Hospital digital twin platform building processing method and system

Abstract

The invention provides a hospital digital twinborn platform building processing method and system, and belongs to the technical field of digital twinborn, and the method specifically comprises the steps: carrying out the building of a digital twinborn platform through the distribution data of traffic accident risk points in an emergency service area, and the congestion data of routes between different traffic accident risk points and a hospital; when it is determined that the traffic accident processing risk coefficient of the hospital is in a preset risk coefficient interval, obtaining association processing data of different hospital departments under different traffic accident risk events, and when it is determined that the association type of the hospital departments belongs to a preset association type based on the association processing data, determining that the association type of the hospital departments belongs to the preset association type; processing matching conditions of hospital departments under different traffic accident risk events and department combinations constructed by associated hospital departments are obtained, and in combination with congestion influence conditions of treatment of the hospital departments in different traffic accident risk events, an access processing strategy of the hospital departments in the digital twin platform is determined; and the reception processing efficiency of emergency events of hospitals is improved.

Description

Technical Field

[0001] The present invention belongs to the field of digital twin technology, and in particular relates to a method and system for building and processing a hospital digital twin platform. Background Art

[0002] The healthcare industry faces numerous challenges, including improving efficiency, optimizing services, and reducing costs. While a foundation for medical informatization exists, issues such as information silos and uneven resource allocation persist. Therefore, building a digital twin platform to integrate medical resources and achieve information interconnection and interoperability has become a pressing technical challenge.

[0003] Specifically, in the invention patent application CN202410834833.1 "A hospital dual carbon monitoring method, system and storage medium based on digital twins", an intelligent energy consumption network monitoring model is adopted to obtain energy consumption data of each subsystem for processing, and obtain subsystems that can be energy-saving controlled and corresponding adjustment parameters, thereby improving energy consumption management capabilities. However, there are the following technical problems:

[0004] When building a digital twin platform, existing technical solutions have ignored the congestion of outpatient medical treatment. When unexpected emergency events such as major car accidents occur, it is difficult to accurately optimize the scheduling of outpatient resources, and the hospital's efficiency in handling emergency events is difficult to meet the requirements.

[0005] In order to solve the above technical problems, this application provides a hospital digital twin platform construction and processing method and system. Summary of the Invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] Specifically, in the first aspect, this application provides a method for building a hospital digital twin platform, which specifically includes:

[0008] S1 obtains the distribution of factors affecting traffic accidents at different locations within the emergency service area of the hospital, and determines the traffic accident risk points at the locations in combination with the traffic flow data of the locations;

[0009] S2: When the traffic accident handling risk coefficient of the hospital is determined to be within a preset risk coefficient range based on the distribution data of traffic accident risk points in the emergency service area and the route congestion data between different traffic accident risk points and the hospital, proceed to the next step;

[0010] S3: obtaining association processing data of different hospital departments under different traffic accident risk events, and determining association types of the hospital departments based on the association processing data;

[0011] S4 When the association type of the hospital department belongs to the preset association type, the processing matching situation of the hospital department and the department combination constructed by the associated hospital departments under different traffic accident risk events is obtained, and combined with the congestion impact of the hospital department's medical treatment in different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined.

[0012] The beneficial effects of the present invention are:

[0013] By utilizing the correlation processing data of different hospital departments under different traffic accident risk events, the correlation types of hospital departments are determined. From the perspective of the number of traffic accident patients handled by hospital departments under different traffic accident risk events, the degree of correlation between hospital departments and the treatment of patients in traffic accident risk events is determined, thereby laying the foundation for adopting differentiated access processing strategies for different hospital departments based on the differences in the degree of correlation.

[0014] The access and processing strategies of hospital departments on the digital twin platform are determined based on the processing matching situations of hospital departments and department combinations constructed by related hospital departments under different traffic accident risk events, and the congestion impact of medical treatment of hospital departments in different traffic accident risk events. This not only takes into account the processing matching situation of hospital departments in traffic accident risk events where the department combination constructed by related hospital departments cannot meet the processing needs of traffic accident patients, but also takes into account the differences in congestion risks of patients in hospital departments. It realizes the determination of the access and processing strategies of hospital departments on the digital twin platform from multiple angles, and on the basis of reducing the amount of access data of the digital twin platform, it also improves the efficiency of handling traffic accident risk events.

[0015] A further technical solution is that the emergency service area is determined according to a preset area divided by the hospital for handling emergency events.

[0016] A further technical solution is that the factors affecting the traffic accident include the length of road damage, the length of shoulder collapse, road slope, and visibility.

[0017] A further technical solution is that the traffic flow data of the location includes the traffic flow of the location at different time periods.

[0018] A further technical solution is that the method for determining the traffic accident risk point in the location is:

[0019] Based on the distribution of factors affecting traffic accidents in different locations, locations with factors affecting traffic accidents that fall within a preset range are identified and designated as potential risk locations;

[0020] Based on the traffic volume at the potential risk location at different time periods, determine the proportion of time periods in which the traffic volume at the location is greater than a preset traffic volume threshold, and use this proportion as the proportion of risk time periods;

[0021] Determine whether the potential risk location is a traffic accident risk point based on the proportion of the risk time periods.

[0022] A further technical solution is that, when the location does not belong to a potential risk location, it is determined that the potential risk location does not belong to a traffic accident risk point.

[0023] A further technical solution is that the method for determining the access processing strategy of the hospital department on the digital twin platform is:

[0024] The department combination formed by the associated hospital departments is used as the associated department combination, and the correlation coefficient of the associated department combination under different traffic accident risk events is determined according to the proportion of the number of patients treated by the associated department combination under different traffic accident risk events, and the associated deviation traffic accident risk event is determined using the correlation coefficient;

[0025] Determine the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events by calculating the proportion of the number of patients treated by the hospital department under different correlation deviation traffic accident risk events;

[0026] Based on the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined.

[0027] A further technical solution is that the associated deviation traffic accident risk event is a traffic accident risk event in which the correlation coefficient of the associated department combination is within a preset correlation coefficient range.

[0028] A further technical solution is to determine the access processing strategy of the hospital department on the digital twin platform based on the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events, which specifically includes:

[0029] When the average value of the correlation coefficient of the hospital department under different correlation deviation accident risk events is greater than the preset value of the correlation coefficient, it is connected to the digital twin platform of the emergency service system according to the preset access processing strategy;

[0030] When the average value of the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is not greater than the preset value of the correlation coefficient, and when the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events is greater than the preset waiting time threshold, then according to the preset access processing strategy, access is made to the digital twin platform of the emergency service system;

[0031] When the average waiting time of traffic accident patients in the hospital department in different traffic accident risk events is greater than the preset waiting time threshold, the hospital department is connected to the digital twin platform of the emergency service system according to the second preset access processing strategy.

[0032] A further technical solution is that when there is no associated deviation traffic accident risk event in the associated department combination, all shallowly associated hospital departments will be connected to the digital twin platform of the emergency service system according to the second preset access processing strategy.

[0033] A further technical solution is that the first preset access processing strategy is to connect the monitoring device data and medical data of the hospital department to the digital twin platform of the emergency service system.

[0034] A further technical solution is that the second preset access processing strategy is to connect the hospital department's medical data to the digital twin platform of the emergency service system.

[0035] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for building a digital twin platform for a hospital when running the computer program.

[0036] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings.

[0039] Figure 1 It is a flowchart of a hospital digital twin platform construction and processing method;

[0040] Figure 2 is a flow chart of a method for determining a traffic accident risk point in a location;

[0041] Figure 3 This is a flow chart of a method for determining a hospital's traffic accident treatment risk factor;

[0042] Figure 4 This is a flowchart of a method for determining association types of hospital departments. DETAILED DESCRIPTION

[0043] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0044] In this application, the processing data of traffic accident patients and the congestion of patients in different hospital departments in different traffic accident risk events are fully taken into consideration, so as to determine the access processing strategy of hospital departments on the digital twin platform.

[0045] Example 1

[0046] like Figure 1 As shown, this application provides a method for building a hospital digital twin platform, which specifically includes:

[0047] S1 obtains the distribution of factors affecting traffic accidents at different locations within the emergency service area of the hospital, and determines the traffic accident risk points at the locations in combination with the traffic flow data of the locations;

[0048] S2: When the traffic accident handling risk coefficient of the hospital is determined to be within a preset risk coefficient range based on the distribution data of traffic accident risk points in the emergency service area and the route congestion data between different traffic accident risk points and the hospital, proceed to the next step;

[0049] S3: obtaining association processing data of different hospital departments under different traffic accident risk events, and determining association types of the hospital departments based on the association processing data;

[0050] S4 When the association type of the hospital department belongs to the preset association type, the processing matching situation of the hospital department and the department combination constructed by the associated hospital departments under different traffic accident risk events is obtained, and combined with the congestion impact of the hospital department's medical treatment in different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined.

[0051] Furthermore, the emergency service area is determined based on the preset areas divided by the hospital for handling emergency events.

[0052] Specifically, the factors affecting traffic accidents include the length of road damage, the length of shoulder collapse, road slope, and visibility.

[0053] Furthermore, the traffic flow data of the location includes the traffic flow of the location at different time periods.

[0054] It is understandable that if Figure 2 As shown, the method for determining the traffic accident risk point in the location is:

[0055] Based on the distribution of factors affecting traffic accidents in different locations, locations with factors affecting traffic accidents that fall within a preset range are identified and designated as potential risk locations;

[0056] Based on the traffic volume at the potential risk location at different time periods, determine the proportion of time periods in which the traffic volume at the location is greater than a preset traffic volume threshold, and use this proportion as the proportion of risk time periods;

[0057] Determine whether the potential risk location is a traffic accident risk point based on the proportion of the risk time periods.

[0058] Further, when the location does not belong to a potential risk location, it is determined that the potential risk location does not belong to a traffic accident risk point.

[0059] It should also be noted that when the proportion of the number of risk time periods of the potential risk point is greater than the proportion of the number of preset time periods, the potential risk point is determined to be a traffic accident risk point.

[0060] In another possible embodiment, the method for determining the traffic accident risk point in the location is:

[0061] Based on the distribution of factors affecting traffic accidents in different locations, determine the proportion of factors affecting traffic accidents in the locations that fall within a preset range, and use this proportion as the risk factor proportion;

[0062] Based on the traffic volume at different time periods, determine the proportion of time periods in which the traffic volume at the location is greater than a preset traffic volume threshold, and use this proportion as the proportion of risk time periods;

[0063] Based on the average value of the proportion of the risk factors and the proportion of the number of risk time periods, the traffic accident risk coefficient of the location is determined, and based on the traffic accident risk coefficient, it is determined whether the location is a traffic accident risk point.

[0064] Furthermore, when the traffic accident risk coefficient is greater than a preset risk coefficient threshold, the location is determined to be a traffic accident risk point.

[0065] In another possible embodiment, the method for determining the traffic accident risk point in the location is:

[0066] S11 determines the number of traffic accident influencing factors that fall within a preset range based on the distribution of traffic accident influencing factors in different locations, and determines the environmental risk influencing factor of the location based on the data volume of different traffic accident influencing factors;

[0067] S12: determining the proportion of time periods in which the traffic volume at the location is greater than a preset traffic volume threshold based on the traffic volume at the location at different time periods, and using the proportion as the proportion of risk time periods, and determining the accident risk factor of the location based on the traffic volume at different time periods;

[0068] S13 determines a traffic accident risk coefficient of the location based on the environmental risk impact factor and the accident risk factor, and determines whether the location is a traffic accident risk point based on the traffic accident risk coefficient.

[0069] Optionally, the traffic accident risk coefficient of the location is determined based on the product of the environmental risk impact factor and the accident risk factor.

[0070] Optionally, the above step S11 includes the following contents:

[0071] S111 determines based on the distribution of accident influencing factors at different locations that no accident influencing factors fall within a preset range at the location, then determines that the location is not an accident risk location. If the location has accident influencing factors that fall within the preset range, then the process proceeds to step S112.

[0072] S112: If the number of factors affecting traffic accidents within the preset range does not meet the requirement, the location is determined to be a traffic accident risk location. If the number of factors affecting traffic accidents within the preset range meets the requirement, the process proceeds to step S113.

[0073] S113 determines the influence factors of different accident influencing factors based on the data volume of different accident influencing factors. If there are accident influencing factors whose influence factors do not meet the requirements, the location is determined to be a accident risk location. If there are no accident influencing factors whose influence factors do not meet the requirements, the process proceeds to step S114.

[0074] S114 determines the environmental risk impact factor of the location based on the number of traffic accident influencing factors that fall within a preset range and combined with the data volume of different traffic accident influencing factors. When the environmental risk impact factor of the location does not meet the requirements, it is determined that the location is a traffic accident risk location. When the environmental risk impact factor of the location meets the requirements, it proceeds to step S12.

[0075] Optionally, the above step S12 includes the following contents:

[0076] Based on the traffic volume at the location in different time periods, determine the proportion of time periods in which the traffic volume at the location is greater than the preset traffic volume threshold, and use it as the proportion of risk time periods, and determine the accident risk factor of the location based on the traffic volume in different time periods.

[0077] If it is determined in step S121 that there is no time period at the location where the traffic volume is greater than the preset traffic volume threshold based on the traffic volume at the location at different time periods, the process proceeds to step S125. If there is a time period at the location where the traffic volume is greater than the preset traffic volume threshold, the process proceeds to step S122.

[0078] S122 determines the proportion of time periods in which the traffic volume at the location is greater than a preset traffic volume threshold, and uses this proportion as the proportion of risk time periods. If the proportion of risk time periods does not meet the requirement, the location is determined to be a traffic accident risk location. If the proportion of risk time periods meets the requirement, the process proceeds to step S123.

[0079] S123: When the proportion of the number of risk periods is within the preset range of the proportion of the number of risk periods, the process proceeds to step S124; when the proportion of the number of risk periods is not within the preset range of the proportion of the number of risk periods, the process proceeds to step S125;

[0080] S124: When the average value of the traffic flow during the period when the traffic flow is greater than the preset traffic flow threshold is greater than the preset traffic flow mean threshold, the location is determined to be a traffic accident risk location; when the average value of the traffic flow during the period when the traffic flow is greater than the preset traffic flow threshold is not greater than the preset traffic flow mean threshold, the process proceeds to step S125;

[0081] S125 determines the accident risk factor of the location based on the proportion of the number of risk time periods and the traffic volume in different time periods. When the accident risk factor does not meet the requirements, it is determined that the location is a traffic accident risk location. When the accident risk factor meets the requirements, it proceeds to step S13.

[0082] Specifically, such as Figure 3 As shown, the method for determining the hospital's traffic accident treatment risk coefficient is:

[0083] Obtaining the number of traffic accident risk points within the emergency service area;

[0084] Determine the number of congested road sections between the traffic accident risk point and the hospital using route congestion data between different traffic accident risk points and the hospital, and use the number as the number of congested road sections at the traffic accident risk point;

[0085] Based on the number of traffic accident risk points and the number of congested road sections at different traffic accident risk points, the total number of congested road sections at traffic accident risk points within the emergency service area is determined, and based on the total number of congested road sections, the traffic accident handling risk coefficient of the hospital is determined.

[0086] Furthermore, the road section in use is a road section in which the number of time periods in which the vehicle travel speed is less than a preset speed threshold on different dates is greater than a preset time period number threshold.

[0087] Specifically, determining the hospital's traffic accident handling risk coefficient based on the total number of congested road sections specifically includes:

[0088] The traffic accident handling risk coefficient of the hospital is determined based on the ratio of the total number of the congested road sections to a preset road section number threshold.

[0089] It is understandable that when the hospital's traffic accident handling risk coefficient is not within the preset risk coefficient range, it is also necessary to determine whether the hospital's traffic accident handling risk coefficient is greater than the preset handling risk coefficient threshold. If so, all hospital departments in the hospital will be connected to the digital twin platform of the emergency service system according to the preset access processing strategy. If not, there is no need to build the digital twin platform of the emergency service system.

[0090] In another possible embodiment, the method for determining the hospital's traffic accident handling risk coefficient is:

[0091] Obtaining the number of traffic accident risk points within the emergency service area. When the number of traffic accident risk points within the emergency service area is greater than a preset risk point number threshold, all hospital departments in the hospital are subject to intervention processing on the digital twin platform according to a preset access processing strategy.

[0092] When the number of traffic accident risk points is not greater than the preset risk point number threshold:

[0093] When the number of traffic accident risk points is within the preset risk point number range:

[0094] When the average distance between different traffic accident risk points is greater than a preset distance threshold, all hospital departments in the hospital will be subject to intervention processing by the digital twin platform according to the preset access processing strategy;

[0095] When the number of the accident wind power points is not within the preset risk point sorting interval or the average distance between different accident risk points is not greater than the preset distance threshold:

[0096] The number of traffic accident risk points on congested sections is determined using the route congestion data between different traffic accident risk points and the hospital. If the number of traffic accident risk points on congested sections does not meet the requirement, all hospital departments in the hospital are accessed and processed by the digital twin platform according to the preset access processing strategy.

[0097] When the number of traffic accident risk points on congested road sections meets the requirements:

[0098] Determine the number of congested road sections between the accident risk point and the hospital, and use it as the number of congested road sections of the accident risk point. When there is an accident risk point with a number of congested road sections greater than a preset congested road section number threshold, all hospital departments in the hospital are intervened by the digital twin platform according to a preset access processing strategy;

[0099] When there is no traffic accident risk point where the number of congested road sections exceeds the preset congested road section threshold:

[0100] Based on the number of congested road sections at different accident risk points and the vehicle speed during congested periods on different dates, the congestion risk coefficients of different accident risk points are determined. If there is an accident risk point whose congestion risk coefficient does not meet the requirements, all hospital departments in the hospital will be intervened by the digital twin platform according to the preset access processing strategy;

[0101] When there is no traffic accident risk point where the congestion risk coefficient does not meet the requirements:

[0102] The traffic accident handling risk coefficient of the hospital is determined based on the congestion risk coefficients of different traffic accident risk points.

[0103] Furthermore, the congested period is a period when the vehicle speed is less than a preset speed threshold.

[0104] Specifically, such as Figure 4 As shown, the method for determining the association type of the hospital departments is:

[0105] Using the associated processing data of different hospital departments under different traffic accident risk events, determine the hospital departments that handle traffic accident patients under different traffic accident risk events;

[0106] Hospital departments that handle traffic accident patients under different traffic accident risk events are considered patient handling departments. Correlation coefficients under different traffic accident risk events are determined based on the proportion of traffic accident patients handled by the patient handling departments under different traffic accident risk events to the number of traffic accident patients under the traffic accident risk events.

[0107] An average value of the correlation coefficients under different traffic accident risk events is used as a department correlation value, and the correlation type of the patient treatment department is determined through the department correlation value.

[0108] Furthermore, when the hospital department does not belong to a patient treatment department, the hospital department is determined to be an unrelated hospital department.

[0109] It should be noted that the association type of the patient treatment department is determined by the department association value, specifically including:

[0110] When the department association value is greater than a preset association value threshold, the association type of the patient treatment department is determined to be an associated hospital department;

[0111] When the department association value is not greater than a preset association value threshold, and when the department association value is within a preset association value interval, determining that the association type of the patient treatment department is a shallowly associated hospital department;

[0112] When the department association value is not within the preset association value range, the association type of the patient treatment department is determined to be an unassociated hospital department.

[0113] Furthermore, when the association type of the hospital department is unassociated hospital department, there is no need to connect the hospital department to the digital twin platform of the emergency service system.

[0114] It can be understood that the association types include associated hospital departments, shallowly associated hospital departments and unassociated hospital departments.

[0115] Optionally, the method for determining the association type of the hospital departments is:

[0116] Using the associated processing data of different hospital departments under different traffic accident risk events, determine the hospital departments that have handled traffic accident patients under different traffic accident risk events; if the hospital department does not handle traffic accident patients under different traffic accident risk events, determine the type of the hospital department as an unassociated hospital department;

[0117] When the hospital department has traffic accident patients to handle under different traffic accident risk events:

[0118] A hospital department that has handled traffic accident patients under different traffic accident risk events is designated as a patient handling department. When the number of traffic accident risk events in which the patient handling department has handled traffic accident patients is greater than a preset risk event number threshold, the type of the hospital department is determined to be an associated hospital department.

[0119] When the number of traffic accident risk events handled by the patient handling department is not greater than the preset risk event number threshold,

[0120] Determining correlation coefficients under different traffic accident risk events based on the proportion of traffic accident patients handled by the patient handling department under different traffic accident risk events to the traffic accident patients under the traffic accident risk events; when a traffic accident risk event with a correlation coefficient greater than a preset correlation coefficient threshold exists in the hospital department, determining the type of the hospital department as a correlated hospital department;

[0121] When there is no traffic accident risk event with a correlation coefficient greater than a preset correlation coefficient threshold in the hospital department:

[0122] Based on the average value of the correlation coefficients under different traffic accident risk events as the department correlation value, when the department correlation value is less than a preset correlation value, the correlation type of the patient treatment department is determined to be a shallowly correlated hospital department;

[0123] When the room correlation value is not less than the preset correlation value:

[0124] Obtain the number of traffic accident risk events in which the patient treatment department handles traffic accident patients and the proportion of the number in the traffic accident risk events, and determine the comprehensive correlation coefficient of the patient treatment department based on the correlation coefficient of the patient treatment department under different traffic accident risk events, and determine the association type of the patient treatment department based on the comprehensive correlation coefficient.

[0125] Specifically, determining the association type of the patient treatment department based on the comprehensive association coefficient includes:

[0126] When the comprehensive correlation coefficient is greater than a preset comprehensive correlation coefficient threshold, determining that the correlation type of the patient treatment department is a related hospital department;

[0127] When the comprehensive correlation coefficient is not greater than a preset comprehensive correlation coefficient threshold, and when the comprehensive correlation coefficient is within a preset comprehensive correlation coefficient interval, determining that the correlation type of the patient treatment department is a shallowly correlated hospital department;

[0128] When the comprehensive correlation coefficient is not within the preset comprehensive correlation coefficient range, the correlation type of the patient treatment department is determined to be an unrelated hospital department.

[0129] Furthermore, the preset association type is a shallow association with hospital departments.

[0130] It should be noted that the method for determining the access processing strategy of the hospital departments on the digital twin platform is as follows:

[0131] Obtain the treatment matching situation of hospital departments and department combinations constructed by related hospital departments under different traffic accident risk events, and combine the congestion impact of hospital departments on medical treatment in different traffic accident risk events

[0132] The department combination formed by the associated hospital departments is used as the associated department combination, and the correlation coefficient of the associated department combination under different traffic accident risk events is determined according to the proportion of the number of patients treated by the associated department combination under different traffic accident risk events, and the associated deviation traffic accident risk event is determined using the correlation coefficient;

[0133] Determine the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events by calculating the proportion of the number of patients treated by the hospital department under different correlation deviation traffic accident risk events;

[0134] Based on the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined.

[0135] Furthermore, the associated deviation traffic accident risk event is a traffic accident risk event in which the correlation coefficient of the associated department combination is within a preset correlation coefficient range.

[0136] It is understandable that, based on the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined, specifically including:

[0137] When the average value of the correlation coefficient of the hospital department under different correlation deviation accident risk events is greater than the preset value of the correlation coefficient, it is connected to the digital twin platform of the emergency service system according to the preset access processing strategy;

[0138] When the average value of the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is not greater than the preset value of the correlation coefficient, and when the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events is greater than the preset waiting time threshold, then according to the preset access processing strategy, access is made to the digital twin platform of the emergency service system;

[0139] When the average waiting time of traffic accident patients in the hospital department in different traffic accident risk events is greater than the preset waiting time threshold, the hospital department is connected to the digital twin platform of the emergency service system according to the second preset access processing strategy.

[0140] Furthermore, when there is no associated deviation traffic accident risk event in the associated department combination, all shallowly associated hospital departments will be connected to the digital twin platform of the emergency service system according to the second preset access processing strategy.

[0141] Specifically, the first preset access processing strategy is to connect the monitoring device data and medical data of the hospital department to the digital twin platform of the emergency service system.

[0142] It should be noted that the second preset access processing strategy is to connect the hospital department's medical data to the digital twin platform of the emergency service system.

[0143] Example 2

[0144] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for building a digital twin platform for a hospital when running the computer program.

[0145] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0146] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for building a hospital digital twin platform, characterized in that: Specifically include: Obtaining the distribution of factors affecting traffic accidents at different locations within the hospital's emergency service area, and determining traffic accident risk points at the locations in combination with traffic flow data at the locations; When the traffic accident handling risk coefficient of the hospital is determined to be within a preset risk coefficient range based on the distribution data of traffic accident risk points within the emergency service area and the route congestion data between different traffic accident risk points and the hospital, proceed to the next step; Acquire association processing data of different hospital departments under different traffic accident risk events, and determine association types of the hospital departments based on the association processing data; When the association type of the hospital department belongs to the preset association type, the processing matching situation of the hospital department and the department combination constructed by the associated hospital departments under different traffic accident risk events is obtained, and combined with the congestion impact of the hospital department's medical treatment in different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined.

2. The hospital digital twin platform construction and processing method according to claim 1, characterized in that: The emergency service area is determined based on the preset areas divided by the hospital for handling emergency events.

3. The hospital digital twin platform construction and processing method according to claim 1, characterized in that: The factors affecting traffic accidents include the length of road damage, the length of shoulder collapse, road slope, and visibility.

4. The hospital digital twin platform construction and processing method according to claim 1, characterized in that: The method for determining the traffic accident risk point in the location is: Based on the distribution of factors affecting traffic accidents in different locations, locations with factors affecting traffic accidents that fall within a preset range are identified and designated as potential risk locations; Based on the traffic volume at the potential risk location at different time periods, determine the proportion of time periods in which the traffic volume at the location is greater than a preset traffic volume threshold, and use this proportion as the proportion of risk time periods; Determine whether the potential risk location is a traffic accident risk point based on the proportion of the risk time periods.

5. The hospital digital twin platform construction and processing method according to claim 4, characterized in that: When the location does not belong to a potential risk location, it is determined that the potential risk location does not belong to a traffic accident risk point.

6. The hospital digital twin platform construction and processing method according to claim 4, characterized in that: When the proportion of the number of risk time periods of the potential risk point is greater than the proportion of the number of preset time periods, the potential risk point is determined to be a traffic accident risk point.

7. The hospital digital twin platform construction and processing method according to claim 1, characterized in that: The method for determining the access processing strategy of the hospital department on the digital twin platform is as follows: The department combination formed by the associated hospital departments is used as the associated department combination, and the correlation coefficient of the associated department combination under different traffic accident risk events is determined according to the proportion of the number of patients treated by the associated department combination under different traffic accident risk events, and the associated deviation traffic accident risk event is determined using the correlation coefficient; Determine the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events by calculating the proportion of the number of patients treated by the hospital department under different correlation deviation traffic accident risk events; Based on the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined.

8. The hospital digital twin platform construction and processing method according to claim 7, characterized in that: The associated deviation traffic accident risk event is a traffic accident risk event in which the correlation coefficient of the associated department combination is within a preset correlation coefficient range.

9. The hospital digital twin platform construction and processing method according to claim 7, characterized in that: Based on the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events, the access processing strategy of the hospital department on the digital twin platform is determined, which specifically includes: When the average value of the correlation coefficient of the hospital department under different correlation deviation accident risk events is greater than the preset value of the correlation coefficient, it is connected to the digital twin platform of the emergency service system according to the preset access processing strategy; When the average value of the correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is not greater than the preset value of the correlation coefficient, and when the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events is greater than the preset waiting time threshold, then according to the preset access processing strategy, access is made to the digital twin platform of the emergency service system; When the average waiting time of traffic accident patients in the hospital department in different traffic accident risk events is greater than the preset waiting time threshold, the hospital department is connected to the digital twin platform of the emergency service system according to the second preset access processing strategy.

10. A computer system comprising: A memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes a hospital digital twin platform construction and processing method as described in any one of claims 1-9.

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