Method and system for prolonging life cycle of unmanned retail terminal

Abstract

The invention provides a method and system for prolonging the life cycle of an unmanned retail terminal, and the system comprises a data collection layer which is used for collecting the sales volume, sales volume, theft loss, maintenance, user behavior and inventory data of the unmanned retail terminal; the data processing layer is used for preprocessing, cleaning and integrating the collected data and training an unmanned retail life cycle model; the data diagnosis and analysis layer is used for evaluating the operation condition of the unmanned retail terminal equipment; the classification processing equipment terminal layer outputs a corresponding classification processing strategy according to the diagnosis analysis report; and the output and feedback layer is used for outputting a life cycle strategy report and regularly optimizing and learning an upgrading model according to a processing result. According to the invention, the life cycle model is established, data such as sales volume, sales volume, theft and loss, maintenance and the like of the unmanned retail terminal equipment are analyzed, and the equipment terminals are classified according to the analysis result, so that the life cycle of the unmanned retail terminal is prolonged, and the value return and use efficiency of the equipment are improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned retail technology, and in particular to a method and system for extending the life cycle of an unmanned retail terminal. Background Art

[0002] The development of unmanned retail technology has promoted the widespread application of unmanned retail terminals in the market; these unmanned retail terminal devices can automatically complete the commodity sales process without human intervention, greatly improving the efficiency and service level of the retail industry; however, with the increase in the number of unmanned retail terminals, some challenges and problems have also emerged, especially regarding the management and maintenance of terminal equipment.

[0003] Disadvantages of existing technology 1. Poor Management: Due to the lack of effective monitoring and management methods, many unmanned retail terminals fail to receive timely maintenance and optimization, resulting in a high equipment failure rate, affecting user experience and the normal operation of the equipment.

[0004] 2. Short equipment life cycle: Due to the existence of the above-mentioned problems, the average life cycle of unmanned retail terminals is often short and requires frequent replacement or repair, which increases operating costs and reduces the return on investment.

[0005] 3. Lack of refined management: Most of the existing unmanned retail terminal management solutions are not sophisticated enough and cannot perform targeted maintenance and improvements based on the actual operating conditions of the equipment.

[0006] 4. Low data utilization: The large amount of data generated by unmanned retail terminals has not been fully utilized. These data include sales volume, sales revenue, theft, loss and repair records. If these data can be effectively utilized, we can better understand the operation of the equipment and make corresponding management decisions.

[0007] 5. Lack of intelligent decision support: The current management solution lacks the support of intelligent tools and cannot automatically make decisions and adjustments based on data analysis results, which limits the efficiency and effectiveness of unmanned retail terminal equipment management.

[0008] Therefore, the prior art has deficiencies and needs further improvement. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides a method and system for extending the life cycle of an unmanned retail terminal.

[0010] To achieve the above object, the specific scheme of the present invention is as follows: The present invention provides a system for extending the life cycle of an unmanned retail terminal, the system comprising: The data collection layer is used to collect sales volume, sales revenue, theft, repair, user behavior, and inventory data of unmanned retail terminals; The data processing layer is used to pre-process, clean, and integrate the collected data and train the unmanned retail lifecycle model; The data diagnosis and analysis layer is used to evaluate the operation and business status of unmanned retail terminal equipment and output diagnosis and analysis reports; The classification processing device terminal layer is used to output the corresponding classification processing strategy according to the diagnosis analysis report; The output and feedback layer is used to output lifecycle policy reports and regularly optimize and learn to upgrade the model based on the processing results.

[0011] Furthermore, the data collection layer further includes: Sensors installed on unmanned retail terminals are used to collect equipment operation status data in real time; The data collection and transmission module is used to upload the collected data to the cloud server.

[0012] Furthermore, the data processing layer also includes: Feature extraction module, used to extract key features from preprocessed data; Model training module, used to train the unmanned retail lifecycle model using key features; The adaptive learning module is used to automatically adjust data processing and model training strategies to adapt to market changes and changes in unmanned retail terminal equipment.

[0013] Furthermore, the data diagnosis and analysis layer further includes: An equipment operation status evaluation module is used to evaluate the operation status of the equipment; Sales trend evaluation module, used to evaluate sales trends; User portrait building module, used to build user portraits; Inventory turnover rate evaluation module, used to evaluate inventory turnover rate; The remote diagnosis module based on the Internet of Things is used to automatically send early warning signals and provide preliminary diagnostic suggestions when abnormalities occur in unmanned retail terminals.

[0014] Furthermore, the classification processing device terminal layer also includes: An automatic matching module, used to automatically match classification processing strategies according to the diagnosis and analysis report; A strategy output module is used to output a classification processing strategy report; An artificial intelligence chatbot module that automatically interacts with customers, collects feedback, and adjusts the service quality of unmanned retail terminal equipment based on customer feedback; The output and feedback layer also includes: Model optimization module, used to regularly collect and process data and update and optimize the model online; The intelligent decision support module provides decision support and suggestions for operators of unmanned retail terminals based on big data analysis and prediction.

[0015] The present invention also provides a method for extending the life cycle of an unmanned retail terminal. Based on the above system, the method comprises the following steps: S1, collect data related to the life cycle of unmanned retail terminals, including sales volume, sales revenue, theft, damage and repair data, and operating status; S2, processing the collected data, including preprocessing, extracting key features, using the key features to train the unmanned retail life cycle model, and outputting the terminal life cycle report; S3, conduct data diagnosis and analysis, evaluate the operation and business status of unmanned retail terminal equipment, and output a diagnosis and analysis report; S4, classification processing equipment terminal, outputs corresponding classification processing strategies according to the diagnosis and analysis report, including: moving points, removing shelves and optimizing and improving; S5, performs output and feedback, outputs lifecycle strategy reports, and regularly optimizes and learns to upgrade the model based on the processing results.

[0016] Furthermore, in step S1, collecting data specifically includes: Collect daily / weekly / monthly / quarterly order sales, sales, theft, loss and repair data of unmanned retail terminal equipment; Sensors installed on unmanned retail terminals collect equipment operating status data in real time; The data collection and transmission module uploads the collected data to the cloud server; Collect ordering user behavior and order sales data; Collect the inventory in and out data of each "inventory replenishment" of the equipment; Collect all equipment repair data; In step S2, data processing specifically includes: Preprocess, clean and integrate the collected data; Extract key features from the preprocessed data; Using key features to train the unmanned retail lifecycle model; Automatically adjust data processing and model training strategies to adapt to market changes and changes in unmanned retail terminal equipment.

[0017] Furthermore, in step S3, the data diagnosis and analysis specifically includes: Assess the operating condition of the equipment; Evaluate sales trends; Build user portraits; Evaluate inventory turnover; When an abnormality occurs at an unmanned retail terminal, it automatically sends an early warning signal and provides preliminary diagnostic suggestions.

[0018] Furthermore, in step S4, the classification processing device terminal specifically includes: Automatically match classification and processing strategies based on diagnostic analysis reports; Output classification processing strategy report; By moving the equipment terminals with low returns, low theft and loss rates, and low maintenance rates to new locations; Remove equipment terminals with low returns, high theft and loss rates, and high maintenance rates and make improvements and optimizations; Regularly initiate inspection and maintenance tasks for equipment terminals with high returns, high theft and loss rates, and high maintenance rates; Use augmented reality to show maintenance personnel the exact location of equipment problems and recommend solutions; The specific processing of moving point includes: Through diagnosis and analysis of the reasons for low returns, such as low customer flow; The system automatically matches high-value new customer sites in nearby areas; Deploy equipment terminals to new sites to reduce site costs, labor maintenance costs, and logistics costs of low-return terminals, thereby increasing the value return of the equipment; The specific processing of the withdrawal includes: Automatically initiate shelf removal through the diagnostic analysis system to reduce site expenses and labor maintenance costs; In-depth analysis of the causes of high theft and high maintenance rates, targeted improvements and optimizations to extend the life cycle of similar high-value equipment terminals.

[0019] Furthermore, in step S5, the output and feedback specifically include: Output terminal life cycle report; Regularly collect and process data and update and optimize the model online; Provide decision support and suggestions to operators of unmanned retail terminals based on big data analysis and prediction; Collect feedback through automatic interaction with customers and adjust the service quality of unmanned retail terminal equipment based on customer feedback; Regular optimization and learning upgrade models include: Continuously accumulate data on unmanned retail terminals; Continuously optimize the unmanned retail life cycle model to improve the efficiency and value return of equipment; Analyze and identify potential problems and improvement points to improve the stability and reliability of the equipment.

[0020] The technical solution of the present invention has the following beneficial effects: 1. Significantly improve equipment stability and reliability: By collecting and analyzing data such as order sales, sales, theft, repairs, and inventory of unmanned retail terminals, we can fully understand the operating status of the equipment. Based on this, we classify the equipment and take different maintenance and management measures for different types of equipment, thereby significantly improving the stability and reliability of the equipment and reducing losses caused by equipment failures.

[0021] 2. Effectively extend the equipment life cycle: The present invention classifies and processes unmanned retail terminals of different types and conditions by means of relocation, shelf removal, and optimization and improvement, thereby effectively extending the life cycle of the equipment. For equipment terminals with low returns, low theft and loss rates, and low maintenance rates, they are placed in more suitable locations by relocation, thereby increasing the value return of the equipment; for equipment terminals with low returns, high theft and loss rates, and high maintenance rates, unnecessary losses are reduced by removing the shelves and improving and optimizing; for equipment terminals with high returns, high theft and loss rates / high maintenance rates, regular inspections, investigations, and maintenance stabilize the high returns and extend the life cycle of the equipment.

[0022] 3. Reduce operating costs and improve economic benefits: The implementation of the present invention can not only extend the life cycle of unmanned retail terminals, but also reduce operating costs through precise equipment maintenance and management. By reducing unnecessary repair and replacement costs, improving the stability and reliability of equipment, reducing losses and customer complaints caused by equipment failures, improving customer satisfaction and brand image, and ultimately bringing higher economic benefits to merchants.

[0023] 4. Promote the sustainable development of the unmanned retail industry: The application of this invention will promote the technological progress and industrial upgrading of the unmanned retail industry, and by extending the life cycle of unmanned retail terminals, improve the efficiency and value return of equipment. This will further promote the popularization and development of the unmanned retail industry and bring positive impact to the entire industry.

[0024] 5. Realize refined management: The method of the present invention enables unmanned retail terminals to achieve refined management, classify and process equipment according to their actual operating conditions, and improve the value return and utilization efficiency of the equipment.

[0025] 6. Continuous learning and optimization: The system of the present invention can continuously optimize the life cycle model of unmanned retail terminals through continuously accumulated data, thereby improving the efficiency and value return of the equipment. By analyzing and discovering potential problems and improvement points, the stability and reliability of the equipment are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a system principle block diagram of the present invention; Figure 2 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments; it can be understood that the specific embodiments described herein are only used to explain the present invention rather than to limit the present invention; it should also be noted that, for ease of description, only the parts related to the present invention rather than all are shown in the accompanying drawings.

[0028] Combination Figure 1-Figure 2 As shown, the present invention provides a system for extending the life cycle of an unmanned retail terminal, the system comprising: The data collection layer is used to collect sales volume, sales revenue, theft, repair, user behavior, and inventory data of unmanned retail terminals; The data processing layer is used to pre-process, clean, and integrate the collected data and train the unmanned retail lifecycle model; The data diagnosis and analysis layer is used to evaluate the operation and business status of unmanned retail terminal equipment and output diagnosis and analysis reports; The classification processing device terminal layer is used to output the corresponding classification processing strategy according to the diagnosis analysis report; The output and feedback layer is used to output lifecycle policy reports and regularly optimize and learn to upgrade the model based on the processing results.

[0029] The data collection layer further includes: Sensors installed on unmanned retail terminals are used to collect equipment operation status data in real time; The data collection and transmission module is used to upload the collected data to the cloud server.

[0030] The data processing layer also includes: Feature extraction module, used to extract key features from preprocessed data; Model training module, used to train the unmanned retail lifecycle model using key features; The adaptive learning module is used to automatically adjust data processing and model training strategies to adapt to market changes and changes in unmanned retail terminal equipment.

[0031] The data diagnosis and analysis layer further includes: An equipment operation status evaluation module is used to evaluate the operation status of the equipment; Sales trend evaluation module, used to evaluate sales trends; User portrait building module, used to build user portraits; Inventory turnover rate evaluation module, used to evaluate inventory turnover rate; The remote diagnosis module based on the Internet of Things is used to automatically send early warning signals and provide preliminary diagnostic suggestions when abnormalities occur in unmanned retail terminals.

[0032] The classification processing equipment terminal layer also includes: An automatic matching module, used to automatically match classification processing strategies according to the diagnosis and analysis report; A strategy output module is used to output a classification processing strategy report; An artificial intelligence chatbot module that automatically interacts with customers, collects feedback, and adjusts the service quality of unmanned retail terminal equipment based on customer feedback; The output and feedback layer also includes: Model optimization module, used to regularly collect and process data and update and optimize the model online; The intelligent decision support module provides decision support and suggestions for operators of unmanned retail terminals based on big data analysis and prediction.

[0033] The present invention also provides a method for extending the life cycle of an unmanned retail terminal. Based on the above system, the method comprises the following steps: S1, collect data related to the life cycle of unmanned retail terminals, including sales volume, sales revenue, theft, damage and repair data, and operating status; S2, processing the collected data, including preprocessing, extracting key features, using the key features to train the unmanned retail life cycle model, and outputting the terminal life cycle report; S3, conduct data diagnosis and analysis, evaluate the operation and business status of unmanned retail terminal equipment, and output a diagnosis and analysis report; S4, classification processing equipment terminal, outputs corresponding classification processing strategies according to the diagnosis and analysis report, including: moving points, removing shelves and optimizing and improving; S5, performs output and feedback, outputs lifecycle strategy reports, and regularly optimizes and learns to upgrade the model based on the processing results.

[0034] In step S1, collecting data specifically includes: Collect daily / weekly / monthly / quarterly order sales, sales, theft, loss and repair data of unmanned retail terminal equipment; Sensors installed on unmanned retail terminals collect equipment operating status data in real time; The data collection and transmission module uploads the collected data to the cloud server; Collect ordering user behavior and order sales data; Collect the inventory in and out data of each "inventory replenishment" of the equipment; Collect all equipment repair data; In step S2, data processing specifically includes: Preprocess, clean and integrate the collected data; Extract key features from the preprocessed data; Using key features to train the unmanned retail lifecycle model; Automatically adjust data processing and model training strategies to adapt to market changes and changes in unmanned retail terminal equipment.

[0035] In step S3, the data diagnosis and analysis specifically includes: Assess the operating condition of the equipment; Evaluate sales trends; Build user portraits; Evaluate inventory turnover; When an abnormality occurs at an unmanned retail terminal, it automatically sends an early warning signal and provides preliminary diagnostic suggestions.

[0036] In step S4, the classification processing device terminal specifically includes: Automatically match classification and processing strategies based on diagnostic analysis reports; Output classification processing strategy report; By moving the equipment terminals with low returns, low theft and loss rates, and low maintenance rates to new locations; Remove equipment terminals with low returns, high theft and loss rates, and high maintenance rates and make improvements and optimizations; Regularly initiate inspection and maintenance tasks for equipment terminals with high returns, high theft and loss rates, and high maintenance rates; Use augmented reality to show maintenance personnel the exact location of equipment problems and recommend solutions; The specific processing of moving point includes: Through diagnosis and analysis of the reasons for low returns, such as low customer flow; The system automatically matches high-value new customer sites in nearby areas; Deploy equipment terminals to new sites to reduce site costs, labor maintenance costs, and logistics costs of low-return terminals, thereby increasing the value return of the equipment; The specific processing of the withdrawal includes: Automatically initiate shelf removal through the diagnostic analysis system to reduce site expenses and labor maintenance costs; In-depth analysis of the causes of high theft and high maintenance rates, targeted improvements and optimizations to extend the life cycle of similar high-value equipment terminals.

[0037] In step S5, the output and feedback specifically include: Output terminal life cycle report; Regularly collect and process data and update and optimize the model online; Provide decision support and suggestions to operators of unmanned retail terminals based on big data analysis and prediction; Collect feedback through automatic interaction with customers and adjust the service quality of unmanned retail terminal equipment based on customer feedback; Regular optimization and learning upgrade models include: Continuously accumulate data on unmanned retail terminals; Continuously optimize the unmanned retail life cycle model to improve the efficiency and value return of equipment; Analyze and identify potential problems and improvement points to improve the stability and reliability of the equipment. Specific embodiment: Step 1: Establish a model to extend the life cycle of unmanned retail terminals: Collect daily / weekly / monthly / quarterly order sales, sales, theft, damage, and repair data of unmanned retail terminal equipment, conduct comparative analysis with similar terminals in the same region, and output terminal life cycle reports through mathematical models and algorithms. The report contains information such as the equipment's operating status, return status, theft, damage, and repair status, providing a basis for subsequent classification processing.

[0039] Step 2: Diagnosis, analysis and classification: (1) Equipment terminals with low returns, low theft and loss rates, and low maintenance rates: For such equipment terminals, the system automatically matches high-value new customer sites in nearby areas by diagnosing and analyzing the reasons for low returns, such as low customer flow. By "moving" the equipment terminals to new sites, the site costs, labor maintenance costs and logistics costs of low-return terminals are reduced, thereby increasing the value return of the equipment.

[0040] (2) Classify and handle equipment terminals with low returns, high theft and loss rates, and high maintenance rates: For such equipment terminals, the diagnostic analysis system automatically initiates "removal of shelves" to reduce site costs and labor maintenance costs. At the same time, the reasons for high theft and high maintenance are deeply analyzed, and improvements and optimizations are made to the problems to extend the life cycle of similar high-value equipment terminals.

[0041] (3) Classify and handle equipment terminals with high returns, high theft and loss rates, and high maintenance rates: For equipment terminals with high returns but high theft or maintenance rates, we automatically initiate regular inspections and maintenance tasks through diagnostic analysis to promptly identify and address potential problems. At the same time, we analyze the causes of high theft and maintenance, and make improvements and optimizations to stabilize high returns and extend the life cycle of unmanned retail terminal equipment.

[0042] Step 3: Continuous learning and optimization: (1) By continuously accumulating data from unmanned retail terminals, the system can continuously optimize the unmanned retail life cycle model, thereby improving the efficiency of equipment use and value return; (2) Through analysis, discover potential problems and improvement points to improve the stability and reliability of equipment; It has the following advantages: 1. Significantly improve equipment stability and reliability The present invention can fully understand the operating status of the equipment by collecting and analyzing the order sales, sales, theft, repair and inventory data of unmanned retail terminals. On this basis, the equipment is classified and different maintenance and management measures are taken for different types of equipment. This precise management method can timely discover and solve potential problems of the equipment, thereby significantly improving the stability and reliability of the equipment and reducing losses caused by equipment failure.

[0043] 2. Effectively extend the life cycle of equipment Through the implementation of the present invention, the life cycle of unmanned retail terminals is effectively extended. For equipment terminals with low returns, low theft and loss rates, and low maintenance rates, they are moved to more suitable locations, which increases the value return of the equipment and extends the equipment's service life. For equipment terminals with low returns, high theft and loss rates, and high maintenance rates, unnecessary losses are reduced and the overall performance and service life of similar equipment are improved through the removal of shelves and improvement and optimization. For equipment terminals with high returns, high theft and loss rates / high maintenance rates, regular inspections, investigations, and maintenance stabilize the high returns and extend the equipment's life cycle.

[0044] 3. Reduce operating costs and improve economic benefits The implementation of the present invention not only extends the life cycle of unmanned retail terminals, but also reduces the company's operating costs. Through precise equipment maintenance and management, unnecessary repair and replacement costs are reduced. At the same time, by improving the stability and reliability of equipment, losses and customer complaints caused by equipment failures are reduced, and customer satisfaction and brand image are improved. These measures work together to bring higher economic benefits to merchants.

[0045] 4. Promote the sustainable development of the unmanned retail industry The promotion and application of this invention will have a positive driving effect on the unmanned retail industry, by extending the life cycle of unmanned retail terminals, improving the efficiency and value return of equipment. This will further promote the popularization and development of the unmanned retail industry and promote technological progress and industrial upgrading of the entire industry.

[0046] The schematic diagram of the patent structure mainly shows the overall process and key components of a method for extending the life cycle of unmanned retail terminals. The entire process covers a complete closed loop from data collection, data processing, classification processing terminal equipment, output and feedback layer.

[0047] 2. Structural Layering 1. Data collection layer: collects data such as "sales volume, sales revenue, theft and loss, repair, user behavior, inventory, etc." of unmanned retail terminal equipment; 2. Data processing layer (1) Preprocess, clean and integrate the collected data, and process the data by time, device terminal, region, type, etc.; (2) Feature extraction and model training module: extract key features from the preprocessed data and use these features to train the unmanned retail lifecycle model; 3. Data diagnosis and analysis layer (1) Conduct multi-dimensional diagnostic analysis of data to evaluate the equipment operation and management status of unmanned retail terminals, including equipment operation status, customer status, sales trends, user portraits, and inventory turnover rate; (2) Output diagnostic analysis report; 4. Classification and processing equipment terminal layer (1) Output the unmanned retail terminal life cycle report based on the diagnostic analysis report, and automatically match the corresponding classification processing device terminal strategy; (2) Output the unmanned retail terminal life cycle device terminal report; 5. Output and feedback layer (1) Output unmanned retail terminal life cycle strategy report ① Equipment terminal processing strategy of "low return, low theft and loss rate, and low maintenance rate"; ② Equipment terminal processing strategy of "low return, high theft rate, high maintenance rate"; ③ Equipment terminal processing strategy of "high return, high theft and loss rate, high maintenance rate"; (2) Regularly collect processing data and optimize and learn to upgrade the model based on the execution of the processing strategy; The implementation process includes the following steps: 1. Collect data: (1) Equipment operation status data: Install sensors on unmanned retail terminal equipment and configure data collection and transmission modules to collect equipment operation status data in real time and upload it to the cloud server; (2) Equipment operation and behavior data: collect data such as ordering user behavior and order sales; (3) Equipment theft and damage data: collect the inventory in and out data of each equipment "inventory replenishment"; (4) Equipment maintenance data: collect all equipment repair data; 2. Establish a model to extend the life cycle of unmanned retail: extract key features from the preprocessed data and use these features to train the unmanned retail life cycle model; 3. Data diagnosis and analysis: Conduct multi-dimensional diagnosis and analysis of data, evaluate the equipment operation and management of unmanned retail terminals, and output diagnostic analysis reports; 4. Classification and processing equipment terminal: automatically match the corresponding classification and processing equipment terminal strategy according to the data diagnosis and analysis report; 5. Continuous learning and optimization: Collect and process new data regularly or according to actual needs, and update and optimize the unmanned retail life cycle model online; Working principle: The present invention relates to a method and system for extending the life cycle of an unmanned retail terminal, and its working principle mainly includes the following steps: Data collection: Collect daily / weekly / monthly / quarterly order sales, sales, theft, damage, and repair data of unmanned retail terminals; The collected data includes equipment operating status data, equipment operation and behavior data, equipment theft and damage data, equipment maintenance data, etc.

[0048] Build a life cycle model: Use the collected data to build a lifecycle model that includes comparative analysis of data from similar terminals in the same region; The data is processed through mathematical models and algorithms to output a terminal life cycle report, which contains information such as the equipment's operating status, feedback, theft and damage, and maintenance status, providing a basis for subsequent classification processing.

[0049] Diagnostic analysis and classification processing: Classify and process unmanned retail terminals according to the reports output by the lifecycle model, including strategies such as relocation, shelf removal, and optimization and improvement; Take different management measures for different types of equipment, for example: For equipment terminals with low returns, low theft and loss rates, and low maintenance rates, they can be moved to new locations with higher value to increase the value return of the equipment. For equipment terminals with low returns, high theft and loss rates, and high maintenance rates, we can reduce losses by removing them from shelves, and make improvements and optimizations based on the problems; For equipment terminals with high returns, high theft and loss rates, and high maintenance rates, regular inspections, investigations, and maintenance can be performed to stabilize high returns and extend the life cycle of unmanned retail terminal equipment.

[0050] Continuous learning and optimization: Continuously accumulate data from unmanned retail terminals, continuously optimize lifecycle models, and improve equipment efficiency and value return; Through analysis, potential problems and improvement points are discovered to improve the stability and reliability of the equipment.

[0051] Specific steps Establish a model to extend the life cycle of unmanned retail terminals: Collect daily / weekly / monthly / quarterly order sales, sales, theft, loss, and repair data of unmanned retail terminal equipment; Conduct comparative analysis with similar terminals in the same region, and output terminal life cycle reports through mathematical models and algorithms.

[0052] Diagnostic analysis, classification and processing: Relocate equipment terminals with low returns, low theft and loss rates, and low maintenance rates; Equipment terminals with low returns, high theft and loss rates, and high maintenance rates will be removed from the shelves, and improvements and optimizations will be made based on the problems; Regular inspections, checks and maintenance are carried out on equipment terminals with high returns, high theft and loss rates, and high maintenance rates, and improvements and optimizations are made based on the problems.

[0053] Continuous learning and optimization: Continuously accumulate data from unmanned retail terminals, continuously optimize lifecycle models, and improve equipment efficiency and value return; Through analysis, potential problems and improvement points are discovered to improve the stability and reliability of the equipment.

[0054] Through the above steps, the present invention can achieve refined management of unmanned retail terminal equipment, improve the value return and use efficiency of the equipment, and thus extend the life cycle of the unmanned retail terminal.

[0055] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the protection scope of the present invention.

Claims

1. A system for extending the life cycle of an unmanned retail terminal, characterized in that: The system includes: The data collection layer is used to collect sales volume, sales revenue, theft, repair, user behavior, and inventory data of unmanned retail terminals; The data processing layer is used to pre-process, clean, and integrate the collected data and train the unmanned retail lifecycle model; The data diagnosis and analysis layer is used to evaluate the operation and business status of unmanned retail terminal equipment and output diagnosis and analysis reports; The classification processing device terminal layer is used to output the corresponding classification processing strategy according to the diagnosis analysis report; The output and feedback layer is used to output lifecycle policy reports and regularly optimize and learn to upgrade the model based on the processing results.

2. The system according to claim 1, characterized in that The data collection layer further includes: Sensors installed on unmanned retail terminals are used to collect equipment operation status data in real time; The data collection and transmission module is used to upload the collected data to the cloud server.

3. The system according to claim 1, characterized in that The data processing layer also includes: Feature extraction module, used to extract key features from preprocessed data; Model training module, used to train the unmanned retail lifecycle model using key features; The adaptive learning module is used to automatically adjust data processing and model training strategies to adapt to market changes and changes in unmanned retail terminal equipment.

4. The system according to claim 1, characterized in that The data diagnosis and analysis layer further includes: An equipment operation status evaluation module is used to evaluate the operation status of the equipment; Sales trend evaluation module, used to evaluate sales trends; User portrait building module, used to build user portraits; Inventory turnover rate evaluation module, used to evaluate inventory turnover rate; The remote diagnosis module based on the Internet of Things is used to automatically send early warning signals and provide preliminary diagnostic suggestions when abnormalities occur in unmanned retail terminals.

5. The system according to claim 1, characterized in that The classification processing equipment terminal layer also includes: An automatic matching module, used to automatically match classification processing strategies according to the diagnosis and analysis report; A strategy output module is used to output a classification processing strategy report; An artificial intelligence chatbot module that automatically interacts with customers, collects feedback, and adjusts the service quality of unmanned retail terminal equipment based on customer feedback; The output and feedback layer also includes: Model optimization module, used to regularly collect and process data and update and optimize the model online; The intelligent decision support module provides decision support and suggestions for operators of unmanned retail terminals based on big data analysis and prediction.

6. A method for extending the life cycle of an unmanned retail terminal, based on the system according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: S1, collect data related to the life cycle of unmanned retail terminals, including sales volume, sales revenue, theft, damage and repair data, and operating status; S2, processing the collected data, including preprocessing, extracting key features, using the key features to train the unmanned retail life cycle model, and outputting the terminal life cycle report; S3, conduct data diagnosis and analysis, evaluate the operation and business status of unmanned retail terminal equipment, and output a diagnosis and analysis report; S4, classification processing equipment terminal, outputs corresponding classification processing strategies according to the diagnosis and analysis report, including: moving points, removing shelves and optimizing and improving; S5, performs output and feedback, outputs lifecycle strategy reports, and regularly optimizes and learns to upgrade the model based on the processing results.

7. The method according to claim 6, characterized in that In step S1, collecting data specifically includes: Collect daily / weekly / monthly / quarterly order sales, sales, theft, loss and repair data of unmanned retail terminal equipment; Sensors installed on unmanned retail terminals collect equipment operating status data in real time; The data collection and transmission module uploads the collected data to the cloud server; Collect ordering user behavior and order sales data; Collect the inventory in and out data of each "inventory replenishment" of the equipment; Collect all equipment repair data; In step S2, data processing specifically includes: Preprocess, clean and integrate the collected data; Extract key features from the preprocessed data; Using key features to train the unmanned retail lifecycle model; Automatically adjust data processing and model training strategies to adapt to market changes and changes in unmanned retail terminal equipment.

8. The method according to claim 6, characterized in that In step S3, the data diagnosis and analysis specifically includes: Assess the operating condition of the equipment; Evaluate sales trends; Build user portraits; Evaluate inventory turnover; When an abnormality occurs at an unmanned retail terminal, it automatically sends an early warning signal and provides preliminary diagnostic suggestions.

9. The method according to claim 6, characterized in that In step S4, the classification processing device terminal specifically includes: Automatically match classification and processing strategies based on diagnostic analysis reports; Output classification processing strategy report; By moving the equipment terminals with low returns, low theft and loss rates, and low maintenance rates to new locations; Remove equipment terminals with low returns, high theft and loss rates, and high maintenance rates and make improvements and optimizations; Regularly initiate inspection and maintenance tasks for equipment terminals with high returns, high theft and loss rates, and high maintenance rates; Use augmented reality to show maintenance personnel the exact location of equipment problems and recommend solutions; The specific processing of moving point includes: Through diagnosis and analysis of the reasons for low returns, such as low customer flow; The system automatically matches high-value new customer sites in nearby areas; Deploy equipment terminals to new sites to reduce site costs, labor maintenance costs, and logistics costs of low-return terminals, thereby increasing the value return of the equipment; The specific processing of the withdrawal includes: Automatically initiate shelf removal through the diagnostic analysis system to reduce site expenses and labor maintenance costs; In-depth analysis of the causes of high theft and high maintenance rates, targeted improvements and optimizations to extend the life cycle of similar high-value equipment terminals.

10. The method according to claim 6, characterized in that In step S5, the output and feedback specifically include: Output terminal life cycle report; Regularly collect and process data and update and optimize the model online; Provide decision support and suggestions to operators of unmanned retail terminals based on big data analysis and prediction; Collect feedback through automatic interaction with customers and adjust the service quality of unmanned retail terminal equipment based on customer feedback; Regular optimization and learning upgrade models include: Continuously accumulate data on unmanned retail terminals; Continuously optimize the unmanned retail life cycle model to improve the efficiency and value return of equipment; Analyze and identify potential problems and improvement points to improve the stability and reliability of the equipment.

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