interactive experience.

8. A cloud store manager and cloud duty rider takeaway pickup management system according to claim 7, characterized in that: The system uses renewable energy to supply power and cooperates with a power management system; The system energy consumption model E can be expressed as: E=h(P,T,C) Where: P represents the power consumption of the system; T represents the operating time; C represents the environmental conditions; and h is an energy consumption estimation function that comprehensively considers the above factors.

9. A cloud store manager cloud duty rider takeaway pickup management system according to claim 8, characterized in that: The system uses high-precision indoor positioning technology to accurately track the riders' movements in the store, ensuring that they can be accurately guided to the target location even in complex environments. The high-precision positioning module has centimeter-level positioning accuracy and can maintain stable positioning performance in dense crowd environments.

10. A cloud store manager cloud duty rider takeaway pickup management system according to claim 8, characterized in that: The social interaction function is added to the system to promote communication and interaction between users and form a good word-of-mouth communication effect. The social interaction platform supports the UGC review mechanism to ensure the quality and security of the published content and maintain a healthy and positive community atmosphere.

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