Online Monitoring Method and System for a Best-Selling Commodity

Abstract

The present invention discloses an online monitoring method and system for hot-selling commodities, which relates to the technical field of e-commerce. The method includes: collecting consumer behavior data and performing preprocessing to form a consumer behavior data set; constructing a consumer behavior heat model based on the consumer behavior data set to obtain the heat score of the commodity; performing geographical location analysis on the sales data of potential hot-selling commodities through geographical information technology to generate a regional commodity sales heat map, identifying and marking regional hot-selling commodities; linking the identified regional hot-selling commodities with the supply chain management system to obtain commodity inventory data, calculating the inventory consumption rate and the inventory depletion time; triggering an inventory warning and generating a replenishment suggestion according to the calculation results, and automatically adjusting inventory management; combining geographical information technology to generate a regional commodity sales heat map and performing spatial autocorrelation analysis using the Moran index, effectively improving the ability to link with the internal supply chain management system of the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce, and particularly to an online monitoring method and system for hot-selling commodities. Background Art

[0002] In the development process of the e-commerce and retail industries, the collection and analysis of consumer behavior data have gradually become key means to optimize supply chain management and improve sales efficiency. With the progress of big data technology and machine learning algorithms, more and more enterprises have started to use these technologies to predict commodity demand trends in order to achieve more accurate commodity management and inventory control. However, most existing solutions focus on static data analysis and fail to fully consider the impact of dynamic changes in consumer behavior on commodity popularity and the regional differences in commodity sales patterns due to geographical factors.

[0003] The deficiencies in the prior art are mainly reflected in two aspects: First, there is a lack of capture of the dynamic characteristics of consumer behavior, that is, the weights are not adjusted according to time changes, ignoring the importance of the evolution of user behavior over time; Second, traditional methods rarely combine geographic information systems (GIS) for spatial autocorrelation analysis and cannot accurately identify regional hot-selling commodities, which limits the enterprise's ability to operate different market regions in a refined manner. Therefore, the current technical solutions are difficult to provide commodity popularity assessment and replenishment suggestions that are both time-sensitive and regionally targeted. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an online monitoring method for hot-selling commodities to solve the problem that the prior art fails to fully reflect the dynamic changes in consumer behavior and the influence of geographical factors.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an online monitoring method for hot-selling commodities, which includes:

[0008] Collect consumer behavior data and perform preprocessing to form a consumer behavior data set;

[0009] Based on the consumer behavior data set, construct a consumer behavior heat model to obtain the heat score of the commodity;

[0010] According to the heat score of the commodity, identify potential hot-selling commodities;

[0011] Through geographic information technology, perform geographical location analysis on the sales data of potential hot-selling commodities to generate a regional commodity sales heat map, and identify and mark regional hot-selling commodities;

[0012] Link the identified regionally popular products with the supply chain management system to obtain product inventory data, and calculate the inventory consumption rate and inventory depletion time;

[0013] Based on the calculation results, trigger an inventory warning and generate a replenishment suggestion to automatically adjust inventory management.

[0014] As a preferred solution of the online monitoring method for popular products described in the present invention, wherein: the consumer behavior data includes browsing records, click counts, add-to-cart behaviors, purchase frequencies, evaluations, and feedback; the preprocessing includes deep cleaning and standardization processing.

[0015] As a preferred solution of the online monitoring method for popular products described in the present invention, wherein: based on the consumer behavior data set, constructing a consumer behavior heat model to obtain the heat score of a product includes the following steps

[0016] Dynamically adjust the weight allocation based on the different degrees of influence of consumers' behaviors on product heat, and the expression is:

[0017] ;

[0018] Wherein, represents the dynamic weight of the th specific consumer behavior at time , represents the importance coefficient of the th specific consumer behavior, represents the importance coefficient of the th type of consumer behavior, represents the specific consumer behavior index, represents the consumer behavior type index, represents time;

[0019] Based on the influence of the mutual influence between users on product heat, introduce a correction factor to correct the social network influence, and the expression is:

[0020] ;

[0021] Wherein, represents the social network influence correction factor of product , represents the social influence strength parameter, represents the user identifier, represents the set of users related to product , represents the PageRank score of user in the social network;

[0022] Based on the adjusted weights and the social network influence correction factor, a consumer behavior heat model is constructed to obtain the heat score of a commodity, and the expression is:

[0023] ;

[0024] wherein, represents the final heat score of the commodity , represents the balance parameter for controlling the importance of behavior indicators, represents the total number of specific consumer behaviors, represents the time decay rate, represents the time difference from the occurrence of the th specific consumer behavior of the th commodity to the present, represents the th quantity of the specific consumer behavior of the th commodity, represents the balance parameter affecting the social network, represents the activation function for the non-linear feature representation of the commodity

[0025] As a preferred solution of the online monitoring method for the hot-selling commodities described in the present invention, wherein: identifying potential hot-selling commodities based on the heat score of the commodity includes the following steps

[0026] Setting a heat threshold according to the heat distribution in historical data, and sorting the heat scores in descending order to form a preliminary candidate list;

[0027] Conducting multi-stage screening for each candidate commodity in the preliminary candidate list;

[0028] Preliminarily screening out the set of commodities whose heat scores are higher than the heat threshold, and conducting secondary screening according to the sales growth rate in the set of commodities;

[0029] Combining seasonality and promotional activities to conduct final screening on the potentially hot-selling commodities after secondary screening, and finally obtaining the potentially hot-selling commodities.

[0030] As a preferred solution of the online monitoring method for the hot-selling commodities described in the present invention, wherein: conducting geographical location analysis on the sales data of potentially hot-selling commodities through geographical information technology to generate a regional commodity sales heat map, and identifying and marking regional hot-selling commodities includes the following steps

[0031] Extracting the sales data of each commodity from the potentially hot-selling commodities and associating it with the geographical location information;

[0032] Construct a spatial weight matrix to evaluate the correlation and influence degree between different regions;

[0033] Use geographic information system tools to generate a regional sales heat map based on the spatial weight matrix and the extracted sales data;

[0034] Apply the Moran's I index to conduct spatial autocorrelation analysis on the regional sales heat map to measure the degree of similarity between observed values and their neighbors. The expression is:

[0035] ;

[0036] where, represents the Moran's I index, represents the total number of geographical regions participating in the analysis, represents the sum of all non-zero elements in the spatial weight matrix, represents the th region and the th region element of the spatial weight matrix, represents the th region's sales volume, represents the th region's sales volume, represents the average value of the sales volumes of all regions, and represent index variables that traverse all regions;

[0037] Based on the results of the spatial autocorrelation analysis, automatically identify the products showing strong spatial aggregation characteristics and mark them as regionally hot-selling products.

[0038] As a preferred scheme of the online monitoring method for the hot-selling products described in the present invention, among them: Link the identified regionally hot-selling products with the supply chain management system to obtain product inventory data, and calculating the inventory consumption rate and the inventory depletion time includes the following steps,

[0039] Obtain the latest inventory data of the product from the supply chain management platform;

[0040] Based on the generated regional sales heat map and the results of the Moran's I index analysis, calculate the inventory consumption rate of each product. The expression is:

[0041] ;

[0042] where, represents the average daily inventory consumption rate of the th product, represents the inventory of the th product at time , represents the The inventory of a product at time , represents the length of the past time window for calculating the consumption rate;

[0043] Using the inventory consumption rate of each product, determine the time when the inventory is exhausted, and the expression is:

[0044] ;

[0045] where, represents the th product's time required for the inventory to be completely exhausted.

[0046] As a preferred solution of the online monitoring method for hot-selling products described in the present invention, wherein: according to the calculation result, trigger an inventory warning and generate a replenishment suggestion, and automatically adjust the inventory management including the following steps,

[0047] Set a safety inventory level and a replenishment lead time for each product. When < , trigger an inventory warning;

[0048] Based on the product that triggers the warning, generate a replenishment suggestion, and the expression is:

[0049] ;

[0050] where, represents the th product's inventory quantity to be replenished, represents the demand volatility parameter;

[0051] According to the generated replenishment suggestion, automatically send an order request to the supplier and update the internal inventory record at the same time.

[0052] In a second aspect, the present invention provides an online monitoring system for hot-selling products, including,

[0053] A data acquisition module, which acquires consumer behavior data and performs preprocessing to form a consumer behavior data set;

[0054] A heat modeling module, which constructs a consumer behavior heat model based on the consumer behavior data set to obtain the heat score of the product;

[0055] A hot-selling identification module, which identifies potential hot-selling products according to the heat score of the product;

[0056] A geographical analysis module, which performs geographical location analysis on the sales data of potential hot-selling products through geographic information technology, generates a regional product sales heat map, and identifies and marks regional hot-selling products;

[0057] An inventory management module links the identified regionally popular products with the supply chain management system to obtain product inventory data, calculate the inventory consumption rate and the inventory depletion time;

[0058] An early warning replenishment module triggers an inventory warning and generates a replenishment suggestion based on the calculation results, and automatically adjusts the inventory management.

[0059] Thirdly, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the online monitoring method of the popular products as described in the first aspect of the present invention is implemented.

[0060] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the online monitoring method of the popular products as described in the first aspect of the present invention is implemented.

[0061] The beneficial effects of the present invention are as follows: By introducing a dynamic weight allocation mechanism and a social network influence correction factor, a consumer behavior heat model that can respond to changes in consumer behavior in real time is constructed, realizing a more accurate quantitative evaluation of product heat; Combining geographic information technology, generating a regional product sales heat map, and using Moran's index for spatial autocorrelation analysis, effectively improving the ability to link with the internal supply chain management system of the enterprise; The inventory warning system triggered based on the calculation results and the automated replenishment suggestion generation process ensure the efficiency and accuracy of inventory management, reduce the risk of out-of-stock, and improve customer satisfaction and the market competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0063] Figure 1 It is a flowchart of the online monitoring method of the popular products in Embodiment 1.

[0064] Figure 2 It is a module diagram of the online monitoring system of the popular products in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0066] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0067] Secondly, as used herein, "one embodiment" or "an embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other.

[0068] Example 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides an online monitoring method for hot-selling products, including the following steps:

[0069] S1. Collect consumer behavior data and perform preprocessing to form a consumer behavior data set.

[0070] S1.1. Consumer behavior data includes browsing records, click counts, add-to-cart behaviors, purchase frequencies, evaluations, and feedback; preprocessing includes deep cleaning and standardization processing.

[0071] Specifically, deep cleaning refers to introducing a hybrid cleaning method based on rules and machine learning to clean consumer behavior data, specifically including:

[0072] For structured fields, use regular expressions to match the correct patterns; for unstructured text content, apply natural language processing techniques to clean up noise words and extract key information; for duplicate item detection problems, use the MinHash algorithm to calculate document similarity, and set similarity thresholds according to different types of consumer behavior data. When the hash values of two entries are lower than the similarity threshold, they are considered duplicate records and thus deleted.

[0073] Standardization processing means making data from different sources have the same representation form. This involves a series of operations such as unifying date / time formats, geographical coding (Geo-coding), currency symbol conversion, etc. In particular, for ratings and reviews, they can be quantified into numerical indicators through a sentiment analysis model.

[0074] S2. Based on the consumer behavior data set, construct a consumer behavior heat model to obtain the heat score of the product.

[0075] S2.1 Dynamically adjust the weight distribution based on the different degrees of influence of consumers' behaviors on product heat, and the expression is:

[0076] ;

[0077] Among them, represents the dynamic weight of the th type of consumer-specific behavior at time , that is, when calculating the popularity score of a product, the proportion of different behaviors according to the time and importance of their occurrence, represents the importance coefficient of the th type of consumer-specific behavior, represents the importance coefficient of the th type of consumer behavior type, represents the consumer-specific behavior index, represents the consumer behavior type index, represents the time.

[0078] S2.2. Based on the influence of the interaction between users on the popularity of products, a correction factor is introduced to correct the social network influence, and the expression is:

[0079] ;

[0080] Among them, represents the social network influence correction factor of the product , represents the social influence strength parameter, represents the user identifier, represents the set of users associated with the product , represents the PageRank score of the user in the social network.

[0081] Specifically, the expression of the PageRank score is:

[0082] ;

[0083] Among them, represents the PageRank score of the product , represents the damping coefficient, represents the set of users associated with the product , represents the th number of products associated with a specific consumer behavior, represents the th basic influence score of a specific consumer behavior.

[0084] It should be noted that the PageRank score not only considers the direct behaviors of users (such as comments, sharing, etc.), but also takes into account the indirect influence of users in the entire social network, thus more comprehensively reflecting the impact of users on the popularity of goods.

[0085] S2.3. Based on the adjusted weights and the social network influence correction factor, construct a consumer behavior heat model to obtain the heat score of the goods. The expression is as follows:

[0086] ;

[0087] Among them, represents the final heat score of the goods , represents the balance parameter for controlling the importance of behavior indicators, represents the total number of specific consumer behaviors, represents the time decay rate, represents the time difference from the occurrence of the th specific consumer behavior of the th good to the present, reflecting the freshness of the behavior. The newer the behavior, the greater the impact on the heat, represents the th quantity of the specific consumer behavior of the th good, represents the balance parameter affecting the social network, represents the activation function of the non-linear feature representation of the goods

[0088] S3. Identify potential hot-selling goods according to the heat score of the goods.

[0089] S3.1. Set a heat threshold according to the heat distribution in historical data, and sort the heat scores in descending order to form a preliminary candidate list; conduct multi-stage screening on each candidate good in the preliminary candidate list; initially screen out the set of goods whose heat scores are higher than the heat threshold, and conduct secondary screening according to the sales growth rate in the set of goods.

[0090] Specifically, the expression of the sales growth rate is as follows:

[0091] ;

[0092] Among them, represents the sales growth rate of the th good, represents the sales amount of the th good at time , represents the The sales amount of a product at time , represents the time window length.

[0093] Based on historical sales data, set a sales growth rate threshold, compare the calculated sales growth rate with the growth rate threshold, and those higher than the sales growth rate threshold are marked as potential hot-selling products after secondary screening.

[0094] S3.2. Combine seasonality and promotional activities to conduct a final screening of the potential hot-selling products after secondary screening, and finally obtain the potential hot-selling products.

[0095] Collect sales records for the same time period in the past few years, and classify and organize the sales records according to the attributes of the products (such as clothing, food, electronic products, etc.); use time series analysis tools (such as STL decomposition, ARIMA model) to decompose the sales data into trend components, seasonal components and random components, and identify obvious seasonal patterns; establish a multiple regression model, with the sales volume as the dependent variable and time and climate variables as independent variables, to evaluate the impact degree of different factors on sales; cluster the products according to the similarity of the sales patterns to find product categories with similar seasonal characteristics;

[0096] Obtain the promotional plans of detailed historical marketing activities from the marketing department, including information such as discount strength, promotion type (such as full reduction, buy one get one free), promotion duration, promotion scope, etc.; refer to the effect evaluation reports of previous promotional activities to understand the specific impact of different types of promotions on sales;

[0097] By analyzing historical sales data and historical marketing activity information, obtain seasonal sales conditions and promotional activities; combine seasonality and promotional activities to conduct a final screening of the potential hot-selling products after secondary screening, and introduce seasonal factors and promotional factors to adjust the heat scores of products significantly affected by seasonality or promotions. The expression is:

[0098] ;

[0099] Among them, represents the adjusted heat score, represents the seasonal factor, represents the promotional factor.

[0100] Compare the adjusted heat score with the heat threshold, and the potential hot-selling products after secondary screening that are higher than the heat threshold are the final potential hot-selling products.

[0101] S4. Conduct a geographical location analysis of the sales data of potential hot-selling products through geographic information technology, generate a regional product sales heat map, and identify and mark regional hot-selling products.

[0102] S4.1. Extract the sales data of each product in each region from potential best-selling products, including product ID, sales date, sales quantity, sales amount, and geographical location information; and associate it with the geographical location information, that is, match the geographical location information in the sales data with the pre-prepared geographical area division table (such as the provincial, municipal, district, and county levels) to ensure that each sales record can correspond to a specific geographical area; construct a spatial weight matrix based on the spatial adjacency relationship of the geographical areas to evaluate the relevance and influence degree between different regions. If two regions are geographically adjacent, the corresponding element value in the matrix is larger; conversely, if the distance is far, the corresponding element value is smaller; use geographic information system tools to generate a regional sales heat map based on the spatial weight matrix and the extracted sales data; apply Moran's Index to conduct spatial autocorrelation analysis on the regional sales heat map to measure the similarity degree between the observed values and their neighbors. The expression is:

[0103] ;

[0104] where, represents Moran's Index, represents the total number of geographical areas participating in the analysis, represents the sum of all non-zero elements in the spatial weight matrix, which is used to standardize the calculation of Moran's Index, represents the element of the spatial weight matrix between the th area and the th area, represents the sales volume of the th area, represents the sales volume of the th area, represents the average value of the sales volumes of all areas, and represent the index variables for traversing all areas;

[0105] Based on the results of the spatial autocorrelation analysis, automatically identify the products that show strong spatial aggregation characteristics and mark them as regional best-selling products.

[0106] Specifically, Moran's Index represents whether there is a certain patterned correlation between all areas in the entire study area. The value range is usually between -1 and +1. Specifically:

[0107] When > 0 indicates the existence of positive spatial autocorrelation, that is, high values (sales volume in a certain geographical area is higher than the average sales volume of all areas, considered as sales hotspots, where the commodity sales are particularly booming) and low values (sales volume in a certain geographical area is lower than the average sales volume of all areas, which are sales cold spots, where the commodity sales are relatively weak) tend to cluster together.

[0108] When < 0, it indicates negative spatial autocorrelation, that is, high values and low values tend to be distributed dispersedly.

[0109] When = 0, it means there is no obvious spatial autocorrelation, and the distribution of data points may be random.

[0110] S5. Link the identified regional best-selling products with the supply chain management system to obtain product inventory data, and calculate the inventory consumption rate and inventory depletion time.

[0111] S5.1. Obtain the latest inventory data of products from the supply chain management platform; based on the generated regional sales heat map and Moran index analysis results, understand the sales patterns and spatial dependencies in different regions, and calculate the inventory consumption rate of each product. The expression is:

[0112] ;

[0113] Where represents the average daily inventory consumption rate of the th product, represents the inventory of the th product at time , represents the inventory of the th product at time , represents the length of the past time window for calculating the consumption rate.

[0114] It should be noted that by introducing spatial effect adjustment, the calculation of the inventory consumption rate is made more in line with the actual sales situation, improving the prediction accuracy.

[0115] S5.2. Use the inventory consumption rate of each product to determine the inventory depletion time. The expression is:

[0116] ;

[0117] Where represents the time required for the th product to be completely depleted of inventory.

[0118] It should be noted that by calculating the inventory depletion time, enterprises can predict in advance when replenishment is needed, avoid sales losses caused by insufficient inventory, help arrange warehousing and logistics resources reasonably, reduce operating costs, and improve supply chain efficiency.

[0119] S6. According to the calculation results, trigger an inventory warning and generate a replenishment suggestion to automatically adjust inventory management.

[0120] Set a safety inventory level for each commodity and a replenishment lead time , when < it is time, trigger an inventory warning; based on the commodity that triggers the warning, generate a replenishment suggestion, and the expression is:

[0121] ;

[0122] wherein, represents the inventory quantity that needs to be replenished for the th commodity, represents the demand volatility parameter;

[0123] According to the generated replenishment suggestion, automatically send an order request to the supplier and update the internal inventory record at the same time.

[0124] It should be noted that setting personalized safety inventory and replenishment lead time according to the characteristics of different commodities and market demands improves the flexibility and response speed of inventory management, effectively reduces the risk of out-of-stock, and ensures the continuous supply of hot-selling commodities.

[0125] This embodiment also provides an online monitoring system for hot-selling commodities, including:

[0126] A data collection module that collects consumer behavior data and performs preprocessing to form a consumer behavior data set;

[0127] A heat modeling module that constructs a consumer behavior heat model based on the consumer behavior data set to obtain the heat score of the commodity;

[0128] A hot-selling identification module that identifies potential hot-selling commodities according to the heat score of the commodity;

[0129] A geographic analysis module that performs geographic location analysis on the sales data of potential hot-selling commodities through geographic information technology, generates a regional commodity sales heat map, and identifies and marks regional hot-selling commodities;

[0130] An inventory management module that links the identified regional hot-selling commodities with the supply chain management system, obtains commodity inventory data, and calculates the inventory consumption rate and inventory depletion time;

[0131] The early warning replenishment module triggers inventory early warning and generates replenishment suggestions according to the calculation results, and automatically adjusts inventory management.

[0132] This embodiment also provides a computer device, which is applicable to the situation of the online monitoring method for hot-selling goods, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the online monitoring method for hot-selling goods as proposed in the above embodiment.

[0133] The computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0134] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the online monitoring method for hot-selling goods as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0135] In summary, the present invention constructs a consumer behavior heat model that can respond in real time to changes in consumer behavior by introducing a dynamic weight allocation mechanism and a social network influence correction factor, achieving a more accurate quantitative evaluation of product heat. Combining geographic information technology, a regional product sales heat map is generated, and Moran's Index is used for spatial autocorrelation analysis, effectively enhancing the ability to link the internal supply chain management system of the enterprise. Based on the inventory warning system triggered by the calculation results and the automated replenishment recommendation generation process, the efficiency and accuracy of inventory management are ensured, the out-of-stock risk is reduced, and customer satisfaction and the market competitiveness of the enterprise are improved.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An online monitoring method for hot-selling products, characterized in that: include, Collect consumer behavior data and pre-process it to form a consumer behavior data set; Based on the consumer behavior dataset, a consumer behavior heat model is constructed to obtain the heat score of the product; Identify potential hot-selling products based on their popularity scores; Use geographic information technology to analyze the geographic location of sales data of potential hot-selling products, generate regional product sales heat maps, and identify and mark regional hot-selling products; Link the identified regional hot-selling products with the supply chain management system to obtain product inventory data and calculate inventory consumption rate and inventory depletion time; According to the calculation results, trigger inventory warnings and generate replenishment suggestions to automatically adjust inventory management; The sales data of potential hot-selling products are analyzed by geographic information technology to generate a regional product sales heat map. The identification and marking of regional hot-selling products include the following steps: Extract sales data for each item from potential hot-selling items and associate it with geographic location information; Construct a spatial weight matrix to assess the correlation and impact between different regions; Generate regional sales heat maps based on spatial weight matrix and extracted sales data using geographic information system tools; The Moran index is used to perform spatial autocorrelation analysis on the regional sales heat map to measure the similarity between the observed value and its neighbors. The expression is: ； in, represents the Moran index, represents the total number of geographic regions participating in the analysis, represents the sum of all non-zero elements in the spatial weight matrix, Indicates Regions and The elements of the regional spatial weight matrix, Indicates Sales volume in each region, Indicates Sales volume in each region, represents the average sales volume of all regions, and Indicates the index variable for traversing all regions; Based on the results of spatial autocorrelation analysis, products that show strong spatial clustering characteristics are automatically identified and marked as regional hot-selling products; Linking the identified regional hot-selling products with the supply chain management system to obtain product inventory data and calculate the inventory consumption rate and inventory depletion time includes the following steps: Get the latest inventory data of goods from the supply chain management platform; Based on the generated regional sales heat map and Moran index analysis results, the inventory consumption rate of each product is calculated as follows: ； in, Indicates The average daily inventory consumption rate of products, Indicates Products at time The inventory volume, Indicates Products at time The inventory volume, Indicates the length of the past time window for calculating the consumption rate; Using the inventory consumption rate of each product, determine the time when the inventory is exhausted. The expression is: ； in, Indicates The time required for the inventory of a product to be completely depleted.

2. The online monitoring method for hot-selling products according to claim 1, characterized in that: The consumer behavior data includes browsing history, number of clicks, add-to-cart behavior, purchase frequency, evaluation and feedback; the preprocessing includes deep cleaning and standardization processing.

3. The online monitoring method for hot-selling products according to claim 2, characterized in that: Based on the consumer behavior dataset, building a consumer behavior heat model and obtaining the heat score of the product includes the following steps: Based on the different degrees of influence of consumer behavior on product popularity, the weight distribution is dynamically adjusted, and the expression is: ； in, Indicates Consumer-specific behaviors over time The dynamic weight of Indicates The importance coefficient of a specific consumer behavior, Indicates The importance coefficient of the consumer behavior type, represents the consumer specific behavior index, represents the consumer behavior type index, Indicates time; Based on the influence of mutual influence between users on the popularity of commodities, a correction factor is introduced to correct the influence of social networks. The expression is: ； in, Display products The social network influence correction factor, represents the social influence strength parameter, represents the user identifier, Display and products Related user collections, Indicates user PageRank scores in social networks; Based on the adjusted weights and social network influence correction factors, a consumer behavior heat model is constructed to obtain the heat score of the product, which is expressed as: ； in, Display products The final heat score of A balance parameter that indicates the importance of the control behavior indicator, represents the total number of consumer-specific behaviors, represents the time decay rate, Indicates that from The first The time difference between the occurrence of a specific consumer behavior and the present. Indicates The first The number of consumer-specific behaviors, represents the equilibrium parameter affecting the social network, Display products The activation function of the nonlinear feature representation.

4. The online monitoring method for hot-selling products according to claim 3, characterized in that: According to the popularity score of the product, identifying potential hot-selling products includes the following steps: Set the heat threshold according to the heat distribution in the historical data, and sort the heat scores from high to low to form a preliminary candidate list; Conduct multi-stage screening for each candidate product in the preliminary candidate list; Preliminarily screen out a set of candidate products whose popularity scores are higher than the popularity threshold, and perform secondary screening in the product set based on sales growth rate; The potential hot-selling products after the secondary screening are finally screened in combination with seasonality and promotional activities to finally obtain potential hot-selling products.

5. The online monitoring method for hot-selling products according to claim 4, characterized in that: According to the calculation results, the inventory warning is triggered and replenishment suggestions are generated. The automatic adjustment of inventory management includes the following steps: Set safety stock levels for each product and replenishment lead time ,when < When the inventory is triggered, the inventory warning is triggered; Generate replenishment suggestions based on the products that trigger the warning. The expression is: ； in, Indicates The number of stocks that need to be replenished for each product. represents the demand volatility parameter; Based on the generated replenishment suggestions, order requests are automatically sent to suppliers and internal inventory records are updated at the same time.

6. An online monitoring system for hot-selling products, based on the online monitoring method for hot-selling products according to any one of claims 1 to 5, characterized in that: include, The data collection module collects consumer behavior data and performs preprocessing to form a consumer behavior data set; The heat modeling module builds a consumer behavior heat model based on the consumer behavior data set to obtain the heat score of the product; Hot-selling identification module, which identifies potential hot-selling products based on their popularity scores; The geographic analysis module uses geographic information technology to analyze the sales data of potential hot-selling products, generate regional product sales heat maps, and identify and mark regional hot-selling products; The inventory management module links the identified regional hot-selling products with the supply chain management system to obtain product inventory data and calculate the inventory consumption rate and inventory depletion time; The early warning replenishment module triggers inventory warnings and generates replenishment suggestions based on calculation results, automatically adjusting inventory management.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the online monitoring method for hot-selling products described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the online monitoring method for hot-selling products described in any one of claims 1 to 5 are implemented.

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