A hospital logistics management system and method based on digital twin

Abstract

The present invention discloses a hospital logistics management system and method based on digital twin, which relates to the technical field of hospital logistics informatization. Through a model unit and an initialization unit, a three-dimensional entity model of the hospital is constructed and the hardware devices are initialized. Business data is obtained through a data acquisition unit. Considering the cross-characteristic of business data among multiple subsystems, a data processing unit is set to calculate the coincidence degree of business data between two subsystems, so as to set a correlation coefficient for the two subsystems. The system eigenvalue of the subsystem is calculated through the first formula in the data integration unit, and the system eigenvalue includes the energy consumption value and risk value of the hardware device. The system eigenvalue is used as the judgment standard for whether there is business warning or risk in a single subsystem. And the comprehensive management unit takes corresponding measures for different ranges of system eigenvalues, which greatly improves the management efficiency of the entire hospital logistics management system.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital logistics informatization, and specifically, to a hospital logistics management system and method based on digital twin. Background Art

[0002] The business areas involved in hospital logistics are very extensive, including energy management, electromechanical system and equipment management, integrated monitoring, central transportation, medical waste treatment, environmental monitoring, infrastructure management, etc. With the continuous expansion of hospital scale and the continuous development of Internet IT technology and digital transformation, the importance of logistics work in hospitals has been gradually highlighted.

[0003] However, most traditional hospital logistics management systems are systems for specific business requirements, facing problems such as decentralized management and information islands. At the same time, in the current logistics management system, the processing of collected data mostly forms reports for presentation, and it is impossible to integrate and process the data between multiple systems, nor to visually present the integration results and provide decision-making for managers based on the integration results, ultimately resulting in low overall efficiency of the existing hospital logistics management. Summary of the Invention

[0004] To solve the deficiencies of the existing technology, the present invention provides a hospital logistics management system based on digital twin, and the system includes:

[0005] Model Unit: Based on the BIM model and UE virtual engine, construct a three-dimensional entity model of the hospital;

[0006] Initialization Unit: Initialize the positions of multiple hardware devices in the three-dimensional entity model and assign coordinate information to all hardware devices;

[0007] Data Acquisition Unit: Based on all hardware devices, collect data and perform data parsing to obtain business data, and all business data contains corresponding coordinate information;

[0008] Data Processing Unit: Obtain the first acquisition result by obtaining the hardware devices included in each subsystem, based on the first acquisition result and all business data, obtain the business data included in each subsystem and record it as the first business data, store the first business data of each subsystem in the corresponding data set, calculate the coincidence degree of the first business data in any two data sets to obtain a calculation result, and based on the calculation result, set the correlation coefficient of any one subsystem with other subsystems, and all correlation coefficients are greater than or equal to 0 and less than 1;

[0009] Data Integration Unit: Calculate the system eigenvalue of each subsystem using the first formula based on all the first business data of each subsystem;

[0010] The first formula includes:

[0011]

[0012] is the system eigenvalue of each subsystem, α j is the energy consumption value of the hardware device corresponding to the same first service data in each subsystem, β k is the risk value of the hardware device corresponding to the same first service data in each subsystem, and r is the number of different first service data in each subsystem;

[0013] Integrated management unit: Set a first threshold, a second threshold, and a third threshold for the system eigenvalue of each subsystem, and the first threshold, the second threshold, and the third threshold in each subsystem are all different;

[0014] Obtain a single subsystem denoted as subsystem E. When the system eigenvalue of the subsystem E is less than or equal to the first threshold, do not act;

[0015] When the system eigenvalue of the subsystem E is greater than the second threshold and less than the third threshold, use the first method to process the subsystem E; The first method is: Mark the hardware devices included in the subsystem E in the entity three-dimensional model to obtain a first marking result, and based on the first service data of the subsystem E, obtain the corresponding coordinate information to obtain a second acquisition result. Based on the first marking result and the second acquisition result, conduct a check on the subsystem E;

[0016] When the system eigenvalue of the subsystem E is greater than or equal to the third threshold, use the first method to process the subsystem E, and obtain all the correlation coefficients of the subsystem E to obtain a third acquisition result. Based on the third acquisition result, process the remaining subsystems.

[0017] The present invention is realized through the following technical solutions: First, construct an entity three-dimensional model of the hospital through the model unit, and initialize the positions of multiple hardware devices in the entity three-dimensional model through the initialization unit. At the same time, assign coordinate information to each hardware device, and then collect data through the hardware devices in the data collection unit and parse it to obtain service data; The entire hospital logistics management system includes multiple subsystems (such as energy management, equipment management, and environmental monitoring, etc.), and the data collection unit completes the collection of service data for each subsystem;

[0018] The business data collected by each subsystem is processed by the data processing unit. In the actual hospital logistics management system, there will be cross - overs of business data between multiple subsystems. Therefore, this solution utilizes the characteristic of cross - over of business data between different subsystems to calculate the coincidence degree of business data of any two subsystems to obtain a calculation result. Based on the calculation result, a correlation coefficient is assigned to any two subsystems. The purpose is that when a business warning or risk occurs in one subsystem, corresponding measures can be taken for the other subsystem based on this correlation coefficient, which not only solves the fragmentation problem between different subsystems but also improves the management efficiency of the entire logistics management system;

[0019] The data integration unit calculates the system eigenvalue of each subsystem by using the first formula. The system eigenvalue includes the energy consumption value and risk value of the hardware device. Whether there is a business warning or risk in the system is judged through this system eigenvalue;

[0020] Finally, the comprehensive management unit takes different measures based on the integration result of the data integration unit. Specifically, a first threshold, a second threshold, and a third threshold are set. When the system eigenvalue of a single subsystem is less than or equal to the first threshold, no action is taken; when the system eigenvalue of a single subsystem is greater than the first threshold and less than the second threshold, the first method is used to process the single subsystem at this time, that is, the hardware devices included in the subsystem are marked in the entity three - dimensional model. At the same time, the business data includes coordinate information. Through the marking result and the corresponding coordinate information, the corresponding subsystem is investigated; when the system eigenvalue of a single subsystem is greater than or equal to the third threshold, the first method is also used to process the corresponding subsystem, but at the same time, all the correlation coefficients of the subsystem are obtained to get an acquisition result. Through this acquisition result, the subsystems related to this subsystem are processed.

[0021] As an alternative technical solution, the second formula is used to calculate the energy consumption value of the hardware device corresponding to the same first business data in each subsystem:

[0022]

[0023] t is a preset time period, and ΔS j is the change amount of the total energy consumption value of the hardware device corresponding to the same first business data within the preset time period.

[0024] As an alternative technical solution, the third formula is used to calculate the risk value of the hardware device corresponding to the same first business data in each subsystem:

[0025]

[0026] P k is the total device risk value of the hardware device corresponding to the same first business data in each subsystem, and Qk The total monitoring risk value of the hardware devices corresponding to the same first business data in each subsystem.

[0027] As an alternative technical solution, calculating the total device risk value of the hardware devices corresponding to the same first business data in each subsystem includes:

[0028] Obtain the service life data, warranty frequency data, and failure frequency data of a single hardware device in a single subsystem;

[0029] Perform data interval partitioning on the service life data, the warranty frequency data, and the failure frequency data in sequence, and assign values to each divided data interval to obtain a first assignment result;

[0030] Based on the first assignment result, convert the service life data of a single hardware device into a service life risk value, the warranty frequency data into a warranty risk value, and the failure frequency data into a failure risk value;

[0031] Calculate the sum of the service life risk value, the warranty risk value, and the failure risk value of a single hardware device to obtain the device risk value of a single hardware device;

[0032] Sum up the device risk values of all hardware devices included in the same first business data in a single subsystem to obtain the total device risk value of the hardware devices corresponding to the same first business data.

[0033] As an alternative technical solution, calculating the total monitoring risk value of the hardware devices corresponding to the same first business data in each subsystem includes:

[0034] Obtain the monitoring data of a single hardware device in a single subsystem;

[0035] Perform data interval partitioning on the monitoring data, and assign values to each divided data interval to obtain a second assignment result;

[0036] Based on the second assignment result, convert the monitoring data of a single hardware device into a monitoring risk value to obtain the monitoring risk value of a single hardware device;

[0037] Sum up the monitoring risk values of all hardware devices included in the same first business data in a single subsystem to obtain the total monitoring risk value of the hardware devices corresponding to the same first business data.

[0038] As an alternative technical solution, based on the third acquisition result, processing the remaining subsystems includes:

[0039] Obtain any subsystem other than subsystem E among all subsystems, denoted as subsystem F;

[0040] Obtain the correlation coefficient between the subsystem E and the subsystem F and denote it as the first correlation coefficient;

[0041] Judge whether the first correlation coefficient is 0. If it is, do nothing. If not, process it using the second method.

[0042] As an alternative technical solution, the second method includes:

[0043] Judge whether the first correlation coefficient is greater than a preset value. If it is, process the subsystem F using the first method. If not, obtain the hardware devices corresponding to all the overlapping first service data of the subsystem E and the subsystem F and denote them as the first hardware devices;

[0044] Mark all the first hardware devices included in the subsystem F in the entity three-dimensional model and obtain the corresponding coordinate information to get the second marking result. Based on the second marking result, conduct a check on the subsystem F.

[0045] As an alternative technical solution, calculating the coincidence degree of the first service data in any two data sets includes:

[0046] The data set C corresponds to the subsystem c, and the data set D corresponds to the subsystem d. Judge whether there is the same first service data in the data set C and the data set D. If there is, denote the same first service data in the data set C and the data set D as the second service data, and calculate the coincidence degree of the data set C and the data set D using the fourth formula. If not, set the correlation coefficient between the subsystem c and the subsystem d to 0;

[0047] The fourth formula is:

[0048]

[0049] η is the coincidence degree of the first service data in the data set C and the data set D, H C is the data volume of all the first service data in the system c per unit time, H D is the data volume of all the first service data in the system d per unit time, H i is the data volume of all the second service data in the subsystem c or the subsystem d per unit time, and n is the number of the second service data in the subsystem c or the subsystem d.

[0050] As an alternative technical solution, based on the calculation result, setting the correlation coefficient between any one subsystem and other subsystems includes:

[0051] Obtain all historical data of any two subsystems, construct a correlation model based on the coincidence degree of the first business data corresponding to the two subsystems, train the model, and set a correlation coefficient for any two subsystems based on the trained correlation model.

[0052] To solve the deficiencies of the existing technology, the present invention provides a hospital logistics management method based on digital twin, and the method includes the following steps:

[0053] Construct a physical three-dimensional model of the hospital based on the BIM model and the UE virtual engine;

[0054] Initialize the positions of multiple hardware devices in the physical three-dimensional model and assign coordinate information to all hardware devices;

[0055] Collect data based on all hardware devices and perform data parsing to obtain business data, and all business data includes corresponding coordinate information;

[0056] Obtain the first acquisition result by getting the hardware devices included in each subsystem. Based on the first acquisition result and all business data, obtain the business data included in each subsystem, denoted as the first business data. Store the first business data of each subsystem in the corresponding dataset, calculate the coincidence degree of the first business data in any two datasets to obtain a calculation result, and based on the calculation result, set the correlation coefficient of any one subsystem with other subsystems. All correlation coefficients are greater than or equal to 0 and less than 1;

[0057] Calculate the system eigenvalue of each subsystem using the first formula based on all the first business data of each subsystem;

[0058] The first formula includes:

[0059]

[0060] is the system eigenvalue of each subsystem, α j is the energy consumption value of the hardware device corresponding to the same first business data in each subsystem, β k is the risk value of the hardware device corresponding to the same first business data in each subsystem, and r is the number of different first business data in each subsystem;

[0061] Set a first threshold, a second threshold, and a third threshold for the system eigenvalue of each subsystem. The first threshold, the second threshold, and the third threshold in each subsystem are all different;

[0062] Obtain a single subsystem, denoted as subsystem E. When the system eigenvalue of subsystem E is less than or equal to the first threshold, do not act;

[0063] When the system eigenvalue of the subsystem E is greater than the second threshold and less than the third threshold, the first method is adopted to process the subsystem E; the first method is: annotate the hardware devices included in the subsystem E in the solid three-dimensional model to obtain a first annotation result, and based on the first service data of the subsystem E, obtain the corresponding coordinate information to obtain a second acquisition result, and based on the first annotation result and the second acquisition result, conduct a check on the subsystem E;

[0064] When the system eigenvalue of the subsystem E is greater than or equal to the third threshold, the first method is adopted to process the subsystem E, and all the correlation coefficients of the subsystem E are obtained to obtain a third acquisition result, and based on the third acquisition result, the remaining subsystems are processed.

[0065] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0066] The present invention provides a hospital logistics management system based on digital twin. Through the model unit and the initialization unit, a three-dimensional solid model of the hospital is constructed and the hardware devices are initialized. The service data is obtained through the data acquisition unit. Considering the cross-characteristic of the service data between multiple subsystems, the data processing unit is set to calculate the coincidence degree of the service data between two subsystems, so as to set the correlation coefficient for the two subsystems. The system eigenvalue of the subsystem is calculated through the first formula in the data integration unit. The system eigenvalue includes the energy consumption value and the risk value of the hardware device. The system eigenvalue is used as the determination criterion for whether there is a service warning or risk in a single subsystem. And the comprehensive management unit takes corresponding measures to process according to the system eigenvalues in different ranges, which greatly improves the management efficiency of the entire hospital logistics management system. Description of the Drawings

[0067] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of the present invention, and do not constitute a limitation to the embodiments of the present invention;

[0068] Figure 1 is a schematic diagram of the composition of a hospital logistics management system based on digital twin in the present invention;

[0069] Figure 2 is a schematic diagram of the flow of a hospital logistics management method based on digital twin in the present invention. Detailed Embodiments

[0070] In order to be able to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0071] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described within the scope hereof. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0072] Embodiment 1

[0073] Please refer to Figure 1 , Figure 1 , which is a schematic diagram of the composition of a hospital logistics management system based on digital twin in the present invention. The system includes:

[0074] Model unit: Based on the BIM model and the UE virtual engine, construct a three-dimensional entity model of the hospital;

[0075] Initialization unit: Initialize the positions of multiple hardware devices in the three-dimensional entity model and assign coordinate information to all hardware devices;

[0076] Data acquisition unit: Based on all hardware devices, collect data and perform data parsing to obtain business data. All business data includes corresponding coordinate information;

[0077] Data processing unit: Obtain the first acquisition result by obtaining the hardware devices included in each subsystem. Based on the first acquisition result and all business data, obtain the business data included in each subsystem, denoted as the first business data. Store the first business data of each subsystem in the corresponding data set, calculate the coincidence degree of the first business data in any two data sets to obtain a calculation result. Based on the calculation result, set the correlation coefficient of any one subsystem with other subsystems. All correlation coefficients are greater than or equal to 0 and less than 1;

[0078] Data integration unit: Based on all the first business data of each subsystem, calculate the system eigenvalue of each subsystem using the first formula;

[0079] The first formula includes:

[0080]

[0081] is the system eigenvalue of each subsystem, α j is the energy consumption value of the hardware device corresponding to the same first business data in each subsystem, β k is the risk value of the hardware device corresponding to the same first business data in each subsystem, and r is the number of different first business data in each subsystem;

[0082] Integrated Management Unit: Set the first threshold, the second threshold, and the third threshold for the system characteristic values of each subsystem. The first threshold, the second threshold, and the third threshold in each subsystem are all different;

[0083] Obtain a single subsystem denoted as Subsystem E. When the system characteristic value of Subsystem E is less than or equal to the first threshold, do not act;

[0084] When the system characteristic value of Subsystem E is greater than the second threshold and less than the third threshold, process Subsystem E using the first method; The first method is: Mark the hardware devices included in Subsystem E in the entity three-dimensional model to obtain a first marking result, and based on the first business data of Subsystem E, obtain the corresponding coordinate information to obtain a second acquisition result. Based on the first marking result and the second acquisition result, conduct a check on Subsystem E;

[0085] When the system characteristic value of Subsystem E is greater than or equal to the third threshold, process Subsystem E using the first method, and obtain all the correlation coefficients of Subsystem E to obtain a third acquisition result. Based on the third acquisition result, process the remaining subsystems.

[0086] The specific embodiments of the present invention are as follows:

[0087] Model Unit: BIM model, which is translated into Building Information Model in Chinese. This technology uses digital means to create a virtual building in a computer. This virtual building will provide a single, complete, and logically related building information database; The UE Unreal Engine is a powerful and flexible real-time 3D creation tool. By applying the BIM model and the UE Unreal Engine, an entity three-dimensional model of the hospital is constructed.

[0088] Initialization Unit: Obtain the actual location information of the hardware devices in the hospital, initialize the positions of each hardware device in the entity three-dimensional model, and assign coordinate information to each hardware device. The coordinate information can be combined using building number, floor, and location data. The hardware devices include but are not limited to water meters, electricity meters, smoke sensors, humidity monitors, etc.

[0089] Data Acquisition Unit: Collect data through all the hardware devices in each subsystem and parse it to obtain the business data of each subsystem.

[0090] Data processing unit: Denote the business data included in each subsystem as the first business data, store the first business data of each subsystem into the corresponding dataset, calculate the coincidence degree of the first business data in any two datasets to obtain the calculation result, that is, the coincidence degree of the two subsystems. Since in the actual hospital logistics management system, there will be cross - business data among multiple subsystems, when there is a business warning or risk in one subsystem, it will also affect other subsystems. Therefore, in this embodiment, based on the calculation result, correlation coefficients are set for any two subsystems, and corresponding measures are taken based on different correlation coefficients.

[0091] This embodiment provides a method for calculating the coincidence degree of the first business data in any two datasets, specifically:

[0092] Dataset C corresponds to subsystem c, and dataset D corresponds to subsystem d. Determine whether there is the same first business data in dataset C and dataset D. If so, denote the same first business data in dataset C and dataset D as the second business data, and calculate the coincidence degree of dataset C and dataset D using the fourth formula. If not, set the correlation coefficient of subsystem c and subsystem d to 0;

[0093] The fourth formula is:

[0094]

[0095] η is the coincidence degree of the first business data in dataset C and dataset D, H C is the data volume of all the first business data in system c within a unit time, H D is the data volume of all the first business data in system d within a unit time, H i is the data volume of all the second business data in subsystem c or subsystem d within a unit time, and n is the number of the second business data in subsystem c or subsystem d.

[0096] This embodiment provides a method for setting the correlation coefficient of any one subsystem with other subsystems, specifically:

[0097] Obtain all the historical data of any two subsystems, construct a correlation model based on the coincidence degree of the first business data corresponding to the two subsystems and train it. Based on the trained correlation model, set the correlation coefficients for any two subsystems.

[0098] Data integration unit: Calculate the system eigenvalue of each subsystem through the first formula. The system eigenvalue includes the energy consumption value and risk value of the hardware device. That is, when the energy consumption of the device is too high or the risk of the device (including the device itself and the monitoring data) is too high, it is determined that there is a business warning or risk in the subsystem. The first formula includes:

[0099]

[0100] is the system eigenvalue of each subsystem, and α j is the energy consumption value of the hardware device corresponding to the same first business data in each subsystem, and β k is the risk value of the hardware device corresponding to the same first business data in each subsystem, and r is the number of different first business data in each subsystem;

[0101] Calculate the energy consumption value of the hardware device corresponding to the same first business data in each subsystem by using the second formula:

[0102]

[0103] t is the preset time period, and ΔS j is the change amount of the total energy consumption value of the hardware device corresponding to the same first business data during the preset time period;

[0104] Calculate the risk value of the hardware device corresponding to the same first business data in each subsystem by using the third formula:

[0105]

[0106] P k is the total device risk value of the hardware device corresponding to the same first business data in each subsystem, and Q k is the total monitoring risk value of the hardware device corresponding to the same first business data in each subsystem.

[0107] Furthermore, the calculation of the total device risk value of the hardware device corresponding to the same first business data in each subsystem includes:

[0108] Obtain the service life data, warranty frequency data, and failure frequency data of a single hardware device in a single subsystem;

[0109] Perform data interval division on the service life data, the warranty frequency data, and the failure frequency data in sequence, and assign values to each divided data interval to obtain the first assignment result;

[0110] Based on the first assignment result, convert the service life data of a single hardware device into a life risk value, the warranty frequency data into a warranty risk value, and the failure frequency data into a failure risk value;

[0111] Calculate the sum of the age risk value, the warranty risk value, and the failure risk value of a single hardware device to obtain the device risk value of the single hardware device;

[0112] Sum up the device risk values of all hardware devices included in the same first service data in a single subsystem to obtain the total device risk value of the hardware devices corresponding to the same first service data.

[0113] Among them, if the single hardware device obtained is a smoke detector, obtain the service life data, warranty frequency data, and failure frequency data of the smoke detector, and by dividing the data intervals and assigning values, convert the service life data, insurance frequency data, and failure frequency data of the smoke detector into the corresponding age risk value, warranty risk value, and failure risk value, calculate the sum of the age risk value, warranty risk value, and failure risk value in a single smoke detector to obtain the device risk value of the single smoke detector, and the sum of the device risk values of multiple smoke detectors is the total device risk value of the hardware devices corresponding to this service data.

[0114] Further, calculating the total monitoring risk value of the hardware devices corresponding to the same first service data in each subsystem includes:

[0115] Obtain the monitoring data of a single hardware device in a single subsystem;

[0116] Divide the monitoring data into data intervals, and assign values to each divided data interval to obtain a second assignment result;

[0117] Based on the second assignment result, convert the monitoring data of a single hardware device into a monitoring risk value to obtain the monitoring risk value of the single hardware device;

[0118] Sum up the monitoring risk values of all hardware devices included in the same first service data in a single subsystem to obtain the total monitoring risk value of the hardware devices corresponding to the same first service data.

[0119] Among them, for example, when calculating the total monitoring risk value for a humidity sensor, the monitoring data obtained for the humidity sensor is humidity, divide multiple data intervals for the humidity, and assign values to each divided data interval. The significance of assigning values through data intervals is to enable all different monitoring data to be calculated under the same measurement system. By dividing the data intervals and assigning values, convert the obtained humidity data into a monitoring risk value, and the sum of the monitoring risk values of multiple humidity sensors is the total monitoring risk value of the hardware devices corresponding to this service data.

[0120] Integrated management unit: For example, if a single subsystem is obtained and denoted as subsystem E, the first threshold, second threshold, and third threshold of subsystem E are ε1, ε2, and ε3 respectively, and ε1 < ε2 < ε3. The system eigenvalue of subsystem E is calculated through the first formula as When it indicates that the possibility of energy consumption and equipment risks in subsystem E is relatively low at this time, and no action is taken at this time; when it indicates that the possibility of energy consumption and equipment risks in subsystem E exists at this time, and the entire subsystem E needs to be investigated. This embodiment provides a first method for investigating subsystem E, specifically: in the entity three-dimensional model, the hardware devices included in subsystem E are marked to obtain a first marking result, and based on the first service data of subsystem E, the corresponding coordinate information is obtained to obtain a second acquisition result. Based on the first marking result and the second acquisition result, subsystem E is investigated. When it indicates that the possibility of energy consumption and equipment risks in subsystem E is relatively high at this time, and there are subsystems that affect other subsystems with cross-service data with subsystem E. Therefore, first, the first method is used to investigate subsystem E, and at the same time, the correlation coefficient between subsystem E and other subsystems is obtained to obtain a third acquisition result. Based on the third acquisition result, the remaining subsystems are processed.

[0121] This embodiment provides a method for processing the remaining subsystems based on the correlation coefficient, specifically:

[0122] Obtain any subsystem other than subsystem E among all subsystems and denote it as subsystem F;

[0123] Obtain the correlation coefficient between subsystem E and subsystem F and denote it as the first correlation coefficient;

[0124] Judge whether the first correlation coefficient is 0. If so, no action is taken. If not, the second method is used for processing;

[0125] The second method includes:

[0126] Judge whether the first correlation coefficient is greater than a preset value. If so, the first method is used to process subsystem F. If not, obtain all the hardware devices corresponding to the overlapping first service data of subsystem E and subsystem F and denote them as the first hardware devices;

[0127] In the entity three-dimensional model, all the first hardware devices included in subsystem F are marked and the corresponding coordinate information is obtained to obtain a second marking result. Based on the second marking result, subsystem F is investigated.

[0128] For example, for subsystem F, the correlation coefficient between subsystem E and subsystem F is denoted as the first correlation coefficient g. Determine whether g is 0. If so, it indicates that there is no cross - business data between subsystem F and subsystem E, and no action is taken at this time. Then, determine whether g is greater than the preset value. If so, it means that the overlap degree between subsystem F and subsystem E is relatively high. When subsystem E has a business warning or risk, it is very likely that the entire subsystem F also has a business warning or risk. Therefore, the first method needs to be used to process subsystem F. The specific processing measures of the first method have been described in the above embodiments and will not be elaborated here. If not, it means that there is business intersection between subsystem F and subsystem E, but the intersection degree is not high. When subsystem E has a business warning or risk, there is a possibility that part of subsystem F has a business warning or risk. Therefore, only the hardware devices that overlap between subsystem F and subsystem E need to be processed. Specifically, the hardware devices that overlap between subsystem F and subsystem E are marked in the entity three - dimensional model to obtain the second marking result, and based on the second marking result, subsystem F is investigated. It should be noted that when investigating the hardware devices that overlap between subsystem F and subsystem E, it does not constitute duplicate work because the system characteristics of the two subsystems are different, and when investigating the same hardware device, the investigation items are not exactly the same.

[0129] In this embodiment, by constructing the entity three - dimensional model of the hospital and initializing all hardware devices, when a certain subsystem needs to be repaired or investigated, the corresponding hardware devices in the entity three - dimensional model are marked, and through the coordinate information of the hardware devices, it is convenient for the manager to plan the investigation path and allocate the investigation tasks; by obtaining the business data of each subsystem, and based on the business data, calculate the overlap degree between any two subsystems, and based on the overlap degree, set the correlation coefficient for any two subsystems; by calculating the system characteristic value of each subsystem, use the system characteristic value as the judgment benchmark for whether the subsystem has risks, and at the same time set different thresholds for the system characteristic values of different subsystems, and set different processing measures for the manager to choose according to different thresholds. And when the system characteristic value of a single subsystem exceeds or is equal to the set highest threshold, not only does the single subsystem need to be investigated, but also the subsystems related to this subsystem need to be processed, and the processing measures also correspond to the correlation coefficient between the two subsystems.

[0130] Embodiment 2

[0131] Please refer to Figure 2 , Figure 2 which is a schematic flow diagram of a hospital logistics management method based on digital twin in the present invention. The method includes the following steps:

[0132] Based on the BIM model and the UE virtual engine, construct the entity three - dimensional model of the hospital;

[0133] Initialize the positions of multiple hardware devices in the entity three-dimensional model, and assign coordinate information to all hardware devices;

[0134] Collect data based on all hardware devices and perform data parsing to obtain service data, and all service data includes corresponding coordinate information;

[0135] Obtain the hardware devices included in each subsystem to get a first acquisition result. Based on the first acquisition result and all service data, obtain the service data included in each subsystem, denoted as first service data. Store the first service data of each subsystem in the corresponding dataset, calculate the coincidence degree of the first service data in any two datasets to get a calculation result. Based on the calculation result, set the correlation coefficient of any one subsystem with other subsystems, and all correlation coefficients are greater than or equal to 0 and less than 1;

[0136] Calculate the system eigenvalue of each subsystem using the first formula based on all the first service data of each subsystem;

[0137] The first formula includes:

[0138]

[0139] is the system eigenvalue of each subsystem, α j is the energy consumption value of the hardware device corresponding to the same first service data in each subsystem, β k is the risk value of the hardware device corresponding to the same first service data in each subsystem, and r is the number of different first service data in each subsystem;

[0140] Set a first threshold, a second threshold, and a third threshold for the system eigenvalue of each subsystem, and the first threshold, the second threshold, and the third threshold in each subsystem are all different;

[0141] Obtain a single subsystem, denoted as subsystem E. When the system eigenvalue of the subsystem E is less than or equal to the first threshold, do nothing;

[0142] When the system eigenvalue of the subsystem E is greater than the second threshold and less than the third threshold, process the subsystem E using the first method; the first method is: perform annotation on the hardware devices included in the subsystem E in the entity three-dimensional model to get a first annotation result, and based on the first service data of the subsystem E, obtain the corresponding coordinate information to get a second acquisition result. Based on the first annotation result and the second acquisition result, conduct a check on the subsystem E;

[0143] When the system eigenvalue of the subsystem E is greater than or equal to the third threshold, the first method is used to process the subsystem E, and all correlation coefficients of the subsystem E are obtained to get a third acquisition result. Based on the third acquisition result, the remaining subsystems are processed.

[0144] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A hospital logistics management system based on digital twins, comprising multiple subsystems, characterized in that: The system further comprises: Model unit: Build a physical 3D model of the hospital based on the BIM model and UE Unreal Engine; Initialization unit: initializing the positions of multiple hardware devices in the solid three-dimensional model and assigning coordinate information to all hardware devices; Data collection unit: Based on all hardware devices, collect data and analyze the data to obtain business data. All business data contains corresponding coordinate information; Data processing unit: obtaining hardware devices included in each subsystem to obtain a first acquisition result, obtaining business data included in each subsystem as first business data based on the first acquisition result and all business data, storing the first business data of each subsystem in a corresponding data set, calculating the overlap of the first business data in any two data sets to obtain a calculation result, and setting a correlation coefficient between any subsystem and other subsystems based on the calculation result, and all correlation coefficients are greater than or equal to 0 and less than 1; Data integration unit: based on all first business data of each subsystem, using a first formula to calculate a system characteristic value of each subsystem; The first formula includes: is the system eigenvalue of each subsystem, α j is the energy consumption value of the hardware device corresponding to the same first business data in each subsystem, β k is the risk value of the hardware device corresponding to the same first business data in each subsystem, and r is the number of different first business data in each subsystem; Comprehensive management unit: setting a first threshold, a second threshold and a third threshold for the system characteristic value of each subsystem, wherein the first threshold, the second threshold and the third threshold in each subsystem are different; A single subsystem is obtained and recorded as subsystem E. When the system characteristic value of the subsystem E is less than or equal to the first threshold, no action is taken; When the system characteristic value of the subsystem E is greater than the second threshold value and less than the third threshold value, the subsystem E is processed by a first method; the first method is: annotating the hardware devices included in the subsystem E in the solid three-dimensional model to obtain a first annotation result, and based on the first business data of the subsystem E, obtaining corresponding coordinate information to obtain a second acquisition result, and checking the subsystem E based on the first annotation result and the second acquisition result; When the system characteristic value of the subsystem E is greater than or equal to the third threshold, the first method is used to process the subsystem E, and all correlation coefficients of the subsystem E are obtained to obtain a third acquisition result, and based on the third acquisition result, the remaining subsystems are processed.

2. According to claim 1, a hospital logistics management system based on digital twins is characterized in that: The second formula is used to calculate the energy consumption value of the hardware device corresponding to the same first business data in each subsystem: t is the preset time period, ΔS j It is the change in the total energy consumption value of the hardware device corresponding to the same first business data within the preset time period.

3. According to claim 1, a hospital logistics management system based on digital twins is characterized in that: The third formula is used to calculate the risk value of the hardware device corresponding to the same first business data in each subsystem: P k is the total equipment risk value of the hardware equipment corresponding to the same first business data in each subsystem, Q k The total monitoring risk value of the hardware equipment corresponding to the same first business data in each subsystem.

4. A hospital logistics management system based on digital twins according to claim 3, characterized in that: Calculating the total equipment risk value of the hardware equipment corresponding to the same first business data in each subsystem includes: Obtain service life data, warranty frequency data, and failure frequency data for a single hardware device in a single subsystem; The service life data, the warranty frequency data and the failure frequency data are divided into data intervals in sequence, and each divided data interval is assigned a value to obtain a first assignment result; Based on the first assignment result, the service life data of a single hardware device is converted into a service life risk value, the warranty frequency data is converted into a warranty risk value, and the failure frequency data is converted into a failure risk value; Calculate the sum of the life risk value, the warranty risk value and the failure risk value of a single hardware device to obtain a device risk value of the single hardware device; The equipment risk values ​​of all hardware devices included in the same first business data in a single subsystem are summed to obtain the total equipment risk value of the hardware devices corresponding to the same first business data.

5. According to claim 3, a hospital logistics management system based on digital twins is characterized in that: Calculating the total monitoring risk value of the hardware device corresponding to the same first business data in each subsystem includes: Get monitoring data of a single hardware device in a single subsystem; Dividing the monitoring data into data intervals, and assigning a value to each divided data interval to obtain a second assignment result; Based on the second assignment result, the monitoring data of the single hardware device is converted into a monitoring risk value to obtain a monitoring risk value of the single hardware device; The monitoring risk values ​​of all hardware devices included in the same first business data in a single subsystem are summed to obtain the total monitoring risk value of the hardware devices corresponding to the same first business data.

6. A hospital logistics management system based on digital twins according to claim 1, characterized in that: Based on the third acquisition result, processing the remaining subsystems includes: Obtain any subsystem among all subsystems except the subsystem E and record it as subsystem F; Obtaining a correlation coefficient between the subsystem E and the subsystem F as a first correlation coefficient; Determine whether the first correlation coefficient is 0, if so, do nothing, if not, use the second method to process.

7. A hospital logistics management system based on digital twins according to claim 6, characterized in that: The second method comprises: Determine whether the first correlation coefficient is greater than a preset value. If so, process the subsystem F using the first method. If not, obtain the hardware devices corresponding to all the overlapping first service data of the subsystem E and the subsystem F as the first hardware devices. All first hardware devices included in the subsystem F are marked in the physical three-dimensional model and corresponding coordinate information is obtained to obtain a second marking result, and the subsystem F is checked based on the second marking result.

8. According to claim 1, a hospital logistics management system based on digital twins is characterized in that: Calculating the overlap of the first business data in any two data sets includes: Data set C corresponds to subsystem c, and data set D corresponds to subsystem d. It is determined whether the data set C and the data set D have the same first business data. If so, the first business data in the data set C that is the same as that in the data set D is recorded as the second business data, and the fourth formula is used to calculate the overlap between the data set C and the data set D. If not, the correlation coefficient between the subsystem c and the subsystem d is set to 0. The fourth formula includes: η is the overlap degree of the first business data in the data set C and the data set D, H C is the data volume of all first service data in the system c per unit time, H D is the data volume of all first service data in the system d per unit time, H i is the data volume of all the second business data in the subsystem c or the subsystem d per unit time, and n is the number of the second business data in the subsystem c or the subsystem d.

9. A hospital logistics management system based on digital twins according to claim 1, characterized in that: Based on the calculation results, the correlation coefficients of any subsystem with other subsystems are set as follows: All historical data of any two subsystems are obtained, and based on the overlap of the first business data corresponding to the two subsystems, a correlation model is constructed and trained, and based on the trained correlation model, a correlation coefficient is set for any two subsystems.

10. A hospital logistics management method based on digital twins, characterized in that: The method comprises the following steps: Build a physical 3D model of the hospital based on the BIM model and UE Unreal Engine; Initializing the positions of multiple hardware devices in the solid three-dimensional model and assigning coordinate information to all the hardware devices; Based on all hardware devices, data is collected and analyzed to obtain business data. All business data contains corresponding coordinate information; Acquire the hardware devices included in each subsystem to obtain a first acquisition result; based on the first acquisition result and all business data, acquire the business data included in each subsystem as first business data; store the first business data of each subsystem in a corresponding data set; calculate the overlap of the first business data in any two data sets to obtain a calculation result; and based on the calculation result, set a correlation coefficient between any subsystem and other subsystems, and all correlation coefficients are greater than or equal to 0 and less than 1; Based on all the first business data of each subsystem, a system characteristic value of each subsystem is calculated using a first formula; The first formula includes: is the system eigenvalue of each subsystem, α j is the energy consumption value of the hardware device corresponding to the same first business data in each subsystem, β k is the risk value of the hardware device corresponding to the same first business data in each subsystem, and r is the number of different first business data in each subsystem; A first threshold, a second threshold and a third threshold are set for the system characteristic value of each subsystem, and the first threshold, the second threshold and the third threshold in each subsystem are different; A single subsystem is obtained and recorded as subsystem E. When the system characteristic value of the subsystem E is less than or equal to the first threshold, no action is taken; When the system characteristic value of the subsystem E is greater than the second threshold value and less than the third threshold value, the subsystem E is processed by a first method; the first method is: annotating the hardware devices included in the subsystem E in the solid three-dimensional model to obtain a first annotation result, and based on the first business data of the subsystem E, obtaining corresponding coordinate information to obtain a second acquisition result, and checking the subsystem E based on the first annotation result and the second acquisition result; When the system characteristic value of the subsystem E is greater than or equal to the third threshold, the first method is used to process the subsystem E, and all correlation coefficients of the subsystem E are obtained to obtain a third acquisition result, and based on the third acquisition result, the remaining subsystems are processed.

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