Product management method and system for self-service retail machines based on blockchain

Abstract

The present invention relates to the field of retail technology, and discloses a method and system for implementing product management of self-service retail machines based on blockchain, including: obtaining product retail scenarios and related parameters of self-service retail machines, determining product supply conditions and inventory requirements, and then analyzing real-time transaction parameters to obtain product fluctuation data and inventory areas, calculating inventory flow values ​​of inventory areas and dividing areas accordingly, generating partition boundaries to formulate partition management goals, clarifying target measures and execution standards to construct management operation instructions, sending instructions to a blockchain system to implement real-time product management, obtaining and verifying blockchain record information, and finally generating a product management report based on verification data. The present invention can improve the product management efficiency of self-service retail machines.

Description

Technical Field

[0001] The present invention relates to a product management method and system for a self-service retail machine based on blockchain, belonging to the field of retail technology. Background Art

[0002] In the modern retail industry, self-service vending machines are increasingly used, especially in unattended scenarios such as airports, subway stations, office buildings, etc., providing consumers with a convenient shopping experience. However, as the number and distribution of self-service vending machines continue to expand, problems in product management are gradually emerging.

[0003] At present, the traditional self-service retail machine product management method usually relies on manual inspection and simple inventory management system. This method has many shortcomings. First, the efficiency of manual inspection is low, and it is impossible to grasp the inventory status of each retail machine in real time, which easily leads to product out-of-stock or expiration, affecting consumers' shopping experience. Secondly, the existing inventory management system lacks intelligent analysis methods and cannot make accurate predictions and replenishment based on historical sales data and market demand, resulting in resource waste or insufficient supply. Therefore, a self-service retail machine product management method based on blockchain technology is needed to improve the product management efficiency of self-service retail machines. Summary of the invention

[0004] The present invention provides a product management method and system for a self-service retail machine based on blockchain, the main purpose of which is to improve the product management efficiency of the self-service retail machine.

[0005] To achieve the above purpose, the present invention provides a product management method for a self-service retail machine based on blockchain, comprising:

[0006] Obtain a product retail scenario corresponding to the self-service retail machine, query product demand parameters in the product retail scenario, determine product supply conditions required for the product retail scenario based on the product demand parameters, and analyze product inventory requirements corresponding to the self-service retail machine based on the product supply conditions;

[0007] Based on the product inventory demand, analyzing the real-time transaction parameters corresponding to the self-service retail machine, determining the product fluctuation data corresponding to the self-service retail machine according to the real-time transaction parameters, and querying the product inventory area corresponding to the self-service retail machine based on the product fluctuation data;

[0008] Calculating the inventory flow value corresponding to the product inventory area, segmenting the product inventory area according to the inventory flow value to obtain inventory partitions, and generating partition boundaries corresponding to the inventory partitions;

[0009] Based on the partition boundary, formulate the partition management target corresponding to the self-service retail machine, analyze the target measures in the partition management target, query the measure execution standard corresponding to the target measure, and construct the management operation instruction corresponding to the self-service retail machine based on the measure execution standard;

[0010] After the management operation instruction is sent to the preset blockchain system, real-time product management is performed on the self-service retail machine to obtain product management data, obtain blockchain record information corresponding to the product management data, perform data verification on the blockchain record information to obtain blockchain verification data, and generate a product management report corresponding to the self-service retail machine based on the blockchain verification data.

[0011] Optionally, analyzing the product inventory demand corresponding to the self-service retail machine based on the product supply condition includes:

[0012] Analyze the key condition parameters in the product supply conditions;

[0013] Identify potential demand indicators corresponding to the key condition parameters;

[0014] Based on the potential demand indicator, determining an inventory fluctuation pattern corresponding to the self-service retail machine;

[0015] Querying the inventory optimization intention corresponding to the inventory fluctuation pattern;

[0016] Based on the inventory optimization intention, the product inventory demand corresponding to the self-service retail machine is analyzed.

[0017] Optionally, analyzing the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand includes:

[0018] Extracting the core demand data in the product inventory demand;

[0019] Based on the core demand data, set key transaction indicators corresponding to the self-service retail machine;

[0020] Collecting real-time flow data corresponding to the self-service retail machine in real time;

[0021] Based on the key transaction indicators, the real-time flow data is screened and processed to obtain real-time screening details;

[0022] Based on the real-time screening details, real-time transaction parameters corresponding to the self-service retail machine are analyzed.

[0023] Optionally, querying the product inventory area corresponding to the self-service retail machine based on the product fluctuation data includes:

[0024] Extracting key fluctuation factors from the product fluctuation data;

[0025] Based on the key fluctuation factors, the products in the self-service retail machine are classified to obtain product category groups;

[0026] Querying the layout characteristics of the product category group in the self-service retail machine;

[0027] Based on the layout characteristics, determining the regional layout range corresponding to the self-service retail machine;

[0028] Based on the regional layout range, query the product inventory area corresponding to the self-service retail machine.

[0029] Optionally, calculating the inventory flow value corresponding to the product inventory area includes:

[0030] The inventory flow value corresponding to the product inventory area is calculated using the following formula:

[0031]

[0032] Among them, CF represents the inventory flow value corresponding to the product inventory area, n represents the total number of sub-areas corresponding to the product inventory area, i represents the quantity index corresponding to the sub-area, and a i represents the weight coefficient corresponding to the i-th sub-region, F i represents the inventory flow assessment value corresponding to the ith sub-region, t 1 and t 2 They represent the start time and end time of the inventory flow cycle respectively, and C(t) represents the dynamic inventory change index at time t.

[0033] Optionally, segmenting the product inventory area according to the inventory flow value to obtain inventory partitions includes:

[0034] Analyzing the flow sequence parameters corresponding to the inventory flow value;

[0035] Querying the flow peak value and flow valley value in the flow sequence parameters;

[0036] Extracting flow time nodes corresponding to the flow peak value and the flow valley value;

[0037] Based on the flow time nodes, dividing the product inventory area into a high flow area and a low flow area;

[0038] Based on the high flow area and the low flow area, the product inventory area is segmented to obtain inventory partitions.

[0039] Optionally, formulating a partition management target corresponding to the self-service retail machine based on the partition boundary includes:

[0040] Analyzing inventory flow characteristics corresponding to products within the partition boundary;

[0041] Querying a standard management model that matches the inventory flow characteristics;

[0042] Adjust the strategy of the standard management model to obtain a preliminary management strategy;

[0043] Based on the preliminary management strategy, calculating the cost-effectiveness value of the self-service retail machine in the operating environment;

[0044] Based on the cost-effectiveness value, the preliminary management strategy is partitioned to obtain a partition management strategy set;

[0045] Based on the partition management strategy set, a partition management target corresponding to the self-service retail machine is formulated.

[0046] Optionally, the calculating the cost-effectiveness value of the self-service retail machine in an operating environment based on the preliminary management strategy includes:

[0047] The cost-effectiveness value of the self-service retail machine in the operating environment is calculated using the following formula:

[0048]

[0049] Wherein, CBV represents the cost-effectiveness value of the self-service retail machine in the operating environment, T′ represents the operating time period corresponding to the operating environment, t′ represents the time node in the operating time period, R t′ represents the sales revenue value at the t′th time node, CB t′ represents the total cost value at the t′th time node, and r represents the discount rate corresponding to the product in the self-service retail machine.

[0050] Optionally, constructing the management operation instruction corresponding to the self-service retail machine based on the measure execution standard includes:

[0051] The implementation standards of the measures are refined into clauses to obtain standard details;

[0052] Extract the key implementation steps in the standard details;

[0053] Arranging the key execution steps in sequence to obtain an action sequence;

[0054] Based on the action sequence, conditional setting is performed on the key execution steps to obtain a conditional execution sequence;

[0055] Based on the conditional execution sequence, a management operation instruction corresponding to the self-service retail machine is constructed.

[0056] In order to solve the above problems, the present invention also provides a product management system for self-service retail machines based on blockchain, the system comprising:

[0057] A demand analysis module, used to obtain a product retail scenario corresponding to the self-service retail machine, query product demand parameters in the product retail scenario, determine product supply conditions required for the product retail scenario based on the product demand parameters, and analyze product inventory requirements corresponding to the self-service retail machine based on the product supply conditions;

[0058] A region query module, configured to analyze the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand, determine the product fluctuation data corresponding to the self-service retail machine according to the real-time transaction parameters, and query the product inventory region corresponding to the self-service retail machine based on the product fluctuation data;

[0059] A boundary generation module, used for calculating the inventory flow value corresponding to the product inventory area, segmenting the product inventory area according to the inventory flow value to obtain inventory partitions, and generating partition boundaries corresponding to the inventory partitions;

[0060] An instruction construction module, used to formulate a zone management target corresponding to the self-service retail machine based on the zone boundary, analyze the target measures in the zone management target, query the measure execution standard corresponding to the target measure, and construct the management operation instruction corresponding to the self-service retail machine based on the measure execution standard;

[0061] The report generation module is used to send the management operation instruction to a preset blockchain system, perform real-time product management on the self-service retail machine, obtain product management data, obtain blockchain record information corresponding to the product management data, perform data verification on the blockchain record information, obtain blockchain verification data, and generate a product management report corresponding to the self-service retail machine based on the blockchain verification data.

[0062] Compared with the problems described in the background technology, the present invention obtains the product retail scene corresponding to the self-service retail machine and queries the product demand parameters in the product retail scene, which helps to accurately locate the consumption characteristics and demand trends of the environment in which the retail machine is located, so that the supplied products are more in line with the preferences of local consumers, improve the targetedness and success rate of sales, and can plan inventory in advance based on these parameters to avoid excessive stockpiling or out-of-stock phenomena and optimize inventory management. The present invention analyzes the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand, can monitor sales dynamics in real time, compare transaction conditions based on inventory demand, and promptly detect sales anomalies so as to quickly adjust marketing strategies. It can accurately predict the timing of replenishment, and plan replenishment plans in advance through the correlation analysis of transaction parameters and inventory demand, so as to avoid out-of-stock or backlogs and ensure supply continuity. Furthermore, the present invention can intuitively understand the circulation speed of products in the inventory area by calculating the inventory flow value corresponding to the product inventory area, so as to provide information for replenishment and inventory allocation. Providing key basis is helpful to optimize inventory layout, reasonably arrange the storage location of goods according to the inventory flow differences in different areas, and improve the utilization rate of inventory space. Further, the present invention formulates the partition management target corresponding to the self-service retail machine based on the partition boundary, which can improve the operation efficiency, clarify the key tasks of each area according to different partition boundaries, such as rapid replenishment in high-flow areas and precise marketing in low-flow areas, reduce the blindness of management, and optimize resource allocation, allocate manpower and material resources according to the characteristics of each partition, and avoid waste and mismatch of resources. Finally, the present invention performs real-time product management on the self-service retail machine after sending the management operation instruction to the preset blockchain system, and obtains product management data. With the help of the immutability and traceability of the blockchain, the product management data is more credible, which is convenient for accurately tracing the operation process and product flow, and can achieve efficient collaboration. All relevant parties can know the management situation in real time through the blockchain, optimize the work connection, and ensure the smooth and efficient operation of the self-service retail machine. Therefore, the product management method and system for implementing self-service retail machines based on blockchain provided in the embodiment of the present invention can improve the product management efficiency of self-service retail machines. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flowchart of a method for implementing product management of a self-service retail machine based on blockchain provided by an embodiment of the present invention;

[0064] Figure 2 A schematic diagram of a module for implementing a product management system for a self-service retail machine based on blockchain provided in one embodiment of the present invention.

[0065] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0067] The embodiment of the present application provides a product management method for a self-service retail machine based on blockchain. The execution subject of the product management method for a self-service retail machine based on blockchain includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the product management method for a self-service retail machine based on blockchain can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0068] Embodiment 1:

[0069] Reference Figure 1 As shown, it is a flowchart of a method for implementing product management of a self-service retail machine based on blockchain provided by an embodiment of the present invention. In this embodiment, the method for implementing product management of a self-service retail machine based on blockchain includes:

[0070] S1. Obtain a product retail scenario corresponding to a self-service retail machine, query product demand parameters in the product retail scenario, determine product supply conditions required for the product retail scenario based on the product demand parameters, and analyze product inventory requirements corresponding to the self-service retail machine based on the product supply conditions.

[0071] The present invention obtains the product retail scene corresponding to the self-service retail machine and queries the product demand parameters in the product retail scene, which helps to accurately locate the consumption characteristics and demand trends of the environment in which the retail machine is located, so that the supplied products are more in line with the preferences of local consumers, improve the targetedness and success rate of sales, and plan inventory in advance based on these parameters to avoid excessive stockpiling or out-of-stock phenomena and optimize inventory management.

[0072] Among them, the self-service retail machine refers to mechanical equipment that can automatically sell goods without manual operation, allowing consumers to complete the shopping process such as selection and payment by themselves. It is often placed in public areas or places with frequent personnel exchanges, so that people can buy the items they need at any time; the product retail scene refers to the specific environment where the self-service retail machine is located, such as airports, railway stations, subway stations, shopping malls, office buildings, schools, communities and other places. Different scenes have different traffic flows, consumer population characteristics, consumption time patterns and consumer demand tendencies, which will affect the selection of products sold by self-service retail machines, inventory management and other aspects; the product demand parameters refer to various quantitative or qualitative indicators related to the demand for products in a specific retail scene, including but not limited to past product sales data, product information and product demand in the scene. The seasonal demand fluctuations, demand change patterns in different time periods, consumer preference ratios for product types, acceptance of product price ranges, and factors affecting product demand through local cultural customs or special events, etc. Optionally, obtaining the product retail scenario corresponding to the self-service retail machine can be achieved through a data analysis method, such as: analyzing the historical operating data of the self-service retail machine (such as sales records, payment methods, consumer behavior, etc.), combined with time, location and other information to obtain the product retail scenario); querying the product demand parameters in the product retail scenario can be achieved through a machine learning algorithm, such as: decision trees, random forests, neural networks and other algorithms, combined with historical data and external factors (such as weather, holidays, special events, etc.) to obtain product demand parameters.

[0073] Furthermore, the present invention determines the product supply conditions required for the product retail scenario based on the product demand parameters, which can accurately adapt the commodity supply to the actual demand, avoid blind distribution of goods, reduce inventory backlogs and out-of-stock phenomena, ensure the smooth circulation of goods in retail machines, and help improve consumer satisfaction. Because the supplied products are in line with the scene requirements, they can better meet consumers' instant shopping expectations.

[0074] Among them, the product supply conditions refer to a series of requirements and standards related to product supply determined in a specific product retail scenario to meet consumer demand and maintain normal sales operations, including but not limited to product category composition, that is, determining which categories of products should be put on the shelves based on demand parameters; product quantity scale, clarifying the reasonable inventory quantity range of each product to cope with demand fluctuations in different periods; product replenishment frequency, formulating appropriate replenishment plans based on sales speed and inventory consumption; product quality standards, ensuring that the supplied products meet consumer expectations and relevant regulatory requirements in the scenario. Optionally, the determination of the product supply conditions required for the product retail scenario can be achieved through clustering analysis methods, such as K-means, hierarchical clustering and other methods, to classify historical sales data and identify the product categories preferred by consumers in different scenarios, thereby obtaining product supply conditions.

[0075] Furthermore, the present invention analyzes the product inventory requirements corresponding to the self-service retail machine based on the product supply conditions, thereby realizing accurate inventory planning, avoiding excessive or insufficient inventory according to the category, quantity and other requirements in the supply conditions, reducing inventory costs and out-of-stock risks, and helping to improve operational efficiency, making the replenishment process more planned and timely, and reducing operational interruptions caused by poor inventory management.

[0076] Among them, the product inventory demand refers to the optimal inventory quantity range, replenishment time nodes and inventory structure configuration that the self-service retail machines should maintain at different time nodes and different product categories, which are accurately determined after comprehensive consideration of key condition parameters, potential demand indicators, inventory fluctuation patterns and inventory optimization intentions.

[0077] As an embodiment of the present invention, analyzing the product inventory demand corresponding to the self-service retail machine based on the product supply conditions includes: analyzing the key condition parameters in the product supply conditions; identifying potential demand indicators corresponding to the key condition parameters; determining the inventory fluctuation pattern corresponding to the self-service retail machine based on the potential demand indicators; querying the inventory optimization intention corresponding to the inventory fluctuation pattern; and analyzing the product inventory demand corresponding to the self-service retail machine based on the inventory optimization intention.

[0078] Among them, the key condition parameters refer to the core elements that have a significant impact on the product inventory demand in the product supply conditions, such as the product sales rate, the product shelf life limit, the sales share in different time periods, the seasonal variation of market demand and the replenishment lead time of each product; the potential demand indicators refer to the quantitative or qualitative signs derived from the key condition parameters that can reflect the possible demand conditions of the product in the future, such as the peak sales volume in the peak season estimated based on the sales rate and seasonal variation, the maximum safety inventory level determined based on the shelf life limit, the demand intensity in each time period analyzed from the sales share in different time periods, and the minimum inventory replenishment trigger point calculated in combination with the replenishment lead time; the inventory fluctuation pattern refers to the regular form of product inventory changes over time presented by the potential demand indicators, including but not limited to the stable low fluctuation pattern (such as the daily office supplies in the office area retail) vending machines), cyclical large fluctuation patterns (such as inventory fluctuations of holiday gifts in vending machines around shopping malls), sudden pulse fluctuation patterns (such as inventory changes of specific support items in vending machines near concert venues), and gradually increasing or decreasing trend fluctuation patterns (such as inventory change trends of certain seasonal drinks with the change of seasons); the inventory optimization intention refers to the goal setting and strategic inclination for specific inventory fluctuation patterns, aiming to improve inventory management efficiency, reduce costs, meet sales needs and reduce out-of-stock and backlog risks, such as the intention to maintain a lower safety inventory and simplify the replenishment process for a stable and low-volatility pattern, formulate a strategic direction for stockpiling in advance and dynamically adjusting the replenishment volume for a cyclical large fluctuation pattern, establish an optimization goal for rapid response and temporary replenishment channel construction for a sudden pulse fluctuation pattern, and form a plan for gradually adjusting the inventory structure along the trend for a trend fluctuation pattern.

[0079] Furthermore, the analysis of the key condition parameters in the product supply conditions can be achieved through statistical analysis tools, such as SPSS, Excel and other tools, to perform descriptive statistics and correlation analysis on the product supply conditions to identify key condition parameters; the identification of potential demand indicators corresponding to the key condition parameters can be achieved through demand forecasting algorithms, such as time series analysis, ARIMA and other algorithms; the determination of the inventory fluctuation pattern corresponding to the self-service retail machine can be achieved through a time series analysis algorithm, such as Prophet, LSTM and other algorithms; the query of the inventory optimization intention corresponding to the inventory fluctuation pattern can be achieved through a rule base query method, such as querying the inventory optimization intention corresponding to the inventory fluctuation pattern through a preset rule base; the analysis of the product inventory demand corresponding to the self-service retail machine can be achieved through simulation tools, such as AnyLogic and other tools.

[0080] S2. Based on the product inventory demand, analyze the real-time transaction parameters corresponding to the self-service retail machine, determine the product fluctuation data corresponding to the self-service retail machine according to the real-time transaction parameters, and query the product inventory area corresponding to the self-service retail machine based on the product fluctuation data.

[0081] The present invention analyzes the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand, can monitor sales dynamics in real time, compare transaction conditions according to inventory demand, promptly detect sales anomalies so as to quickly adjust marketing strategies, accurately predict replenishment opportunities, and plan replenishment plans in advance through correlation analysis between transaction parameters and inventory demand, so as to avoid out-of-stock or backlogs and ensure supply continuity.

[0082] Among them, the real-time transaction parameters refer to quantitative indicators and characteristic descriptions that are obtained through comprehensive analysis and calculation of real-time screening details, which can accurately reflect the current transaction status of the self-service retail machine and its degree of adaptation to inventory demand, such as the deviation value between the actual sales rate and the expected sales rate, the real-time changes in inventory turnover rate, the comparative relationship between the real-time inventory level and the safety inventory level of each product, the difference in transaction flow between the peak and trough sales periods of popular products, etc.

[0083] As an embodiment of the present invention, the real-time transaction parameters corresponding to the self-service retail machine are analyzed based on the product inventory demand, including: extracting the core demand data in the product inventory demand; setting the key transaction indicators corresponding to the self-service retail machine based on the core demand data; collecting the real-time flow data corresponding to the self-service retail machine in real time; based on the key transaction indicators, filtering and processing the real-time flow data to obtain real-time filtering details; based on the real-time filtering details, analyzing the real-time transaction parameters corresponding to the self-service retail machine.

[0084] The core demand data refers to information extracted from product inventory demand that is of key guiding significance for transaction parameter analysis, such as the target inventory level of each product, the number of products corresponding to replenishment points, the inventory ratio structure of different products, and the ideal sales rate of each product determined based on historical data and market forecasts; the key transaction indicators refer to specific measurement dimensions determined based on the core demand data that can reflect the degree of correlation between the transaction status of self-service retail machines and inventory, including the sales quantity and frequency of specific products, the distribution of transaction amounts and their changing trends over time, the proportion of various payment methods used, the time interval pattern of transactions, and the relationship between different product categories. the proportion of related sales, etc.; the real-time flow data refers to the detailed information set of each transaction generated in real time by the self-service retail machine through its built-in transaction record system (such as scan code payment terminal, cash collection module, etc.) during the actual operation, covering all original data points related to the transaction, such as transaction timestamp, product name and quantity, payment amount, payment method, consumer membership information (if any), etc.; the real-time screening details refer to the targeted and relevant transaction record subsets screened out from the massive real-time flow data in accordance with the requirements of key transaction indicators, such as only retaining the sales records of specific products, transaction data within a specific time period, and transaction information that meets a certain amount range, etc.

[0085] Furthermore, the extraction of the core demand data from the product inventory demand can be achieved through machine learning models, such as regression analysis, support vector machine and other models; the setting of key transaction indicators corresponding to the self-service retail machine can be achieved through optimization algorithms, such as linear programming and other algorithms, which comprehensively consider the core demand data and generate key transaction indicators; the real-time collection of real-time flow data corresponding to the self-service retail machine can be achieved through a data collection platform, such as Kafka, Flink and other platforms; the screening and processing of the real-time flow data can be achieved through a data filtering algorithm, such as Bloom filter, which can filter out targeted and relevant transaction records from massive real-time flow data; the analysis of the real-time transaction parameters corresponding to the self-service retail machine can be achieved through simulation tools, such as AnyLogic and other tools, which simulate the effects of different transaction strategies and determine real-time transaction parameters.

[0086] The present invention determines the product fluctuation data corresponding to the self-service retail machine based on the real-time transaction parameters, can keenly capture market dynamic changes, and timely detect the fluctuations in consumer demand based on the fluctuations in transaction parameters so as to flexibly adjust product supply strategies. It can accurately predict inventory change trends. Product fluctuation data reflects the unstable factors of sales, and inventory adjustments can be planned in advance to reduce the risks of out-of-stock and backlog.

[0087] Among them, the product fluctuation data refers to a data set that reflects the fluctuations in sales quantity, sales amount, sales speed and purchase frequency of products sold by self-service retail machines over time, which covers the increase or decrease in product sales in different time periods, such as the sales difference between weekdays and weekends, holidays and non-holidays; the fluctuation curve of sales amount reflects the changes in consumers' purchasing tendencies for products in different price ranges; the changes in sales speed, that is, the changes in the rate at which products are sold per unit time, can intuitively present the degree of popularity or slow sales of the products. Optionally, the determination of the product fluctuation data corresponding to the self-service retail machine can be achieved through machine learning models, such as: LSTM, XGBoost and other models.

[0088] Furthermore, the present invention queries the product inventory area corresponding to the self-service retail machine based on the product fluctuation data, so as to optimize the inventory layout, place the best-selling products in a more convenient access area according to the fluctuation data, improve the efficiency of replenishment and shipment, and effectively reduce the inventory management cost. The accurate positioning of the inventory area makes the inventory counting and sorting more targeted, thereby reducing unnecessary manpower and time consumption.

[0089] Among them, the product inventory area refers to the specific physical space location inside the self-service retail machine that is specially used to store and hold various types of products. These areas are based on the regional layout range, combined with the actual structure of the retail machine (such as the number of shelf layers, compartment divisions, refrigerated area and normal temperature area settings, etc.) and product storage requirements (such as special storage conditions for fragile and perishable goods), and ultimately determine the actual inventory storage location of each product category group.

[0090] As an embodiment of the present invention, querying the product inventory area corresponding to the self-service retail machine based on the product fluctuation data includes: extracting key fluctuation factors in the product fluctuation data; classifying the products in the self-service retail machine based on the key fluctuation factors to obtain product category groups; querying the layout characteristics of the product category groups in the self-service retail machine; determining the regional layout range corresponding to the self-service retail machine based on the layout characteristics; and querying the product inventory area corresponding to the self-service retail machine based on the regional layout range.

[0091] Among them, the key fluctuation factors refer to the information that is screened out from the product fluctuation data and has key guiding significance for the division of product inventory areas, including but not limited to the time patterns of the peak and valley values ​​of product sales quantity, the time nodes of the rapid growth and decline stages of sales rate, the difference in sales volume in different time periods (such as weekdays and weekends, daytime and night, etc.), the fluctuation frequency within the product sales cycle, and the sudden sales volume changes caused by market promotion activities or special events; the product category group refers to the collection formed by classifying and dividing the products in the self-service retail machines according to the similarity of the key fluctuation factors. For example, beverages with peak sales concentrated in the morning on weekdays and with relatively stable sales fluctuations and similar rates are classified into one category; beverages with sharp sales increases on holidays and average sales during holidays are classified into another category. Gift products with relatively low sales and large fluctuations are classified into another category; the layout characteristics refer to the positional relationship, space proportion, relative position to the entrance and exit of the retail machine and the operating interface of each product category group when placed in the internal space of the self-service retail machine. For example, some high-profit and frequently purchased product category groups may be arranged in the golden position in the upper middle of the retail machine, which is easy for customers to reach and operate; the regional layout range refers to the spatial area boundary range roughly occupied by each category group in the overall space of the self-service retail machine determined according to the layout characteristics of the product category group. It is defined by coordinates, area numbers or relative position descriptions, clarifying the spatial distribution framework of each product category group in the retail machine.

[0092] Furthermore, the extraction of key fluctuation factors in the product fluctuation data can be achieved through volatility calculation tools, such as Excel, Python's pandas library and other tools; the classification processing of the products in the self-service retail machine can be achieved through clustering analysis algorithms, such as K-means, hierarchical clustering and other algorithms, and the products are classified and divided according to the similarity of key fluctuation factors; the query of the layout characteristics of the product category group in the self-service retail machine can be achieved through image recognition technology, such as deep learning models, etc., to analyze the positional relationship and space proportion of the product category group in the self-service retail machine to obtain layout characteristics; the determination of the regional layout range corresponding to the self-service retail machine can be achieved through spatial analysis tools, such as ArcGIS, QGIS and other tools; the query of the product inventory area corresponding to the self-service retail machine can be achieved through data visualization tools, such as Tableau, Matplotlib and other tools.

[0093] S3. Calculate the inventory flow value corresponding to the product inventory area, segment the product inventory area according to the inventory flow value to obtain inventory partitions, and generate partition boundaries corresponding to the inventory partitions.

[0094] By calculating the inventory flow value corresponding to the product inventory area, the present invention can intuitively understand the circulation speed of the product in the inventory area, provide a key basis for replenishment and inventory allocation, and help optimize the inventory layout. According to the inventory flow differences in different areas, the storage location of goods can be reasonably arranged to improve the utilization rate of inventory space.

[0095] Among them, the inventory flow value refers to a comprehensive indicator used to measure the flow of inventory in the product inventory area. This value reflects the changes in product inventory in the inventory area within a specific time period, including the impact of factors such as product entry and exit and storage time on inventory liquidity.

[0096] As an embodiment of the present invention, the calculating the inventory flow value corresponding to the product inventory area includes:

[0097] The inventory flow value corresponding to the product inventory area is calculated using the following formula:

[0098]

[0099] Among them, CF represents the inventory flow value corresponding to the product inventory area, n represents the total number of sub-areas corresponding to the product inventory area, i represents the quantity index corresponding to the sub-area, and a i represents the weight coefficient corresponding to the i-th sub-region, F i represents the inventory flow assessment value corresponding to the ith sub-region, t 1 and t 2 They represent the start time and end time of the inventory flow cycle respectively, and C(t) represents the dynamic inventory change index at time t.

[0100] In detail, the sub-area refers to each small area after the product inventory area is further subdivided. In a large product inventory area, it will be divided into multiple sub-areas according to factors such as product type, storage location, storage conditions, etc.; the weight coefficient refers to a coefficient used to measure the relative importance of each sub-area in the entire inventory area. The weight coefficient is determined based on these factors. For example, a sub-area storing high-turnover products will be assigned a higher weight coefficient because its inventory flow situation has a greater impact on the entire inventory area; the inventory flow evaluation value refers to the value obtained by individually evaluating the inventory flow situation of each sub-area, which comprehensively considers factors such as the frequency of entry and exit of products in the sub-area, storage time, inventory turnover rate, etc.; the inventory flow cycle refers to the time range used to evaluate the inventory flow value, which is composed of the cycle start time (t 1 ) and cycle end time (t 2) is defined, the choice of inventory turnover cycle can be determined according to actual needs. For example, a time period of days, weeks, months or quarters can be selected to evaluate the inventory turnover value; the dynamic inventory change indicator refers to an indicator that changes with time, which is used to reflect the dynamic changes of inventory in the inventory area at a specific time point, which may include factors such as the product's incoming and outgoing quantities and inventory loss.

[0101] Furthermore, the present invention divides the product inventory area into regions according to the inventory flow value to obtain inventory partitions, which can improve inventory management efficiency. Partitioning based on flow value can make replenishment and inventory work more targeted, which helps to improve operational efficiency. Reasonable partitioning facilitates rapid positioning and retrieval of goods and speeds up inventory turnover.

[0102] Among them, the inventory partition refers to the different areas obtained after dividing the product inventory area based on high-flow areas and low-flow areas. These partitions are divided according to the characteristics of the inventory flow value. Each partition has different inventory management requirements. Through reasonable partitioning, inventory management operations can be optimized and inventory turnover efficiency and space utilization can be improved.

[0103] As an embodiment of the present invention, the regional segmentation of the product inventory area according to the inventory flow value to obtain inventory partitions includes: analyzing the flow sequence parameters corresponding to the inventory flow value; querying the flow peak value and the flow valley value in the flow sequence parameters; extracting the flow time nodes corresponding to the flow peak value and the flow valley value; dividing the product inventory area into high flow areas and low flow areas based on the flow time nodes; and regional segmentation of the product inventory area based on the high flow area and the low flow area to obtain inventory partitions.

[0104] Among them, the flow sequence parameter refers to a series of data formed by the change of inventory flow value over time, which reflects the specific situation of inventory flow in the product inventory area at different time points, including a collection of relevant data such as inventory inflow, outflow, turnover speed, etc.; the flow peak value refers to the maximum value reached by the inventory flow value in the flow sequence parameter, which represents the most active inventory flow in the product inventory area at a specific time point, and is the high point of the inventory flow value caused by the concentrated warehousing and outbound transportation of a large number of products or high-frequency inventory turnover operations; the flow valley value refers to the minimum value reached by the inventory flow value in the flow sequence parameter, which indicates the slowest inventory flow in the product inventory area at a specific time point, which may be caused by fewer inventory operations, unsalable products or other factors that slow down inventory flow. The low point of the value; the flow time node refers to the specific time point corresponding to the flow peak value and the flow valley value. These time points clarify the moment when the inventory flow value reaches the maximum or minimum, and are the key basis for dividing different inventory flow status areas. Through these time nodes, the changing law of inventory flow can be tracked and analyzed; the high flow area refers to the area with relatively high inventory flow value divided according to the flow time node in the product inventory area. In these areas, the inventory turnover of products is frequent, and the inflow and outflow operations are relatively intensive. Usually, best-selling products or products with fast turnover are stored; the low flow area refers to the area with relatively low inventory flow value divided according to the flow time node in the product inventory area. In these areas, the inventory turnover of products is slow, and the inflow and outflow operations are less. Usually, unsalable products or products with slow turnover are stored.

[0105] Furthermore, the analysis of the flow sequence parameters corresponding to the inventory flow value can be implemented by a time series analysis algorithm, such as ARIMA, Prophet and other algorithms, analyzing the data of inventory flow value changes over time to generate flow sequence parameters; the query of the flow peak and flow valley in the flow sequence parameters can be implemented by an extreme value analysis algorithm, such as Peak Detection algorithm, identifying the flow peak and flow valley from the flow sequence parameters; the extraction of the flow time nodes corresponding to the flow peak and the flow valley can be implemented by a timestamp extraction tool, such as Python's pandas library, extracting the time nodes corresponding to the flow peak and the flow valley from the flow sequence parameters; the division of the high flow area and the low flow area in the product inventory area can be implemented by a clustering analysis algorithm, such as K-means, hierarchical clustering and other algorithms, dividing the high flow area and the low flow area according to the flow time nodes; the regional segmentation of the product inventory area can be implemented by an inventory management system, such as SAP, Oracle and other platforms, which can perform regional segmentation according to the high flow area and the low flow area to generate inventory partitions.

[0106] The present invention can make inventory management more accurate by generating partition boundaries corresponding to the inventory partitions. Clear boundaries prevent management operations of different partitions from interfering with each other, facilitate employee identification and operation, and help optimize inventory space. Partition boundaries make product placement more orderly, avoid space waste, and improve inventory counting efficiency.

[0107] Among them, the partition boundary refers to the boundary mark used to clearly define the scope of each inventory partition after the product inventory area is partitioned according to the inventory flow value. It can be a physical mark such as shelf dividing lines, ground markings, wall partitions in the actual physical space, or it can be a digital mark such as a virtual coordinate range and area code boundary set in the inventory management system. These boundaries clearly distinguish different inventory partitions such as high-flow areas and low-flow areas, so that inventory management personnel can intuitively and accurately know the scope of each partition, so as to accurately implement the specific management strategy of the corresponding partition in operations such as cargo storage, handling, inventory counting and replenishment, ensure the efficiency, orderliness and accuracy of the inventory management process, and avoid confusion and misplacement of products between different partitions. Optionally, the generation of the partition boundary corresponding to the inventory partition can be achieved through spatial analysis tools, such as ArcGIS, QGIS and other tools to generate physical marks such as shelf dividing lines, ground markings in the actual physical space according to the spatial distribution of inventory partitions.

[0108] S4. Based on the partition boundary, formulate the partition management target corresponding to the self-service retail machine, analyze the target measures in the partition management target, query the measure execution standard corresponding to the target measure, and construct the management operation instruction corresponding to the self-service retail machine based on the measure execution standard.

[0109] The present invention formulates the zoning management goals corresponding to the self-service retail machines based on the zoning boundaries, which can improve operational efficiency, clarify the key tasks of each area according to different zoning boundaries, such as rapid replenishment in high-flow areas and precise marketing in low-flow areas, reduce the blindness of management, and optimize resource allocation, allocate manpower and material resources according to the characteristics of each zone, and avoid waste and mismatch of resources.

[0110] Among them, the zone management goal refers to a clear, specific and measurable goal system formulated based on the zone management strategy set. For each zone, there is a corresponding goal setting. For example, the goal of a high-flow zone can be to increase the inventory turnover rate by a certain percentage and reduce the replenishment cost while ensuring that there is no shortage of goods; the goal of a low-flow zone can be to reduce the inventory backlog to a specific level within a certain period of time and improve the sales conversion rate.

[0111] As an embodiment of the present invention, the partition management target corresponding to the self-service retail machine is formulated based on the partition boundary, including: analyzing the inventory flow characteristics corresponding to the products within the partition boundary; querying the standard management mode that matches the inventory flow characteristics; adjusting the strategy of the standard management mode to obtain a preliminary management strategy; based on the preliminary management strategy, calculating the cost-effectiveness value of the self-service retail machine in the operating environment; based on the cost-effectiveness value, partitioning the preliminary management strategy to obtain a partition management strategy set; based on the partition management strategy set, formulating the partition management target corresponding to the self-service retail machine.

[0112] The inventory flow characteristics refer to the unique properties of product inventory over time in each area defined by the partition boundary, including the turnover speed of inventory, such as the frequency of rapid entry and exit of products in high-flow areas and the relatively slow turnover in low-flow areas; the fluctuation pattern of inventory, such as whether there are seasonal or cyclical changes in inventory increase and decrease; and the risk tendency of inventory backlog and out-of-stock, that is, the possibility of long-term backlog or frequent out-of-stock of products in certain areas; the standard management model refers to the general management method and process for products with specific inventory flow characteristics formed in the retail industry through long-term practice and experience summary, for example, the common mode of timely replenishment and optimized display to promote sales for high-flow products; the mode of regular inventory and precision marketing to reduce inventory backlog for low-flow products; the preliminary management strategy refers to the management method set obtained by preliminary modification and adjustment of the standard model after querying the standard management model matching the inventory flow characteristics of each partition, combined with some basic conditions of the self-service retail machine itself, such as the storage space layout of the machine, the existing personnel operation process, etc., which may include preliminary replenishment plan, product Display plan, inventory counting cycle setting, etc.; the operating environment refers to the actual working scene of the self-service retail machine, covering multiple factors, including the physical environment, such as the location of the retail machine (shopping mall, school, office building, etc.), the surrounding flow of people and the consumption habits of the crowd; the market environment, such as the local economic level, competitive situation, and market demand trends of products; and internal operating conditions, such as the technical performance of the retail machine, maintenance, and cooperation with suppliers; the cost-effectiveness value refers to a comprehensive indicator used to measure the relationship between the economic benefits and cost expenditures brought about by the self-service retail machine under the operating environment based on the preliminary management strategy adopted. This value reflects the overall profitability of the self-service retail machine operation under a specific operating strategy; the partition management strategy set refers to a complete, detailed and targeted management strategy combination for each partition obtained after the preliminary management strategy is partitioned and optimized according to the cost-effectiveness value. The management strategy of each partition fully considers its inventory flow characteristics, operating environment and cost-effectiveness requirements, including accurate replenishment strategies, reasonable inventory layout, effective marketing promotion activities, and appropriate staffing.

[0113] Furthermore, the analysis of the inventory flow characteristics corresponding to the products within the partition boundary can be achieved through feature extraction tools, such as Pandas, NumPy and other tools; the query of the standard management mode matching the inventory flow characteristics can be achieved through machine learning models, such as KNN, SVM and other models, which can query the standard management mode matching the inventory flow characteristics; the strategy adjustment of the standard management mode can be achieved through simulation tools, such as AnyLogi tool, to simulate the effects of different management strategies and generate a preliminary management strategy; the calculation of the cost-effectiveness value of the self-service retail machine in the operating environment is achieved by the following algorithm formula; the partitioning of the preliminary management strategy can be achieved through a clustering analysis algorithm, such as K-means, hierarchical clustering and other algorithms, to partition the preliminary management strategy according to the cost-effectiveness value; the formulation of the partition management goals corresponding to the self-service retail machine can be achieved through a goal setting tool, such as OKR and other tools.

[0114] As an embodiment of the present invention, the calculating the cost-effectiveness value of the self-service retail machine in the operating environment based on the preliminary management strategy includes:

[0115] The cost-effectiveness value of the self-service retail machine in the operating environment is calculated using the following formula:

[0116]

[0117] Wherein, CBV represents the cost-effectiveness value of the self-service retail machine in the operating environment, T′ represents the operating time period corresponding to the operating environment, t′ represents the time node in the operating time period, R t′ represents the sales revenue value at the t′th time node, CB t′ represents the total cost value at the t′th time node, and r represents the discount rate corresponding to the product in the self-service retail machine.

[0118] In detail, the cost-effectiveness value refers to a comprehensive indicator used to measure the relationship between the economic benefits and cost expenditures brought about by the self-service retail machine under the operating environment based on the initial management strategy adopted. This value reflects the overall profitability of the self-service retail machine operation under a specific operating strategy; the operating time period refers to a time period selected when conducting a cost-effectiveness analysis on the self-service retail machine, which can be a month, a quarter, a year or other time period determined according to actual needs, and is used to evaluate the operating performance of the self-service retail machine during this period; the time node refers to a specific time point within the operating time cycle. When calculating the cost-effectiveness value, the entire operating time cycle is divided into multiple discrete time nodes to facilitate the calculation of the sales revenue and total cost at each time point. For example, if the operation time cycle is one year, the time node may be the end of each month, or the end of each quarter, etc.; the sales revenue value refers to the amount of revenue obtained by the self-service retail machine from selling products at each time node within the operation time cycle, reflecting the sales performance of the self-service retail machine at a specific moment; the total cost value refers to all costs and expenses incurred by the operation of the self-service retail machine at each time node within the operation time cycle, including but not limited to procurement costs, inventory costs, operation and maintenance costs, labor costs, etc.; the discount rate refers to a ratio used to consider the time value of money, which reflects the change in value of money over time, that is, the value of a sum of money at a certain point in the future will be lower than its current value.

[0119] The present invention helps to ensure the standardization of management operations by analyzing the target measures in the partition management targets and querying the measure execution standards corresponding to the target measures. Operating in accordance with the execution standards can avoid management chaos caused by arbitrariness, improve management efficiency, and facilitate the evaluation of management effects. By comparing with the execution standards, the implementation effectiveness of the target measures can be accurately judged, which is convenient for timely adjustment of strategies.

[0120] Among them, the target measures refer to the specific action plans and strategies formulated to achieve the zoning management goals, which are operational steps broken down according to the zoning management goals. For example, if the management goal of high-flow zones is to improve replenishment efficiency, the target measures may include adopting a real-time inventory monitoring system, establishing a fast replenishment channel, and optimizing the work flow of replenishment personnel; the management goal of low-flow zones is to reduce inventory backlogs, and the target measures may include implementing precision marketing plans, regularly counting and analyzing inventory, and negotiating return or exchange policies with suppliers; the measure implementation standards refer to the specific specifications and requirements set for each target measure to ensure that the target measures can be accurately and effectively implemented. For example, for the target measure of a real-time inventory monitoring system for high-flow zones, the implementation standards may include inventory numbers; The update frequency of the data (such as updating every 10 minutes), the accuracy of the monitoring system (such as the error rate is controlled within 1%), the response time of the system failure (such as issuing an alarm and launching an emergency plan within 5 minutes after the failure occurs), etc.; for the target measure of the precision marketing program implemented in the low-mobility partition, the execution standard may involve the target customer positioning accuracy of the marketing activities (such as reaching 80%), the push frequency of marketing information (such as pushing once a week), the conversion rate of the marketing activities (such as reaching 10%), etc. Optionally, the analysis of the target measures in the partition management goals can be implemented by a rule engine, such as: generating a decomposition according to the partition management goals through the rule engine to obtain the target measures; the query of the measure execution standard corresponding to the target measure can be implemented by a machine learning model, such as: regression analysis, XGBoost and other models.

[0121] Furthermore, the present invention constructs management operation instructions corresponding to the self-service retail machine based on the measure execution standards, which helps to improve operational efficiency. Standardized operation instructions make the workflow smoother, reduce unnecessary waste of time, and facilitate management and supervision. Managers can effectively evaluate employees' work based on the operation instructions, discover and correct problems in a timely manner, and ensure the stable operation of the retail machine.

[0122] Among them, the management operation instructions refer to the conversion of conditional execution sequences into specific instruction texts or electronic information that can be directly followed by self-service retail machine managers and operators. These instructions are presented in clear, unambiguous and concise language or in a visual form, and detail the specific tasks, operating methods, execution conditions, precautions and expected effects of each operation link.

[0123] As an embodiment of the present invention, the construction of the management operation instructions corresponding to the self-service retail machine based on the measure execution standard includes: refining the clauses of the measure execution standard to obtain standard details; extracting the key execution steps in the standard details; arranging the key execution steps in sequence to obtain an action sequence; based on the action sequence, setting conditions for the key execution steps to obtain a conditional execution sequence; based on the conditional execution sequence, constructing the management operation instructions corresponding to the self-service retail machine.

[0124] Among them, the standard details refer to the specific clauses formed after the detailed decomposition and explanation of the implementation standards of the measures. For example, for the implementation standard of "optimizing product display" in a certain section of the self-service retail machine, the standard details may include the specific display positions of various types of goods (such as high-profit goods are displayed in the middle of the middle shelf, and best-selling goods are displayed at a position that is at eye level and easy to pick up, etc.), display methods (such as specific requirements for horizontal display, vertical display, and related display), display quantity (the range of the number of goods placed on each shelf layer), display spacing (standard spacing between goods) and other detailed operating specifications; the key implementation steps refer to the key points extracted from the standard details that have a key role in achieving the goals of the measure. The key execution steps of the action sequence are the operation process chain formed by arranging and combining the key execution steps in a reasonable order. For example, for the key execution step of "optimizing commodity display", the shelf cleaning is first performed (including wiping the shelf, checking the shelf stability, etc.); the conditional execution sequence refers to a more accurate and intelligent operation process formed by setting corresponding trigger conditions and restriction conditions for each key execution step on the basis of the action sequence. For example, in the action sequence of "fast replenishment mechanism based on real-time sales data", for the step of "calculating the replenishment quantity", the condition is set that the replenishment quantity calculation operation is performed only when the real-time sales data shows that the inventory of a certain commodity is lower than the preset safety stock and the sales speed in the past hour exceeds a certain multiple of the average sales speed.

[0125] Furthermore, the detailed clauses of the implementation standards of the measures can be achieved through document generation tools, such as Notion, Overleaf and other tools; the extraction of key execution steps in the standard details can be achieved through NLP tools, such as NLTK, SpaCy and other tools; the sequential arrangement of the key execution steps can be achieved through process optimization tools, such as BPMN tools, which arrange the key execution steps in sequence and generate an action sequence; the conditional setting of the key execution steps can be achieved through conditional logic tools, such as Excel, Google Sheets and other tools; the construction of management operation instructions corresponding to the self-service retail machine can be achieved through automated scripting tools, such as Python, JavaScript and other tools.

[0126] S5. After sending the management operation instruction to the preset blockchain system, perform real-time product management on the self-service retail machine to obtain product management data, obtain blockchain record information corresponding to the product management data, perform data verification on the blockchain record information to obtain blockchain verification data, and generate a product management report corresponding to the self-service retail machine based on the blockchain verification data.

[0127] After the management operation instruction is sent to a preset blockchain system, the present invention performs real-time product management on the self-service retail machine to obtain product management data. With the help of the immutability and traceability of the blockchain, the product management data is more credible, which facilitates accurate tracing of the operation process and product flow, and can achieve efficient collaboration. All relevant parties can know the management situation in real time through the blockchain, optimize work connection, and ensure the smooth and efficient operation of the self-service retail machine.

[0128] Among them, the preset blockchain system refers to the blockchain network architecture and related technical platform specially built or selected for the operation and management of self-service retail machines. It has the core characteristics of blockchain, such as decentralized distributed ledgers, data stored in multiple nodes rather than a single central server, to ensure data security and stability; encryption algorithms are used to ensure confidentiality during data transmission and storage to prevent information leakage and tampering. In this system, all entities related to self-service retail machines, such as retailers, suppliers, logistics providers, maintenance service providers, etc., can be connected as nodes; the product management data refers to a series of product-related data information generated and collected in the process of sending management operation instructions to the blockchain system and executing real-time product management, including real-time changes in the inventory quantity of products in self-service retail machines, such as each time Inventory increase or decrease after replenishment and sales; detailed sales data such as product sales time, sales price, sales quantity, etc., which are helpful for analyzing product sales trends and consumer purchasing behavior; product inventory location information, that is, the specific storage location of the product in the self-service retail machine, which is convenient for quick search and inventory; product quality monitoring data, such as temperature and humidity data of food products monitored by sensors to ensure product quality and safety, or operating status data of electronic products to determine whether there are hidden dangers of failure, etc. Optionally, sending the management operation instructions to the preset blockchain system can be implemented through a blockchain client, such as: Geth, Besu and other clients; the real-time management of products on the self-service retail machine can be implemented through an automated control system, such as: PLC, SCADA, etc.

[0129] Furthermore, the present invention can enhance data reliability by obtaining blockchain record information corresponding to the product management data and performing data verification on the blockchain record information to obtain blockchain verification data. Verification can ensure that the data has not been maliciously tampered with during blockchain transmission and storage, provide a credible basis for decision-making, and help discover potential problems, such as inconsistent data or abnormal records, so as to promptly troubleshoot the source of errors and ensure the normal operation of business processes.

[0130] Among them, the blockchain record information refers to a collection of all relevant records about the product management data of self-service retail machines in the blockchain system. For example, it records the timestamp of each product inventory change and sales transaction, and clarifies the exact time when the data is generated; it records the digital signature of the operation initiator to identify the authenticity and non-repudiation of the data source; it also records the detailed content of the data itself, such as the specific inventory increase and decrease quantity, the type and price of the sales product and other information; the blockchain verification data refers to the result data obtained after processing the blockchain record information through a specific data verification algorithm and process. During the verification process, the data integrity in the blockchain record information will be checked, for example, confirming whether all required data fields exist and are in the correct format; verifying the consistency of the data, such as whether the inventory data of the same product at different time nodes is logically coherent, and whether the sales data matches the inventory change data. Optionally, the acquisition of the blockchain record information corresponding to the product management data can be implemented through an API interface, such as: Alchemy, Infura and other interfaces; the data verification of the blockchain record information can be implemented through a digital signature algorithm, such as: ECDSA and other algorithms.

[0131] Furthermore, the present invention generates a product management report corresponding to the self-service retail machine based on the blockchain verification data, which can ensure the transparency, credibility and non-tamperability of the data, provide a reliable basis for decision-making, accurately trace each step of the product management process, and promptly discover and solve potential problems, thereby improving management efficiency and the smoothness of business processes.

[0132] Among them, the product management report refers to a document or electronic information material formed after a comprehensive, systematic and detailed summary and analysis of the product management of the self-service retail machine based on blockchain verification data. Its content covers multiple key aspects of the product in the operation process of the self-service retail machine, including but not limited to the inventory dynamics of the product, such as changes in inventory levels in different time periods, inventory turnover rate and other data and related charts; sales situation analysis, such as sales quantity, sales volume, sales trend (by day, week, month and other cycles) of various products and ranking and sales characteristics of best-selling and slow-selling products; product replenishment, including information such as the number of replenishments, replenishment time interval, and the matching degree of replenishment quantity with sales demand; product quality, through the quality monitoring data in the blockchain record to reflect the quality status of the product during storage and sales (such as the freshness of food, the operating stability of electronic products, etc.); it will also involve the effect evaluation caused by the adjustment of product management strategies, such as changes in product sales during promotional activities and the impact on inventory, etc. Optionally, the generation of the product management report corresponding to the self-service retail machine can be achieved through a report generation tool, such as: Tableau, Power BI and other tools.

[0133] Compared with the problems described in the background technology, the present invention obtains the product retail scene corresponding to the self-service retail machine and queries the product demand parameters in the product retail scene, which helps to accurately locate the consumption characteristics and demand trends of the environment in which the retail machine is located, so that the supplied products are more in line with the preferences of local consumers, improve the targetedness and success rate of sales, and can plan inventory in advance based on these parameters to avoid excessive stockpiling or out-of-stock phenomena and optimize inventory management. The present invention analyzes the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand, can monitor sales dynamics in real time, compare transaction conditions based on inventory demand, and promptly detect sales anomalies so as to quickly adjust marketing strategies. It can accurately predict the timing of replenishment, and plan replenishment plans in advance through the correlation analysis of transaction parameters and inventory demand, so as to avoid out-of-stock or backlogs and ensure supply continuity. Furthermore, the present invention can intuitively understand the circulation speed of products in the inventory area by calculating the inventory flow value corresponding to the product inventory area, so as to provide information for replenishment and inventory allocation. Providing key basis is helpful to optimize inventory layout, reasonably arrange the storage location of goods according to the inventory flow differences in different areas, and improve the utilization rate of inventory space. Further, the present invention formulates the partition management target corresponding to the self-service retail machine based on the partition boundary, which can improve the operation efficiency, clarify the key tasks of each area according to different partition boundaries, such as rapid replenishment in high-flow areas and precise marketing in low-flow areas, reduce the blindness of management, and optimize resource allocation, allocate manpower and material resources according to the characteristics of each partition, and avoid waste and mismatch of resources. Finally, the present invention performs real-time product management on the self-service retail machine after sending the management operation instruction to the preset blockchain system, and obtains product management data. With the help of the immutability and traceability of the blockchain, the product management data is more credible, which is convenient for accurately tracing the operation process and product flow, and can achieve efficient collaboration. All relevant parties can know the management situation in real time through the blockchain, optimize the work connection, and ensure the smooth and efficient operation of the self-service retail machine. Therefore, the product management method and system for implementing self-service retail machines based on blockchain provided in the embodiment of the present invention can improve the product management efficiency of self-service retail machines.

[0134] Embodiment 2:

[0135] like Figure 2 As shown, it is a functional module diagram of a product management system for a self-service retail machine based on blockchain according to the present invention.

[0136] The product management system 200 for implementing a self-service retail machine based on blockchain described in the present invention can be installed in an electronic device. According to the functions implemented, the product management system for implementing a self-service retail machine based on blockchain can include a demand analysis module 201, an area query module 202, a boundary generation module 203, an instruction construction module 204 and a report generation module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0137] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0138] The demand analysis module 201 is used to obtain a product retail scenario corresponding to the self-service retail machine, query product demand parameters in the product retail scenario, determine product supply conditions required for the product retail scenario based on the product demand parameters, and analyze product inventory requirements corresponding to the self-service retail machine based on the product supply conditions;

[0139] The area query module 202 is used to analyze the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand, determine the product fluctuation data corresponding to the self-service retail machine according to the real-time transaction parameters, and query the product inventory area corresponding to the self-service retail machine based on the product fluctuation data;

[0140] The boundary generation module 203 is used to calculate the inventory flow value corresponding to the product inventory area, segment the product inventory area according to the inventory flow value to obtain inventory partitions, and generate partition boundaries corresponding to the inventory partitions;

[0141] The instruction construction module 204 is used to formulate the zone management target corresponding to the self-service retail machine based on the zone boundary, analyze the target measures in the zone management target, query the measure execution standard corresponding to the target measure, and construct the management operation instruction corresponding to the self-service retail machine based on the measure execution standard;

[0142] The report generation module 205 is used to send the management operation instruction to the preset blockchain system, perform real-time product management on the self-service retail machine, obtain product management data, obtain blockchain record information corresponding to the product management data, perform data verification on the blockchain record information, obtain blockchain verification data, and generate a product management report corresponding to the self-service retail machine based on the blockchain verification data.

[0143] In detail, the modules in the product management system 200 for implementing a self-service retail machine based on blockchain in the embodiment of the present invention are used in the same manner as described above. Figure 1 The technical means described in the method for implementing product management of self-service retail machines based on blockchain are the same as those described in the method and can produce the same technical effects, so they will not be repeated here.

[0144] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A product management method for self-service retail machines based on blockchain, characterized in that: The method comprises: Obtain a product retail scenario corresponding to the self-service retail machine, query product demand parameters in the product retail scenario, determine product supply conditions required for the product retail scenario based on the product demand parameters, and analyze product inventory requirements corresponding to the self-service retail machine based on the product supply conditions; Based on the product inventory demand, analyzing the real-time transaction parameters corresponding to the self-service retail machine, determining the product fluctuation data corresponding to the self-service retail machine according to the real-time transaction parameters, and querying the product inventory area corresponding to the self-service retail machine based on the product fluctuation data; Calculating the inventory flow value corresponding to the product inventory area, segmenting the product inventory area according to the inventory flow value to obtain inventory partitions, and generating partition boundaries corresponding to the inventory partitions; Based on the partition boundary, formulate the partition management target corresponding to the self-service retail machine, analyze the target measures in the partition management target, query the measure execution standard corresponding to the target measure, and construct the management operation instruction corresponding to the self-service retail machine based on the measure execution standard; After the management operation instruction is sent to the preset blockchain system, real-time product management is performed on the self-service retail machine to obtain product management data, obtain blockchain record information corresponding to the product management data, perform data verification on the blockchain record information to obtain blockchain verification data, and generate a product management report corresponding to the self-service retail machine based on the blockchain verification data.

2. The method for implementing product management of a self-service retail machine based on blockchain as claimed in claim 1, characterized in that: The analyzing the product inventory demand corresponding to the self-service retail machine based on the product supply condition includes: Analyze the key condition parameters in the product supply conditions; Identify potential demand indicators corresponding to the key condition parameters; Based on the potential demand indicator, determining an inventory fluctuation pattern corresponding to the self-service retail machine; Querying the inventory optimization intention corresponding to the inventory fluctuation pattern; Based on the inventory optimization intention, the product inventory demand corresponding to the self-service retail machine is analyzed.

3. The method for implementing product management of self-service retail machines based on blockchain as claimed in claim 1, characterized in that: The analyzing the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand includes: Extracting the core demand data in the product inventory demand; Based on the core demand data, set key transaction indicators corresponding to the self-service retail machine; Collecting real-time flow data corresponding to the self-service retail machine in real time; Based on the key transaction indicators, the real-time flow data is screened and processed to obtain real-time screening details; Based on the real-time screening details, real-time transaction parameters corresponding to the self-service retail machine are analyzed.

4. The method for implementing product management of a self-service retail machine based on blockchain as claimed in claim 1, characterized in that: The querying of the product inventory area corresponding to the self-service retail machine based on the product fluctuation data includes: Extracting key fluctuation factors from the product fluctuation data; Based on the key fluctuation factors, the products in the self-service retail machine are classified to obtain product category groups; Querying the layout characteristics of the product category group in the self-service retail machine; Based on the layout characteristics, determining the regional layout range corresponding to the self-service retail machine; Based on the regional layout range, query the product inventory area corresponding to the self-service retail machine.

5. The method for implementing product management of self-service retail machines based on blockchain as claimed in claim 1, characterized in that: The calculating the inventory flow value corresponding to the product inventory area includes: The inventory flow value corresponding to the product inventory area is calculated using the following formula: Among them, CF represents the inventory flow value corresponding to the product inventory area, n represents the total number of sub-areas corresponding to the product inventory area, i represents the quantity index corresponding to the sub-area, and a i represents the weight coefficient corresponding to the i-th sub-region, F i represents the inventory flow evaluation value corresponding to the i-th sub-area, t1 and t2 represent the cycle start time and cycle end time corresponding to the inventory flow cycle, respectively, and C(t) represents the dynamic inventory change index at time t.

6. The method for implementing product management of a self-service retail machine based on blockchain as claimed in claim 1, characterized in that: The step of segmenting the product inventory area according to the inventory flow value to obtain inventory partitions includes: Analyzing the flow sequence parameters corresponding to the inventory flow value; Querying the flow peak value and flow valley value in the flow sequence parameters; Extracting flow time nodes corresponding to the flow peak value and the flow valley value; Based on the flow time nodes, dividing the product inventory area into a high flow area and a low flow area; Based on the high flow area and the low flow area, the product inventory area is segmented to obtain inventory partitions.

7. The method for implementing product management of a self-service retail machine based on blockchain as claimed in claim 1, characterized in that: The formulating of the partition management target corresponding to the self-service retail machine based on the partition boundary includes: Analyzing inventory flow characteristics corresponding to products within the partition boundary; Querying a standard management model that matches the inventory flow characteristics; Adjust the strategy of the standard management model to obtain a preliminary management strategy; Based on the preliminary management strategy, calculating the cost-effectiveness value of the self-service retail machine in the operating environment; Based on the cost-effectiveness value, the preliminary management strategy is partitioned to obtain a partition management strategy set; Based on the partition management strategy set, a partition management target corresponding to the self-service retail machine is formulated.

8. The method for implementing product management of a self-service retail machine based on blockchain as claimed in claim 7, characterized in that: The calculating the cost-effectiveness value of the self-service retail machine in the operating environment based on the preliminary management strategy includes: The cost-effectiveness value of the self-service retail machine in the operating environment is calculated using the following formula: Wherein, CBV represents the cost-effectiveness value of the self-service retail machine in the operating environment, T′ represents the operating time period corresponding to the operating environment, t′ represents the time node in the operating time period, R t′ represents the sales revenue value at the t′th time node, CB t′ represents the total cost value at the t′th time node, and r represents the discount rate corresponding to the product in the self-service retail machine.

9. The method for implementing product management of a self-service retail machine based on blockchain as claimed in claim 1, characterized in that: The step of constructing the management operation instructions corresponding to the self-service retail machine based on the measure execution standard includes: The implementation standards of the measures are refined into clauses to obtain standard details; Extract the key implementation steps in the standard details; Arranging the key execution steps in sequence to obtain an action sequence; Based on the action sequence, conditional setting is performed on the key execution steps to obtain a conditional execution sequence; Based on the conditional execution sequence, a management operation instruction corresponding to the self-service retail machine is constructed.

10. A product management system for self-service retail machines based on blockchain, characterized in that: The system comprises: A demand analysis module, used to obtain a product retail scenario corresponding to the self-service retail machine, query product demand parameters in the product retail scenario, determine product supply conditions required for the product retail scenario based on the product demand parameters, and analyze product inventory requirements corresponding to the self-service retail machine based on the product supply conditions; A region query module, configured to analyze the real-time transaction parameters corresponding to the self-service retail machine based on the product inventory demand, determine the product fluctuation data corresponding to the self-service retail machine according to the real-time transaction parameters, and query the product inventory region corresponding to the self-service retail machine based on the product fluctuation data; A boundary generation module, used for calculating the inventory flow value corresponding to the product inventory area, segmenting the product inventory area according to the inventory flow value to obtain inventory partitions, and generating partition boundaries corresponding to the inventory partitions; An instruction construction module, used to formulate a zone management target corresponding to the self-service retail machine based on the zone boundary, analyze the target measures in the zone management target, query the measure execution standard corresponding to the target measure, and construct the management operation instruction corresponding to the self-service retail machine based on the measure execution standard; The report generation module is used to send the management operation instruction to a preset blockchain system, perform real-time product management on the self-service retail machine, obtain product management data, obtain blockchain record information corresponding to the product management data, perform data verification on the blockchain record information, obtain blockchain verification data, and generate a product management report corresponding to the self-service retail machine based on the blockchain verification data.

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