An elevator health state evaluation method, device, medium and equipment

Abstract

Embodiments of the present application disclose an elevator health state evaluation method, device, medium and equipment, and relate to the elevator monitoring technical field, and the present application realizes the monitoring of the car and the guide rail when the elevator runs through the vibration sensor. The vibration sensor is arranged along the guide rail, multiple vibration sensors can comprehensively realize monitoring, and can monitor subtle changes, improve the effectiveness of the evaluation result, after respectively comparing to obtain fluctuation data, only the linear change of the data of the abnormal fluctuation part is used to reflect the size of the relative distance, so as to determine the interval where the guide rail is abnormal, using the detected vibration data of the car, using the part where the car vibration is abnormal to more accurately locate the abnormal point in the suspected interval, and finally combining the historical abnormal point and the current abnormal point to realize effective and timely evaluation of the health state of the elevator.

Description

Technical Field

[0001] This application relates to the field of elevator monitoring technology, specifically to a method, device, medium, and equipment for assessing the health status of an elevator. Background Technology

[0002] An elevator is a permanent transportation device that serves several specific floors within a building, with its car moving along at least two rigid tracks perpendicular to the horizontal plane or at an angle of less than 15° to the vertical. To ensure the safe operation of elevators, health status assessments are necessary. However, current assessment methods primarily rely on manual operation, with maintenance personnel periodically dispatched to inspect the condition of key components. Based on the condition of each component, a health assessment is conducted to determine if any safety hazards exist, and if so, the corresponding components are repaired or replaced. For example, one crucial indicator is the straightness of the guide rails. Abnormal straightness can affect the safety of elevator operation, potentially causing vibrations and abnormal noises during operation. Current assessment methods are not timely; abnormalities in the guide rails may have already occurred within the time intervals between maintenance cycles. Even with significant time investment, the inspection of the entire guide rail system may be incomplete, and manual processing may miss minor anomalies, leading to lower validity of the assessment results. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, medium, and equipment for assessing the health status of elevators, aiming to solve the problems of poor timeliness and low effectiveness of assessment results in the prior art for assessing the health status of elevators.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0005] In a first aspect, embodiments of this application provide a method for assessing the health status of an elevator, comprising the following steps:

[0006] The system acquires vibration data of the elevator car and guide rail during operation; the guide rail vibration data is obtained based on multiple vibration sensors evenly distributed along the guide rail.

[0007] The car vibration data and guide rail vibration data are compared with the car standard vibration data and guide rail standard vibration data, respectively, to obtain the car vibration fluctuation data and guide rail vibration fluctuation data.

[0008] If the number of frames of guide rail vibration fluctuation data is greater than the preset number of frames, the suspected abnormal range of the guide rail is determined based on the linear change of the guide rail vibration fluctuation data.

[0009] Based on the car vibration fluctuation data, the target abnormal point is determined within the suspected abnormal range of the guide rail;

[0010] Assess the health status of elevators based on historical and target anomaly points.

[0011] In one possible implementation of the first aspect, the suspected abnormal interval of the guide rail is determined based on the linear change of the guide rail vibration fluctuation data, including:

[0012] Based on the vibration fluctuation data of the guide rail, multiple fluctuation deviation values ​​are obtained; each fluctuation deviation value corresponds to one vibration sensor.

[0013] Based on the magnitude of multiple fluctuation deviation values, the linear change pattern is obtained to determine the suspected abnormal range of the guide rail.

[0014] In one possible implementation of the first aspect, the target abnormal point is determined within the suspected abnormal zone of the guide rail based on the car vibration fluctuation data, including:

[0015] Based on the car vibration fluctuation data, determine the interval position of abnormal data relative to complete data;

[0016] Map the interval position to the guide rail to determine the target abnormal point position within the suspected abnormal interval of the guide rail.

[0017] In one possible implementation of the first aspect, before comparing the car vibration data and guide rail vibration data with the car standard vibration data and guide rail standard vibration data respectively to obtain the car vibration fluctuation data and guide rail vibration fluctuation data, the method further includes:

[0018] Based on the vibration sensor corresponding to the guide rail vibration data and the distance between the car and the vibration sensor when collecting each frame of data, the guide rail vibration data is adjusted to obtain the target vibration data of the guide rail.

[0019] In one possible implementation of the first aspect, the guide rail vibration data is adjusted based on the vibration sensor corresponding to the guide rail vibration data and the distance between the car and the vibration sensor when each frame of data is acquired, to obtain the target vibration data of the guide rail, including:

[0020] The adjustment curve is obtained based on the vibration sensor corresponding to the guide rail vibration data and the distance between the car and the vibration sensor when each frame of data is collected;

[0021] Based on the adjustment curve, adjust the guide rail vibration data corresponding to each frame number so that the guide rail vibration data presents a horizontal linear distribution, thereby obtaining the guide rail target vibration data.

[0022] In one possible implementation of the first aspect, before comparing the car vibration data and guide rail vibration data with the car standard vibration data and guide rail standard vibration data respectively to obtain the car vibration fluctuation data and guide rail vibration fluctuation data, the method further includes:

[0023] By unifying the standard vibration data of the car and the standard vibration data of the guide rail, the target standard data can be obtained.

[0024] The car vibration data and guide rail vibration data are compared with the standard car vibration data and standard guide rail vibration data, respectively, to obtain the car vibration fluctuation data and guide rail vibration fluctuation data, including:

[0025] The car vibration data and guide rail vibration data are compared with the target standard data to obtain the car vibration fluctuation data and guide rail vibration fluctuation data.

[0026] In one possible implementation of the first aspect, acquiring car vibration data and guide rail vibration data during elevator operation includes:

[0027] Obtain raw car vibration data and raw guide rail vibration data during elevator operation;

[0028] The original car vibration data and original guide rail vibration data were cleaned, and the cleaned data were cropped based on the effective time series to obtain car vibration data and guide rail vibration data with the same number of frames.

[0029] Secondly, embodiments of this application provide an elevator health status assessment device, comprising:

[0030] The acquisition module is used to acquire car vibration data and guide rail vibration data during elevator operation; the guide rail vibration data is obtained based on multiple vibration sensors evenly distributed along the guide rail.

[0031] The comparison module is used to compare the car vibration data and guide rail vibration data with the standard car vibration data and guide rail vibration data, respectively, to obtain the car vibration fluctuation data and guide rail vibration fluctuation data.

[0032] The determination module is used to determine the suspected abnormal range of the guide rail based on the linear change of the guide rail vibration fluctuation data when the number of frames of the guide rail vibration fluctuation data is greater than the preset number of frames.

[0033] The anomaly module is used to determine the target anomaly point within the suspected anomaly range of the guide rail based on the car vibration fluctuation data.

[0034] The assessment module is used to assess the health status of the elevator based on historical anomaly points and target anomaly points.

[0035] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the elevator health status assessment method provided in any of the first aspects above.

[0036] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein,

[0037] Memory is used to store computer programs;

[0038] The processor is used to load and execute computer programs to cause the electronic device to perform the elevator health assessment method provided in any of the first aspects above.

[0039] Compared with the prior art, the beneficial effects of this application are:

[0040] This application proposes an elevator health status assessment method, apparatus, medium, and equipment. The method includes: acquiring car vibration data and guide rail vibration data during elevator operation; wherein the guide rail vibration data is obtained based on multiple vibration sensors evenly distributed along the guide rail; comparing the car vibration data and guide rail vibration data with standard car vibration data and standard guide rail vibration data respectively to obtain car vibration fluctuation data and guide rail vibration fluctuation data; when the number of frames in the guide rail vibration fluctuation data is greater than a preset number of frames, determining a suspected abnormal interval of the guide rail based on the linear change of the guide rail vibration fluctuation data; determining a target abnormal point within the suspected abnormal interval of the guide rail based on the car vibration fluctuation data; and assessing the elevator health status based on historical abnormal points and the target abnormal point. This application utilizes vibration sensors to monitor the elevator car and guide rails during operation. Multiple vibration sensors are deployed along the guide rails, enabling more comprehensive monitoring and detecting subtle changes, thus improving the effectiveness of the assessment results. By comparing the collected vibration data with standard vibration data, fluctuations in the vibration data can be identified. If the number of frames in the guide rail vibration fluctuation data exceeds the preset number, it indicates that the abnormal fluctuations are not caused by accidental factors. Considering that changes in the distance between the vibration sensors and the moving car affect the overall level of the vibration data, after comparing the fluctuation data separately, only the linear changes in the abnormal fluctuation data are used to reflect the magnitude of the relative distance, thereby determining the interval where the guide rail is abnormal. Since the vibration sensors cannot be deployed too densely, this interval is relatively large and can only be identified as a suspected interval. At this point, the detected car vibration data can be used to more accurately locate the abnormal point within the suspected interval by focusing on the abnormal part of the car vibration. Finally, by combining historical abnormal points with the current abnormal point, an effective and timely assessment of the elevator's health status can be achieved. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application;

[0042] Figure 2 A flowchart illustrating the elevator health status assessment method provided in this application embodiment;

[0043] Figure 3 This is a schematic diagram of the elevator health status assessment device provided in the embodiments of this application;

[0044] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0045] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0046] See attached document Figure 1 , attached Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface or a wireless interface. The network interface 103 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0047] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0048] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an elevator health status assessment device.

[0049] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the elevator health status assessment device stored in the memory 105 through the processor 101 and executes the elevator health status assessment method provided in the embodiment of this application.

[0050] See attached document Figure 2 Based on the hardware device described in the foregoing embodiments, embodiments of this application provide a method for assessing the health status of an elevator, comprising the following steps:

[0051] S10: Acquire car vibration data and guide rail vibration data during elevator operation; wherein, the guide rail vibration data is obtained based on multiple vibration sensors evenly distributed along the guide rail.

[0052] In practical implementation, the elevator guide rail is the safe path for the elevator to travel up and down the shaft. The guide rail is installed on the shaft wall and fixed to the shaft wall by guide rail brackets and supports. The straightness of the guide rail is a key factor in ensuring the smooth operation of the elevator. Detecting the vibration data of the guide rail during elevator operation can determine whether its straightness is affected. If a single vibration sensor is deployed, the data from the sensor may not reflect abnormal vibration changes if the location of the anomaly is far from the sensor. Therefore, multiple vibration sensors evenly distributed along the guide rail are used to achieve comprehensive monitoring. The vibration data of the car can be obtained by installing vibration sensors on the car. The vibration sensors move with the car, so multiple sensors are not required.

[0053] In one embodiment, acquiring elevator car vibration data and guide rail vibration data during elevator operation includes:

[0054] Obtain raw car vibration data and raw guide rail vibration data during elevator operation;

[0055] The original car vibration data and original guide rail vibration data were cleaned, and the cleaned data were cropped based on the effective time series to obtain car vibration data and guide rail vibration data with the same number of frames.

[0056] In the specific implementation process, due to the start-stop and stop of elevator operation, as well as the influence of the usage environment and personnel, the raw data collected may have missing, discontinuous, or abrupt effects. Data cleaning can be considered to remove duplicate values, eliminate obviously discrete data, and interpolate and complete missing data to improve the integrity and quality of the data. After cleaning, the data is trimmed according to the time series to ensure that the car vibration data and guide rail vibration data have the same number of frames for subsequent processing and analysis.

[0057] S20: Compare the car vibration data and guide rail vibration data with the standard car vibration data and guide rail vibration data respectively to obtain the car vibration fluctuation data and guide rail vibration fluctuation data.

[0058] In the specific implementation process, abnormal data can be obtained by comparing the monitored data with the corresponding standard data. This abnormal data manifests as fluctuating vibration data. The standard data used for comparison can be the vibration data collected by each vibration sensor under conditions where the elevator is running normally. Since multiple sensors are installed on the guide rail, the data collected by each vibration sensor is different, and the standard data used for comparison is also different. Comparing them separately would be somewhat complex. Therefore, it is considered whether the data can be adjusted so that all sensors can be compared using the same standard. This application provides a method: before comparing the car vibration data and guide rail vibration data with the car standard vibration data and guide rail standard vibration data respectively to obtain the car vibration fluctuation data and guide rail vibration fluctuation data, the method further includes:

[0059] Based on the vibration sensor corresponding to the guide rail vibration data and the distance between the car and the vibration sensor when collecting each frame of data, the guide rail vibration data is adjusted to obtain the target vibration data of the guide rail.

[0060] In practical implementation, since multiple vibration sensors are installed, the relative distance between the elevator car and different vibration sensors remains unchanged during elevator operation. Based on vibration data, regardless of whether there is an abnormality in the guide rail, the closer the vibration sensor is to the car, the higher the overall level of the feedback data. Therefore, the vibration data is adjusted by changing the distance between the car and the vibration sensors, increasing the smaller values ​​reported by the farther sensors. The adjusted data is the target vibration data for the guide rail. Specifically: based on the vibration sensor corresponding to the guide rail vibration data and the distance between the car and the vibration sensor at the time of data acquisition for each frame, the guide rail vibration data is adjusted to obtain the target vibration data for the guide rail, including:

[0061] The adjustment curve is obtained based on the vibration sensor corresponding to the guide rail vibration data and the distance between the car and the vibration sensor when each frame of data is collected;

[0062] Based on the adjustment curve, adjust the guide rail vibration data corresponding to each frame number so that the guide rail vibration data presents a horizontal linear distribution, thereby obtaining the guide rail target vibration data.

[0063] In practical implementation, vibration data is collected only when the elevator car moves, causing vibration. The distance between the constantly moving car and the sensor is constantly changing, requiring different adjustments for each frame of data. Therefore, the change in distance is quantified as a curve, which serves as an adjustment curve for each frame of data. After plotting the curve on a coordinate system, time is plotted on the x-axis, and the adjustment coefficient for the corresponding frame number is plotted on the y-axis. Without adjustment, the vibration data curve fed back by the vibration sensor is diverse. Even during smooth operation without abnormalities, the curve may be a straight line with a positive slope (the vibration sensor gradually approaches the car), a curve with a negative slope (the vibration sensor gradually moves away from the car), or an inverted V-shaped curve (the vibration sensor first gradually approaches and then gradually moves away from the car). The adjustment curve obtained based on the change in relative distance is used to adjust the guide rail vibration data, resulting in a horizontal linear distribution of the guide rail vibration data. To further illustrate the curve representation of the vibration data, let's continue with the previous example:

[0064] For example, if the vibration data curve is a straight line with a positive slope, the smaller the point on the vertical axis, the farther away from the car it is. The corresponding vertical axis value on the adjustment curve will be larger, and the vibration data at that frame number needs to be adjusted more. As a result, the slope of the straight line with a positive slope will get closer and closer to zero, and it will become a horizontal straight line.

[0065] Since the vibration sensor for the car moves with the car, the aforementioned problem does not occur. The linearity of its feedback data is inherently a horizontal straight line. The vibration data of the guide rail is adjusted to a similar state, thus the standard data for comparison can be unified. The standard data for comparison is simply a horizontal straight line. The comparison is based on the levelness of the line, and the discrete portion of the vibration data relative to the horizontal straight line can be considered as data with vibration fluctuations. That is, before comparing the car vibration data and guide rail vibration data with the standard car vibration data and standard guide rail vibration data respectively to obtain the car vibration fluctuation data and guide rail vibration fluctuation data, the method also includes:

[0066] By unifying the standard vibration data of the car and the standard vibration data of the guide rail, the target standard data can be obtained.

[0067] Based on the aforementioned steps, the car vibration data and guide rail vibration data are compared with the standard car vibration data and guide rail vibration data, respectively, to obtain the car vibration fluctuation data and guide rail vibration fluctuation data, including:

[0068] The car vibration data and guide rail vibration data are compared with the target standard data to obtain the car vibration fluctuation data and guide rail vibration fluctuation data.

[0069] S30: If the number of frames of guide rail vibration fluctuation data is greater than the preset number of frames, determine the suspected abnormal range of the guide rail based on the linear change of the guide rail vibration fluctuation data.

[0070] In practical implementation, ideally, the elevator operates smoothly without faults, and the vibration data will not show abnormal fluctuations. However, in reality, the elevator's start and stop, as well as the influence of sensor transmission signals, can cause abnormal fluctuations in the feedback vibration data. Considering that these effects are not prolonged (e.g., elevator start and stop last only a few seconds), and sensor signal anomalies may be even smaller, lasting only a few seconds or even milliseconds, these situations differ from the continuous existence of abnormal conditions. Therefore, a frame rate is set as a threshold to identify data anomalies caused by these sudden factors. These abnormal fluctuations are directly affected by abnormal parts of the guide rail and are related to their relative distance; the greater the distance, the more pronounced the abnormal fluctuations. Therefore, by observing the linear changes in the guide rail vibration fluctuation data, the suspected abnormal range of the guide rail can be determined.

[0071] Specifically: Based on the linear changes in the guide rail vibration fluctuation data, the suspected abnormal range of the guide rail is determined, including:

[0072] Based on the vibration fluctuation data of the guide rail, multiple fluctuation deviation values ​​are obtained; each fluctuation deviation value corresponds to one vibration sensor.

[0073] Based on the magnitude of multiple fluctuation deviation values, the linear change pattern is obtained to determine the suspected abnormal range of the guide rail.

[0074] In practice, different vibration sensors are located at different distances from the abnormal location. This distance directly affects the fluctuation deviation of the guide rail vibration data. A larger fluctuation deviation indicates the vibration sensor is closer to the abnormal location, and the linear relationship formed by the fluctuation deviation points to the location closest to the abnormality. However, due to the long guide rail, sensors are not densely arranged along it. Therefore, this only preliminarily identifies the suspected abnormal area, requiring further confirmation using car vibration data.

[0075] S40: Based on the car vibration fluctuation data, determine the target abnormal point within the suspected abnormal range of the guide rail.

[0076] In the specific implementation process, after identifying the suspected abnormal zone using guide rail vibration data, the car vibration data is then used to determine the more precise abnormal location. Specifically: based on the car vibration fluctuation data, the target abnormal point is determined within the suspected abnormal zone of the guide rail, including:

[0077] Based on the car vibration fluctuation data, determine the interval position of abnormal data relative to complete data;

[0078] Map the interval position to the guide rail to determine the target abnormal point position within the suspected abnormal interval of the guide rail.

[0079] In the specific implementation process, since the vibration of the car is directly affected by the distance from the abnormal part, the vibration fluctuation data of the car can directly reflect the abnormal part. As abnormal data, the position of the car vibration fluctuation data in the interval of the complete data corresponds to the position of the abnormal point on the guide rail. By forming a mapping relationship, the interval position is mapped onto the guide rail, and then combined with the suspected interval determined by the linear change of the guide rail vibration fluctuation data, if the deviation between the target abnormal point and the abnormal position pointed to by the linear change is within a reasonable range, then the target abnormal point can be determined to be reliable.

[0080] S50: Assess the health status of the elevator based on historical and target abnormal points.

[0081] In the specific implementation process, once the target anomaly point is obtained, it indicates that the elevator's health status is poor. In order to achieve a more comprehensive assessment, historical data is combined for evaluation. Historical anomaly points also represent the occurrence of anomalies. Based on this information, it is possible to determine the current target anomaly point or the location and frequency of anomalies that have occurred in the past. Taking into account the health status of the elevator, for example, if anomalies have occurred multiple times in a certain section, then the health status of that elevator is poor, and that section needs to be inspected in particular.

[0082] In this embodiment, vibration sensors are deployed to monitor the elevator car and guide rails during operation. Multiple vibration sensors are deployed along the guide rails, enabling more comprehensive monitoring and detecting subtle changes, thus improving the effectiveness of the evaluation results. By comparing the collected vibration data with standard vibration data, fluctuations in the vibration data can be identified. If the number of frames in the guide rail vibration fluctuation data exceeds the preset number of frames, it indicates that the abnormal fluctuations are not caused by accidental factors. Considering that changes in the distance between the vibration sensors and the moving car affect the overall level of the vibration data, after comparing the fluctuation data separately, only the linear changes in the abnormal fluctuation data are used to reflect the relative distance, thereby determining the interval where the guide rail is abnormal. Since the vibration sensors cannot be deployed too densely, this interval is relatively large and can only be identified as a suspected interval. At this point, the detected car vibration data can be used to more accurately locate the abnormal point within the suspected interval by using the abnormal part of the car vibration. Finally, by combining historical abnormal points with the current abnormal point, the health status of the elevator can be effectively and timely evaluated.

[0083] See attached document Figure 3 Based on the same inventive concept as in the foregoing embodiments, this application also provides an elevator health status assessment device, comprising:

[0084] The acquisition module is used to acquire car vibration data and guide rail vibration data during elevator operation; the guide rail vibration data is obtained based on multiple vibration sensors evenly distributed along the guide rail.

[0085] The comparison module is used to compare the car vibration data and guide rail vibration data with the standard car vibration data and guide rail vibration data, respectively, to obtain the car vibration fluctuation data and guide rail vibration fluctuation data.

[0086] The determination module is used to determine the suspected abnormal range of the guide rail based on the linear change of the guide rail vibration fluctuation data when the number of frames of the guide rail vibration fluctuation data is greater than the preset number of frames.

[0087] The anomaly module is used to determine the target anomaly point within the suspected anomaly range of the guide rail based on the car vibration fluctuation data.

[0088] The assessment module is used to assess the health status of the elevator based on historical anomaly points and target anomaly points.

[0089] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated into one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the elevator health status assessment device in this embodiment corresponds one-to-one with each step in the elevator health status assessment method in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned elevator health status assessment method, which will not be repeated here.

[0090] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the elevator health status assessment method provided in the embodiments of this application.

[0091] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein,

[0092] Memory is used to store computer programs;

[0093] The processor is used to load and execute computer programs to enable electronic devices to perform elevator health status assessment methods as provided in the embodiments of this application.

[0094] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0095] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0096] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0097] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0098] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0099] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0101] In summary, this application provides an elevator health status assessment method, apparatus, medium, and equipment. The method includes: acquiring car vibration data and guide rail vibration data during elevator operation; wherein the guide rail vibration data is obtained based on multiple vibration sensors evenly distributed along the guide rail; comparing the car vibration data and guide rail vibration data with standard car vibration data and standard guide rail vibration data, respectively, to obtain car vibration fluctuation data and guide rail vibration fluctuation data; when the number of frames in the guide rail vibration fluctuation data is greater than a preset number of frames, determining the suspected abnormal interval of the guide rail based on the linear change of the guide rail vibration fluctuation data; determining the target abnormal point within the suspected abnormal interval of the guide rail based on the car vibration fluctuation data; and assessing the elevator health status based on historical abnormal points and the target abnormal point. This application utilizes vibration sensors to monitor the elevator car and guide rails during operation. Multiple vibration sensors are deployed along the guide rails, enabling more comprehensive monitoring and detecting subtle changes, thus improving the effectiveness of the assessment results. By comparing the collected vibration data with standard vibration data, fluctuations in the vibration data can be identified. If the number of frames in the guide rail vibration fluctuation data exceeds the preset number, it indicates that the abnormal fluctuations are not caused by accidental factors. Considering that changes in the distance between the vibration sensors and the moving car affect the overall level of the vibration data, after comparing the fluctuation data separately, only the linear changes in the abnormal fluctuation data are used to reflect the magnitude of the relative distance, thereby determining the interval where the guide rail is abnormal. Since the vibration sensors cannot be deployed too densely, this interval is relatively large and can only be identified as a suspected interval. At this point, the detected car vibration data can be used to more accurately locate the abnormal point within the suspected interval by focusing on the abnormal part of the car vibration. Finally, by combining historical abnormal points with the current abnormal point, an effective and timely assessment of the elevator's health status can be achieved.

[0102] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating the health state of an elevator, characterized by, The method comprises the following steps: obtaining car vibration data and guide rail vibration data during elevator operation; wherein the guide rail vibration data is obtained based on a plurality of vibration sensors evenly distributed along the guide rail; comparing the car vibration data and the guide rail vibration data with car standard vibration data and guide rail standard vibration data respectively to obtain car vibration fluctuation data and guide rail vibration fluctuation data; in the case that the number of frames of the guide rail vibration fluctuation data is greater than a preset number of frames, determining a guide rail suspected abnormal interval according to the linear change of the guide rail vibration fluctuation data; determining a target abnormal point within the guide rail suspected abnormal interval according to the car vibration fluctuation data; evaluating the health status of the elevator according to the historical abnormal point and the target abnormal point.

2. The elevator health state evaluation method according to claim 1, characterized by, The method for determining the guide rail suspected abnormal interval according to the linear change of the guide rail vibration fluctuation data comprises: obtaining a plurality of fluctuation deviation values according to the guide rail vibration fluctuation data; wherein one fluctuation deviation value corresponds to one vibration sensor; obtaining a linear change to determine the guide rail suspected abnormal interval according to the size of the plurality of fluctuation deviation values.

3. The elevator health state evaluation method according to claim 1, characterized by, The method for determining the target abnormal point within the guide rail suspected abnormal interval according to the car vibration fluctuation data comprises: determining the interval position of abnormal data relative to complete data according to the car vibration fluctuation data; mapping the interval position to the guide rail to determine the target abnormal point within the guide rail suspected abnormal interval.

4. The elevator health state evaluation method according to claim 1, characterized by, Before comparing the car vibration data and the guide rail vibration data with car standard vibration data and guide rail standard vibration data respectively to obtain car vibration fluctuation data and guide rail vibration fluctuation data, the method further comprises: adjusting the guide rail vibration data according to the distance between the vibration sensor corresponding to the guide rail vibration data and the car and the vibration sensor when collecting each frame of data to obtain guide rail target vibration data.

5. The elevator health state evaluation method according to claim 4, characterized by The method for adjusting the guide rail vibration data according to the distance between the vibration sensor corresponding to the guide rail vibration data and the car and the vibration sensor when collecting each frame of data to obtain guide rail target vibration data comprises: obtaining an adjustment curve according to the distance between the vibration sensor corresponding to the guide rail vibration data and the car and the vibration sensor when collecting each frame of data; adjusting the guide rail vibration data corresponding to each frame number according to the adjustment curve to make the guide rail vibration data present a horizontal linear distribution to obtain guide rail target vibration data.

6. The elevator health state evaluation method according to claim 5, characterized by Before comparing the car vibration data and the guide rail vibration data with car standard vibration data and guide rail standard vibration data respectively to obtain car vibration fluctuation data and guide rail vibration fluctuation data, the method further comprises: unifying the car standard vibration data and the guide rail standard vibration data to obtain target standard data; The method for comparing the car vibration data and the guide rail vibration data with car standard vibration data and guide rail standard vibration data respectively to obtain car vibration fluctuation data and guide rail vibration fluctuation data comprises: The car vibration data and the guide rail vibration data are compared with the target standard data respectively to obtain car vibration fluctuation data and guide rail vibration fluctuation data.

7. The elevator health state evaluation method according to claim 1, characterized by, The car vibration data and the guide rail vibration data during the operation of the elevator are obtained, including: The original car vibration data and the original guide rail vibration data during the operation of the elevator are obtained; The original car vibration data and the original guide rail vibration data are cleaned, and the cleaned data are cut based on effective time sequences to obtain car vibration data and guide rail vibration data with the same number of frames.

8. An elevator health state evaluation device characterized by comprising: including: The car vibration data and the guide rail vibration data during the operation of the elevator are obtained by the obtaining module; wherein the guide rail vibration data is obtained based on a plurality of vibration sensors evenly distributed along the guide rail; The car vibration data and the guide rail vibration data are compared with the car standard vibration data and the guide rail standard vibration data respectively by the comparison module to obtain car vibration fluctuation data and guide rail vibration fluctuation data; The determination module is configured to determine a guide rail suspected abnormal interval according to the linear change of the guide rail vibration fluctuation data when the number of frames of the guide rail vibration fluctuation data is greater than a preset number of frames; The anomaly module is configured to determine a target abnormal point in the guide rail suspected abnormal interval according to the car vibration fluctuation data; The evaluation module is configured to evaluate the health status of the elevator according to the historical abnormal point and the target abnormal point.

9. A computer-readable storage medium storing a computer program, characterized in that, The computer program is loaded and executed by the processor to implement the elevator health status evaluation method of any one of claims 1-7.

10. An electronic device, comprising: including a processor and a memory, wherein, The memory is configured to store a computer program; The processor is configured to load and execute the computer program to enable the electronic device to perform the elevator health status evaluation method of any one of claims 1-7.

Images