Medical supply chain data analysis early warning method and system

Abstract

The invention provides a medical supply chain data analysis early warning method and system, and belongs to the technical field of medical safety. The medical supply chain data analysis early warning method comprises the following steps: acquiring historical replenishment data and historical drug consumption data; according to the historical replenishment data and the historical drug consumption data, analyzing drug replenishment threshold data of each type of drugs, and according to the drug replenishment threshold data, determining early warning content data; calculating total medicine supplementing amount data according to the medicine supplementing threshold value data; determining a medicine loading scheme according to the total medicine supplementing amount data and preset medicine transport vehicle specification data, adjusting the medicine supplementing amount data in the early warning content data according to the medicine loading scheme, and updating the early warning content data; through the method of the scheme, timely early warning can be realized when the residual amount of the medicine is insufficient, and the flexibility and toughness of a medical supply chain can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of medical safety, and in particular to a medical supply chain data analysis and early warning method and system. Background Art

[0002] The medical supply chain is a complex and critical system, which involves all links from raw material procurement, production, distribution to final delivery to drug demanders, such as medical institutions or patients. Ensuring the stability and efficiency of the medical supply chain is essential to protecting public health and patient safety. Drug delivery, as the last link in the medical supply chain, is also crucial. Generally speaking, drug suppliers will periodically ship drugs to drug demanders, that is, for drug demanders, there is a fixed replenishment cycle. When the drug receiving agency finds that the remaining amount of drugs is insufficient, the upstream of the medical supply chain needs to deliver the drugs in time for medical institutions or patients to use. Therefore, in order to ensure the adequacy of drugs and ensure that medical institutions or patients can use the corresponding drugs in a timely manner, it is necessary to issue an early warning when the remaining amount of drugs is insufficient. Summary of the invention

[0003] The present application provides a medical supply chain data analysis and early warning method and system, which can provide timely early warning when the remaining amount of medicines is insufficient, which is conducive to improving the flexibility and resilience of the medical supply chain.

[0004] In a first aspect, the present application provides a medical supply chain data analysis and early warning method. The method comprises:

[0005] Acquire historical replenishment data and historical drug consumption data; the historical replenishment data includes the time of the last replenishment of each type of drug; the historical drug consumption data includes the drug consumption quantity of each type of drug;

[0006] Analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold value of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the medicine remaining quantity triggers the warning;

[0007] Calculate the total amount of replenishment medicine data according to the replenishment medicine threshold data; the total amount of replenishment medicine data is the sum of the amount of each type of medicine that needs to be replenished;

[0008] A drug loading plan is determined according to the total amount data of replenished medicines and the preset specification data of the drug transport vehicle, and the replenished medicine amount data in the warning content data is adjusted according to the drug loading plan and the warning content data is updated.

[0009] Further, the determining of a drug loading scheme according to the total amount of replenished medicine data and preset drug transport vehicle specification data, adjusting the replenished medicine amount data in the warning content data according to the drug loading scheme and updating the warning content data includes:

[0010] Determine the number of medicine transport vehicles required for medicine replenishment and the number of medicines loaded on each medicine transport vehicle according to the total amount of medicine replenishment data and the preset medicine transport vehicle specification data; the preset medicine transport vehicle specification data includes the standard amount of medicines that can be accommodated by the medicine transport vehicle;

[0011] Screening the drug transport vehicle to be adjusted; the drug transport vehicle to be adjusted is a drug transport vehicle whose loaded drug quantity is less than the standard drug capacity;

[0012] Determine the usage frequency data of each type of medicine between the last replenishment and the current time point according to the historical replenishment data and the historical medicine consumption data;

[0013] analyzing the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data;

[0014] A drug loading plan is determined according to the drug replenishment urgency data and the loading quantity of the drug transport vehicle to be adjusted.

[0015] Further, analyzing the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data includes:

[0016] Obtaining fixed replenishment time for each type of medicine; the fixed replenishment time includes multiple preset replenishment cycles; the preset replenishment cycle is a fixed cycle for replenishing medicines for the demand side;

[0017] Determine the remaining time for drug replenishment based on the fixed replenishment time and the current time node;

[0018] Obtain drug remaining quantity data for each type of drug;

[0019] Calculate the remaining drug usage time according to the usage frequency data and the historical drug consumption data;

[0020] The replenishment urgency data of each type of medicine is analyzed according to the replenishment remaining time and the medicine remaining usage time.

[0021] Further, the determining of the drug loading plan according to the drug replenishment urgency data and the amount of loaded drugs of the drug transport vehicle to be adjusted includes:

[0022] Determine the difference in drug quantity according to the drug quantity loaded on the drug transport vehicle to be adjusted and the standard drug quantity;

[0023] Determine the direction of drug adjustment according to the drug quantity difference; the drug adjustment direction includes adding drug types and quantities and reducing drug types and quantities;

[0024] A drug loading plan is determined according to the drug replenishment urgency data and the drug adjustment direction.

[0025] Further, the replenishment threshold data of each type of medicine is analyzed according to the historical replenishment data and the historical medicine consumption data, and the warning content data is determined according to the replenishment threshold data; the replenishment threshold data is the threshold of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the medicine remaining quantity triggers the warning, including:

[0026] Analyzing the drug consumption quantity of each type of drug on each day from the last drug replenishment to the current time point according to the historical replenishment data and the historical drug consumption data, and calculating the first drug usage average value data based on the drug consumption quantity;

[0027] Analyzing the historical average drug consumption quantity of each type of drug on each day from the current time point to the next drug replenishment according to the historical drug consumption data and the fixed replenishment time, and calculating the second drug usage average value data based on the historical average drug consumption quantity;

[0028] The replenishment threshold data of each type of medicine is analyzed based on the first medicine usage average data and the second medicine usage average data.

[0029] In a second aspect, the present application provides a medical supply chain data analysis and early warning system. The system comprises:

[0030] An acquisition module, used to acquire historical replenishment data and historical drug consumption data; the historical replenishment data includes the time of the last replenishment of each type of drug; the historical drug consumption data includes the drug consumption quantity of each type of drug;

[0031] an analysis module, configured to analyze replenishment threshold data of various types of drugs according to the historical replenishment data and the historical drug consumption data, and determine warning content data according to the replenishment threshold data; the replenishment threshold data is a threshold value of the drug quantity that requires replenishment of drugs; the warning content data includes a drug remaining quantity triggering warning and replenishment quantity data; the drug remaining quantity triggering warning is a warning triggered when the remaining quantity of drugs of various types is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of drugs that need to be replenished when the drug remaining quantity triggers the warning;

[0032] A calculation module, used to calculate total medicine replenishment data according to the medicine replenishment threshold data; the total medicine replenishment data is the sum of the quantities of medicines of various types that need to be replenished;

[0033] The determination module is used to determine the drug loading plan according to the total amount of replenishment data and the preset drug transport vehicle specification data, adjust the replenishment amount data in the warning content data according to the drug loading plan, and update the warning content data.

[0034] Furthermore, the determination module is further configured to determine the drug loading plan according to the total amount of replenishment data and the preset drug transport vehicle specification data, and adjust the replenishment amount data in the warning content data according to the drug loading plan and update the warning content data, including:

[0035] Determine the number of medicine transport vehicles required for medicine replenishment and the number of medicines loaded on each medicine transport vehicle according to the total amount of medicine replenishment data and the preset medicine transport vehicle specification data; the preset medicine transport vehicle specification data includes the standard amount of medicines that can be accommodated by the medicine transport vehicle;

[0036] Screening the drug transport vehicle to be adjusted; the drug transport vehicle to be adjusted is a drug transport vehicle whose loaded drug quantity is less than the standard drug capacity;

[0037] Determine the usage frequency data of each type of medicine between the last replenishment and the current time point according to the historical replenishment data and the historical medicine consumption data;

[0038] analyzing the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data;

[0039] A drug loading plan is determined according to the drug replenishment urgency data and the loading quantity of the drug transport vehicle to be adjusted.

[0040] Furthermore, the determination module is further configured to analyze the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data, including:

[0041] Obtaining fixed replenishment time for each type of medicine; the fixed replenishment time includes multiple preset replenishment cycles; the preset replenishment cycle is a fixed cycle for replenishing medicines for the demand side;

[0042] Determine the remaining time for drug replenishment based on the fixed replenishment time and the current time node;

[0043] Obtain drug remaining quantity data for each type of drug;

[0044] Calculate the remaining drug usage time according to the usage frequency data and the historical drug consumption data;

[0045] The replenishment urgency data of each type of medicine is analyzed according to the replenishment remaining time and the medicine remaining usage time.

[0046] Furthermore, the determination module is further configured to determine the drug loading plan according to the drug replenishment urgency data and the number of loaded drugs of the drug transport vehicle to be adjusted, including:

[0047] Determine the difference in drug quantity according to the drug quantity loaded on the drug transport vehicle to be adjusted and the standard drug quantity;

[0048] Determine the direction of drug adjustment according to the drug quantity difference; the drug adjustment direction includes adding drug types and quantities and reducing drug types and quantities;

[0049] A drug loading plan is determined according to the drug replenishment urgency data and the drug adjustment direction.

[0050] Further, the analysis module is further configured to analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the medicine remaining quantity triggers the warning, including:

[0051] Analyzing the drug consumption quantity of each type of drug on each day from the last drug replenishment to the current time point according to the historical replenishment data and the historical drug consumption data, and calculating the first drug usage average value data based on the drug consumption quantity;

[0052] Analyzing the historical average drug consumption quantity of each type of drug on each day from the current time point to the next drug replenishment according to the historical drug consumption data and the fixed replenishment time, and calculating the second drug usage average value data based on the historical average drug consumption quantity;

[0053] The replenishment threshold data of each type of medicine is analyzed based on the first medicine usage average data and the second medicine usage average data.

[0054] 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

[0055] 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:

[0056] Figure 1 A flowchart of a medical supply chain data analysis and early warning method in an embodiment of the present application is shown;

[0057] Figure 2 A block diagram of a medical supply chain data analysis and early warning system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] 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.

[0059] 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.

[0060] The present application provides a medical supply chain data analysis and early warning method and system, which can provide timely early warning when the remaining amount of medicines is insufficient, which is conducive to improving the flexibility and resilience of the medical supply chain.

[0061] In a first aspect, the present application provides a medical supply chain data analysis and early warning method. Figure 1 The specific steps included in the method are as follows.

[0062] Step S110: Acquire historical replenishment data and historical drug consumption data; the historical replenishment data includes the time of the last replenishment of each type of drug; the historical drug consumption data includes the drug consumption quantity of each type of drug.

[0063] It is understandable that the medical supply chain includes drug suppliers and drug demanders. Generally speaking, drug suppliers are manufacturers such as pharmaceutical factories that can produce and provide drugs, and drug demanders are places such as hospitals and pharmacies that demand drugs. For drug demanders, the various types of drugs they store need to be replenished regularly, and each replenishment has a fixed replenishment time. The historical replenishment data includes the last replenishment time of each drug, and the last replenishment time here refers to the replenishment time of the last replenishment closest to the current time node.

[0064] It is understandable that the replenishment time for different types of medicines is different, as different types of medicines are consumed differently under different seasons, sanitary conditions, epidemics and other influencing factors. Therefore, the replenishment time is different; and for the drug demanders as a whole, the replenishment cycle is fixed, for example, drugs are replenished twice every half a month, and the amount of drugs replenished each time is the full load of drugs for three drug transport trucks, etc.

[0065] In the scheme of the present application, the analysis is conducted from the perspective of different types of medicines; the historical drug consumption data here can be obtained through the drug inventory data; for hospitals, they treat patients or provide drugs to patients, the types and quantities of the drugs will be recorded when the drugs are shipped out, so the daily drug consumption quantity can be obtained based on the drug inventory data.

[0066] Step S120: Analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold value of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the medicine remaining quantity triggers the warning.

[0067] Specifically, in an embodiment of the present application, the replenishment threshold data of each type of medicine is analyzed according to the historical replenishment data and the historical drug consumption data, and the warning content data is determined according to the replenishment threshold data. Specifically, the drug consumption quantity of each type of medicine on each day from the last replenishment to the current time node is analyzed according to the historical replenishment data and the historical drug consumption data, and first drug usage average value data is calculated based on the drug consumption quantity; the historical drug consumption quantity of each type of medicine on each day from the current time node to the next replenishment is analyzed according to the historical drug consumption data and the fixed replenishment time, and second drug usage average value data is calculated based on the historical drug consumption quantity; and the replenishment threshold data of each type of medicine is analyzed according to the first drug usage average value data and the second drug usage average value data.

[0068] It is understandable that, since different types of medicines are consumed differently in different seasons and periods, generally speaking, the replenishment threshold data of medicines needs to be adjusted dynamically in real time; and the adjustment and update of the replenishment threshold data depends on the consumption of medicines during the period from the last replenishment to the current moment; the above-mentioned first drug usage average data is obtained by calculating the average consumption quantity of each type of medicine on each day from the last replenishment to the current time node; for example, the consumption quantity of drug A in three days is 5 units, 4 units and 6 units respectively, then the first drug usage average data of drug A is the average of the three, 5 units.

[0069] The replenishment threshold data of drugs also depends on the daily drug consumption between the current time node and the next replenishment. The historical drug consumption data can be used to obtain the historical drug consumption corresponding to the daily historical drug consumption between the current time node and the next replenishment in the historical period, select the preset number of historical drug consumptions with the same date, and then calculate the average of these historical drug consumptions to obtain the historical average drug consumption. It can be understood that the next replenishment here refers to the fixed replenishment time for different types of drugs. It can be understood that the preset replenishment cycle is the time node for replenishing drugs at fixed intervals. The fixed replenishment time for each type of drug is a preset number of preset replenishment cycles. The preset replenishment cycle means that for the demand side, it needs to replenish a batch of drugs at fixed intervals. This replenishment cycle is fixed, such as replenishing two trucks every half a month. However, for each drug, its own replenishment time includes a preset number of replenishment cycles. For example, drug A is replenished once a month, then the replenishment time of drug A is two preset replenishment cycles. Therefore, in the historical drug consumption data and the historical date data, for the same drug in each year time node, the current moment and the next time of replenishing medicine are the same; when selecting the preset number of historical drug consumption, you can select the daily historical drug consumption between the current moment and the next time of replenishing medicine last year, the daily historical drug consumption between the current moment and the next time of replenishing medicine the year before last, etc.; during this period, the average value of the historical drug consumption on each same date is the historical average drug consumption; then the average value of the historical drug consumption from the first day to the last day during this period is calculated to obtain the second drug usage average value data; finally, according to the first drug usage average value data and the second drug usage average value data, the average value of the two is taken to obtain the unit replenishment quantity data; and the replenishment threshold data is the preset number of unit replenishment quantity data; that is, if the unit replenishment quantity data is 5 units of Class A drugs, the unit quantity can be boxes, etc.; the replenishment threshold data is the preset number of unit replenishment quantity data, the preset number is 3, then the replenishment threshold data of Class A drugs is 15 boxes, and when the remaining amount of Class A drugs is 15 boxes or less, an early warning will be triggered.

[0070] It is understandable that the last replenishment and the next replenishment mentioned here refer to the fixed replenishment time of each of the above-mentioned medicines.

[0071] It can be understood that the remaining drug quantity triggering warning here is what is mentioned in the above example. When the remaining drug quantity triggers the replenishment threshold data, a warning of insufficient remaining drug quantity is issued; and the preset content data also includes replenishment quantity data for each drug; the replenishment quantity data here can be calculated based on the historical drug consumption data; based on the historical drug consumption data, the average historical drug consumption of each type of drug per day between the current time node and the next replenishment time node of this type of drug is calculated; the average historical drug consumption here is calculated in the same way as the average historical drug consumption quantity mentioned above, which is also for each preset number of historical drug consumption on the same date, and then the average value is calculated; then the average historical drug consumption per day between the current time node and the next replenishment time node of this type of drug is added to obtain the total replenishment quantity; it can be understood that the replenishment quantity here is the replenishment quantity for different types of drugs themselves, not the replenishment quantity for all drugs.

[0072] Step S130: Calculating total amount of replenishment medicine data according to the replenishment medicine threshold data; the total amount of replenishment medicine data is the sum of the amount of each type of medicine that needs to be replenished.

[0073] It can be understood that the total amount of replenishment data can be obtained by adding up the replenishment amounts of all types of drugs that trigger replenishment warnings.

[0074] Step S140: determining a drug loading plan according to the total drug replenishment data and preset drug transport vehicle specification data, adjusting the drug replenishment quantity data in the warning content data according to the drug loading plan, and updating the warning content data.

[0075] In an embodiment of the present application, a drug loading plan is determined based on the total amount of replenishment data and the preset drug transport vehicle specification data, and the replenishment amount data in the warning content data is adjusted and the warning content data is updated based on the drug loading plan. Specifically, the number of drug transport vehicles required for replenishment and the number of loaded drugs on each drug transport vehicle are determined based on the total amount of replenishment data and the preset drug transport vehicle specification data; the preset drug transport vehicle specification data includes the standard drug capacity of the drug transport vehicle; the drug transport vehicle to be adjusted is screened; the drug transport vehicle to be adjusted is a drug transport vehicle whose loaded drug quantity is less than the standard drug capacity; the usage frequency data of each type of drug between the last replenishment and the current time node is determined based on the historical replenishment data and the historical drug consumption data; the replenishment urgency data of each type of drug is analyzed based on the historical drug consumption data and the usage frequency data; the drug loading plan is determined based on the replenishment urgency data and the loaded drug quantity of the drug transport vehicle to be adjusted.

[0076] It can be understood that after the total amount of replenished medicines is determined, since the specifications of the drug transport vehicles involved in the solution of the present application are consistent and fixed, the number of drug transport vehicles required for this replenishment and the number of drugs loaded on each vehicle can be determined based on the total amount of replenished medicines and the standard drug capacity of the drug transport vehicles; the standard drug capacity here is the maximum cargo capacity of the drug transport vehicle; generally speaking, there are two situations, the first is that if two drug transport vehicles are needed, the first one is full and the second one is almost full; the second is that if three drug transport vehicles are needed, the first two are full and the third one is only slightly full; these two situations can be summarized as one is that one vehicle is almost full, but the amount of drugs loaded exceeds 50% of the capacity; the other is that one vehicle is only slightly loaded, but the amount of drugs loaded does not exceed 50% of the capacity.

[0077] In order to save transportation costs and improve the cost-effectiveness of drug transportation, it is necessary to deal with the above two situations accordingly; for the first situation, you can choose additional medicines to replenish and fill up the unfilled medicine transport vehicle; for the second situation, you can choose to reduce the medicines and empty the unfilled medicine transport vehicle.

[0078] In the scheme of the present application, the urgency of replenishment of each type of medicine is determined by analyzing and calculating the replenishment urgency data of each type of medicine, thereby determining the replenishment priority of each medicine. Based on the replenishment priority, you can choose to replenish the medicines with high replenishment priorities first, or choose to clear the medicines with low replenishment priorities.

[0079] Among them, analyzing the replenishment urgency data of each type of medicine based on the historical drug consumption data and the usage frequency data specifically includes: obtaining a fixed replenishment time for each type of medicine; the fixed replenishment time includes multiple preset replenishment cycles; the preset replenishment cycle is a fixed cycle for replenishing medicines for the demand side; determining the remaining time for drug replenishment based on the fixed replenishment time and the current time node; obtaining drug remaining quantity data for each type of medicine; calculating the remaining drug usage time based on the usage frequency data and the historical drug consumption data; analyzing the replenishment urgency data of each type of medicine based on the remaining replenishment time and the remaining drug usage time.

[0080] It can be understood that in the scheme of the present application, the longer the remaining time for drug replenishment, the higher the replenishment priority of the drug, because the next replenishment time is too long, and more drugs are needed to fill this replenishment gap; the shorter the remaining time for drug use, the higher the replenishment priority of the drug, because the time it can be used is too short, and more drugs need to be replenished urgently; and the more frequently used the drug, the higher the replenishment priority of the drug, because the frequency and number of uses and quantity required are increased, and more drugs need to be replenished.

[0081] It should be noted that the remaining time for drug replenishment mentioned above is the time length between the time node of the most recent replenishment to the current time node determined based on the fixed replenishment time and the current time node.

[0082] It can be understood that the usage frequency data is obtained by dividing the drug usage by time; and based on the historical drug consumption data, the average drug frequency data from the current time node to the next replenishment time node can be obtained; the average drug frequency data is the usage frequency of each drug in the same period of each year; that is, the historical usage frequency is obtained by dividing the usage of each drug in the same period of each year by the duration, and then the average of all historical usage frequencies is calculated to obtain the average drug frequency data.

[0083] After obtaining the average drug frequency data, the remaining drug usage time is calculated by combining the usage frequency data and the remaining drug quantity.

[0084] Specifically,

[0085]

[0086] Where, T2 is the remaining drug usage time, M 余 is the remaining amount of medicine, F1 is the usage frequency data, and F2 is the average medicine frequency data; k1 and k2 are the preset first frequency weight and the preset second frequency weight respectively; wherein, k1+k2=1.

[0087] In the solution of the present application, the preset first frequency weight and the preset second frequency weight may be set to 0.5 and 0.5.

[0088] After obtaining the remaining usage time of the medicine and the remaining time for replenishment, the replenishment urgency data of each type of medicine can be calculated.

[0089] Specifically,

[0090]

[0091] Wherein, U is the urgency data for replenishing medicine, T1 is the remaining time for replenishment, and T2 is the remaining usage time of medicine; K1 and K2 are the preset first time weight and the preset second time weight, respectively; B1 and B2 are the preset first adjustment parameter and the preset second adjustment parameter, respectively; wherein, K1+K2=1.

[0092] In the solution of the present application, the preset first time weight and the preset second time weight may be set to 0.7 and 0.3.

[0093] Among them, determining the drug loading plan based on the drug replenishment urgency data and the loaded drug quantity of the drug transport vehicle to be adjusted specifically includes: determining the drug quantity difference based on the loaded drug quantity of the drug transport vehicle to be adjusted and the standard drug capacity; determining the drug adjustment direction based on the drug quantity difference; the drug adjustment direction includes adding drug types and quantities and reducing drug types and quantities; determining the drug loading plan based on the drug replenishment urgency data and the drug adjustment direction.

[0094] After obtaining the medicine replenishment urgency data and the medicine quantity difference, different quantities and types of medicines are selected for addition and reduction based on the medicine replenishment urgency data; it can be understood that when medicines need to be added, medicines with high medicine replenishment urgency are selected for addition; when medicines need to be reduced, medicines with low medicine replenishment urgency are selected for reduction; it should be noted that when medicines are reduced, the number of medicines reduced shall not be greater than the preset number of reductions; the preset number of reductions is obtained based on the difference in medicine quantities; in the scheme of the present application, the preset number of reductions is one-fifth of the difference in medicine quantities; specifically, the preset number of reductions can be adjusted in real time according to the difference in medicine quantities.

[0095] It can be understood that the solution of the present application is actually a machine learning model, which realizes the training of various parameters by continuously calculating the urgency data of medicine replenishment, and the trainable parameters include a preset first frequency weight and a preset second frequency weight, a preset first time weight and a preset second time weight, a preset first adjustment parameter and a preset second adjustment parameter, etc.; through continuous training and adjustment of various parameters, the urgency data of medicine replenishment is made more accurate, thereby ensuring the rationality of the medicine loading plan and making it more in line with the actual situation.

[0096] 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.

[0097] In the second aspect, the present application provides a medical supply chain data analysis and early warning system. Figure 2As shown, the system includes an acquisition module 210, which is used to acquire historical replenishment data and historical drug consumption data; the historical replenishment data includes the replenishment time of each type of drug last replenishment; the historical drug consumption data includes the drug consumption quantity of each type of drug; the analysis module 220 is used to analyze the replenishment threshold data of each type of drug according to the historical replenishment data and the historical drug consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the drug quantity threshold value that requires replenishment of the drug; the warning content data includes the drug remaining quantity triggering warning and replenishment quantity data ; The remaining medicine quantity trigger warning is a warning triggered when the remaining medicine quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicines that need to be replenished when the remaining medicine quantity triggers the warning; a calculation module 230 is used to calculate the total replenishment quantity data according to the replenishment threshold data; the total replenishment quantity data is the sum of the quantities of medicines that need to be replenished for each type of medicine; a determination module 240 is used to determine a medicine loading plan according to the total replenishment quantity data and preset medicine transport vehicle specification data, adjust the replenishment quantity data in the warning content data according to the medicine loading plan, and update the warning content data.

[0098] Furthermore, the analysis module 220 is further configured to analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold value of the medicine quantity that needs to be replenished; the warning content data includes the remaining quantity of medicine triggering warning and replenishment quantity data; the remaining quantity of medicine triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the remaining quantity of medicine triggers the warning, including, according to the historical replenishment data, The drug consumption quantity of each type of drug on each day from the last replenishment to the current time node is analyzed based on the historical drug consumption data and the historical drug consumption data, and the first drug usage average value data is calculated based on the drug consumption quantity; the historical drug consumption quantity of each type of drug on each day from the current time node to the next replenishment is analyzed based on the historical drug consumption data and the fixed replenishment time, and the second drug usage average value data is calculated based on the historical drug consumption quantity; the replenishment threshold data of each type of drug is analyzed based on the first drug usage average value data and the second drug usage average value data.

[0099] Furthermore, the determination module 240 is further configured to determine a drug loading plan based on the total amount of replenishment data and the preset drug transport vehicle specification data, adjust the replenishment amount data in the warning content data based on the drug loading plan and update the warning content data, including determining the number of drug transport vehicles required for replenishment and the number of loaded drugs on each drug transport vehicle based on the total amount of replenishment data and the preset drug transport vehicle specification data; the preset drug transport vehicle specification data includes the standard drug capacity of the drug transport vehicle; screening the drug transport vehicle to be adjusted; the drug transport vehicle to be adjusted is a drug transport vehicle whose loaded drug quantity is less than the standard drug capacity; determining the usage frequency data of each type of drug between the last replenishment and the current time node based on the historical replenishment data and the historical drug consumption data; analyzing the replenishment urgency data of each type of drug based on the historical drug consumption data and the usage frequency data; determining the drug loading plan based on the replenishment urgency data and the loaded drug quantity of the drug transport vehicle to be adjusted; and Analyzing the replenishment urgency data of each type of medicine with the usage frequency data includes: obtaining a fixed replenishment time for each type of medicine; the fixed replenishment time includes multiple preset replenishment cycles; the preset replenishment cycle is a fixed cycle for replenishing medicines for the demand side; determining the remaining replenishment time of medicines according to the fixed replenishment time and the current time node; obtaining the remaining quantity data of each type of medicine; calculating the remaining medicine usage time according to the usage frequency data and the historical medicine consumption data; analyzing the replenishment urgency data of each type of medicine according to the replenishment remaining time and the remaining medicine usage time; determining the medicine loading plan according to the replenishment urgency data and the loading quantity of the medicine transport vehicle to be adjusted, including: determining the difference in the number of medicines according to the loading quantity of the medicine transport vehicle to be adjusted and the standard capacity of medicines; determining the medicine adjustment direction according to the difference in the number of medicines; the medicine adjustment direction includes adding medicine types and quantities and reducing medicine types and quantities; determining the medicine loading plan according to the replenishment urgency data and the medicine adjustment direction.

[0100] 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.

[0101] 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 medical supply chain data analysis and early warning method, characterized in that: include: Acquire historical replenishment data and historical drug consumption data; the historical replenishment data includes the time of the last replenishment of each type of drug; the historical drug consumption data includes the drug consumption quantity of each type of drug; Analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold value of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the medicine remaining quantity triggers the warning; Calculate the total amount of replenishment medicine data according to the replenishment medicine threshold data; the total amount of replenishment medicine data is the sum of the amount of each type of medicine that needs to be replenished; A drug loading plan is determined according to the total amount data of replenished medicines and the preset specification data of the drug transport vehicle, and the replenished medicine amount data in the warning content data is adjusted according to the drug loading plan and the warning content data is updated.

2. The medical supply chain data analysis and early warning method according to claim 1 is characterized in that: The step of determining a drug loading plan according to the total amount of replenished medicine data and the preset drug transport vehicle specification data, adjusting the replenished medicine amount data in the warning content data according to the drug loading plan, and updating the warning content data includes: Determine the number of medicine transport vehicles required for medicine replenishment and the number of medicines loaded on each medicine transport vehicle according to the total amount of medicine replenishment data and the preset medicine transport vehicle specification data; the preset medicine transport vehicle specification data includes the standard amount of medicines that can be accommodated by the medicine transport vehicle; Screening the drug transport vehicle to be adjusted; the drug transport vehicle to be adjusted is a drug transport vehicle whose loaded drug quantity is less than the standard drug capacity; Determine the usage frequency data of each type of medicine between the last replenishment and the current time point according to the historical replenishment data and the historical medicine consumption data; analyzing the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data; A drug loading plan is determined according to the drug replenishment urgency data and the loading quantity of the drug transport vehicle to be adjusted.

3. The medical supply chain data analysis and early warning method according to claim 2 is characterized in that: The analyzing the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data includes: Obtaining fixed replenishment time for each type of medicine; the fixed replenishment time includes multiple preset replenishment cycles; the preset replenishment cycle is a fixed cycle for replenishing medicines for the demand side; Determine the remaining time for drug replenishment based on the fixed replenishment time and the current time node; Obtain drug remaining quantity data for each type of drug; Calculate the remaining drug usage time according to the usage frequency data and the historical drug consumption data; The replenishment urgency data of each type of medicine is analyzed according to the replenishment remaining time and the medicine remaining usage time.

4. The medical supply chain data analysis and early warning method according to claim 3 is characterized in that: The step of determining the drug loading plan according to the drug replenishment urgency data and the drug loading quantity of the drug transport vehicle to be adjusted includes: Determine the difference in drug quantity according to the drug quantity loaded on the drug transport vehicle to be adjusted and the standard drug quantity; Determine the direction of drug adjustment according to the drug quantity difference; the drug adjustment direction includes adding drug types and quantities and reducing drug types and quantities; A drug loading plan is determined according to the drug replenishment urgency data and the drug adjustment direction.

5. The medical supply chain data analysis and early warning method according to claim 4 is characterized in that: The replenishment threshold data of each type of medicine is analyzed according to the historical replenishment data and the historical medicine consumption data, and the warning content data is determined according to the replenishment threshold data; the replenishment threshold data is the threshold value of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; The replenishment quantity data is the quantity of medicine that needs to be replenished when the remaining medicine quantity triggers the warning, including: Analyzing the drug consumption quantity of each type of drug on each day from the last drug replenishment to the current time point according to the historical replenishment data and the historical drug consumption data, and calculating the first drug usage average value data based on the drug consumption quantity; Analyzing the historical average drug consumption quantity of each type of drug on each day from the current time point to the next drug replenishment according to the historical drug consumption data and the fixed replenishment time, and calculating the second drug usage average value data based on the historical average drug consumption quantity; The replenishment threshold data of each type of medicine is analyzed based on the first medicine usage average data and the second medicine usage average data.

6. A medical supply chain data analysis and early warning system, characterized in that: include: An acquisition module (210) is used to acquire historical replenishment data and historical drug consumption data; the historical replenishment data includes the time of the last replenishment of each type of drug; the historical drug consumption data includes the drug consumption quantity of each type of drug; The analysis module (220) is used to analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold value of the medicine quantity that needs to be replenished; the warning content data includes a medicine remaining quantity triggering warning and replenishment quantity data; the medicine remaining quantity triggering warning is a warning triggered when the remaining quantity of each type of medicine is not greater than the replenishment threshold data; the replenishment quantity data is the quantity of medicine that needs to be replenished when the medicine remaining quantity triggers the warning; A calculation module (230) is used to calculate total amount of replenishment medicine data according to the replenishment medicine threshold data; the total amount of replenishment medicine data is the sum of the amount of each type of medicine that needs to be replenished; The determination module (240) is used to determine a drug loading plan based on the total amount of drug replenishment data and preset drug transport vehicle specification data, adjust the drug replenishment amount data in the warning content data according to the drug loading plan, and update the warning content data.

7. The medical supply chain data analysis and early warning system according to claim 6 is characterized in that: The determination module (240) is further configured to determine the drug loading plan according to the total amount of replenished medicine data and the preset drug transport vehicle specification data, and adjust the replenished medicine amount data in the warning content data according to the drug loading plan and update the warning content data, including: Determine the number of medicine transport vehicles required for medicine replenishment and the number of medicines loaded on each medicine transport vehicle according to the total amount of medicine replenishment data and the preset medicine transport vehicle specification data; the preset medicine transport vehicle specification data includes the standard amount of medicines that can be accommodated by the medicine transport vehicle; Screening the drug transport vehicle to be adjusted; the drug transport vehicle to be adjusted is a drug transport vehicle whose loaded drug quantity is less than the standard drug capacity; Determine the usage frequency data of each type of medicine between the last replenishment and the current time point according to the historical replenishment data and the historical medicine consumption data; analyzing the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data; A drug loading plan is determined according to the drug replenishment urgency data and the loading quantity of the drug transport vehicle to be adjusted.

8. The medical supply chain data analysis and early warning system according to claim 7 is characterized in that: The determination module (240) is further configured to analyze the drug replenishment urgency data of each type of drug according to the historical drug consumption data and the usage frequency data, including: Obtaining fixed replenishment time for each type of medicine; the fixed replenishment time includes multiple preset replenishment cycles; the preset replenishment cycle is a fixed cycle for replenishing medicines for the demand side; Determine the remaining time for drug replenishment based on the fixed replenishment time and the current time node; Obtain drug remaining quantity data for each type of drug; Calculate the remaining drug usage time according to the usage frequency data and the historical drug consumption data; The replenishment urgency data of each type of medicine is analyzed according to the replenishment remaining time and the medicine remaining usage time.

9. The medical supply chain data analysis and early warning system according to claim 8, characterized in that: The determination module (240) is further configured to determine the drug loading plan according to the drug replenishment urgency data and the number of loaded drugs on the drug transport vehicle to be adjusted, including: Determine the difference in drug quantity according to the drug quantity loaded on the drug transport vehicle to be adjusted and the standard drug quantity; Determine the direction of drug adjustment according to the drug quantity difference; the drug adjustment direction includes adding drug types and quantities and reducing drug types and quantities; A drug loading plan is determined according to the drug replenishment urgency data and the drug adjustment direction.

10. The medical supply chain data analysis and early warning system according to claim 9, characterized in that: The analysis module (220) is further configured to analyze the replenishment threshold data of each type of medicine according to the historical replenishment data and the historical medicine consumption data, and determine the warning content data according to the replenishment threshold data; the replenishment threshold data is the threshold of the medicine quantity that needs to be replenished; the warning content data includes the remaining medicine quantity triggering warning and the replenishment quantity data; the remaining medicine quantity triggering warning is a warning triggered when the remaining medicine quantity of each type of medicine is not greater than the replenishment threshold data; The replenishment quantity data is the quantity of medicine that needs to be replenished when the remaining medicine quantity triggers the warning, including: Analyzing the drug consumption quantity of each type of drug on each day from the last drug replenishment to the current time point according to the historical replenishment data and the historical drug consumption data, and calculating the first drug usage average value data based on the drug consumption quantity; Analyzing the historical average drug consumption quantity of each type of drug on each day from the current time point to the next drug replenishment according to the historical drug consumption data and the fixed replenishment time, and calculating the second drug usage average value data based on the historical average drug consumption quantity; The replenishment threshold data of each type of medicine is analyzed based on the first medicine usage average data and the second medicine usage average data.

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