Intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring

Abstract

The invention relates to the technical field of intelligent cabinet inventory monitoring and replenishment management, is used for solving the problems of early warning lag, slow replenishment response and low operation efficiency caused by dependence on a static inventory threshold value and manual regular patrol in the prior art, and particularly discloses an intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring. Comprising a commodity identification module, an inventory influence analysis module, an inventory critical proofreading module and a replenishment management module. According to the invention, multi-dimensional analysis is carried out on the sales state, the user behavior state and the marketing environment state of various commodities in the intelligent cabinet, a sales trend value, a user trend value and a marketing environment trend value are obtained through calculation, an inventory influence value is generated through fusion, an original inventory critical value is dynamically corrected based on the inventory influence value, and a calibrated inventory critical value is obtained. And according to the triggered replenishment instruction, optimal personnel matching is completed in combination with the replenishment personnel state and distance, so that efficient and accurate intelligent perception and dynamic response of inventory changes are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of smart cabinet inventory monitoring and replenishment management, and specifically to a smart cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring. Background Art

[0002] In recent years, with the continuous development of technologies such as artificial intelligence, big data, and image recognition, the retail industry is accelerating its evolution towards intelligent, unmanned, and data-driven operations. As an emerging unmanned retail terminal, smart lockers have been widely used in various scenarios such as office buildings, transportation hubs, shopping malls, supermarkets, and campuses due to their advantages such as flexible deployment, convenient operation, and no need for human supervision. They have shown good market prospects and development potential.

[0003] However, existing smart cabinets still face many shortcomings in inventory monitoring and replenishment management;

[0004] First, most current inventory monitoring mechanisms are still based on static thresholds, lacking comprehensive consideration of multi-dimensional factors such as product sales trends, changes in user behavior, and the marketing environment. This makes it difficult to dynamically adjust inventory thresholds, leading to product shortages or excess inventory.

[0005] Second, replenishment management relies on periodic inspections by fixed personnel and lacks intelligent and refined scheduling strategies. This not only reduces the timeliness of replenishment response, but also affects overall operational efficiency and service quality.

[0006] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0007] The purpose of this invention is to address the technical defects of existing smart cabinets in inventory monitoring and replenishment management, and to propose an intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring, so as to solve the problems of delayed inventory warning, untimely replenishment response and low operational efficiency caused by the current technical solutions that still mainly rely on static thresholds for inventory monitoring and fixed manual periodic inspections for replenishment management.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] Smart cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring, including:

[0010] The commodity identification module is used to identify and analyze the commodities stored in the smart cabinet and obtain information about various commodities placed in the smart cabinet;

[0011] The inventory impact analysis module is used to analyze the sales status of various commodities placed in the smart cabinet through the sales analysis unit to obtain the sales trend value of each commodity. The sales trend value is obtained by combining the consumption rate index value, the sales peak index value and the replenishment index value;

[0012] The user analysis unit analyzes the behavior of visiting users and obtains the user trend value of each product;

[0013] The marketing environment status of the smart cabinet is analyzed through the environmental analysis unit to obtain the marketing environment trend value of each type of commodity. The marketing environment trend value is obtained by combining the promotion response factor and the holiday approaching factor.

[0014] Extract the sales trend value, user trend value, and marketing environment trend value of each product and normalize them to obtain the inventory impact value;

[0015] The inventory critical value calibration module is used to obtain the inventory critical value of each commodity and analyze the calibration inventory critical value based on the inventory impact value and inventory critical value of each commodity to obtain the calibration inventory critical value of each commodity;

[0016] The replenishment management module matches target replenishment managers for each type of merchandise placed in the smart cabinet based on the calibrated inventory critical values ​​of each type of merchandise, and obtains the target replenishment managers for each type of merchandise.

[0017] Furthermore, the specific solution process for the consumption rate index value is as follows:

[0018] The time point when each commodity transaction is completed in the smart cabinet is defined as the selling moment. Within the set time period, the entire selling moment sequence of each type of commodity is recorded. By calculating the time difference between two adjacent selling moments, the selling time interval set of each type of commodity within the set time period is obtained. Then, the average of all selling time intervals is taken to obtain the average selling interval time of each type of commodity within the set time period. The reciprocal of the average selling interval time is the average sales frequency of each type of commodity within the set time period, and it is used as the consumption rate indicator value of each type of commodity within the set time period.

[0019] Furthermore, the specific process of solving the sales peak index value is as follows:

[0020] Setting a sales time interval threshold, comparing and analyzing each sales time interval in the sales time interval set of each type of commodity within the set time period with the sales time interval threshold, and marking the sales time interval as a high-fluctuation time interval if the sales time interval is less than or equal to the sales time interval threshold;

[0021] Integrate the adjacent high-fluctuation time intervals of each commodity within the set time period to obtain the peak sales periods of each commodity within the set time period;

[0022] The total sales volume corresponding to each peak sales period of each type of commodity within the set time period is obtained, and the total sales volume corresponding to each peak sales period of each type of commodity within the set time period is integrated to obtain the sales peak volume index value of each type of commodity within the set time period.

[0023] Furthermore, the specific solution process for the replenishment index value is as follows:

[0024] The time from the issuance of each replenishment request instruction to the completion of replenishment for each type of commodity within the set time period is obtained, and the replenishment time of each type of commodity within the set time period is obtained. The average of the replenishment time is then calculated to obtain the average replenishment time of each type of commodity within the set time period, which is used as the replenishment index value of each type of commodity within the set time period.

[0025] Furthermore, the specific process of solving the user trend value of each product is as follows:

[0026] Obtain the behavioral status parameters of users accessing the smart cabinet within a set time period and extract three key parameters from them: visit frequency, purchase quantity, and product repetition rate;

[0027] Based on the above three key parameters, determine the behavioral status evaluation value of the visiting user;

[0028] Matching and analyzing the behavior status evaluation value of the visiting user with the preset behavior status table to obtain the behavior status level of the visiting user, and matching it with the life cycle stage corresponding to the behavior status level to obtain the life cycle stage of the visiting user;

[0029] Based on the life cycle stage of the visiting user, corresponding attention weights are set for each type of product, and the user trend value of each type of product is determined based on the proportion of the life cycle stage in the total number of users visiting the smart cabinet.

[0030] Furthermore, the specific solution process for the promotion response factor is as follows:

[0031] Obtain the promotional discount value of each type of merchandise placed in the smart cabinet within a set time period, and average them to obtain the average promotional discount value;

[0032] The promotion response factor is further determined by combining the average sales volume of various commodities placed in the smart cabinet during the promotion period and the non-promotion period within the set time period.

[0033] Furthermore, the specific solution process for the holiday approaching factor is as follows:

[0034] Get the time interval between each monitoring time point of the smart cabinet and the holidays within the set time period and mark it as gjz t, t represents the monitoring time points divided within the set time period, and T represents the total monitoring time points divided within the set time period;

[0035] According to the set model: The holiday approach factor δ2 is calculated, where e represents the set natural constant, k represents the set holiday impact attenuation coefficient, and δ2∈(0,1].

[0036] Furthermore, the calibrated inventory critical value is analyzed based on the inventory impact value and inventory critical value of each commodity. The specific analysis process is as follows:

[0037] Match the inventory impact value of each commodity with the inventory critical impact value corresponding to each set impact value to obtain the inventory critical impact value of each commodity. Subtract the inventory critical value of each commodity from the inventory critical impact value to obtain the difference between the inventory critical value and the inventory critical impact value of each commodity, which is used as the calibrated inventory critical value of each commodity.

[0038] Furthermore, the target replenishment management personnel are matched with each type of merchandise placed in the smart cabinet. The specific matching process is as follows:

[0039] When the inventory of each type of goods placed in the smart cabinet reaches the calibrated inventory threshold, a replenishment instruction is generated;

[0040] Based on the captured replenishment instructions of various types of goods, they are matched with the stored commodity replenishment management personnel table to obtain the replenishment management personnel of various types of goods, obtain the working status of the replenishment management personnel of various types of goods, select the replenishment management personnel with an idle working status and record them as preliminary replenishment management personnel, and at the same time obtain the distance between various types of goods and the preliminary replenishment management personnel, mark it as the replenishment distance value, and determine the preliminary replenishment management personnel corresponding to the minimum replenishment distance value between various types of goods and the preliminary replenishment management personnel as the target replenishment management personnel, so as to dispatch the target replenishment management personnel to replenish the goods.

[0041] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0042] 1. This invention uses high-definition camera components deployed in smart cabinets, combined with image recognition technology and a product database, to perform multi-dimensional feature recognition of products. This not only accurately extracts product category, quantity, and location information, but also has the ability to learn and train models for newly added products, thereby significantly improving the adaptability and intelligence level of product recognition.

[0043] 2. This invention builds a multi-factor driven inventory impact analysis mechanism to model and quantify inventory changes based on product sales characteristics, user behavior characteristics, and marketing environment. It then calculates an inventory impact value, which is used to dynamically adjust and correct the original inventory thresholds for various products. This enables intelligent calibration of inventory monitoring thresholds, effectively overcoming the limited adaptability of traditional inventory strategies based on static thresholds in complex dynamic scenarios.

[0044] 3. This invention uses replenishment instructions issued by various commodities to match corresponding replenishment managers in real time. Based on the replenishment manager's work status and relative distance, it intelligently selects the target replenishment manager most suitable for the replenishment task. This avoids the scheduling inefficiencies caused by manual experience judgment and static task allocation, and improves the intelligence and execution efficiency of the entire replenishment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0046] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] like Figure 1 As shown, the smart cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring includes: a product identification module, an inventory impact analysis module, an inventory criticality proofreading module and a replenishment management module, wherein the inventory impact analysis module includes a sales analysis unit, a user analysis unit and an environment analysis unit;

[0049] The commodity identification module is used to identify and analyze commodities stored in the smart cabinet. The specific analysis process is as follows:

[0050] The high-definition camera components integrated in the smart cabinet capture multi-angle images of the products inside the smart cabinet periodically or when triggered (such as opening and closing the door, or when products are put in and out). The captured images are uploaded to the image recognition terminal in real time. The image recognition terminal compares the product feature information in the image (product feature information includes appearance outline, color, label, packaging text, and location in the cabinet, etc.) with the pre-built product database for multi-dimensional analysis, and obtains information about the various products placed in the smart cabinet, including category, quantity, and specific storage location. The information about various products placed in the smart cabinet is then fed back to the cloud management terminal.

[0051] It should be noted that the product database not only supports the rapid identification of entered products, but also has the ability to learn and train models for newly added unidentified products, thereby continuously improving product adaptability and recognition accuracy;

[0052] In an embodiment of the present invention, by executing periodic or triggered event image acquisition, image recognition analysis and product database comparison and other processing processes, automatic identification of products in smart cabinets and extraction of structured placement information are achieved, which is convenient for providing reliable data support for subsequent inventory monitoring, product circulation tracking and replenishment decisions.

[0053] The inventory impact analysis module includes sales analysis unit, user analysis unit and environment analysis unit;

[0054] The sales analysis unit analyzes the sales status of various commodities placed in the smart cabinet. The specific analysis process is as follows:

[0055] The time point at which each commodity transaction is completed in the smart cabinet is defined as the sales moment. Within a set time period, the entire sales moment sequence of each commodity is recorded. By calculating the time difference between two adjacent sales moments, the sales time interval set of each commodity within the set time period is obtained. Then, the average of all sales time intervals is taken to obtain the average sales interval time of each commodity within the set time period. The reciprocal of the average sales frequency is the average sales frequency of each commodity within the set time period, which is used as the consumption rate indicator value of each commodity within the set time period.

[0056] A selling time interval threshold is set, and each selling time interval in the selling time interval set of each type of commodity within the set time period is compared and analyzed with the selling time interval threshold. If a selling time interval in the selling time interval set of each type of commodity within the set time period is less than or equal to the selling time interval threshold, then the selling time interval in the selling time interval set of each type of commodity within the set time period is marked as a high-fluctuation time interval. Conversely, if a selling time interval in the selling time interval set of each type of commodity within the set time period is greater than the selling time interval threshold, then the selling time interval in the selling time interval set of each type of commodity within the set time period is marked as a low-fluctuation time interval.

[0057] Integrate the adjacent high-fluctuation time intervals of each commodity within the set time period to obtain the peak sales periods of each commodity within the set time period;

[0058] Obtain the total sales volume corresponding to each peak sales period of each type of commodity within the set time period, obtain the total sales volume corresponding to each peak sales period of each type of commodity within the set time period, and integrate them to obtain the sales peak volume index value of each type of commodity within the set time period;

[0059] The time from the issuance of each replenishment request instruction to the completion of replenishment for each type of commodity within the set time period is obtained, and the average of the replenishment times is calculated to obtain the average replenishment time for each type of commodity within the set time period, which is used as the replenishment index value for each type of commodity within the set time period;

[0060] It should be noted that if a replenishment request is issued within the set time period but the replenishment is not actually completed, the end time of the set time period will be used as the "replenishment completion time" of the replenishment by default. This is to avoid missing the impact of this abnormal data on the replenishment efficiency evaluation and to ensure data integrity and continuity.

[0061] The consumption rate index value, sales peak index value and replenishment index value of each commodity in the set time period are calibrated as Xhaoz j 、Xfenz j and Xhuoz j , where j represents the number of each product, (j = 1, 2, 3...n, n represents the total number of product numbers)

[0062] According to the formula: Calculate the sales trend value η1 of each commodity within the set time period, where and They represent the benchmark values ​​of the consumption rate index value, the sales peak index value and the replenishment index value respectively, and a1, a2 and a3 represent the set weight coefficients respectively;

[0063] It should be pointed out that the benchmark value refers to the average value of the corresponding indicator in the historical data sample, which is calculated based on the average indicators of similar products in multiple historical periods.

[0064] The user analysis unit analyzes the behavior of the visiting user. The specific analysis process is as follows:

[0065] The behavioral state parameters of users accessing smart lockers within a set time period are monitored and obtained, and three key parameters are extracted from them: visit frequency, purchase quantity, and product repetition rate, and are calibrated as Wfanz, Wgouz, and Wchoz respectively;

[0066] Based on the above three key parameters, the following formula is used to calculate the behavior status evaluation value zqp of the visiting user;

[0067] zqp=Wfanz×λ1+Wgouz×λ2+Wchoz×λ3

[0068] Among them, λ1, λ2 and λ3 represent the weight coefficients of visit frequency, purchase quantity and product repetition rate respectively;

[0069] The behavior status evaluation value of the visiting user is matched and analyzed with the preset behavior status table to obtain the behavior status level of the visiting user. Each visiting user's behavior status evaluation value corresponds to a behavior status level. At the same time, it is matched with the life cycle stage corresponding to the behavior status level to obtain the life cycle stage of the visiting user. The life cycle stages include the new user stage, active user stage, loyal user stage and returning user stage.

[0070] According to the life cycle stage of the visiting user, the corresponding attention weight θ is set for each type of product ij , where i represents the number of the life cycle stage (i=1 represents the new user stage, i=2 represents the active user stage, i=3 represents the loyal user stage, and i=4 represents the returning user stage);

[0071] Combined with the proportion of life cycle stage in the total number of smart cabinet access users fwz i , according to the formula: Calculate the user trend value η2 of each product within the set time period;

[0072] The marketing environment of the smart cabinet is analyzed through the environmental analysis unit. The specific analysis process is as follows:

[0073] Get the promotion discount value of each type of goods placed in the smart cabinet within the set time period, get the promotion discount value of each type of goods placed in the smart cabinet within the set time period, and mark it as cxzj t , according to the formula: Calculate the average promotional discount value of various commodities placed in the smart cabinet within a set time period Wherein, t represents the monitoring time point divided within the set time period, and T represents the total monitoring time points divided within the set time period;

[0074] It should be noted that the promotional discount value refers to the price discount amount or percentage given by merchants on the basis of the original price in order to attract consumers to buy goods during promotional activities;

[0075] Further, the promotion response factor δ1 is calculated by combining the average sales of various commodities placed in the smart cabinet during the promotion period and the non-promotion period within the set time period. j , the specific calculation formula is: in, represents the average sales volume during the promotion period, represents the average sales volume during non-promotion period;

[0076] Get the time interval between each monitoring time point of the smart cabinet and the holidays within the set time period, and mark it as gjz t (Unit is day, where the set time period is greater than 24 hours, if gjz t =0, indicating that the monitoring time point is a holiday);

[0077] According to the set model: Calculate the holiday proximity factor δ2, where e represents the set natural constant, k represents the set holiday impact attenuation coefficient, δ2∈(0,1], the closer to the holiday (i.e. gjz t The closer δ2 is to 1, the stronger the impact of holidays is;

[0078] Combined promotion response factor δ1 j The marketing environment trend value η3 of each commodity in the set time period is calculated by the holiday approaching factor δ2. The specific calculation formula is: η3=a4×δ1 j +a5×δ2, where a4 and a5 represent the weight coefficients of the promotion response factor and the holiday approaching factor, respectively.

[0079] The sales trend value η1, user trend value η2 and marketing environment trend value η3 of each type of commodity within the set time period are extracted and normalized, and the inventory impact value YXZ is calculated according to the formula: YXZ = η1×b1+η2×b2+η3×b3, where b1, b2 and b3 represent the influencing factors of the sales trend value, user trend value and marketing environment trend value, respectively.

[0080] The inventory critical calibration module is used to obtain the inventory critical value of each commodity within a set time period, and analyze the calibrated inventory critical value of each commodity based on the inventory impact value and inventory critical value of each commodity within the set time period to obtain the calibrated inventory critical value of each commodity. The specific analysis process is as follows:

[0081] Match the inventory impact values ​​of various commodities within the set time period with the inventory critical impact values ​​corresponding to the set impact values ​​to obtain the inventory critical impact values ​​of various commodities within the set time period. Subtract the inventory critical values ​​of various commodities within the set time period from the inventory critical impact values ​​to obtain the difference between the inventory critical values ​​and the inventory critical impact values ​​of various commodities within the set time period, which is used as the calibrated inventory critical values ​​of various commodities within the set time period.

[0082] The replenishment management module analyzes and processes the replenishment of various commodities placed in the smart cabinet based on the calibrated inventory thresholds of various commodities within a set time period. The specific analysis process is as follows:

[0083] When the inventory of each type of goods placed in the smart cabinet reaches the calibrated inventory threshold, a replenishment instruction is generated. Based on the generated replenishment instruction, the corresponding replenishment management personnel are matched to each type of goods placed in the smart cabinet. The specific matching analysis is as follows:

[0084] Based on the captured replenishment instructions of various types of goods, they are matched with the stored commodity replenishment management personnel table to obtain the replenishment management personnel of various types of goods, obtain the working status of the replenishment management personnel of various types of goods, select the replenishment management personnel with an idle working status and record them as preliminary replenishment management personnel, and at the same time obtain the distance between various types of goods and the preliminary replenishment management personnel, mark it as the replenishment distance value, and determine the preliminary replenishment management personnel corresponding to the minimum replenishment distance value between various types of goods and the preliminary replenishment management personnel as the target replenishment management personnel, so as to dispatch the target replenishment management personnel to replenish the goods.

[0085] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring, characterized by: include: The inventory impact analysis module is used to analyze the consumption rate index value, sales peak index value, and replenishment index value of each type of goods placed in the smart cabinet through the sales analysis unit to obtain the sales trend value of each type of goods; the consumption rate index value is determined by the inverse of the average sales interval time within a set time period, the sales peak index value is determined by the total sales volume corresponding to each peak sales period within the set time period, and the replenishment index value is determined by the average replenishment time within the set time period; The user analysis unit analyzes the behavior of visiting users and obtains the user trend value of each product; The marketing environment status of the smart cabinet is analyzed through the environmental analysis unit to obtain the marketing environment trend value of each type of commodity. The marketing environment trend value is obtained by combining the promotion response factor and the holiday approaching factor. Extract the sales trend value, user trend value, and marketing environment trend value of each product and normalize them to obtain the inventory impact value; The inventory critical value calibration module is used to obtain the inventory critical value of each commodity and analyze the calibration inventory critical value based on the inventory impact value and inventory critical value of each commodity to obtain the calibration inventory critical value of each commodity; The replenishment management module matches target replenishment managers for each type of merchandise placed in the smart cabinet based on the calibrated inventory critical values ​​of each type of merchandise, and obtains the target replenishment managers for each type of merchandise.

2. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The specific solution process for the consumption rate index value is as follows: The time point when each commodity transaction is completed in the smart cabinet is defined as the selling moment. Within the set time period, the entire selling moment sequence of each type of commodity is recorded. By calculating the time difference between two adjacent selling moments, the selling time interval set of each type of commodity within the set time period is obtained. Then, the average of all selling time intervals is taken to obtain the average selling interval time of each type of commodity within the set time period. The reciprocal of the average selling interval time is the average sales frequency of each type of commodity within the set time period, and it is used as the consumption rate indicator value of each type of commodity within the set time period.

3. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 2 is characterized in that: The specific solution process for the sales peak index value is as follows: Setting a sales time interval threshold, comparing and analyzing each sales time interval in the sales time interval set of each type of commodity within the set time period with the sales time interval threshold, and marking the sales time interval as a high-fluctuation time interval if the sales time interval is less than or equal to the sales time interval threshold; Integrate the adjacent high-fluctuation time intervals of each commodity within the set time period to obtain the peak sales periods of each commodity within the set time period; The total sales volume corresponding to each peak sales period of each type of commodity within the set time period is obtained, and the total sales volume corresponding to each peak sales period of each type of commodity within the set time period is integrated to obtain the sales peak volume index value of each type of commodity within the set time period.

4. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The specific solution process for the replenishment index value is as follows: The time from the issuance of each replenishment request instruction to the completion of replenishment for each type of commodity within the set time period is obtained, and the replenishment time of each type of commodity within the set time period is obtained. The average of the replenishment time is then calculated to obtain the average replenishment time of each type of commodity within the set time period, which is used as the replenishment index value of each type of commodity within the set time period.

5. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The specific process of solving the user trend value of each product is as follows: Obtain the behavioral status parameters of users accessing the smart cabinet within a set time period, and extract three key parameters from them: visit frequency, purchase quantity, and product repetition rate; Based on the above three key parameters, determine the behavioral status evaluation value of the visiting user; Matching and analyzing the behavior status evaluation value of the visiting user with the preset behavior status table to obtain the behavior status level of the visiting user, and matching it with the life cycle stage corresponding to the behavior status level to obtain the life cycle stage of the visiting user; Based on the life cycle stage of the visiting user, corresponding attention weights are set for each type of product, and the user trend value of each type of product is determined based on the proportion of the life cycle stage in the total number of users visiting the smart cabinet.

6. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The specific solution process for the promotion response factor is as follows: Obtain the promotional discount value of each type of merchandise placed in the smart cabinet within a set time period, and average them to obtain the average promotional discount value; The promotion response factor is further determined by combining the average sales volume of various commodities placed in the smart cabinet during the promotion period and the non-promotion period within the set time period.

7. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The specific solution process for the holiday approaching factor is as follows: Get the time interval between each monitoring time point of the smart cabinet and the holidays within the set time period and mark it as gjz t , t represents the monitoring time points divided within the set time period, and T represents the total monitoring time points divided within the set time period; According to the set model: The holiday approach factor δ2 is calculated, where e represents the set natural constant, k represents the set holiday impact attenuation coefficient, and δ2∈(0,1].

8. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The calibrated inventory critical value is analyzed based on the inventory impact value and inventory critical value of each commodity. The specific analysis process is as follows: Match the inventory impact value of each commodity with the inventory critical impact value corresponding to each set impact value to obtain the inventory critical impact value of each commodity. Subtract the inventory critical value of each commodity from the inventory critical impact value to obtain the difference between the inventory critical value and the inventory critical impact value of each commodity, which is used as the calibrated inventory critical value of each commodity.

9. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: The target replenishment management personnel are matched with various commodities placed in the smart cabinet. The specific matching process is as follows: When the inventory of each type of goods placed in the smart cabinet reaches the calibrated inventory threshold, a replenishment instruction is generated; Based on the captured replenishment instructions of various types of goods, they are matched with the stored commodity replenishment management personnel table to obtain the replenishment management personnel of various types of goods, obtain the working status of the replenishment management personnel of various types of goods, select the replenishment management personnel with an idle working status and record them as preliminary replenishment management personnel, and at the same time obtain the distance between various types of goods and the preliminary replenishment management personnel, mark it as the replenishment distance value, and determine the preliminary replenishment management personnel corresponding to the minimum replenishment distance value between various types of goods and the preliminary replenishment management personnel as the target replenishment management personnel, so as to dispatch the target replenishment management personnel to replenish the goods.

10. The intelligent cabinet inventory monitoring and replenishment management system based on intelligent AI monitoring according to claim 1 is characterized in that: It also includes a commodity identification module for identifying and analyzing the commodities stored in the smart cabinet and obtaining information about various commodities placed in the smart cabinet.

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