A terminal intelligent configuration method, system and medium based on medical services

Abstract

The present invention discloses a terminal intelligent configuration method, system and medium based on medical services. A three-dimensional visual map model is constructed based on the spatial layout information of medical units. The user mobile terminal and historical video data are collected through the medical platform, the user movement path is analyzed and the historical path data is generated. The medical unit area is divided into M sub-areas, and the high-frequency sub-areas are screened out according to the historical path data. The ant colony algorithm is used to simulate the movement of user groups, the starting point and the end point are set to initialize the corresponding pheromone, and the predicted path is obtained after the cyclic simulation. Finally, the terminal distribution and configuration are optimized according to the predicted path to form a terminal configuration plan.

Description

Technical Field

[0001] The present invention relates to the field of medical service analysis, and more specifically, to a terminal intelligent configuration method, system and medium based on medical services. Background Art

[0002] In modern medical services, terminal devices such as self-service registration machines, medicine machines, and information inquiry machines play an important role in improving service efficiency and optimizing patient experience. However, how to reasonably deploy these terminal devices to meet medical service needs has become an urgent problem to be solved. Traditional terminal device configuration methods are often based on experience or simple statistics, lacking scientificity and flexibility.

[0003] In addition, in the existing technology, there is a lack of effective distribution analysis and effective configuration solutions for medical terminal devices, and the location, distribution and other configurations of medical terminals will affect the efficiency and experience of medical services. Therefore, how to effectively configure and optimize terminals is an important issue for improving the efficiency and quality of information-based medical services. Summary of the invention

[0004] The present invention overcomes the defects of the prior art and proposes a terminal intelligent configuration method, system and medium based on medical services.

[0005] A first aspect of the present invention provides a terminal intelligent configuration method based on medical services, comprising:

[0006] Acquire spatial layout information of a preset medical unit, and construct a three-dimensional visualization-based map model through the spatial layout information;

[0007] Within a preset time period, the medical platform collects user mobile terminal data and historical video data, analyzes the user's movement path, and generates user historical movement path data in combination with the map model;

[0008] Based on the map model, the preset medical unit area is divided into M sub-areas. According to the user's historical mobile path data, the route area statistics are performed in the map model to screen out high-frequency sub-areas from the M sub-areas.

[0009] Based on the ant colony algorithm, the user crowd movement simulation is carried out from the map model. The simulation process uses the sub-area as the movement path, sets the starting and end areas based on the spatial layout information, and initializes the pheromone based on the distribution of the high-frequency sub-area. The final movement path is formed through multiple cycles of simulation, and the final movement path is recorded to obtain the predicted path of the user crowd.

[0010] The terminal distribution and configuration are optimized and analyzed through the user population prediction path, and the terminal configuration plan is generated in combination with the map model.

[0011] In this solution, the spatial layout information of the preset medical unit is obtained, and a map model based on three-dimensional visualization is constructed through the spatial layout information, which has the following features:

[0012] Obtaining spatial layout information of a preset medical unit, the spatial layout information including distribution of departments, distribution of consultation areas, user movable areas, staff movable areas, equipment distribution information on each floor, area of ​​the preset medical unit, and area outline information;

[0013] A map model based on three-dimensional visualization is constructed through the spatial layout information.

[0014] In this solution, within a preset time period, the medical platform collects user mobile terminal data and historical video data, analyzes the user's movement path, and generates user historical movement path data in combination with the map model, specifically:

[0015] Collect user mobile terminal data through the medical platform within a preset time period;

[0016] Extract location data and medical information based on user mobile terminal data;

[0017] Analyze the user's mobile path based on the positioning data and the medical information, record the path in combination with the map model, and generate first mobile path data;

[0018] In a preset time period, historical video data is obtained through the medical platform, and related frames are extracted based on the historical video data to obtain a corresponding image set. The image set is used for user identification and user movement tracking through an image recognition module, and user positioning analysis is performed in combination with a map model to generate second movement path data;

[0019] The first movement path data and the second movement path data are integrated to obtain the user's historical movement path data.

[0020] In this solution, the preset medical unit area is divided into M sub-areas based on the map model, and the route area statistics are performed in the map model according to the user's historical mobile path data, and high-frequency sub-areas are screened out from the M sub-areas, specifically:

[0021] Divide the preset medical unit area into M sub-areas based on the map model;

[0022] Generate multiple route data based on the user's historical mobile path data, combine with the map model, simulate the user's movement on the multiple route data, and count the number of passes, frequency, and duration of each sub-area to obtain sub-area statistical data;

[0023] Based on the preset threshold standard, high-frequency areas are screened according to sub-area statistical data to obtain high-frequency sub-areas, and the remaining sub-areas are marked as low-frequency sub-areas.

[0024] In this solution, based on the ant colony algorithm, the user crowd movement simulation is performed from the map model. The simulation process uses the sub-area as the movement path, sets the starting and end areas based on the spatial layout information, and initializes the pheromone based on the distribution of the high-frequency sub-area. The final movement path is formed through multiple cycles of simulation, and the final movement path is recorded to obtain the user crowd predicted path, which is specifically:

[0025] Obtain user quantity change data through the medical platform within a preset time period, perform quantity change analysis based on linear regression based on the user quantity change data, perform linear fitting using periodic quantity values ​​as change data, and predict the number of users in the next preset period based on the fitting equation, and obtain the predicted number of users N;

[0026] Based on the user's historical movement path data, the starting and ending locations of all paths are recorded and counted to generate the starting and ending point information;

[0027] Based on the ant colony algorithm, the user population is simulated and analyzed from the map model. During the simulation, each sub-area is used as a mobile unit. The corresponding starting and ending positions are set based on the starting and ending information. The number of ants at the starting position is set to N. A preset pheromone is assigned to each mobile unit. If the mobile unit corresponds to a high-frequency sub-area, the first initial pheromone is assigned. If the mobile unit corresponds to a low-frequency sub-area, the second initial pheromone is assigned. The ant path is traversed according to the pheromone and heuristic information. Each ant starts from the starting point and updates the pheromones of all mobile units each time a traversal is completed until the preset number of traversals are completed.

[0028] The last K paths traversed are recorded to form the predicted path of the user population.

[0029] In this solution, the terminal distribution and configuration are optimized and analyzed by predicting the user population path, and the terminal configuration solution is generated in combination with the map model, specifically:

[0030] The shortest path is selected from the predicted paths of the user group as the optimal path, and the feasible area of ​​the equipment is marked through the map model and spatial layout information;

[0031] Terminal distribution analysis is performed through user group prediction paths, and terminal distribution information is generated in combination with the feasible area of ​​the device;

[0032] Based on the optimal path and terminal distribution information, the terminal equipment is analyzed for specific quantity and location, and a terminal configuration plan is generated.

[0033] The second aspect of the present invention further provides a terminal intelligent configuration system based on medical services, the system comprising: a memory, a processor, the memory comprising a terminal intelligent configuration program based on medical services, the terminal intelligent configuration program based on medical services being executed by the processor to implement the following steps:

[0034] Acquire spatial layout information of a preset medical unit, and construct a three-dimensional visualization-based map model through the spatial layout information;

[0035] Within a preset time period, the medical platform collects user mobile terminal data and historical video data, analyzes the user's movement path, and generates user historical movement path data in combination with the map model;

[0036] Based on the map model, the preset medical unit area is divided into M sub-areas. According to the user's historical mobile path data, the route area statistics are performed in the map model to screen out high-frequency sub-areas from the M sub-areas.

[0037] Based on the ant colony algorithm, the user crowd movement simulation is carried out from the map model. The simulation process uses the sub-area as the movement path, sets the starting and end areas based on the spatial layout information, and initializes the pheromone based on the distribution of the high-frequency sub-area. The final movement path is formed through multiple cycles of simulation, and the final movement path is recorded to obtain the predicted path of the user crowd.

[0038] The terminal distribution and configuration are optimized and analyzed through the user population prediction path, and the terminal configuration plan is generated in combination with the map model.

[0039] The third aspect of the present invention also provides a computer-readable storage medium, which includes a terminal intelligent configuration program based on medical services. When the terminal intelligent configuration program based on medical services is executed by a processor, the steps of the terminal intelligent configuration method based on medical services as described in any one of the above items are implemented.

[0040] The present invention discloses a terminal intelligent configuration method, system and medium based on medical services. A three-dimensional visual map model is constructed based on the spatial layout information of medical units. The user mobile terminal and historical video data are collected through the medical platform, the user movement path is analyzed and the historical path data is generated. The medical unit area is divided into M sub-areas, and the high-frequency sub-areas are screened out according to the historical path data. The ant colony algorithm is used to simulate the movement of user groups, the starting point and the end point are set to initialize the corresponding pheromone, and the predicted path is obtained after the cyclic simulation. Finally, the terminal distribution and configuration are optimized according to the predicted path to form a terminal configuration plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of a terminal intelligent configuration method based on medical services of the present invention is shown;

[0042] Figure 2 A block diagram of a terminal intelligent configuration system based on medical services of the present invention is shown. DETAILED DESCRIPTION

[0043] 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.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0045] Figure 1 A flow chart of a terminal intelligent configuration method based on medical services of the present invention is shown.

[0046] like Figure 1 As shown, the first aspect of the present invention provides a terminal intelligent configuration method based on medical services, comprising:

[0047] S102, obtaining spatial layout information of a preset medical unit, and constructing a three-dimensional visualization-based map model according to the spatial layout information;

[0048] S104, within a preset time period, collecting user mobile terminal data and historical video data through the medical platform, analyzing the user's movement path, and generating user historical movement path data in combination with a map model;

[0049] S106, dividing the preset medical unit area into M sub-areas based on the map model, performing route area statistics in the map model according to the user's historical movement path data, and screening out high-frequency sub-areas from the M sub-areas;

[0050] S108, based on the ant colony algorithm, simulate the movement of the user group from the map model. In the simulation process, the sub-area is used as the movement path, the starting and ending areas are set based on the spatial layout information, and the pheromone is initialized based on the distribution of the high-frequency sub-area. The final movement path is formed through multiple cycles of simulation, and the final movement path is recorded to obtain the predicted path of the user group;

[0051] S110, optimizing and analyzing the terminal distribution and configuration through the user group prediction path, and generating a terminal configuration plan in combination with the map model.

[0052] It should be noted that the preset time period is generally a relatively long period, such as several months to a year, which is used to collect enough path data of patients in medical institutions, and then conduct simulation analysis of patient paths. Medical users are generally patients. The user's historical movement path data includes information such as the starting point, terminal, and passage area.

[0053] According to an embodiment of the present invention, the spatial layout information of the preset medical unit is obtained, and a map model based on three-dimensional visualization is constructed by using the spatial layout information, which has the following features:

[0054] Obtaining spatial layout information of a preset medical unit, the spatial layout information including distribution of departments, distribution of consultation areas, user movable areas, staff movable areas, equipment distribution information on each floor, area of ​​the preset medical unit, and area outline information;

[0055] A map model based on three-dimensional visualization is constructed through the spatial layout information.

[0056] It should be noted that the map model includes information such as various department areas and treatment areas. Through the map model, effective visualization analysis of medical units can be carried out.

[0057] According to an embodiment of the present invention, within a preset time period, the user's mobile terminal data and historical video data are collected through the medical platform, the user's movement path is analyzed, and the user's historical movement path data is generated in combination with the map model, specifically:

[0058] Collect user mobile terminal data through the medical platform within a preset time period;

[0059] Extract location data and medical information based on user mobile terminal data;

[0060] Analyze the user's mobile path based on the positioning data and the medical information, record the path in combination with the map model, and generate first mobile path data;

[0061] In a preset time period, historical video data is obtained through the medical platform, and related frames are extracted based on the historical video data to obtain a corresponding image set. The image set is used for user identification and user movement tracking through an image recognition module, and user positioning analysis is performed in combination with a map model to generate second movement path data;

[0062] The first movement path data and the second movement path data are integrated to obtain the user's historical movement path data.

[0063] It should be noted that the medical platform is a medical system platform that interacts with user mobile terminals in real time based on the Internet. The historical video data is acquired based on the preset camera device of the medical unit, and the data is stored and analyzed through the medical platform. The image recognition module can adopt a recognition model based on deep learning, learn through image data such as user portraits, and perform real-time recognition and analysis. In an embodiment of the present invention, the walking path of the user in the historical time period is recorded and collected through the two dimensions of positioning analysis and video analysis of the mobile terminal to form a corresponding historical moving path, and the collected path data is more comprehensive, and through the historical path analysis, the corresponding high-frequency areas are subsequently marked. The positioning analysis of the mobile terminal includes the GPS positioning information of the user's mobile terminal, etc.

[0064] According to an embodiment of the present invention, the preset medical unit area is divided into M sub-areas based on the map model, and the route area statistics are performed in the map model according to the user's historical mobile path data, and high-frequency sub-areas are screened out from the M sub-areas, specifically:

[0065] Divide the preset medical unit area into M sub-areas based on the map model;

[0066] Generate multiple route data based on the user's historical mobile path data, combine with the map model, simulate the user's movement on the multiple route data, and count the number of passes, frequency, and duration of each sub-area to obtain sub-area statistical data;

[0067] Based on the preset threshold standard, high-frequency areas are screened according to sub-area statistical data to obtain high-frequency sub-areas, and the remaining sub-areas are marked as low-frequency sub-areas.

[0068] It should be noted that the M sub-areas are the smallest unit areas where users can move, and are used to study the choice of user movement paths. The path frequency is the average frequency within a preset unit time. The preset threshold standard is generally based on a predetermined frequency value or a predetermined threshold of other statistical data types for comparison and screening. The obtained high-frequency sub-area is an area where people frequently enter and exit. Screening out this area can provide data support for subsequent scientific and efficient terminal configuration.

[0069] According to an embodiment of the present invention, based on the ant colony algorithm, a user crowd movement simulation is performed from a map model. The simulation process uses sub-areas as movement paths, sets the starting and end areas based on spatial layout information, initializes pheromones based on the distribution of high-frequency sub-areas, forms a final movement path through multiple cycles of simulation, records the final movement path, and obtains a predicted path for the user crowd, specifically:

[0070] Obtain user quantity change data through the medical platform within a preset time period, perform quantity change analysis based on linear regression based on the user quantity change data, perform linear fitting using periodic quantity values ​​as change data, and predict the number of users in the next preset period based on the fitting equation, and obtain the predicted number of users N;

[0071] Based on the user's historical movement path data, the starting and ending locations of all paths are recorded and counted to generate the starting and ending point information;

[0072] Based on the ant colony algorithm, the user population is simulated and analyzed from the map model. During the simulation, each sub-area is used as a mobile unit. The corresponding starting and ending positions are set based on the starting and ending information. The number of ants at the starting position is set to N. A preset pheromone is assigned to each mobile unit. If the mobile unit corresponds to a high-frequency sub-area, the first initial pheromone is assigned. If the mobile unit corresponds to a low-frequency sub-area, the second initial pheromone is assigned. The ant path is traversed according to the pheromone and heuristic information. Each ant starts from the starting point and updates the pheromones of all mobile units each time a traversal is completed until the preset number of traversals are completed.

[0073] The last K paths traversed are recorded to form the predicted path of the user population.

[0074] It should be noted that the user quantity change data is obtained by analyzing the change data under multiple preset periods. A preset time period includes multiple preset periods, and the corresponding user quantity change data is obtained by periodic user quantity statistical analysis. The first and second initial pheromones are preset pheromones, and different pheromone values ​​are assigned to simulate the selection of people in different regions. The first initial pheromone is greater than the second initial pheromone.

[0075] In the present invention, an improved ant colony algorithm is used to simulate the mobile path of the user group in the medical service, and the initial pheromone distribution is performed by judging the high and low frequency areas, so as to more realistically reflect the path selection of the user group in the path simulation, obtain an accurate predicted path, and predict the initial population based on the population change in the historical time period, and use the predicted number as the initial population of the simulation process to more realistically reflect the user population. In addition, in the last traversal of the simulation process, multiple result paths obtained by the last simulation are selected as the predicted path, and K is a preset value, which can be equal to N.

[0076] By applying the improved ant colony algorithm of the present invention to the path prediction of user groups, firstly, it is possible to simulate and predict the path direction of user groups within a preset period, and then conduct precise user terminal demand analysis, and provide a data basis for adjustment analysis of the medical unit's medical treatment process.

[0077] Second, the ant colony algorithm can be used to predict the optimal path (screening in the predicted path). The optimal path can effectively reflect the optimal ideal path that exists when users receive medical services in medical institutions. Based on the optimal ideal path, targeted location configuration of medical terminal equipment can be performed, which can make the movement path of the user group deviate from the ideal path, thereby improving the efficiency and quality of medical services.

[0078] According to an embodiment of the present invention, the terminal distribution and configuration are optimized and analyzed by predicting the user population path, and a terminal configuration plan is generated in combination with a map model, specifically:

[0079] The shortest path is selected from the predicted paths of the user group as the optimal path, and the feasible area of ​​the equipment is marked through the map model and spatial layout information;

[0080] Terminal distribution analysis is performed through user group prediction paths, and terminal distribution information is generated in combination with the feasible area of ​​the device;

[0081] Based on the optimal path and terminal distribution information, the terminal equipment is analyzed for specific quantity and location, and a terminal configuration plan is generated.

[0082] It should be noted that the user population prediction path can effectively reflect the user's mobility trend when receiving medical services. Analyzing the corresponding mobility trend and mobility area can obtain the user's terminal demand distribution, and then analyze the terminal distribution data that matches the mobility trend. The terminal distribution information is a roughly optimized distribution information that meets the personalized terminal needs of the target medical unit. The device feasible area represents the area range that the device is allowed to set.

[0083] According to an embodiment of the present invention, it also includes:

[0084] Configure terminal equipment in the medical area based on the terminal configuration plan;

[0085] In a preset time period, the user's mobile terminal data and real-time video data are collected in real time, and the user's location is analyzed in real time, and the frequency of the user passing through each terminal device is counted to obtain the user's terminal frequency distribution information;

[0086] Based on the medical platform, the usage frequency information of each terminal device is collected and combined with the map model to obtain the usage frequency distribution information;

[0087] Setting target medical service areas;

[0088] Based on the target medical service area, frequency difference analysis of corresponding terminal devices is performed from user path terminal frequency distribution information and usage frequency distribution information. Based on the analysis results, effectiveness evaluation of different terminal devices is performed, and terminal configuration evaluation results are generated;

[0089] Based on the terminal configuration evaluation results, the existing terminal configuration is optimized and adjusted, and based on the effectiveness of different terminal devices, the recommended data for different terminals is displayed in a differentiated manner.

[0090] It should be noted that the preset time period is a time period after the terminal configuration scheme is implemented. In the analysis results, the smaller the frequency difference, the more the configuration result is in line with expectations. It is worth mentioning that the prior art often relies on manual experience analysis and evaluation for the rationality and effectiveness of existing terminal configuration information, and lacks scientific and effective evaluation means. In the terminal configuration scheme of the present invention, the use analysis of the terminal configuration is performed based on regionality. For example, through the terminal configuration scheme, the comprehensive use of its terminal equipment in a certain medical service area is analyzed, and the effectiveness evaluation is further performed based on the relevant differences (differences in usage frequency and path frequency), so as to realize the effectiveness evaluation and configuration optimization adjustment of the terminal configuration. Furthermore, it is possible to realize differentiated recommendation of recommended data for the terminal based on the evaluation results. For example, the core content of medical services is recommended for terminal devices with higher effectiveness and higher utilization rate, and advertising data is recommended for terminal devices with lower effectiveness, so as to comprehensively utilize the terminal configuration and improve the benefits of medical services while ensuring user experience.

[0091] The target medical service area is a certain area in a medical unit.

[0092] Figure 2 A block diagram of a terminal intelligent configuration system based on medical services of the present invention is shown.

[0093] The second aspect of the present invention further provides a terminal intelligent configuration system 2 based on medical services, the system comprising: a memory 21, a processor 22, the memory comprising a terminal intelligent configuration program based on medical services, the terminal intelligent configuration program based on medical services when executed by the processor implements the following steps:

[0094] Acquire spatial layout information of a preset medical unit, and construct a three-dimensional visualization-based map model through the spatial layout information;

[0095] Within a preset time period, the medical platform collects user mobile terminal data and historical video data, analyzes the user's movement path, and generates user historical movement path data in combination with the map model;

[0096] Based on the map model, the preset medical unit area is divided into M sub-areas. According to the user's historical mobile path data, the route area statistics are performed in the map model to screen out high-frequency sub-areas from the M sub-areas.

[0097] Based on the ant colony algorithm, the user crowd movement simulation is carried out from the map model. The simulation process uses the sub-area as the movement path, sets the starting and end areas based on the spatial layout information, and initializes the pheromone based on the distribution of the high-frequency sub-area. The final movement path is formed through multiple cycles of simulation, and the final movement path is recorded to obtain the predicted path of the user crowd.

[0098] The terminal distribution and configuration are optimized and analyzed through the user population prediction path, and the terminal configuration plan is generated in combination with the map model.

[0099] It should be noted that the preset time period is generally a relatively long period, such as several months to a year, which is used to collect enough path data of patients in medical institutions, and then conduct simulation analysis of patient paths. Medical users are generally patients. The user's historical movement path data includes information such as the starting point, terminal, and passage area.

[0100] According to an embodiment of the present invention, the spatial layout information of the preset medical unit is obtained, and a map model based on three-dimensional visualization is constructed by using the spatial layout information, which has the following features:

[0101] Obtaining spatial layout information of a preset medical unit, the spatial layout information including distribution of departments, distribution of consultation areas, user movable areas, staff movable areas, equipment distribution information on each floor, area of ​​the preset medical unit, and area outline information;

[0102] A map model based on three-dimensional visualization is constructed through the spatial layout information.

[0103] It should be noted that the map model includes information such as various department areas and treatment areas. Through the map model, effective visualization analysis of medical units can be carried out.

[0104] According to an embodiment of the present invention, within a preset time period, the user's mobile terminal data and historical video data are collected through the medical platform, the user's movement path is analyzed, and the user's historical movement path data is generated in combination with the map model, specifically:

[0105] Collect user mobile terminal data through the medical platform within a preset time period;

[0106] Extract location data and medical information based on user mobile terminal data;

[0107] Analyze the user's mobile path based on the positioning data and the medical information, record the path in combination with the map model, and generate first mobile path data;

[0108] In a preset time period, historical video data is obtained through the medical platform, and related frames are extracted based on the historical video data to obtain a corresponding image set. The image set is used for user identification and user movement tracking through an image recognition module, and user positioning analysis is performed in combination with a map model to generate second movement path data;

[0109] The first movement path data and the second movement path data are integrated to obtain the user's historical movement path data.

[0110] It should be noted that the medical platform is a medical system platform that interacts with user mobile terminals in real time based on the Internet. The historical video data is acquired based on the preset camera device of the medical unit, and the data is stored and analyzed through the medical platform. The image recognition module can adopt a recognition model based on deep learning, learn through image data such as user portraits, and perform real-time recognition and analysis. In an embodiment of the present invention, the walking path of the user in the historical time period is recorded and collected through the two dimensions of positioning analysis and video analysis of the mobile terminal to form a corresponding historical moving path, and the collected path data is more comprehensive, and through the historical path analysis, the corresponding high-frequency areas are subsequently marked. The positioning analysis of the mobile terminal includes the GPS positioning information of the user's mobile terminal, etc.

[0111] According to an embodiment of the present invention, the preset medical unit area is divided into M sub-areas based on the map model, and the route area statistics are performed in the map model according to the user's historical mobile path data, and high-frequency sub-areas are screened out from the M sub-areas, specifically:

[0112] Divide the preset medical unit area into M sub-areas based on the map model;

[0113] Generate multiple route data based on the user's historical mobile path data, combine with the map model, simulate the user's movement on the multiple route data, and count the number of passes, frequency, and duration of each sub-area to obtain sub-area statistical data;

[0114] Based on the preset threshold standard, high-frequency areas are screened according to sub-area statistical data to obtain high-frequency sub-areas, and the remaining sub-areas are marked as low-frequency sub-areas.

[0115] It should be noted that the M sub-areas are the smallest unit areas where users can move, and are used to study the choice of user movement paths. The path frequency is the average frequency within a preset unit time. The preset threshold standard is generally based on a predetermined frequency value or a predetermined threshold of other statistical data types for comparison and screening. The obtained high-frequency sub-area is an area where people frequently enter and exit. Screening out this area can provide data support for subsequent scientific and efficient terminal configuration.

[0116] According to an embodiment of the present invention, based on the ant colony algorithm, a user crowd movement simulation is performed from a map model. The simulation process uses sub-areas as movement paths, sets the starting and end areas based on spatial layout information, initializes pheromones based on the distribution of high-frequency sub-areas, forms a final movement path through multiple cycles of simulation, records the final movement path, and obtains a predicted path for the user crowd, specifically:

[0117] Obtain user quantity change data through the medical platform within a preset time period, perform quantity change analysis based on linear regression based on the user quantity change data, perform linear fitting using periodic quantity values ​​as change data, and predict the number of users in the next preset period based on the fitting equation, and obtain the predicted number of users N;

[0118] Based on the user's historical movement path data, the starting and ending locations of all paths are recorded and counted to generate the starting and ending point information;

[0119] Based on the ant colony algorithm, the user population is simulated and analyzed from the map model. During the simulation, each sub-area is used as a mobile unit. The corresponding starting and ending positions are set based on the starting and ending information. The number of ants at the starting position is set to N. A preset pheromone is assigned to each mobile unit. If the mobile unit corresponds to a high-frequency sub-area, the first initial pheromone is assigned. If the mobile unit corresponds to a low-frequency sub-area, the second initial pheromone is assigned. The ant path is traversed according to the pheromone and heuristic information. Each ant starts from the starting point and updates the pheromones of all mobile units each time a traversal is completed until the preset number of traversals are completed.

[0120] The last K paths traversed are recorded to form the predicted path of the user population.

[0121] It should be noted that the user quantity change data is obtained by analyzing the change data under multiple preset periods. A preset time period includes multiple preset periods, and the corresponding user quantity change data is obtained by periodic user quantity statistical analysis. The first and second initial pheromones are preset pheromones, and different pheromone values ​​are assigned to simulate the selection of people in different regions. The first initial pheromone is greater than the second initial pheromone.

[0122] In the present invention, an improved ant colony algorithm is used to simulate the mobile path of the user group in the medical service, and the initial pheromone distribution is performed by judging the high and low frequency areas, so as to more realistically reflect the path selection of the user group in the path simulation, obtain an accurate predicted path, and predict the initial population based on the population change in the historical time period, and use the predicted number as the initial population of the simulation process to more realistically reflect the user population. In addition, in the last traversal of the simulation process, multiple result paths obtained by the last simulation are selected as the predicted path, and K is a preset value, which can be equal to N.

[0123] By applying the improved ant colony algorithm of the present invention to the path prediction of user groups, firstly, it is possible to simulate and predict the path direction of user groups within a preset period, and then conduct precise user terminal demand analysis, and provide a data basis for adjustment analysis of the medical unit's medical treatment process.

[0124] Second, the ant colony algorithm can be used to predict the optimal path (screening in the predicted path). The optimal path can effectively reflect the optimal ideal path that exists when users receive medical services in medical institutions. Based on the optimal ideal path, targeted location configuration of medical terminal equipment can be performed, which can make the movement path of the user group deviate from the ideal path, thereby improving the efficiency and quality of medical services.

[0125] According to an embodiment of the present invention, the terminal distribution and configuration are optimized and analyzed by predicting the user population path, and a terminal configuration plan is generated in combination with a map model, specifically:

[0126] The shortest path is selected from the predicted paths of the user group as the optimal path, and the feasible area of ​​the equipment is marked through the map model and spatial layout information;

[0127] Terminal distribution analysis is performed through user group prediction paths, and terminal distribution information is generated in combination with the feasible area of ​​the device;

[0128] Based on the optimal path and terminal distribution information, the terminal equipment is analyzed for specific quantity and location, and a terminal configuration plan is generated.

[0129] It should be noted that the user population prediction path can effectively reflect the user's mobility trend when receiving medical services. Analyzing the corresponding mobility trend and mobility area can obtain the user's terminal demand distribution, and then analyze the terminal distribution data that matches the mobility trend. The terminal distribution information is a roughly optimized distribution information that meets the personalized terminal needs of the target medical unit. The device feasible area represents the area range that the device is allowed to set.

[0130] The third aspect of the present invention also provides a computer-readable storage medium, which includes a terminal intelligent configuration program based on medical services. When the terminal intelligent configuration program based on medical services is executed by a processor, the steps of the terminal intelligent configuration method based on medical services as described in any one of the above items are implemented.

[0131] The present invention discloses a terminal intelligent configuration method, system and medium based on medical services. A three-dimensional visual map model is constructed based on the spatial layout information of medical units. The user mobile terminal and historical video data are collected through the medical platform, the user movement path is analyzed and the historical path data is generated. The medical unit area is divided into M sub-areas, and the high-frequency sub-areas are screened out according to the historical path data. The ant colony algorithm is used to simulate the movement of user groups, the starting point and the end point are set to initialize the corresponding pheromone, and the predicted path is obtained after the cyclic simulation. Finally, the terminal distribution and configuration are optimized according to the predicted path to form a terminal configuration plan.

[0132] In the several embodiments provided in 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: 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 components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0133] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0134] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0135] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0136] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0137] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A terminal intelligent configuration method based on medical services, characterized in that: include: Acquire spatial layout information of a preset medical unit, and construct a three-dimensional visualization-based map model through the spatial layout information; Within a preset time period, the medical platform collects user mobile terminal data and historical video data, analyzes the user's movement path, and generates user historical movement path data in combination with the map model; Based on the map model, the preset medical unit area is divided into M sub-areas. According to the user's historical mobile path data, the route area statistics are performed in the map model to screen out high-frequency sub-areas from the M sub-areas. Based on the ant colony algorithm, the user crowd movement simulation is carried out from the map model. The simulation process uses the sub-area as the movement path, sets the starting and end areas based on the spatial layout information, and initializes the pheromone based on the distribution of the high-frequency sub-area. The final movement path is formed through multiple cycles of simulation, and the final movement path is recorded to obtain the predicted path of the user crowd. Optimize terminal distribution and configuration through user group prediction paths, and generate terminal configuration plans based on map models; Among them, based on the ant colony algorithm, the user crowd movement simulation is performed from the map model. The simulation process uses the sub-area as the movement path, sets the starting and end areas based on the spatial layout information, initializes the pheromone based on the distribution of the high-frequency sub-area, and forms the final movement path through multiple cycles of simulation. The final movement path is recorded and the user crowd predicted path is obtained, which is specifically: Obtain user quantity change data through the medical platform within a preset time period, perform quantity change analysis based on linear regression based on the user quantity change data, perform linear fitting using periodic quantity values ​​as change data, and predict the number of users in the next preset period based on the fitting equation, and obtain the predicted number of users N; Based on the user's historical movement path data, the starting and ending locations of all paths are recorded and counted to generate the starting and ending point information; Based on the ant colony algorithm, the user population is simulated and analyzed from the map model. During the simulation, each sub-area is used as a mobile unit. The corresponding starting and ending positions are set based on the starting and ending information. The number of ants at the starting position is set to N. A preset pheromone is assigned to each mobile unit. If the mobile unit corresponds to a high-frequency sub-area, the first initial pheromone is assigned. If the mobile unit corresponds to a low-frequency sub-area, the second initial pheromone is assigned. The ant path is traversed according to the pheromone and heuristic information. Each ant starts from the starting point and updates the pheromones of all mobile units each time a traversal is completed until the preset number of traversals are completed. Record the last K paths traversed to form a user group prediction path; The optimization analysis of terminal distribution and configuration by predicting the user population path and generating a terminal configuration plan in combination with the map model are as follows: The shortest path is selected from the predicted paths of the user group as the optimal path, and the feasible area of ​​the equipment is marked through the map model and spatial layout information; Terminal distribution analysis is performed through user group prediction paths, and terminal distribution information is generated in combination with the feasible area of ​​the device; Based on the optimal path and terminal distribution information, the terminal equipment is analyzed for specific quantity and location, and a terminal configuration plan is generated.

2. According to claim 1, a terminal intelligent configuration method based on medical services is characterized in that: The obtaining of the spatial layout information of the preset medical unit and the construction of a three-dimensional visualization-based map model through the spatial layout information have the following features: Obtaining spatial layout information of a preset medical unit, the spatial layout information including distribution of departments, distribution of consultation areas, user movable areas, staff movable areas, equipment distribution information on each floor, area of ​​the preset medical unit, and area outline information; A map model based on three-dimensional visualization is constructed through the spatial layout information.

3. A terminal intelligent configuration method based on medical services according to claim 2, characterized in that: In a preset time period, the medical platform collects user mobile terminal data and historical video data, analyzes the user's movement path, and generates user historical movement path data in combination with the map model, specifically: Collect user mobile terminal data through the medical platform within a preset time period; Extract location data and medical information based on user mobile terminal data; Analyze the user's mobile path based on the positioning data and the medical information, record the path in combination with the map model, and generate first mobile path data; In a preset time period, historical video data is obtained through the medical platform, and related frames are extracted based on the historical video data to obtain a corresponding image set. The image set is used for user identification and user movement tracking through an image recognition module, and user positioning analysis is performed in combination with a map model to generate second movement path data; The first movement path data and the second movement path data are integrated to obtain the user's historical movement path data.

4. A terminal intelligent configuration method based on medical services according to claim 3, characterized in that: The preset medical unit area is divided into M sub-areas based on the map model, and route area statistics are performed on the map model according to the user's historical mobile path data, and high-frequency sub-areas are screened out from the M sub-areas, specifically: Divide the preset medical unit area into M sub-areas based on the map model; Generate multiple route data based on the user's historical mobile path data, combine with the map model, simulate the user's movement on the multiple route data, and count the number of passes, frequency, and duration of each sub-area to obtain sub-area statistical data; Based on the preset threshold standard, high-frequency areas are screened according to sub-area statistical data to obtain high-frequency sub-areas, and the remaining sub-areas are marked as low-frequency sub-areas.

5. A terminal intelligent configuration system based on medical services, characterized in that: The system includes: a memory and a processor, wherein the memory includes a terminal intelligent configuration program based on medical services, and when the terminal intelligent configuration program based on medical services is executed by the processor, the steps of the terminal intelligent configuration method based on medical services as described in any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a terminal intelligent configuration program based on medical services. When the terminal intelligent configuration program based on medical services is executed by a processor, the steps of the terminal intelligent configuration method based on medical services as described in any one of claims 1 to 4 are implemented.

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