A method and system for building a digital twin platform for hospitals

Abstract

This invention provides a method and system for building a digital twin platform for hospitals, belonging to the field of digital twin technology. Specifically, it includes: using data on the distribution of traffic accident risk points within an emergency service area and route congestion data between different traffic accident risk points and the hospital, determining the hospital's traffic accident handling risk coefficient within a preset risk coefficient range; acquiring associated processing data for different hospital departments under different traffic accident risk events; determining, based on the associated processing data, that the association type of the hospital departments belongs to a preset association type; acquiring the processing matching status of hospital departments and the department combinations constructed by associated hospital departments under different traffic accident risk events; and combining the congestion impact of hospital departments' treatment in different traffic accident risk events to determine the access processing strategy for hospital departments on the digital twin platform, thereby improving the hospital's emergency reception and processing efficiency.

Description

Technical Field

[0001] This 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 Technology

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

[0003] Specifically, the invention patent application CN202410834833.1, "A Hospital Dual-Carbon Monitoring Method, System and Storage Medium Based on Digital Twin," employs an intelligent energy consumption network monitoring model. This model processes the energy consumption data of each subsystem to derive the subsystems that can be controlled for energy conservation and their corresponding adjustment parameters, thereby improving energy management capabilities. However, it suffers from the following technical problems:

[0004] Existing technical solutions for building digital twin platforms have neglected outpatient congestion. In the event of unexpected emergencies such as major traffic accidents, it is difficult to accurately optimize the allocation of outpatient resources, which in turn makes it difficult for hospitals to meet the requirements for emergency response efficiency.

[0005] To address the aforementioned technical issues, this application provides a method and system for building and processing a hospital digital twin platform. Summary of the Invention

[0006] To achieve the objectives of this invention, the following technical solution is adopted:

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

[0008] S1 obtains the distribution of traffic accident influencing factors in different locations within the hospital's emergency service area, and, in conjunction with traffic flow data at those locations, determines the traffic accident risk points at those locations.

[0009] S2 uses 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 to determine that the traffic accident handling risk coefficient of the hospital is within the preset risk coefficient range, and then proceeds to the next step.

[0010] S3 acquires the associated processing data of different hospital departments under different car accident risk events, and determines the association type of the hospital department based on the associated processing data;

[0011] S4 When the association type of the hospital department belongs to the preset association type, obtain the processing matching status of the hospital department and the associated hospital department combination under different traffic accident risk events, and combine the congestion impact of the hospital department's treatment in different traffic accident risk events to determine the access processing strategy of the hospital department on the digital twin platform.

[0012] The beneficial effects of this invention are as follows:

[0013] By utilizing the associated processing data of different hospital departments under different traffic accident risk events, the association type of hospital departments can be determined. This enables the determination of the degree of association between hospital departments and the patient processing of traffic accident risk events from the perspective of the number of traffic accident patients processed by hospital departments under different traffic accident risk events. This lays the foundation for adopting differentiated access processing strategies for different hospital departments based on the differences in the degree of association.

[0014] By analyzing the matching of hospital departments with related hospital departments under different traffic accident risk events, and the congestion impact of hospital departments on patient care under different traffic accident risk events, the access and processing strategies of hospital departments on the digital twin platform are determined. This approach not only considers the matching of hospital departments with related hospital departments in traffic accident risk events where the combination of departments cannot meet the treatment needs of traffic accident patients, but also takes into account the differences in patient congestion risk among hospital departments. This approach achieves the determination of hospital departments' access and processing strategies on the digital twin platform from multiple perspectives, thereby reducing the amount of data accessed to the digital twin platform while improving the efficiency of handling traffic accident risk events.

[0015] A further technical solution is that the emergency service area is determined according to the preset area for the hospital to handle 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, the 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 involves determining the traffic accident risk points at the aforementioned location as follows:

[0019] Based on the distribution of traffic accident influencing factors in different locations, identify locations where traffic accident influencing factors fall within a preset range and designate them as potential risk locations;

[0020] Based on the traffic flow at the potential risk locations at different times, determine the percentage of time periods during which the traffic flow at the locations exceeds a preset traffic flow threshold, and use this percentage as the percentage of risk periods.

[0021] The potential risk location is determined as a traffic accident risk point based on the proportion of the number of risk periods.

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

[0023] A further technical solution involves determining the access and processing strategy for the hospital departments on the digital twin platform as follows:

[0024] The combination of departments in the associated hospitals is taken as the associated department combination. Based on the proportion of patients treated by the associated department combination under different traffic accident risk events, the correlation coefficient of the associated department combination under different traffic accident risk events is determined. The correlation coefficient is used to determine the associated deviation traffic accident risk events.

[0025] The correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is determined by the proportion of patients treated by the hospital department under different correlation deviation traffic accident risk events.

[0026] Based on the correlation coefficients of the hospital departments under different correlation deviation traffic accident risk events and the average waiting time of traffic accident patients in the hospital departments under different traffic accident risk events, the access processing strategy of the hospital departments 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 involves determining the access processing strategy for 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. Specifically, this includes:

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

[0030] When the average correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is not greater than the preset correlation coefficient value, 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 the hospital department is connected to the digital twin platform of the emergency service system according to the preset access processing strategy.

[0031] When the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events exceeds the preset waiting time threshold, the patient 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 risk of a traffic accident due to a correlation deviation in the combination of related departments, all shallowly related hospital departments are 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 treatment data of the hospital departments 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 access the hospital department's patient visit data to the digital twin platform of the emergency service system.

[0035] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for building a digital twin platform for a hospital when running the computer program.

[0036] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0038] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0039] Figure 1 This is a flowchart illustrating a method for building a digital twin platform for a hospital.

[0040] Figure 2 This is a flowchart illustrating the method for determining traffic accident risk points in a location;

[0041] Figure 3 This is a flowchart illustrating the method for determining the risk factor in hospital traffic accident handling;

[0042] Figure 4 This is a flowchart illustrating the method for determining the association type of hospital departments. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0044] This application fully considers the processing data of traffic accident patients in different hospital departments under different traffic accident risk events, as well as the congestion of patients seeking medical treatment, so as to determine the access and 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, specifically including:

[0047] S1 obtains the distribution of traffic accident influencing factors in different locations within the hospital's emergency service area, and, in conjunction with traffic flow data at those locations, determines the traffic accident risk points at those locations.

[0048] S2 uses 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 to determine that the traffic accident handling risk coefficient of the hospital is within the preset risk coefficient range, and then proceeds to the next step.

[0049] S3 acquires the associated processing data of different hospital departments under different car accident risk events, and determines the association type of the hospital department based on the associated processing data;

[0050] S4 When the association type of the hospital department belongs to the preset association type, obtain the processing matching status of the hospital department and the associated hospital department combination under different traffic accident risk events, and combine the congestion impact of the hospital department's treatment in different traffic accident risk events to determine the access processing strategy of the hospital department on the digital twin platform.

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

[0052] Specifically, the factors influencing the traffic accident include the length of road surface damage, the length of road shoulder collapse, road surface slope, and visibility.

[0053] Furthermore, the traffic flow data for the location includes the traffic flow at the location at different times.

[0054] It is understandable that, such as Figure 2 As shown, the method for determining the traffic accident risk points in the location is as follows:

[0055] Based on the distribution of traffic accident influencing factors in different locations, identify locations where traffic accident influencing factors fall within a preset range and designate them as potential risk locations;

[0056] Based on the traffic flow at the potential risk locations at different times, determine the percentage of time periods during which the traffic flow at the locations exceeds a preset traffic flow threshold, and use this percentage as the percentage of risk periods.

[0057] The potential risk location is determined as a traffic accident risk point based on the proportion of the number of risk periods.

[0058] Furthermore, if the location is not a potential risk location, then the potential risk location is determined not to be a traffic accident risk point.

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

[0060] In another possible embodiment, the method for determining the traffic accident risk points at the location is as follows:

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

[0062] Based on the traffic flow at different times, determine the percentage of time periods in which the traffic flow at the location exceeds a preset traffic flow threshold, and use this percentage as the percentage of risky time periods.

[0063] Based on the average of the proportion of risk factors and the proportion of risk 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 points at the location is as follows:

[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 by combining the data volume of different traffic accident influencing factors.

[0067] S12 uses the traffic flow at the location at different times to determine the percentage of time periods when the traffic flow at the location is greater than a preset traffic flow threshold, and uses this percentage as the percentage of risk periods. The accident risk factor of the location is determined according to the traffic flow at different times.

[0068] S13 determines the 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 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, step S11 above includes the following:

[0071] S111 If the distribution of traffic accident influencing factors in different locations determines that there are no traffic accident influencing factors within the preset range at the location, then the location is determined not to be a traffic accident risk location. If there are traffic accident influencing factors within the preset range at the location, proceed to step S112.

[0072] S112 If the number of accident-influencing factors within the preset range of the location does not meet the requirements, the location is determined to be a car accident risk location. If the number of accident-influencing factors within the preset range of the location meets the requirements, proceed to step S113.

[0073] S113 Based on the amount of data of different car accident influencing factors, determine the influencing factors of different car accident influencing factors. When there are car accident influencing factors whose influencing factors do not meet the requirements, the location is determined to be a car accident risk location. When there are no car accident influencing factors whose influencing factors do not meet the requirements, proceed to step S114.

[0074] S114 determines the environmental risk impact factor of the location based on the number of traffic accident influencing factors falling within the preset range and in combination with the data volume of different traffic accident influencing factors. If the environmental risk impact factor of the location does not meet the requirements, the location is determined to be a traffic accident risk location. If the environmental risk impact factor of the location meets the requirements, the process proceeds to step S12.

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

[0076] Based on the traffic flow at the location at different times, determine the percentage of time periods during which the traffic flow at the location exceeds a preset traffic flow threshold, and use this percentage as the proportion of high-risk time periods. Then, determine the accident risk factor for the location based on the traffic flow during different time periods.

[0077] If, based on the traffic flow at the location at different times, it is determined that there is no time period in which the traffic flow at the location exceeds the preset traffic flow threshold, then proceed to step S125; if there is a time period in which the traffic flow at the location exceeds the preset traffic flow threshold, then proceed to step S122.

[0078] S122 determines the percentage of time periods in the location where the traffic flow is greater than a preset traffic flow threshold, and uses this percentage as the percentage of risky time periods. If the percentage of risky time periods does not meet the requirements, the location is determined to be a location at risk of traffic accidents. If the percentage of risky time periods meets the requirements, the process proceeds to step S123.

[0079] S123 When the proportion of risk periods is within the preset range of risk periods, proceed to step S124; when the proportion of risk periods is not within the preset range of risk periods, proceed to step S125.

[0080] S124 When the average traffic flow during a period when the traffic flow is greater than the preset traffic flow threshold is greater than the preset average traffic flow threshold, the location is determined to be a location at risk of traffic accidents. When the average traffic flow during a period when the traffic flow is greater than the preset traffic flow threshold is not greater than the preset average traffic flow threshold, proceed to step S125.

[0081] S125 determines the accident risk factor of the location based on the proportion of the number of risk periods and the traffic flow in different periods. If the accident risk factor does not meet the requirements, the location is determined to be a traffic accident risk location. If the accident risk factor meets the requirements, the process proceeds to step S13.

[0082] Specifically, such as Figure 3 As shown, the method for determining the risk factor of traffic accident handling at the hospital is as follows:

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

[0084] Using the route congestion data between different accident risk points and the hospital, determine the number of congested road segments between the accident risk points and the hospital, and use this number as the number of congested road segments between the accident risk points.

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

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

[0087] Specifically, the accident handling risk coefficient of the hospital is determined based on the total number of congested road sections, including:

[0088] The risk coefficient for handling traffic accidents at the hospital is determined by the ratio of the total number of congested road sections to a preset threshold number of road sections.

[0089] It is understandable that when the accident handling risk coefficient of the hospital is not within the preset risk coefficient range, it is also necessary to determine whether the accident handling risk coefficient of the hospital 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 risk factor for traffic accident handling at the hospital is as follows:

[0091] The number of traffic accident risk points in the emergency service area is obtained. When the number of traffic accident risk points in the emergency service area is greater than the preset risk point number threshold, all hospital departments in the hospital are processed by the digital twin platform according to the preset access processing strategy.

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

[0093] When the number of traffic accident risk points is within a preset 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 processed by the digital twin platform according to the preset access processing strategy.

[0095] When the number of accident-related wind farm sites is not within the preset risk point analysis range, or when the average distance between different accident-related risk points is not greater than a preset distance threshold:

[0096] Based on the route congestion data between different accident risk points and the hospital, the number of accident risk points with congested road sections is determined. When the number of accident risk points with congested road sections does not meet the requirements, all hospital departments in the hospital are processed by the digital twin platform according to the preset access processing strategy.

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

[0098] The number of congested road segments between the accident risk point and the hospital is determined and used as the number of congested road segments of the accident risk point. When there is an accident risk point with a number of congested road segments greater than the preset threshold for the number of congested road segments, all hospital departments in the hospital are intervened and processed by the digital twin platform according to the preset access processing strategy.

[0099] When there are no traffic accident risk points where the number of congested road segments exceeds the preset threshold for the number of congested road segments:

[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 coefficient for different accident risk points is determined. When there are accident risk points whose congestion risk coefficient does not meet the requirements, all hospital departments in the hospital are intervened and processed by the digital twin platform according to the preset access processing strategy.

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

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

[0103] Furthermore, the congestion period is the 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 as follows:

[0105] By using the associated processing data of different hospital departments under different traffic accident risk events, the hospital departments that handle traffic accident patients under different traffic accident risk events can be identified.

[0106] Hospital departments that handle traffic accident patients under different traffic accident risk events are designated as patient handling departments. Based on the proportion of traffic accident patients handled by the patient handling department under different traffic accident risk events, the correlation coefficient under different traffic accident risk events is determined.

[0107] The departmental association value is determined by using the average correlation coefficient under different traffic accident risk events as the departmental association value.

[0108] Furthermore, if the hospital department is not a patient treatment department, then the hospital department is determined to be an unrelated hospital department.

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

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

[0111] When the department association value is not greater than the preset association value threshold, or when the department association value is within the preset association value range, the association type of the patient's treatment department is determined to be 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 unrelated hospital department.

[0113] Furthermore, when the association type of the hospital department is "unrelated hospital department", it is not necessary to connect the hospital department to the digital twin platform of the emergency service system.

[0114] It is 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 department is as follows:

[0116] Based on the associated processing data of different hospital departments under different car accident risk events, the hospital departments that handle car accident patients under different car accident risk events are identified. When the hospital department does not handle car accident patients under any of the different car accident risk events, the hospital department is identified as an unassociated hospital department.

[0117] When the hospital department handles car accident patients under different car accident risk events:

[0118] Hospital departments that handle car accident patients under different car accident risk events are designated as patient handling departments. When the number of car accident risk events that a patient handling department handles car accident patients exceeds a preset risk event number threshold, the hospital department is determined to be an associated hospital department.

[0119] When the number of traffic accident risk events involving traffic accident patients handled in the patient treatment department does not exceed a preset risk event number threshold.

[0120] Based on the proportion of car accident patients handled by the patient treatment department under different car accident risk events, the correlation coefficient under different car accident risk events is determined. When there is a car accident risk event in the hospital department with a correlation coefficient greater than a preset correlation coefficient threshold, the hospital department is determined to be an associated hospital department.

[0121] When the hospital department does not have any traffic accident risk events with a correlation coefficient greater than a preset correlation coefficient threshold:

[0122] The average correlation coefficient under different traffic accident risk events is used as the department correlation value. When the department correlation value is less than the preset correlation value, the correlation type of the patient's 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] The number of traffic accident risk events involving traffic accident patients handled by the patient treatment department and the proportion of such events are obtained. Combined with the correlation coefficients of the patient treatment department under different traffic accident risk events, the comprehensive correlation coefficient of the patient treatment department is determined. Based on the comprehensive correlation coefficient, the correlation type of the patient treatment department is determined.

[0125] Specifically, the association type of the patient's treatment department is determined based on the comprehensive correlation coefficient, including:

[0126] When the comprehensive correlation coefficient is greater than the preset comprehensive correlation coefficient threshold, the correlation type of the patient treatment department is determined to be an associated hospital department.

[0127] When the comprehensive correlation coefficient is not greater than the preset comprehensive correlation coefficient threshold, or when the comprehensive correlation coefficient is within the preset comprehensive correlation coefficient range, the correlation type of the patient treatment department is determined to be 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 of hospital departments.

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

[0131] This study obtains the processing matching results of hospital departments and related hospital departments under different traffic accident risk events, and combines this with the congestion impact of hospital departments on patient treatment under different traffic accident risk events.

[0132] The combination of departments in the associated hospitals is taken as the associated department combination. Based on the proportion of patients treated by the associated department combination under different traffic accident risk events, the correlation coefficient of the associated department combination under different traffic accident risk events is determined. The correlation coefficient is used to determine the associated deviation traffic accident risk events.

[0133] The correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is determined by the proportion of patients treated by the hospital department under different correlation deviation traffic accident risk events.

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

[0135] Furthermore, the associated deviation traffic accident risk event refers to a traffic accident risk event where the association coefficient of the associated department combination is within a preset association coefficient range.

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

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

[0138] When the average correlation coefficient of the hospital department under different correlation deviation traffic accident risk events is not greater than the preset correlation coefficient value, 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 the hospital department is connected to the digital twin platform of the emergency service system according to the preset access processing strategy.

[0139] When the average waiting time of traffic accident patients in the hospital department under different traffic accident risk events exceeds the preset waiting time threshold, the patient 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 combination of related departments, all shallowly related hospital departments are 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 treatment data of the hospital departments to the digital twin platform of the emergency service system.

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

[0143] Example 2

[0144] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described 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 or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

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

[0147] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A hospital digital twin platform setup processing method, characterized in that, Specifically comprising: Obtain the distribution of car accident influencing factors in different locations within the emergency service area of the hospital, and combine the traffic volume data of the locations to determine the car accident risk points in the locations; When the distribution data of the car accident risk points within the emergency service area, the route congestion data between different car accident risk points and the hospital, and the car accident handling risk coefficient of the hospital are within the preset risk coefficient interval, proceed to the next step; Obtain the associated handling data of different hospital departments under different car accident risk events, and determine the association type of the hospital departments based on the associated handling data; The method for determining the association type of the hospital departments is: Determine the hospital departments that exist car accident patient handling under different car accident risk events based on the associated handling data of different hospital departments under different car accident risk events; Take the hospital departments that exist car accident patient handling under different car accident risk events as patient handling departments, and determine the association coefficient under different car accident risk events based on the proportion of the number of car accident patients handled by the patient handling departments under different car accident risk events to the number of car accident patients under the car accident risk events; Take the average value of the association coefficient under different car accident risk events as the department association value, and determine the association type of the patient handling departments through the department association value; The association type includes unassociated hospital departments, associated hospital departments, and shallowly associated hospital departments; When the association type of the hospital departments belongs to the associated hospital departments, the hospital departments are connected to the digital twin platform of the emergency service system; When the association type of the hospital departments belongs to the shallowly associated hospital departments, obtain the handling matching situation of the department combination constructed by the hospital departments and the associated hospital departments under different car accident risk events, and combine the congestion influence situation of the hospital departments in the treatment of different car accident risk events to determine the access processing strategy of the hospital departments in the digital twin platform.

2. The hospital digital twin platform onboarding process of claim 1, wherein, The emergency service area is determined according to the preset area divided by the hospital in handling emergency events.

3. The hospital digital twin platform onboarding process of claim 1, wherein, The car accident influencing factors include road damage length, shoulder collapse length, road slope, and visibility.

4. The hospital digital twin platform onboarding process of claim 1, wherein, The method for determining the car accident risk points in the locations is: Determine the locations with car accident influencing factors falling within the preset range based on the distribution of car accident influencing factors in different locations, and take them as potential risk locations; Determine the proportion of the number of time periods with traffic volume greater than the preset traffic volume threshold in the potential risk locations based on the traffic volume of the potential risk locations in different time periods, and take it as the risk time period proportion; Determine whether the potential risk location is a car accident risk point based on the risk time period proportion.

5. The hospital digital twin platform onboarding process of claim 4, wherein, When the location does not belong to the potential risk location, it is determined that the potential risk location is not a car accident risk point.

6. The hospital digital twin platform onboarding process of claim 4, wherein, When the risk time period proportion of the potential risk point is greater than the preset time period proportion, it is determined that the potential risk point is a car accident risk point.

7. The hospital digital twin platform onboarding process of claim 1, wherein, The method for determining the access processing strategy of the hospital departments in the digital twin platform is: The department combination of the associated hospital departments is taken as an associated department combination, the association coefficients of the associated department combination in different car accident risk events are determined according to the proportion of the number of patients treated by the associated department combination in different car accident risk events, and the association deviation car accident risk events are determined by using the association coefficients; The proportion of the number of patients treated by the hospital department in different association deviation car accident risk events is determined, and the association coefficients of the hospital department in different association deviation car accident risk events are determined; The access processing strategy of the hospital department in the digital twin platform is determined according to the association coefficients of the hospital department in different association deviation car accident risk events and the average waiting time of car accident patients in different car accident risk events.

8. The hospital digital twin platform onboarding process of claim 7, wherein, The association deviation car accident risk event is a car accident risk event in which the association coefficient of the associated department combination is within a preset association coefficient range.

9. The hospital digital twin platform onboarding process of claim 7, wherein, The access processing strategy of the hospital department in the digital twin platform is determined according to the association coefficients of the hospital department in different association deviation car accident risk events and the average waiting time of car accident patients in different car accident risk events, and specifically includes: When the average value of the association coefficients of the hospital department in different association deviation car accident risk events is greater than a preset association coefficient value, the hospital department accesses the digital twin platform of the emergency service system according to a preset access processing strategy; When the average value of the association coefficients of the hospital department in different association deviation car accident risk events is not greater than the preset association coefficient value, and when the average waiting time of car accident patients of the hospital department in different car accident risk events is greater than a preset waiting time threshold, the hospital department accesses the digital twin platform of the emergency service system according to the preset access processing strategy; When the average waiting time of car accident patients of the hospital department in different car accident risk events is greater than the preset waiting time threshold, the hospital department accesses the digital twin platform of the emergency service system according to a second preset access processing strategy.

10. A computer system comprising: The memory and the processor connected in communication, and the computer program stored on the memory and capable of running on the processor, characterized in that the processor executes the computer program to perform the hospital digital twin platform building processing method of any one of claims 1-9.

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