Establishment method and management system of smart medical multi-mode digital twinborn model

Abstract

The invention belongs to the field of medical equipment supervision, and particularly relates to an establishment method and a management system of an intelligent medical multi-mode digital twinning model, and the method comprises the steps: marking the equipment type information and position information of medical equipment into a medical institution digital twinning model through the establishment of the medical institution digital twinning model, and carrying out the management of the medical institution digital twinning model. By obtaining the actual action video and the action standard image data in the operation process of the medical equipment, the repeated area ratio of the standard image to the same action standard image is judged, the operation state of the medical equipment can be reflected preliminarily, a doctor can judge the illness state conveniently, misdiagnosis caused by the medical equipment is avoided, and the diagnosis efficiency is improved. By combining the repeated area ratio, the equipment mainboard temperature difference characterization value and the working electric energy coefficient characterization value, whether the medical equipment is abnormal or not can be more accurately judged, the accuracy of the operation result of the medical equipment can be judged, the fault problem can be found in time, the position of the medical equipment can be quickly positioned, and the response speed is high.

Description

Technical Field

[0001] The present invention belongs to the field of medical device supervision, and specifically relates to a method for establishing and a management system of a smart medical multi-modal digital twin model. Background Art

[0002] With the continuous progress of medical technology and the increasing medical needs, the complexity and quantity of medical devices are both increasing. Traditional management methods often rely on manual inspections and regular maintenance, which are not only inefficient but also difficult to detect and solve potential problems in a timely manner. As an emerging digital means, digital twin technology can achieve real-time monitoring and predictive analysis of device status by constructing virtual models corresponding one-to-one with physical devices, providing a new solution for device maintenance and management.

[0003] There are still some problems in the application of existing digital twin technology in the medical field, such as single data collection, only regularly checking the device by recording the maintenance time, but unable to judge the actual operating condition of the device, unable to judge whether the device operation monitoring results are accurate according to multiple parameter factors, prone to incorrect device monitoring results, and slow response speed in the whole process.

[0004] Therefore, the present invention provides a method for establishing and a management system of a smart medical multi-modal digital twin model. Summary of the Invention

[0005] In order to make up for the deficiencies of the prior art and solve at least one of the technical problems proposed in the background art.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows: A management system of a smart medical multi-modal digital twin model according to the present invention includes:

[0007] S1. Establish a digital twin model of a medical institution, and calibrate medical device information in the digital twin model of the medical institution;

[0008] S2. Shoot the actions during the operation of the medical device to obtain the actual action video; obtain the standard image data of the actions during the operation of the medical device, intercept the standard pictures from the actual action video, intercept the same-action standard pictures from the standard image data, compare the standard pictures with the same-action standard pictures to obtain the repeated area ratio, and compare the repeated area ratio with the area ratio threshold to obtain an action abnormal signal or an action standard signal;

[0009] S3. According to the action abnormal signal, obtain the medical device information corresponding to the actual action video and generate a device monitoring analysis signal;

[0010] S4. Analyze the signal according to the device monitoring, obtain the temperature difference characterization value of the device main board and the working power coefficient characterization value of the medical device, analyze the device deviation coefficient based on the repeated area ratio, the temperature difference characterization value of the device main board and the working power coefficient characterization value, compare the device deviation coefficient with the preset deviation coefficient threshold to obtain an operation signal, and manage and control the medical device based on the operation signal.

[0011] Preferably, compare the repeated area ratio with the area ratio threshold to obtain an action abnormal signal or an action standard signal, specifically:

[0012] If the repeated area ratio is greater than the area ratio threshold, it indicates that the overlapping area of the standard picture and the same action standard picture is large, and an action standard signal is generated;

[0013] If the repeated area ratio is less than or equal to the area ratio threshold, it indicates that the overlapping area of the standard picture and the same action standard picture is small, and an action abnormal signal is generated.

[0014] Preferably, the operation signal includes a device power-off signal, an alarm signal, and a no-abnormality signal.

[0015] Preferably, compare the device deviation coefficient with the preset deviation coefficient threshold to obtain an operation signal, specifically:

[0016] The preset deviation coefficient threshold includes a first deviation threshold and a second deviation threshold, where the first deviation threshold is greater than the second deviation threshold;

[0017] If the device deviation coefficient is greater than the first deviation threshold, it indicates that the device deviation coefficient is too large, the device operation result is incorrect, and it has no reference meaning. Generate a device power-off signal for automatic power-off processing, and relevant staff need to be arranged for maintenance;

[0018] If the device deviation coefficient is less than or equal to the first deviation threshold and the device deviation coefficient is greater than or equal to the second deviation threshold, it indicates that the device operation result may be abnormal, and an alarm signal is generated to remind the staff to pay attention to inspection;

[0019] If the device deviation coefficient is less than the second deviation threshold, it indicates that the device operation result is accurate and normal, and a no-abnormality signal is generated.

[0020] Preferably, the acquisition method of the temperature difference characterization value of the device main board of the medical device is specifically:

[0021] Set a detection period, divide the monitoring period into several acquisition moments, obtain the temperature of the device main board and the temperature of the device outer surface at each acquisition moment, obtain the ratio of the temperature of the device main board to the temperature of the device outer surface, and record it as the temperature difference characterization value of the device main board at the current acquisition moment; sum and average the temperature difference characterization values of all acquisition moments to obtain the temperature difference characterization value of the device main board of the medical device.

[0022] Preferably, the working electrical energy coefficient characterization value of the medical device is obtained by summing the current coefficient value and the voltage coefficient value; wherein, the obtaining method of the current coefficient value is as follows:

[0023] Obtain the current values at all acquisition times, calculate the difference between the current value at each acquisition time and the rated current of the medical device respectively to obtain the current difference, and compare the current difference with the current difference threshold:

[0024] If the current difference is greater than or equal to the current difference threshold, mark the current acquisition time as the current abnormal time;

[0025] If the current difference is less than the current difference threshold, mark the current acquisition time as the current normal time;

[0026] Obtain the ratio of the number of current normal times to the number of current abnormal times within the detection period, and denote it as the current coefficient value.

[0027] Preferably, the obtaining method of the voltage coefficient value is as follows:

[0028] Obtain the voltage values at all acquisition times, calculate the difference between the voltage value at each acquisition time and the rated voltage of the medical device respectively to obtain the voltage difference, and compare the voltage difference with the voltage difference threshold:

[0029] If the voltage difference is greater than or equal to the voltage difference threshold, mark the current acquisition time as the voltage abnormal time;

[0030] If the voltage difference is less than the voltage difference threshold, mark the current acquisition time as the voltage normal time;

[0031] Obtain the ratio of the number of voltage normal times to the number of voltage abnormal times within the detection period, and denote it as the voltage coefficient value.

[0032] Preferably, the device deviation coefficient is obtained by analyzing the repeated area ratio, the device main board temperature difference characterization value, and the working electrical energy coefficient characterization value, specifically:

[0033] Mark the repeated area ratio, the device main board temperature difference characterization value, and the working electrical energy coefficient characterization value as CFB, WCB, and DNB respectively, and obtain the device deviation coefficient PL through the formula where a1, a2, and a3 are all coefficient factors.

[0034] Preferably, a method for establishing a smart medical multi-modal digital twin model is as follows:

[0035] Build a simulation 3D model of the medical institution building. Based on the simulation 3D model of the medical institution building, mark the building information, and mark the positions of the departments on the corresponding floors in each building. According to the marked information, obtain the medical equipment information in the departments, where the medical equipment information includes equipment type information and location information, and calibrate the medical equipment information in the simulation 3D model of the medical institution building to obtain a digital twin model of the medical institution.

[0036] Preferably, building a simulation 3D model of the medical institution building is specifically as follows:

[0037] According to the architectural design drawings of the medical institution building, start modeling from the foundation part of the building through 3D modeling software;

[0038] Use the basic geometric bodies in the 3D modeling software to build a rough framework of the medical institution building;

[0039] According to the actual environment around the building, add environmental elements to enhance the realism and vividness of the scene, and complete the establishment of the simulation 3D model of the medical institution building.

[0040] The beneficial effects of the present invention are as follows:

[0041] 1. For the method for establishing and management system of a smart medical multi-modal digital twin model described in the present invention, by establishing a digital twin model of the medical institution, the equipment type information and location information of the medical equipment are marked in the digital twin model of the medical institution. By obtaining the actual action video and the standard image data of the actions during the operation of the medical equipment, and judging the ratio of the overlapping area between the standard pictures and the same action standard pictures, it can initially reflect the operation state of the medical equipment, which is beneficial for doctors to diagnose diseases and avoid misdiagnosis caused by medical equipment reasons;

[0042] 2. For the method for establishing and management system of a smart medical multi-modal digital twin model described in the present invention, after obtaining an abnormal action signal, by acquiring the medical device information corresponding to the actual action video, generating a device monitoring and analysis signal, it can remind the relevant medical staff in the corresponding department to pay attention to the operation result of the medical device. Then, acquire the temperature difference characterization value of the device main board and the working power coefficient characterization value of the medical device, analyze to obtain a device deviation coefficient based on the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value, compare the device deviation coefficient with a preset deviation coefficient threshold to obtain an operation signal, and manage and control the medical device based on the operation signal, so as to realize secondary analysis and monitoring of the operation information of the medical device. Combining factors such as the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value, it can more accurately determine whether the medical device is abnormal, judge the accuracy of the operation result of the medical device, timely discover fault problems, and can quickly locate the position of the medical device and notify the medical staff in the relevant department, with a fast response speed. Description of the Drawings

[0043] The present invention will be further described below with reference to the drawings.

[0044] Figure 1 is the flowchart of the management system of a smart medical multi-modal digital twin model of the present invention;

[0045] Figure 2 is the method flowchart of the method for establishing a smart medical multi-modal digital twin model of the present invention. Detailed Embodiments

[0046] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0047] Embodiment 1

[0048] As Figure 1 shown, a management system of a smart medical multi-modal digital twin model described in an embodiment of the present invention includes:

[0049] S1. Establish a digital twin model of a medical institution, and calibrate medical device information in the digital twin model of the medical institution;

[0050] S2. Shoot the actions during the operation of the medical device to obtain an actual action video; obtain the standard image data of the actions during the operation of the medical device, intercept the actual action video to obtain standard pictures, intercept the standard image data to obtain standard pictures of the same actions, compare the standard pictures with the standard pictures of the same actions to obtain a repeated area ratio, compare the repeated area ratio with an area ratio threshold to obtain an abnormal action signal or an action standard signal;

[0051] S3. Obtain the medical device information corresponding to the actual action video according to the action anomaly signal, and generate a device monitoring and analysis signal;

[0052] S4. Obtain the temperature difference characterization value of the device main board and the working power coefficient characterization value of the medical device according to the device monitoring and analysis signal. Analyze the device deviation coefficient based on the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value. Compare the device deviation coefficient with the preset deviation coefficient threshold to obtain an operation signal, and perform management and control on the medical device based on the operation signal.

[0053] Specifically, by establishing a digital twin model of a medical institution, the device type information and location information of the medical device are marked in the digital twin model of the medical institution. By obtaining the actual action video and the standard image data of the actions during the operation of the medical device, and judging the repeated area ratio between the standard picture and the same-action standard picture, it can initially reflect the operating state of the medical device, which is beneficial for doctors to distinguish the condition and avoid misdiagnosis caused by medical device reasons;

[0054] Based on the action anomaly signal, obtain the medical device information corresponding to the actual action video, and generate a device monitoring and analysis signal, which can remind the relevant medical staff in the corresponding department to pay attention to the operation result of the medical device. According to the device monitoring and analysis signal, obtain the temperature difference characterization value of the device main board and the working power coefficient characterization value of the medical device. Analyze the device deviation coefficient based on the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value. Compare the device deviation coefficient with the preset deviation coefficient threshold to obtain an operation signal, and perform management and control on the medical device based on the operation signal, so as to realize secondary analysis and monitoring of the operation information of the medical device. Combining factors such as the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value, it can more accurately judge whether the medical device is abnormal, determine the accuracy of the operation result of the medical device, timely discover fault problems, and can quickly locate the position of the medical device and notify the medical staff in the relevant department, with a fast response speed.

[0055] In one embodiment, compare the repeated area ratio with an area ratio threshold (the area ratio threshold is set by those skilled in the art according to historical experience) to obtain an action anomaly signal or an action standard signal. Specifically:

[0056] If the repeated area ratio is greater than the area ratio threshold, it indicates that the overlapping area between the standard picture and the same-action standard picture is large, and an action standard signal is generated;

[0057] If the repeated area ratio is less than or equal to the area ratio threshold, it indicates that the overlapping area between the standard picture and the same-action standard picture is small, and an action anomaly signal is generated.

[0058] It should be noted that the action standard signal is obtained by calculating the ratio of the overlapping area between the standard picture and the same-action standard picture, and when the ratio of the overlapping area is greater than the area ratio threshold, the greater the ratio of the overlapping area, the higher the degree of similarity between the standard picture and the same-action standard picture, the more standard the action, and the smaller the possibility of errors in the operation result of the device. When generating the action standard signal, it indicates that the device is operating normally and can work properly; while the action abnormal signal is obtained when the ratio of the overlapping area is less than or equal to the area ratio threshold, indicating that the degree of similarity between the standard picture and the same-action standard picture is small, and the possibility of errors in the operation result of the device is greater. It is necessary to further analyze the reasons for the abnormal action of the device to judge whether the operation result of the device is accurate, which is of great significance for the accuracy of the doctor's subsequent disease diagnosis; by obtaining the actual action video and the action standard image data during the operation of the medical device, and judging the ratio of the overlapping area between the standard picture and the same-action standard picture, it can initially reflect the operation state of the medical device, which is beneficial for the doctor to diagnose the disease and avoid misdiagnosis caused by the medical device.

[0059] In one embodiment, the operation signal includes a device power-off signal, an alarm signal, and a no-abnormality signal.

[0060] In one embodiment, the device deviation coefficient is compared with a preset deviation coefficient threshold to obtain an operation signal, specifically:

[0061] The preset deviation coefficient threshold includes a first deviation threshold and a second deviation threshold (both the first deviation threshold and the second deviation threshold are summarized by those skilled in the art based on historical experience), where the first deviation threshold is greater than the second deviation threshold;

[0062] If the device deviation coefficient is greater than the first deviation threshold, it indicates that the device deviation coefficient is too large, the operation result of the device is incorrect, and there is no reference significance. A device power-off signal is generated for automatic power-off processing, and relevant staff need to be arranged for maintenance;

[0063] If the device deviation coefficient is less than or equal to the first deviation threshold and the device deviation coefficient is greater than or equal to the second deviation threshold, it indicates that the operation result of the device may be abnormal, and an alarm signal is generated to remind the staff to pay attention to inspection;

[0064] If the device deviation coefficient is less than the second deviation threshold, it indicates that the operation result of the device is accurate and abnormal-free, and a no-abnormality signal is generated.

[0065] In one embodiment, the specific method for obtaining the temperature difference characterization value of the device main board of the medical device is:

[0066] Set a detection period, divide the monitoring period into several acquisition moments, obtain the temperature of the device main board and the temperature of the device exterior at each acquisition moment, obtain the ratio of the temperature of the device main board to the temperature of the device exterior, and record it as the device main board temperature difference characterization value at the current acquisition moment; sum and average the device main board temperature difference characterization values at all acquisition moments to obtain the device main board temperature difference characterization value of the medical device.

[0067] It should be noted that the main board temperature and the exterior temperature of the medical device can reflect the current operating state of the medical device. When the medical device is working properly, the main board temperature and the exterior temperature of the device should be in a relatively stable state. The device main board temperature difference characterization value can reflect whether the internal main board of the medical device is operating properly.

[0068] In one embodiment, the working electrical energy coefficient characterization value of the medical device is obtained by summing the current coefficient value and the voltage coefficient value; wherein, the method for obtaining the current coefficient value is as follows:

[0069] Obtain the current values at all acquisition moments, calculate the difference between the current value at each acquisition moment and the rated current of the medical device respectively to obtain a current difference, and compare the current difference with a current difference threshold (the current difference threshold is set by those skilled in the art according to the device production standard parameters):

[0070] If the current difference is greater than or equal to the current difference threshold, mark the current acquisition moment as an abnormal current moment;

[0071] If the current difference is less than the current difference threshold, mark the current acquisition moment as a normal current moment;

[0072] Obtain the ratio of the number of normal current moments to the number of abnormal current moments within the detection period, and record it as the current coefficient value.

[0073] In one embodiment, the method for obtaining the voltage coefficient value is as follows:

[0074] Obtain the voltage values at all acquisition moments, calculate the difference between the voltage value at each acquisition moment and the rated voltage of the medical device respectively to obtain a voltage difference, and compare the voltage difference with a voltage difference threshold (the voltage difference threshold is set by those skilled in the art according to the device production standard parameters):

[0075] If the voltage difference is greater than or equal to the voltage difference threshold, mark the current acquisition moment as an abnormal voltage moment;

[0076] If the voltage difference is less than the voltage difference threshold, mark the current acquisition moment as a normal voltage moment;

[0077] Obtain the ratio of the number of normal voltage moments to the number of abnormal voltage moments within the detection period, and record it as the voltage coefficient value.

[0078] In one embodiment, the device deviation coefficient is obtained by analyzing the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value. Specifically:

[0079] Mark the repeated area ratio, the temperature difference characterization value of the device main board, and the working power coefficient characterization value as CFB, WCB, and DNB respectively. Through the formula the device deviation coefficient PL is obtained, where a1, a2, and a3 are all coefficient factors.

[0080] Embodiment 2

[0081] As Figure 2 shown, a method for establishing a smart medical multi-modal digital twin model is as follows:

[0082] Establish a simulation 3D model of the medical institution building. Based on the simulation 3D model of the medical institution building, label the building information, and mark the positions of the departments on each corresponding floor in the building. According to the marking information, obtain the medical device information in the departments, where the medical device information includes device type information and location information, and calibrate the medical device information in the simulation 3D model of the medical institution building to obtain the digital twin model of the medical institution.

[0083] In one embodiment, establishing the simulation 3D model of the medical institution building is specifically:

[0084] According to the architectural design drawings of the medical institution building, start modeling from the foundation part of the building using 3D modeling software;

[0085] Use the basic geometric bodies in the 3D modeling software to build the general framework of the institutional building.

[0086] According to the actual environment around the building, add environmental elements to increase the realism and vividness of the scene, and complete the establishment of the simulation 3D model of the medical institution building.

[0087] According to the architectural design drawings of the medical institution building, start modeling from the foundation part of the building. The modeling can use 3D building modeling, such as 3ds Max, Maya, Blender, SketchUp, Revit, etc. Use the basic geometric bodies (such as cuboids, cylinders, spheres, etc.) in the software to build the general framework of the building. For simple building shapes, the basic geometric bodies can be transformed into the various parts of the building through operations such as stretching, rotating, and Boolean operations. For complex building exteriors, more refined modeling methods may be required, such as polygon modeling or parametric design.

[0088] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A management system for a smart medical multimodal digital twin model, characterized by: include: S1, establishing a digital twin model of a medical institution, and calibrating the medical equipment information in the digital twin model of the medical institution; S2, filming the operation process of the medical device to obtain the actual operation video; obtaining the standard image data of the operation process of the medical device, intercepting the actual operation video to obtain the standard image, intercepting the standard image data to obtain the standard image of the same operation, comparing the standard image with the standard image of the same operation, obtaining the repeated area ratio, comparing the repeated area ratio with the area ratio threshold, and obtaining the abnormal operation signal or the standard operation signal; S3, according to the abnormal action signal, obtaining the medical device information corresponding to the actual action video, and generating a device monitoring and analysis signal; S4, according to the equipment monitoring and analysis signal, obtain the equipment mainboard temperature difference characterization value and the working power coefficient characterization value of the medical equipment, analyze the equipment deviation coefficient according to the repeated area ratio, the equipment mainboard temperature difference characterization value and the working power coefficient characterization value, compare the equipment deviation coefficient with the preset deviation coefficient threshold, obtain the operation signal, and manage and regulate the medical equipment based on the operation signal.

2. According to claim 1, a management system for a smart medical multimodal digital twin model is characterized by: Compare the repeated area ratio with the area ratio threshold to obtain an abnormal action signal or a standard action signal, specifically: If the repeated area ratio is greater than the area ratio threshold, it indicates that the standard image has a large overlap area compared with the standard image of the same action, and an action standard signal is generated; If the repeated area ratio is less than or equal to the area ratio threshold, it indicates that the overlap area between the standard image and the standard image of the same action is small, and an abnormal action signal is generated.

3. According to claim 2, a management system for a smart medical multimodal digital twin model is characterized by: The operation signals include equipment power-off signals, alarm signals, and normal operation signals.

4. The management system of a smart medical multimodal digital twin model according to claim 3 is characterized by: The equipment deviation coefficient is compared with the preset deviation coefficient threshold to obtain an operation signal, specifically: The preset deviation coefficient threshold includes a first deviation threshold and a second deviation threshold, wherein the first deviation threshold is greater than the second deviation threshold; If the equipment deviation coefficient is greater than the first deviation threshold, it indicates that the equipment deviation coefficient is too large, the equipment operation result is wrong and has no reference significance, and a power-off signal is generated for the equipment to be automatically powered off, and relevant staff need to be arranged for maintenance; If the equipment deviation coefficient is less than or equal to the first deviation threshold and the equipment deviation coefficient is greater than or equal to the second deviation threshold, it indicates that the equipment operation result may be abnormal, and an alarm signal is generated to remind the staff to pay attention to the inspection; If the equipment deviation coefficient is less than the second deviation threshold, it indicates that the equipment operation result is accurate and has no abnormalities, and a no-abnormality signal is generated.

5. The management system of a smart medical multimodal digital twin model according to claim 4 is characterized by: The method for obtaining the temperature difference characterization value of the device mainboard of the medical device is specifically as follows: Set a detection cycle, divide the monitoring cycle into several collection moments, obtain the device mainboard temperature and the device surface temperature at each collection moment, obtain the ratio of the device mainboard temperature to the device surface temperature, and record it as the device mainboard temperature difference characterization value at the current collection moment; sum and average the device mainboard temperature difference characterization values ​​at all collection moments to obtain the device mainboard temperature difference characterization value of the medical device.

6. The management system of a smart medical multimodal digital twin model according to claim 5, characterized in that: The working power coefficient representation value of the medical device is obtained by summing the current coefficient value and the voltage coefficient value; wherein the current coefficient value is obtained in the following manner: Obtain the current values ​​at all acquisition moments, calculate the difference between the current value at each acquisition moment and the rated current of the medical device, obtain the current difference, and compare the current difference with the current difference threshold: If the current difference is greater than or equal to the current difference threshold, the current acquisition moment is marked as the current abnormal moment; If the current difference is less than the current difference threshold, the current acquisition moment is marked as the normal current moment; The ratio of the number of normal current moments to the number of abnormal current moments within the detection period is obtained and recorded as the current coefficient value.

7. The management system of a smart medical multimodal digital twin model according to claim 6 is characterized by: The voltage coefficient value is obtained as follows: Get the voltage values ​​at all acquisition moments, calculate the difference between the voltage value at each acquisition moment and the rated voltage of the medical device, get the voltage difference, and compare the voltage difference with the voltage difference threshold: If the voltage difference is greater than or equal to the voltage difference threshold, the current acquisition time is marked as the voltage abnormality time; If the voltage difference is less than the voltage difference threshold, the current acquisition time is marked as the voltage normal time; The ratio of the number of normal voltage moments to the number of abnormal voltage moments within the detection period is obtained and recorded as the voltage coefficient value.

8. The management system of a smart medical multimodal digital twin model according to claim 7 is characterized by: The equipment deviation coefficient is obtained by analyzing the repetitive area ratio, the temperature difference characteristic value of the equipment mainboard, and the working power coefficient characteristic value, which is specifically: The repeated area ratio, the temperature difference characteristic value of the equipment mainboard, and the working power coefficient characteristic value are marked as CFB, WCB, and DNB, respectively, and the formula The equipment deviation coefficient PL is obtained, where a1, a2, and a3 are all coefficient factors.

9. A method for establishing a smart medical multimodal digital twin model, applied to a management system of a smart medical multimodal digital twin model according to claim 1, characterized in that: Specifically: A simulated three-dimensional model of a medical institution building is established. Based on the simulated three-dimensional model of the medical institution building, the building information is marked, and the department locations on the corresponding floors in each building are marked with dots. According to the marked information, the medical equipment information in the department is obtained, wherein the medical equipment information includes equipment type information and location information, and the medical equipment information is calibrated in the simulated three-dimensional model of the medical institution building to obtain a digital twin model of the medical institution.

10. The method for establishing a smart medical multimodal digital twin model according to claim 9, characterized in that: Establish a simulated 3D model of a medical institution building, specifically: According to the architectural design drawings of the medical institution building, modeling is carried out using 3D modeling software starting from the foundation of the building; Use basic geometric shapes in 3D modeling software to build the general framework of the institution's building; According to the actual environment around the building, environmental elements are added to increase the realism and vividness of the scene, and the establishment of a simulated 3D model of the medical institution building is completed.

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