A method and system for optimizing the selection of a deflector cover for an ultra-high-speed elevator

Abstract

A method and system for optimizing and selecting a guide cover for an ultra-high-speed elevator relates to the technical field of elevator optimization. An initial twin model of the elevator is constructed, monitoring points are set to obtain actual operation data, an operation twin model of the elevator is constructed according to the actual operation data and the initial twin model, grid surfaces and grid blocks are obtained, the operation twin model is used to simulate theoretical operation data, and visual coefficients of each grid surface and grid block under visualization processing are obtained. A visual curve is constructed and the guide coefficient of the elevator is obtained. The guide cover is adjusted to obtain the guide coefficient under different adjustment modes, and the guide cover is selected according to the guide coefficient. Through the technical solution of the present invention, the guide performance of the guide cover can be comprehensively reflected in combination with multi-dimensional data, which is conducive to providing effective reference for relevant personnel in the optimization and selection of the guide cover.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator optimization, and in particular to a method and system for optimizing and selecting a shroud for an ultra-high-speed elevator. Background Art

[0002] With the continuous construction of high-rise buildings, elevators have emerged as indispensable transportation equipment. High speed, comfort, stability and intelligence are the iconic features of modern elevators. With the increase in the operating speed of ultra-high-speed elevators, higher and higher requirements are put forward for the operating quality of the car, and the selection of the deflector has become an important factor affecting the operating quality of the car.

[0003] In the prior art, there is a lack of effective evaluation methods for the flow-guiding performance of the air deflector, which makes it impossible to accurately and intuitively grasp the degree of influence of the air deflector on the elevator during operation. In addition, in the prior art, the optimization and selection of the air deflector are often based on one-sided factors, and fail to comprehensively consider various factors generated during the operation of the elevator. In view of the shortcomings of the prior art, the present invention provides a method and system for optimizing the selection of air deflectors for ultra-high-speed elevators. Summary of the invention

[0004] The object of the present invention is to provide a method and system for optimizing the selection of a deflector for an ultra-high-speed elevator.

[0005] The object of the present invention can be achieved by the following technical solutions: A system for optimizing and selecting a deflector for an ultra-high-speed elevator, comprising a main control center, wherein the main control center is communicatively connected to a data acquisition module, a data monitoring module, a data visualization module, a data analysis module, and a data optimization module;

[0006] The data acquisition module is used to respectively collect the elevator car information and the shaft information, and construct an initial twin model of the elevator according to the collected car information and shaft information;

[0007] The data monitoring module is used to set a number of monitoring points in the car and the shaft, use the set monitoring points to monitor the actual operation data of the elevator respectively, and build an operation twin model of the elevator according to the actual operation data obtained and the initial twin model;

[0008] The data visualization module is used to obtain the guide plane and guide space of the elevator, perform grid processing on the obtained guide plane and guide space to obtain a plurality of grid surfaces and grid blocks, use the operation twin model to obtain the theoretical operation data of the elevator during operation, and perform visualization processing on the obtained theoretical operation data;

[0009] The data analysis module is used to obtain the visual coefficients of each mesh surface and mesh block under visualization processing, construct a visual curve according to the obtained visual coefficients and obtain the elevator's conductance coefficient;

[0010] The data optimization module is used to adjust the air deflector to obtain the air conduction coefficients under different adjustment modes, and select the air deflector according to the obtained air conduction coefficients.

[0011] Furthermore, the data acquisition module collects the car information and the shaft information of the elevator respectively, and the process of constructing the initial twin model of the elevator according to the collected car information and shaft information includes:

[0012] The car information refers to the size parameters of each component structure of the car, and the shaft information refers to the size parameters of each component structure of the shaft;

[0013] Digital twin technology is used to build digital twin models of the car and shaft according to the car information and shaft information respectively, and the digital twin models of the car and shaft are integrated to obtain the initial twin model of the elevator.

[0014] Furthermore, the data monitoring module sets a number of monitoring points in the car and the shaft, and uses the set monitoring points to monitor the actual operation data of the elevator respectively. The process of constructing the operation twin model of the elevator according to the obtained actual operation data and the initial twin model includes:

[0015] The monitoring points of the car are divided into force monitoring points and amplitude monitoring points. The actual resistance data is obtained through the force monitoring points, and the actual amplitude data is obtained through the amplitude monitoring points. The monitoring points of the well are divided into force monitoring points and flow rate monitoring points. The actual pressure data is obtained through the force monitoring points, and the actual flow rate data is obtained through the flow rate monitoring points.

[0016] The actual operation data includes actual resistance data, actual amplitude data, actual pressure data, and actual flow rate data. The airflow changes of the elevator during operation are simulated based on the actual operation data combined with the initial twin model to obtain the operation twin model.

[0017] Furthermore, the data visualization module obtains the guide plane and guide space of the elevator, and the process of gridding the obtained guide plane and guide space to obtain a plurality of grid surfaces and grid blocks includes:

[0018] The outer surface of the deflector cover of the car is converted into a deflection plane, which is divided into several grid surfaces with equal areas, each of which contains a force monitoring point. The internal space of the hoistway is marked as the deflection space, which is divided into several grid blocks with equal volumes, each of which contains a flow velocity monitoring point.

[0019] Furthermore, the data visualization module uses the operation twin model to obtain theoretical operation data of the elevator during operation, and the process of visualizing the obtained theoretical operation data includes:

[0020] The operation twin model is continuously trained and tuned using actual operation data, and theoretical operation data is obtained using the latest operation twin model, wherein the theoretical operation data includes theoretical resistance data, theoretical amplitude data, theoretical pressure data, and theoretical flow rate data;

[0021] The theoretical resistance data are visualized on the grid surface, and the theoretical flow velocity data are visualized on the grid block. The theoretical resistance data and theoretical flow velocity data are divided into different numerical intervals, respectively. A color of corresponding wavelength is set for each numerical interval, and each grid surface and grid block is rendered with the color corresponding to its theoretical resistance data and theoretical flow velocity data, respectively.

[0022] Furthermore, the data analysis module obtains the visual coefficients of each mesh surface and mesh block under visualization processing, and the process of constructing a visual curve according to the obtained visual coefficients and obtaining the flow conduction coefficient of the elevator includes:

[0023] The average wavelengths corresponding to different colors in a single grid surface and a single grid block are used as the visibility coefficients of the grid surface and the grid block, and the visible curves include a resistance visible curve and a flow velocity visible curve;

[0024] Record the visibility coefficient of each grid surface in real time and construct a resistance visual curve with the horizontal axis as the recording time and the vertical axis as the visibility coefficient; record the visibility coefficient of the grid block contacted by each grid surface in real time and construct a velocity visual curve with the horizontal axis as the recording time and the vertical axis as the visibility coefficient;

[0025] Integrate different resistance visual curves into a first integrated curve, obtain the resistance visual area enclosed by each resistance visual curve and the horizontal coordinate, take the average of each resistance visual area as the resistance area standard, and obtain the obstructed grid surface according to the resistance area standard;

[0026] Different flow velocity visual curves are integrated into a second integrated curve to obtain the flow velocity visual area of ​​each flow velocity visual curve, and the elevator's conductance coefficient is obtained according to the resistance visual area, flow velocity visual area, theoretical amplitude data, and theoretical pressure data at different recording times.

[0027] Furthermore, the data optimization module adjusts the air deflector to obtain the air conduction coefficients under different adjustment modes, and the process of selecting the air deflector according to the obtained air conduction coefficients includes:

[0028] Adjustment of the air deflector is divided into overall adjustment and local adjustment. The overall adjustment replaces the entire air deflector, and the local adjustment optimizes the obstructed mesh surface.

[0029] The adjusted air deflector is marked as an adjusted air deflector, and the adjusted air deflection coefficient of the adjusted air deflector is obtained in the running twin model. The overall adjusted air deflector and the local adjusted air deflector corresponding to the maximum adjusted air deflection coefficient under the overall adjustment and the local adjustment are obtained respectively, and the obtained overall adjusted air deflector and the local adjusted air deflector are fed back to relevant personnel.

[0030] A method for optimizing and selecting a deflector cover for an ultra-high-speed elevator comprises the following steps:

[0031] Step S1: Collect the car information and shaft information of the elevator respectively, build the initial twin model of the elevator according to the collected car information and shaft information, set up several monitoring points in the car and shaft, use the set monitoring points to monitor the actual operation data of the elevator respectively, and build the operation twin model of the elevator according to the actual operation data obtained and the initial twin model;

[0032] Step S2: obtaining the guide plane and guide space of the elevator, meshing the obtained guide plane and guide space to obtain a number of mesh surfaces and mesh blocks, using the operation twin model to obtain theoretical operation data of the elevator during operation, and visualizing the obtained theoretical operation data;

[0033] Step S3: Obtain the visual coefficients of each mesh surface and mesh block under visualization processing, construct a visual curve based on the obtained visual coefficients and obtain the elevator's flow diversion coefficient, adjust the deflector, obtain the flow diversion coefficients under different adjustment methods, and select the deflector based on the obtained flow diversion coefficients.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention constructs an initial twin model of the elevator, and constructs an operation twin model of the elevator in combination with the actual operation data of the elevator. It can simulate the operation of the elevator in a digital twin space, and obtain the theoretical operation data of each grid surface and grid block by dividing it. This is beneficial to improving the accuracy of data monitoring. The theoretical operation data is visualized to obtain the visual coefficient, and the resistance visual curve and the flow velocity visual curve are constructed. Different visual curves are integrated to obtain the elevator's diversion coefficient. It can combine multi-dimensional data to comprehensively reflect the diversion performance of the deflector cover. By obtaining the optimal deflector cover under two different adjustment methods, it is beneficial to provide effective reference for relevant personnel in the optimization and selection of the deflector cover. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0037] like Figure 1 As shown, a system for optimizing and selecting a guide cover for an ultra-high-speed elevator includes a main control center, wherein the main control center is communicatively connected to a data acquisition module, a data monitoring module, a data visualization module, a data analysis module, and a data optimization module;

[0038] The data acquisition module is used to respectively collect the elevator car information and the shaft information, and construct an initial twin model of the elevator according to the collected car information and shaft information;

[0039] The data monitoring module is used to set a number of monitoring points in the car and the shaft, use the set monitoring points to monitor the actual operation data of the elevator respectively, and build an operation twin model of the elevator according to the actual operation data obtained and the initial twin model;

[0040] The data visualization module is used to obtain the guide plane and guide space of the elevator, perform grid processing on the obtained guide plane and guide space to obtain a plurality of grid surfaces and grid blocks, use the operation twin model to obtain the theoretical operation data of the elevator during operation, and perform visualization processing on the obtained theoretical operation data;

[0041] The data analysis module is used to obtain the visual coefficients of each mesh surface and mesh block under visualization processing, construct a visual curve according to the obtained visual coefficients and obtain the elevator's conductance coefficient;

[0042] The data optimization module is used to adjust the air deflector to obtain the air conduction coefficients under different adjustment modes, and select the air deflector according to the obtained air conduction coefficients.

[0043] It should be further explained that, in the specific implementation process, the data acquisition module collects the elevator car information and the shaft information respectively, and the process of constructing the initial twin model of the elevator according to the collected car information and shaft information includes:

[0044] A collection unit is provided, through which the car information and shaft information of the elevator are collected respectively, the car information refers to the size parameters of each component structure of the car, including the size parameters of the car bottom, car wall, car top, car door, deflector, etc., and the shaft information refers to the size parameters of each component structure of the shaft, including shaft width, shaft depth, top floor height, bottom pit depth, wall width, etc.;

[0045] Digital twin technology is used to construct physical models of the car and shaft respectively according to the collected car information and shaft information, and the physical models of the car and shaft are simulated to obtain their digital twin models, and the digital twin models of the two are integrated to obtain the initial twin model of the elevator. At this time, the constructed initial twin model can only reflect the physical structure of the car and shaft and perform simulation, but does not include the relative movement of the two during operation and the changes in airflow.

[0046] It should be further explained that, in the specific implementation process, the data monitoring module sets a number of monitoring points in the car and the shaft, and uses the set monitoring points to monitor the actual operation data of the elevator respectively. The process of constructing the operation twin model of the elevator according to the actual operation data obtained and the initial twin model includes:

[0047] By setting a number of monitoring points in the car and the shaft, the actual operation data of the elevator can be monitored. For the car, the monitoring points are divided into force monitoring points and amplitude monitoring points. The force monitoring points are set on the deflector. The setting rule is: a number of force monitoring points are set at equal intervals on the deflector, and each force monitoring point includes a force monitoring unit. The actual resistance data of each force monitoring point is monitored by the force monitoring unit;

[0048] The amplitude monitoring points are set on the inner wall of the car, and the setting rules are as follows: a number of amplitude monitoring points are randomly set on the inner wall of the car, each amplitude monitoring point includes an amplitude monitoring unit, and the actual amplitude data of each amplitude monitoring point is monitored by the amplitude monitoring unit. For the hoistway, its monitoring points are divided into force monitoring points and flow rate monitoring points, and the setting rules are as follows:

[0049] A number of force monitoring points and flow rate monitoring points are arranged at equal intervals on the inner wall of the well, and the force monitoring points and flow rate monitoring points at the same position are bound together, each force monitoring point comprises a force monitoring unit, and the actual pressure data of each force monitoring point is monitored by the force monitoring unit, and each flow rate monitoring point comprises a flow rate monitoring unit, and the actual flow rate data of each flow rate monitoring point is monitored by the flow rate monitoring unit;

[0050] Since the actual resistance data, actual amplitude data, actual pressure data, and actual flow rate data are all obtained through actual monitoring at each monitoring point, they are marked as the actual operation data of the elevator. CFD technology is used to simulate the airflow changes of the elevator during operation based on the actual operation data obtained and the constructed initial twin model to obtain the corresponding operation twin model. Among them, CFD technology (Computational Fluid Dynamics) is the abbreviation of computational fluid dynamics, and is an emerging cross-technology that integrates fluid mechanics and computer science.

[0051] It should be further explained that, in a specific implementation process, the data visualization module obtains the guide plane and guide space of the elevator, and the process of gridding the obtained guide plane and guide space to obtain a plurality of grid surfaces and grid blocks includes:

[0052] In an embodiment of the present invention, the outer surface of the deflector cover of the car is converted into a corresponding two-dimensional plane, and is marked as a deflector plane. The gridding method of the deflector plane is as follows: the deflector plane is divided into a plurality of sub-areas of equal area, each sub-area includes a force monitoring point, and the divided sub-areas are marked as grid surfaces;

[0053] Similarly, the internal space of the wellbore is marked as a diversion space, which is a three-dimensional space. The gridding method for the diversion space is: divide the diversion space into several sub-areas of equal volume, each sub-area contains a flow rate monitoring point, and mark the divided sub-areas as grid blocks.

[0054] It should be further explained that, in the specific implementation process, the data visualization module uses the operation twin model to obtain the theoretical operation data of the elevator during operation, and the process of visualizing the obtained theoretical operation data includes:

[0055] Since the monitoring points are all set on the surface of fixed objects, they can only monitor the actual operation data within a certain area. For other areas where monitoring points cannot be set, it is necessary to simulate by running the twin model to obtain the corresponding theoretical operation data;

[0056] The constructed operation twin model is continuously trained and tuned using the monitored actual operation data, and the obtained operation twin model is used to simulate various theoretical operation data of the elevator during operation. The theoretical operation data corresponds to the actual operation data, including theoretical resistance data, theoretical amplitude data, theoretical pressure data, and theoretical flow rate data;

[0057] The obtained theoretical resistance data and theoretical flow rate data are respectively visualized, wherein the visualization result of the theoretical resistance data is reflected on the grid surface, and the visualization result of the theoretical flow rate data is reflected on the grid block, taking the theoretical resistance data as an example;

[0058] The theoretical resistance data is divided into different numerical intervals according to the numerical size, and a color corresponding to the wavelength is set for each numerical interval. The larger the numerical value, the larger the wavelength. Each grid surface is rendered with the color corresponding to its theoretical resistance data to realize the visualization of the grid surface. The same method is adopted to render each grid block with the color corresponding to its theoretical flow velocity data to realize the visualization of the grid block.

[0059] It should be further explained that, in a specific implementation process, the data analysis module obtains the visual coefficients of each mesh surface and mesh block under visualization processing, and the process of constructing a visual curve according to the obtained visual coefficients and obtaining the elevator's flow conduction coefficient includes:

[0060] In the embodiment of the present invention, since a single grid surface and a single grid block often contain theoretical resistance data and theoretical flow rate data of different numerical values, there will also be colors of different wavelengths in a single grid surface and a single grid block, and the average wavelengths corresponding to different colors in a single grid surface and a single grid block are used as the visual coefficients of the grid surface and the grid block, and the visual curves include a resistance visual curve and a flow rate visual curve;

[0061] The steps for constructing the resistance visual curve are as follows: taking any grid surface as an example, when the elevator starts working, the theoretical resistance data on the grid surface will change accordingly, and its visual coefficient will also change accordingly. The visual coefficient on the grid surface is recorded in real time, and a line graph with the horizontal axis as the recording time and the vertical axis as the visual coefficient is constructed, and it is marked as the resistance visual curve of the grid surface. The same method is adopted to obtain the resistance visual curve of each grid surface;

[0062] The steps for constructing the flow velocity visual curve are as follows: taking any grid surface as an example, obtaining the grid blocks that the grid surface contacts in real time when the elevator is working. Since the grid blocks are fixed and the grid surface is relatively movable, a single grid surface will contact different grid blocks during the movement. The visibility coefficients in each grid block are recorded in real time, and a line graph with the horizontal axis being the recording time and the vertical axis being the visibility coefficient is constructed, and the line graph is marked as the flow velocity visual curve of the grid surface. The flow velocity visual curves of each grid surface are obtained by the same method;

[0063] The resistance visual curves of different grid surfaces are integrated into the same coordinate system to obtain a first integrated curve, which is used to reflect the change in resistance of the fairing when the elevator is working, and the area enclosed by each resistance visual curve and the horizontal coordinate is obtained, which is marked as the resistance visual area. The method for obtaining the resistance visual area is:

[0064] Connect the coordinate point corresponding to the latest visual coefficient with the coordinate point corresponding to the recording time on its horizontal coordinate, and then obtain the area enclosed by the resistance visual curve and the horizontal coordinate, take the average of each resistance visual area as the resistance area standard of the first integrated curve, and mark the grid surfaces corresponding to the resistance visual curves whose resistance visual areas are greater than (excluding or equal to) the resistance area standard as obstructed grid surfaces;

[0065] The velocity visual curves of different grid surfaces are integrated into the same coordinate system to obtain a second integrated curve, which is used to reflect the velocity change of the deflector when the elevator is working. The same method is adopted to obtain the velocity visual area of ​​each velocity visual curve, and the obtained recording time is marked as t, and the resistance visual area and velocity visual area at different recording times are marked as Z respectively. t and S t , obtain the theoretical amplitude data and theoretical pressure data corresponding to different recording times, marked as F t and Y t Based on this, the elevator conductance coefficient R is obtained t ;

[0066] ; where i represents the total number of recorded moments in the second integrated curve, i=1, 2, ..., R t represents the conductance coefficient of the elevator at the recording time t, and It is a preset fixed parameter. The larger the diversion coefficient, the better the diversion performance of the diversion cover, and the smaller the diversion coefficient, the worse the diversion performance of the diversion cover.

[0067] It should be further explained that, in a specific implementation process, the data optimization module adjusts the air deflector to obtain the air conduction coefficient under different adjustment modes, and the process of selecting the air deflector according to the obtained air conduction coefficient includes:

[0068] In the embodiment of the present invention, the air deflector is adjusted in two ways: one is overall adjustment and the other is local adjustment. The overall adjustment is based on the existing air deflector, and the entire air deflector is replaced, and the air deflector currently used by the elevator is replaced with another air deflector. The local adjustment is based on the obstructed mesh surface, and the obstructed mesh surface is optimized, and its cross-sectional shape, curved surface transition, supporting structure, etc. are changed, so that the visible resistance area of ​​the obstructed mesh surface is less than (including equal to) the original resistance area standard;

[0069] The adjusted air deflector is marked as an adjusted air deflector, and the same method is used to obtain the diversion coefficient of the adjusted air deflector in the running twin model. The average of the diversion coefficients of the same adjusted air deflector at different recording times is used as the adjusted diversion coefficient of the adjusted air deflector. The adjusted air deflectors corresponding to the maximum adjusted diversion coefficients under the two adjustment methods are obtained respectively, and the two are marked as the overall adjusted air deflector and the local adjusted air deflector respectively. For the overall adjusted air deflector, it is recommended that relevant personnel replace the current air deflector with the overall adjusted air deflector. For the local adjusted air deflector, it is recommended that relevant personnel optimize the current air deflector with reference to the local adjusted air deflector.

[0070] The embodiment of the present invention also includes a method for optimizing and selecting a shroud for an ultra-high-speed elevator, comprising the following steps:

[0071] Step S1: Collect the car information and shaft information of the elevator respectively, build the initial twin model of the elevator according to the collected car information and shaft information, set up several monitoring points in the car and shaft, use the set monitoring points to monitor the actual operation data of the elevator respectively, and build the operation twin model of the elevator according to the actual operation data obtained and the initial twin model;

[0072] Step S2: obtaining the guide plane and guide space of the elevator, meshing the obtained guide plane and guide space to obtain a number of mesh surfaces and mesh blocks, using the operation twin model to obtain theoretical operation data of the elevator during operation, and visualizing the obtained theoretical operation data;

[0073] Step S3: Obtain the visual coefficients of each mesh surface and mesh block under visualization processing, construct a visual curve based on the obtained visual coefficients and obtain the elevator's flow diversion coefficient, adjust the deflector, obtain the flow diversion coefficients under different adjustment methods, and select the deflector based on the obtained flow diversion coefficients.

[0074] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A system for optimizing and selecting a deflector cover for an ultra-high-speed elevator, characterized in that: It includes a main control center, which is communicatively connected to a data acquisition module, a data monitoring module, a data visualization module, a data analysis module, and a data optimization module; The data acquisition module is used to respectively collect the elevator car information and the shaft information, and construct an initial twin model of the elevator according to the collected car information and shaft information; The data monitoring module is used to set a number of monitoring points in the car and the shaft, use the set monitoring points to monitor the actual operation data of the elevator respectively, and build an operation twin model of the elevator according to the actual operation data obtained and the initial twin model; The data visualization module is used to obtain the guide plane and guide space of the elevator, perform grid processing on the obtained guide plane and guide space to obtain a plurality of grid surfaces and grid blocks, use the operation twin model to obtain the theoretical operation data of the elevator during operation, and perform visualization processing on the obtained theoretical operation data; The data analysis module is used to obtain the visual coefficients of each mesh surface and mesh block under visualization processing, construct a visual curve according to the obtained visual coefficients and obtain the elevator's conductance coefficient; The data optimization module is used to adjust the deflector cover to obtain the flow conduction coefficient under different adjustment modes, and select the deflector cover according to the obtained flow conduction coefficient; The process of the data analysis module constructing a visual curve and obtaining the elevator's flow conductivity coefficient includes: The average wavelengths corresponding to different colors in a single grid surface and a single grid block are used as the visibility coefficients of the grid surface and the grid block, and the visible curves include a resistance visible curve and a flow velocity visible curve; Record the visibility coefficient of each grid surface in real time and construct a resistance visibility curve with the horizontal axis as the recording time and the vertical axis as the visibility coefficient; record the visibility coefficient of the grid block contacted by each grid surface in real time and construct a velocity visibility curve with the horizontal axis as the recording time and the vertical axis as the visibility coefficient; Integrate different resistance visual curves into a first integrated curve, obtain the resistance visual area enclosed by each resistance visual curve and the horizontal coordinate, take the average of each resistance visual area as the resistance area standard, and obtain the obstructed grid surface according to the resistance area standard; Different flow velocity visual curves are integrated into a second integrated curve to obtain the flow velocity visual area of ​​each flow velocity visual curve, and the elevator's conductance coefficient is obtained according to the resistance visual area, flow velocity visual area, theoretical amplitude data, and theoretical pressure data at different recording times.

2. The system for optimizing and selecting a shroud for an ultra-high-speed elevator according to claim 1, characterized in that: The data acquisition module collects car information and shaft information, and the process of building an initial twin model of the elevator includes: The car information refers to the size parameters of each component structure of the car, and the shaft information refers to the size parameters of each component structure of the shaft; Digital twin technology is used to build digital twin models of the car and shaft according to the car information and shaft information respectively, and the digital twin models of the car and shaft are integrated to obtain the initial twin model of the elevator.

3. The system for optimizing and selecting a shroud for an ultra-high-speed elevator according to claim 2, characterized in that: The data monitoring module sets monitoring points to monitor the actual operation data of the elevator, and the process of building the elevator operation twin model includes: The monitoring points of the car are divided into force monitoring points and amplitude monitoring points. The actual resistance data is obtained through the force monitoring points, and the actual amplitude data is obtained through the amplitude monitoring points. The monitoring points of the well are divided into force monitoring points and flow rate monitoring points. The actual pressure data is obtained through the force monitoring points, and the actual flow rate data is obtained through the flow rate monitoring points. The actual operation data includes actual resistance data, actual amplitude data, actual pressure data, and actual flow rate data. The airflow changes of the elevator during operation are simulated based on the actual operation data combined with the initial twin model to obtain the operation twin model.

4. The system for optimizing and selecting a shroud for an ultra-high-speed elevator according to claim 3, characterized in that: The process of the data visualization module performing grid processing on the diversion plane and the diversion space includes: The outer surface of the deflector cover of the car is converted into a deflection plane, which is divided into several grid surfaces with equal areas, each of which contains a force monitoring point. The internal space of the hoistway is marked as the deflection space, which is divided into several grid blocks with equal volumes, each of which contains a flow velocity monitoring point.

5. The system for optimizing and selecting a shroud for an ultra-high-speed elevator according to claim 4, characterized in that: The process of the data visualization module acquiring and visualizing the theoretical operation data includes: The operation twin model is continuously trained and tuned using actual operation data, and theoretical operation data is obtained using the latest operation twin model, wherein the theoretical operation data includes theoretical resistance data, theoretical amplitude data, theoretical pressure data, and theoretical flow rate data; The theoretical resistance data are visualized on the grid surface, and the theoretical flow velocity data are visualized on the grid block. The theoretical resistance data and theoretical flow velocity data are divided into different numerical intervals, respectively. A color of corresponding wavelength is set for each numerical interval, and each grid surface and grid block is rendered with the color corresponding to its theoretical resistance data and theoretical flow velocity data, respectively.

6. The system for optimizing and selecting a shroud for an ultra-high-speed elevator according to claim 5, characterized in that: The process of selecting the deflector cover according to the flow conductivity by the data optimization module includes: Adjustment of the air deflector is divided into overall adjustment and local adjustment. The overall adjustment replaces the entire air deflector, and the local adjustment optimizes the obstructed mesh surface. The adjusted air deflector is marked as an adjusted air deflector, and the adjusted air deflection coefficient of the adjusted air deflector is obtained in the running twin model. The overall adjusted air deflector and the local adjusted air deflector corresponding to the maximum adjusted air deflection coefficient under the overall adjustment and the local adjustment are obtained respectively, and the obtained overall adjusted air deflector and the local adjusted air deflector are fed back to relevant personnel.

7. A method for optimizing the selection of a shroud for an ultra-high-speed elevator, which is implemented based on the system for optimizing the selection of a shroud for an ultra-high-speed elevator according to any one of claims 1 to 6, characterized in that: The method comprises: Step S1: Collect the car information and shaft information of the elevator respectively, build the initial twin model of the elevator according to the collected car information and shaft information, set up several monitoring points in the car and shaft, use the set monitoring points to monitor the actual operation data of the elevator respectively, and build the operation twin model of the elevator according to the actual operation data obtained and the initial twin model; Step S2: obtaining the guide plane and guide space of the elevator, meshing the obtained guide plane and guide space to obtain a number of mesh surfaces and mesh blocks, using the operation twin model to obtain theoretical operation data of the elevator during operation, and visualizing the obtained theoretical operation data; Step S3: Obtain the visual coefficients of each mesh surface and mesh block under visualization processing, construct a visual curve based on the obtained visual coefficients and obtain the elevator's flow diversion coefficient, adjust the deflector, obtain the flow diversion coefficients under different adjustment methods, and select the deflector based on the obtained flow diversion coefficients.

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