A home elevator maintenance control system and method

Abstract

An embodiment of the present invention provides a home elevator maintenance control system and method, the method comprising: obtaining historical weight data in the elevator car; obtaining historical induced current and historical magnetic flux of the magnetic damper; establishing a correspondence between the historical weight data, historical induced current and historical magnetic flux; generating a weight current fitting curve for home elevator maintenance based on the correspondence; establishing a specific model based on multiple weight current fitting curves; receiving a maintenance request for the elevator car and guide rails, querying the specific model, obtaining first fault information, and determining the first fault information as a maintenance result; outputting the maintenance result; inputting the above-mentioned weight information and corresponding induced current information into a trained artificial intelligence model to predict fault information; being able to accurately predict elevator stall problems, improve the durability of elevator buffers, improve elevator quality control, and ensure the safety of carried users.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a home elevator maintenance control method, a home elevator maintenance control system, a computer device, and a computer-readable storage medium. Background Art

[0002] A home elevator is an elevator installed in a private residence for use by a single family member. It can also be installed in buildings not used by a single family as a means of access for a single family, but is inaccessible to the public or other residents. The rated speed of a home elevator must not exceed 0.4 m / s (or 0.3 m / s for a doorless home elevator). The car travel must not exceed 12 m, and the rated load capacity must not exceed 400 kg. These elevators are primarily categorized as traction elevators, screw elevators, and hydraulic elevators. Existing home elevators rely primarily on manual maintenance, which is inconvenient and unsafe. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a home elevator maintenance control method, a home elevator maintenance control system, a computer device and a computer-readable storage medium that overcome the above problems or at least partially solve the above problems.

[0004] To solve the above problems, an embodiment of the present invention discloses a maintenance control method for a home elevator, wherein the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions on the guide rail; the method includes:

[0005] Obtaining historical weight data in the elevator car;

[0006] Obtaining historical induced current and historical magnetic flux of the magnetic damper;

[0007] Establishing a corresponding relationship between the historical weight data, the historical induced current, and the historical magnetic flux;

[0008] Generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship;

[0009] establishing a specific model according to a plurality of weight current fitting curves;

[0010] receiving a maintenance request for an elevator car and a guide rail, querying the specific model to obtain first fault information, and determining the first fault information as a maintenance result;

[0011] Output the inspection results.

[0012] Preferably, the elevator car and guide rails are provided with a plurality of cameras; the method further comprises:

[0013] Upon receiving a maintenance request for the elevator car and guide rails, a plurality of elevator images captured by a camera within a preset time period are obtained;

[0014] An abnormal feature comparison is performed on the multiple elevator images to obtain second fault information.

[0015] Preferably, the establishing of the correspondence between the historical weight data, the historical induced current and the historical magnetic flux includes:

[0016] Obtaining weight current data corresponding to the historical weight data;

[0017] establishing a first corresponding relationship between the weight current and the historical induced current;

[0018] A second corresponding relationship between the weight current and the historical magnetic flux is established.

[0019] Preferably, generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship includes:

[0020] Fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates to generate a first weight-current fitting curve for home elevator maintenance;

[0021] The data in the second corresponding relationship is fitted according to the vertical and horizontal coordinates to generate a second weight-current fitting curve for home elevator maintenance.

[0022] Preferably, establishing a specific model according to the plurality of weight-current fitting curves comprises:

[0023] Extracting metadata corresponding to the first weight current fitting curve and the second weight current fitting curve, associating the metadata with historical fault information to form associated data, and converting the associated data into training set data;

[0024] The training set data is input into the artificial intelligence model for training to obtain a trained artificial intelligence model.

[0025] Preferably, the method further comprises:

[0026] The first fault information is matched with the second fault information to obtain comprehensive fault information of the home elevator.

[0027] An embodiment of the present invention discloses a maintenance control system for a home elevator, the home elevator comprising an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions on the guide rail; the system comprises:

[0028] A first acquisition module, configured to acquire historical weight data of the elevator car;

[0029] A second acquisition module is used to obtain the historical induced current and historical magnetic flux of the magnetic damper;

[0030] An establishing module for establishing a corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux;

[0031] A generating module, configured to generate a weight-current fitting curve for home elevator maintenance according to the corresponding relationship;

[0032] A specific model establishment module, used to establish a specific model according to the plurality of weight-current fitting curves;

[0033] a fault information module, configured to receive a maintenance request for the elevator car and the guide rail, query the specific model, obtain first fault information, and determine the first fault information as a maintenance result;

[0034] Output module, used to output maintenance results.

[0035] Preferably, the elevator car and guide rails are provided with a plurality of cameras; the system further comprises:

[0036] an elevator image acquisition module, configured to acquire a plurality of elevator images captured by a camera within a preset time period upon receiving a maintenance request for the elevator car and guide rails;

[0037] The feature comparison module is used to perform abnormal feature comparison on the multiple elevator images to obtain second fault information.

[0038] An embodiment of the present invention discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned home elevator maintenance control method when executing the computer program.

[0039] An embodiment of the present invention discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned home elevator maintenance control method are implemented.

[0040] The embodiments of the present invention include the following advantages:

[0041] In an embodiment of the present invention, the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions of the guide rail; the method includes: obtaining historical weight data in the elevator car; obtaining historical induced current and historical magnetic flux of the magnetic damper; establishing a correspondence between the historical weight data, historical induced current and historical magnetic flux; generating a weight current fitting curve for home elevator maintenance based on the correspondence; establishing a specific model based on multiple weight current fitting curves; receiving a maintenance request for the elevator car and guide rail, querying the specific model, obtaining first fault information, and determining the first fault information as the maintenance result; outputting the maintenance result; inputting the above-mentioned weight information and corresponding induced current information into the trained artificial intelligence model to predict fault information; being able to accurately predict elevator stall problems, improve the durability of elevator buffers, improve elevator quality control, and ensure the safety of carried users. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 1 is a schematic diagram of an embodiment of a home elevator maintenance control method according to an embodiment of the present invention;

[0044] Figure 2 This is a structural block diagram of an embodiment of a home elevator maintenance control system according to an embodiment of the present invention;

[0045] Figure 3 The diagram is an internal structural diagram of a computer device according to an embodiment. DETAILED DESCRIPTION

[0046] In order to make the technical problems, technical solutions and beneficial effects solved by the embodiments of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In a core concept of an embodiment of the present invention, in terms of internal electrical data, a data correspondence between the car load weight and the magnetic buffer is established, and a data curve is generated by fitting according to the data correspondence. The data in the data curve is used as training metadata and input into the artificial intelligence model for training to obtain a trained artificial intelligence model. When a maintenance request for the elevator car and guide rail is received, the current car weight information and corresponding induced current information are obtained, and the above-mentioned weight information and corresponding induced current information are input into the trained artificial intelligence model to predict fault information; it can accurately predict the elevator stall problem, improve the durability of the elevator buffer, improve the elevator quality control, and ensure the safety of the users; on the other hand, in terms of external hardware maintenance, it is mainly through the use of a camera installed outside the car to obtain multiple images of the elevator during operation. The speed exceeding the threshold can be calculated based on the moving distance between the image pixels, and it can be judged whether the car speed is too high, and whether the appearance of the hardware device is damaged can be identified, further enhancing the detectability of the device appearance, and achieving the effect of detecting whether the appearance of the device is damaged.

[0048] Reference Figure 1 , a schematic diagram showing an embodiment of a maintenance control method for a home elevator according to an embodiment of the present invention is shown, wherein the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions of the guide rail; and the method may specifically include the following steps:

[0049] Step S101: Acquire historical weight data of the elevator car;

[0050] In an embodiment of the present invention, the home elevator includes an elevator car and guide rails, and further includes a control device such as a controller. The control device can be connected to various systems of the home elevator, including a traction system, a guide system, a door system, a weight balancing system, and an electric traction system.

[0051] The guide system may include guide rails, which can be used to limit the freedom of movement of the car and counterweight, so that they can only move up and down along the guide rails. The guide system may also include components such as guide shoes and guide rail frames. In addition, the traction system may include traction wire ropes, guide pulleys, and return pulleys, which are mainly responsible for outputting and transmitting power to move the elevator up and down. The door system includes car doors, landing doors, door openers, and door locking devices, which are used to seal or open the car entrance.

[0052] The weight balancing system may include a counterweight and a weight compensation device. The weight balancing system can balance the weight of the car to ensure the normal operation of the elevator's traction transmission. The electric traction system is a system that cooperates with the traction system, which mainly includes a traction motor, a power supply system, a speed feedback device, and a motor speed regulating device. The traction motor is connected to the traction wire rope of the traction system to provide power for the up and down movement of the elevator. On the other hand, the control device can also be connected to devices such as a position display device, a control panel, and a floor selector to control the displayed information. In addition, the home elevator may also include other safety protection devices, such as a speed limiter, a safety clamp, a buffer, a magnetic damper, an end station protection device, a weight sensor, etc., and the embodiments of the present invention do not impose too many restrictions on this.

[0053] Specifically applied to an embodiment of the present invention, the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions on the guide rail; further applied to an embodiment of the present invention, a plurality of cameras may be provided at different positions outside the elevator car or outside the guide rail frame;

[0054] The magnetic damper is used to slow down the descent of the elevator car. It can be a number of different induction coils, each of which is equipped with an electromagnet. The electromagnet generates a magnetic field, and the descending elevator car cuts the magnetic flux of the magnetic field. The historical magnetic flux passing through is changing, and the descending elevator car is subjected to electromagnetic damping, that is, it is subjected to upward resistance, which slows down the descent of the car.

[0055] In an embodiment of the present invention, historical weight data in the elevator car can be obtained, that is, the control device may include a storage device, which can store weight data carried on multiple elevator cars; that is, the historical weight data refers to the weight data carried on the elevator car within a preset time period in the past.

[0056] Step S102: obtaining the historical induced current and historical magnetic flux of the magnetic damper;

[0057] Further applied to the embodiments of the present invention, the historical induced current and historical magnetic flux of multiple magnetic dampers set at different positions of the guide rail can also be obtained; the historical induced current refers to the change in induced current generated when the magnetic damper is operating within a preset time period; the historical magnetic flux refers to the change in magnetic flux generated when the magnetic damper is operating within a preset time period; for example, the historical induced current can be: k1A, k2A, k3A, k4A, k5A, k6A, k7A, k8A, k9A; the historical magnetic flux can be: m1Wb, m2Wb, m3Wb, m4Wb, m5Wb, m7Wb, m8Wb, m9Wb, m10Wb.

[0058] Step S103: establishing a corresponding relationship between the historical weight data, the historical induced current, and the historical magnetic flux;

[0059] After the historical weight data, the historical induced current, and the historical magnetic flux are acquired, a corresponding relationship among the historical weight data, the historical induced current, and the historical magnetic flux may be further established.

[0060] In a specific example of an embodiment of the present invention, establishing the correspondence between the historical weight data, historical induced current and historical magnetic flux includes: obtaining weight current data corresponding to the historical weight data; establishing a first correspondence between the weight current and the historical induced current; and establishing a second correspondence between the weight current and the historical magnetic flux.

[0061] In an embodiment of the present invention, weight data can first be converted into weight current data; the weight current data is the current data generated by the weight sensor when the corresponding weight data is measured, and the two have a corresponding relationship. Converting weight data into current data can improve the efficiency of model training; in addition, a first corresponding relationship between weight current and historical induced current can be established, that is, the weight current of the weight sensor and the historical induced current of the magnetic damper at the same time can be summarized and associated; in addition, a second corresponding relationship between the weight current and the historical magnetic flux is also established, that is, the weight current of the weight sensor and the historical magnetic flux of the magnetic damper at the same time can be summarized and associated.

[0062] Step S104: generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship;

[0063] Specifically applied to the embodiment of the present invention, after obtaining the first corresponding relationship between the weight current and the historical induced current and the second corresponding relationship between the weight current and the historical magnetic flux, a weight current fitting curve for home elevator maintenance can be generated according to the above corresponding relationships. Specifically, generating the weight current fitting curve for home elevator maintenance according to the corresponding relationships includes:

[0064] Fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates to generate a first weight-current fitting curve for home elevator maintenance;

[0065] The data in the second corresponding relationship is fitted according to the vertical and horizontal coordinates to generate a second weight-current fitting curve for home elevator maintenance.

[0066] Specifically, the weight current in the first corresponding relationship is taken as the X-axis and the historical induced current is taken as the Y-axis, and the first weight current fitting curve of the home elevator maintenance is generated by fitting; in addition, the weight current in the first corresponding relationship is taken as the X-axis and the historical magnetic flux is taken as the Y-axis, and the second weight current fitting curve of the home elevator maintenance is generated by fitting. The corresponding data is smoothed by fitting to improve the efficiency of the model input and output.

[0067] Regarding the fitting method, the embodiment of the present invention can also perform fitting through various methods such as linear regression, quadratic regression, nonlinear least squares fitting, spline interpolation, etc., and the embodiment of the present invention does not impose too many restrictions on this.

[0068] Step S105: establishing a specific model according to the plurality of weight-current fitting curves;

[0069] After obtaining the first weight current fitting curve and the second weight current fitting curve, a specific model is established based on the weight current fitting curve. The specific model is established based on multiple weight current fitting curves, including: extracting metadata corresponding to the first weight current fitting curve and the second weight current fitting curve, associating the metadata with historical fault information to form associated data, and converting the associated data into training set data; inputting the training set data into the artificial intelligence model for training to obtain a trained artificial intelligence model.

[0070] In actual application in the embodiments of the present invention, historical fault information may be fault information occurring in an elevator car, for example, "the elevator car stalled in a certain period of time" or "the elevator car stopped lifting or decreasing in a certain period of time"; the above historical fault information is combined with weight current, historical induced current or weight current, and historical magnetic flux to be associated as training set data. Furthermore, the specific model may include multiple neural network models, and the training set data is input into the artificial intelligence model for training, and the model parameters are adjusted until the iteration is completed to obtain a trained artificial intelligence model.

[0071] In an embodiment of the present invention, the loss function M of the artificial intelligence model is: Where N is the number of samples in the training set, φ is the magnetic flux in the training set, A is the mean of the induced current and weight current in the training set, and y i is the predicted value of sample i. By combining the loss function with the magnetic flux and current data, the accuracy of the function is effectively improved.

[0072] Regarding the type of neural network model, it can be multiple types of supervised neural network models, such as convolutional neural networks, recursive neural networks, etc., and an unsupervised neural network model can also be used to implement the functions of the embodiment of the present invention. It is only necessary to obtain the data to be input into the model. The unsupervised neural network model can include a K-means algorithm model, a hierarchical clustering algorithm model, a PCA principal component analysis model, etc. The embodiment of the present invention does not impose too many restrictions on this.

[0073] That is, in another embodiment, an unsupervised neural network model is used as the initial model, and data of multiple weight-current fitting curves are input into the unsupervised neural network model to obtain output first fault information, which can be predicted fault information.

[0074] Step S106: receiving a maintenance request for the elevator car and the guide rail, querying the specific model, obtaining first fault information, and determining the first fault information as a maintenance result;

[0075] Step S107: Output the inspection result.

[0076] Specifically applied to the embodiment of the present invention, after obtaining the trained artificial intelligence model and receiving the maintenance request for the elevator car and guide rail, the current car weight information and the corresponding induced current information, etc., can be input into the trained artificial intelligence model to predict the fault information, that is, to obtain the first fault information.

[0077] In an embodiment of the present invention, the elevator car and guide rails are provided with a plurality of cameras; the method further comprises: upon receiving a maintenance request for the elevator car and guide rails, obtaining a plurality of elevator images taken by the camera within a preset time period; performing abnormal feature comparison on the plurality of elevator images to obtain second fault information, and calculating a speed exceeding a threshold value based on the moving distance between image pixels, determining whether the car speed is too high, and identifying whether the appearance of the hardware device is damaged based on the image edge.

[0078] In actual application, the method also includes: matching the first fault information with the second fault information to obtain comprehensive fault information of the home elevator; that is, the comprehensive fault information can be a combination of predicted software fault information and hardware component appearance damage information. Finally, the maintenance result can be output. For example, the maintenance result can display: "The elevator may stall, please suspend use and contact professional maintenance personnel in time", or "A certain component of the elevator may be damaged, please contact professional maintenance personnel in time."

[0079] In an embodiment of the present invention, the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions of the guide rail; the method includes: obtaining historical weight data in the elevator car; obtaining historical induced current and historical magnetic flux of the magnetic damper; establishing a correspondence between the historical weight data, historical induced current and historical magnetic flux; generating a weight current fitting curve for home elevator maintenance based on the correspondence; establishing a specific model based on multiple weight current fitting curves; receiving a maintenance request for the elevator car and guide rail, querying the specific model, obtaining first fault information, and determining the first fault information as the maintenance result; outputting the maintenance result; inputting the above-mentioned weight information and corresponding induced current information into the trained artificial intelligence model to predict fault information; being able to accurately predict elevator stall problems, improve the durability of elevator buffers, improve elevator quality control, and ensure the safety of carried users.

[0080] It should be noted that for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should be aware that the embodiments are not limited by the order of the actions described, as certain steps can be performed in other orders or simultaneously according to the embodiments. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments.

[0081] Reference Figure 2 , shows a structural block diagram of an embodiment of a home elevator maintenance control system according to this embodiment, wherein the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions of the guide rail; and specifically, the following modules may be included:

[0082] A first acquisition module 301 is used to acquire historical weight data of the elevator car;

[0083] A second acquisition module 302 is configured to acquire historical induced current and historical magnetic flux of the magnetic damper;

[0084] Establishing module 303, for establishing a correspondence between the historical weight data, the historical induced current and the historical magnetic flux;

[0085] A generating module 304 is used to generate a weight-current fitting curve for home elevator maintenance according to the corresponding relationship;

[0086] A specific model building module 305 is used to build a specific model according to the plurality of weight-current fitting curves;

[0087] a fault information module 306 for receiving a maintenance request for the elevator car and the guide rail, querying the specific model, obtaining first fault information, and determining the first fault information as a maintenance result;

[0088] The output module 307 is used to output the maintenance results.

[0089] Preferably, the elevator car and guide rails are provided with a plurality of cameras; the system further comprises:

[0090] an elevator image acquisition module, configured to acquire a plurality of elevator images captured by a camera within a preset time period upon receiving a maintenance request for the elevator car and guide rails;

[0091] The feature comparison module is used to perform abnormal feature comparison on the multiple elevator images to obtain second fault information.

[0092] Preferably, the establishment module includes:

[0093] An acquisition submodule, configured to acquire weight current data corresponding to the historical weight data;

[0094] A first corresponding relationship establishing submodule, configured to establish a first corresponding relationship between the weight current and the historical induced current;

[0095] The second corresponding relationship establishing submodule is used to establish a second corresponding relationship between the weight current and the historical magnetic flux.

[0096] Preferably, the generating module includes:

[0097] A first generating submodule is configured to generate a first weight-current fitting curve for home elevator maintenance by fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates;

[0098] The first generating submodule is used to generate a second weight-current fitting curve for home elevator maintenance by fitting the data in the second corresponding relationship according to the vertical and horizontal coordinates.

[0099] Preferably, the specific model building module includes:

[0100] an extraction submodule, configured to extract metadata corresponding to the first weight-current fitting curve and the second weight-current fitting curve, associate the metadata with historical fault information to form associated data, and convert the associated data into training set data;

[0101] The training submodule is used to input the training set data into the artificial intelligence model for training to obtain a trained artificial intelligence model.

[0102] Preferably, the system further comprises:

[0103] The comprehensive fault information acquisition module is used to match the first fault information with the second fault information to obtain comprehensive fault information of the home elevator.

[0104] Each module in the above-mentioned home elevator maintenance control system can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0105] The home elevator maintenance control system provided above can be used to execute the home elevator maintenance control method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0106] In one embodiment, a computer device is provided, whose internal structure diagram can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for a home elevator maintenance control device is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0107] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0108] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above embodiment are implemented:

[0109] Obtaining historical weight data in the elevator car;

[0110] Obtaining historical induced current and historical magnetic flux of the magnetic damper;

[0111] Establishing a corresponding relationship between the historical weight data, the historical induced current, and the historical magnetic flux;

[0112] Generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship;

[0113] Establishing a specific model according to a plurality of weight current fitting curves;

[0114] receiving a maintenance request for an elevator car and a guide rail, querying the specific model to obtain first fault information, and determining the first fault information as a maintenance result;

[0115] Output the inspection results.

[0116] Preferably, the elevator car and guide rails are provided with a plurality of cameras; the method further comprises:

[0117] Upon receiving a maintenance request for the elevator car and guide rails, a plurality of elevator images captured by a camera within a preset time period are obtained;

[0118] An abnormal feature comparison is performed on the multiple elevator images to obtain second fault information.

[0119] Preferably, the establishing of the correspondence between the historical weight data, the historical induced current and the historical magnetic flux includes:

[0120] Obtaining weight current data corresponding to the historical weight data;

[0121] Establishing a first corresponding relationship between the weight current and the historical induced current;

[0122] A second corresponding relationship between the weight current and the historical magnetic flux is established.

[0123] Preferably, generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship includes:

[0124] Fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates to generate a first weight-current fitting curve for home elevator maintenance;

[0125] The data in the second corresponding relationship is fitted according to the vertical and horizontal coordinates to generate a second weight-current fitting curve for home elevator maintenance.

[0126] Preferably, establishing a specific model according to the plurality of weight-current fitting curves comprises:

[0127] Extracting metadata corresponding to the first weight current fitting curve and the second weight current fitting curve, associating the metadata with historical fault information to form associated data, and converting the associated data into training set data;

[0128] The training set data is input into the artificial intelligence model for training to obtain a trained artificial intelligence model.

[0129] Preferably, the method further comprises:

[0130] The first fault information is matched with the second fault information to obtain comprehensive fault information of the home elevator.

[0131] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above embodiment are implemented:

[0132] Obtaining historical weight data in the elevator car;

[0133] Obtaining historical induced current and historical magnetic flux of the magnetic damper;

[0134] Establishing a corresponding relationship between the historical weight data, the historical induced current, and the historical magnetic flux;

[0135] Generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship;

[0136] establishing a specific model according to a plurality of weight current fitting curves;

[0137] receiving a maintenance request for an elevator car and a guide rail, querying the specific model to obtain first fault information, and determining the first fault information as a maintenance result;

[0138] Output the inspection results.

[0139] Preferably, the elevator car and guide rails are provided with a plurality of cameras; the method further comprises:

[0140] Upon receiving a maintenance request for the elevator car and guide rails, a plurality of elevator images captured by a camera within a preset time period are obtained;

[0141] An abnormal feature comparison is performed on the multiple elevator images to obtain second fault information.

[0142] Preferably, the establishing of the correspondence between the historical weight data, the historical induced current and the historical magnetic flux includes:

[0143] Obtaining weight current data corresponding to the historical weight data;

[0144] establishing a first corresponding relationship between the weight current and the historical induced current;

[0145] A second corresponding relationship between the weight current and the historical magnetic flux is established.

[0146] Preferably, generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship includes:

[0147] Fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates to generate a first weight-current fitting curve for home elevator maintenance;

[0148] The data in the second corresponding relationship is fitted according to the vertical and horizontal coordinates to generate a second weight-current fitting curve for home elevator maintenance.

[0149] Preferably, establishing a specific model according to the plurality of weight-current fitting curves comprises:

[0150] Extracting metadata corresponding to the first weight current fitting curve and the second weight current fitting curve, associating the metadata with historical fault information to form associated data, and converting the associated data into training set data;

[0151] The training set data is input into the artificial intelligence model for training to obtain a trained artificial intelligence model.

[0152] Preferably, the method further comprises:

[0153] The first fault information is matched with the second fault information to obtain comprehensive fault information of the home elevator.

[0154] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0155] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, embodiments of the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0159] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0160] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0161] The above is a detailed introduction to a home elevator maintenance control method, a home elevator maintenance control device, a computer device and a computer-readable storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A home elevator maintenance control method, characterized in that: The home elevator comprises an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions of the guide rail; and the method comprises: Obtaining historical weight data in the elevator car; Obtaining historical induced current and historical magnetic flux of the magnetic damper; Establishing a corresponding relationship between the historical weight data, the historical induced current, and the historical magnetic flux; Generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship; establishing a specific model according to a plurality of weight current fitting curves; receiving a maintenance request for an elevator car and a guide rail, querying the specific model to obtain first fault information, and determining the first fault information as a maintenance result; Output maintenance results; The establishing of the corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux includes: Obtaining weight current data corresponding to the historical weight data; establishing a first corresponding relationship between the weight current and the historical induced current; establishing a second corresponding relationship between the weight current and the historical magnetic flux; Generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship includes: Fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates to generate a first weight-current fitting curve for home elevator maintenance; Fitting the data in the second corresponding relationship according to the vertical and horizontal coordinates to generate a second weight-current fitting curve for home elevator maintenance; The specific model is established according to the plurality of weight current fitting curves, comprising: Extracting metadata corresponding to the first weight current fitting curve and the second weight current fitting curve, associating the metadata with historical fault information to form associated data, and converting the associated data into training set data; The training set data is input into an artificial intelligence model for training to obtain a trained artificial intelligence model; the artificial intelligence model includes a supervised neural network model.

2. The home elevator maintenance control method according to claim 1, characterized in that: The elevator car and the guide rails are provided with a plurality of cameras; the method further comprises: Upon receiving a maintenance request for the elevator car and guide rails, a plurality of elevator images captured by a camera within a preset time period are obtained; An abnormal feature comparison is performed on the multiple elevator images to obtain second fault information.

3. The home elevator maintenance control method according to claim 2, characterized in that: The method further comprises: The first fault information is matched with the second fault information to obtain comprehensive fault information of the home elevator.

4. A home elevator maintenance control system, using the home elevator maintenance control method according to claims 1-3, characterized in that: The home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are provided at different positions of the guide rail; and the system includes: A first acquisition module, configured to acquire historical weight data of the elevator car; A second acquisition module is used to obtain the historical induced current and historical magnetic flux of the magnetic damper; An establishing module for establishing a corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux; A generating module, configured to generate a weight-current fitting curve for home elevator maintenance according to the corresponding relationship; A specific model establishment module, used to establish a specific model according to the plurality of weight-current fitting curves; a fault information module, configured to receive a maintenance request for the elevator car and the guide rail, query the specific model, obtain first fault information, and determine the first fault information as a maintenance result; Output module, used to output maintenance results; The establishing of the corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux includes: Obtaining weight current data corresponding to the historical weight data; establishing a first corresponding relationship between the weight current and the historical induced current; establishing a second corresponding relationship between the weight current and the historical magnetic flux; Generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship includes: Fitting the data in the first corresponding relationship according to the vertical and horizontal coordinates to generate a first weight-current fitting curve for home elevator maintenance; Fitting the data in the second corresponding relationship according to the vertical and horizontal coordinates to generate a second weight-current fitting curve for home elevator maintenance; The specific model is established according to the plurality of weight current fitting curves, comprising: Extracting metadata corresponding to the first weight current fitting curve and the second weight current fitting curve, associating the metadata with historical fault information to form associated data, and converting the associated data into training set data; The training set data is input into an artificial intelligence model for training to obtain a trained artificial intelligence model; the artificial intelligence model includes a supervised neural network model.

5. The home elevator maintenance control system according to claim 4, characterized in that: The elevator car and guide rails are provided with a plurality of cameras; the system further comprises: an elevator image acquisition module, configured to acquire a plurality of elevator images captured by a camera within a preset time period upon receiving a maintenance request for the elevator car and guide rails; The feature comparison module is used to perform abnormal feature comparison on the multiple elevator images to obtain second fault information.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the home elevator maintenance control method according to any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the home elevator maintenance control method according to any one of claims 1 to 3 are implemented.

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