Discipline planning system and method of digital hospital model

Abstract

The present disclosure relates to a discipline planning system and method for a digital hospital model, the system comprising: a discipline planning module plans the department ratio of the digital hospital model according to the hospital scale parameter of the digital hospital model input by the user, and generates the discipline planning data of the digital hospital model; a room digital module ratios the room parameters and area parameters of each department and section in the digital hospital model, and generates the room ratio parameters of the digital hospital model; an equipment and material module generates the equipment and material configuration of the digital hospital model according to the room ratio parameters and the discipline planning data; and a discipline planning module amends the initial discipline planning data according to the room ratio parameters and the equipment and material configuration, and generates the target discipline planning data of the digital hospital model. Thus, the distribution of each discipline in the digital hospital model is scientifically planned, the accuracy of the digital hospital model is improved, and a scientific data reference is provided for the discipline planning construction in the hospital.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a subject planning system and method for a digital hospital model. Background Art

[0002] "Digital human" is a virtual character that can work continuously by simulating various language forms of human beings. The virtual character can imitate the human body and have bionic conversations. "Digital hospital" is to profile hospitals of different types and sizes based on a large amount of construction drawing data in the past hospital operation practice, and use AI models and AI algorithms to determine the mapping rules between construction drawings and hospital scales. By defining the framework structure and components of the hospital, including building department structures, room databases and related equipment and material warehouses, the corresponding department structures are selected according to different types and sizes of hospitals, so as to quickly generate a "digital hospital" model. Summary of the invention

[0003] In order to overcome the technical problems existing in the related technologies, the present disclosure provides a discipline planning system and method for a digital hospital model.

[0004] According to a first aspect of an embodiment of the present disclosure, a discipline planning system of a digital hospital model is provided, the system comprising: a discipline planning module, a room digital module and an equipment and material module;

[0005] The discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameter of the digital hospital model input by the user, and generate initial discipline planning data of the digital hospital model, wherein the initial discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data;

[0006] The room digital module is used to obtain the planned bed parameters of the digital hospital model for input, and to match the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the subject planning data, so as to generate the room matching parameters of the digital hospital model;

[0007] The equipment and material module is used to generate the equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data, and associate the equipment and material configuration with the various department planning data;

[0008] The discipline planning module is also used to modify the initial discipline planning data according to the room ratio parameters and the equipment and material configuration to generate target discipline planning data for the digital hospital model.

[0009] Optionally, the system further includes a model generation module, which is used to generate the digital hospital model through an artificial intelligence algorithm and preset algorithm rules according to the target discipline planning data, the room ratio parameters and the equipment and material configuration.

[0010] Optionally, matching room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the subject planning data to generate room matching parameters of the digital hospital model includes:

[0011] According to the planned bed parameters, the number of first rooms and the area of ​​public areas matched in the digital hospital model are determined by the room digital AI model, where the first rooms include at least one of the consulting room, treatment room, operating room, and laboratory room of the digital hospital model;

[0012] Divide the room use area of ​​the digital hospital model according to the subject planning data to generate area division data for each department in the digital hospital model;

[0013] Adjusting the area division data according to the area of ​​the public area and the first number of rooms to generate target area planning data for the digital hospital model;

[0014] The room matching parameters are generated according to the target area planning data and the subject planning data.

[0015] Optionally, generating the room ratio parameter according to the target area planning data and the subject planning data includes:

[0016] Obtaining a subject room number planning mapping table, wherein the subject room number planning mapping table includes mapping relationships among a plurality of subjects, a plurality of room planning numbers, and a plurality of planning areas;

[0017] Determining the planning area information of each subject in the subject planning data according to the target area planning data;

[0018] From the subject room number planning mapping table, obtain the room planning number that matches the planned area information as the room ratio parameter.

[0019] Optionally, generating the equipment and material configuration of the digital hospital model according to the room ratio parameter and the subject planning data includes:

[0020] According to the subject planning data, determine the functional room list corresponding to each subject in the digital hospital model, and the number of bed scales corresponding to the subject planning data;

[0021] Obtaining preset usage requirements of the functional rooms corresponding to the functional room list;

[0022] According to the preset usage requirements, determine the equipment and material type list and equipment quantity list required for the corresponding room;

[0023] The equipment and material configuration is determined based on the number of beds, the equipment and material type list, and the equipment quantity list.

[0024] Optionally, determining the equipment and material type list and equipment quantity list required for the corresponding room according to the preset usage requirements includes:

[0025] Obtain multiple historical equipment configuration lists for each discipline in the discipline planning data;

[0026] According to the preset usage requirements, a weighted average is performed on the multiple historical equipment configuration lists to generate the equipment material type list and the equipment quantity list.

[0027] Optionally, the number of beds, the equipment and material type list, and the equipment quantity list include:

[0028] According to the number of beds, determine the area data information of the rooms corresponding to each discipline in the digital hospital model;

[0029] According to the area data information, the equipment and material list, the equipment and material type list and the equipment quantity list, the equipment and materials in each subject room are selected to generate the equipment and material configuration.

[0030] Optionally, the equipment and materials module includes an equipment and materials management unit;

[0031] The equipment and material management unit is used to perform life cycle management on the equipment in each discipline according to the equipment and material configuration to generate monitoring data for each device, wherein the monitoring data includes material parameter information, performance parameter information, service life information, and equipment identity information of each device.

[0032] Optionally, the system further comprises a data export module;

[0033] The data export module is used to obtain the data export type input by the user, and the data export type includes exporting by room, exporting by subject, or exporting by equipment and material type, and generating digital model export data according to the data export type and the digital hospital model.

[0034] According to a second aspect of an embodiment of the present disclosure, a discipline planning method for a digital hospital model is provided, which is applied to the system described in any one of the first aspects of the present disclosure, and the method includes:

[0035] According to the hospital scale parameter of the digital hospital model input by the user, the department ratio of the digital hospital model is planned to generate the discipline planning data of the digital hospital model, wherein the discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data;

[0036] Acquire the planned bed parameters of the digital hospital model for input, and match the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the subject planning data to generate room matching parameters of the digital hospital model;

[0037] Generating equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data, and associating the equipment and material configuration with the various department planning data;

[0038] According to the room ratio parameters and the equipment and material configuration, the initial discipline planning data is modified to generate target discipline planning data of the digital hospital model.

[0039] According to a third aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the second aspect are implemented.

[0040] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0041] processor;

[0042] a memory for storing processor-executable instructions;

[0043] The processor is configured to execute executable instructions in the memory to implement the method described in any one of the second aspects.

[0044] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0045] The discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameters of the digital hospital model input by the user, and generate the discipline planning data of the digital hospital model. The discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data; the room digital module is used to obtain the planned bed parameters of the digital hospital model for input, and according to the planned bed parameters and the discipline planning data, the room parameters and area parameters of each department and department in the digital hospital model are matched to generate the room ratio parameters of the digital hospital model; the equipment and material module is used to generate the equipment and material configuration of the digital hospital model according to the room ratio parameters and the discipline planning data, and associate the equipment and material configuration with the multiple department planning data; the discipline planning module is also used to modify the initial discipline planning data according to the room ratio parameters and the equipment and material configuration, and generate the target discipline planning data of the digital hospital model. Therefore, the disciplines of the digital hospital model are initially planned according to the hospital scale parameters, and the discipline planning data are corrected based on the room data and equipment and material data to generate matching discipline planning data. Through data processing, the distribution of various disciplines in the digital hospital model is scientifically planned, which improves the accuracy of the digital hospital model and provides a scientific data reference for discipline planning and construction in the hospital.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0048] Figure 1 It is a block diagram of a discipline planning system of a digital hospital model according to an exemplary embodiment.

[0049] Figure 2 The present invention is a flowchart of a subject planning method of a digital hospital model according to an exemplary embodiment.

[0050] Figure 3 The present invention is a flowchart of a method for generating a digital hospital model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0052] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

[0053] Figure 1 FIG. 1 is a block diagram of a discipline planning system of a digital hospital model according to an exemplary embodiment. Figure 1 As shown, the system includes: a subject planning module, a room digital module and an equipment and material module;

[0054] The discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameters of the digital hospital model input by the user, and generate discipline planning data of the digital hospital model. The discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data.

[0055] For example, in this embodiment, by analyzing multiple groups of hospital cases, the discipline structure of each department of the hospital, the number of each key treatment and diagnosis auxiliary room, and the configuration mode of equipment and materials are determined, so as to generate an efficient digital hospital model through the intelligent generation system of the digital hospital model. The discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameters of the digital hospital model input by the user, and generate the discipline planning data of the digital hospital model. The discipline planning data includes a variety of department planning data, and each department planning data includes a variety of department planning data. The discipline planning module plans the departments and disciplines of the digital hospital model. The user inputs relevant parameters and outputs the department configuration and discipline configuration in the virtual standardized digital hospital of the same scale through the artificial intelligence algorithm.

[0056] Optionally, the hospital parameters include: planned bed parameters of the digital hospital model, floor area parameters of the digital hospital model, planning type parameters of the digital hospital model, and regional address parameters of the digital hospital model.

[0057] For example, the user inputs the hospital scale parameter as: 1000 beds. The system matches the department type corresponding to the digital hospital model under the bed scale, as well as the discipline planning type under each department type. For example, department types can be divided into: outpatient departments, medical technology departments, health management departments, ward departments, logistics support departments, office research departments, etc.; among them, outpatient departments can also include dermatology, surgical clinics, infection clinics, outpatient halls, otolaryngology and other discipline types; medical technology departments also include radiotherapy centers, nuclear medicine, ultrasound departments and other discipline types. In this embodiment, by analyzing multiple examples, the hospital scale is used as the model input, and the corresponding discipline planning data is used as the output to train the artificial intelligence model, so that the artificial intelligence model determines the mapping relationship between the input and the output. The discipline planning data corresponding to the hospital scale parameter input by the user is determined according to the trained artificial intelligence model.

[0058] The room digital module is used to obtain the planned bed parameters of the digital hospital model for input, and according to the planned bed parameters and the subject planning data, to match the room parameters and area parameters of each department and section in the digital hospital model to generate the room matching parameters of the digital hospital model.

[0059] For example, the room digital module is used to plan the room area, number of rooms and room location of each discipline in the digital hospital model. The user inputs the number of beds into the system, and other data associated with the room can be automatically calculated. In this embodiment, the actual construction data of multiple historical hospitals are analyzed and learned to train the AI ​​model, so that the AI ​​model can plan the room area, room location and number of rooms in the digital hospital based on the hospital size and planned number of beds, and generate a room digital model. The room digital model matches the planned bed parameters with the discipline planning data generated in the above embodiment, matches the room parameters and area of ​​each department and section in the digital hospital model, and generates the room ratio parameters of the digital hospital model.

[0060] Optionally, in some embodiments, the above step of “matching the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the discipline planning data to generate room matching parameters of the digital hospital model” includes:

[0061] According to the planned bed parameters, the number of first rooms and the area of ​​public areas matched in the digital hospital model are determined by the room digital AI model, where the first rooms include at least one of the consulting room, treatment room, operating room, and laboratory room of the digital hospital model;

[0062] Divide the room use area of ​​the digital hospital model according to the subject planning data to generate area division data for each department in the digital hospital model;

[0063] Adjusting the area division data according to the area of ​​the public area and the first number of rooms to generate target area planning data for the digital hospital model;

[0064] The room matching parameters are generated according to the target area planning data and the subject planning data.

[0065] For example, the system is configured with a room digital AI model. In this embodiment, the planned bed parameters are input into the room digital AI model completed through training, and the number of first rooms and the area of ​​public areas matched in the digital hospital model are determined based on the room digital AI model, wherein the first room is a key room in the digital hospital model, and the key room includes: diagnosis and treatment room, treatment room, operating room, laboratory, etc. The public area is a public use area at the intersection of various disciplines in the digital hospital model. After the room digital AI model determines the scale of the digital hospital model according to the discipline planning data, it determines the planning area and initial planning area of ​​the digital hospital model, and then divides the initial planning area according to the discipline planning data to generate the regional division data of each department in the digital hospital model. In this embodiment, when the room digital AI model divides the area of ​​the digital hospital model, it first divides the area based on the discipline planning data generated in the above embodiment, but does not consider the first room position and the public area position in the hospital area planning process. Therefore, it is necessary to adjust the regional division data based on the number of first rooms and the area of ​​public areas to ensure that there are public areas, treatment areas corresponding to the first room, office areas and logistics support areas in the planning area, so as to generate target area planning data. Based on the discipline planning data, each department of the digital hospital model is planned into the target area, and the room ratio parameters of the digital hospital model are generated.

[0066] Optionally, in some embodiments, the above step of “generating the room matching parameters according to the target area planning data and the subject planning data” includes:

[0067] For example, in this embodiment, the discipline ratio parameters of multiple historical hospitals that have been built can be analyzed to determine the number of rooms for each discipline in the built hospitals, the room planning area, and the mapping relationship between disciplines. By weighted average, a discipline room number planning mapping table is generated. The discipline room number planning mapping table is used to indicate the mapping relationship between disciplines, room planning numbers, and planning areas. According to the discipline room number planning mapping table, the planning area information corresponding to the target area planning data is determined. From the discipline room number planning mapping table, the planning area information is determined to correspond to the room planning number as the room ratio parameter.

[0068] For example, in this embodiment, the process of generating a digital hospital model is a visualization process. During the generation process, the user can manually modify the indicator data in the generation process based on actual needs. For example, for the subject planning data generated in the above embodiment, the user can adjust the proportion of each subject based on building needs, and increase or delete the number of subjects; based on the room ratio parameters generated by the room digital module in the above embodiment, the user can adjust the number of rooms, room size and room location in the room ratio parameters based on actual needs to meet the user's adaptive editing needs. It can be understood that when the user types the corresponding modification information, the digital hospital model will adjust the overall data in the model based on the modification information. For example, if the number or area of ​​any room in a single subject plan is modified, the overall area measurement will be automatically adjusted.

[0069] The equipment and material module is used to generate the equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data, and associate the equipment and material configuration with the various department planning data.

[0070] For example, in this embodiment, during the digital hospital model planning process, based on the room ratio parameters of the above-mentioned disciplines, the equipment in each room is configured, and the equipment and material configuration of the digital medical model is generated according to the room ratio parameters and the discipline planning data. The equipment in each room in the digital hospital model includes large medical equipment, small medical equipment, household, low-value consumables, firefighting items, office meeting items and other configuration equipment. According to the discipline planning of each room and the area planning of each room, the equipment in the room is configured to generate the equipment and material configuration of the room.

[0071] In this embodiment, an equipment and material library can be set up in the system, and each device can be classified in the equipment and material library. The equipment and material configuration of each room generated in the above embodiment can be modified and adjusted through the equipment and material library, so that the user can adjust the equipment and materials of the room according to actual needs. For example, when the user modifies the equipment configured in each room based on the equipment and material library, the overall equipment and materials and the corresponding equipment and material cost calculation will be automatically modified.

[0072] Optionally, in some embodiments, the above step of "generating equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data" includes:

[0073] Determine the equipment and material list corresponding to each discipline in the digital hospital model according to the discipline planning data;

[0074] Determine the equipment and material configuration according to the room ratio parameters and the equipment and material list.

[0075] It should be understood that the room areas corresponding to the same discipline in the digital hospital model are different, and the equipment and materials that need to be configured are different. In this embodiment, the equipment and materials list that needs to be configured under the discipline is first determined based on the discipline planning data, and then the room size and number of rooms configured for the discipline in the digital hospital model are determined based on the room ratio parameters corresponding to the discipline. Based on the room size and the number of rooms, the equipment and material models, equipment and material sizes, and equipment and material quantities that need to be configured under the current equipment and material list are determined, thereby generating the equipment and material configuration for the room.

[0076] Optionally, in some embodiments, the above step of “determining the equipment and material list corresponding to each discipline in the digital hospital model according to the discipline planning data” includes:

[0077] Obtain multiple historical equipment configuration lists for each discipline in the discipline planning data;

[0078] The equipment and material list is generated by taking a weighted average of the multiple historical equipment configuration lists.

[0079] For example, in this embodiment, multiple historical equipment configuration lists of various disciplines in existing historical hospitals are analyzed, the scale of the existing hospitals corresponding to the multiple historical equipment configuration lists is used as a weighted average coefficient, and the multiple historical equipment configuration lists are weighted averaged to generate the corresponding equipment and material lists in each room.

[0080] Optionally, in some embodiments, the above step of “determining the equipment and material configuration according to the room matching parameters and the equipment and material list” includes:

[0081] According to the room ratio parameters, determine the area data information of the rooms corresponding to each discipline in the digital hospital model;

[0082] According to the area data information and the equipment and material list, the equipment and materials in each subject room are selected to generate the equipment and material configuration.

[0083] For example, in this embodiment, the area data of the rooms corresponding to each discipline in the digital hospital model is determined according to the room ratio parameters, and then the area data is matched with the equipment and material list to select the equipment and materials in the rooms of each discipline, wherein the selection includes selecting the equipment and material size, equipment and material model and equipment and material size of the corresponding type of equipment and materials, thereby generating the equipment and material configuration.

[0084] Optionally, in some embodiments, the equipment and material module includes an equipment and material management unit;

[0085] The equipment and material management unit is used to perform life cycle management on the equipment in each discipline according to the equipment and material configuration to generate monitoring data for each device, wherein the monitoring data includes material parameter information, performance parameter information, service life information, and equipment identity information of each device.

[0086] For example, in this embodiment, the digital hospital model can also be used to monitor and manage the equipment and materials after the construction of the digital hospital is completed. The equipment and materials management unit monitors the flow, service life, damage, etc. of each equipment and material. An identity ID is set for each material, which includes the equipment and material number, QR code, location and other information to facilitate the management of its entire life cycle based on the equipment and materials management unit. Users can scan the QR code to view the basic parameters, performance parameters, prices and management information of the entire process from leaving the factory to scrapping of equipment and materials.

[0087] The equipment and materials management unit manages equipment and materials including their warehousing, repair, maintenance, replacement, depreciation, and scrapping. The ID records the time and place of major events, people, and resolution results. Briefing information and detailed information of each event process are set in the management interface of a single equipment and material. By consulting it, the detailed flow process of the corresponding equipment and materials can be determined. Including the time of the operation and maintenance on that day, the main person in charge, pictures of the actual situation, and evaluation after maintenance.

[0088] For example, in some embodiments, the system also includes a room equipment library module, which includes more than 6,000 different types of equipment, and the search method can be directly searched according to the keywords in the name. It avoids the situation where it is difficult to search after a large number of classifications and there are too many sub-items. The room equipment library module defines the type, price and quantity of the materials equipped in each room. The room equipment library module can configure the different functional rooms in the digital hospital model according to the relevant use and space requirements of medical and architectural. The same type of room can be divided into multiple different room configurations according to the use function. For example, the operating room can be gynecological, ophthalmic, anorectal, cardiothoracic, orthopedic surgery, emergency surgery, and outpatient surgery. Under the condition that the main configuration remains unchanged, the configuration of the equipment or small medical instruments and equipment can be differentiated according to the specific type of surgery. The operating room can also be based on the different types of surgery it performs, such as interventional surgery, hybrid robot surgery, etc. There will also be corresponding differences in the size and internal layout of the room, resulting in different overall equipment configurations. The operating room can also be divided into 100-level, 1,000-level, and 10,000-level operating rooms based on the cleanliness of the air conditioner. The room size and interior layout will also have corresponding differences, resulting in different overall equipment configurations. The same type of room, such as offices and conference rooms, can also have different sizes according to the different users. The difference is caused by the different sizes of purchased furniture or equipment. It is also possible to configure the same furniture and equipment with different price positioning according to their administrative level and the group of people using them. The price of equipment and materials is determined in the early stage based on the recently approved expenses. In the actual procurement process, the selection will be based on the parameters set in the early stage, and the system will be updated after confirmation. For example, the price fluctuation of the same type of materials can be around 10%.

[0089] The model generation module is used to generate the digital hospital model according to the subject planning data, the room ratio parameters and the equipment and material configuration.

[0090] For example, in this embodiment, after determining the subject planning data, room configuration parameters and equipment and material configuration of the digital hospital model through the above steps, the corresponding digital hospital model is generated through the AI ​​model.

[0091] Optionally, in some embodiments, the system further comprises a data export module;

[0092] The data export module is used to obtain the data export type for input, and the data export type includes room export, subject export or equipment type export; and generate digital model export data according to the data export type and the digital hospital model.

[0093] For example, after the digital hospital model is generated based on the above embodiment, the digital hospital model can be exported. In this embodiment, the generated digital hospital model can be exported based on the data export type input by the user. The export of the digital hospital model has multiple modes. It can be exported by room, or classified and exported by department and equipment type, which is suitable for different application scenarios. Export by room: The configuration of the corresponding equipment and materials will be listed under the room. Including information such as the type, number, unit price, total price, number, and location of the department. Export by department: Usually, the area where all rooms in the department are located, as well as the area and number of rooms can be exported, and the proportion of the area of ​​each discipline can be counted. Export by equipment type: The equipment type will be automatically classified and counted based on the data exported by room, and the quantity and total price of different equipment types will be counted.

[0094] The discipline planning module is also used to modify the initial discipline planning data according to room ratio parameters and equipment and material configuration, and generate target discipline planning data for the digital hospital model.

[0095] For example, in this embodiment, after determining the room ratio parameters and equipment and material configuration of each discipline in the above manner, the initial discipline planning data of the initial planning is revised so that the revised discipline planning data can meet the actual planning requirements of the current digital hospital model, and then generate target discipline planning data that matches the digital hospital model.

[0096] Through the above method, the discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameters of the digital hospital model input by the user, and generate the discipline planning data of the digital hospital model. The discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data; the room digital module is used to obtain the planned bed parameters of the digital hospital model for input, and according to the planned bed parameters and the discipline planning data, the room parameters and area parameters of each department and department in the digital hospital model are matched to generate the room ratio parameters of the digital hospital model; the equipment and material module is used to generate the equipment and material configuration of the digital hospital model according to the room ratio parameters and the discipline planning data, and associate the equipment and material configuration with the multiple department planning data; the discipline planning module is also used to modify the initial discipline planning data according to the room ratio parameters and the equipment and material configuration, and generate the target discipline planning data of the digital hospital model. Therefore, the disciplines of the digital hospital model are initially planned according to the hospital scale parameters, and the discipline planning data are corrected based on the room data and equipment and material data to generate matching discipline planning data. Through data processing, the distribution of various disciplines in the digital hospital model is scientifically planned, which improves the accuracy of the digital hospital model and provides a scientific data reference for discipline planning and construction in the hospital.

[0097] Figure 2 is a flow chart of a discipline planning method of a digital hospital model according to an exemplary embodiment. Figure 2 As shown, the method is applied to any of the above systems, and the method includes:

[0098] Step S11, planning the department ratio of the digital hospital model according to the hospital scale parameters of the digital hospital model input by the user, generating the discipline planning data of the digital hospital model, the discipline planning data includes a variety of department planning data, and each department planning data includes a variety of department planning data.

[0099] Step S12, obtaining the planned bed parameters of the digital hospital model for input, and matching the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and subject planning data to generate room matching parameters of the digital hospital model.

[0100] Step S13, generating equipment and material configuration of the digital hospital model according to the room ratio parameters and the discipline planning data, and associating the equipment and material configuration with the planning data of various departments.

[0101] Step S14, modifying the initial discipline planning data according to the room ratio parameters and equipment and material configuration, and generating target discipline planning data of the digital hospital model.

[0102] For example, in this embodiment, by analyzing multiple groups of hospital cases, the discipline structure of each department of the hospital, the number of each key treatment and diagnosis auxiliary room, and the configuration mode of equipment and materials are determined, so as to generate an efficient digital hospital model through the intelligent generation system of the digital hospital model. The discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameters of the digital hospital model input by the user, and generate the discipline planning data of the digital hospital model. The discipline planning data includes a variety of department planning data, and each department planning data includes a variety of department planning data. The discipline planning module plans the departments and disciplines of the digital hospital model. The user inputs relevant parameters and outputs the department configuration and discipline configuration in the virtual standardized digital hospital of the same scale through the artificial intelligence algorithm.

[0103] Plan the room area, number of rooms, and room location of each discipline in the digital hospital model. The user inputs the number of beds into the system, and other data associated with the room can be automatically calculated. In this embodiment, the actual construction data of multiple historical hospitals is analyzed and learned to train the AI ​​model, so that the AI ​​model can plan the room area, room location, and number of rooms in the digital hospital based on the hospital scale and planned number of beds, and generate a digital model of the room. The digital model of the room matches the planned bed parameters with the discipline planning data generated in the above embodiment, matches the room parameters and areas of each department and section in the digital hospital model, and generates the room ratio parameters of the digital hospital model.

[0104] In the digital hospital model planning process, the equipment in each room is configured based on the room ratio parameters of the above-mentioned disciplines, and the equipment and material configuration of the digital medical model is generated according to the room ratio parameters and discipline planning data. The equipment in each room in the digital hospital model includes large medical equipment, small medical equipment, household appliances, low-value consumables, firefighting items, office meeting items and other configuration equipment. According to the discipline planning of each room and the area planning of each room, the equipment in the room is configured to generate the equipment and material configuration of the room. After determining the discipline planning data, room configuration parameters and equipment and material configuration of the digital hospital model through the above steps, the corresponding digital hospital model is generated through the AI ​​model.

[0105] Through the above method, according to the hospital scale parameter of the digital hospital model input by the user, the department ratio of the digital hospital model is planned, and the discipline planning data of the digital hospital model is generated. The discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data. The planned bed parameters of the digital hospital model for input are obtained, and the room parameters and area parameters of each department in the digital hospital model are matched according to the planned bed parameters and the discipline planning data to generate the room ratio parameters of the digital hospital model. According to the room ratio parameters and the discipline planning data, the equipment and material configuration of the digital hospital model is generated, and the equipment and material configuration is associated with the multiple department planning data. The discipline planning module is also used to correct the initial discipline planning data according to the room ratio parameters and the equipment and material configuration, and generate the target discipline planning data of the digital hospital model. Therefore, the disciplines of the digital hospital model are initially planned according to the hospital scale parameters, and the discipline planning data are corrected in the room data and the equipment and material data to generate matching discipline planning data. Through data processing, the distribution of each discipline in the digital hospital model is scientifically planned, the accuracy of the digital hospital model is improved, and a scientific data reference is provided for the discipline planning construction in the hospital.

[0106] Figure 3is a flow chart of a method for generating a digital hospital model according to an embodiment of the present disclosure. Figure 3 As shown, the method includes the following steps.

[0107] For example, according to the different sizes of hospitals (number of beds), "digital hospital models" with 500-1000-2000-3000 beds are built. According to the actual cases of hospitals that have been built, hospitals of different types and sizes are combined by building a department database of discipline planning, and then selecting different discipline models through hospitals of different sizes, so that hospital construction can emulate past successful experiences and improve efficiency. More standardized, normalized, and high-quality development.

[0108] 1. Based on thousands of department planning prototype data in the database, the operating models and rules of different hospitals are summarized, and digital models of standard hospitals with sizes of 500 beds, 1,000 beds, 1,500 beds, and 3,000 beds are refined and generated.

[0109] 2. The framework structure of the "digital hospital" model was built, and the space and configuration of the hospital were simulated in a refined manner according to the architecture of the department database, room database, equipment database, and material database.

[0110] 3. Divide the "digital hospital model" into "standard hospital" and "real hospital". By comparing the real hospital and the virtual hospital, including verifying the drawings and actual scenes, a customized hospital can be quickly generated and the corresponding functions can be used simultaneously.

[0111] For example, in this embodiment, the digital model of the hospital can be virtually generated through the AI ​​automatic algorithm, thereby providing an interface for the later full life cycle management, intelligent and information application system. The digital model can interact with the three-dimensional BIM (Building Information Modeling), so that the virtual model can be deleted or added according to the actual project progress, realizing the fitting of the virtual world and the real world, thereby building a platform for hospital construction, operation and management, so that all the data of the hospital can be accessed through a platform, breaking through the information island.

[0112] This embodiment solves the technical problems related to the three-dimensional modeling of digital twin hospitals, including lightweight conversion and visual display of BIM. Due to the file size and storage space issues of BIM, it is impossible to fully present all the material configurations of all rooms without any size or detail. This will make the model too large and make it impossible for computer equipment to open it. Therefore, in the process of building a digital hospital, this embodiment converts the equipment and material information into a digital presentation related to the equipment rather than a full three-dimensional description.

[0113] Focus on the core elements of equipment and materials, including the quantity and price of configurations in the start-up phase, as well as the parameters required in the operation phase, such as energy consumption, water and electricity consumption, gas consumption and other core data. Avoid the excessive development requirements for computing power and algorithms caused by integrating all non-important information.

[0114] Through the above methods, a model of efficient operation is formed. These digital models of actual successful operation are used to guide practice. Or the model can be used for various optimization tests to improve efficiency and save costs.

[0115] The present disclosure also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in any one of the aforementioned embodiments are implemented.

[0116] The present disclosure also provides an electronic device, including:

[0117] processor;

[0118] a memory for storing processor-executable instructions;

[0119] The processor is configured to execute executable instructions in the memory to implement the method described in any one of the aforementioned embodiments.

[0120] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0121] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A discipline planning system for a digital hospital model, characterized in that: The system includes: a subject planning module, a room digital module and an equipment and material module; The discipline planning module is used to plan the department ratio of the digital hospital model according to the hospital scale parameter of the digital hospital model input by the user, and generate initial discipline planning data of the digital hospital model, wherein the initial discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data; The room digital module is used to obtain the planned bed parameters of the digital hospital model input by the user, and to match the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the subject planning data, so as to generate the room matching parameters of the digital hospital model; The step of matching the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the discipline planning data to generate room matching parameters of the digital hospital model includes: According to the planned bed parameters, the number of first rooms and the area of ​​public areas matched in the digital hospital model are determined by the room digital AI model, where the first rooms include at least one of the consulting room, treatment room, operating room, and laboratory room of the digital hospital model; Divide the room use area of ​​the digital hospital model according to the subject planning data to generate area division data for each department in the digital hospital model; Adjusting the area division data according to the area of ​​the public area and the first number of rooms to generate target area planning data for the digital hospital model; Generating the room ratio parameters according to the target area planning data and the subject planning data; The generating the room ratio parameter according to the target area planning data and the subject planning data comprises: Obtaining a subject room number planning mapping table, wherein the subject room number planning mapping table includes mapping relationships among a plurality of subjects, a plurality of room planning numbers, and a plurality of planning areas; Determining the planning area information of each subject in the subject planning data according to the target area planning data; From the subject room number planning mapping table, obtaining the room planning number matching the planned area information as the room ratio parameter; The equipment and material module is used to generate the equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data, and associate the equipment and material configuration with the various department planning data; Generating the equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data includes: According to the subject planning data, determine the functional room list corresponding to each subject in the digital hospital model, and the number of bed scales corresponding to the subject planning data; Obtaining preset usage requirements of the functional rooms corresponding to the functional room list; According to the preset usage requirements, determine the equipment and material type list and equipment quantity list required for the corresponding room; Determine the equipment and material configuration according to the number of beds, the equipment and material type list, and the equipment quantity list; Determining the equipment and material type list and equipment quantity list required for the corresponding room according to the preset usage requirements includes: Obtain multiple historical equipment configuration lists for each discipline in the discipline planning data; According to the preset usage requirements, weighted averaging the multiple historical equipment configuration lists is performed to generate the equipment material type list and the equipment quantity list; The number of beds, the list of equipment and materials, and the list of equipment quantity include: According to the number of beds, determine the area data information of the rooms corresponding to each discipline in the digital hospital model; Selecting equipment and materials in each subject room according to the area data information, the equipment and material type list and the equipment quantity list to generate the equipment and material configuration; The discipline planning module is also used to modify the initial discipline planning data according to the room ratio parameters and the equipment and material configuration to generate target discipline planning data for the digital hospital model.

2. The discipline planning system of the digital hospital model according to claim 1 is characterized in that: The system also includes a model generation module; The model generation module is used to generate the digital hospital model through artificial intelligence algorithm and preset algorithm rules according to the target discipline planning data, the room ratio parameters and the equipment and material configuration.

3. The discipline planning system of the digital hospital model according to any one of claims 1-2, characterized in that: The equipment and materials module includes an equipment and materials management unit; The equipment and material management unit is used to perform life cycle management on the equipment in each discipline according to the equipment and material configuration to generate monitoring data for each device, wherein the monitoring data includes material parameter information, performance parameter information, service life information, and equipment identity information of each device.

4. The discipline planning system of the digital hospital model according to claim 2 is characterized in that: The system also includes a data export module; The data export module is used to obtain the data export type input by the user, and the data export type includes exporting by room, exporting by subject, or exporting by equipment and material type, and generating digital model export data according to the data export type and the digital hospital model.

5. A discipline planning method for a digital hospital model, characterized in that: Applied to the system of any one of claims 1 to 4, the method comprising: According to the hospital scale parameter of the digital hospital model input by the user, the department ratio of the digital hospital model is planned to generate the discipline planning data of the digital hospital model, wherein the discipline planning data includes multiple department planning data, and each department planning data includes multiple department planning data; Acquire the planned bed parameters of the digital hospital model input by the user, and match the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the subject planning data to generate room matching parameters of the digital hospital model; The step of matching the room parameters and area parameters of each department and section in the digital hospital model according to the planned bed parameters and the discipline planning data to generate room matching parameters of the digital hospital model includes: According to the planned bed parameters, the number of first rooms and the area of ​​public areas matched in the digital hospital model are determined by the room digital AI model, where the first rooms include at least one of the consulting room, treatment room, operating room, and laboratory room of the digital hospital model; Divide the room use area of ​​the digital hospital model according to the subject planning data to generate area division data for each department in the digital hospital model; Adjusting the area division data according to the area of ​​the public area and the first number of rooms to generate target area planning data for the digital hospital model; Generating the room ratio parameters according to the target area planning data and the subject planning data; The generating the room ratio parameter according to the target area planning data and the subject planning data comprises: Obtaining a subject room number planning mapping table, wherein the subject room number planning mapping table includes mapping relationships among a plurality of subjects, a plurality of room planning numbers, and a plurality of planning areas; Determining the planning area information of each subject in the subject planning data according to the target area planning data; From the subject room number planning mapping table, obtaining the room planning number matching the planned area information as the room ratio parameter; Generating equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data, and associating the equipment and material configuration with the various department planning data; According to the room ratio parameters and the equipment and material configuration, the initial discipline planning data is modified to generate target discipline planning data of the digital hospital model; Generating the equipment and material configuration of the digital hospital model according to the room ratio parameters and the subject planning data includes: According to the subject planning data, determine the functional room list corresponding to each subject in the digital hospital model, and the number of bed scales corresponding to the subject planning data; Obtaining preset usage requirements of the functional rooms corresponding to the functional room list; According to the preset usage requirements, determine the equipment and material type list and equipment quantity list required for the corresponding room; Determine the equipment and material configuration according to the number of beds, the equipment and material type list, and the equipment quantity list; Determining the equipment and material type list and equipment quantity list required for the corresponding room according to the preset usage requirements includes: Obtain multiple historical equipment configuration lists for each discipline in the discipline planning data; According to the preset usage requirements, weighted averaging the multiple historical equipment configuration lists is performed to generate the equipment material type list and the equipment quantity list; The number of beds, the list of equipment and materials, and the list of equipment quantity include: According to the number of beds, determine the area data information of the rooms corresponding to each discipline in the digital hospital model; According to the area data information, the equipment and material type list and the equipment quantity list, the equipment and materials in each subject room are selected to generate the equipment and material configuration.

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