Medical supply chain demand intelligent management and control method and system based on big data

Abstract

The invention provides a medical supply chain demand intelligent management and control method and system based on big data, and belongs to the technical field of medical safety. The medical supply chain demand intelligent management and control method based on big data comprises the steps of obtaining logistics transportation data of a medicine supplier and medicine demand data of a plurality of medicine demanders; analyzing medicine supply logistics influence data according to the logistics transportation data; determining abnormal drug supply influence data according to the drug demand data; according to the medicine supply logistics influence data and the medicine supply abnormal influence data, calculating supply priority data of each medicine demand side, and determining a supply sequence of each medicine demand side based on the supply priority data; through the method, the supply priority of each drug demand side can be sequenced, so that the drug demand of each drug demand side is met, and the stability of a medical supply chain is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of medical safety, and in particular to a method and system for intelligent management and control of medical supply chain demand based on big data. Background Art

[0002] The medical supply chain is a relatively complex system, which involves all links from the procurement, production, distribution to final delivery of raw materials. In the medical supply chain, there are drug suppliers and drug demanders. The relationship between drug suppliers and drug demanders can be one-to-many, that is, one drug supplier provides drugs to multiple drug demanders. When drug demanders need to replenish drugs, drug suppliers will also provide corresponding drugs. However, due to the limitations of the logistics and transportation capacity of drug suppliers, when multiple drug demanders need to replenish drugs at the same time, how to determine the supply order to drug demanders becomes very important. Summary of the invention

[0003] The present application provides a method and system for intelligent management and control of medical supply chain demand based on big data, which can analyze and calculate the drug supply priority of each drug demander, and thus supply drugs to the drug demanders in sequence according to the drug supply priority, helping the drug demanders to reasonably arrange drug supply under limited transportation capacity, thereby ensuring the stability of the medical supply chain and ensuring that the drug needs of multiple drug demanders are met.

[0004] In the first aspect, the present application provides a method for intelligent management and control of medical supply chain demand based on big data. The method comprises:

[0005] Obtaining logistics and transportation data of a drug supplier and drug demand data of multiple drug demanders; the logistics and transportation data includes the logistics and transportation distance between the drug supplier and the multiple drug demanders; the drug demand data includes the type and quantity of drugs required by each drug demander, abnormal events that lead to increased drug demand and the number of abnormal events, and drug remaining quantity data;

[0006] Analyze the drug supply logistics impact data according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance;

[0007] Determine drug supply abnormality impact data according to the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data;

[0008] The supply priority data of each of the drug demanders is calculated according to the drug supply logistics impact data and the drug supply abnormality impact data, and the supply order of each of the drug demanders is determined based on the supply priority data; the supply priority data is positively correlated with the drug supply logistics impact data and the drug supply abnormality impact data.

[0009] Further, the drug supply logistics impact data is analyzed according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance, including:

[0010] Obtaining the periodic supply time data of each drug demander; the periodic supply time data is the supply time of the drug supplier periodically providing drugs to each drug demander; the periodic supply time data includes the supply time node of each supply to the drug demander;

[0011] Obtaining the delivery time data according to the periodic delivery time data and the current time node; the delivery time data is the time between the next delivery time node of the current time node and the current time node;

[0012] The drug supply logistics impact data is analyzed according to the logistics transportation distance and the waiting time data; the drug supply logistics impact data is positively correlated with the waiting time data.

[0013] Further, the drug supply abnormality impact data is determined according to the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data, including:

[0014] Obtaining drug remaining quantity and historical usage data of the drug demanded by each drug demander, and determining usage frequency data of each drug demanded based on the historical usage data;

[0015] Determine the remaining drug usage time of each drug demander according to the remaining drug amount and the usage frequency data;

[0016] Calculate event importance data based on the abnormal events that lead to increased drug demand and the number of abnormal events;

[0017] The drug supply abnormality impact data is determined according to the remaining drug use time and the event importance data; the drug supply abnormality impact data is negatively correlated with the remaining drug use time, and the drug supply abnormality impact data is positively correlated with the event importance data.

[0018] Furthermore, the method for calculating the event importance data includes:

[0019] Determine the basic urgency data of each abnormal event based on the preset event urgency comparison table;

[0020] Obtaining the event existence time of the abnormal event;

[0021] Assume there are n abnormal events, then the basic urgency data of the ith abnormal event is C i , the event existence time of the i-th abnormal event is T i ;

[0022] Assume that any drug demander demands m kinds of drugs, then the importance data of the sub-event of the jth drug is W j The sub-event importance data is the sum of the event importance of each abnormal event corresponding to the j-th drug that leads to an increase in the demand for the j-th drug;

[0023]

[0024]

[0025] Wherein, H is the event importance data; p is the first preset event weight, and q is the second preset event weight;

[0026] in,

[0027] Furthermore, the method for calculating the supply priority data includes:

[0028] P=L×K1+E×K2

[0029] Wherein, P is the supply priority data, L is the drug supply logistics impact data, and E is the drug supply abnormality impact data; K1 and K2 are the first preset weight and the second preset weight respectively;

[0030] Among them, K1+K2=1.

[0031] In the second aspect, the present application provides a medical supply chain demand intelligent management and control system based on big data. The system includes:

[0032] An acquisition module is used to acquire the logistics and transportation data of a drug supplier and the drug demand data of multiple drug demanders; the logistics and transportation data includes the logistics and transportation distance between the drug supplier and the multiple drug demanders; the drug demand data includes the type and quantity of drugs required by each drug demander, the abnormal events that lead to an increase in drug demand and the number of abnormal events, and the remaining amount of drugs;

[0033] An analysis module, used for analyzing drug supply logistics impact data according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance;

[0034] A determination module, configured to determine drug supply abnormality impact data according to the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data;

[0035] A calculation module is used to calculate the supply priority data of each of the drug demanders based on the drug supply logistics impact data and the drug supply abnormality impact data, and determine the supply order of each of the drug demanders based on the supply priority data; the supply priority data is positively correlated with the drug supply logistics impact data and the drug supply abnormality impact data.

[0036] Furthermore, the analysis module is further configured to analyze the drug supply logistics impact data according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance, including:

[0037] Obtaining the periodic supply time data of each drug demander; the periodic supply time data is the supply time of the drug supplier periodically providing drugs to each drug demander; the periodic supply time data includes the supply time node of each supply to the drug demander;

[0038] Obtaining the delivery time data according to the periodic delivery time data and the current time node; the delivery time data is the time between the next delivery time node of the current time node and the current time node;

[0039] The drug supply logistics impact data is analyzed according to the logistics transportation distance and the waiting time data; the drug supply logistics impact data is positively correlated with the waiting time data.

[0040] Furthermore, the determination module is further configured to determine the abnormal drug supply impact data according to the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data, including:

[0041] Obtaining drug remaining quantity and historical usage data of the drug demanded by each drug demander, and determining usage frequency data of each drug demanded based on the historical usage data;

[0042] Determine the remaining drug usage time of each drug demander according to the remaining drug amount and the usage frequency data;

[0043] Calculate event importance data based on the abnormal events that lead to increased drug demand and the number of abnormal events;

[0044] The drug supply abnormality impact data is determined according to the remaining drug use time and the event importance data; the drug supply abnormality impact data is negatively correlated with the remaining drug use time, and the drug supply abnormality impact data is positively correlated with the event importance data.

[0045] Furthermore, the determination module (230) is further configured such that the method for calculating the event importance data includes:

[0046] Determine the basic urgency data of each abnormal event based on the preset event urgency comparison table;

[0047] Obtaining the event existence time of the abnormal event;

[0048] Assume there are n abnormal events, then the basic urgency data of the ith abnormal event is C i , the event existence time of the i-th abnormal event is T i ;

[0049] Assume that any drug demander demands m kinds of drugs, then the importance data of the sub-event of the jth drug is W j The sub-event importance data is the sum of the event importance of each abnormal event corresponding to the j-th drug that leads to an increase in the demand for the j-th drug;

[0050]

[0051]

[0052] Wherein, H is the event importance data; p is the first preset event weight, and q is the second preset event weight;

[0053] in,

[0054] Furthermore, the calculation module is further configured such that the calculation method of the supply priority data includes:

[0055] P=L×K1+E×K2

[0056] Wherein, P is the supply priority data, L is the drug supply logistics impact data, and E is the drug supply abnormality impact data; K1 and K2 are the first preset weight and the second preset weight respectively;

[0057] Among them, K1+K2=1.

[0058] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0060] Figure 1 A flowchart of a method for intelligent management and control of medical supply chain demand based on big data in an embodiment of the present application is shown;

[0061] Figure 2 A block diagram of a medical supply chain demand intelligent management and control system based on big data in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0064] The present application provides a method and system for intelligent management and control of medical supply chain demand based on big data, which can determine the supply order for multiple drug demanders, thereby meeting the needs of drug demanders and ensuring the stability of the medical supply chain.

[0065] In the first aspect, the present application provides a method for intelligent management and control of medical supply chain demand based on big data. Figure 1 The specific steps included in the method are as follows.

[0066] Step S110: Obtain the logistics and transportation data of the drug supplier and the drug demand data of multiple drug demanders; the logistics and transportation data include the logistics and transportation distance between the drug supplier and the multiple drug demanders; the drug demand data include the type and quantity of drugs required by each drug demander, the abnormal events that lead to increased drug demand and the number of abnormal events, and the remaining drug data.

[0067] In the embodiment of the present application, there is a one-to-many relationship between drug suppliers and drug demanders; that is, in the scheme of the present application, one drug supplier provides drugs to multiple drug demanders, and the supply relationship is long-term; therefore, under normal circumstances, the logistics transportation road between the drug supplier and the drug demander is fixed, so the logistics transportation distance is also known.

[0068] Regarding the drug demand data mentioned above, in the solution of the present application, when multiple drug demanders propose the need to replenish certain drugs, the drug replenishment information they propose must include the types and quantities of the required drugs; it can be understood that the drug remaining quantity data here is the remaining quantity of each type of drug required by the drug demanders.

[0069] It can be understood that in the scheme of the present application, abnormal events include seriously ill patients, scarce drugs and public health events; and the number of abnormal events mentioned here refers to the number of abnormal events corresponding to the drugs demanded by the drug demander; it can be understood that each seriously ill patient here is an abnormal event, because different seriously ill patients require different drugs; and scarce drugs are also based on patient needs; and public health events are epidemics or large-scale diseases.

[0070] Step S120: analyzing the drug supply logistics impact data according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance.

[0071] In an embodiment of the present application, analyzing the drug supply logistics impact data based on the logistics transportation data specifically includes: obtaining the periodic supply time data of each drug demander; the periodic supply time data is the supply time of the drug supplier periodically providing drugs to each of the drug demanders; the periodic supply time data includes the supply time nodes for each supply to the drug demander; obtaining the waiting time data based on the periodic supply time data and the current time node; the waiting time data is the time between the next supply time node of the current time node and the current time node; analyzing the drug supply logistics impact data based on the logistics transportation distance and the waiting time data; the drug supply logistics impact data is positively correlated with the waiting time data.

[0072] It is understandable that the influencing factor of logistics and transportation includes two elements, one is the cycle delivery time, and the other is the logistics transportation distance; the cycle delivery time here refers to the periodic delivery time nodes when the drug supplier supplies drugs to the drug demander under normal circumstances; such as supplying drugs once every half a month, etc.; and for different types of drugs, the next delivery time starting from the current node is uncertain, and the larger the calculated delivery time data, the longer the next delivery time, so the drug needs to be supplied first; conversely, the smaller the supply time data, the shorter the next delivery time, so the supply priority of the drug is lower.

[0073] Specifically, drug supply logistics impact data = delivery time data * preset time conversion coefficient * preset time weight - logistics transportation distance * preset distance conversion coefficient * preset distance weight; it can be understood that the preset time conversion coefficient and preset distance conversion coefficient here are coefficients designed to assist calculations.

[0074] Step S130: Determine the drug supply abnormality impact data based on the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data.

[0075] In an embodiment of the present application, determining the abnormal drug supply impact data based on the drug demand data specifically includes: obtaining the remaining drug quantity and historical usage data of the demanded drugs of each drug demander, and determining the usage frequency data of each demanded drug based on the historical usage data; determining the remaining drug usage time of each drug demander based on the remaining drug quantity and the usage frequency data; calculating the event importance data based on the abnormal events that cause the increase in drug demand and the number of abnormal events; determining the abnormal drug supply impact data based on the remaining drug usage time and the event importance data; the abnormal drug supply impact data is negatively correlated with the remaining drug usage time, and the abnormal drug supply impact data is positively correlated with the event importance data.

[0076] It is understandable that the usage frequency data is obtained by dividing the number of times the drug is used by the time. Therefore, when the usage frequency data and the remaining amount of the drug are known, the remaining usage time of the drug can be obtained by dividing the usage frequency data by the remaining amount of the drug; when calculating the usage frequency data, a preset number of historical drug usage days are selected, the amount of drug used in all historical drug usage days is calculated, and then the usage frequency data is obtained by dividing the sum of the usage amounts by the sum of the usage dates; it should be noted that when calculating the remaining time of specific different types of drugs, the above calculation method is adopted; and the remaining usage time of the drug for each drug demander finally obtained is the remaining usage time of the drug with the shortest remaining usage time among the drugs required by the drug demander as the remaining usage time of the drug demander; it is understandable that for the drug demander, it needs a variety of drugs due to various special circumstances, and the drug with the shortest remaining time means that it is the most urgent. Even if the remaining time of other drugs is longer, in order to meet the needs of the drug demander, it is necessary to select the shortest remaining time as the remaining usage time of the drug demander.

[0077] Specifically, the method for calculating the event importance data includes determining the basic urgency data of each abnormal event based on a preset event urgency comparison table; obtaining the event existence time of the abnormal event;

[0078] Assume there are n abnormal events, then the basic urgency data of the ith abnormal event is C i , the event existence time of the i-th abnormal event is T i ;

[0079] Assume that any drug demander demands m kinds of drugs, then the importance data of the sub-event of the jth drug is W j The sub-event importance data is the sum of the event importance of each abnormal event corresponding to the j-th drug that leads to an increase in the demand for the j-th drug;

[0080]

[0081]

[0082] In the formula, H is the event importance data; p is the first preset event weight, q is the second preset event weight;

[0083] It is understandable that the preset event urgency comparison table includes the correspondence between abnormal events and basic urgency data.

[0084] It can be understood that after obtaining the remaining drug usage time and the event importance data, the drug supply abnormality impact data = event importance data * preset importance conversion coefficient * preset event weight - remaining drug usage time * preset usage time conversion coefficient * preset usage time weight.

[0085] It should be noted that the preset importance conversion coefficient and the preset usage time conversion coefficient here are coefficients designed to assist in calculation.

[0086] Step S140: Calculate the supply priority data of each of the drug demanders according to the drug supply logistics impact data and the drug supply abnormality impact data, and determine the supply order of each of the drug demanders based on the supply priority data; the supply priority data is positively correlated with the drug supply logistics impact data and the drug supply abnormality impact data.

[0087] In the embodiment of the present application, the method for calculating the supply priority data includes:

[0088] P=L×K1+E×K2

[0089] Wherein, P is the supply priority data, L is the drug supply logistics impact data, and E is the drug supply abnormality impact data; K1 and K2 are the first preset weight and the second preset weight respectively; wherein, K1+K2=1.

[0090] It can be understood that after obtaining the supply priority data of each drug demander, the supply order of each drug demander can be determined by arranging the supply priority data in order from large to small.

[0091] It can be understood that the method of the present application is essentially a machine learning model, which realizes the training of various parameters by continuously calculating the supply priority data; among them, the parameters that can be trained include preset time conversion coefficient, preset time weight, preset distance conversion coefficient, preset distance weight, preset importance conversion coefficient, preset event weight, preset usage time weight, event urgency comparison table, first preset weight and second preset weight, etc.; by continuously training the parameters, the calculation results of the supply priority data are more accurate, so as to more reasonably arrange the drug supply for multiple drug demanders and ensure the stability of the medical supply chain.

[0092] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0093] In the second aspect, the present application provides a smart management and control system for medical supply chain demand based on big data. Figure 2 As shown, the system includes an acquisition module 210, which is used to acquire the logistics and transportation data of the drug supplier and the drug demand data of multiple drug demanders; the logistics and transportation data includes the logistics and transportation distance between the drug supplier and the multiple drug demanders; the drug demand data includes the type and quantity of drugs required by each drug demander, the abnormal events that lead to increased drug demand and the number of abnormal events and the remaining drug quantity data; the analysis module 220 is used to analyze the drug supply logistics impact data based on the logistics and transportation data; the drug supply logistics impact data is associated with the logistics and transportation data, and the drug supply logistics impact data is negatively correlated with the logistics and transportation distance; the determination module 230 is used to determine the drug supply logistics impact data. Determine the drug supply abnormality impact data according to the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data; a calculation module 240 is used to calculate the supply priority data of each of the drug demanders according to the drug supply logistics impact data and the drug supply abnormality impact data, and determine the supply order of each of the drug demanders based on the supply priority data; the supply priority data is positively correlated with the drug supply logistics impact data and the drug supply abnormality impact data.

[0094] The analysis module 220 is further configured to analyze the drug supply logistics impact data based on the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance, including obtaining the periodic supply time data of each drug demander; the periodic supply time data is the supply time of the drug supplier periodically providing drugs to each of the drug demanders; the periodic supply time data includes the supply time nodes for each supply to the drug demander; according to the periodic supply time data and the current time node, the waiting time data is obtained; the waiting time data is the time between the next supply time node of the current time node and the current time node; the drug supply logistics impact data is analyzed according to the logistics transportation distance and the waiting time data; the drug supply logistics impact data is positively correlated with the waiting time data.

[0095] The determination module 230 is further configured to determine the drug supply abnormality impact data based on the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; the drug supply demand impact data is negatively correlated with the drug remaining quantity data, including obtaining the drug remaining quantity and historical usage data of the demanded drugs of each drug demander, and determining the usage frequency data of each demanded drug based on the historical usage data; determining the remaining drug usage time of each drug demander based on the drug remaining quantity and the usage frequency data; calculating the event importance data based on the abnormal events that cause the increase in drug demand and the number of abnormal events; determining the drug supply abnormality impact data based on the drug remaining usage time and the event importance data; the drug supply abnormality impact data is negatively correlated with the drug remaining usage time, and the drug supply abnormality impact data is positively correlated with the event importance data.

[0096] The calculation method of the event importance data includes determining the basic urgency data of each abnormal event based on a preset event urgency comparison table;

[0097] Obtaining the event existence time of the abnormal event;

[0098] Assume there are n abnormal events, then the basic urgency data of the ith abnormal event is C i , the event existence time of the i-th abnormal event is T i ;

[0099] Assume that any drug demander demands m kinds of drugs, then the importance data of the sub-event of the jth drug is W jThe sub-event importance data is the sum of the event importance of each abnormal event corresponding to the j-th drug that leads to an increase in the demand for the j-th drug;

[0100]

[0101]

[0102] Wherein, H is the event importance data; p is the first preset event weight, and q is the second preset event weight;

[0103] in,

[0104] The calculation module 240 is further configured to calculate the supply priority data in the following manner:

[0105] P=L×K1+E×K2

[0106] Wherein, P is the supply priority data, L is the drug supply logistics impact data, and E is the drug supply abnormality impact data; K1 and K2 are the first preset weight and the second preset weight respectively;

[0107] Among them, K1+K2=1.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0109] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for intelligent management and control of medical supply chain demand based on big data, characterized in that: include: Obtain logistics and transportation data from drug suppliers and drug demand data from multiple drug demanders; The logistics transportation data includes the logistics transportation distance between the drug supplier and multiple drug demanders; the drug demand data includes the type and quantity of drugs required by each drug demander, the abnormal events that lead to increased drug demand and the number of abnormal events, and the remaining drug quantity data; Analyze the drug supply logistics impact data according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance; Determine drug supply abnormality impact data based on the drug demand data; The drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; The drug supply demand impact data is negatively correlated with the drug remaining quantity data; The supply priority data of each of the drug demanders is calculated according to the drug supply logistics impact data and the drug supply abnormality impact data, and the supply order of each of the drug demanders is determined based on the supply priority data; the supply priority data is positively correlated with the drug supply logistics impact data and the drug supply abnormality impact data.

2. The method for intelligent management and control of medical supply chain demand based on big data according to claim 1 is characterized in that: said analyzing the drug supply logistics impact data based on said logistics transportation data; The drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance and includes: Obtaining the periodic supply time data of each drug demander; the periodic supply time data is the supply time of the drug supplier periodically providing drugs to each drug demander; the periodic supply time data includes the supply time node of each supply to the drug demander; Obtaining the delivery time data according to the periodic delivery time data and the current time node; the delivery time data is the time between the next delivery time node of the current time node and the current time node; The drug supply logistics impact data is analyzed according to the logistics transportation distance and the waiting time data; the drug supply logistics impact data is positively correlated with the waiting time data.

3. The method for intelligent management and control of medical supply chain demand based on big data according to claim 2 is characterized in that: Determining drug supply abnormality impact data based on the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; The drug supply demand impact data is positively correlated with the number of abnormal events; The drug supply demand impact data negatively correlated with the drug remaining quantity data include: Obtaining drug remaining quantity and historical usage data of the drug demanded by each drug demander, and determining usage frequency data of each drug demanded based on the historical usage data; Determine the remaining drug usage time of each drug demander according to the remaining drug amount and the usage frequency data; Calculate event importance data based on the abnormal events that lead to increased drug demand and the number of abnormal events; The drug supply abnormality impact data is determined according to the remaining drug use time and the event importance data; the drug supply abnormality impact data is negatively correlated with the remaining drug use time, and the drug supply abnormality impact data is positively correlated with the event importance data.

4. The method for intelligent management and control of medical supply chain demand based on big data according to claim 3 is characterized in that: The method for calculating the event importance data includes: Determine the basic urgency data of each abnormal event based on the preset event urgency comparison table; Obtaining the event existence time of the abnormal event; Assume there are n abnormal events, then the basic urgency data of the ith abnormal event is C i , the event existence time of the i-th abnormal event is T i ; Assume that any drug demander demands m kinds of drugs, then the importance data of the sub-event of the jth drug is W j The sub-event importance data is the sum of the event importance of each abnormal event corresponding to the j-th drug that leads to an increase in the demand for the j-th drug; Wherein, H is the event importance data; p is the first preset event weight, and q is the second preset event weight; in, 5. The method for intelligent management and control of medical supply chain demand based on big data according to claim 4 is characterized in that: The calculation method of the supply priority data includes: P=L×K1+E×K2 Wherein, P is the supply priority data, L is the drug supply logistics impact data, and E is the drug supply abnormality impact data; K1 and K2 are the first preset weight and the second preset weight respectively; Among them, K1+K2=1.

6. A medical supply chain demand intelligent management and control system based on big data, characterized by: include: An acquisition module (210), used to acquire logistics and transportation data of a drug supplier and drug demand data of a plurality of drug demanders; The logistics transportation data includes the logistics transportation distance between the drug supplier and multiple drug demanders; the drug demand data includes the type and quantity of drugs required by each drug demander, the abnormal events that lead to increased drug demand and the number of abnormal events, and the remaining drug quantity data; An analysis module (220) is used to analyze drug supply logistics impact data according to the logistics transportation data; the drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance; A determination module (230) is used to determine drug supply abnormality impact data according to the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; the drug supply demand impact data is positively correlated with the number of abnormal events; The drug supply demand impact data is negatively correlated with the drug remaining quantity data; A calculation module (240) is used to calculate the supply priority data of each of the drug demanders based on the drug supply logistics impact data and the drug supply abnormality impact data, and determine the supply order of each of the drug demanders based on the supply priority data; the supply priority data is positively correlated with the drug supply logistics impact data and the drug supply abnormality impact data.

7. The big data-based intelligent management and control system for medical supply chain demand according to claim 6 is characterized in that: The analysis module (220) is further configured to analyze the drug supply logistics impact data based on the logistics transportation data; The drug supply logistics impact data is associated with the logistics transportation data, and the drug supply logistics impact data is negatively correlated with the logistics transportation distance and includes: Obtaining the periodic supply time data of each drug demander; the periodic supply time data is the supply time of the drug supplier periodically providing drugs to each drug demander; the periodic supply time data includes the supply time node of each supply to the drug demander; Obtaining the delivery time data according to the periodic delivery time data and the current time node; the delivery time data is the time between the next delivery time node of the current time node and the current time node; The drug supply logistics impact data is analyzed according to the logistics transportation distance and the waiting time data; the drug supply logistics impact data is positively correlated with the waiting time data.

8. The big data-based intelligent management and control system for medical supply chain demand according to claim 7 is characterized in that: The determination module (230) is further configured to determine drug supply abnormality impact data based on the drug demand data; the drug supply demand impact data is associated with the number of abnormal events and the drug remaining quantity data; The drug supply demand impact data is positively correlated with the number of abnormal events; The drug supply demand impact data negatively correlated with the drug remaining quantity data include: Obtaining drug remaining quantity and historical usage data of the drug demanded by each drug demander, and determining usage frequency data of each drug demanded based on the historical usage data; Determine the remaining drug usage time of each drug demander according to the remaining drug amount and the usage frequency data; Calculate event importance data based on the abnormal events that lead to increased drug demand and the number of abnormal events; The drug supply abnormality impact data is determined according to the remaining drug use time and the event importance data; the drug supply abnormality impact data is negatively correlated with the remaining drug use time, and the drug supply abnormality impact data is positively correlated with the event importance data.

9. The big data-based intelligent management and control system for medical supply chain demand according to claim 8 is characterized in that: The determination module (230) is further configured such that the method for calculating the event importance data includes: Determine the basic urgency data of each abnormal event based on the preset event urgency comparison table; Obtaining the event existence time of the abnormal event; Assume there are n abnormal events, then the basic urgency data of the ith abnormal event is C i , the event existence time of the i-th abnormal event is T i ; Assume that any drug demander demands m kinds of drugs, then the importance data of the sub-event of the jth drug is W j The sub-event importance data is the sum of the event importance of each abnormal event corresponding to the j-th drug that leads to an increase in the demand for the j-th drug; Wherein, H is the event importance data; p is the first preset event weight, and q is the second preset event weight; in, 10. The big data-based intelligent management and control system for medical supply chain demand according to claim 9 is characterized in that: The calculation module (240) is further configured such that the calculation method of the supply priority data includes: P=L×K1+E×K2 Wherein, P is the supply priority data, L is the drug supply logistics impact data, and E is the drug supply abnormality impact data; K1 and K2 are the first preset weight and the second preset weight respectively; Among them, K1+K2=1.

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