Intelligent management system for pharmacy real-time inventory early warning

Abstract

The invention relates to the technical field of data monitoring, in particular to an intelligent management system for pharmacy real-time inventory early warning. The system comprises a data acquisition module which is used for acquiring the daily sales volume and stock quantity of each type of medicines in a pharmacy; the sales volume analysis module is used for analyzing current drug sales volume characteristics based on the drug sales volume similarity and the drug ex-warehouse state; the sales state analysis module is used for analyzing a sales volume stability index of the medicine in combination with the sales volume trend coefficient based on a single-day growth coefficient difference of the sales volume of the medicine in adjacent dates; the day number expectation module is used for analyzing the expected sales day number through the single-day growth coefficient of the multi-day drug sales in combination with the drug demand development rule; the early warning module is used for carrying out pharmacy inventory early warning based on the expected sales days; the problem that it is difficult to effectively analyze drug sales changes in different seasons through regular detection is avoided, the manpower resource expenditure in the pharmacy inventory process is reduced, and the pharmacy management efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of data monitoring, and specifically relates to an intelligent management system for real-time inventory warning in a pharmacy. Background Art

[0002] The pharmacy is mainly used for storing drugs. The drug inventory depends on the pharmacy staff to regularly detect the access volume of drugs and uses the retrospective method to dynamically analyze the pharmacy inventory. Although the existing technology uses the dynamic inventory analysis based on the retrospective method to adjust the drug stock in the pharmacy, in actual operation, a large amount of manpower is still required to regularly query the pharmacy inventory and the drug inflow and outflow volume through the network, and rely on manual control of the inventory. This not only consumes unnecessary manpower and material resources, but also cannot realize the intelligent analysis and control of the drug stock. And due to the fixed detection cycle, it is difficult to match the sudden seasonal changes in real time. There may be a phenomenon of misanalyzing the severity through manual analysis of the drug stock demand, resulting in an obvious deviation between the inventory quantity and the 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 in a pharmacy, and the specific technical solutions adopted are as follows: This application proposes an intelligent management system for real-time inventory warning in a pharmacy, and the system includes: Data acquisition module: Collect the daily sales volume and inventory quantity of each type of drug in the pharmacy; Sales analysis module: For each type of drug, calculate the proportion of the total daily drug growth based on the difference between the drug sales volumes in adjacent dates and the average daily drug sales volume in the historical time; Based on the difference between the drug sales volume on the current date and the drug sales volume during the period when the sales volume has not increased, combined with the proportion of the total daily drug growth, calculate the sales trend coefficient of the drug on the current date; Sales status analysis module: Calculate the new change parameter of the drug every day based on the difference in the single-day growth coefficient of the drug sales volume in adjacent dates, combined with the sales trend coefficient; Based on the difference between the single-day growth coefficient of the drug sales volume on the current day and the previous day, combined with the new change parameter, construct the sales stability index corresponding to the drug on the current day; Days prediction module: During the period of drug sales growth, based on the single-day growth coefficient of the drug sales volume for multiple days and the sales stability index, combined with the data fitting formula, determine the expected sales volume of the drug every day in the future, and construct a relationship formula between the total expected sales volume and the expected number of sales days; Based on the remaining inventory of the drug on the current date, combined with the relationship formula, obtain the expected number of sales days of the drug on the current date; Warning module: Conduct pharmacy inventory warning based on the expected number of sales days.

[0004] In one embodiment, the ratio of the total daily growth of a drug is calculated based on the difference in drug sales between adjacent dates and the average daily drug sales over a historical period, specifically as follows: Calculate the daily growth coefficient of the daily drug sales based on the difference in drug sales between adjacent dates; Calculate the sum of the daily growth coefficients of the drug sales of all categories on the i-th day; calculate the average daily sales of the k-th category of drugs in the week before the i-th day; calculate the ratio of the daily growth coefficient of the k-th category of drugs on the i-th day to the sum of the daily growth coefficients, denoted as the first ratio; the ratio of the total daily sales growth of the k-th category of drugs on the i-th day is positively correlated with the first ratio and the average daily sales of the k-th category of drugs on the i-th day.

[0005] In one embodiment, the process of calculating the daily growth coefficient of the daily drug sales is as follows: Take the difference between the drug sales on the i-th day and the (i - 1)-th day as the sales growth of the drug on the i-th day; take the ratio of the sales growth to the drug sales on the (i - 1)-th day as the daily growth coefficient of the drug sales on the i-th day.

[0006] In one embodiment, the expression of the sales trend coefficient is: , where is the sales trend coefficient of the k-th category of drugs on the i-th day; is the ratio of the total daily sales growth of the k-th category of drugs on the i-th day; is the sales of the k-th category of drugs on the i-th day; is the average value of the drug sales on all non-increasing dates before the i-th day; is a normalization function; among them, the non-increasing date is the date when the sales growth is less than 0.

[0007] In one embodiment, the process of obtaining the new change parameter is as follows: Take the difference between the daily growth coefficients of the drug sales on the i-th day and the (i - 1)-th day as the incremental difference coefficient of the drug on the i-th day; calculate the absolute value of the ratio of the incremental difference coefficient to the daily growth coefficient of the drug sales on the (i - 1)-th day; the new 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 of drugs on the i-th day.

[0008] In one embodiment, the expression of the sales stability index is: ; where is the sales stability index corresponding to the k-th category of drugs on the i-th day; , They are respectively the new change parameter and the incremental difference coefficient of the k-th type of drug on the i-th day.

[0009] In one embodiment, the single-day growth coefficient of the drug sales volume based on multiple days and the sales volume stability index are combined with a data fitting formula to determine the expected daily sales volume of the drug in the future, and a relationship between the expected total sales volume and the expected number of sales days is constructed. Specifically: During the period of drug sales volume growth, the logistic formula of the drug is calculated using the single-day growth coefficient of the known consecutive multi-day drug sales volume. Through the calculated logistic formula, the predicted value of the single-day growth coefficient of the drug sales volume on any day is obtained. Based on the predicted value of the single-day growth coefficient and the sales volume stability index corresponding to the drug in the future every day, the expected daily sales volume of the drug in the future is determined, and a relationship between the expected total sales volume and the expected number of sales days is constructed.

[0010] In one embodiment, the determination of the expected daily sales volume of the drug in the future and the construction of the relationship between the expected total sales volume and the expected number of sales days are expressed as: ; ; In the formula, N is the expected total sales volume from the current date to the U-th day after it; is the expected number of sales days; is the actual sales volume on the current date; is the expected sales volume on the -th day after the current date; The expected sales volume on the -th day after the current date; is the predicted value of the single-day growth coefficient of the sales volume on the -th day after the current date obtained through the logistic formula; is based on all the data of the dates before the -th day that have been obtained, and is calculated in the same way as the sales volume stability index, and is the sales volume stability index corresponding to the drug on the -th day.

[0011] In one embodiment, the process of obtaining the expected number of sales days of the drug on the current date is as follows: Take the remaining inventory on the current date as the expected total sales volume in the relationship formula, calculate the expected number of sales days, and round down the calculated expected number of sales days to obtain the expected number of sales days of the drug on the current date.

[0012] In one embodiment, the pharmacy inventory warning based on the expected number of sales days is specifically as follows: Obtain the drugs with a single-day growth coefficient greater than 0 within the current date as the drugs with increasing sales volume on the current date; for each type of drug with increasing sales volume, when the expected sales days of the drug are less than the preset inventory depletion threshold, issue an inventory warning; otherwise, do not issue an inventory warning.

[0013] The present application has the following beneficial effects: The present application realizes the adaptive adjustment of the drug inventory warning value by monitoring the recent sales volume changes of each drug in real time, analyzing the current drug sales characteristics based on the similarity of drug growth patterns and the drug outbound status, and analyzing the drug demand based on historical data and current real-time data, thereby improving the pharmacy management efficiency; The present application monitors the pharmacy inventory and drug sales volume in real time, calculates the single-day growth coefficient of the daily drug sales volume based on the difference between the drug sales volumes on adjacent dates, constructs the sales volume trend coefficient of the daily drug based on the difference between the single-day growth coefficient of each type of drug and all drugs, and the drug sales volume in historical time; analyzes the drug sales stability index based on the difference in the single-day growth coefficient of the drug sales volume on adjacent dates and the sales volume trend coefficient; predicts the single-day growth coefficient of the daily drug through the single-day growth coefficient of the drug sales volume for multiple days, combines the drug demand development law, deduces the drug demand trend, and calculates the expected sales days of the current drug inventory; issues a pharmacy inventory warning based on the expected sales days. Realize the dynamic adaptive adjustment of the inventory warning value, thereby improving the pharmacy management efficiency, avoiding the problem that it is difficult to effectively analyze the drug demand in different seasons through regular detection, and reducing the human resource expenditure in the pharmacy inventory process. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0015] Figure 1 It is a block diagram of an intelligent management system for real-time pharmacy inventory warning provided by an embodiment of the present application; Figure 2 It is a flowchart of an intelligent management system for real-time pharmacy inventory warning; Figure 3 It is a schematic diagram of the acquisition process of the proportion of the total daily drug growth. Detailed Embodiments

[0016] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, elaborate in detail on a smart management system for real-time inventory warning in a pharmacy proposed according to this application, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0018] The following will specifically describe the specific solution of a smart management system for real-time inventory warning in a pharmacy provided by this application in conjunction with the accompanying drawings.

[0019] Please refer to Figure 1 , which shows a block diagram of a smart management system for real-time inventory warning in a pharmacy provided by an embodiment of this application. The system includes: A data acquisition module 101 that collects the daily sales volume and inventory quantity of each type of drug in the pharmacy.

[0020] Real-time supervision of the drug status in the pharmacy to obtain drug information and access data in the pharmacy, including: (1) Drugs are the main items stored in the pharmacy. When recording the drugs in the pharmacy, the drugs are classified and numbered according to the uses of each type of drug. Among them, the number of the kth type of drug is recorded as k, and the total number of drug types in the pharmacy is recorded as .

[0021] (2) When accessing drugs, obtain the inventory quantity of a single-name drug in the pharmacy; each deposit or withdrawal operation will cause a change in the drug stock. There may be multiple access operations within a single day. For the convenience of data recording, the daily drug sales volume is statistically analyzed at 0:00 every day, and the inventory quantity of the drug on the current day is recorded. Among them, the sales volume of the kth type of drug on the ith day is recorded as , and the drug inventory quantity is recorded as .

[0022] A sales volume analysis module 102 that, for each type of drug, calculates the single-day growth coefficient of the daily drug sales volume based on the difference in drug sales volume between adjacent dates; calculates the proportion of the total single-day drug growth volume of the drug based on the difference between the single-day growth coefficient of each type of drug and all drugs, and the average daily drug sales volume in historical time; calculates the sales volume trend coefficient of the drug on the current date based on the difference between the drug sales volume on the current date and the drug sales volume during the period when the sales volume did not increase, in combination with the proportion of the total single-day drug growth volume.

[0023] Since people have different demands for different drugs at different times, the sales volume of drugs in pharmacies also fluctuates accordingly. For example, affected by seasons or emergencies, there will be a large-scale regional procurement phenomenon for certain types of drugs, with a large number of purchasers and a large quantity. The purchase demand for drugs increases sharply. After that, the demand for drugs decreases, and the quantity of drugs to be purchased decreases, only meeting the daily sales volume. Overstocking may lead to an oversupply of drugs.

[0024] The sales volume of drugs can well reflect the changes in people's demand for drugs. When the sales volume of drugs is high, people's demand for drugs is relatively high. When affected by seasons, people's demand for some drugs is relatively high, and the sales volume of drugs increases more than usual. By observing the sales volume growth of drugs over several consecutive days, it is possible to determine whether there is a suspected seasonal change at the current time. Specifically: (1)For each type of drug, obtain the sales volume growth of this type of drug on the current day compared to the previous day. Among them, denote the sales volume growth of the k-th type of drug on the i-th day compared to the previous day (the (i - 1)-th day) as , ; Further, calculate the single-day growth coefficient of the sales volume of the k-th type of drug on the i-th day , and the expression is: . Among them, and are the sales volumes of the k-th type of drug on the i-th day and the (i - 1)-th day respectively.

[0025] (2)Based on the sales volume growth of each type of drug, calculate the proportion of the total single-day growth of each type of drug in the total sales volume, and the expression is: ; In the formula, is the proportion of the total single-day growth of the k-th type of drug on the i-th day; is the single-day growth coefficient of the sales volume of the k-th type of drug on the i-th day; is the sum of the single-day growth coefficients of the sales volumes of all types of drugs on the i-th day; is the average daily sales volume of the k-th type of drug within one week before the i-th day. The average daily sales volume is the total sales volume divided by the number of sales days. Among them, is the first ratio.

[0026] By calculating , the problem of accidental phenomena affecting the authenticity of the results due to too low a base number is avoided; is the ratio of the sales volume growth of drug k on a single day to the total sales volume growth of all drugs. The larger this value is, the higher the growth of drug k on a single day.

[0027] (3)Customers who have purchased will not purchase the drug again in the short term, but there are still new consumers going to purchase, which reduces the growth rate of drug sales while the sales volume remains at a relatively high level.

[0028] Therefore, by comparing the drug sales volume in the current season with that during the period when no sales growth occurred, the sales trend coefficient of drugs in the current season is analyzed, and the expression is: ; In the formula, is the sales trend coefficient of the k-th type of drug on the i-th day; is the proportion of the total daily sales growth of the k-th type of drug on the i-th day; is the sales volume of the k-th type of drug on the i-th day; is the average sales volume of drugs on all non-increment dates before the i-th day; is the normalization function. Among them, the non-increment date is the date when the sales growth is less than 0.

[0029] is the comparison value between the actual sales volume after the drug generates an increment and the sales volume during the non-increment period. The larger this value is, the higher the drug sales volume is in a continuous high state.

[0030] represents the maintenance state parameter of the drug sales volume after a high growth. The larger this value is, the larger the number of people purchasing the drug, and the stable growth. The normalization function is used to normalize the result. The higher the sales trend coefficient is, the greater the probability that the drug has a high demand brought by the season in the current season.

[0031] The sales status analysis module 103 calculates the new change parameter of the drug every day based on the difference in the daily growth coefficient of the drug sales volume within adjacent dates and in combination with the sales trend coefficient; and constructs the sales stability index corresponding to the drug on the current day based on the difference between the daily growth coefficient of the drug sales volume on the current day and the previous day and in combination with the new change parameter.

[0032] The growth of the number of purchasers is positively correlated with the growth of drug sales. When the demand of the crowd for drugs increases, the drug sales volume will increase accordingly. Similarly, when the demand of the crowd for drugs decreases, the drug sales volume will also show a decreasing trend. Therefore, to obtain the drug sales status, it is necessary to analyze the change trend of the sales increment during the drug sales process. If the sales volume continues to grow, the demand of people for drugs is still increasing continuously.

[0033] By comparing the drug sales status on adjacent dates and in combination with the sales trend coefficient of each type of drug, the new change parameter of each type of drug is obtained, and the expression is: ; In the formula, is the new change parameter of the k-th type of drug on the i-th day; , are the daily growth coefficients of the sales volume of the k-th type of drug on the i-th day and the (i - 1)-th day respectively; is the sales volume trend coefficient of the k-th type of drug on the i-th day.

[0034] represents the difference in newly added sales volume between adjacent days. The smaller this value, the closer the newly added number of purchasers on adjacent dates. Conversely, the difference in newly added number of purchasers is larger. is the drug sales volume trend coefficient, which is positively correlated with the difference in newly added number of purchasers per day.

[0035] (2) When the newly added number of purchasers on the i-th day is less than that of the previous day, the actual drug sales volume decreases. For the newly added change parameter, it is necessary to analyze whether the newly added number of purchasers increases or decreases day by day.

[0036] By the difference between the single-day growth coefficients of the drug sales volume on the current day and the previous day, the incremental difference coefficient of the drug on the current day is obtained, and the expression is: , where in the formula, is the incremental difference coefficient of the k-th type of drug on the i-th day.

[0037] When , the newly added number of purchasers increases with the date, and the larger the value, the more newly added number of purchasers; when , the newly added purchase quantity decreases or remains unchanged with the date.

[0038] (3) Further, the difference in newly added number of purchasers reflects the degree of drug demand. By analyzing whether the newly added number of people per day increases or decreases through the sign value of the newly added number of people, the sales volume stability index corresponding to the drug on the current day is obtained. Among them, the sales volume stability index corresponding to the k-th type of drug on the i-th day is respectively negatively correlated with the newly added change parameter of the k-th type of drug on the i-th day and the incremental difference coefficient.

[0039] Preferably, in the embodiment of the present application, the expression of is: ; where in the formula,

[0040] is the product of the newly added number stability parameter and the incremental difference, representing the sales volume stability index. When , it indicates that the drug sales volume is stable; otherwise, it indicates that the drug sales volume changes rapidly.

[0041] The number of days prediction module 104, within the period of increasing drug sales volume, based on the daily growth coefficient of the drug sales volume over multiple days, combines with a data fitting formula to obtain the predicted value of the daily growth coefficient of the drug sales volume for any future date; based on the predicted value of the daily growth coefficient and the sales stability index corresponding to each day's drug in the future, determines the expected sales volume of the drug for each future day, constructs a relationship between the total expected sales volume and the expected number of sales days; based on the remaining inventory of the drug on the current date, combines with the relationship to obtain the expected number of sales days of the drug on the current date.

[0042] After passing through the seasonal high-demand period of the drug, the demand for the drug decreases. Therefore, for the urgency of the supply of the existing drug stock, it is necessary to analyze the available supply duration of the current drug stock based on the sales stability index of drug k and the actual sales volume of drug k.

[0043] (1) Taking the k-th type of drug as an example, obtain the sales volume of this type of drug within the previous week of any date as the single-week sales volume of this type of drug on that date; calculate the daily growth rate of the sales volume of this type of drug on that date compared to the previous day; when the daily growth rate exceeds 0.2 of the corresponding single-week sales volume for the first time as the date changes, then take the day before the date corresponding to the daily growth rate as the starting date of the drug sales volume growth period. At this time, the demand for the drug is in a rapid growth state.

[0044] (2) On the j-th day after the starting date, use the same acquisition method as the daily growth coefficient in step (1) of the sales analysis module 102 to obtain the daily growth coefficient of the sales volume of the k-th type of drug on the j-th day. .

[0045] (3) The logistic formula is often used in scenarios with an S-shaped growth trend phenomenon. Therefore, taking the k-th type of drug as an example, after the starting date, use the known daily growth coefficients of the sales volume of this type of drug for consecutive Q days to calculate the logistic formula of this type of drug, where Q is greater than 3; based on the calculated logistic formula, obtain the predicted value of the daily growth coefficient of the sales volume of this type of drug on the j-th day. . Preferably, this application uses the data of consecutive 5 days to calculate the logistic formula. As other embodiments of this application, the implementer can set the value of Q according to the actual situation.

[0046] (4) Further, through the sales stability index of the current drug k is corrected. The more stable the sales state, the more the drug sales volume can be reduced, and vice versa, then it is increased to obtain the expected total sales volume of this type of drug on the U-th day after the current date. The expression is: ; ; Wherein, N is the total expected sales volume from the current date to the Uth day after this date; is the expected number of sales days; is the actual sales volume on the current date; is the number of days after the current date is the expected sales volume on the The expected sales volume on the day after the current date; is the predicted value of the daily growth coefficient of the sales volume on the day after the current date obtained through the logistic formula; is based on all the data of the dates before the th day that have been obtained, and by using the same calculation method as the above sales volume stability index, the sales volume stability index corresponding to the drug on the th day is obtained.

[0047] (5) Take the remaining inventory on the current date as the value of N, and substitute the actual sales volume on the current date to calculate the expected number of sales days. Since the expected number of sales days should be an integer, round down the calculated expected number of sales days to obtain the expected number of sales days of the drug on the current date.

[0048] The warning module 105 performs pharmacy inventory warning based on the expected number of sales days.

[0049] This application is an intelligent management system for real-time inventory warning in a pharmacy. Based on the analysis of the change of pharmacy drug data parameters to analyze the predicted drug sales volume, and then realizing the intelligent warning of drug inventory. The specific operation steps are as follows: (1) Calculate the daily growth coefficient of each type of drug in the pharmacy, and obtain the drugs with a daily growth coefficient greater than 0 within this date as the drugs with increasing sales volume within this date.

[0050] (2) For each type of drug with increasing sales volume, calculate the expected number of sales days of the current remaining inventory of this drug, and set the inventory depletion threshold , preferably, in the embodiment of this application, is set to 2. As other embodiments of this application, the implementer can set the value of by himself according to the actual situation.

[0051] When the expected number of sales days of each type of drug with increasing sales volume is less than , the pharmacy intelligent management system issues an inventory warning, and it is necessary to urgently purchase this type of drug; when the expected number of sales days of each type of drug with increasing sales volume is greater than or equal to , the pharmacy intelligent management system does not issue an inventory warning, but will remind the pharmacy management staff to reasonably arrange the purchase.

[0052] An intelligent management system for real-time inventory warning in a pharmacy provided in this embodiment further includes a basic support layer, a platform data processing layer, and a system application layer, which realizes a point-to-point closed-loop from data storage to business value and helps with pharmacy quality control management.

[0053] Basic support layer: Store the real-time inventory data of the pharmacy in the cloud server platform, complete data storage, raw data collection, and infrastructure management (including progress management, transaction management, etc.). The system administrator is responsible for data maintenance and data security.

[0054] Platform data processing layer: Develop an intelligent management system for real-time inventory warning in a pharmacy using mainstream programming languages to realize data management, data statistics, and personnel permission allocation, etc.

[0055] System application layer: Use low-code development tools to build the system to realize data collection, sales volume analysis, sales status analysis, days prediction, and warning, and finally realize the visualization of business scenarios and the real-time management of warning situations. Among them, data collection, sales volume analysis, sales status analysis, days prediction, and warning can be realized through existing technologies, and no special restrictions are made in this embodiment.

[0056] A flowchart of an intelligent management system for real-time inventory warning in a pharmacy is as Figure 2 shown; A schematic diagram of the process for obtaining the proportion of the total daily drug growth volume is as Figure 3 shown.

[0057] In summary, the embodiment of this application realizes the adaptive adjustment of the drug inventory warning value by monitoring the recent sales volume changes of each drug in real time, analyzing the current drug sales characteristics based on the similarity of drug growth patterns and the drug outbound status, and analyzing the drug demand based on historical data and current real-time data, thereby improving the pharmacy management efficiency; This application monitors the pharmacy inventory and drug sales volume in real time, calculates the single-day growth coefficient of the daily drug sales volume based on the difference between the drug sales volumes on adjacent dates, constructs the sales volume trend coefficient of each daily drug based on the difference between the single-day growth coefficient of each type of drug and that of all drugs, and the drug sales volume in historical time; analyzes the sales volume stability index of drugs based on the difference in the single-day growth coefficient of drug sales volume on adjacent dates and in combination with the sales volume trend coefficient; predicts the single-day growth coefficient of each daily drug through the single-day growth coefficient of the drug sales volume over multiple days, combines the development law of drug demand, deduces the drug demand trend, calculates the expected sales days of the current drug inventory; issues a pharmacy inventory warning based on the expected sales days. Realize the dynamic adaptive adjustment of the inventory warning value, thereby enhancing the pharmacy management efficiency, avoiding the problem that it is difficult to effectively analyze the drug demand in different seasons through regular inspections, and reducing the human resource expenditure in the pharmacy inventory process.

[0058] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0060] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; any modification to the technical solutions recorded in the foregoing embodiments, or any equivalent replacement of some of the technical features, does not deviate from the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. An intelligent management system for real-time inventory warning in a pharmacy, characterized in that The system includes: A data collection module: collecting the daily sales volume and inventory quantity of each type of drug in the pharmacy; A sales volume analysis module: for each type of drug, calculating the proportion of the total daily growth of the drug based on the difference in drug sales volume between adjacent dates and the average daily sales volume of the drug over a historical period; calculating the sales volume trend coefficient of the drug on the current date based on the difference between the drug sales volume on the current date and the drug sales volume during the period when the sales volume did not increase, in combination with the proportion of the total daily growth of the drug; A sales status analysis module: calculating the new change parameter of the drug every day based on the difference in the daily growth coefficient of the drug sales volume between adjacent dates, in combination with the sales volume trend coefficient; constructing the sales volume stability index corresponding to the drug on the current date based on the difference between the daily growth coefficient of the drug sales volume on the current date and the previous day, in combination with the new change parameter; A days prediction module: during the period of drug sales volume growth, determining the expected sales volume of the drug every day in the future based on the daily growth coefficient of the drug sales volume over multiple days and the sales volume stability index, in combination with a data fitting formula, and constructing a relationship between the expected total sales volume and the expected number of sales days; obtaining 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; An early warning module: performing an early warning on the pharmacy inventory based on the expected number of sales days.

2. The intelligent management system for real-time inventory warning in a pharmacy according to claim 1, characterized in that The calculation of the proportion of the total daily growth of the drug based on the difference in drug sales volume between adjacent dates and the average daily sales volume of the drug over a historical period is specifically as follows: Calculating the daily growth coefficient of the drug sales volume based on the difference in drug sales volume between adjacent dates; Calculating the sum of the daily growth coefficients of the sales volume of all types of drugs on the i-th day; calculating the average daily sales volume of the k-th type of drug within one week before the i-th day; calculating the ratio of the daily growth coefficient of the sales volume of the k-th type of drug on the i-th day to the sum of the daily growth coefficients, denoted as the first ratio; the proportion of the total daily sales volume growth of the k-th type of drug on the i-th day is positively correlated with the first ratio and the average daily sales volume of the k-th type of drug on the i-th day.

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

4. The intelligent management system for real-time inventory warning in a pharmacy according to claim 2, wherein, The expression of the sales volume trend coefficient is: , where is the sales volume trend coefficient of the k-th type of drug on the i-th day; is the proportion of the total daily sales volume increase of the k-th type of drug on the i-th day; is the sales volume of the k-th type of drug on the i-th day; is the average sales volume of drugs on all non-increasing days before the i-th day; is a normalization function; wherein, the non-increasing days are the days when the sales volume increase is less than 0.

5. The intelligent management system for real-time inventory warning in a pharmacy according to claim 1, characterized in that, The process of obtaining the new change parameter is: Taking the difference between the daily growth coefficient of the drug sales volume on the i-th day and the daily growth coefficient of the drug sales volume on the (i - 1)-th day as the incremental difference coefficient of the drug on the i-th day; calculating the absolute value of the ratio of the incremental difference coefficient to the daily growth coefficient of the drug sales volume on the (i - 1)-th day; The new change parameter of the drug on the i-th day is positively correlated with the absolute value of the ratio and the sales volume trend coefficient of the k-th type of drug on the i-th day respectively.

6. The intelligent management system for real-time inventory warning in a pharmacy according to claim 5, wherein, The expression of the sales volume stability index is: ; wherein, is the sales volume stability index corresponding to the k-th type of drug on the i-th day; , are respectively the new change parameter and the incremental difference coefficient of the k-th type of drug on the i-th day.

7. An intelligent management system for real-time inventory warning in a pharmacy according to claim 1, characterized in that The determination of the expected sales volume of the drug every day in the future based on the daily growth coefficient of the drug sales volume over multiple days and the sales volume stability index, in combination with a data fitting formula, and the construction of a relationship between the expected total sales volume and the expected number of sales days is specifically as follows: During the period of increasing drug sales volume, use the single-day growth coefficient of the known drug sales volume for consecutive days to calculate the logistic formula of the drug. Through the calculated logistic formula, obtain the predicted value of the single-day growth coefficient of the drug sales volume for any day. Based on the predicted value of the single-day growth coefficient and the sales volume stability index corresponding to the drug every day in the future, determine the expected sales volume of the drug every day in the future, and construct a relationship between the total expected sales volume and the expected number of sales days.

8. The intelligent management system for real-time inventory warning in a pharmacy according to claim 7, characterized in that, The process of determining the expected sales volume of the drug every day in the future and constructing the relationship between the total expected sales volume and the expected number of sales days is expressed as: ； ； Where N is the total expected sales volume from the current date to the Uth day after it; is the expected number of sales days; is the actual sales volume on the current date; is the expected sales volume on the th day after the current date; The expected sales volume on the th day after the current date; is the predicted value of the daily growth coefficient of the sales volume on the th day after the current date obtained through the logistic formula; is based on all the data of the dates before the th day that have been obtained, and using the same calculation method as the sales volume stability index, the sales volume stability index corresponding to the drug on the th day is obtained.

9. The intelligent management system for real-time inventory warning in a pharmacy according to claim 1, characterized in that, The process of obtaining the expected number of sales days of the drug on the current date is as follows: Take the remaining inventory on the current date as the total expected sales volume in the above relationship, calculate the expected number of sales days, and round down the calculated expected number of sales days to obtain the expected number of sales days of the drug on the current date.

10. The intelligent management system for real-time inventory warning in a pharmacy according to claim 1, characterized in that, The pharmacy inventory warning based on the expected number of sales days is specifically as follows: Obtain the drugs with a single-day growth coefficient greater than 0 within the current date as the drugs with increasing sales volume on the current date; for each type of drug with increasing sales volume, when the expected number of sales days of the drug is less than the preset inventory depletion threshold, issue an inventory warning. Otherwise, no inventory warning is issued.

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