An elevator remote monitoring system, an elevator fault early warning method, and an elevator

Abstract

This application relates to an elevator remote monitoring system, an elevator fault early warning method, and an elevator, applied in the field of elevator early warning technology. In this system: an intelligent sensing module is used to acquire the elevator's operating speed during elevator operation; a real-time monitoring module is used to acquire a target speed reflecting the elevator car's movement speed during elevator operation; an intelligent algorithm module is used to determine the elevator operation flow level, which characterizes the elevator's operational fluctuations, based on a preset quantization strategy and the elevator's operating speed and target speed; and a fault early warning module is used to determine whether to issue a fault warning based on the elevator operation flow level and a preset historical fault data threshold. This application solves the problem of insufficient targeting in current elevator fault early warning parameter identification, leading to low accuracy in elevator fault early warning.

Description

Technical Field

[0001] This application relates to the field of elevator early warning technology, and in particular to an elevator remote monitoring system, an elevator fault early warning method, and an elevator. Background Technology

[0002] With the continuous acceleration of urbanization, the number of high-rise buildings has increased significantly. As a core piece of vertical transportation equipment, elevators have been deeply integrated into people's production and life. Their operational safety and reliability are directly related to the safety of public life and property. Therefore, accurate monitoring and fault early warning of elevator operation status has become a core requirement of the industry.

[0003] Currently, IoT technology is gradually penetrating the elevator monitoring field, and various IoT-based elevator monitoring systems are emerging, which has improved the level of intelligence in elevator operation and maintenance management to a certain extent. Most IoT-based elevator monitoring systems currently employ a multi-dimensional data collection mode, performing unified analysis on various types of data collected in a balanced manner to obtain the elevator's operating status and subsequently provide fault warnings. For example, patent CN118877674A discloses an elevator monitoring and early warning method and related device, which mainly acquires multi-dimensional elevator operating data, including elevator running acceleration data, traction sheave wire rope condition data, traction machine temperature rise data, actual load data, door opening and closing time data, and single-run door opening and closing data. This multi-dimensional elevator operating data is then input into a model as input variables for unified analysis to obtain elevator anomaly data, thereby monitoring and issuing early warnings about the elevator's current status.

[0004] However, this method does not focus on monitoring elevator operating speed, a core fault characteristic parameter, resulting in insufficient targeting for identifying elevator faults. In particular, when it is not targeted enough for identifying faults in core elevator components such as speed runaway and operating fluctuations, false alarms and missed alarms are likely to occur, leading to low accuracy of elevator early warning and failure to avoid major safety risks in a timely manner.

[0005] There is currently no effective solution to the problem that the identification of elevator fault warning parameters in related technologies is not specific enough, resulting in low warning accuracy. Summary of the Invention

[0006] This embodiment provides an elevator remote monitoring system, an elevator fault early warning method, and an elevator to solve the problem in related technologies where elevator speed is not considered as an independent monitoring object when issuing fault early warnings, resulting in insufficient targeted identification of faults caused by elevator speed loss and thus low early warning accuracy.

[0007] Firstly, this embodiment provides a system comprising: an intelligent sensing module, a real-time monitoring module, an intelligent algorithm module, and a fault early warning module; the intelligent sensing module, the real-time monitoring module, and the fault early warning module are respectively connected to the intelligent algorithm module;

[0008] The intelligent sensing module is used to obtain the elevator's operating speed during elevator operation.

[0009] The real-time monitoring module is used to acquire the speed of the target fixedly installed on the elevator car during elevator operation; the target speed is used to reflect the moving speed of the elevator car.

[0010] The intelligent algorithm module is used to determine the elevator operation flow level during elevator operation based on a preset quantization strategy, according to the elevator operating speed and the target speed; the elevator operation flow level is used to characterize the elevator's operation fluctuation.

[0011] The fault warning module is used to determine whether to issue a fault warning for the elevator based on the elevator's operating flow level and a preset historical fault data threshold.

[0012] In some embodiments, the intelligent algorithm module is further configured to perform consistency verification on the elevator running speed and the target speed based on a preset consistency verification strategy, and obtain a consistency verification result.

[0013] The intelligent algorithm module is also used to determine the elevator operation flow level of the elevator based on the elevator running speed and the target speed when the consistency verification result indicates that the verification has passed.

[0014] In some embodiments, the intelligent sensing module includes an Internet of Things (IoT) sensor unit, which is equipped with multi-dimensional auxiliary sensors.

[0015] The multi-dimensional auxiliary sensor is used to acquire multi-dimensional operating data of the elevator and send the multi-dimensional operating data to the intelligent algorithm module; the multi-dimensional operating data includes elevator operating temperature data, elevator car humidity data, elevator car gas data, current and voltage data, and elevator operating sound data.

[0016] The intelligent algorithm module is used to combine the multi-dimensional operating data and the elevator operating flow level to determine whether to issue a fault warning for the elevator.

[0017] In some embodiments, the IoT sensor unit is also equipped with a vibration sensor;

[0018] The vibration sensor is used to collect the physical flow level during the operation of the elevator and send the physical flow level to the intelligent algorithm module;

[0019] The intelligent algorithm module is used to determine whether the elevator has a fault based on the elevator running speed and the target speed, the elevator running flow degree and the physical running flow degree, and to generate a fault diagnosis result to determine whether to issue a fault warning for the elevator.

[0020] In some embodiments, the intelligent sensing module further includes a signal conditioning unit; the signal conditioning unit is connected to the IoT sensor unit;

[0021] The signal conditioning unit is used to receive the multi-dimensional operation data and physical operation flow level of the elevator collected by the Internet of Things sensor unit, and to perform filtering and noise reduction processing on the multi-dimensional operation data and physical operation flow level.

[0022] The intelligent algorithm module is used to receive the multi-dimensional operating data and physical operating flow level after being filtered and denoised by the signal conditioning unit, and to determine whether the elevator has a fault based on the multi-dimensional operating data and physical operating flow level after being filtered and denoised, and to generate a fault diagnosis result.

[0023] In some embodiments, the elevator remote monitoring system further includes a data processing and verification module; the data processing and verification module is connected to the intelligent sensing module and the real-time monitoring module respectively; the data processing and verification module is also connected to the intelligent algorithm module;

[0024] The data processing and verification module is used to receive the elevator running speed, the multi-dimensional running data and the physical running flow degree sent by the intelligent sensing module, and to receive the target speed sent by the real-time monitoring module.

[0025] It is also used to perform validity verification on the received elevator running speed, multi-dimensional running data, physical running flow degree and target speed based on a preset verification strategy, and send the data that passes the validity verification to the intelligent algorithm module.

[0026] In some embodiments, the system further includes an elevator position tracking module; the elevator position tracking module is used to acquire the elevator's running trajectory;

[0027] The elevator position tracking module includes a positioning unit and a trajectory recording unit;

[0028] The positioning unit is used to locate the elevator floors at different times during the operation of the elevator, as well as the running trajectory information generated during the change of elevator floors;

[0029] The trajectory recording unit is used to record the elevator floor and the running trajectory information, and associate the running trajectory information with the elevator running speed and the target speed.

[0030] In some embodiments, the system further includes a remote control platform; the remote control platform is connected to the fault early warning module; the remote control platform is also connected to the elevator position tracking module;

[0031] The remote control platform is used to receive the fault warning signal generated by the fault warning module, and generate elevator speed control instructions based on the fault warning signal and the elevator's running trajectory information in the elevator position tracking module.

[0032] Secondly, this embodiment provides a method for remote elevator monitoring, the method being applied to the elevator remote monitoring system described in the first aspect, the method comprising:

[0033] During elevator operation, the elevator's operating speed and the target's speed are acquired; the target is fixedly installed on the elevator car to reflect the elevator car's moving speed.

[0034] Based on a preset quantization strategy, the elevator operation flow level is determined according to the elevator operating speed and the target speed; the elevator operation flow level is used to characterize the elevator's operation fluctuation.

[0035] Based on the elevator's operational flow level and the elevator's historical fault data threshold, determine whether to issue a fault warning for the elevator.

[0036] Thirdly, this embodiment provides an elevator, which includes: an elevator car and the elevator remote monitoring system described in the first aspect above; the elevator remote monitoring system is used to provide early warning of elevator malfunctions.

[0037] Fourthly, this embodiment provides a storage medium storing a computer program that, when executed by a processor, implements the elevator fault early warning method described in the second aspect above.

[0038] Compared with related technologies, the elevator remote monitoring system, elevator fault early warning method, and elevator provided in this embodiment use elevator operating speed as the core indicator for elevator fault early warning. The system acquires the elevator operating speed through an intelligent sensing module and the target speed of a target installed on the elevator car through a real-time monitoring module. Then, based on the dual-source speed data acquired above, an intelligent algorithm module determines the current elevator operating flow level, reflecting fluctuations in elevator operation. The fault early warning module compares the current elevator operating flow level with a preset historical fault data threshold to determine whether to issue a fault warning. When an elevator fault occurs, the fault early warning module issues a warning based on the resulting operational fluctuations, specifically identifying faults in core elevator components such as speed loss and operational fluctuations, thereby improving the accuracy of elevator fault early warning.

[0039] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a hardware structure block diagram of the terminal of the elevator fault early warning method provided in the embodiments of this application;

[0042] Figure 2 This is a schematic diagram of the elevator remote monitoring system provided in the embodiments of this application;

[0043] Figure 3 This is a flowchart of the elevator fault early warning method provided in the embodiments of this application;

[0044] Figure 4 This is a schematic diagram of the overall structure of the elevator remote monitoring system provided in this application embodiment;

[0045] Figure 5 This is a flowchart of the elevator fault warning signal generation method provided in the embodiments of this application;

[0046] Figure 6 This is a flowchart of an IoT-based remote fault early warning method for elevators provided in this specific embodiment.

[0047] Reference numerals: 102, processor; 104, memory; 106, transmission device; 108, input / output device. Detailed Implementation

[0048] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0049] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0050] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal for the elevator fault early warning method provided in this application embodiment. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0051] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the elevator fault early warning method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0052] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0053] In existing IoT-based elevator monitoring systems, most elevator data monitoring adopts a multi-dimensional data collection mode, which does not focus on the core fault characterization parameter of elevator operating speed. Instead, it collects and analyzes various types of data in a balanced manner, resulting in insufficient targeted identification of faults in core elevator components such as speed loss and operation fluctuations. This easily leads to false alarms and missed alarms, and makes it impossible to avoid major safety risks in a timely manner.

[0054] Therefore, in order to solve the technical problems of existing elevator monitoring systems, such as insufficient focus on the core parameter elevator operating speed, low data accuracy, poor module linkage and lack of continuous optimization capabilities, resulting in inaccurate fault warnings and low handling efficiency, this embodiment provides an elevator remote monitoring system. This system is used to execute an elevator fault warning method, which uses elevator speed as the core judgment parameter to warn of whether there is a fault in the elevator. Figure 2 This is a schematic diagram of the elevator remote monitoring system provided in an embodiment of this application, for reference. Figure 2 The system includes: an intelligent sensing module, a real-time monitoring module, an intelligent algorithm module, and a fault early warning module; the intelligent sensing module, the real-time monitoring module, and the fault early warning module are respectively connected to the intelligent algorithm module.

[0055] The intelligent sensing module is used to obtain the elevator's speed during elevator operation;

[0056] The real-time monitoring module is used to acquire the target speed of a target fixedly installed on the elevator car during elevator operation; the target speed is used to reflect the moving speed of the elevator car.

[0057] The intelligent algorithm module is used to determine the elevator operation flow level based on the elevator running speed and the target speed according to the preset quantization strategy; the elevator operation flow level is used to characterize the elevator's operation fluctuation.

[0058] The fault warning module is used to determine whether to issue a fault warning for the elevator based on the elevator's operating flow and preset historical fault data thresholds.

[0059] In the elevator remote monitoring system provided in this application embodiment, the elevator operating speed is used as the core indicator for elevator fault early warning. The elevator operating speed is acquired through an intelligent sensing module, and the target speed installed on the elevator car is acquired through a real-time monitoring module. Then, based on the dual-source speed data acquired above, an intelligent algorithm module determines the current elevator operating flow level, reflecting elevator operating fluctuations. Finally, the fault early warning module compares the current elevator operating flow level with a preset historical fault data threshold to determine whether to issue a fault warning for the elevator.

[0060] When an elevator malfunctions, the fault warning module issues a warning based on the operational fluctuations caused by the malfunction. This allows for the targeted identification of faults in core elevator components, such as speed loss and operational fluctuations, thereby improving the accuracy of elevator fault warnings.

[0061] The intelligent sensing module is deployed at key locations in the elevator. Its core function is to collect elevator operation flow and related operation data. The operation flow is used as the core to determine the elevator's operating status. After processing, the data is uploaded to the real-time monitoring module and the intelligent algorithm module.

[0062] The real-time monitoring module connects the intelligent sensing module and the location tracking module. It includes two monitoring functions: a video monitoring unit and a car speed monitoring unit. The video monitoring unit uses a wide-angle camera to monitor the elevator interior and passenger status, while the car speed monitoring unit reflects the elevator car's operating speed by monitoring the speed of a target installed on the car.

[0063] The intelligent algorithm module receives data from multiple modules, including elevator operating speed and other multi-dimensional operating data. Specifically, the multi-dimensional operating data includes at least one of the following: elevator operating temperature data, elevator car humidity data, elevator car gas data, current and voltage data, and elevator operating sound data. The module processes the data using fault diagnosis algorithms and quantitative calculation equations to identify faults, classify warning levels, and output diagnostic results to the fault warning module. The quantitative strategies in the intelligent algorithm module include operating flow degree quantification formulas, target speed and perceived speed deviation formulas, and multi-dimensional data fusion warning coefficient formulas, used to quantitatively analyze the elevator's operating status.

[0064] The fault early warning module includes a data storage unit and a data analysis and comparison algorithm unit. The data storage unit specifically stores the operating data, fault type, and handling records corresponding to the occurrence of historical elevator faults, i.e., a historical fault database. The data analysis and comparison algorithm unit compares the real-time operating data (including elevator operating speed data and multi-dimensional operating data) obtained by the intelligent sensing module with the threshold range of the corresponding parameters in the historical fault data one by one.

[0065] Based on the above Figure 2 The elevator remote monitoring system provided in this embodiment offers an elevator fault early warning method. This method is based on Internet of Things (IoT) technology and is applied to the aforementioned elevator remote monitoring system. Figure 3 This is a flowchart of the elevator fault early warning method provided in the embodiments of this application, such as... Figure 3 As shown, the process includes the following steps:

[0066] Step S310: During elevator operation, the elevator speed and the target speed are obtained; the target is fixedly installed on the elevator car.

[0067] When it is necessary to monitor the elevator's operating status, it is necessary to first obtain data during the elevator's operation. However, since elevator malfunctions usually cause changes in elevator speed, this embodiment uses elevator speed data as an important detection basis and focuses on speed changes.

[0068] However, in terms of speed data acquisition, existing systems mostly rely on a single sensor to obtain speed information. Factors such as mechanical vibration, light interference in the shaft, and electromagnetic interference during elevator operation can easily lead to speed data distortion and make it difficult to guarantee acquisition accuracy. The accuracy of speed data directly determines the reliability of fault diagnosis, and data deviation will further aggravate the problem of fault misjudgment and affect the effectiveness of subsequent operation and maintenance.

[0069] To improve the accuracy of speed data acquisition, this embodiment uses an intelligent sensing module to acquire the elevator's operating speed during elevator operation; and a real-time monitoring module to acquire the target speed fixedly installed on the car during elevator operation. Furthermore, the elevator's operating speed and the target speed are not detected by sensors located at the same position. That is, it does not rely on a single sensor to acquire a single speed data point, which helps improve the accuracy of subsequent elevator speed determination.

[0070] Step S320: Based on a preset quantization strategy, determine the elevator operation flow level during elevator operation according to the elevator running speed and the target speed; the elevator operation flow level is used to characterize the elevator's operation fluctuation.

[0071] Specifically, after the intelligent sensing module obtains the elevator's running speed and the real-time monitoring module obtains the target speed, the elevator's running speed and the target speed are transmitted to the data processing and verification module, respectively. The data processing and verification module verifies the validity of the data to ensure that the elevator's running speed and the target speed subsequently sent to the intelligent algorithm module are valid data.

[0072] For example, the rated operating speed of the elevator at the time of manufacture can be used as the standard speed to determine the validity of the elevator operating speed and the target speed. If the collected elevator operating speed and target speed are within a reasonable range corresponding to the standard speed (pre-set), then it means that the current elevator operating speed is normal data; based on the elevator operating speed and target speed calibrated according to the standard speed, accurate data is provided for subsequent verification of elevator operating speed and judgment of operating status.

[0073] Furthermore, after the data processing and verification module sends the valid elevator running speed and target speed during elevator operation to the intelligent algorithm module, the intelligent algorithm module needs to quantify the valid elevator running speed and target speed according to the preset quantization strategy, thereby determining the elevator running flow degree used to characterize the elevator's running fluctuations, which helps to ensure the accuracy of system fault identification.

[0074] Step S330: Determine whether to issue a fault warning for the elevator based on the elevator operation flow level and the elevator's historical fault data threshold.

[0075] Specifically, an intelligent algorithm module determines whether to issue a fault warning for the elevator based on the elevator's operational flow level and historical fault data thresholds. This is achieved by comparing the elevator's operational flow level with the historical fault data thresholds and determining the appropriate warning based on the comparison result.

[0076] In addition to using elevator operation flow to provide early warning of elevator malfunctions, other data during elevator operation can also be used, such as multi-dimensional operational data acquired during elevator operation. This multi-dimensional operational data includes elevator operating temperature data, elevator car humidity data, elevator car gas data, current and voltage data, and elevator operating sound data. By combining multi-dimensional operational data with elevator operation flow, it can be determined whether to provide early warning of elevator malfunctions.

[0077] Through the above steps, the elevator operating speed is used as the core indicator for elevator fault early warning. The elevator operating speed and the target speed installed on the car are obtained separately. By using dual-source speed data, including the elevator operating speed and the target speed, the current operating fluctuation of the elevator is determined. Then, when the elevator has a fault, the elevator is given an early warning based on the operating fluctuation caused by the elevator fault. This allows for targeted identification of faults in the core components of the elevator, such as speed loss and operating fluctuations, thereby improving the accuracy of elevator fault early warning.

[0078] In some embodiments, the intelligent algorithm module is also used to perform consistency verification on the elevator running speed and the target speed based on a preset consistency verification strategy, and obtain a consistency verification result; if the consistency verification result indicates that the verification has passed, the elevator running flow degree is determined according to the elevator running speed and the target speed.

[0079] The intelligent algorithm module analyzes the received dual-source speed data, including elevator operating speed and target speed, through standard quantitative equations to accurately assess the elevator's operating status. Based on the elevator operating speed and target speed, it determines the degree of elevator operation flow, including: determining multiple instantaneous elevator speeds sampled during operation based on the elevator operating speed and target speed; and determining the degree of elevator operation flow based on the multiple instantaneous elevator speeds, the preset average rated elevator operating speed, and the number of samplings corresponding to the multiple instantaneous elevator speeds.

[0080] Specifically, the elevator operation flow level is determined through a preset quantification strategy, including the following quantification equation for the operation flow level:

[0081] ;

[0082] Where S represents the quantified value corresponding to the elevator operation flow level, that is, the elevator operation flow level. The instantaneous speed of the elevator, which is collected in real time during elevator operation, is determined by the target speed collected by the real-time monitoring module and the elevator running speed collected by the intelligent sensing module. Both the elevator running speed and the target speed here conform to the preset elevator calibration speed, that is, the standard speed set by the elevator at the factory in the aforementioned embodiment. S represents the preset average rated operating speed of the elevator, and n represents the number of samples per unit time. The larger the S value, the worse the smoothness of the elevator operation and the more obvious the fluctuations in operation.

[0083] In addition, before the intelligent algorithm module determines the elevator's operating flow level by using the elevator's operating speed and the target speed, it is necessary to perform a consistency check on the elevator's operating speed and the target speed to avoid a decrease in the accuracy of fault warnings due to invalid speed data.

[0084] Furthermore, the intelligent algorithm module performs consistency verification on the elevator running speed and the target speed based on a preset consistency verification strategy. Specifically, it is necessary to calculate the difference between the target speed and the elevator running speed and determine the ratio of the difference to the preset rated elevator running speed. The comparison result of the ratio with the preset speed deviation threshold is determined as the consistency verification result.

[0085] Preferably, the consistency between the elevator running speed and the target speed can be verified using the following speed data consistency verification formula:

[0086] ;

[0087] in, The calculated target-sensing speed deviation rate, i.e., the above comparison result, is used to characterize the consistency verification result. To monitor the target velocity collected by the real-time monitoring module, The elevator's operating speed is collected by the intelligent sensing module; This is the preset rated operating speed of the elevator.

[0088] For example, when the consistency check result When the data exceeds a preset threshold (e.g., 5%), a consistency check anomaly is detected between the target speed and elevator running speed data, triggering a secondary consistency check. If the secondary consistency check still exceeds the preset threshold (e.g., 5%), a fault warning module needs to issue a warning based on the portion exceeding the preset threshold.

[0089] In some embodiments, to help improve the accuracy of fault early warning based on the collected elevator speed data, the intelligent sensing module further expands the sensor configuration. Specifically, the intelligent sensing module includes an IoT sensor unit, which is equipped with multi-dimensional auxiliary sensors.

[0090] Multi-dimensional auxiliary sensors are used to acquire multi-dimensional operating data of the elevator and send the multi-dimensional operating data to the intelligent algorithm module; the multi-dimensional operating data includes elevator operating temperature data, elevator car humidity data, elevator car gas data, current and voltage data, and elevator operating sound data;

[0091] The intelligent algorithm module receives data from multiple modules, including multi-dimensional operational data, and combines this data with the elevator's operational flow to determine if the elevator has a fault and generate a fault diagnosis result.

[0092] In the above embodiment, the intelligent sensing module acquires the elevator's operating speed, specifically through an IoT sensor unit within the intelligent sensing module. For example, the IoT sensor unit includes a Hall effect speed sensor, which collects the elevator's operating speed.

[0093] Compared to existing technologies that rely solely on a single sensor to acquire elevator speed data, this embodiment expands upon the core speed data from multiple sources by collecting multi-dimensional operational data, including temperature, humidity, gas, and sound, to aid in the identification process. This approach facilitates both targeted and comprehensive elevator fault warnings, thereby enhancing the safety of elevator operation.

[0094] For example, the IoT sensor unit also includes temperature sensors, humidity sensors, odor sensors, and sound sensors; the temperature sensor is deployed in the elevator car and traction machine, the humidity sensor is deployed inside the car, the gas sensor is deployed on the top of the car, and the sound sensor is deployed on the traction machine and bottom of the car; through the sensors in the above-mentioned IoT sensor unit, the operating temperature of the elevator car and traction machine (i.e., elevator operating temperature data), the humidity inside the car (i.e., elevator car humidity data), the abnormal gas data inside the car (i.e., elevator car gas data), and the abnormal elevator operating sound data (i.e., elevator operating sound data) are collected simultaneously.

[0095] Furthermore, the IoT sensor unit also includes a current and voltage sensor to collect current and voltage data during elevator operation.

[0096] After collecting multi-dimensional operational data through the multi-dimensional auxiliary sensors of the IoT sensor unit in the intelligent sensing module, the data is sent to the intelligent algorithm module. Based on this multi-dimensional operational data, the intelligent algorithm module assists in judging dual-source speed data, including elevator operating speed and target speed. For example, abnormal temperature can indicate the risk of speed runaway due to traction machine overheating; abnormal sound can help locate speed fluctuations caused by mechanical faults; and abnormal temperature, humidity, and gas can simultaneously investigate environmental and safety hazards affecting elevator operation, further improving the accuracy of elevator speed-related fault judgment while ensuring the comprehensiveness of elevator operation safety monitoring, thereby improving the accuracy of elevator fault early warning judgment.

[0097] In some embodiments, the IoT sensor unit is further equipped with a vibration sensor; the vibration sensor is used to collect the physical flow level during elevator operation and send the physical flow level to the intelligent algorithm module; the intelligent algorithm module is used to determine whether there is a fault in the elevator based on the elevator operating speed and the target speed, the elevator operating flow level, and the physical flow level, and generate a fault diagnosis result.

[0098] For example, Hall effect speed sensors and vibration sensors are installed in the elevator car and traction machine, while current and voltage sensors are deployed in the elevator control cabinet to help determine the impact of equipment load on speed.

[0099] The intelligent sensing module is also used to collect the physical flow level during elevator operation, such as through vibration sensors in the IoT sensor unit. Based on the elevator's operating speed and the target speed, the determined elevator flow level indicates that the elevator is not currently experiencing a malfunction. Furthermore, the physical flow level collected by the vibration sensor can be used to determine whether a fault warning is needed. This can be achieved by sending the collected physical flow level to the fault warning module, which uses a preset physical flow level threshold for judgment, or by using other methods to judge the data collected by the vibration sensor; specific limitations are not specified here.

[0100] The intelligent algorithm module determines whether there is a fault in the elevator based on the elevator operation data collected by various sensors in the intelligent sensing module, including elevator operating speed, physical operation flow level and multi-dimensional operation data, as well as the target speed collected by the real-time monitoring module, and generates fault diagnosis results to the fault warning module. The fault warning module determines whether to issue a fault warning to the elevator based on the fault diagnosis results.

[0101] In addition, the intelligent algorithm module is also equipped with a single-item anomaly warning judgment rule. For the quantitative value of the operating flow degree, speed deviation rate, temperature anomaly value, humidity anomaly value, gas anomaly value and sound anomaly value mentioned in the above embodiment, a safety threshold is set respectively. When any parameter exceeds the corresponding safety threshold, an alarm is triggered separately. The alarm level is divided according to the severity of the anomaly: a mild anomaly (5%) alarm requires staff to carry out maintenance and repair, and a severe anomaly (20%) alarm requires the elevator to be located and rescue preparations to be made.

[0102] The fault diagnosis results can be generated by comparing the elevator operation data collected by the sensors in the current intelligent sensing module and real-time monitoring module with the historical elevator fault data stored in the fault early warning module, or by making a judgment based on, for example, the aforementioned quantitative calculation equation. No limitation is placed on the method of generating fault diagnosis results here.

[0103] The intelligent sensing module also includes a signal conditioning unit; the signal conditioning unit is connected to the IoT sensor unit; the signal conditioning unit is used to receive multi-dimensional operation data and physical operation flow level of the elevator collected by the IoT sensor unit, and to perform filtering and noise reduction processing on the multi-dimensional operation data and physical operation flow level.

[0104] The intelligent algorithm module is used to receive multi-dimensional operating data and physical operating flow level after filtering and noise reduction by the signal conditioning unit, and to determine whether there is a fault in the elevator based on the multi-dimensional operating data and physical operating flow level after filtering and noise reduction, and to generate fault diagnosis results.

[0105] Furthermore, the signal conditioning unit is also used to receive the elevator running speed and the target speed, and to filter and reduce noise on the elevator running speed and the target speed; the intelligent algorithm module receives the elevator running speed and the target speed after the signal conditioning unit has filtered and reduced noise, determines whether there is a fault in the elevator, and generates a fault diagnosis result.

[0106] In another embodiment, Figure 4 This is a schematic diagram of the overall structure of the elevator remote monitoring system provided in this embodiment of the application. (Reference) Figure 4 In addition to the elevator remote monitoring system, Figure 2 In addition to the intelligent sensing module, real-time monitoring module, intelligent algorithm module, and fault early warning module shown, it also includes a data processing and verification module, an elevator position tracking module, and a remote control platform.

[0107] The signal conditioning unit sends the filtered and noise-reduced elevator speed and target speed, as well as multi-dimensional operating data and physical operating flow, to the data processing and verification module to verify the validity of the data. Data that passes the validity verification is sent to the intelligent algorithm module, while data that fails the validity verification is sent to the fault warning module.

[0108] The data processing and verification module is connected to both the intelligent sensing module and the real-time monitoring module. It is also connected to the intelligent algorithm module. The data processing and verification module receives elevator operating speed, multi-dimensional operating data, and physical flow intensity from the intelligent sensing module, and receives target speed from the real-time monitoring module. Based on a preset verification strategy, it performs validity verification on the received elevator operating speed, multi-dimensional operating data, physical flow intensity, and target speed, and sends the data that passes the validity verification to the intelligent algorithm module.

[0109] In this process, after the intelligent sensing module obtains the elevator's operating speed, the IoT sensor unit in the intelligent sensing module obtains the elevator's multi-dimensional operating data and physical operating flow, and the real-time monitoring module obtains the target speed installed in the elevator car, the above data needs to be validated. For example, the preset validation strategy is to validate the above data based on the elevator's physical operating characteristics and standard limits.

[0110] Data that conforms to the physical operating characteristics of the elevator passes validity verification and is sent to the intelligent algorithm module; data that fails validity verification is considered invalid and is not sent to the intelligent algorithm module, but instead sent to the fault warning module. This verification strategy can be limited according to the actual elevator usage scenario; no specific limitations are imposed on the validity verification strategy here. The remote control platform is connected to the fault warning module and also to the elevator position tracking module. The remote control platform receives fault warning signals generated by the fault warning module and, based on the fault warning signals and the elevator's running trajectory information in the elevator position tracking module, generates elevator speed control commands.

[0111] The elevator position tracking module is used to acquire the elevator's running trajectory. The elevator position tracking module includes a positioning unit and a trajectory recording unit. The positioning unit is used to locate the elevator floor at different times during operation, as well as the running trajectory information generated during the change of elevator floor. The trajectory recording unit is used to record the elevator floor and running trajectory information, and associate the running trajectory information with the elevator running speed and the target speed.

[0112] The remote control platform serves as the central hub for management and control, enabling remote visual monitoring, fault handling, and command issuance, and pushing early warning information to management personnel. The elevator position tracking module is deployed in the car and shaft to achieve accurate positioning and trajectory tracing, and uploads position data to the remote control platform to support fault location and emergency rescue. The fault early warning module connects all core modules, stores the operating data and fault records corresponding to historical elevator faults, and compares and analyzes the real-time data received from multiple modules with historical fault data.

[0113] Furthermore, the target of the real-time monitoring module is a high-contrast, wear-resistant target adapted to the elevator operation scenario, fixedly installed on the outside of the car in an unobstructed location. The real-time monitoring module includes a target monitoring unit. This target monitoring unit is located at the top of the elevator shaft. By continuously tracking the target's displacement changes and combining this with the time dimension, it calculates the real-time operating speed of the elevator car, i.e., the target speed in the aforementioned embodiment. It possesses vibration and light interference resistance capabilities, ensuring accurate speed data acquisition during both high-speed and low-speed elevator operation. The real-time monitoring module also includes camera equipment installed inside the elevator car, such as a wide-angle high-definition camera, to monitor the elevator interior and personnel status in real time.

[0114] After receiving the target speed data collected by the target monitoring unit, the data processing and verification module retrieves the elevator operating speed data uploaded by the intelligent sensing module in real time. It then uses conventional data comparison and consistency verification methods to perform linked analysis and validity verification of the dual-source speed data, including both the elevator operating speed and the target speed. Simultaneously, it retains the analysis process data using conventional data recording methods, which enhances the correlation and accuracy of speed-related fault analysis and provides fundamental data support for subsequent fault analysis, early warning, and tracing.

[0115] For example, historical data or conventional discrimination methods in other technical fields can be used to determine whether dual-source speed data is normal. According to existing conventional communication and data distribution methods, the verified speed data is synchronously transmitted to the intelligent algorithm module and the fault warning module. Specifically, the speed data that passes the verification is transmitted to the intelligent algorithm module for subsequent calculation, and the dual-source speed data that fails the verification is transmitted to the fault warning module for further discrimination and warning.

[0116] The remote control platform includes a visual interactive interface and an early warning push unit. The visual interactive interface prioritizes displaying elevator operating speed, target speed, speed fluctuation curves, and speed anomaly alarm indicators collected by the intelligent sensing module and real-time monitoring module. It simultaneously integrates elevator location information collected by the elevator location tracking module, real-time images of the car collected by the real-time monitoring module, and operating status data from the aforementioned modules. It also supports retrieving historical speed data, fault warning records, and handling logs from the fault warning module, achieving integrated visual monitoring of "speed core + multi-dimensional data." The early warning push unit sets the highest push priority for speed anomaly warnings, simultaneously pushing them to relevant personnel through multiple channels such as pop-ups, SMS, and APP pushes. The push information includes the elevator's real-time operating speed value, duration of the anomaly, elevator location, historical similar faults, and handling suggestions, while also marking the warning level to ensure rapid response from management personnel.

[0117] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0118] In some of these embodiments, the elevator's operating speed is the core focus. Figure 5 This is a flowchart illustrating the method for generating elevator fault warning signals according to an embodiment of this application. (Reference) Figure 5 In step S330, based on the elevator's operational flow level and historical fault data thresholds, it is determined whether to issue a fault warning for the elevator, including:

[0119] Step S331: During the elevator operation, the elevator's trajectory is acquired; the elevator's trajectory is related to the elevator's operating speed and the target speed.

[0120] Step S332: Based on the comparison results of the elevator operation flow level and the historical fault data threshold, indicating that the elevator has a fault, the current position coordinate information of the elevator is matched in the elevator's running trajectory according to the real-time fault running speed of the elevator; and a fault warning signal including the position coordinate information is generated.

[0121] The elevator position tracking module includes a positioning unit and a trajectory recording and association unit. The positioning unit accurately collects real-time floor and trajectory coordinate information of the elevator; the trajectory recording and association unit is specifically used to store the elevator's trajectory data and synchronously associates it with the Hall effect speed data from the intelligent sensing module and the target speed data from the real-time monitoring module, forming a three-dimensional "position-speed-time" associated dataset.

[0122] Once the positioning unit collects location information, the associated data is simultaneously uploaded to the remote control platform. When the elevator experiences an abnormal speed warning, the platform can quickly dispatch rescue personnel using the "real-time speed + precise location" information to determine the elevator's floor or operating location, avoid misjudgments during rescue, and significantly improve the efficiency and safety of emergency rescue.

[0123] For example, the positioning unit uses an infrared + Bluetooth dual-mode positioning sensor adapted to the elevator shaft scenario, which is deployed on the top of the elevator car and on the inner wall of the shaft at key floor locations.

[0124] This embodiment also provides an elevator, which includes an elevator car and the aforementioned elevator remote monitoring system; the elevator remote monitoring system is used to execute the aforementioned elevator fault early warning method.

[0125] The present embodiment will be described and explained below through specific examples.

[0126] Figure 6 This is a flowchart of the IoT-based remote fault early warning method for elevators provided in this specific embodiment, as follows: Figure 6 As shown, the method includes the following steps:

[0127] First, data collection is performed after the elevator remote monitoring system is started.

[0128] The intelligent sensing module prioritizes collecting elevator operating speed via Hall effect speed sensors and physical flow data via vibration sensors, while simultaneously collecting multi-dimensional operational data such as temperature, humidity, gas anomalies, sound anomalies, and current and voltage data. The real-time monitoring module collects data on the elevator interior, passenger status, and the speed of fixed targets on the car. The elevator position tracking module collects data on elevator floors and operating trajectory. This achieves multi-dimensional data acquisition through intelligent sensing, real-time monitoring, and position tracking.

[0129] Subsequently, the collected elevator operation data will be transmitted, processed, and verified.

[0130] The intelligent sensing module, real-time monitoring module, and elevator position tracking module in the elevator remote monitoring system collect elevator operation data, including dual-source speed data, elevator floor data, and running trajectory data. After the data is processed and verified by the data processing and verification module, it is synchronously transmitted to the intelligent algorithm module, fault early warning module, and remote control platform.

[0131] The elevator operation data is validated by the data processing and verification module. The specific verification methods refer to the preset verification strategy in the above embodiments and will not be described in detail here. Subsequently, the elevator operation data that has passed the validity verification by the data processing and verification module is sent to the intelligent algorithm module. The intelligent algorithm module then evaluates the elevator operation data, including the elevator's dual-source speed data as the core data and the elevator's multi-dimensional operation data and physical operation flow degree as auxiliary data.

[0132] The intelligent algorithm module prioritizes the calculation and analysis of dual-source speed data, including elevator operating speed and target speed, using quantization equations (i.e., quantization strategies) and consistency verification formulas to determine whether the elevator is currently malfunctioning. Subsequently, it uses historical fault data from the fault warning module to assess other auxiliary data, including multi-dimensional operating data and physical operating flow levels. For example, based on historical fault data from the fault warning module, it sets safety thresholds for each auxiliary multi-dimensional operating data item other than speed data to determine whether there are any anomalies in the multi-dimensional operating data and the severity of any such anomalies. Specifically, the anomaly assessment method and severity assessment method described in the aforementioned embodiments can be referenced. This allows for the priority assessment of speed data anomalies while supplementing the analysis with multi-dimensional elevator operating data, including elevator operating temperature data, elevator car humidity data, elevator car gas data, current and voltage data, and elevator operating sound data, and then verifying this multi-dimensional operating data.

[0133] After the elevator operation data is validated by the data processing and verification module, the elevator operation data that fails the validation by the data processing and verification module is sent to the fault warning module so that the fault warning module can directly judge the fault based on the elevator operation data.

[0134] The elevator operation data includes multi-dimensional operation data, elevator operating speed, and target speed.

[0135] After verifying and processing the elevator operation data during elevator operation, fault determination is required.

[0136] Specifically, the fault warning module retrieves stored historical fault data, prioritizes comparing real-time speed data with historical fault speed thresholds, and then compares other real-time abnormal elevator operation data (including abnormal elevator car and traction machine operating temperature data, abnormal humidity data in the car, abnormal gas data in the car, and abnormal elevator operating sound data) with the corresponding stored historical fault data thresholds in the fault warning module one by one, thereby achieving complete verification of multi-dimensional operation data.

[0137] An alert is triggered when an abnormality is detected in the current elevator operation. Otherwise, data on the elevator's operation is continuously collected in a loop.

[0138] If the real-time speed data exceeds the historical fault speed threshold, a separate warning message including abnormal information and elevator coordinate information will be triggered first, and its warning level will be marked. Other real-time abnormal elevator operation data other than real-time speed data that exceed the corresponding historical fault data threshold will also trigger a separate warning message, generating a warning message containing abnormal data, historical fault association information and elevator handling suggestions, and generating a corresponding warning message instruction.

[0139] Subsequently, the fault early warning module transmits the warning information to the remote control platform. The remote control platform displays the elevator's current operating status and pushes the corresponding elevator fault information and handling methods to the management personnel terminal, enabling the terminal to execute the warning instructions and conduct on-site handling. After on-site handling by management personnel, the feedback data is used for iterative optimization. This involves updating the historical fault database in the fault early warning module based on the current warning information and optimizing the speed threshold or the speed acquisition frequency of the intelligent sensing module and real-time monitoring module. This, in turn, adjusts the data acquisition and monitoring accuracy during subsequent cyclical elevator fault monitoring.

[0140] The elevator cycle fault monitoring process includes: data acquisition, where elevator speed data is synchronously and preferentially collected through the Hall speed sensor of the intelligent sensing module and the target monitoring unit of the real-time monitoring module. Simultaneously, associated auxiliary data, including current and voltage data (i.e., the multi-dimensional operational data in the above embodiment), is collected based on the IoT sensor unit in the intelligent sensing module to ensure synchronization with the dual-source speed data, ultimately obtaining the elevator operation data. Additionally, the elevator position tracking module collects elevator floor and trajectory data and correlates it with the currently collected elevator speed to form "speed-position" correlated data.

[0141] Data transmission and processing: The intelligent sensing module and real-time monitoring module send the collected elevator operation data to the data processing and verification module. The data processing and verification module verifies the validity of the received elevator operation data and transmits the valid elevator operation data to the intelligent algorithm module; the valid elevator operation data is transmitted to the fault early warning module so that the fault early warning module can directly make threshold judgments on the elevator operation data.

[0142] After receiving the elevator operation data that has undergone validity verification, the intelligent algorithm module prioritizes the quantitative analysis of the dual-source speed data within the elevator operation data, verifies the speed consistency of the dual-source speed data, obtains the analysis results, and synchronously transmits the analysis results to the fault early warning module. The fault early warning module prioritizes comparing the speed data with historical fault thresholds. The method used by the intelligent algorithm module to perform data quantitative analysis and consistency verification can be referred to the above embodiment, and will not be described in detail here.

[0143] The warning and command linkage is as follows: if a speed abnormality warning is triggered in the fault warning module, the fault warning module will push the speed abnormality information to the remote control platform first. The remote control platform, in conjunction with the "speed-position" correlation data uploaded by the elevator position tracking module, will issue speed control commands such as deceleration and stopping, as well as rescue dispatch commands.

[0144] In terms of handling and data feedback, once the speed control command is executed, the position tracking module, intelligent sensing module, and real-time monitoring module simultaneously feed back speed changes, elevator position, and on-site status data, which are then transmitted back to the fault early warning module.

[0145] The cyclic optimization closed loop, after determining that a fault has occurred in the current elevator operation and executing the corresponding speed control command, updates the current speed anomaly data and handling results to the historical fault database, feeding back to the intelligent algorithm module to optimize the elevator operating speed threshold parameters, and then guiding the intelligent sensing module and real-time monitoring module to adjust the speed acquisition frequency and target monitoring accuracy, forming a continuous closed loop of "acquisition-processing-early warning-handling-optimization-reacquisition", continuously improving the accuracy of speed monitoring and the reliability of fault early warning.

[0146] This specific embodiment uses the elevator speed during elevator operation as the core monitoring parameter. Through dual-source speed acquisition of Hall sensors and targets, as well as a consistency verification mechanism for speed deviation, it accurately captures key fault characteristics such as speed runaway and fluctuation, avoids the errors of a single acquisition method, effectively improves the accuracy of fault identification of core components such as braking and traction systems, and prevents major safety hazards from the source.

[0147] Meanwhile, based on focusing on core speed data, the system expands the collection of multi-dimensional operational data such as temperature, humidity, gas, and sound. Through data collaboration, it analyzes the correlation between speed anomalies and environmental and mechanical hazards, enabling targeted fault warnings and comprehensively covering the safety of elevator operating environment and equipment status monitoring needs, thereby improving the overall operational safety of elevators.

[0148] Furthermore, relying on the remote control platform's visualized speed monitoring, multi-channel early warning push, and command issuance functions, managers can monitor elevator speed status in real time, accurately locate faults, and complete fault response and control command issuance without on-site supervision, significantly reducing the frequency of on-site inspections and lowering maintenance labor costs. Through a multi-module closed-loop mechanism, accumulated fault data feeds back into the intelligent algorithm to optimize speed thresholds and collection parameters, continuously improving speed monitoring accuracy and fault early warning sensitivity. This drives the transformation of elevator maintenance from "passive handling" to "proactive early warning and precise maintenance," extending equipment lifespan and reducing long-term maintenance costs.

[0149] It should be noted that the steps shown in the above process or in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions.

[0150] This embodiment also provides an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0151] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0152] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0153] S1, during elevator operation, acquires the elevator's running speed and the target's speed; the target is fixedly installed inside the elevator.

[0154] S2, based on a preset quantization strategy, determines the elevator operation flow level during elevator operation according to the elevator running speed and the target speed; the elevator operation flow level is used to characterize the elevator's operation fluctuation.

[0155] S3 determines whether to issue a fault warning for the elevator based on the elevator's operating flow level and historical fault data thresholds.

[0156] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0157] Furthermore, in conjunction with the elevator fault early warning method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. The storage medium stores a computer program; when executed by a processor, the computer program implements any one of the elevator fault early warning methods in the above embodiments.

[0158] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0159] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0160] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0161] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. An elevator remote monitoring system, characterized in that, The system includes: an intelligent sensing module, a real-time monitoring module, an intelligent algorithm module, and a fault early warning module; the intelligent sensing module, the real-time monitoring module, and the fault early warning module are respectively connected to the intelligent algorithm module. The intelligent sensing module is used to obtain the elevator's operating speed during elevator operation. The real-time monitoring module is used to acquire the speed of the target fixedly installed on the elevator car during elevator operation; the target speed is used to reflect the moving speed of the elevator car. The intelligent algorithm module is used to determine the elevator operation flow level during elevator operation based on a preset quantization strategy, according to the elevator operating speed and the target speed; the elevator operation flow level is used to characterize the elevator's operation fluctuation. The fault warning module is used to determine whether to issue a fault warning for the elevator based on the elevator's operating flow level and a preset historical fault data threshold.

2. The elevator remote monitoring system according to claim 1, characterized in that, The intelligent algorithm module is also used to perform consistency verification on the elevator running speed and the target speed based on a preset consistency verification strategy, and obtain the consistency verification result. The intelligent algorithm module is further configured to determine the elevator operation flow level based on the elevator running speed and the target speed, when the consistency verification result indicates that the verification has passed.

3. The elevator remote monitoring system according to claim 1, characterized in that, The intelligent sensing module includes an Internet of Things (IoT) sensor unit, which is equipped with multi-dimensional auxiliary sensors. The multi-dimensional auxiliary sensor is used to acquire multi-dimensional operating data of the elevator and send the multi-dimensional operating data to the intelligent algorithm module; the multi-dimensional operating data includes elevator operating temperature data, elevator car humidity data, elevator car gas data, current and voltage data, and elevator operating sound data. The intelligent algorithm module is used to combine the multi-dimensional operating data and the elevator operating flow level to determine whether to issue a fault warning for the elevator.

4. The elevator remote monitoring system according to claim 3, characterized in that, The IoT sensor unit is also equipped with a vibration sensor; The vibration sensor is used to collect the physical flow level during the operation of the elevator and send the physical flow level to the intelligent algorithm module. The intelligent algorithm module is used to determine whether the elevator has a fault based on the elevator running speed and the target speed, the elevator running flow degree and the physical running flow degree, and generate a fault diagnosis result.

5. The elevator remote monitoring system according to claim 4, characterized in that, The intelligent sensing module further includes a signal conditioning unit; the signal conditioning unit is connected to the IoT sensor unit; The signal conditioning unit is used to receive the multi-dimensional operation data and physical operation flow level of the elevator collected by the Internet of Things sensor unit, and to perform filtering and noise reduction processing on the multi-dimensional operation data and physical operation flow level. The intelligent algorithm module is used to receive the multi-dimensional operating data and physical operating flow level after being filtered and denoised by the signal conditioning unit, and to determine whether the elevator has a fault based on the multi-dimensional operating data and physical operating flow level after being filtered and denoised, and to generate a fault diagnosis result.

6. The elevator remote monitoring system according to claim 4, characterized in that, The elevator remote monitoring system also includes a data processing and verification module; the data processing and verification module is connected to the intelligent sensing module and the real-time monitoring module respectively; the data processing and verification module is also connected to the intelligent algorithm module; The data processing and verification module is used to receive the elevator running speed, the multi-dimensional running data and the physical running flow degree sent by the intelligent sensing module, and to receive the target speed sent by the real-time monitoring module. It is also used to perform validity verification on the received elevator running speed, multi-dimensional running data, physical running flow degree and target speed based on a preset verification strategy, and send the data that passes the validity verification to the intelligent algorithm module.

7. The elevator remote monitoring system according to any one of claims 1 to 6, characterized in that, The system also includes an elevator position tracking module; the elevator position tracking module is used to acquire the elevator's running trajectory; The elevator position tracking module includes a positioning unit and a trajectory recording unit; The positioning unit is used to locate the elevator floors at different times during the operation of the elevator, as well as the running trajectory information generated during the change of elevator floors; The trajectory recording unit is used to record the elevator floor and the running trajectory information, and associate the running trajectory information with the elevator running speed and the target speed.

8. The elevator remote monitoring system according to claim 7, characterized in that, The system also includes a remote control platform; the remote control platform is connected to the fault early warning module; the remote control platform is also connected to the elevator position tracking module; The remote control platform is used to receive the fault warning signal generated by the fault warning module, and generate elevator speed control instructions based on the fault warning signal and the elevator's running trajectory information in the elevator position tracking module.

9. A method for early warning of elevator malfunctions, characterized in that, The method is applied to an elevator remote monitoring system as described in any one of claims 1 to 8, and the method includes: During elevator operation, the elevator's operating speed and the target's speed are acquired; the target is fixedly installed on the elevator car to reflect the elevator car's moving speed. Based on a preset quantization strategy, the elevator operation flow level is determined according to the elevator operating speed and the target speed; the elevator operation flow level is used to characterize the elevator's operation fluctuation. Based on the elevator's operational flow level and the elevator's historical fault data threshold, determine whether to issue a fault warning for the elevator.

10. An elevator, characterized in that, The elevator includes: an elevator car and an elevator remote monitoring system as described in any one of claims 1 to 8, for providing early warning of elevator malfunctions.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the elevator fault early warning method according to claim 9.

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