A Self-Service Terminal Data Acquisition Method, System and Medium Based on Multiple Scenarios

Abstract

The present invention discloses a self-service terminal data collection method, system and medium based on multiple scenarios. A preset medical area is displayed by constructing a three-dimensional map model. Disease diagnosis data is collected, and medical keywords are extracted through semantic analysis to generate content requirements. Medical data is collected in real time, and the distribution of interactive needs is statistically analyzed on the map in combination with location information. Areas are divided based on the location of the medical terminal, knowledge data is retrieved and recommended, and operation priorities and page setting plans are generated in combination with interactive demand analysis. Finally, the page layout and knowledge recommendation plan are sent to the medical terminal to achieve personalized display and knowledge push. Through the present invention, it is possible to effectively realize intelligent dynamic adjustment of terminals in multiple scenarios, improve user experience and efficient operation of medical operations.

Description

Technical Field

[0001] The present invention relates to the field of terminal data analysis, and more specifically, to a self-service terminal data collection method, system and medium based on multiple scenarios. Background Art

[0002] With the rapid development of the medical industry and the in-depth digital transformation, the application of self-service terminals in medical places such as hospitals and clinics is becoming increasingly widespread. However, when providing services, existing self-service terminals often fail to fully consider the specific scenarios where users are located and their real-time medical treatment process requirements, resulting in single service content and lack of pertinence, especially for the recommendation and analysis of medical knowledge and advertising data. In addition, the massive growth of medical data provides rich resources for data analysis, but how to effectively utilize these data to achieve accurate service push and display in multiple scenarios is still an urgent problem to be solved. Summary of the Invention

[0003] The present invention overcomes the defects of the prior art and proposes a self-service terminal data collection method, system and medium based on multiple scenarios.

[0004] The first aspect of the present invention provides a self-service terminal data collection method based on multiple scenarios, including:

[0005] Obtain the map basic information of a preset medical area, and build a map model based on three-dimensional visualization through the map basic information;

[0006] Within a preset time period, collect the disease diagnosis data of all users through the medical platform, perform semantic recognition analysis based on the semantic analysis model through the disease diagnosis data, and extract medical keywords, and generate content demand data through the medical keywords;

[0007] Within an analysis period, collect the medical treatment data of users in real time, perform user medical treatment interaction demand analysis based on the medical treatment data, and generate the terminal interaction demand information of the users. Through the mobile terminal, collect the location information of the users in real time, and perform demand distribution statistical analysis based on the terminal interaction demand information and the location information in the map model to form interaction demand distribution data;

[0008] In the map model, divide the area based on the locations of multiple medical terminals to form multiple terminal areas, perform knowledge data retrieval in the system database through the content demand data to form knowledge recommendation data, perform interaction demand analysis in multiple scenarios for each terminal area based on the interaction demand distribution data, and form interaction operation priority information. Generate a page setting scheme through the interaction operation priority information, and generate a knowledge content recommendation scheme for the medical terminal based on the knowledge recommendation data;

[0009] Send to multiple medical terminals through the page setting scheme and the knowledge content recommendation scheme and perform the setting of the display content.

[0010] In this solution, obtain the map basic information of the preset medical area, and build a map model based on three-dimensional visualization through the map basic information. Specifically:

[0011] Obtain the map basic information of the preset medical area, where the map basic information includes the building layout, area, map outline, number and location information of terminal devices in the preset medical area;

[0012] Build a map model based on three-dimensional visualization through the map basic information.

[0013] In this solution, within a preset time period, collect the disease diagnosis data of all users through the medical platform, perform semantic recognition analysis based on the semantic analysis model through the disease diagnosis data, and extract medical keywords, and generate content requirement data through the medical keywords. Specifically:

[0014] Within a preset time period, collect the disease diagnosis data of all users through the medical platform;

[0015] Perform data cleaning, standardization and text format conversion processing on the disease diagnosis data to form diagnostic text data;

[0016] Build a semantic analysis model based on CNN, import the diagnostic text data into the semantic analysis model for semantic recognition analysis, and perform word segmentation processing on the diagnostic text data through the bag-of-words model, and build a vocabulary based on the segmented diagnostic text data;

[0017] Perform word frequency calculation and statistics through the vocabulary, and form word frequency statistical data;

[0018] Screen out the phrases with a word frequency higher than the preset word frequency from the word frequency statistical data to obtain medical keywords, and sort the medical keywords based on the word frequency size to form a keyword sorting table;

[0019] Based on the medical keywords and the keyword sorting table, perform content priority analysis and similarity medical content analysis of keywords for each keyword to generate content requirement data.

[0020] In this solution, within an analysis period, collect the user's medical treatment data in real time, perform user medical treatment interaction requirement analysis based on the medical treatment data, and generate the user's terminal interaction requirement information. Through the mobile terminal, collect the user's location information in real time, and perform demand distribution statistical analysis on the map model based on the terminal interaction requirement information and the location information to form interaction demand distribution data. Specifically:

[0021] During an analysis cycle, the medical data of users is collected in real time, user real-time process analysis is performed based on the medical data, and medical interaction requirement analysis is carried out based on the current medical process of users to generate interactive operation requirement information;

[0022] Based on the historical interaction time of the medical terminal, the interaction time of different medical processes is statistically analyzed and the average time is calculated, and the average time is used as the interaction requirement time for different medical processes;

[0023] The interactive operation requirement information and the interaction requirement time are integrated to form terminal interaction requirement information.

[0024] In this solution, during an analysis cycle, the medical data of users is collected in real time, medical interaction requirement analysis of users is performed based on the medical data, and terminal interaction requirement information of users is generated. Through the mobile terminal, the location information of users is collected in real time. In the map model, distribution statistics analysis of requirements is performed based on the terminal interaction requirement information and the location information to form interaction requirement distribution data, and it further includes:

[0025] A data connection is established between the medical platform and the user mobile terminal within the preset medical area;

[0026] The medical platform collects the location information of users from the user mobile terminal in real time;

[0027] Based on the terminal interaction requirement information and the location information of users, in the map model, distribution statistics of different requirements for the terminal interaction requirement are performed to form interaction requirement distribution data.

[0028] In this solution, in the map model, based on the locations of multiple medical terminals, regional division is performed to form multiple terminal regions. Through the content requirement data, knowledge data retrieval is carried out in the system database to form knowledge recommendation data. Based on the interaction requirement distribution data, interactive requirement analysis in multiple scenarios is performed for each terminal region respectively, and interactive operation priority information is formed. Through the interactive operation priority information, a page setting scheme is generated, and based on the knowledge recommendation data, a knowledge content recommendation scheme for the medical terminal is generated. Specifically:

[0029] In the map model, based on the locations of multiple medical terminals, regional division is performed to form multiple terminal regions;

[0030] Triple data extraction is performed based on the medical knowledge text data in the system database, and a medical knowledge graph is constructed;

[0031] The content requirement data is imported into the semantic analysis model for semantic analysis and entity data extraction to form requirement entity data;

[0032] In the system database, based on the requirement entity data, knowledge retrieval and analysis are carried out in the medical knowledge graph, and knowledge recommendation data is generated based on the retrieval results.

[0033] In the map model, based on the interactive demand distribution data, each terminal area is divided based on demand to obtain the multi-scenario demand distribution of each terminal area.

[0034] Taking one terminal area as the analysis unit, based on the multi-scenario demand distribution, the quantity of interactive demands is counted, sorted based on the quantity of different demands, and priorities are set based on the quantity size to form interactive operation priority information.

[0035] Obtain the page interaction layout information of the medical terminal, and based on the interactive operation priority information, update and set the multi-level pages of the interaction layout information. The interactive functions with higher interactive demand priorities are set in higher-level page units to obtain the page setting scheme.

[0036] Perform interactive priority analysis on each terminal area to obtain the page setting scheme corresponding to each terminal area.

[0037] The second aspect of the present invention also provides a self-service terminal data acquisition system based on multiple scenarios. The system includes: a memory and a processor. The memory includes a self-service terminal data acquisition program based on multiple scenarios. When the self-service terminal data acquisition program based on multiple scenarios is executed by the processor, the following steps are implemented:

[0038] Obtain the map basic information of the preset medical area, and build a map model based on three-dimensional visualization through the map basic information.

[0039] Within a preset time period, collect the disease diagnosis data of all users through the medical platform, perform semantic recognition analysis based on the semantic analysis model through the disease diagnosis data, extract medical keywords, and generate content demand data through the medical keywords.

[0040] Within an analysis period, collect the user's medical treatment data in real time, analyze the user's medical treatment interaction needs based on the medical treatment data, and generate the user's terminal interaction need information. Through the mobile terminal, collect the user's location information in real time, and perform demand distribution statistical analysis based on the terminal interaction need information and the location information in the map model to form interactive demand distribution data.

[0041] In the map model, the regions are divided based on the locations of multiple medical terminals to form multiple terminal regions. Knowledge data is retrieved in the system database through content demand data to form knowledge recommendation data. Based on the interactive demand distribution data, the interactive demand analysis under multiple scenarios is performed for each terminal region, and interactive operation priority information is formed. The page setting plan is generated through the interactive operation priority information, and the knowledge content recommendation plan is generated for the medical terminal based on the knowledge recommendation data.

[0042] Through the page setting plan and knowledge content recommendation plan, it is sent to multiple medical terminals and the display content is set.

[0043] The third aspect of the present invention also provides a computer-readable storage medium, which includes a self-service terminal data collection program based on multiple scenarios. When the self-service terminal data collection program based on multiple scenarios is executed by a processor, the steps of the self-service terminal data collection method based on multiple scenarios as described in any one of the above items are implemented.

[0044] The present invention discloses a self-service terminal data collection method, system and medium based on multiple scenarios. A preset medical area is displayed by constructing a three-dimensional map model. Disease diagnosis data is collected, and medical keywords are extracted through semantic analysis to generate content requirements. Medical data is collected in real time, and the distribution of interactive needs is statistically analyzed on the map in combination with location information. Areas are divided based on the location of the medical terminal, knowledge data is retrieved and recommended, and operation priorities and page setting plans are generated in combination with interactive demand analysis. Finally, the page layout and knowledge recommendation plan are sent to the medical terminal to achieve personalized display and knowledge push. Through the present invention, it is possible to effectively realize intelligent dynamic adjustment of terminals in multiple scenarios, improve user experience and efficient operation of medical operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a self-service terminal data collection method based on multiple scenarios of the present invention is shown;

[0046] Figure 2 A block diagram of a self-service terminal data collection system based on multiple scenarios of the present invention is shown. DETAILED DESCRIPTION

[0047] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0048] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0049] Figure 1 The flowchart of a self-service terminal data collection method based on multiple scenarios according to the present invention is shown.

[0050] As Figure 1 shown, in the first aspect of the present invention, a self-service terminal data collection method based on multiple scenarios is provided, including:

[0051] S102, obtaining the map basic information of a preset medical area, and building a map model based on three-dimensional visualization through the map basic information;

[0052] S104, within a preset time period, collecting the disease diagnosis data of all users through the medical platform, performing semantic recognition analysis based on a semantic analysis model through the disease diagnosis data, and extracting medical keywords, and generating content requirement data through the medical keywords;

[0053] S106, within an analysis period, collecting the user's medical treatment data in real time, analyzing the user's medical treatment interaction requirements based on the medical treatment data, and generating the terminal interaction requirement information of the user. Through the mobile terminal, collecting the user's location information in real time, and performing demand distribution statistical analysis based on the terminal interaction requirement information and the location information in the map model to form interaction demand distribution data;

[0054] S108, in the map model, dividing regions based on the locations of multiple medical terminals to form multiple terminal regions, performing knowledge data retrieval in the system database through the content requirement data to form knowledge recommendation data, based on the interaction demand distribution data, performing interaction demand analysis in multiple scenarios for each terminal region respectively, and forming interaction operation priority information, generating a page setting scheme through the interaction operation priority information, and generating a knowledge content recommendation scheme for the medical terminal based on the knowledge recommendation data;

[0055] S110, sending the page setting scheme and the knowledge content recommendation scheme to multiple medical terminals and setting the display content.

[0056] It should be noted that the medical platform includes a medical data analysis platform and a database. The platform is connected to the medical terminal through the cloud intranet, and the terminal display data and data transmission can be controlled in real time through the medical platform.

[0057] Medical terminal devices are set at multiple locations in a preset medical area, which is determined according to the actual situation and can be used to interact with medical service users. The terminal can perform medical service contents such as content information recommendation display, medical appointment information query, medical appointment process interaction, drug query, payment process interaction, etc.

[0058] According to an embodiment of the present invention, the method for obtaining the map basic information of the preset medical area and building a map model based on three-dimensional visualization through the map basic information is specifically as follows:

[0059] Obtain the map basic information of the preset medical area, where the map basic information includes the building layout, area, map outline, number and location information of terminal devices in the preset medical area;

[0060] Build a map model based on three-dimensional visualization through the map basic information.

[0061] It should be noted that the map model can display relevant dynamic data in real time. Through the map model, users can view the current distribution of terminals, the usage situation of terminal devices and user location information, etc., enabling users to more intuitively view the current medical terminal service situation.

[0062] According to an embodiment of the present invention, within a preset time period, collect the disease diagnosis data of all users through the medical platform, perform semantic recognition analysis based on a semantic analysis model through the disease diagnosis data, and extract medical keywords, and generate content requirement data through the medical keywords, specifically as follows:

[0063] Within a preset time period, collect the disease diagnosis data of all users through the medical platform;

[0064] Perform data cleaning, standardization and text format conversion processing on the disease diagnosis data to form diagnostic text data;

[0065] Build a semantic analysis model based on CNN, import the diagnostic text data into the semantic analysis model for semantic recognition analysis, and perform word segmentation processing on the diagnostic text data through a bag-of-words model, and build a vocabulary based on the segmented diagnostic text data;

[0066] Perform word frequency calculation and statistics through the vocabulary to form word frequency statistical data;

[0067] Select the phrases with a word frequency higher than the preset word frequency from the word frequency statistical data to obtain medical keywords, and sort the medical keywords based on the word frequency to form a keyword sorting table;

[0068] Based on the medical keywords and the keyword sorting table, perform content priority analysis and similarity medical content analysis of keywords for each keyword to generate content requirement data.

[0069] It should be noted that each term in the vocabulary corresponds to a unique index. The content requirement data includes keywords and their corresponding priorities, as well as the medical content (medical content similar to the keywords) involved in the keywords. The priority is divided based on word frequency. The higher the priority of a keyword, the higher the corresponding content requirement. By forming the content requirement data, it is possible to retrieve medical knowledge content in the subsequent process and form knowledge data for display.

[0070] The keywords generally include corresponding symptom description words, disease names, historical situations of corresponding bad habits, etc. For example, within a preset time period, just during the high-incidence period of seasonal influenza, the keywords generally correspond to information such as the symptoms, situations, and causes of the influenza disease. When an epidemic disease breaks out, based on the periodic analysis of the platform, it is possible to perform semantic data mining on the corresponding medical treatment data, record the corresponding keywords, and display the recommended knowledge on the terminal in the subsequent process, improving the experience of medical users and having high practicality for medical services.

[0071] According to an embodiment of the present invention, within an analysis period, the medical treatment data of the user is collected in real time, the user's medical treatment interaction requirement analysis is performed based on the medical treatment data, and the terminal interaction requirement information of the user is generated. Through the mobile terminal, the location information of the user is collected in real time, and based on the terminal interaction requirement information and the location information, the demand distribution statistical analysis is performed in the map model to form the interaction demand distribution data, specifically:

[0072] Within an analysis period, the medical treatment data of the user is collected in real time, the real-time process analysis of the user is performed based on the medical treatment data, and the medical treatment interaction requirement analysis is performed based on the current medical treatment process of the user to generate the interaction operation requirement information;

[0073] Based on the historical interaction time of the medical terminal, the interaction times of different medical treatment processes are statistically analyzed and the average time is calculated, and the average time is used as the interaction requirement time of different medical treatment processes;

[0074] The interaction operation requirement information and the interaction requirement time are integrated to form the terminal interaction requirement information.

[0075] It should be noted that the interaction operation requirement information is specifically the requirement for the terminal operation process steps analyzed for the user in real time. For example: requirements for operations such as registration, payment, drug query, report query, printing, etc. When analyzing the current medical treatment step process of the user in real time, the next step process is used as the demand interaction operation. For example, when the current user has completed the registration process, the interaction demand operation can be medical treatment payment.

[0076] According to an embodiment of the present invention, within an analysis cycle, medical treatment data of a user is collected in real time, the medical treatment interaction requirements of the user are analyzed based on the medical treatment data, and terminal interaction requirement information of the user is generated. Through a mobile terminal, the location information of the user is collected in real time. In a map model, distribution statistical analysis of requirements is performed based on the terminal interaction requirement information and the location information to form interaction requirement distribution data, further including:

[0077] Establish a data connection with the user's mobile terminal within a preset medical area through a medical platform;

[0078] Collect the user's location information from the user's mobile terminal in real time through the medical platform;

[0079] Based on the terminal interaction requirement information and the user's location information, in the map model, distribution statistics of different requirements for the terminal interaction requirements are performed to form interaction requirement distribution data.

[0080] It should be noted that the interaction requirement distribution data is display data based on the map model, specifically including the distribution status of different interaction requirements on the map. The present invention performs binding analysis by binding the interaction requirement information and the user positioning, and can more intuitively master the terminal requirements of the current user group in the map model, and analyze the requirement quantity and distribution through user positioning analysis. For example, within the medical treatment area or in the area near the medical treatment, there are multiple user mobile terminals (the location of the user can be obtained correspondingly), and the interaction requirements collected through the terminal are respectively registration requirements, payment requirements, query requirements, etc., and the quantities of different requirements are different. Based on the analysis of the distribution status of the requirements and the location, generally speaking, registration requirements, payment requirements, etc. are the main interaction requirements, with a large number of requirements and a relatively dense distribution. In subsequent analysis, the requirement area division of each terminal area can be performed through the interaction requirement distribution data to obtain the multi-scenario requirement distribution of each terminal area. Specifically, the interaction requirement distribution of a terminal area is analyzed through the interaction requirement distribution data (that is, only the requirement distribution status within the terminal area is studied), and the corresponding distribution information is extracted, and different requirements are scenario-based to obtain the multi-scenario requirement distribution corresponding to a terminal area. The multi-scenario requirement distribution includes the quantity and distribution status of different interaction requirements within the terminal area, and one requirement corresponds to one scenario mode, such as the payment mode.

[0081] According to an embodiment of the present invention, in the map model, area division is performed based on the locations of multiple medical terminals to form multiple terminal areas. Knowledge data retrieval is performed in the system database through content requirement data to form knowledge recommendation data. Based on the interaction requirement distribution data, multi-scenario interaction requirement analysis is performed for each terminal area respectively to form interaction operation priority information. A page setting scheme is generated through the interaction operation priority information, and a knowledge content recommendation scheme is generated for the medical terminal based on the knowledge recommendation data. Specifically:

[0082] In the map model, regional division is carried out based on the locations of multiple medical terminals to form multiple terminal regions;

[0083] Triple data extraction is performed based on the medical knowledge text data in the system database, and a medical knowledge graph is constructed;

[0084] The content requirement data is imported into the semantic analysis model for semantic analysis and entity data extraction to form requirement entity data;

[0085] In the system database, based on the requirement entity data, knowledge retrieval analysis is carried out in the medical knowledge graph, and knowledge recommendation data is generated based on the retrieval results;

[0086] In the map model, demand-based regional division is carried out for each terminal region through the interactive demand distribution data to obtain the multi-scenario demand distribution of each terminal region;

[0087] Taking one terminal region as the analysis unit, based on the multi-scenario demand distribution, the quantity of interactive demands is counted, sorted based on the quantities of different demands, and priorities are set based on the quantity sizes to form interactive operation priority information;

[0088] The page interaction layout information of the medical terminal is obtained, and based on the interactive operation priority information, the multi-level pages of the interaction layout information are updated and set. The interactive functions with higher interactive demand priorities are set in the higher-level page units to obtain the page setting scheme;

[0089] Interactive priority analysis is carried out for each terminal region to obtain the page setting scheme corresponding to each terminal region.

[0090] It should be noted that in the terminal area, a terminal area generally includes one terminal device, which is used to analyze the user interaction demand information existing in the terminal device area. Through area division, refined management of user demands and catering to the demands of the terminal are carried out. The medical knowledge text data is the knowledge data existing in the database, which is generally set by user input and includes knowledge recommendation data for various diseases, and is used to display to users through the terminal to improve users' understanding and prevention awareness of diseases. Traditional terminal knowledge display is often based on fixed content, while the present invention collects user data for a certain period of time and generates corresponding demand information, and then retrieves corresponding knowledge data through a knowledge graph. In the knowledge graph, generally, the disease name is entity data, the types, knowledge, prevention measures, examination means, etc. of the disease are attribute data, and the relationships and mutual influences between diseases are relationship data. In the interaction operation priority information, the higher the quantity, the higher the priority of the interaction demand. In the page setting scheme, due to the limited amount of information on the display page of the terminal device, it is often necessary to set multiple levels of pages, and users perform interaction operations through the jump of multiple levels of pages. In the present invention, through the analysis of the demand distribution and operation priority of users in the area, the interaction demands with higher demands are used as high-priority interaction functions and set on the first-level page, and the lower-priority ones are set on the second-level page or other pages, thereby greatly improving the efficiency of users using the terminal, and the scheme setting can be dynamically updated periodically or reset based on the demand distribution to adapt to the real-time changing terminal demands.

[0091] Through the present invention, it is possible to effectively achieve intelligent dynamic adjustment of the terminal in multiple scenarios, improve the user experience and the efficient operation of medical operations.

[0092] According to an embodiment of the present invention, it further includes:

[0093] Based on the page setting scheme and the knowledge content recommendation scheme, it is set as the first terminal display strategy;

[0094] In an analysis period, taking one terminal area as the analysis unit, through the map model, the N user mobile terminals closest to the corresponding medical terminal are marked, and the corresponding interaction demands of the marked user mobile terminals are extracted through the interaction demand distribution data to obtain the marked interaction demands;

[0095] Through the marked interaction demands, the priority analysis of the interaction demands is carried out, and a second page setting scheme is further generated;

[0096] Through a medical platform, based on the marked user mobile terminals, obtain disease diagnosis data of the marked users to get real-time diagnosis data, perform semantic analysis and medical keyword extraction on the real-time diagnosis data, retrieve recommended knowledge content from the medical knowledge graph in the system database based on the medical keywords, and generate a second knowledge content recommendation scheme;

[0097] Based on the second page setting scheme and the second knowledge content recommendation scheme, set it as the second terminal display strategy;

[0098] Statistical marked interaction requirements, obtain the types and quantities of interaction requirements, calculate the current medical treatment pressure value based on the two values, and the treatment pressure value is positively correlated with the two values;

[0099] Real-time judgment and analysis of the treatment pressure value. When the treatment pressure value is higher than the preset threshold, adopt the first terminal display strategy. When the treatment pressure value is lower than the preset threshold, adopt the second terminal display strategy.

[0100] It should be noted that each user corresponds to one type of interaction requirement. The quantity of interaction requirements is generally consistent with the current number of users, reflecting the number of users. The treatment pressure value is equal to the sum of the products of the type number of interaction requirements and the quantity of interaction requirements and the preset coefficients respectively. The formula is as follows:

[0101] P = K1×N1 + K2×N2

[0102] P is the treatment pressure value, K1 and K2 are preset coefficients, and N1 and N2 are the type number of interaction requirements and the quantity of interaction requirements.

[0103] It can be seen that the pressure value is positively correlated with the type number of requirements and the quantity of requirements, which reflects the complexity of the current terminal requirements. The larger this value is, the higher the complexity of the terminal requirements. Based on this value, compare with the preset threshold. When it is higher than a certain value, it means that the current terminal demand is large and the terminal requirements are relatively complex. Therefore, adopt the first strategy for terminal display to meet the demand situation of the user terminal through a scheme based on overall analysis. When it is lower than a certain value, the current terminal requirements are relatively simple and it also means that the number of users is small. At this time, adopt the second strategy to meet the real-time user requirements. Thus, effectively improve the operation efficiency of the medical terminal. The analysis process of the second knowledge content recommendation scheme and the second page setting scheme is the same as that of the scheme generated by the first analysis in this embodiment, but there are differences in the analysis data involved.

[0104] The marking of the nearest N user mobile terminals can be obtained through marking within a preset range in a certain area of the terminal device.

[0105] In the present invention, the first terminal display strategy tends to perform terminal display based on the overall demand distribution, and the second terminal display strategy caters to the terminal demands of users within a certain range according to the principle of proximity of terminal devices. The two strategies can meet the terminal demands in different scenarios. The present invention uses the medical treatment pressure value as the evaluation reference value for the current complex situation of the terminal. When the terminal demand is high, the first strategy is adopted. On the contrary, when the terminal demand is low, the second strategy is adopted, and a dynamic adjustment mode is implemented to improve the self - adaptability and interaction efficiency of the medical terminal.

[0106] Figure 2 The block diagram of a self - service terminal data acquisition system based on multiple scenarios according to the present invention is shown.

[0107] In the second aspect of the present invention, a self - service terminal data acquisition system 2 based on multiple scenarios is further provided. The system includes: a memory 21 and a processor 22. The memory includes a self - service terminal data acquisition program based on multiple scenarios. When the self - service terminal data acquisition program based on multiple scenarios is executed by the processor, the following steps are implemented:

[0108] Obtain the map basic information of the preset medical area, and build a map model based on three - dimensional visualization through the map basic information;

[0109] Within a preset time period, collect the disease diagnosis data of all users through the medical platform, perform semantic recognition and analysis based on the semantic analysis model through the disease diagnosis data, and extract medical keywords, and generate content demand data through the medical keywords;

[0110] Within an analysis period, collect the medical treatment data of users in real - time, analyze the medical treatment interaction requirements of users based on the medical treatment data, and generate the terminal interaction requirement information of users. Through the mobile terminal, collect the location information of users in real - time, and perform demand distribution statistical analysis based on the terminal interaction requirement information and the location information in the map model to form interaction demand distribution data;

[0111] In the map model, divide the area based on the locations of multiple medical terminals to form multiple terminal areas, perform knowledge data retrieval in the system database through the content demand data to form knowledge recommendation data, perform interaction demand analysis in multiple scenarios for each terminal area based on the interaction demand distribution data, and form interaction operation priority information. Generate a page setting scheme through the interaction operation priority information, and generate a knowledge content recommendation scheme for the medical terminal based on the knowledge recommendation data;

[0112] Send the page setting scheme and the knowledge content recommendation scheme to multiple medical terminals and set the display content.

[0113] It should be noted that the medical platform includes a medical data analysis platform and a database. The platform is connected to medical terminals through a cloud intranet, and through the medical platform, the terminal display data and data transmission can be controlled in real time.

[0114] Medical terminal devices are set at multiple positions in a preset medical area, which is determined by the actual situation. They can be used to interact with medical service users. The terminal can perform medical service contents such as content information recommendation display, medical appointment information query, medical appointment process interaction, drug query, and payment process interaction.

[0115] According to an embodiment of the present invention, the map basic information of the preset medical area is obtained, and a map model based on three-dimensional visualization is built through the map basic information. Specifically:

[0116] Obtain the map basic information of the preset medical area, where the map basic information includes the building layout, area, map contour, number and location information of terminal devices in the preset medical area;

[0117] Build a map model based on three-dimensional visualization through the map basic information.

[0118] It should be noted that the map model can display relevant dynamic data in real time. Through the map model, users can view the current distribution of terminals, the usage of terminal devices, and user location information, etc., enabling users to more intuitively view the current medical terminal service situation.

[0119] According to an embodiment of the present invention, within a preset time period, the disease diagnosis data of all users is collected through the medical platform, semantic recognition analysis based on a semantic analysis model is performed on the disease diagnosis data, and medical keywords are extracted. Content demand data is generated through the medical keywords. Specifically:

[0120] Within a preset time period, collect the disease diagnosis data of all users through the medical platform;

[0121] Perform data cleaning, standardization, and text format conversion processing on the disease diagnosis data to form diagnostic text data;

[0122] Build a semantic analysis model based on CNN, import the diagnostic text data into the semantic analysis model for semantic recognition analysis, and perform word segmentation processing on the diagnostic text data through a bag-of-words model, and build a vocabulary based on the segmented diagnostic text data;

[0123] Perform word frequency calculation and statistics through the vocabulary to form word frequency statistical data;

[0124] Select phrases with a word frequency higher than the preset word frequency from the word frequency statistical data to obtain medical keywords, and sort the medical keywords based on the word frequency size to form a keyword sorting table;

[0125] Based on the medical keyword and the keyword sorting table, content priority analysis is performed on each keyword, and similarity medical content analysis of the keyword is carried out to generate content requirement data.

[0126] It should be noted that each word in the vocabulary corresponds to a unique index. The content requirement data includes the keyword and the corresponding priority, and the medical content involved in the keyword (medical content similar to the keyword). The priority is divided based on the word frequency. The higher the priority of the keyword, the higher the corresponding content requirement. By forming the content requirement data, it is possible to retrieve medical knowledge content in the subsequent process and form knowledge data for display.

[0127] The keywords generally include corresponding symptom description words, disease names, historical conditions of corresponding bad habits, etc. For example, within a preset time period, just during the high-incidence period of seasonal influenza, the keywords generally correspond to information such as the symptoms, conditions, and causes of the influenza disease. When a pandemic breaks out, based on the periodic analysis of the platform, semantic data mining can be performed on the corresponding medical treatment data, the corresponding keywords can be recorded, and the recommended knowledge can be displayed on the terminal subsequently, improving the experience of medical users and having high practicality for medical services.

[0128] According to an embodiment of the present invention, within an analysis period, the medical treatment data of the user is collected in real time, the user's medical treatment interaction requirement analysis is performed based on the medical treatment data, and the terminal interaction requirement information of the user is generated. Through the mobile terminal, the location information of the user is collected in real time, and demand distribution statistical analysis is performed based on the terminal interaction requirement information and the location information in the map model to form interaction demand distribution data, specifically:

[0129] Within an analysis period, the medical treatment data of the user is collected in real time, the real-time process analysis of the user is performed based on the medical treatment data, and the medical treatment interaction requirement analysis is performed based on the user's current medical treatment process to generate interaction operation requirement information;

[0130] Based on the historical interaction time of the medical terminal, the interaction time of different medical treatment processes is statistically analyzed and the average time is calculated, and the average time is used as the interaction requirement time of different medical treatment processes;

[0131] The interaction operation requirement information and the interaction requirement time are integrated to form terminal interaction requirement information.

[0132] It should be noted that the interactive operation requirement information is specifically the requirement for the terminal operation process steps analyzed for the user in real time. For example, requirements operations such as registration operation, payment operation, drug query, report query, printing, etc. In the real-time analysis of the user's current medical treatment step process, the next step process is used as the requirement interactive operation. For example, when the current user is in the completed state of the registration process, the interactive requirement operation can be medical treatment payment.

[0133] According to an embodiment of the present invention, within an analysis period, the medical treatment data of the user is collected in real time, the user's medical treatment interactive requirement analysis is performed based on the medical treatment data, and the terminal interactive requirement information of the user is generated. Through the mobile terminal, the location information of the user is collected in real time. In the map model, the requirement distribution statistical analysis is performed based on the terminal interactive requirement information and the location information to form the interactive requirement distribution data, further including:

[0134] Establish a data connection with the user's mobile terminal within the preset medical area through the medical platform;

[0135] Collect the user's location information in real time from the user's mobile terminal through the medical platform;

[0136] Based on the terminal interactive requirement information and the user's location information, in the map model, the distribution statistics of different requirements for the terminal interactive requirements are performed to form the interactive requirement distribution data.

[0137] It should be noted that the interactive requirement distribution data is the display data based on the map model, specifically including the distribution status of different interactive requirements on the map. The present invention can more intuitively grasp the terminal requirements of the current user group in the map model through the binding analysis of the interactive requirement information and the user positioning, and analyze the requirement quantity and distribution through the user positioning. For example, within the medical treatment area or in the area near the medical treatment, there are multiple user mobile terminals (the corresponding user locations can be obtained). Through this terminal, their interactive requirements are respectively registration requirements, payment requirements, query requirements, etc., and the quantities of different requirements are different. Based on the analysis of the distribution status of the requirements and the locations, generally speaking, registration requirements, payment requirements, etc. are the main interactive requirements, with a large number of requirements and a relatively dense distribution. In subsequent analysis, the requirement area division can be performed for each terminal area through the interactive requirement distribution data to obtain the multi-scenario requirement distribution of each terminal area. Specifically, it is divided into analyzing the interactive requirement distribution situation of a terminal area through the interactive requirement distribution data (that is, only studying the requirement distribution status within this terminal area), extracting the corresponding distribution information, and performing scenario-based processing on different requirements to obtain the multi-scenario requirement distribution corresponding to a terminal area. The multi-scenario requirement distribution includes the quantity and distribution status of different interactive requirements within the terminal area, and one requirement corresponds to one scenario mode, such as the payment mode.

[0138] According to an embodiment of the present invention, in the map model, regional division is performed based on the positions of multiple medical terminals to form multiple terminal regions. Knowledge data retrieval is performed in the system database through content requirement data to form knowledge recommendation data. Based on the interactive requirement distribution data, interactive requirement analysis in multiple scenarios is performed for each terminal region respectively, and interactive operation priority information is formed. A page setting scheme is generated through the interactive operation priority information, and a knowledge content recommendation scheme is generated for the medical terminals based on the knowledge recommendation data. Specifically:

[0139] In the map model, regional division is performed based on the positions of multiple medical terminals to form multiple terminal regions;

[0140] Triple data extraction is performed based on the medical knowledge text data in the system database, and a medical knowledge graph is constructed;

[0141] The content requirement data is imported into the semantic analysis model for semantic analysis and entity data extraction to form requirement entity data;

[0142] In the system database, based on the requirement entity data, knowledge retrieval analysis is performed in the medical knowledge graph, and knowledge recommendation data is generated based on the retrieval results;

[0143] In the map model, requirement-based regional division is performed for each terminal region through the interactive requirement distribution data to obtain the multi-scenario requirement distribution of each terminal region;

[0144] Taking one terminal region as the analysis unit, based on the multi-scenario requirement distribution, the number of interactive requirements is counted, sorted based on the number of different requirements, and priorities are set based on the quantity size to form interactive operation priority information;

[0145] The page interaction layout information of the medical terminal is obtained, and based on the interactive operation priority information, the multi-level pages of the interaction layout information are updated and set. The interactive functions with higher interactive requirement priorities are set at higher-level page units to obtain a page setting scheme;

[0146] Interactive priority analysis is performed for each terminal region to obtain the page setting scheme corresponding to each terminal region.

[0147] It should be noted that in the terminal area, generally one terminal area includes one terminal device, which is used to analyze the user interaction demand information existing in the terminal device area. Through area division, refined management of user demands and catering to the demands of the terminal are carried out. The medical knowledge text data is the knowledge data existing in the database, which is generally set by user input and includes knowledge recommendation data for various diseases, and is used to be displayed to users through the terminal to improve users' understanding and prevention awareness of diseases. Traditional terminal knowledge display is often based on fixed content, while the present invention collects user data for a certain period of time, generates corresponding demand information, and then retrieves corresponding knowledge data through a knowledge graph. In the knowledge graph, generally the disease name is entity data, the disease type, knowledge, prevention measures, examination means, etc. are attribute data, and the relationships and mutual influences between diseases are relationship data. In the interaction operation priority information, the higher the quantity, the higher the priority of the interaction demand. In the page setting scheme, due to the limited amount of information on the display page of the terminal device, multiple-level pages often need to be set, and users perform interaction operations through the jump of multiple-level pages. In the present invention, through the analysis of the demand distribution and operation priority of users in the area, the demands with higher interaction demands are used as high-priority interaction functions and set on the first-level page, and the lower-priority ones are set on the second-level page or other pages, thereby greatly improving the efficiency of users using the terminal. Moreover, the scheme setting can be dynamically updated periodically or reset based on the demand distribution to adapt to the real-time changing terminal demands.

[0148] Through the present invention, intelligent dynamic adjustment of the terminal in multiple scenarios can be effectively realized, improving user experience and the efficient operation of medical operations.

[0149] The third aspect of the present invention also provides a computer-readable storage medium, which includes a self-service terminal data collection program based on multiple scenarios. When the self-service terminal data collection program based on multiple scenarios is executed by a processor, the steps of the self-service terminal data collection method based on multiple scenarios as described in any one of the above are realized.

[0150] The present invention discloses a self-service terminal data collection method, system and medium based on multiple scenarios. By constructing a three-dimensional map model to display a preset medical area, collecting disease diagnosis data, extracting medical keywords through semantic analysis to generate content demands, collecting real-time medical treatment data, combining location information, and counting the interaction demand distribution on the map. Dividing areas based on the location of medical terminals, retrieving knowledge data and making recommendations, and generating operation priorities and page setting schemes by combining interaction demand analysis. Finally, sending the page layout and knowledge recommendation scheme to the medical terminal to achieve personalized display and knowledge push. Through the present invention, intelligent dynamic adjustment of the terminal in multiple scenarios can be effectively realized, improving user experience and the efficient operation of medical operations.

[0151] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0152] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, in each embodiment of the present invention, the various functional units can all be integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0154] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0155] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0156] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. A self-service terminal data collection method based on multiple scenarios, characterized in that: include: Obtain basic map information of the preset medical area, and build a three-dimensional visualization-based map model based on the basic map information; Within a preset time period, the disease diagnosis data of all users are collected through the medical platform, semantic recognition analysis based on the semantic analysis model is performed on the disease diagnosis data to extract medical keywords, and content demand data is generated through the medical keywords; In one analysis cycle, the user's medical data is collected in real time, and the user's medical interaction needs are analyzed based on the medical data, and the user's terminal interaction demand information is generated. The user's location information is collected in real time through the mobile terminal, and the demand distribution statistics analysis is performed on the map model based on the terminal interaction demand information and location information to form interaction demand distribution data; In the map model, the regions are divided based on the locations of multiple medical terminals to form multiple terminal regions. Knowledge data is retrieved in the system database through content demand data to form knowledge recommendation data. Based on the interactive demand distribution data, the interactive demand analysis under multiple scenarios is performed for each terminal region, and interactive operation priority information is formed. The page setting plan is generated through the interactive operation priority information, and the knowledge content recommendation plan is generated for the medical terminal based on the knowledge recommendation data. Send to multiple medical terminals and set the display content through page setting scheme and knowledge content recommendation scheme; Among them, in the map model, the area is divided based on the locations of multiple medical terminals to form multiple terminal areas, knowledge data is retrieved in the system database through content demand data to form knowledge recommendation data, and based on the interactive demand distribution data, interactive demand analysis is performed in multiple scenarios for each terminal area, and interactive operation priority information is formed, and a page setting plan is generated through the interactive operation priority information, and a knowledge content recommendation plan is generated for the medical terminal based on the knowledge recommendation data, specifically: In the map model, the regions are divided based on the locations of multiple medical terminals to form multiple terminal regions; Extract triple data based on medical knowledge text data in the system database and construct a medical knowledge graph; Import content demand data into the semantic analysis model for semantic analysis and entity data extraction to form demand entity data; In the system database, based on the demand entity data, knowledge retrieval analysis is performed in the medical knowledge graph, and knowledge recommendation data is generated based on the retrieval results; In the map model, each terminal area is divided into demand-based areas through interactive demand distribution data to obtain multi-scenario demand distribution for each terminal area; Taking a terminal area as the analysis unit, the interactive demands are counted based on the distribution of multi-scenario demands, sorted based on the quantity of different demands, and set priorities based on the quantity to form interactive operation priority information; Obtaining the page interaction arrangement information of the medical terminal, and updating and setting the multi-level pages of the interaction arrangement information based on the interaction operation priority information, setting the interaction functions with higher interaction demand priority in the higher-level page units, and obtaining the page setting scheme; An interaction priority analysis is performed on each terminal area to obtain a page setting solution corresponding to each terminal area.

2. The self-service terminal data collection method based on multiple scenarios according to claim 1 is characterized in that: The obtaining of basic map information of the preset medical area and building a three-dimensional visualized map model based on the basic map information are specifically as follows: Obtaining basic map information of the preset medical area, wherein the basic map information includes the building layout, area, map outline, number and location information of terminal devices of the preset medical area; Construct a map model based on three-dimensional visualization through basic map information.

3. The self-service terminal data collection method based on multiple scenarios according to claim 2 is characterized in that: In a preset time period, the disease diagnosis data of all users are collected through the medical platform, semantic recognition analysis based on the semantic analysis model is performed on the disease diagnosis data to extract medical keywords, and content demand data is generated through the medical keywords, specifically: Collect disease diagnosis data of all users through the medical platform within a preset time period; Clean, standardize and convert disease diagnosis data into text format to form diagnostic text data; Construct a semantic analysis model based on CNN, import the diagnostic text data into the semantic analysis model for semantic recognition analysis, and use the bag-of-words model to segment the diagnostic text data, and build a vocabulary based on the segmented diagnostic text data; Calculate and count word frequencies through vocabulary lists and form word frequency statistics; Filter out phrases with a higher frequency than a preset frequency from the word frequency statistics to obtain medical keywords, and sort the medical keywords based on the frequency to form a keyword sorting table; Based on medical keywords and keyword ranking tables, content priority analysis and keyword similarity medical content analysis are performed on each keyword to generate content demand data.

4. The self-service terminal data collection method based on multiple scenarios according to claim 3 is characterized in that: In one analysis cycle, the user's medical data is collected in real time, and the user's medical interaction needs are analyzed based on the medical data, and the user's terminal interaction demand information is generated. The user's location information is collected in real time through the mobile terminal, and the demand distribution statistics analysis is performed on the map model based on the terminal interaction demand information and location information to form the interaction demand distribution data, which is specifically: In one analysis cycle, the user's medical data is collected in real time, and the user's real-time process analysis is performed based on the medical data. The medical interaction demand analysis is performed based on the user's current medical process to generate interactive operation demand information; Based on the historical interaction time of medical terminals, the interaction time of different medical processes is counted and the average time is calculated, and the average time is used as the interaction requirement time of different medical processes; The interactive operation demand information and the interactive demand time are integrated to form the terminal interaction demand information.

5. The self-service terminal data collection method based on multiple scenarios according to claim 4 is characterized in that: In one analysis cycle, the user's medical data is collected in real time, the user's medical interaction needs are analyzed based on the medical data, and the user's terminal interaction demand information is generated. The user's location information is collected in real time through the mobile terminal, and the demand distribution statistics analysis is performed on the map model based on the terminal interaction demand information and the location information to form the interaction demand distribution data, which also includes: Establishing a data connection with a user's mobile terminal in a preset medical area through the medical platform; Collect user location information from user mobile terminals in real time through the medical platform; Based on the terminal interaction demand information and user location information, the distribution statistics of different terminal interaction demands are performed in the map model to form interaction demand distribution data.

6. A self-service terminal data collection system based on multiple scenarios, characterized in that: The system includes: a memory and a processor, wherein the memory includes a self-service terminal data collection program based on multiple scenarios, and when the self-service terminal data collection program based on multiple scenarios is executed by the processor, the following steps are implemented: Obtain basic map information of the preset medical area, and build a three-dimensional visualization-based map model based on the basic map information; Within a preset time period, the disease diagnosis data of all users are collected through the medical platform, semantic recognition analysis based on the semantic analysis model is performed on the disease diagnosis data to extract medical keywords, and content demand data is generated through the medical keywords; In one analysis cycle, the user's medical data is collected in real time, and the user's medical interaction needs are analyzed based on the medical data, and the user's terminal interaction demand information is generated. The user's location information is collected in real time through the mobile terminal, and the demand distribution statistics analysis is performed on the map model based on the terminal interaction demand information and location information to form interaction demand distribution data; In the map model, the regions are divided based on the locations of multiple medical terminals to form multiple terminal regions. Knowledge data is retrieved in the system database through content demand data to form knowledge recommendation data. Based on the interactive demand distribution data, the interactive demand analysis under multiple scenarios is performed for each terminal region, and interactive operation priority information is formed. The page setting plan is generated through the interactive operation priority information, and the knowledge content recommendation plan is generated for the medical terminal based on the knowledge recommendation data. Send to multiple medical terminals and set the display content through page setting scheme and knowledge content recommendation scheme; Among them, in the map model, the area is divided based on the locations of multiple medical terminals to form multiple terminal areas, knowledge data is retrieved in the system database through content demand data to form knowledge recommendation data, and based on the interactive demand distribution data, interactive demand analysis is performed in multiple scenarios for each terminal area, and interactive operation priority information is formed, and a page setting plan is generated through the interactive operation priority information, and a knowledge content recommendation plan is generated for the medical terminal based on the knowledge recommendation data, specifically: In the map model, the regions are divided based on the locations of multiple medical terminals to form multiple terminal regions; Extract triple data based on medical knowledge text data in the system database and construct a medical knowledge graph; Import content demand data into the semantic analysis model for semantic analysis and entity data extraction to form demand entity data; In the system database, based on the demand entity data, knowledge retrieval analysis is performed in the medical knowledge graph, and knowledge recommendation data is generated based on the retrieval results; In the map model, each terminal area is divided into demand-based areas through interactive demand distribution data to obtain multi-scenario demand distribution for each terminal area; Taking a terminal area as the analysis unit, the interactive demands are counted based on the distribution of multi-scenario demands, sorted based on the quantity of different demands, and set priorities based on the quantity to form interactive operation priority information; Obtaining the page interaction arrangement information of the medical terminal, and updating and setting the multi-level pages of the interaction arrangement information based on the interaction operation priority information, setting the interaction functions with higher interaction demand priority in the higher-level page units, and obtaining the page setting scheme; An interaction priority analysis is performed on each terminal area to obtain a page setting solution corresponding to each terminal area.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a self-service terminal data collection program based on multiple scenarios. When the self-service terminal data collection program based on multiple scenarios is executed by the processor, the steps of the self-service terminal data collection method based on multiple scenarios as described in any one of claims 1 to 5 are implemented.

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