Home elevator maintenance control system and method

Abstract

The embodiment of the invention provides a home elevator maintenance control system and method. The method comprises the steps that historical weight data in an elevator car is obtained; acquiring historical induction current and historical magnetic flux of the magnetic damper; establishing a corresponding relation among the historical weight data, the historical induction current and the historical magnetic flux; according to the corresponding relation, a weight current fitting curve for home elevator maintenance is generated; establishing a specific model according to the plurality of weight current fitting curves; a maintenance request for the elevator car and the guide rail is received, the specific model is inquired, first fault information is obtained, and the first fault information is determined as a maintenance result; and outputting a maintenance result. Inputting the weight information and the corresponding induction current information into a trained artificial intelligence model, and predicting to obtain fault information; the elevator stall problem can be accurately predicted, the durability of an elevator buffer is improved, elevator quality control is improved, and the safety of bearing users is guaranteed.

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 only. It can also be installed in a building that is not used by a single family as a tool for a single family to enter their residence, but it is not accessible and usable by the public or other residents in the building. The rated speed of a home elevator does not exceed 0.4m / s, and the rated speed of a home elevator without a door does not exceed 0.3m / s. The car travel does not exceed 12m, and the rated load is not more than 400KG. It is mainly divided into traction elevators, screw elevators, and hydraulic elevators. The maintenance of existing home elevators mainly relies on manual methods, which is very inconvenient and has low safety. 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] In order to solve the above problems, an embodiment of the present invention discloses a maintenance control method for a home elevator, wherein the home elevator comprises an elevator car and a guide rail; a plurality of magnetic dampers are arranged at different positions of the guide rail; the method comprises:

[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] Generate 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, obtaining first fault information, and determining the first fault information as a maintenance result;

[0011] Output the inspection results.

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

[0013] After receiving a maintenance request for the elevator car and the guide rail, 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] Acquire 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 comprises:

[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] The embodiment of the present invention discloses a maintenance control system for a home elevator, wherein the home elevator comprises an elevator car and a guide rail; a plurality of magnetic dampers are arranged at different positions of the guide rail; the system comprises:

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

[0029] A second acquisition module, used for acquiring the historical induced current and the historical magnetic flux of the magnetic damper;

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

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

[0032] A specific model building module, used to build a specific model according to a 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 the guide rail are provided with a plurality of cameras; the system further comprises:

[0036] An elevator image acquisition module, for acquiring a plurality of elevator images taken 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 on which a computer program is stored. 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 arranged 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 according to the correspondence; establishing a specific model according to a plurality of weight current fitting curves; 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; 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 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 It 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 a diagram of the internal structure 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 used 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 the guide rail is received, the current car weight information and the corresponding induced current information are obtained, and the above-mentioned weight information and the corresponding induced current information are input into the trained artificial intelligence model to predict fault information; the elevator stall problem can be accurately predicted, the durability of the elevator buffer is improved, the elevator quality control is improved, and the safety of the carrying users is ensured; on the other hand, in terms of external hardware maintenance, it is mainly through the camera installed outside the car to obtain multiple images of the elevator during operation, and the speed exceeding the threshold can be calculated according to the moving distance between the image pixels, and it is judged whether the car speed is too high, and whether the appearance of the hardware device is damaged can be identified, and the detectability of the device appearance can be further enhanced, so as to achieve the effect of detecting whether the appearance of the device is damaged.

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

[0049] Step S101, obtaining historical weight data in the elevator car;

[0050] In an embodiment of the present invention, the home elevator includes an elevator car and a guide rail, and further includes a control device such as a controller, and the control device can be connected to various systems of the home elevator, and the various systems may include a traction system, a guide system, a door system, a weight balance 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 wheels and anti-rope wheels, which are mainly responsible for outputting and transmitting power to make the elevator move up and down; the door system includes car doors, floor doors, door openers and door locks, which are used to seal or open the car entrance;

[0052] The weight balancing system may include a counterweight and a weight compensation device, and the weight balancing system may balance the weight of the car to ensure the normal traction transmission of the elevator; the electric traction system is a system that cooperates with the traction system, and 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 may also be connected to devices such as a position display device, a control panel, and a layer 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, a terminal 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 the embodiment of the present invention, the home elevator includes an elevator car and a guide rail; a plurality of magnetic dampers are arranged at different positions of the guide rail; further applied to the embodiment of the present invention, a plurality of cameras may be arranged at different positions outside the elevator car or outside the guide rail frame;

[0054] The magnetic damper is used to slow down the speed of the car's descent. It can be a plurality of different induction coils, in which an electromagnet is arranged, so that the electromagnet generates a magnetic field. The descending elevator car will cut 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 speed of the car's descent.

[0055] In an embodiment of the present invention, historical weight data in the elevator car can be obtained, that is, the control device can 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 running within a preset time period; the historical magnetic flux refers to the change in magnetic flux generated when the magnetic damper is running 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, first, weight data can 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 the weight data into current data can improve the efficiency of model training; in addition, a first corresponding relationship between the weight current and the 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 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, the weight current fitting curve for home elevator maintenance is generated according to the corresponding relationship, including:

[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 for 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 for home elevator maintenance is generated by fitting. The corresponding data is smoothed by fitting to improve the efficiency of model input and output.

[0067] Regarding the fitting method, the embodiment of the present invention can also perform fitting through linear regression, quadratic regression, nonlinear least squares fitting, spline interpolation and other methods, 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 according to the weight current fitting curve. The specific model is established according to the 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 to the embodiments of the present invention, historical fault information may be fault information occurring in an elevator car, for example, "the elevator car stalled during a certain period of time" or "the elevator car stopped lifting or lowering during 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 a variety of 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 the 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 network, recursive neural network, 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 K-means algorithm model, hierarchical clustering algorithm model, 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 embodiments of the present invention, after obtaining the trained artificial intelligence model and receiving the maintenance request for the elevator car and the 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 a moving distance between image pixels, determining whether the speed of the car is too high, and identifying whether the appearance of the hardware device is damaged based on the edge of the image.

[0078] In actual applications, 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 arranged 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 according to the correspondence; establishing a specific model according to a plurality of weight current fitting curves; 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; 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 method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that this embodiment is not limited by the order of the actions described, because according to this embodiment, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this embodiment.

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

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

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

[0084] Establishing module 303, used to establish the corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux;

[0085] A generating module 304, for generating 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 a plurality of weight current fitting curves;

[0087] A fault information module 306 is used 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;

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

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

[0090] An elevator image acquisition module, for acquiring a plurality of elevator images taken 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, used for acquiring weight current data corresponding to the historical weight data;

[0094] A first corresponding relationship establishing submodule, used 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 comprises:

[0097] A first generating submodule, used for 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;

[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 by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[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 through a system bus. Among them, 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 through 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 covered on the display screen, or a button, trackball or touchpad set 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0108] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, 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] Generate 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, obtaining first fault information, and determining the first fault information as a maintenance result;

[0115] Output the inspection results.

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

[0117] After receiving a maintenance request for the elevator car and the guide rail, 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] Acquire 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 comprises:

[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] Generate 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, obtaining first fault information, and determining the first fault information as a maintenance result;

[0138] Output the inspection results.

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

[0140] After receiving a maintenance request for the elevator car and the guide rail, 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] Acquire 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 comprises:

[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] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[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 generate 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 processes 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 capable of directing 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 A 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 The steps for the functions specified 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 have learned the basic creative concept. 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 article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0161] The above is a detailed introduction to a household elevator maintenance control method, a household 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 a person skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on 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 arranged 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; Generate 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, obtaining first fault information, and determining the first fault information as a maintenance result; Output the inspection results.

2. The home elevator maintenance control method according to claim 1, characterized in that: The elevator car and the guide rail are provided with a plurality of cameras; the method further comprises: After receiving a maintenance request for the elevator car and the guide rail, 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 1, characterized in that: The establishing of the corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux includes: Acquire weight current data corresponding to the historical weight data; Establishing a first corresponding relationship between the weight current and the historical induced current; A second corresponding relationship between the weight current and the historical magnetic flux is established.

4. The home elevator maintenance control method according to claim 3, characterized in that: The step of generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship comprises: 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; 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.

5. The home elevator maintenance control method according to claim 3, characterized in that: 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 the artificial intelligence model for training to obtain a trained artificial intelligence model.

6. 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.

7. A home elevator maintenance control system, characterized in that: The home elevator comprises an elevator car and a guide rail; a plurality of magnetic dampers are arranged at different positions of the guide rail; the system comprises: A first acquisition module, used to acquire historical weight data in the elevator car; A second acquisition module, used for acquiring the historical induced current and the historical magnetic flux of the magnetic damper; An establishing module, used for establishing a corresponding relationship between the historical weight data, the historical induced current and the historical magnetic flux; A generating module, used for generating a weight-current fitting curve for home elevator maintenance according to the corresponding relationship; A specific model building module, used to build a specific model according to a 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.

8. The home elevator maintenance control system according to claim 1, characterized in that: The elevator car and the guide rail are provided with a plurality of cameras; the system further comprises: An elevator image acquisition module, for acquiring a plurality of elevator images taken 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.

9. 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 described in any one of claims 1 to 6 are implemented.

10. 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 described in any one of claims 1 to 6 are implemented.

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