CN116703528A - Medical sales management system and management method thereof - Google Patents
Medical sales management system and management method thereof Download PDFInfo
- Publication number
- CN116703528A CN116703528A CN202310943332.2A CN202310943332A CN116703528A CN 116703528 A CN116703528 A CN 116703528A CN 202310943332 A CN202310943332 A CN 202310943332A CN 116703528 A CN116703528 A CN 116703528A
- Authority
- CN
- China
- Prior art keywords
- disease
- target
- time
- target disease
- infected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007726 management method Methods 0.000 title claims abstract description 46
- 201000010099 disease Diseases 0.000 claims abstract description 306
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 306
- 239000003814 drug Substances 0.000 claims abstract description 101
- 229940079593 drug Drugs 0.000 claims abstract description 37
- 230000005541 medical transmission Effects 0.000 claims abstract description 33
- 230000008859 change Effects 0.000 claims abstract description 27
- 238000004519 manufacturing process Methods 0.000 claims abstract description 22
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 230000009471 action Effects 0.000 claims abstract description 11
- 230000001419 dependent effect Effects 0.000 claims abstract description 7
- 208000015181 infectious disease Diseases 0.000 claims description 49
- 238000011084 recovery Methods 0.000 claims description 23
- 230000034994 death Effects 0.000 claims description 20
- 231100000517 death Toxicity 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 20
- 238000000556 factor analysis Methods 0.000 claims description 10
- 238000009792 diffusion process Methods 0.000 claims description 4
- 230000004044 response Effects 0.000 abstract description 2
- 230000005540 biological transmission Effects 0.000 description 9
- 230000000694 effects Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 6
- 230000008901 benefit Effects 0.000 description 5
- 238000012377 drug delivery Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 230000005180 public health Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 230000004043 responsiveness Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 208000035473 Communicable disease Diseases 0.000 description 1
- 206010061217 Infestation Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000002301 combined effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000002458 infectious effect Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0605—Supply or demand aggregation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Public Health (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Medical Informatics (AREA)
- Game Theory and Decision Science (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of medicine management, in particular to a medicine sales management system and a management method thereof. The system comprises: a sales target area basic data acquisition section configured to acquire basic data of a sales target area in real time; a target disease analysis model configured to generate a target disease infected person number variation function based on the base data; the independent variable of the function of the change of the number of infected people of the target disease is time, and the dependent variable is the number of infected people of the target disease; a target disease drug analysis model configured to calculate an attenuation factor of a target disease under the action of the target disease drug; and a medical sales management section configured to substitute the attenuation factor into a function of a change in the number of infected persons of the target disease. The invention comprehensively considers the disease transmission condition, the drug production and supply condition and the market demand, optimizes the drug supply quantity and the selling price, and realizes the quick response and the high-efficiency management during the disease outbreak.
Description
Technical Field
The invention belongs to the technical field of medicine management, and particularly relates to a medicine sales management system and a management method thereof.
Background
In modern society, the pharmaceutical industry plays a vital role in guaranteeing the health of people. The medicine sales management system is an important tool for coordinating the links of medicine supply chain, adjusting medicine storage, distribution, sales and the like. However, with the development of society and the advancement of technology, conventional medical sales management systems are facing a number of challenges.
First, most conventional medical sales management systems are designed primarily to focus on back-end storage management and sales flows, and these systems often lack sufficient flexibility and responsiveness in the presence of public health events such as outbreaks. For example, when a new infection outbreak occurs, there is a need to quickly and efficiently predict drug needs in order to adjust production and supply plans in time. However, existing systems often lack the ability to predict and handle such complications.
Second, conventional pharmaceutical sales management systems typically do not take into account dynamic adjustment mechanisms of drug prices. The price of the medicine is not only influenced by factors such as production cost, market demand and the like, but also is related to various factors such as policies, disease epidemic situations and the like. If the price of the drug cannot be dynamically adjusted based on these factors, it may lead to a mismatch in drug supply and demand and even affect the accessibility of the drug.
Furthermore, existing pharmaceutical sales management systems often lack overall optimization of the pharmaceutical supply chain. The production, supply and sales of pharmaceuticals is a complex process involving multiple participants and multiple links. If optimization of only a single link is concerned, but global coordination and integration are ignored, inefficiency may result, or even supply interruption may occur.
Therefore, there are many problems to be solved in the existing pharmaceutical sales management system. First, these systems lack sufficient responsiveness and flexibility in the face of incidents such as outbreaks. Second, these systems do not take into account dynamic adjustment of drug prices, which may lead to drug supply and demand mismatch, affecting drug accessibility. Finally, the lack of an overall optimization of the medical supply chain by existing systems may lead to inefficient and even interruption of the supply. In view of these problems, it is desirable to design a new medical sales management system that better addresses various challenges and improves the efficiency and stability of the medical supply chain.
Disclosure of Invention
The invention aims to provide a medicine sales management system and a management method thereof, comprehensively consider disease transmission conditions, medicine production and supply conditions and market demands, optimize medicine supply quantity and sales price, and realize quick response and efficient management during disease outbreak.
In order to solve the problems, the technical scheme of the invention is realized as follows:
a pharmaceutical sales management system, the system comprising: a sales target area basic data acquisition section configured to acquire basic data of a sales target area in real time; the basic data includes: average population income, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality; a target disease analysis model configured to generate a target disease infected person number variation function based on the base data; the independent variable of the function of the change of the number of infected people with the target disease is time, and the dependent variable is the number of infected people with the target disease; the target disease medicine analysis model is configured to calculate an attenuation factor of the target disease under the action of the target disease medicine by using a preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease; and the medicine sales management part is configured to substitute attenuation factors into a change function of the number of infected patients of the target diseases, take the total number of infected patients of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, minimize the integral of the number of infected patients of the target diseases in a time domain, calculate the optimal supply quantity and the optimal supply time of the target diseases, calculate the optimal sales price according to the average income of population, the production cost and the target profit margin of the target diseases, and sell the target diseases according to the optimal supply quantity, the optimal supply time and the optimal sales price.
Further, the function of the change in the number of infected persons of the target disease is expressed using the following formula:
。
wherein, time of presentationThe number of infected people at the moment;indicating the number of initial infections;time of presentationIntensity of disease transmission at time;representation ofThe number of infected people at the moment;indicating recovery rate of infected people;representing the rate of spread of the disease;is a time variable.
Further, the disease transmission intensity is calculated using the following formula:
。
wherein, time of presentationIntensity of disease transmission at time;time of presentationThe number of infected people at the moment;time of presentationPopulation number at time;indicating the disease infection rate.
Further, the method comprises the steps of,calculated using the following formula:
。
wherein, time of presentationPopulation number at time;representing an initial population number;representing population growth rate; the value range is 1.1-1.5;indicating mortality of disease infection;indicating recovery rate of infected people.
Further, the method for calculating the attenuation factor of the target disease under the action of the target disease medicine by using the preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease by the target disease medicine analysis model comprises the following steps: substituting the index of the cure rate of the target disease medicine for the target disease into the following formula to calculate the attenuation factor：
。
Wherein, the cure rate of the medicine for the target diseases.
Further, the number of target disease deaths is expressed using the following formula:
。
wherein, time of presentationDeath number at the moment;indicating mortality of disease infection;time of presentationThe number of infected people at the moment;representing the rate of spread of the disease; the total number of patients with the target disease is expressed by the following formula:
。
wherein, time of presentationThe total number of people at the moment;time of presentationThe number of infected people at the moment;time of presentationThe number of people recovered at the moment;time of presentationDeath number at the moment; wherein:
。
time of presentationThe number of people recovered at the moment;indicating recovery rate of infected people;time of presentationThe number of infected people at the moment;representing the rate of spread of the disease; the target disease regression time is expressed using the following formula:
。
wherein, time of presentationThe number of people recovered at the moment;indicating the number of initial infections;representing the rate of spread of the disease.
Further, substituting the attenuation factor into the function of the change of the number of infected persons of the target disease, taking the total number of diseased persons of the target disease, the death number of the target disease and the regression time of the target disease as constraint conditions, minimizing the integral of the infected persons of the target disease in a time domain, and calculating the optimal supply quantity and the optimal supply time of the target disease medicine according to the integral, wherein the method comprises the following steps: using the modified lagrangian multiplier algorithm, a following objective function is constructed:
。
the problem is then solved by minimizing the objective function, namely solving the following system of equations:
。
solving to obtainAndthe updated expression of (2) is put into constraint conditions, and the values of other variables are obtained; obtaining the optimal target disease infection number and optimal time corresponding to the minimum integral of the target disease infection number in the time domain according to the values of all the variables obtained by solving; taking the optimal number of target disease infected people as the optimal supply amount of the target disease medicine; taking the optimal time as the upper limit value of the optimal supply time, and calculating the target disease diffusion speed to obtain a disease outbreak time function; obtaining an optimal supply time set based on the disease outbreak function model and an upper limit value of the optimal supply time; the optimal supply time set comprises a plurality of optimal supply times.
Further, the disease outbreak time function is expressed using the following formula:
。
wherein, representing a disease outbreak time function;time of presentationThe number of infected people at the moment;indicating the number of initial infections;time of presentationIntensity of disease transmission at time;indicating recovery rate of infected people;representing the rate of spread of the disease.
Further, the method for calculating the optimal selling price according to the average income of population, the production cost of the target disease medicine and the target profit margin comprises the following steps: the best selling price is calculated using the following formula:
。
wherein, is the best selling price;the value range of the first weight coefficient is 0.3-0.6;the value range of the second weight coefficient is 0.4-0.7;average income for population;the production cost of the target disease medicine is;is the target profit margin.
A pharmaceutical sales management method, the method comprising:
acquiring basic data of a sales target area in real time; the basic data includes: average population income, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality; generating a target disease infected person number change function based on the basic data; the independent variable of the function of the change of the number of infected people with the target disease is time, and the dependent variable is the number of infected people with the target disease; calculating the attenuation factor of the target disease under the action of the target disease medicine by using a preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease; substituting the attenuation factors into a change function of the number of infected persons of the target diseases, taking the total number of diseased persons of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, minimizing the integral of the number of infected persons of the target diseases in a time domain, calculating to obtain the optimal supply quantity and the optimal supply time of the target diseases, calculating the optimal selling price according to the average income of population, the production cost and the target profit margin of the target diseases, and selling the target diseases according to the optimal supply quantity, the optimal supply time and the optimal selling price.
The medical sales management system and the management method thereof have the following beneficial effects:
firstly, the invention can provide accurate sales target area information through real-time acquisition and analysis of basic data. By obtaining basic data such as average income of population, population number, population growth rate, population mortality, disease infection rate, etc., the system can better understand the population characteristics and disease transmission condition of the targeted sales area. The method provides important basis for developing targeted sales strategies and drug supply plans, thereby improving sales effects and meeting market demands.
Secondly, the invention realizes the comprehensive evaluation and prediction of the target diseases through the target disease analysis model and the medicine analysis model. By analyzing the basic data and the drug characteristics, the system can generate a function of the change of the number of infected people of the target disease and the attenuation factor of the target disease, and further calculate the total number of the infected people, the death number and the regression time of the target disease. This information is critical to the strategy of formulating drug supply, sales price and sales time. According to the invention, the medical company can more accurately predict the propagation trend and market demand of the target diseases, so that the medicine supply and sales strategies can be flexibly adjusted, the market demand can be met to the greatest extent, and the sales profits can be improved.
The invention accurately predicts the disease transmission condition and the number of infected people through accurate mathematical models and algorithms, and provides scientific basis for calculating the optimal supply quantity and supply time of medicines. This approach ensures timely and adequate drug supply during an outbreak of disease, avoiding social panic and secondary disasters due to drug shortage. More importantly, the medicine can inhibit the further transmission of the diseases by providing enough medicines, so that the number of infected people with the diseases is reduced to a certain extent, and the public health is protected.
The system calculates the optimal medicine selling price through a reasonable price strategy. The strategy comprehensively considers factors such as average population income, production cost of medicines, target profit margin and the like, so that the price of the medicines has market competitiveness and can ensure economic benefits of medicine suppliers. The method ensures the normal operation and profit of the medicine suppliers while ensuring the affordability of the public, thereby finding a balance point between economic benefit and social benefit.
Drawings
Fig. 1 is a schematic system structure diagram of a medical sales management system according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The following will describe in detail.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein.
Example 1: a pharmaceutical sales management system, the system comprising: a sales target area basic data acquisition section configured to acquire basic data of a sales target area in real time; the basic data includes: average population income, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality; a target disease analysis model configured to generate a target disease infected person number variation function based on the base data; the independent variable of the function of the change of the number of infected people with the target disease is time, and the dependent variable is the number of infected people with the target disease; the target disease medicine analysis model is configured to calculate an attenuation factor of the target disease under the action of the target disease medicine by using a preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease; and the medicine sales management part is configured to substitute attenuation factors into a change function of the number of infected patients of the target diseases, take the total number of infected patients of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, minimize the integral of the number of infected patients of the target diseases in a time domain, calculate the optimal supply quantity and the optimal supply time of the target diseases, calculate the optimal sales price according to the average income of population, the production cost and the target profit margin of the target diseases, and sell the target diseases according to the optimal supply quantity, the optimal supply time and the optimal sales price.
Specifically, the system first configures and acquires basic data related to a sales target area, such as average income of population, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality, and the like, through a sales target area basic data acquisition section. These data provide important information about demographics and disease transmission.
By analyzing the underlying data, the system configures a target disease analysis model that generates a function of the number of infected persons for the target disease. The argument of this function is time and the argument is the number of people with the target disease infection. The target disease analysis model predicts the propagation trend and rule of the target disease in a specific area according to population characteristics and disease propagation rules. The target disease medicine analysis model is based on the cure rate of the target disease medicine for the target disease, and a preset attenuation factor analysis model is used for calculating the attenuation factor of the target disease under the action of the target disease medicine. The attenuation factor reflects the inhibition effect of the drug on disease transmission. And (5) evaluating the influence degree of the target disease drug on disease transmission by analyzing the attenuation factors.
The medical sales management section substitutes the attenuation factor into the target disease infected person number change function, and takes the target disease total number, the target disease death number, and the target disease resolution time as constraints. The goal of the system is to minimize the integral of the number of target disease-infected people over the time domain, thereby calculating the optimal supply of the target disease drug and the optimal supply time. Through this step, the system finds a drug delivery strategy that minimizes the number of target disease infestations.
After obtaining the optimal supply amount and the optimal supply time, the system further calculates an optimal selling price according to the average income of the population, the production cost of the target disease medicine and the target profit margin. This price aims to achieve maximum sales profits while taking into account the cost of the targeted disease drug and the purchasing power of the consumer.
Finally, the system performs sales of the targeted disease drug according to the optimal supply amount, the optimal supply time, and the optimal sales price. The system will rationally arrange drug supply and marketing campaigns to meet market demand and maximize sales benefits based on predicted target disease spread trends and demand conditions.
The function of the change in the number of infected persons of the target disease is expressed using the following formula:
。
wherein, time of presentationThe number of infected people at the moment;indicating the number of initial infections;time of presentationIntensity of disease transmission at time;representation ofThe number of infected people at the moment;indicating recovery rate of infected people;representing the rate of spread of the disease;is a time variable.
In particular, the method comprises the steps of,: this section describes the difference in disease transmission intensity from the number of infected persons. When the transmission intensity is greater than the number of people infected, the disease is represented as a trend of transmission; when the transmission intensity is smaller than the number of infected persons, it indicates that the disease has a tendency to shrink.
: this is an attenuation factor, which represents the attenuation of time. Over time, the number of infected peopleThe effect gradually diminishes.
The integral term is subjected to integral operation to obtain the time periodA change in the number of infected persons. The result of the integral plus the number of initial infectionsThe time is obtainedThe number of people infected by the target disease at the moment。
By setting different disease transmission intensitiesPeople suffering from infectionRecovery rate of infected peopleAnd the transmission rate of the diseasePredicting and simulating the change trend of the number of infected people of the target disease at different time points according to a formula.
The disease transmission intensity is calculated using the following formula:
。
wherein, time of presentationIntensity of disease transmission at time;time of presentationThe number of infected people at the moment;time of presentationPopulation number at time;indicating the disease infection rate.
Specifically, the disease transmission intensity at a specific time point is calculated according to the ratio of the number of infected persons to the population number and the disease infection rate parameter.
The intensity of disease transmission was used to assess the extent of disease transmission at this time point. When the disease transmission intensity is high, the disease transmission speed in the crowd is high, and the transmission range is wide. Conversely, when the intensity of disease transmission is low, it means that the transmission of the disease is limited or inhibited to some extent.
In a medical sales management system, this formula is used to estimate the spread intensity of the disease to assist in the target disease analysis model. By combining the basic data with other analysis models, the system predicts the transmission trend of the target disease in a specific area and optimizes the supply and sales strategies of the medicines according to the evaluation result of the disease transmission intensity.
Through evaluation and control of disease transmission intensity, medical companies better deal with transmission challenges of target diseases, and targeted sales strategies are adopted to improve sales management effects and benefits. In addition, the formula also helps decision makers to know the disease transmission more accurately so as to formulate corresponding preventive and control measures to protect public health and safety.
Calculated using the following formula:
。
wherein, time of presentationPopulation number at time;representing an initial population number;representing population growth rate; the value range is 1.1-1.5;indicating mortality of disease infection;indicating recovery rate of infected people.
Specifically, population numberBy initial population sizeMultiplying by an exponential term. The exponential portion of the exponential term is composed of a number of factors:
: this section accounts for the combined effects of population growth rate, mortality from disease infection, disease infection rate, and recovery rate in infected individuals.
: this is an exponential function that represents the trend of an exponential increase or decay in population size over time.
By counting the initial populationMultiplying the index term to obtain timePopulation number of time of day。
The population number trend at different time points is predicted based on the initial population number, population growth rate, disease infection mortality, and recovery rate of the infected population. It helps to understand population growth or decay over time and provides population data as one of the inputs to the target disease analysis model and disease propagation intensity calculation.
The method for calculating the attenuation factor of the target disease under the action of the target disease medicine by using the preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease by using the target disease medicine analysis model comprises the following steps: substituting the index of the cure rate of the target disease medicine for the target disease into the following formula to calculate the attenuation factor：
。
Wherein, the cure rate of the medicine for the target diseases.
The number of deaths from the target disease is expressed using the following formula:
。
wherein, time of presentationDeath number at the moment;indicating mortality of disease infection;time of presentationThe number of infected people at the moment;representing the rate of spread of the disease; by taking into account the number of infected personsAnd mortality of disease infectionIs estimated at time by the product ofDeath number of the patient. Wherein, is an attenuation factor, shows that the contribution of early infectious people to the current death number is reduced with the lapse of time, and reflects the dynamics of disease transmission.
The total number of patients with the target disease is expressed by the following formula:
。
wherein, time of presentationThe total number of people at the moment;time of presentationThe number of infected people at the moment;time of presentationThe number of people recovered at the moment;time of presentationDeath number at the moment; wherein:
。
time of presentationThe number of people recovered at the moment;indicating recovery rate of infected people;time of presentationThe number of infected people at the moment;representing the rate of spread of the disease; the target disease regression time is expressed using the following formula:
。
wherein, time of presentationThe number of people recovered at the moment;indicating the number of initial infections;representing the rate of spread of the disease.
In particular, the number of people who regress the disease and recover the infected person to healthyIn proportion to the number of people initially infectedInversely proportional. Over time, the number of recovery people will increase and the disease will eventually subside. This formula reflects this relationship by a logarithmic function because the logarithmic function amplifies small changes and suppresses large changes with assurance that the result is positive. In addition, in the case of the optical fiber,is the rate of disease transmission, which indicates the ability of the disease to spread, and this factor is present to regulate the rate of disease regression.
Substituting the attenuation factors into a function of changing the number of infected persons of the target diseases, taking the total number of diseased persons of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, and minimizing the integral of the infected persons of the target diseases on a time domain, so that the method for calculating the optimal supply quantity and the optimal supply time of the medicines of the target diseases comprises the following steps: using the modified lagrangian multiplier algorithm, a following objective function is constructed:
。
the problem is then solved by minimizing the objective function, namely solving the following system of equations:
。
solving to obtainAndthe updated expression of (2) is put into constraint conditions, and the values of other variables are obtained; obtaining the optimal target disease infection number and optimal time corresponding to the minimum integral of the target disease infection number in the time domain according to the values of all the variables obtained by solving; taking the optimal number of target disease infected people as the optimal supply amount of the target disease medicine; taking the optimal time as the upper limit value of the optimal supply time, and calculating the target disease diffusion speed to obtain a disease outbreak time function; obtaining an optimal supply time set based on the disease outbreak function model and an upper limit value of the optimal supply time; the optimal supply time set comprises a plurality of optimal supply times.
Specifically, the Lagrangian multiplier method is a common technique for dealing with optimization problems with constraints. It introduces constraints into the objective function through a multiplier (lagrangian multiplier) so that the problem is converted into an unconstrained optimization problem. In this example, this approach is used to minimize the time integral of the number of infected persons while meeting given constraints, namely the total number of cases of the disease, the number of deaths, and the time to regression of the disease.
The basic idea of the Lagrangian multiplier method is: for a constrained optimization problem, if a solution is known to exist, at the optimal point, the gradient of the objective function is linearly represented by the gradient of the constrained function. This means that at the optimum point, the gradient of the objective function and the gradient of each constraint function are orthogonal.
This model first sets an objective function that contains the objective to be minimized and a set of constraints. Then, an optimal solution is found by solving the condition that the first derivative of the objective function is equal to zero. Since this objective function involves the integration of multiple variables, it is in fact a variational problem.
First, it uses the number of infected persons represented by the integral to reflect the spread of the disease over the time domain, which allows a more comprehensive assessment of the effect of the disease than a single infected person. Second, constraints in the model, such as the total number of diseases, the number of deaths, and the time to disease regression, are all factors that need to be considered in practice, and their existence makes the model closer to reality.
An optimal drug delivery strategy can be found where a series of constraints are met. This is of great importance for inhibiting the spread of diseases in limited resources, especially for severe infectious diseases.
Furthermore, this model can give not only an optimal drug supply amount but also an optimal supply time. This is also instructive for the scheduling of the production and distribution of pharmaceuticals.
Objective functionConsists of four parts:
is the goal of minimizing, i.e., the integral of the change in the number of infected persons over the time domain.
Is a constraint on the total number of diseases, whereinIs the corresponding lagrangian multiplier.
Is a constraint on the number of deaths from illness, in whichIs the corresponding lagrangian multiplier.
Is a constraint on disease regression time, in whichIs the corresponding lagrangian multiplier.
Then, solving the condition that the first derivative of the function is equal to zero to obtain updated number of infected peopleAnd the number of people recoveredAnd thus other variables. In this way, an optimal number of infected persons and a corresponding time are found that minimizes the time integral of the number of infected persons, which can be regarded as the optimal supply amount of the target disease drug, and the optimal time can be used as the upper limit of the optimal supply time.
And then calculating the diffusion speed of the target disease to obtain a disease outbreak time function. Based on this model and the upper limit of the optimal supply time, a set of optimal supply times is obtained. Thus, the drug delivery schedule is effectively performed while considering both the disease propagation dynamics and the delivery limit.
The disease outbreak time function is expressed using the following formula:
。
wherein, representing a disease outbreak time function;time of presentationThe number of infected people at the moment;indicating the number of initial infections;time of presentationIntensity of disease transmission at time;indicating recovery rate of infected people;representing the rate of spread of the disease.
Specifically, it calculates the rate of increase in the number of infected persons by differential and logarithmic transformation over time. This rate of increase varies with time and is therefore used to describe the spread of the disease. Finally, it divides the rate of increase by the rate of spread of the disease, resulting in the time of the outbreak of the disease.
The definition of an outbreak of disease is considered to be the time at which the spread of the disease begins to grow rapidly, which is related to the rate of increase in the number of infected persons. This explains why we are concerned about the growth rate of the number of infected people.
Second, since the rate of disease transmission may be affected by many factors, such as population density, medical resources, etc., this factor should be considered in calculating the time of disease outbreak. This explains why there is one division of disease propagation rate in the formula.
The method for calculating the optimal selling price according to the average income of population, the production cost of the target disease medicine and the target profit margin comprises the following steps: the best selling price is calculated using the following formula:
。
wherein, is the best selling price;the value range of the first weight coefficient is 0.3-0.6;the value range of the second weight coefficient is 0.4-0.7;average income for population;the production cost of the target disease medicine is;is the target profit margin.
In particular, in economics, the price of a product is typically determined based on supply and demand. In this formula, population average revenue is considered an important factor in demand. Communities with higher average revenues may be willing to pay higher prices, while communities with lower average revenues may only be burdened with lower prices.
On the other hand, the production cost of the medicine is a key factor in the supply. Generally, the higher the cost, the higher the sales price should be in order to maintain profits. Meanwhile, profit margin is an important component of enterprise operation targets and can also influence the selling price of medicines.
The impact of revenue and cost on price is balanced by two weight coefficients. These two weights are adjusted according to the actual situation. For example, if an enterprise considers market competition to be intense and consumers are very price sensitive, the revenue may be weighted up to maintain price competitiveness. Conversely, if the cost of production of the drug is very high, or the enterprise requires higher profits to support research and development activities, the cost and profit margin may be weighted.
Example 2: a pharmaceutical sales management method, the method comprising:
acquiring basic data of a sales target area in real time; the basic data includes: average population income, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality; generating a target disease infected person number change function based on the basic data; the independent variable of the function of the change of the number of infected people with the target disease is time, and the dependent variable is the number of infected people with the target disease; calculating the attenuation factor of the target disease under the action of the target disease medicine by using a preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease; substituting the attenuation factors into a change function of the number of infected persons of the target diseases, taking the total number of diseased persons of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, minimizing the integral of the number of infected persons of the target diseases in a time domain, calculating to obtain the optimal supply quantity and the optimal supply time of the target diseases, calculating the optimal selling price according to the average income of population, the production cost and the target profit margin of the target diseases, and selling the target diseases according to the optimal supply quantity, the optimal supply time and the optimal selling price.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A pharmaceutical sales management system, the system comprising: a sales target area basic data acquisition section configured to acquire basic data of a sales target area in real time; the base data includes: average population income, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality; a target disease analysis model configured to generate a target disease infected person number variation function based on the base data; the independent variable of the function of the change of the number of infected people of the target disease is time, and the dependent variable is the number of infected people of the target disease; the target disease medicine analysis model is configured to calculate an attenuation factor of the target disease under the action of the target disease medicine by using a preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease; and the medicine sales management part is configured to substitute attenuation factors into a change function of the number of infected patients of the target diseases, take the total number of infected patients of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, minimize the integral of the number of infected patients of the target diseases in a time domain, calculate the optimal supply quantity and the optimal supply time of the target diseases, calculate the optimal sales price according to the average income of population, the production cost and the target profit margin of the target diseases, and sell the target diseases according to the optimal supply quantity, the optimal supply time and the optimal sales price.
2. The pharmaceutical sales management system of claim 1, wherein the target disease infection number change function is expressed using the following formula:
；
wherein, time of presentation->The number of infected people at the moment; />Indicating the number of initial infections; />Time of presentation->Intensity of disease transmission at time; />Representation->The number of infected people at the moment; />Indicating recovery rate of infected people; />Representing the rate of spread of the disease;is a time variable.
3. The pharmaceutical sales management system of claim 2, wherein the disease transmission intensity is calculated using the formula:
；
wherein, time of presentation->Intensity of disease transmission at time; />Time of presentation->The number of infected people at the moment; />Time of presentation->Population number at time; />Indicating the disease infection rate.
4. A medical sales management system according to claim 3, wherein theCalculated using the following formula:
；
wherein, time of presentation->Population number at time; />Representing an initial population number; />Representing population growth rate; the value range is 1.1-1.5; />Indicating mortality of disease infection; />Indicating recovery rate of infected people.
5. The medical sales management system of claim 4, wherein the method for calculating the attenuation factor of the target disease under the action of the target disease drug based on the cure rate of the target disease drug for the target disease using the preset attenuation factor analysis model by the target disease drug analysis model comprises: substituting the index of the cure rate of the target disease medicine for the target disease into the following formula to calculate the attenuation factor：
；
Wherein, the cure rate of the medicine for the target diseases.
6. The pharmaceutical sales management system of claim 5, wherein the number of target disease deaths is expressed using the formula:
；
wherein, time of presentation->Death number at the moment; />Indicating mortality of disease infection; />Time of presentation->The number of infected people at the moment; />Representing the rate of spread of the disease; the total number of patients with the target disease is expressed by the following formula:
；
wherein, time of presentation->The total number of people at the moment; />Time of presentation->The number of infected people at the moment; />Time of presentation->The number of people recovered at the moment; />Time of presentation->Death number at the moment; wherein:
；
time of presentation->The number of people recovered at the moment; />Indicating recovery rate of infected people; />Time of presentation->The number of infected people at the moment; />Representing the rate of spread of the disease; the target disease regression time is expressed using the following formula:
；
wherein, time of presentation->The number of people recovered at the moment; />Indicating the number of initial infections; />Representing the rate of spread of the disease.
7. The medical sales management system of claim 6, wherein the method of substituting the attenuation factor into the target disease infection count change function, minimizing the integral of the target disease infection count over the time domain using the target disease population, the target disease death count, and the target disease resolution time as constraints, thereby calculating the optimal supply of the target disease drug and the optimal supply time comprises: using the modified lagrangian multiplier algorithm, a following objective function is constructed:
；
the problem is then solved by minimizing the objective function, namely solving the following system of equations:
；
wherein, 、/>and->Lagrangian multipliers, which are functions of multiplication therewith;
solving to obtainAnd->The updated expression of (2) is put into constraint conditions, and the values of other variables are obtained; obtaining the optimal target disease infection number and optimal time corresponding to the minimum integral of the target disease infection number in the time domain according to the values of all the variables obtained by solving; taking the optimal number of target disease infected people as the optimal supply amount of the target disease medicine; the optimal time is used as the upper limit value of the optimal supply time, and the disease is obtained by calculating the target disease diffusion speedA disease outbreak time function; obtaining an optimal supply time set based on the disease outbreak function model and an upper limit value of the optimal supply time; the optimal supply time set comprises a plurality of optimal supply times.
8. The pharmaceutical sales management system of claim 7, wherein the disease outbreak time function is expressed using the formula:
；
wherein, representing a disease outbreak time function; />Time of presentation->The number of infected people at the moment; />Indicating the number of initial infections; />Time of presentation->Intensity of disease transmission at time; />Indicating recovery rate of infected people; />Representing the rate of spread of the disease.
9. The medical sales management system of claim 8, wherein the method of calculating the optimal sales price according to the population average income, the production cost of the target disease medicine, and the target profit margin comprises: the best selling price is calculated using the following formula:
；
wherein, is the best selling price; />The value range of the first weight coefficient is 0.3-0.6; />The value range of the second weight coefficient is 0.4-0.7; />Average income for population; />The production cost of the target disease medicine is; />Is the target profit margin.
10. A medical sales management method for implementing the system of any one of claims 1 to 9, the method comprising:
acquiring basic data of a sales target area in real time; the base data includes: average population income, initial population number, population growth rate, population mortality, disease infection rate, recovery rate of infected population, initial infected population, disease infection mortality; generating a target disease infected person number change function based on the basic data; the independent variable of the function of the change of the number of infected people of the target disease is time, and the dependent variable is the number of infected people of the target disease; calculating the attenuation factor of the target disease under the action of the target disease medicine by using a preset attenuation factor analysis model based on the cure rate of the target disease medicine for the target disease; substituting the attenuation factors into a change function of the number of infected persons of the target diseases, taking the total number of diseased persons of the target diseases, the death number of the target diseases and the regression time of the target diseases as constraint conditions, minimizing the integral of the number of infected persons of the target diseases in a time domain, calculating to obtain the optimal supply quantity and the optimal supply time of the target diseases, calculating the optimal selling price according to the average income of population, the production cost and the target profit margin of the target diseases, and selling the target diseases according to the optimal supply quantity, the optimal supply time and the optimal selling price.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310943332.2A CN116703528B (en) | 2023-07-31 | 2023-07-31 | Medical sales management system and management method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310943332.2A CN116703528B (en) | 2023-07-31 | 2023-07-31 | Medical sales management system and management method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116703528A true CN116703528A (en) | 2023-09-05 |
| CN116703528B CN116703528B (en) | 2023-11-17 |
Family
ID=87836054
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310943332.2A Active CN116703528B (en) | 2023-07-31 | 2023-07-31 | Medical sales management system and management method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116703528B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020111828A1 (en) * | 2000-10-25 | 2002-08-15 | Robert Bloder | Method for selling and distributing pharmaceuticals |
| US20110125529A1 (en) * | 2009-11-24 | 2011-05-26 | Walgreen Co. | System and Method for Disease State Marketing |
| CN112102957A (en) * | 2020-09-02 | 2020-12-18 | 四川骏逸富顿科技有限公司 | Epidemic disease early warning method and early warning system thereof |
| CN113095869A (en) * | 2021-03-16 | 2021-07-09 | 东华大学 | Mask dynamic supply and pricing decision method for respiratory infectious disease outbreak period |
| CN115114979A (en) * | 2022-06-23 | 2022-09-27 | 中国人民解放军国防科技大学 | Infectious disease propagation state prediction method and device based on clustering |
| CN115760210A (en) * | 2022-11-17 | 2023-03-07 | 江西药葫芦科技有限公司 | Medicine sales prediction system and method based on IPSO-LSTM model |
-
2023
- 2023-07-31 CN CN202310943332.2A patent/CN116703528B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020111828A1 (en) * | 2000-10-25 | 2002-08-15 | Robert Bloder | Method for selling and distributing pharmaceuticals |
| US20110125529A1 (en) * | 2009-11-24 | 2011-05-26 | Walgreen Co. | System and Method for Disease State Marketing |
| CN112102957A (en) * | 2020-09-02 | 2020-12-18 | 四川骏逸富顿科技有限公司 | Epidemic disease early warning method and early warning system thereof |
| CN113095869A (en) * | 2021-03-16 | 2021-07-09 | 东华大学 | Mask dynamic supply and pricing decision method for respiratory infectious disease outbreak period |
| CN115114979A (en) * | 2022-06-23 | 2022-09-27 | 中国人民解放军国防科技大学 | Infectious disease propagation state prediction method and device based on clustering |
| CN115760210A (en) * | 2022-11-17 | 2023-03-07 | 江西药葫芦科技有限公司 | Medicine sales prediction system and method based on IPSO-LSTM model |
Non-Patent Citations (2)
| Title |
|---|
| 张环宇 等: "基于两种定价模型分析下的基本药物定价模型研究", 中国全科医学, vol. 16, no. 5, pages 425 - 426 * |
| 王慧 等: "中成药国家基本药物保障供应综合评估模型的探索与研究", 中国中药杂志, vol. 42, no. 13, pages 2612 - 2618 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116703528B (en) | 2023-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shahzad et al. | COVID-19 impact on e-commerce usage: An empirical evidence from Malaysian healthcare industry | |
| Xu et al. | Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat | |
| Gaynor et al. | Competition in health care markets | |
| Salarpour et al. | A multicountry, multicommodity stochastic game theory network model of competition for medical supplies inspired by the Covid-19 pandemic | |
| Khaldi et al. | Artificial neural network based approach for blood demand forecasting: Fez transfusion blood center case study | |
| Nikmanesh et al. | Macroeconomic determinants of stock market volatility: An empirical study of Malaysia and Indonesia | |
| Aghababaei et al. | A two-stage fuzzy optimization model for scarce drugs supply and ration planning under uncertainty: A case study | |
| Giokas | The use of goal programming, regression analysis and data envelopment analysis for estimating efficient marginal costs of hospital services | |
| Attema et al. | Investment in antiviral drugs: a real options approach | |
| Freire | Impact of HIV/AIDS on saving behaviour in South Africa | |
| Dowd et al. | Can data envelopment analysis provide a scalar index of ‘value’? | |
| Lu et al. | Analysis of early warning of RMB exchange rate fluctuation and value at risk measurement based on deep learning | |
| Wanjau et al. | The health and economic impact and cost effectiveness of interventions for the prevention and control of overweight and obesity in Kenya: a stakeholder engaged modelling study | |
| Hanspach | The home bias in procurement. Cross-border procurement of medical supplies during the Covid-19 pandemic. | |
| CN116703528B (en) | Medical sales management system and management method thereof | |
| Afonso et al. | Dealing with uncertainty in healthcare performance assessment: a fuzzy network‐DEA approach with undesirable outputs | |
| Bodurtha et al. | Historical and implied measures of value at risk: The dm and yen case | |
| Svensson et al. | Long-term fiscal implications of subsidizing in-vitro fertilization in Sweden: a lifetime tax perspective | |
| Sabripoor et al. | Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA | |
| Alves | Pricing and waiting time decisions in a health care market with private and public provision | |
| Dzupire et al. | Constructing Malawian Ordinary Actuarial Tables: Reflection of the COVID-19 Era | |
| Kang et al. | Search, infection, and government policy | |
| Atanasov et al. | Modelling the innovative potential of companies in the pharmaceutical industry in Bulgaria | |
| Mathouraparsad et al. | Economic Impacts of an Epidemiologic Model: The Senegalese Case of COVID-19 in a Computable General Equilibrium-Multi-Agent System Model | |
| Vasileiou | Is the turn of the month an anomaly on which an investment strategy could be based? Evidence from Bitcoin and Ethereum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |