A spark-based cardiovascular disease drug recommendation method and system

Abstract

The application discloses a cardiovascular disease drug recommendation method and system based on Spark, wherein the cardiovascular disease drug recommendation system is provided with a drug information collection module, a user demand determination module, a data screening module, a target extraction module, a combination recommendation module and a user server; the data screening module screens out recommended cardiovascular disease drugs according to a demand determination benchmark and performs sorting; and the combination recommendation module screens out associated purchase drugs according to the use conditions of the associated drugs of the corresponding recommended cardiovascular disease drugs and performs sorting. The user demand determination module and the data screening module are used to obtain a drug sorting list meeting the requirements of demanders, so that the drug selection efficiency is improved, and the drug selection pertinence and accuracy are improved; in addition, the combination recommendation module is used to screen out the associated purchase drugs and perform sorting, so that the demanders can obtain the associated purchase drugs with the best combination treatment effect and select the associated purchase drugs, and the drug selection accuracy is further improved.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical technology, specifically to a method and system for recommending cardiovascular drugs based on Spark. Background Technology

[0002] Drug recommendation systems typically use a drug search function to search for drugs based on drug names or specific keywords. However, with a large drug database, patients may not be able to find drugs that meet certain efficacy, dosage, and price criteria in a timely manner, resulting in low efficiency in drug selection and difficulty in ensuring the relevance and accuracy of the selection.

[0003] In addition, for some diseases that are prone to causing various complications, multiple medications are often needed in combination to achieve a certain therapeutic effect. The existing drug recommendation system cannot recommend the corresponding combination of drugs with the best efficacy for patients, which affects the overall therapeutic effect of some drugs on the disease. Summary of the Invention

[0004] Therefore, this invention provides a Spark-based method and system for recommending cardiovascular drugs, which effectively solves the problems of low efficiency in drug selection, difficulty in ensuring the specificity and accuracy of drug selection, and inability to recommend the corresponding combination of drugs with the best therapeutic effect to patients in the existing technology.

[0005] To address the aforementioned technical problems, the present invention specifically provides the following technical solution: a Spark-based cardiovascular drug recommendation system, comprising:

[0006] The drug information collection module is used to collect drug information, drug purchase information, and drug usage information for cardiovascular drugs to be recommended.

[0007] The user demand determination module is communicatively connected to the drug information collection module. The user demand determination module is used to determine the demand data group based on drug information, drug purchase status and drug usage status, and generate a demand determination selection group which is transmitted to the user server for the user to select and confirm, so as to generate a demand determination benchmark group.

[0008] The data filtering module is communicatively connected to the drug information collection module and the user demand determination module. The data filtering module is used to filter recommended cardiovascular drugs based on the demand-determined benchmark group, drug information of the cardiovascular drugs to be recommended, drug purchase status, and drug usage status, and sort them according to the demand-determined benchmark group.

[0009] The target extraction module is communicatively connected to the data filtering module, the user server, and the drug information collection module. The target extraction module is used to send the recommended cardiovascular drugs to the user server for selection based on the ranking of the recommended cardiovascular drugs, and to extract relevant drug information from the drug information collection module based on the selection result of the user server and send it to the user server for display.

[0010] The combined recommendation module is connected to the drug information collection module and the user server. The combined recommendation module is used to filter and sort related purchase drugs based on the usage of related drugs of the corresponding recommended cardiovascular disease drugs, and send the top three related purchase drugs to the user server for display.

[0011] The user server is connected to the user demand determination module, the target extraction module, and the combination recommendation module. The user server is used to confirm the filtering and ranking factors in the demand determination benchmark group and transmit them to the data filtering module, select recommended cardiovascular drugs for display, and receive related purchase drugs from the combination recommendation module for display.

[0012] Furthermore, the drug information collection module includes a drug information collection unit, a usage information collection unit, and a storage database;

[0013] The drug information collection unit is used to collect drug information, and the usage information collection unit is used to collect drug purchase information and drug usage information. Both the drug information collection unit and the usage information collection unit are communicatively connected to the storage database and transmit the collected information to the storage database for storage.

[0014] Furthermore, the drug information includes the drug code, drug name, drug specifications, drug price, and drug usage guidelines for the cardiovascular disease drug to be recommended;

[0015] The drug purchase information includes the quantity of drugs purchased, the drug purchase return rate, and the other drugs purchased in a single drug purchase order and their combined use. The drug usage information includes drug ratings and drug usage feedback.

[0016] Furthermore, the user requirement determination module includes a first requirement processing unit, a second requirement processing unit, a third requirement processing unit, and a requirement communication unit;

[0017] The first demand processing unit, the second demand processing unit, and the third demand processing unit are all connected to the demand communication unit. The first demand processing unit is used to generate a main search bar. The second demand processing unit is used to generate a drug factor sorting bar based on drug price, drug purchase quantity, and drug purchase return rate. The third demand processing unit is used to generate multiple drug factor filtering bars based on drug number, drug name, drug specification, drug price, drug purchase quantity, and drug purchase return rate.

[0018] Furthermore, the demand communication unit is used to receive the main search bar, the drug factor sorting bar, and the drug factor filtering bar from the first demand processing unit, the second demand processing unit, and the third demand processing unit, integrate them into the demand determination selection group, and send it to the user server.

[0019] Furthermore, the data filtering module includes a first data filtering unit, a data sorting unit, and a second data filtering unit;

[0020] The first data filtering unit is communicatively connected to the data sorting unit, and the data sorting unit is communicatively connected to the second data filtering unit. The first data filtering unit is used to extract data from the storage database based on the input content of the main search bar in the benchmark group according to the requirements for a first filtering. The data sorting unit is used to extract data from the storage database based on the drug factor sorting bar in the benchmark group according to the requirements for a second filtering. The second data filtering unit is used to extract data from the storage database based on the drug factor filtering bar in the benchmark group according to the requirements for a second filtering, so as to generate a drug sorting list.

[0021] Furthermore, the target extraction module includes a communication transmission unit and an information extraction unit;

[0022] The information extraction end is communicatively connected to the second data filtering unit and the user server, and transmits the extracted drug sorting list to the user server via the communication transmission unit. The information extraction end extracts the selection result of the user server and extracts the relevant drug information in the drug information collection module according to the selection result of the user server, and sends it to the user server via the communication transmission unit.

[0023] Furthermore, the combined recommendation module includes an association sorting unit, an association filtering unit, and an association communication unit;

[0024] The association sorting unit is communicatively connected to the storage database. The association sorting unit is used to extract other purchased drug data from the corresponding drug single purchase order of the drug information collection module according to the cardiovascular drug selected by the user server, calculate the associated purchase quantity and combination treatment effect score of other purchased drugs in the drug single purchase order, and sort them. The association filtering unit is communicatively connected to the association sorting unit. The association filtering unit is used to filter out the top three associated purchased drugs and send them to the user server for display. The association communication unit is communicatively connected to the association filtering unit. The association communication unit receives the associated purchased drugs and transmits them to the user server.

[0025] Furthermore, the user server selects and confirms the contents of the main search bar, the drug factor sorting bar, and the drug factor filtering bar to generate the demand determination benchmark group and send it to the data filtering module;

[0026] The user server receives the drug sorting list and selects cardiovascular drugs from it, and receives and displays relevant drug information transmitted by the target extraction module;

[0027] The user server receives the top three related drugs for purchase from the combined recommendation module and displays them.

[0028] To address the aforementioned technical problems, the present invention further provides the following technical solution: a recommendation method for a Spark-based cardiovascular drug recommendation system, comprising the following steps:

[0029] Step 100: Pre-collect and store information on the cardiovascular drugs to be recommended, including drug purchase and usage details;

[0030] Step 200: Analyze and generate a demand determination selection group based on drug information, drug purchase status and drug usage status, and transmit it to the user server for the user to select and confirm, so as to generate a demand determination baseline group.

[0031] Step 300: Determine the baseline group based on the requirements, screen and sort the recommended cardiovascular drugs to generate a drug ranking list, and transmit it to the user server for display.

[0032] Step 400: The user server selects cardiovascular drugs from the drug sorting list and extracts relevant drug information to be displayed to the user server for users to choose from.

[0033] Step 500: Based on the usage of related drugs of the corresponding selected recommended cardiovascular disease drugs, filter the related purchase drugs and sort them. Send the top three related purchase drugs to the user server for display so that users can choose.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] (1) The present invention generates a demand determination benchmark group through the user demand determination module, and performs a first screening, sorting and a second screening in sequence according to the demand determination benchmark group that meets the requirements of the demander through the data filtering module, so as to obtain a drug sorting list that meets the requirements of the demander, thereby improving the efficiency of drug selection and improving the targeting and accuracy of drug selection.

[0036] (2) The present invention uses a combination recommendation module to filter and sort related purchase drugs based on the usage of related drugs of the corresponding recommended cardiovascular drugs, and recommends the top-ranked related purchase drugs to the user, so that the user can obtain the related purchase drugs with the best combination treatment effect and make a selection, thereby further improving the accuracy of drug selection. Attached Figure Description

[0037] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0038] Figure 1 A schematic diagram of the structure of a Spark-based cardiovascular drug recommendation system provided in an embodiment of the present invention;

[0039] Figure 2 This is a flowchart illustrating a recommendation method for a Spark-based cardiovascular drug recommendation system, as provided in an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] like Figure 1 As shown, this invention provides a method and system for recommending cardiovascular drugs based on Spark. The cardiovascular drug recommendation system includes a drug information collection module, a user needs determination module, a data filtering module, a target extraction module, a combination recommendation module, and a user server.

[0042] The system comprises the following modules: a drug information collection module, used to collect drug information, purchase details, and usage information for recommended cardiovascular drugs; a user demand determination module, communicating with the drug information collection module, used to determine demand data groups based on drug information, purchase details, and usage information, and generates demand determination selection groups which are transmitted to the user server for selection and confirmation by the user, thus generating a demand determination benchmark group; a data filtering module, communicating with both the drug information collection module and the user demand determination module, used to filter recommended cardiovascular drugs based on the demand determination benchmark group, drug information, purchase details, and usage information, and sort them according to the demand determination benchmark group; and a target extraction module, communicating with the data filtering module, the user server, and the drug information collection module. The target extraction module is used to send recommended cardiovascular disease drugs to the user server for selection based on the ranking of recommended cardiovascular disease drugs. It also extracts relevant drug information from the drug information collection module based on the user server's selection and sends it to the user server for display. The combined recommendation module communicates with the drug information collection module and the user server. This module filters and ranks related drugs based on their usage, and sends the top three related drugs to the user server for display. The user server communicates with the user demand determination module, the target extraction module, and the combined recommendation module. It confirms the ranking factors in the demand determination benchmark group and transmits them to the data filtering module, selects recommended cardiovascular disease drugs for display, and receives related drugs from the combined recommendation module for display.

[0043] In this embodiment of the invention, a user demand determination module generates a demand determination benchmark group, and a data filtering module sequentially performs a first filtering, sorting, and a second filtering based on the demand determination benchmark group to obtain a drug ranking list that meets the user's requirements. This improves drug selection efficiency and enhances the targeting and accuracy of drug selection. In addition, a combination recommendation module filters and sorts related purchase drugs based on the usage of related drugs of the corresponding recommended cardiovascular disease drugs, and recommends the top-ranked related purchase drugs to the user. This allows the user to obtain and select the related purchase drugs with the best combination treatment effect, further improving the accuracy of drug selection.

[0044] This invention collects drug information, purchase details, and usage information of recommended cardiovascular drugs through a drug information collection module. The preferred embodiment of this invention's drug information collection module includes a drug information collection unit, a usage information collection unit, and a storage database. The drug information collection unit collects drug information, and the usage information collection unit collects drug purchase details and usage information. Both the drug information collection unit and the usage information collection unit are communicatively connected to the storage database and transmit the collected information to the storage database for storage.

[0045] In the above embodiments, the drug information collection unit mainly collects the basic information of the drug, while the usage information collection unit collects and statistically analyzes the usage of the drug through big data.

[0046] The drug information includes the drug code, drug name, drug specifications, drug price, and drug usage guidelines for the cardiovascular drugs to be recommended; drug purchase information includes the amount of drugs purchased, drug purchase return rate, other drugs purchased in a single purchase order and their combination use; and drug usage information includes drug ratings and drug usage feedback.

[0047] In the above embodiments, the other purchased drugs and their combined use in a single drug purchase order include the usage score of the combined drugs and the usage feedback of the combined drugs.

[0048] In this invention, a user demand determination module determines a demand data group based on drug information, drug purchase status, and drug usage, and generates a demand determination selection group which is transmitted to the user server for selection and confirmation by the user to generate a demand determination benchmark group. The user demand module of this invention mainly adopts the following preferred embodiments: the user demand determination module includes a first demand processing unit, a second demand processing unit, a third demand processing unit, and a demand communication unit; the first demand processing unit, the second demand processing unit, and the third demand processing unit are all communicatively connected to the demand communication unit. The first demand processing unit is used to generate a main search bar; the second demand processing unit is used to generate a drug factor sorting bar based on drug price, drug purchase quantity, and drug purchase return rate; and the third demand processing unit is used to generate multiple drug factor filtering bars based on drug number, drug name, drug specification, drug price, drug purchase quantity, and drug purchase return rate.

[0049] In the above embodiments, the main search bar is filled in by the user. The user can fill in the symptoms, drug name, etc. Filling in the symptoms will search for all drugs that treat the symptoms, and filling in the drug name will search for drugs of that type produced by various pharmaceutical companies. In actual application, the symptoms are usually filled in by the user and the server will generate the main search bar with content.

[0050] The drug factor sorting column sorts drugs based on a specific factor. For example, the drug factor sorting column can provide multiple factors such as drug price, drug purchase volume, and drug purchase repurchase rate for the user server to select. Assuming that drug price is selected, the subsequent sorting will be based on drug price. After the user server makes the selection, a drug factor sorting column with content will be generated.

[0051] The drug factor filtering section allows users to sort drugs based on specific factors. Each factor can be configured via the user interface. These factors include drug ID, drug name, drug specification, drug price, purchase quantity, and repurchase rate. Entering the drug ID in the drug ID field will filter for drugs, but usually, entering the drug ID in the main search bar will yield the desired results. Similarly, entering the drug name in the drug name field will filter for drugs with that name, but usually, entering the drug name in the main search bar will also yield the desired results. The drug specification field allows users to select different drug specifications for further filtering. The system provides options for the highest and lowest prices for drugs with corresponding specifications. For drug prices, it offers options for the lowest and highest purchase quantities, allowing users to filter drugs within these ranges. Similarly, it offers options for the highest and lowest repeat purchase rates, also allowing users to filter drugs within these ranges. The system allows users to select specific fields for their needs, and the system generates a complete drug factor filter after the user's input and selection.

[0052] In the above embodiments, the demand communication unit is used to receive the main search bar, drug factor sorting bar and drug factor filtering bar of the first demand processing unit, the second demand processing unit and the third demand processing unit, and send them to the user server for selection and filling. The main search bar, drug factor sorting bar and drug factor filtering bar after the above-mentioned content are the demand determination benchmark group.

[0053] This invention uses a data filtering module to determine a baseline group based on requirements, drug information of cardiovascular drugs to be recommended, drug purchase information, and drug usage information to filter recommended cardiovascular drugs and sort them according to the baseline group determined by requirements. The data filtering module of this invention mainly adopts the following preferred embodiments, which include a first data filtering unit, a data sorting unit, and a second data filtering unit.

[0054] The first data filtering unit is communicatively connected to the data sorting unit, and the data sorting unit is communicatively connected to the second data filtering unit. The first data filtering unit is used to extract data from the storage database based on the input content of the main search bar in the benchmark group according to the requirements for the first filtering. The data sorting unit is used to extract data from the storage database based on the drug factor sorting bar in the benchmark group according to the requirements for the second filtering. The second data filtering unit is used to extract data from the storage database based on the drug factor filtering bar in the benchmark group according to the requirements for the second filtering, so as to generate a drug sorting list.

[0055] In the above embodiments, the first data filtering unit performs the first filtering by searching for drugs in the main search bar. The data sorting unit sorts drugs according to drug price, drug purchase quantity, or drug purchase return rate. In actual application, other factors can also be used for sorting. The third data filtering unit performs the second filtering according to the drug number, drug name, drug specification, drug price, drug purchase quantity, and drug purchase return rate in the drug factor filtering bar. The second filtering is to filter out drugs that do not meet the standards, and the sorting order will not be affected during the process.

[0056] This invention uses a target extraction module to send recommended cardiovascular drugs in a sorted order to a user server for selection. Based on the user server's selection, it also extracts relevant drug information from a drug information collection module and sends it to the user server for display. The target extraction module primarily functions as a data transmission unit and includes a communication transmission unit and an information extraction unit. The information extraction unit communicates with a second data filtering unit and the user server, transmitting the extracted drug sorting list to the user server via the communication transmission unit. The information extraction unit also extracts the user server's selection result and, based on that, extracts relevant drug information from the drug information collection module and sends it to the user server via the communication transmission unit.

[0057] This invention uses a combined recommendation module to filter and sort related purchase drugs based on the usage of associated drugs of corresponding recommended cardiovascular disease drugs, and sends the top three related purchase drugs to the user server for display. The combined recommendation module of this invention mainly adopts the following preferred embodiment, which includes an associated sorting unit, an associated filtering unit, and an associated communication unit.

[0058] The association sorting unit communicates with the storage database. The association sorting unit is used to calculate and sort the associated purchase quantity and combination treatment effect score of other purchased drugs in the corresponding drug purchase order in the cardiovascular disease drug extraction drug information collection module selected by the user server. The association filtering unit communicates with the association sorting unit. The association filtering unit is used to filter out the top three associated purchased drugs and send them to the user server for display. The association communication unit communicates with the association filtering unit. The association communication unit receives associated purchased drugs and transmits them to the user server.

[0059] In the above embodiments, the association sorting unit can extract data on other purchased drugs in the corresponding single drug purchase order from the data repository or drug information collection module, calculate the associated purchase quantity and combined treatment effect score of other purchased drugs in the single drug purchase order, wherein the combined treatment effect score can be based on the associated purchase quantity and the drug score at the time of associated purchase as the overall score standard to reduce the calculation error of the purchaser's active scoring, and sort according to the final combined treatment effect score. In addition, the different drug combinations in this embodiment can be two or more drug combinations, and the final combined treatment effect score is derived from the overall drug combination. The screening result of the association screening unit is also based on the combined treatment effect score to select and display the top three drug combinations.

[0060] In addition, the user server plays a role in selection, confirmation, and display. Specifically, the user server selects and confirms the contents of the main search bar, drug factor sorting bar, and drug factor filtering bar to generate a demand determination benchmark group and send it to the data filtering module; the user server receives the drug sorting list and selects cardiovascular drugs from it, as well as receives relevant drug information transmitted by the target extraction module for display; the user server receives the top three related purchase drugs sent by the combination recommendation module for display.

[0061] In summary, such as Figure 2 As shown, a recommendation method for a Spark-based cardiovascular drug recommendation system includes the following steps:

[0062] Step 100: Pre-collect and store information on the cardiovascular drugs to be recommended, including drug purchase and usage details;

[0063] Step 200: Analyze and generate a demand determination selection group based on drug information, drug purchase status and drug usage status, and transmit it to the user server for the user to select and confirm, so as to generate a demand determination baseline group.

[0064] Step 300: Determine the baseline group based on the requirements, screen and sort the recommended cardiovascular drugs to generate a drug ranking list, and transmit it to the user server for display.

[0065] Step 400: The user server selects cardiovascular drugs from the drug sorting list and extracts relevant drug information to be displayed to the user server for users to choose from.

[0066] Step 500: Based on the usage of related drugs of the corresponding selected recommended cardiovascular disease drugs, filter the related purchase drugs and sort them. Send the top three related purchase drugs to the user server for display so that users can choose.

[0067] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A Spark-based cardiovascular drug recommendation system, characterized in that, have: The drug information collection module is used to collect drug information, drug purchase information, and drug usage information for cardiovascular drugs to be recommended. The user demand determination module is communicatively connected to the drug information collection module. The user demand determination module is used to determine the demand data group based on drug information, drug purchase status and drug usage status, and generate a demand determination selection group which is transmitted to the user server for the user to select and confirm, so as to generate a demand determination benchmark group. The data filtering module is communicatively connected to the drug information collection module and the user demand determination module. The data filtering module is used to filter recommended cardiovascular drugs based on the demand-determined benchmark group, drug information of the cardiovascular drugs to be recommended, drug purchase status, and drug usage status, and sort them according to the demand-determined benchmark group. The target extraction module is communicatively connected to the data filtering module, the user server, and the drug information collection module. The target extraction module is used to send the recommended cardiovascular drugs to the user server for selection based on the ranking of the recommended cardiovascular drugs, and to extract relevant drug information from the drug information collection module based on the selection result of the user server and send it to the user server for display. The combined recommendation module is connected to the drug information collection module and the user server. The combined recommendation module is used to filter and sort related purchase drugs based on the usage of related drugs of the corresponding recommended cardiovascular disease drugs, and send the top three related purchase drugs to the user server for display. The user server is connected to the user demand determination module, the target extraction module, and the combination recommendation module. The user server is used to confirm the filtering and ranking factors in the demand determination benchmark group and transmit them to the data filtering module, select recommended cardiovascular drugs for display, and receive related purchase drugs from the combination recommendation module for display.

2. The Spark-based cardiovascular drug recommendation system according to claim 1, characterized in that, The drug information collection module includes a drug information collection unit, a usage information collection unit, and a storage database; The drug information collection unit is used to collect drug information, and the usage information collection unit is used to collect drug purchase information and drug usage information. Both the drug information collection unit and the usage information collection unit are communicatively connected to the storage database and transmit the collected information to the storage database for storage.

3. The Spark-based cardiovascular drug recommendation system according to claim 2, characterized in that, The drug information includes the drug code, drug name, drug specifications, drug price, and drug usage guidelines for the cardiovascular drugs to be recommended. The drug purchase information includes the quantity of drugs purchased, the drug purchase return rate, and the other drugs purchased in a single drug purchase order and their combined use. The drug usage information includes drug ratings and drug usage feedback.

4. A Spark-based cardiovascular drug recommendation system according to claim 3, characterized in that, The user requirement determination module includes a first requirement processing unit, a second requirement processing unit, a third requirement processing unit, and a requirement communication unit. The first demand processing unit, the second demand processing unit, and the third demand processing unit are all connected to the demand communication unit. The first demand processing unit is used to generate a main search bar. The second demand processing unit is used to generate a drug factor sorting bar based on drug price, drug purchase quantity, and drug purchase return rate. The third demand processing unit is used to generate multiple drug factor filtering bars based on drug number, drug name, drug specification, drug price, drug purchase quantity, and drug purchase return rate.

5. A Spark-based cardiovascular drug recommendation system according to claim 4, characterized in that, The demand communication unit is used to receive the main search bar, the drug factor sorting bar, and the drug factor filtering bar from the first demand processing unit, the second demand processing unit, and the third demand processing unit, integrate them into the demand determination selection group, and send it to the user server.

6. A Spark-based cardiovascular drug recommendation system according to claim 5, characterized in that, The data filtering module includes a first data filtering unit, a data sorting unit, and a second data filtering unit; The first data filtering unit is communicatively connected to the data sorting unit, and the data sorting unit is communicatively connected to the second data filtering unit. The first data filtering unit is used to extract data from the storage database based on the input content of the main search bar in the benchmark group according to the requirements for a first filtering. The data sorting unit is used to extract data from the storage database based on the drug factor sorting bar in the benchmark group according to the requirements for a second filtering. The second data filtering unit is used to extract data from the storage database based on the drug factor filtering bar in the benchmark group according to the requirements for a second filtering, so as to generate a drug sorting list.

7. A Spark-based cardiovascular drug recommendation system according to claim 6, characterized in that, The target extraction module includes a communication transmission unit and an information extraction unit; The information extraction end is communicatively connected to the second data filtering unit and the user server, and transmits the extracted drug sorting list to the user server via the communication transmission unit. The information extraction end extracts the selection result of the user server and extracts the relevant drug information in the drug information collection module according to the selection result of the user server, and sends it to the user server via the communication transmission unit.

8. A Spark-based cardiovascular drug recommendation system according to claim 7, characterized in that, The combined recommendation module includes an associated sorting unit, an associated filtering unit, and an associated communication unit; The association sorting unit is communicatively connected to the storage database. The association sorting unit is used to extract other purchased drug data from the corresponding drug single purchase order of the drug information collection module according to the cardiovascular drug selected by the user server, calculate the associated purchase quantity and combination treatment effect score of other purchased drugs in the drug single purchase order, and sort them. The association filtering unit is communicatively connected to the association sorting unit. The association filtering unit is used to filter out the top three associated purchased drugs and send them to the user server for display. The association communication unit is communicatively connected to the association filtering unit. The association communication unit receives the associated purchased drugs and transmits them to the user server.

9. A Spark-based cardiovascular drug recommendation system according to claim 8, characterized in that, The user server selects and confirms the contents of the main search bar, the drug factor sorting bar, and the drug factor filtering bar to generate the demand determination benchmark group and send it to the data filtering module. The user server receives the drug sorting list and selects cardiovascular drugs from it, and receives and displays relevant drug information transmitted by the target extraction module; The user server receives the top three related drugs for purchase from the combined recommendation module and displays them.

10. A recommendation method for a Spark-based cardiovascular drug recommendation system according to claim 1, characterized in that, Includes the following steps, Step 100: Pre-collect and store information on the cardiovascular drugs to be recommended, including drug purchase and usage details; Step 200: Analyze and generate a demand determination selection group based on drug information, drug purchase status and drug usage status, and transmit it to the user server for the user to select and confirm, so as to generate a demand determination baseline group. Step 300: Determine the baseline group based on the requirements, screen and sort the recommended cardiovascular drugs to generate a drug ranking list, and transmit it to the user server for display. Step 400: The user server selects cardiovascular drugs from the drug sorting list and extracts relevant drug information to be displayed to the user server for users to choose from. Step 500: Based on the usage of related drugs of the corresponding selected recommended cardiovascular disease drugs, filter the related purchase drugs and sort them. Send the top three related purchase drugs to the user server for display so that users can choose.

Images