Operation work order generation method for unmanned retail terminal

Abstract

The invention provides an operation work order generation method for an unmanned retail terminal, and the method comprises the steps: employing an operation work order generation server, pre-storing a historical operation work order in the operation work order generation server, carrying out the work order analysis of the consumption data corresponding to each region and the environment data corresponding to each region through the operation work order generation server, and carrying out the calculation of the work order. Analyzing to obtain a matching value corresponding to the region; according to the matching value corresponding to the area, the unmanned retail terminal of the area analyzes a service demand index corresponding to the current matching value of the area; analyzing a current goods demand index of the area according to the service demand index corresponding to the area; and obtaining the historical data processing efficiency of each work order in a preset area of the unmanned retail terminal, and calculating the service demand time corresponding to the service area of each work order according to the historical data processing efficiency of each work order and the service demand quantity corresponding to the area corresponding to the service demand index corresponding to the service area of each work order.

Description

Technical Field

[0001] The present invention belongs to the field of terminal operation, and in particular relates to a method for generating an operation work order for an unmanned retail terminal. Background Art

[0002] In the modern retail industry, the rise of unmanned retail terminals provides consumers with a convenient shopping experience. However, the operation and management of this emerging field faces some technical bottlenecks, especially in the generation of operation work orders. Existing unmanned retail terminals usually rely on traditional manual processes to manage inventory, equipment maintenance and sales records, which significantly affects operational efficiency.

[0003] Most unmanned retail systems lack an effective automated work order generation mechanism. Many existing systems still rely on manual monitoring and recording of equipment status and inventory. This approach is not only prone to human error, but also causes a lag in data updates, promotes asymmetric management information, and fails to reflect the actual operating status of equipment and product inventory levels in real time. This delayed response mechanism prevents operators from taking timely measures when equipment fails or inventory is short, thus affecting customers' shopping experience and sales performance.

[0004] The existing system has defects in data integration and analysis. Although unmanned retail terminals can collect a large amount of data (including sales data, customer behavior, and equipment status, etc.), this data is often stored in isolation and lacks comprehensive analysis and processing capabilities. This situation hinders the operator's ability to fully understand the terminal's operating conditions and fails to realize the potential of data-driven decision-making. The isolation of information makes it difficult to grasp business trends from a macro perspective, and it is difficult for operators to make reasonable predictions and adjustments based on real-time data, which affects the flexibility and adaptability of the business.

[0005] Customer service lacks a direct feedback mechanism. In an unmanned retail environment, consumers cannot get timely help and support from traditional service staff, which makes it difficult for them to communicate effectively when they encounter problems. This lack of interaction means that customer problems may not be solved for a long time, further leading to a negative customer experience. In addition, if the system fails to respond quickly to customer feedback and complaints, it will damage the brand image and ultimately affect customer loyalty.

[0006] There are also deficiencies in technical support and maintenance. Many unmanned retail terminals still rely on traditional manual inspections for equipment maintenance and updates. The lack of intelligent maintenance solutions makes it impossible to quickly locate and resolve equipment failures. The lag in technical services leads to extended system downtime, which not only affects sales revenue but may also lead to customer loss. These shortcomings emphasize the need to build a more intelligent and automated operation work order generation system. By integrating artificial intelligence and IoT technologies, it is possible to monitor equipment status in real time, automatically generate maintenance work orders, and optimize inventory management, thereby improving overall operational efficiency and customer satisfaction. Summary of the invention

[0007] The present invention proposes a method for generating operating work orders for unmanned retail terminals. This method for generating operating work orders for unmanned retail terminals solves the problems of untimely work order generation and inaccurate data analysis in traditional retail management. Through comprehensive analysis of regional consumption data and environmental data, intelligent generation of work orders based on real-time needs is realized, thereby improving operational efficiency and response speed.

[0008] The technical solution of the present invention is implemented as follows: a method for generating an operation work order for an unmanned retail terminal, comprising an operation work order generation server, the operation work order generation server pre-stores historical operation work orders, and the operation work order generation server performs work order analysis within the region on consumption data corresponding to each region and environmental data corresponding to the region, and obtains matching values ​​corresponding to the region through analysis;

[0009] According to the matching value corresponding to the area, the unmanned retail terminal in the area analyzes the service demand index corresponding to the current matching value of the area; according to the service demand index corresponding to the area, the current product demand index of the area is analyzed;

[0010] Obtain the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal, and calculate the service demand time corresponding to the service area of ​​each work order based on the historical data processing efficiency of each work order and the number of service demands corresponding to the area corresponding to the service demand index corresponding to the service area of ​​each work order;

[0011] Sort the service demand time corresponding to the service area of ​​each work order to obtain the service time corresponding to each work order; classify the work orders according to the service time corresponding to each work order, obtain the work order group corresponding to each service time, and sort the work order group according to the service time;

[0012] The step of performing work order analysis on the region according to the historical operation work order, the consumption data corresponding to the region and the environment data corresponding to the region, and obtaining the matching value corresponding to the region through analysis includes: obtaining the historical consumption data and the historical environment data of the region between the last time the operation work order was generated in the region and the analysis time;

[0013] Obtain regional historical consumption data, and obtain regional historical environmental change data from regional environmental data through historical consumption data; integrate regional historical environmental data through neural networks to obtain regional historical environmental change data; integrate regional historical environmental change data through neural networks based on regional historical consumption data to obtain environmental analysis data corresponding to regional historical environmental change data;

[0014] The current regional environmental data is integrated through a neural network to obtain the current regional environmental statistical data; the current regional environmental statistical data is analyzed, and the change parameters of the regional consumption data are analyzed; the regional change parameters are compared with the environmental analysis data corresponding to the regional historical environmental change data to obtain the corresponding matching value of the region.

[0015] Compared with the existing technology, this method of generating operational work orders shows many significant differences and innovations. Traditional work order generation systems usually rely on manual input and fixed processes, and lack the ability to dynamically respond to real-time data. In the traditional model, operators often need to manually generate work orders based on experience, which easily causes delays in work order generation and makes it difficult to cope with rapidly changing market demands. This system uses an operational work order generation server to combine and analyze historical operational work orders with consumption and environmental data corresponding to the region, and can generate work orders for specific regions in real time. This real-time and dynamic nature enables the system to quickly respond to consumer demand and environmental changes, greatly improving the efficiency of work order generation.

[0016] Existing technologies often rely on simple statistical methods in data analysis and are unable to deeply explore the potential patterns behind the data. Many systems only summarize historical data and lack intelligent analysis methods, which makes it impossible to accurately identify the service and product needs of the current region. However, this system uses neural network technology to obtain more accurate regional matching values ​​through the deep integration of historical consumption data and environmental change data. This artificial intelligence-based analysis method improves the accuracy and intelligence level of data processing, allowing the system to better adapt to complex market environments.

[0017] Traditional work order classification and sorting often rely on static rules, lack flexibility, and cannot adapt to the differences in demand in different regions. This method ensures the reasonable allocation and timely processing of work orders by dynamically calculating and sorting the service time of work orders. The system can intelligently adjust the priority of work orders based on the service demand index and historical data processing efficiency of each work order, thereby improving the utilization efficiency of resources. This work order management method based on dynamic data significantly improves the problem of resource waste in traditional systems.

[0018] Existing retail management systems often lack effective monitoring methods when dealing with environmental changes, and are unable to accurately assess the impact of environmental factors on consumer behavior. This system comprehensively monitors and analyzes environmental changes by acquiring regional historical environmental data and real-time environmental statistics, which enables the system to adjust operating strategies in a timely manner, optimize product configuration, and improve user experience. This comprehensive environmental analysis capability is not common in traditional systems and provides support for the flexible operation of unmanned retail.

[0019] The design of this system emphasizes the combination of intelligence and automation, which can effectively reduce labor costs and improve management efficiency. Traditional retail systems often require a large amount of manpower to generate and manage work orders, but this system can greatly reduce manual intervention and improve work efficiency through the application of intelligent algorithms. This increase in automation allows merchants to focus more on optimizing operational strategies and improving customer experience.

[0020] As a preferred implementation mode, according to the matching value corresponding to the area, the unmanned retail terminal of the area analyzes the service demand index corresponding to the current matching value of the area; the analysis steps of the unmanned retail terminal are: first, obtain the environmental data of the time area when the unmanned retail terminal analyzes and the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order; according to the number of goods corresponding to the area in the historical consumption data of the area; through a neural network, integrate the historical consumption data of the area and the number of goods corresponding to the area in the historical consumption data of the area to obtain the economic variables corresponding to the historical consumption data of the area; according to the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order, analyze the historical operation work order; analyze the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order and the service demand index corresponding to the current regional matching value; integrate the service demand index corresponding to the time area when the unmanned retail terminal generates the historical operation work order, the economic variables corresponding to the historical consumption data of the area and the service demand index corresponding to the current regional matching value to obtain the service demand index corresponding to the current matching value of the area.

[0021] As a preferred implementation, the steps of analyzing the current product demand index of a region by the service demand index corresponding to the region are:

[0022] Obtain the product data of the unmanned retail terminal in the unmanned retail terminal analysis time area; obtain the product data of the unmanned retail terminal in the preset historical time area of ​​the area, and the preset historical time area is the time when the operation work order is generated;

[0023] By analyzing the product data of the unmanned retail terminal in the time zone and the product data of the unmanned retail terminal in the preset historical time zone, the quantity of products in the current time zone of the unmanned retail terminal is obtained;

[0024] Based on the quantity of goods in the current time area of ​​the unmanned retail terminal and the quantity of goods corresponding to the area in the historical consumption data of the area, the demand index of goods in the current time area of ​​the unmanned retail terminal is obtained.

[0025] As a preferred implementation, the priority of each work order corresponding to the processing time is analyzed based on the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal, the preset processing time of each work order in the preset area of ​​the unmanned retail terminal, the regional historical service demand variable of the preset area of ​​the unmanned retail terminal, and the service demand index corresponding to the preset area; the work order with the longest service time is given the highest priority, the work order analysis is performed according to the time period corresponding to each work order, and the work orders are sorted in chronological order.

[0026] As a preferred implementation, the work order with the longest service time is given the highest priority, and the work order analysis is performed according to the time period corresponding to each work order, and the work orders are sorted in chronological order. The sorting steps are:

[0027] Obtain the processing time of each work order in the latest preset time period of the regional preset area, and obtain the processing time of the latest preset time period of the regional preset area by weighted calculation of the processing time of each work order;

[0028] Obtain the regional historical service demand variable corresponding to the service area of ​​each work order, and obtain the service speed variable of the most recent preset time period of the regional preset area according to the service duration corresponding to each service data of the service area of ​​each work order;

[0029] Obtain the service quantity, processing speed and service speed corresponding to the service speed variable of the latest preset time period in the regional preset area, and obtain the service processing balance variable of the latest preset time period in the regional preset area;

[0030] Obtain the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal, and obtain the preset processing time corresponding to each work order in the preset area of ​​the unmanned retail terminal according to the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal;

[0031] The work orders are generated and sorted according to the preset processing time.

[0032] After adopting the above technical solution, the beneficial effects of the present invention are as follows: the unmanned container operation system based on consumption data has many beneficial effects, significantly improving operational efficiency, resource utilization and user experience. First of all, the real-time data analysis and work order generation capabilities enable the system to respond quickly to consumer needs. By comprehensively analyzing the consumption data and environmental data in the area, the system can generate targeted work orders in a short time to ensure timely replenishment and adjustment of goods. This efficient response capability greatly improves the flexibility of operations, ensuring that unmanned containers can always meet consumer needs and avoid waste of resources due to out-of-stock or overstocking.

[0033] Through intelligent analysis methods, the system can deeply explore the potential patterns behind the data and help merchants better understand consumer behavior and market trends. This in-depth analysis not only enables merchants to accurately grasp market trends, but also effectively guides the purchase and configuration of goods, improves the accuracy of sales, and thus improves overall profitability. The dynamic work order management system effectively improves the efficiency of resource utilization. By intelligently calculating and sorting the service time of work orders, the system can give priority to high-demand work orders and ensure the timeliness of service response. This optimization not only reduces the waste of human resources, but also ensures that consumers can get a better service experience when using unmanned containers, improving overall customer satisfaction.

[0034] Comprehensive environmental monitoring and analysis capabilities enable merchants to promptly identify and respond to the impact of environmental changes on consumer behavior. Through the analysis of historical and real-time environmental data, the system can provide merchants with valuable decision-making support, helping them to formulate more scientific operating strategies and enhance market competitiveness. The system has a high degree of automation, which reduces manual intervention and improves work efficiency. Merchants can focus more time and energy on optimizing operating strategies and improving customer experience, without having to spend a lot of time on work order management and data monitoring. This efficient management model not only improves operational results, but also provides merchants with greater advantages in the fierce market competition. The unmanned container operation system based on consumer data provides merchants with a comprehensive, flexible and efficient operation solution through intelligent design and efficient data management, significantly improving the operational efficiency and customer experience of unmanned containers, and promoting the development of the unmanned retail industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example:

[0039] like Figure 1 As shown, a method for generating an operation work order for an unmanned retail terminal includes an operation work order generating server, in which historical operation work orders are pre-stored, and the operation work order generating server performs work order analysis within the region on consumption data corresponding to each region and environmental data corresponding to the region, and obtains matching values ​​corresponding to the region through analysis;

[0040] According to the matching value corresponding to the area, the unmanned retail terminal in the area analyzes the service demand index corresponding to the current matching value of the area; according to the service demand index corresponding to the area, the current product demand index of the area is analyzed;

[0041] Obtain the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal, and calculate the service demand time corresponding to the service area of ​​each work order based on the historical data processing efficiency of each work order and the number of service demands corresponding to the area corresponding to the service demand index corresponding to the service area of ​​each work order;

[0042] Sort the service demand time corresponding to the service area of ​​each work order to obtain the service time corresponding to each work order; classify the work orders according to the service time corresponding to each work order, obtain the work order group corresponding to each service time, and sort the work order group according to the service time;

[0043] The step of performing work order analysis on the region according to the historical operation work order, the consumption data corresponding to the region and the environment data corresponding to the region, and obtaining the matching value corresponding to the region through analysis includes: obtaining the historical consumption data and the historical environment data of the region between the last time the operation work order was generated in the region and the analysis time;

[0044] Obtain regional historical consumption data, and obtain regional historical environmental change data from regional environmental data through historical consumption data; integrate regional historical environmental data through neural networks to obtain regional historical environmental change data; integrate regional historical environmental change data through neural networks based on regional historical consumption data to obtain environmental analysis data corresponding to regional historical environmental change data;

[0045] The current regional environmental data is integrated through a neural network to obtain the current regional environmental statistical data; the current regional environmental statistical data is analyzed, and the change parameters of the regional consumption data are analyzed; the regional change parameters are compared with the environmental analysis data corresponding to the regional historical environmental change data to obtain the corresponding matching value of the region.

[0046] The technical solution involves a method for generating operation work orders for unmanned retail terminals, which aims to automatically generate operation work orders that adapt to regional needs through analysis of consumption data and environmental data, thereby improving the operational efficiency of unmanned retail terminals. The solution performs data processing and analysis through an operation work order generation server, and the specific workflow is as follows.

[0047] Historical operation work orders are pre-stored in the system's operation work order generation server. These work orders not only contain past service records, but also the corresponding consumption data and environmental data, which provide a basis for subsequent analysis. In the first step of realizing the generation of operation work orders, the system will conduct regional work order analysis on the consumption data and environmental data of each specified area. This process includes obtaining the historical consumption data and environmental data of each area, and performing matching analysis to obtain the corresponding matching value of the area. By analyzing the consumption behavior and environmental changes of the area, the system can evaluate the service demand and product demand of the area.

[0048] The system will analyze the current service demand index of the region based on the matching value corresponding to the region. The service demand index is a quantitative indicator that reflects the intensity of consumer demand for goods and services in the region. By comparing the current matching value with historical data, the system can determine the trend of service demand changes in the current region. The key to this step is to be able to capture the changes in consumer demand in the region in real time and provide data support for subsequent work order generation.

[0049] After obtaining the service demand index, the system will further analyze the current product demand index of the region. The product demand index is actually a refinement of the service demand index. It specifically reflects the degree of consumer demand for specific products and helps the operation team understand which products are more popular in the region, so that product replenishment and inventory management can be more reasonably performed.

[0050] The system obtains the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal. The purpose of this step is to evaluate the past processing effect of each work order through historical data, including processing time, success rate and other indicators. Combined with the service demand index corresponding to the service area of ​​each work order, the system calculates the service demand time of each work order. This calculation process involves evaluating the number of service demands and predicting future service response time in combination with historical processing efficiency.

[0051] Once the service time required for all work orders is calculated, the system will sort these times to obtain the service time corresponding to each work order. This sorting process helps to classify the work orders by priority, ensuring that the most urgent or most demanding work orders are handled first.

[0052] The system will form different work order groups by classifying the work orders according to the service time of each work order. These work order groups are sorted according to the service time for subsequent allocation and scheduling. Such a classification and sorting mechanism can improve the flexibility and response speed of operations, and ensure that each work order can be processed in a timely manner according to actual needs.

[0053] In the process of analyzing the regional matching value, the system also needs to obtain the historical consumption data and historical environmental data between the last time the regional operation work order was generated and the time of analysis. This step is to ensure the timeliness of the data and to reflect the latest market dynamics and environmental changes. The system extracts the regional historical environmental change data through historical consumption data, and integrates these data using neural network technology to obtain a more accurate historical environmental change situation.

[0054] Through neural networks, the system can conduct in-depth learning and analysis of regional historical consumption data and historical environmental change data, and obtain environmental analysis data corresponding to regional historical environmental change data. This process ensures the accuracy and scientificity of the data, so that the generated work orders can better meet actual needs.

[0055] The system needs to perform the same neural network integration processing on the environmental data of the current area to obtain the statistical data of the current environment. By analyzing the current environmental statistical data, the system can identify the change parameters of the regional consumption data. These change parameters will be compared with the environmental analysis data corresponding to the historical environmental change data of the region to obtain the matching value corresponding to the region.

[0056] This technical solution uses in-depth analysis of historical data and environmental data, combined with neural network technology, to form an efficient method for generating operational work orders. This method not only improves the operational efficiency of unmanned retail terminals, but also ensures that services can accurately meet the needs of consumers in the region. Through dynamic analysis and real-time feedback, the operation team can respond to market changes more flexibly and achieve more efficient resource allocation and management.

[0057] According to the matching value corresponding to the area, the unmanned retail terminal of the area analyzes the service demand index corresponding to the current matching value of the area; the analysis steps of the unmanned retail terminal are: first, obtain the environmental data of the time area when the unmanned retail terminal analyzes and the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order; according to the number of goods corresponding to the area in the historical consumption data of the area; through a neural network, integrate the historical consumption data of the area and the number of goods corresponding to the area in the historical consumption data of the area to obtain the economic variables corresponding to the historical consumption data of the area; according to the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order, analyze the historical operation work order; analyze the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order and the service demand index corresponding to the current regional matching value; integrate the service demand index corresponding to the time area when the unmanned retail terminal generates the historical operation work order, the economic variables corresponding to the historical consumption data of the area and the service demand index corresponding to the current regional matching value to obtain the service demand index corresponding to the current matching value of the area.

[0058] In the operation of unmanned retail terminals, traditional technologies often rely on simple historical data statistics and lack in-depth analysis of the current environment and consumer behavior. This solution comprehensively considers multi-dimensional information by obtaining the environmental data of the time area of ​​the unmanned retail terminal analysis and the historical consumption data of the time area of ​​the historical operation work order generation. This method not only obtains the quantity of goods in the regional historical consumption data, but also integrates the regional historical consumption data through neural networks to obtain the corresponding economic variables. This process reflects the intelligence and automation of data analysis and can more accurately reflect the actual needs of the region. Existing technologies usually only focus on static data and fail to effectively integrate dynamically changing environmental and consumption data, resulting in the inability to adjust the operation strategy in a timely manner. By combining the service demand index corresponding to the time area of ​​the historical operation work order with the economic variables of the regional historical consumption data, this solution realizes the accurate calculation of the service demand index corresponding to the current matching value. This innovative data integration method enables unmanned retail terminals to respond flexibly to changing market environments and improve operational efficiency.

[0059] The steps of analyzing the current product demand index of a region by the service demand index corresponding to the region are as follows:

[0060] Obtain the product data of the unmanned retail terminal in the unmanned retail terminal analysis time area; obtain the product data of the unmanned retail terminal in the preset historical time area of ​​the area, and the preset historical time area is the time when the operation work order is generated;

[0061] By analyzing the product data of the unmanned retail terminal in the time zone and the product data of the unmanned retail terminal in the preset historical time zone, the quantity of products in the current time zone of the unmanned retail terminal is obtained;

[0062] Based on the quantity of goods in the current time area of ​​the unmanned retail terminal and the quantity of goods corresponding to the area in the historical consumption data of the area, the demand index of goods in the current time area of ​​the unmanned retail terminal is obtained.

[0063] In the step of analyzing the current product demand index of the region with the service demand index corresponding to the region, this solution further demonstrates its difference from the existing technology. Traditional methods often rely on fixed historical data when analyzing product demand, and lack flexible response to real-time situations. However, this technical solution can obtain the quantity of goods in the current area in real time by obtaining the product data of the analysis time area of ​​the unmanned retail terminal and combining it with the product data of the preset historical time area. The product demand index is obtained by analyzing the quantity of goods in the current time area of ​​the unmanned retail terminal and the quantity of goods in the historical consumption data. The innovation of this solution is that it emphasizes the timeliness and accuracy of data analysis to ensure that it can reflect the immediate needs of consumers. This dynamic analysis capability enables unmanned retail terminals to achieve more accurate product replenishment and inventory management, thereby improving user experience and sales efficiency.

[0064] Based on the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal, the preset processing time of each work order in the preset area of ​​the unmanned retail terminal, the regional historical service demand variables of the preset area of ​​the unmanned retail terminal, and the service demand index corresponding to the preset area, the priority of each work order is analyzed according to the processing time of each work order; the work order with the longest service time is taken as the highest priority, the work order analysis is performed according to the time period corresponding to each work order, and the work orders are sorted in chronological order.

[0065] When analyzing the priority corresponding to the processing time of each work order, this solution relies on the historical data processing efficiency and preset processing time of each work order of the unmanned retail terminal. This is different from the existing technology, which often lacks scientific evaluation of the priority of work orders and usually processes them only based on experience. However, this technical solution can quantify the processing time of each work order through analysis of specific historical data and service demand variables, so that the work order with the longest service time is given the highest priority. Through this method, the operation team can ensure that the most urgent work orders are processed in a timely manner, rather than relying solely on manual judgment. This data-based prioritization method can effectively improve the efficiency of work order processing and ensure the rational allocation of resources.

[0066] The work order with the longest service time is given the highest priority. Work order analysis is performed based on the time period corresponding to each work order, and the work orders are sorted in chronological order. The sorting steps are as follows:

[0067] Obtain the processing time of each work order in the latest preset time period of the regional preset area, and obtain the processing time of the latest preset time period of the regional preset area by weighted calculation of the processing time of each work order;

[0068] Obtain the regional historical service demand variable corresponding to the service area of ​​each work order, and obtain the service speed variable of the most recent preset time period of the regional preset area according to the service duration corresponding to each service data of the service area of ​​each work order;

[0069] Obtain the service quantity, processing speed and service speed corresponding to the service speed variable of the latest preset time period in the regional preset area, and obtain the service processing balance variable of the latest preset time period in the regional preset area;

[0070] Obtain the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal, and obtain the preset processing time corresponding to each work order in the preset area of ​​the unmanned retail terminal according to the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal;

[0071] The work orders are generated and sorted according to the preset processing time.

[0072] In the specific implementation of the sorting step, this solution obtains the processing time of each work order in the most recent set time period of the regional preset area, and performs weighted calculation to obtain a more accurate processing time. This refined processing time calculation capability is relatively rare in the prior art, which often relies only on simple statistical methods. By obtaining the historical service demand variables and service speed variables corresponding to each work order service area, the system can comprehensively analyze the current number of services, processing speed and service speed to form a service processing balance variable. The introduction of this balance variable further enhances the scientific evaluation of the work order processing capability, making the sorting basis more comprehensive and reasonable. Finally, according to the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal, a more targeted preset processing time can be obtained, and the work orders are generated and sorted based on this. Compared with the empirical method of the prior art, this sorting method based on historical efficiency is more scientific and efficient, and improves the operational management level of unmanned retail terminals.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating an operation work order for an unmanned retail terminal, characterized in that: It includes an operation work order generation server, in which historical operation work orders are pre-stored. The operation work order generation server performs work order analysis within the region on the consumption data corresponding to each region and the environment data corresponding to the region, and obtains the matching value corresponding to the region through the analysis; According to the matching value corresponding to the area, the unmanned retail terminal in the area analyzes the service demand index corresponding to the current matching value of the area; according to the service demand index corresponding to the area, the current product demand index of the area is analyzed; Obtain the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal, and calculate the service demand time corresponding to the service area of ​​each work order based on the historical data processing efficiency of each work order and the number of service demands corresponding to the area corresponding to the service demand index corresponding to the service area of ​​each work order; Sort the service demand time corresponding to the service area of ​​each work order to obtain the service time corresponding to each work order; classify the work orders according to the service time corresponding to each work order, obtain the work order group corresponding to each service time, and sort the work order group according to the service time; The step of performing work order analysis on the region according to the historical operation work order, the consumption data corresponding to the region and the environment data corresponding to the region, and obtaining the matching value corresponding to the region through analysis includes: obtaining the historical consumption data and the historical environment data of the region between the last time the operation work order was generated in the region and the analysis time; Obtain regional historical consumption data, and obtain regional historical environmental change data from regional environmental data through historical consumption data; integrate regional historical environmental data through neural networks to obtain regional historical environmental change data; integrate regional historical environmental change data through neural networks based on regional historical consumption data to obtain environmental analysis data corresponding to regional historical environmental change data; The current regional environmental data is integrated through a neural network to obtain the current regional environmental statistical data; the current regional environmental statistical data is analyzed, and the change parameters of the regional consumption data are analyzed; the regional change parameters are compared with the environmental analysis data corresponding to the regional historical environmental change data to obtain the corresponding matching value of the region.

2. The method for generating an operation work order for an unmanned retail terminal according to claim 1, characterized in that: According to the matching value corresponding to the area, the unmanned retail terminal of the area analyzes the service demand index corresponding to the current matching value of the area; the analysis steps of the unmanned retail terminal are: first, obtain the environmental data of the time area when the unmanned retail terminal analyzes and the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order; according to the number of goods corresponding to the area in the historical consumption data of the area; through a neural network, integrate the historical consumption data of the area and the number of goods corresponding to the area in the historical consumption data of the area to obtain the economic variables corresponding to the historical consumption data of the area; according to the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order, analyze the historical operation work order; analyze the historical consumption data of the time area when the unmanned retail terminal generates the historical operation work order and the service demand index corresponding to the current regional matching value; integrate the service demand index corresponding to the time area when the unmanned retail terminal generates the historical operation work order, the economic variables corresponding to the historical consumption data of the area and the service demand index corresponding to the current regional matching value to obtain the service demand index corresponding to the current matching value of the area.

3. The method for generating an operation work order for an unmanned retail terminal according to claim 1, characterized in that: The steps of analyzing the current product demand index of a region by the service demand index corresponding to the region are as follows: Obtain the product data of the unmanned retail terminal in the unmanned retail terminal analysis time area; obtain the product data of the unmanned retail terminal in the preset historical time area of ​​the area, and the preset historical time area is the time when the operation work order is generated; By analyzing the product data of the unmanned retail terminal in the time zone and the product data of the unmanned retail terminal in the preset historical time zone, the quantity of products in the current time zone of the unmanned retail terminal is obtained; Based on the quantity of goods in the current time area of ​​the unmanned retail terminal and the quantity of goods corresponding to the area in the historical consumption data of the area, the demand index for goods in the current time area of ​​the unmanned retail terminal is obtained.

4. The method for generating an operation work order for an unmanned retail terminal according to claim 1, characterized in that: Analyze the priority of each work order based on the historical data processing efficiency of each work order in the preset area of ​​the unmanned retail terminal, the preset processing time of each work order in the preset area of ​​the unmanned retail terminal, the regional historical service demand variable of the preset area of ​​the unmanned retail terminal, and the service demand index corresponding to the preset area; The work order with the longest service time is given the highest priority, and the work orders are analyzed according to the time period corresponding to each work order, and the work orders are sorted in chronological order.

5. The method for generating an operation work order for an unmanned retail terminal according to claim 4, characterized in that: The work order with the longest service time is given the highest priority. Work order analysis is performed based on the time period corresponding to each work order, and the work orders are sorted in chronological order. The sorting steps are as follows: Obtain the processing time of each work order in the latest preset time period of the regional preset area, and obtain the processing time of the latest preset time period of the regional preset area by weighted calculation of the processing time of each work order; Obtain the regional historical service demand variable corresponding to the service area of ​​each work order, and obtain the service speed variable of the most recent preset time period of the regional preset area according to the service duration corresponding to each service data of the service area of ​​each work order; Obtain the service quantity, processing speed and service speed corresponding to the service speed variable of the latest preset time period in the regional preset area, and obtain the service processing balance variable of the latest preset time period in the regional preset area; Obtain the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal, and obtain the preset processing time corresponding to each work order in the preset area of ​​the unmanned retail terminal according to the historical data processing efficiency corresponding to each work order in the preset area of ​​the unmanned retail terminal; The work orders are generated and sorted according to the preset processing time.

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