An intelligent management system for real-time inventory warning in pharmacies

Abstract

The present application relates to the field of data monitoring technology, and specifically to an intelligent management system for real-time inventory warnings in pharmacies. The system comprises: a data acquisition module for collecting daily sales and inventory quantities of each type of drug in the pharmacy; a sales analysis module for analyzing current drug sales characteristics based on drug sales similarity and drug delivery status; a sales status analysis module for analyzing the drug sales stability index based on the difference in daily growth coefficients of drug sales on adjacent dates, combined with the sales trend coefficient; a days prediction module for analyzing the expected sales days based on the daily growth coefficients of drug sales over multiple days, combined with the development pattern of drug demand; and an early warning module for providing pharmacy inventory early warnings based on the expected sales days. This system avoids the problem of regular testing being unable to effectively analyze changes in drug sales during different seasons, reduces human resource expenditure during the pharmacy inventory process, and improves pharmacy management efficiency.

Description

Technical Field

[0001] The present application relates to the field of data monitoring technology, and in particular to an intelligent management system for real-time inventory warning in pharmacies. Background Art

[0002] Pharmacies primarily store medications. Drug inventory relies on regular checks of drug availability by pharmacy personnel, who also conduct dynamic inventory analysis using a retrospective approach. While existing technologies utilize dynamic inventory analysis based on retrospective methods to adjust drug inventory, in practice, this still requires significant manpower to regularly query pharmacy inventory and drug inflows and outflows online, and relies on manual inventory control. This not only wastes unnecessary manpower and resources, but also prevents intelligent analysis and control of drug inventory. Furthermore, due to fixed monitoring cycles, it is difficult to adapt to sudden seasonal fluctuations in real time. Manual analysis of drug inventory demand can lead to significant misanalysis, resulting in significant deviations between inventory levels and actual demand. Summary of the Invention

[0003] In order to solve the above technical problems, the purpose of this application is to provide an intelligent management system for real-time inventory warning of pharmacies. The technical solutions adopted are as follows:

[0004] This application proposes an intelligent management system for real-time inventory warning of pharmacies, which includes:

[0005] Data collection module: collects daily sales and inventory quantities of each type of medicine in the pharmacy;

[0006] Sales Analysis Module: For each drug category, the percentage of a drug's total daily sales growth is calculated based on the difference between drug sales on adjacent dates and the average daily sales of the drug in historical time. The sales trend coefficient of the drug on the current date is calculated based on the difference between the drug sales on the current date and the drug sales during the period of no sales growth, combined with the percentage of the total daily sales growth;

[0007] Sales Status Analysis Module: Calculates daily drug change parameters based on the difference in daily drug sales growth coefficients between adjacent days, combined with the sales trend coefficient. Constructs a sales stability index for the drug on the current day based on the difference in daily drug sales growth coefficients between the current day and the previous day, combined with the new change parameter.

[0008] Days prediction module: During a period of drug sales growth, based on the daily growth coefficient of drug sales over multiple days and the sales stability index, combined with a data fitting formula, the expected sales volume of the drug for each day in the future is determined, and a relationship between the expected total sales volume and the expected number of days of sales is constructed. Based on the remaining inventory of the drug on the current date and combined with the relationship, the expected number of days of sales for the drug on the current date is obtained;

[0009] Early warning module: Provides early warning of pharmacy inventory based on expected sales days.

[0010] In one embodiment, the daily drug sales ratio of a drug is calculated based on the difference between drug sales on adjacent days and the average daily drug sales in history, specifically:

[0011] Calculate the daily growth coefficient of drug sales based on the difference between drug sales on adjacent days;

[0012] Calculate the sum of the daily growth coefficients of sales of all types of drugs on the i-th day; calculate the average daily sales of the k-th drug in the week before the i-th day; calculate the ratio of the daily growth coefficient of sales of the k-th drug on the i-th day to the sum of the said daily growth coefficients, recorded as the first ratio; the proportion of the total daily sales growth of the k-th drug on the i-th day is positively correlated with the first ratio and the average daily sales of the k-th drug on the i-th day.

[0013] In one embodiment, the process of calculating the daily growth coefficient of daily drug sales is as follows:

[0014] The difference between the sales of the drug on the i-th day and the i-1-th day is taken as the sales growth of the drug on the i-th day; the ratio of the sales growth to the sales of the drug on the i-1-th day is taken as the daily growth coefficient of the drug sales on the i-th day.

[0015] In one embodiment, the sales trend coefficient is expressed as:

[0016] , where is the sales trend coefficient of the k-th drug category on day i; is the percentage of the total daily sales growth of the k-th category of drugs on day i; is the sales volume of the k-th category drug on day i; is the average sales volume of the drug on all non-incremental dates before day i; is a normalization function; wherein, the non-incremental date is the date when the sales growth is less than 0.

[0017] In one embodiment, the process of acquiring the newly added change parameter is as follows:

[0018] The difference between the daily growth coefficients of drug sales on the i-th day and the i-1-th day is taken as the incremental difference coefficient of the drug on the i-th day; the absolute value of the ratio of the incremental difference coefficient to the daily growth coefficient of drug sales on the i-1-th day is calculated; the newly added change parameters of the drug on the i-th day are positively correlated with the absolute value of the ratio and the sales trend coefficient of the k-th category drug on the i-th day.

[0019] In one embodiment, the sales stability index is expressed as:

[0020] Where, is the sales stability index corresponding to the k-th category of drugs on day i; 、 are the newly added change parameters and incremental difference coefficients of the kth category of drugs on day i.

[0021] In one embodiment, the daily growth coefficient of the drug sales over multiple days and the sales stability index are combined with a data fitting formula to determine the expected sales volume of the drug every day in the future, and to construct a relationship between the expected total sales volume and the expected number of sales days, specifically:

[0022] During the period of drug sales growth, the drug's logistic formula is calculated using the known daily growth coefficients of drug sales for multiple consecutive days. The predicted value of the daily growth coefficient of drug sales on any day is obtained through the calculated logistic formula.

[0023] Based on the predicted value of the daily growth coefficient and the corresponding sales stability index of the drug every day in the future, the expected sales volume of the drug every day in the future is determined, and a relationship between the expected total sales volume and the expected number of sales days is constructed.

[0024] In one embodiment, the expected sales volume of the drug each day in the future is determined, and a relationship between the expected total sales volume and the expected number of sales days is constructed, which is expressed as follows:

[0025] ;

[0026] ;

[0027] Where N is the expected total sales from the current date to the next U day; is the expected number of days to sell; is the actual sales volume on the current date; is the expected sales volume on the jth day after the current date; Expected sales volume j´-1 days after the current date; is the predicted value of the daily sales growth coefficient on the j´-1 day after the current date obtained by the logistic formula; The sales stability index corresponding to the drug on day j´-1 is obtained based on the data of all dates before the j´th day using the same calculation method as the sales stability index.

[0028] In one embodiment, the process of obtaining the expected sales days of the drug on the current date is:

[0029] The remaining inventory on the current date is used as the expected total sales volume in the relationship, and the expected sales days are calculated. The calculated expected sales days are rounded down to obtain the expected sales days of the drug on the current date.

[0030] In one embodiment, the pharmacy inventory warning based on the expected sales days is specifically as follows:

[0031] Obtain drugs with a daily growth coefficient greater than 0 on the current date as the sales growth drugs for the current date; for each type of sales growth drug, when the expected sales days of the drug are less than the preset inventory depletion threshold, an inventory warning is issued; otherwise, no inventory warning is issued.

[0032] This application has the following beneficial effects:

[0033] This application monitors the recent sales changes of various drugs in real time, analyzes the current sales characteristics of drugs based on the similarity of drug growth patterns and drug delivery status, and analyzes drug demand based on historical data and current real-time data, thereby achieving adaptive adjustment of drug inventory warning values and improving pharmacy management efficiency;

[0034] This application monitors the pharmacy inventory and drug sales in real time, calculates the daily growth coefficient of drug sales based on the difference between drug sales on adjacent dates, constructs the daily drug sales trend coefficient based on the difference between the daily growth coefficient of each type of drug and all drugs, and the drug sales in historical time; analyzes the drug sales stability index based on the difference in the daily growth coefficient of drug sales on adjacent dates in combination with the sales trend coefficient; predicts the daily drug growth coefficient based on the daily growth coefficient of drug sales for multiple days in combination with the law of drug demand development, deduces the drug demand trend in combination with historical drug use laws and real-time demand data, and calculates the expected sales days of the current drug inventory; and issues pharmacy inventory warnings based on the expected sales days. This achieves dynamic and adaptive adjustment of inventory warning values, thereby improving pharmacy management efficiency, avoiding the problem that regular inspections are difficult to effectively analyze drug demand in different seasons, and reducing human resource expenditures in the pharmacy inventory process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 A block diagram of an intelligent management system for real-time inventory warnings in pharmacies provided by one embodiment of the present application;

[0037] Figure 2 This is a flow chart of an intelligent management system for real-time inventory warning in pharmacies;

[0038] Figure 3 This is a schematic diagram of the process of obtaining the proportion of total drug growth in a single day. DETAILED DESCRIPTION

[0039] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an intelligent management system for real-time pharmacy inventory warnings proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The specific solution of an intelligent management system for real-time inventory warning of pharmacies provided by this application is described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a block diagram of an intelligent management system for real-time inventory warning of a pharmacy provided by one embodiment of the present application, the system includes:

[0043] The data collection module 101 collects the daily sales and inventory quantity of each type of medicine in the pharmacy.

[0044] Monitor the status of pharmacies' medicines in real time, obtain information about the medicines in the pharmacy, and access data, including:

[0045] (1) Medicines are the main items stored in the pharmacy. When recording the medicines in the pharmacy, the medicines are classified and numbered according to the purpose of each type of medicine. The number of the kth type of medicine is recorded as k, and the total number of medicines in the pharmacy is recorded as .

[0046] (2) When storing or retrieving drugs, the inventory quantity of a single-name drug in the pharmacy is obtained; each storage or retrieval operation will cause the inventory quantity of the drug to change, and there may be multiple storage and retrieval operations in a single day. To facilitate data recording, the sales volume of the drug on that day is counted at 0:00 every day, and the inventory quantity of the drug on that day is recorded. Among them, the sales volume of the k-th drug on the i-th day is recorded as , the drug inventory quantity is recorded as .

[0047] The sales analysis module 102 calculates the daily growth coefficient of drug sales for each type of drug based on the difference between drug sales on adjacent dates; calculates the proportion of the drug's daily total drug growth based on the difference between the daily growth coefficient of each type of drug and all drugs, and the average daily sales of drugs in historical time; calculates the sales trend coefficient of the drug on the current date based on the difference between the drug sales on the current date and the drug sales during the period when sales did not increase, combined with the said daily total drug growth proportion.

[0048] Since people have different demands for different medicines at different times, the sales volume of medicines in pharmacies also fluctuates. For example, due to seasonal or sudden events, there will be a large regional purchase of a certain type of medicine, and the number of purchasers and the quantity will increase sharply. After that, the demand for medicines will decrease, and the amount of medicines required to be purchased will decrease, only to meet daily sales. Overstocking may lead to a surplus of medicines.

[0049] Drug sales can be a good indicator of changes in people's demand for drugs. High drug sales indicate a high demand for drugs. Due to seasonal influences, demand for certain drugs is high, and drug sales increase significantly compared to usual. By analyzing the sales growth of drugs over several consecutive days, we can determine whether there is any suspected seasonal change in the current season. Specifically:

[0050] (1) For each type of drug, obtain the sales growth of the drug on the current day compared to the previous day. The sales growth of the k-th drug on the i-th day compared to the previous day (i-1 day) is recorded as , ; Further, calculate the daily growth coefficient of the sales volume of the k-th drug on the i-th day , the expression is: .in, and are the sales volume of the k-th category drug on day i and day i-1 respectively.

[0051] (2) Based on the sales growth of each type of drug, the proportion of each type of drug in the total daily drug growth is calculated using the following expression:

[0052] , where is the percentage of the total daily sales growth of the k-th category of drugs on day i; is the daily growth coefficient of the sales volume of the k-th category of drugs on the i-th day; is the sum of the daily growth coefficients of sales of all types of drugs on day i; is the average daily sales volume of the kth category drug in the week before the i-th day, where the average daily sales volume is the total sales volume divided by the number of sales days. is the first ratio.

[0053] By calculation , to avoid the problem of accidental phenomena affecting the authenticity of the results due to a low base number; It is the ratio of the sales growth of drug K in a single day to the total sales growth of all drugs. The larger the value, the higher the growth of drug K in a single day.

[0054] (3) Customers who have purchased drugs will not buy drugs again in the short term, but there are still new consumers who go to buy drugs, which reduces the growth rate of drug sales while the sales volume remains at a high level.

[0055] Therefore, by comparing the sales volume of drugs in the current season with the sales volume of drugs in the period without sales growth, the sales trend coefficient of drugs in the current season is analyzed, and the expression is:

[0056] ;

[0057] Where, is the sales trend coefficient of the k-th drug category on day i; is the percentage of the total daily sales growth of the k-th category of drugs on day i; is the sales volume of the k-th category drug on day i; is the average sales volume of the drug on all non-incremental dates before day i; is a normalization function. The non-incremental date is a date when the sales growth is less than 0.

[0058] It is the comparison between the actual sales volume after the drug generates an increase and the sales volume during the period without an increase. The larger the value is, the more it indicates that the drug sales volume is at a sustained high level.

[0059] This parameter represents the maintenance parameter for drug sales after experiencing high growth. The larger the value, the more people are purchasing the drug, and the more stable the growth. The results are normalized using the normalization function. The higher the sales trend coefficient, the greater the probability that the drug is experiencing high seasonal demand during the current season.

[0060] The sales status analysis module 103 calculates the daily drug change parameters based on the difference in the daily growth coefficients of drug sales on adjacent dates, combined with the sales trend coefficient; and constructs the sales stability index corresponding to the drug on that day based on the difference between the daily growth coefficients of drug sales on that day and the previous day, combined with the new change parameters.

[0061] Growth in the number of drug buyers is positively correlated with drug sales growth. When demand for drugs increases, drug sales increase. Similarly, when demand decreases, drug sales also decrease. To understand drug sales status, it's necessary to analyze the changing trends in sales increments during the sales process. If sales volume continues to grow, demand for drugs is also increasing.

[0062] Compare the drug sales status on adjacent dates and combine the sales trend coefficient of each type of drug to obtain the new change parameter of each type of drug. The expression is:

[0063] , where is the newly added change parameter of the k-th category drug on day i; 、 are the daily growth coefficients of the sales volume of the k-th category of drugs on day i and day i-1 respectively; is the sales trend coefficient of the k-th category of drugs on the i-th day.

[0064] Indicates the difference in new sales between adjacent days. The smaller the value, the closer the number of new purchases on adjacent days. Conversely, the difference in the number of new purchases is greater. is the drug sales trend coefficient, which is positively correlated with the difference in the number of new purchasers per day.

[0065] (2) When the number of new purchasers on day i is less than that on the previous day, the actual sales volume of the drug decreases. For the newly added change parameter, it is necessary to analyze whether the number of new purchasers increases or decreases day by day.

[0066] The difference between the daily growth coefficients of drug sales on the current day and the previous day is used to obtain the incremental difference coefficient of the drug sales on the current day. The expression is: , where is the incremental difference coefficient of the k-th category drug on day i.

[0067] when When the number of new purchases increases with the date, and The larger the value, the more new purchases there will be; When the new purchase quantity decreases or remains unchanged with the date.

[0068] (3) Furthermore, the difference in the number of new purchases reflects the degree of demand for drugs, and by analyzing the symbol value of the new number of people to see whether the number of new purchases is increasing or decreasing, the sales stability index corresponding to the drug on that day is obtained. Among them, the sales stability index corresponding to the k-th drug on the i-th day is The newly added change parameters of the k-th drug on day i are and incremental variance coefficient There is a negative correlation.

[0069] Preferably, in the embodiment of the present application, The expression is: Where, is the normalization function.

[0070] The product of the stability parameter of the number of new people and the incremental difference represents the sales stability index. When the number of drug sales is stable, it means that the number of drug sales is stable; otherwise, it means that the number of drug sales is changing rapidly.

[0071] The days prediction module 104 obtains the predicted value of the daily growth coefficient of drug sales on any future date based on the daily growth coefficient of drug sales over multiple days in combination with the data fitting formula during the drug sales growth period; determines the expected sales volume of the drug on each day in the future based on the predicted value of the daily growth coefficient and the sales stability index corresponding to the drug on each day in the future, and constructs a relationship between the expected total sales volume and the expected number of sales days; obtains the expected number of sales days of the drug on the current date based on the remaining inventory of the drug on the current date in combination with the relationship.

[0072] After the seasonal high demand period for drugs, the demand for drugs decreases. In view of the urgency of the supply of existing drug stocks, it is necessary to analyze the supply period of the current drug stocks based on the sales stability index of drug K and the actual sales volume of drug K.

[0073] (1) Taking the kth category of drugs as an example, obtain the sales volume of this category of drugs in the week before any date as the weekly sales volume of this category of drugs on any date; calculate the daily month-on-month growth rate of the sales volume of this category of drugs on any date and the day before it; when the daily month-on-month growth rate exceeds 0.2 of the corresponding weekly sales volume for the first time as the date changes, the day before the corresponding date of the daily month-on-month growth rate is taken as the starting date of the sales growth period of the drug, at which time people's demand for drugs is in a state of rapid growth.

[0074] (2) On the jth day after the starting date, the same method as the daily growth coefficient in step (1) of the sales analysis module 102 is used to obtain the daily growth coefficient of the sales volume of the kth category drug on the jth day. .

[0075] (3) The logistic formula is often used in scenarios with S-shaped growth trends. Therefore, taking the k-th type of drug as an example, after the starting date, the logistic formula of the drug is calculated using the known daily growth coefficient of the sales of the drug for Q consecutive days, where Q is greater than 3; based on the calculated logistic formula, the predicted value of the daily growth coefficient of the sales of the drug for the j-th day is obtained. Preferably, the present application uses data from 5 consecutive days to calculate the logistic formula. As other embodiments of the present application, the implementer can set the value of Q according to actual conditions.

[0076] (4) Further, the current drug sales stability index K Correction processing is performed. The more stable the sales status is, the less the drug sales can be. On the contrary, it can be increased. In this way, the expected total sales volume of this type of drug on the Uth day after the current date is obtained. The expression is:

[0077] ;

[0078] ;

[0079] Where N is the expected total sales from the current date to the Uth day after the date; is the expected number of days to sell; is the actual sales volume on the current date; is the expected sales volume on the jth day after the current date; Expected sales volume j´-1 days after the current date; is the predicted value of the daily sales growth coefficient on the j´-1 day after the current date obtained by the logistic formula; The sales stability index corresponding to the drug on day j´-1 is obtained based on the data obtained for all dates before the j´th day, using the same calculation method as the above-mentioned sales stability index.

[0080] (5) The remaining inventory on the current date As the value of N, and the actual sales volume of the current date is substituted to calculate the expected sales days. Since the expected sales days should be an integer, the calculated expected sales days are rounded down to obtain the expected sales days of the drug on the current date.

[0081] The early warning module 105 performs early warning of pharmacy inventory based on the expected sales days.

[0082] This application is an intelligent management system for real-time inventory warning of pharmacies. It analyzes the expected sales volume of drugs based on the changes in drug data parameters in the pharmacy, and then realizes intelligent early warning of drug inventory. The specific operation steps are as follows:

[0083] (1) Calculate the daily growth coefficient of each type of medicine in the pharmacy every day, and obtain the medicines with a daily growth coefficient greater than 0 on that day as the medicines with sales growth on that day.

[0084] (2) For each type of drug with increasing sales, calculate the expected sales days of the drug’s current remaining inventory and set the inventory depletion threshold , preferably, in the embodiment of the present application, Set to 2. As other embodiments of this application, the implementer can set it according to the actual situation. value.

[0085] When the expected sales days of each sales growth drug are less than When the pharmacy intelligent management system issues an inventory warning, it is necessary to urgently purchase the drugs of this type; when the expected sales days of each type of sales growth drug is greater than or equal to When the pharmacy intelligent management system is full, it will not issue an inventory warning, but will remind pharmacy managers to arrange purchases reasonably.

[0086] The intelligent management system for real-time inventory warning of pharmacies provided in this embodiment also includes a basic support layer, a platform data processing layer and a system application layer, realizing a point-to-point closed loop from data storage to business value, which is helpful for the quality control management of pharmacies.

[0087] Basic support layer: Store the pharmacy's real-time inventory data on the cloud server platform, complete data storage, raw data collection and infrastructure management (including progress management, transaction management, etc.), and the system administrator is responsible for data maintenance and data security.

[0088] Platform data processing layer: Use mainstream programming languages to develop an intelligent management system for real-time inventory warnings in pharmacies, and implement data management, data statistics, and personnel authority allocation.

[0089] System application layer: Utilize low-code development tools to build the system, enabling data collection, sales analysis, sales status analysis, forecasting of sales days, and early warning. This ultimately enables business scenario visualization and real-time management of early warning situations. Data collection, sales analysis, sales status analysis, forecasting of sales days, and early warning can be implemented using existing technologies and are not specifically limited in this embodiment.

[0090] A flow chart of an intelligent management system for real-time inventory warning in pharmacies Figure 2 As shown; the process of obtaining the proportion of total daily drug growth is shown in the following figure: Figure 3 shown.

[0091] In summary, the embodiment of the present application monitors the recent sales changes of each drug in real time, analyzes the current drug sales characteristics based on the similarity of drug growth patterns and drug delivery status, and analyzes drug demand based on historical data and current real-time data, thereby achieving adaptive adjustment of drug inventory warning values and improving pharmacy management efficiency;

[0092] This application monitors the pharmacy inventory and drug sales in real time, calculates the daily growth coefficient of drug sales based on the difference between drug sales on adjacent dates, constructs the daily drug sales trend coefficient based on the difference between the daily growth coefficient of each type of drug and all drugs, and the drug sales in historical time; analyzes the drug sales stability index based on the difference in the daily growth coefficient of drug sales on adjacent dates in combination with the sales trend coefficient; predicts the daily drug growth coefficient based on the daily growth coefficient of drug sales for multiple days in combination with the law of drug demand development, deduces the drug demand trend in combination with historical drug use laws and real-time demand data, and calculates the expected sales days of the current drug inventory; and issues pharmacy inventory warnings based on the expected sales days. This achieves dynamic and adaptive adjustment of inventory warning values, thereby improving pharmacy management efficiency, avoiding the problem that regular inspections are difficult to effectively analyze drug demand in different seasons, and reducing human resource expenditures in the pharmacy inventory process.

[0093] It should be noted that the order of the embodiments of the present application is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0095] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them. Modifications to the technical solutions described in the aforementioned embodiments, or equivalent replacements of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent management system for real-time inventory warning in pharmacies, characterized by: The system comprises: Data collection module: collects daily sales and inventory quantities of each type of medicine in the pharmacy; Sales volume analysis module: For each type of drug, the daily sales growth coefficient is calculated based on the difference between drug sales on adjacent days; Calculate the sum of the daily growth coefficients of sales of all drug categories on day i; calculate the average daily sales of drugs in category k in the week before day i; calculate the ratio of the daily growth coefficient of sales of drugs in category k on day i to the sum of the daily growth coefficients, recorded as the first ratio; the proportion of the total daily sales growth of drugs in category k on day i is positively correlated with the first ratio and the average daily sales of drugs in category k on day i; calculate the sales trend coefficient of the drug on the current date, expressed as: , where is the sales trend coefficient of the k-th drug category on day i; is the percentage of the total daily sales growth of the k-th category of drugs on day i; is the sales volume of the k-th category drug on day i; is the average sales volume of the drug on all non-incremental dates before day i; is a normalization function; wherein, the non-incremental date is the date when the sales growth is less than 0; Sales status analysis module: The difference between the daily growth coefficients of drug sales on the i-th day and the i-1-th day is used as the incremental difference coefficient of the drug on the i-th day; the absolute value of the ratio of the incremental difference coefficient to the daily growth coefficient of drug sales on the i-1-th day is calculated; the newly added change parameter of the drug on the i-th day is positively correlated with the absolute value of the ratio and the sales trend coefficient of the k-th category drug on the i-th day; the sales stability index corresponding to the drug on that day is calculated, and the expression is: Where, is the sales stability index corresponding to the k-th category of drugs on day i; 、 are the newly added change parameter and incremental difference coefficient of the k-th category drug on day i; Days prediction module: During a period of drug sales growth, based on the daily growth coefficient of drug sales over multiple days and the sales stability index, combined with a data fitting formula, the expected sales volume of the drug for each day in the future is determined, and a relationship between the expected total sales volume and the expected number of days of sales is constructed. Based on the remaining inventory of the drug on the current date and combined with the relationship, the expected number of days of sales for the drug on the current date is obtained; Early warning module: Provides early warning of pharmacy inventory based on expected sales days.

2. The intelligent management system for real-time inventory warning of a pharmacy according to claim 1 is characterized in that: The process of calculating the daily growth coefficient of daily drug sales is as follows: The difference between the sales of the drug on the i-th day and the i-1-th day is taken as the sales growth of the drug on the i-th day; the ratio of the sales growth to the sales of the drug on the i-1-th day is taken as the daily growth coefficient of the drug sales on the i-th day.

3. The intelligent management system for real-time inventory warning of a pharmacy according to claim 1 is characterized in that: The daily growth coefficient of the drug sales volume over multiple days and the sales stability index are combined with the data fitting formula to determine the expected sales volume of the drug every day in the future, and to construct a relationship between the expected total sales volume and the expected number of sales days, specifically: During the period of drug sales growth, the drug's logistic formula is calculated using the known daily growth coefficients of drug sales for multiple consecutive days. The predicted value of the daily growth coefficient of drug sales on any day is obtained through the calculated logistic formula. Based on the predicted value of the daily growth coefficient and the corresponding sales stability index of the drug every day in the future, the expected sales volume of the drug every day in the future is determined, and a relationship between the expected total sales volume and the expected number of sales days is constructed.

4. The intelligent management system for real-time inventory warning of a pharmacy according to claim 3 is characterized in that: The expected sales volume of the drug every day in the future is determined, and a relationship between the expected total sales volume and the expected number of sales days is constructed, which is expressed as follows: ； ； Where N is the expected total sales from the current date to the next U day; is the expected number of days to sell; is the actual sales volume on the current date; is the expected sales volume on the jth day after the current date; Expected sales volume j´-1 days after the current date; is the predicted value of the daily sales growth coefficient on the j´-1 day after the current date obtained by the logistic formula; The sales stability index corresponding to the drug on day j´-1 is obtained based on the data of all dates before the j´th day using the same calculation method as the sales stability index.

5. The intelligent management system for real-time inventory warning of a pharmacy according to claim 1 is characterized in that: The process of obtaining the expected sales days of the drug on the current date is: The remaining inventory on the current date is used as the expected total sales volume in the relationship, and the expected sales days are calculated. The calculated expected sales days are rounded down to obtain the expected sales days of the drug on the current date.

6. The intelligent management system for real-time inventory warning of a pharmacy according to claim 1 is characterized in that: The pharmacy inventory warning based on the expected sales days is specifically as follows: Obtain drugs with a daily growth coefficient greater than 0 on the current date and use them as the drugs with sales growth on the current date. For each type of drug with sales growth, issue an inventory warning when the expected sales days of the drug are less than the preset stock-out threshold. Otherwise, no inventory warning will be issued.

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