Elevator internal noise monitoring method and device
Through cloud-based monitoring methods and neural network models, the problem of low accuracy in traditional elevator noise monitoring has been solved, efficient and accurate monitoring of elevator internal noise has been achieved, and the stability of elevator operation and user experience have been improved.
Patent Information
- Application Number
- CN202510652457.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-21
- Publication Date
- 2025-09-12
AI Technical Summary
Traditional elevator noise monitoring methods rely on manual judgment and lack precise quantitative standards. The monitoring results are inaccurate and inefficient, and cannot meet the modern society's demand for precise control of elevator operating status.
A cloud-based monitoring method is used to obtain elevator internal noise and operating speed data, perform feature extraction and feature fusion, use delay influencing factors to process noise and speed data, and combine neural network models to perform noise prediction to improve monitoring accuracy.
It achieves efficient and accurate monitoring of elevator internal noise, protects user health, and provides timely fault detection and smooth operation support for elevator operation status.
Smart Images
Figure CN120622253A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of elevator noise monitoring, and in particular to a method and device for monitoring elevator internal noise. Background Art
[0002] Amidst the rapid growth of urbanization, elevators, an integral part of modern life, have reached unprecedented levels of popularity. Whether in towering office buildings or cozy, comfortable residential communities, elevators play a crucial role in people's daily commute. However, the downside is the increasingly prominent noise generated by elevators, a factor that seriously impacts user experience.
[0003] Traditional elevator noise monitoring methods have numerous limitations. Determining the rated speed range of an elevator relies heavily on subjective judgment and lacks precise quantitative standards. Using simple noise meters requires manual operation, which is labor-intensive and inefficient. Measuring test distances relies solely on rough estimates using a steel tape measure, a primitive method with significant errors. These shortcomings directly lead to low monitoring accuracy and efficiency, making it difficult to meet the modern demand for precise control of elevator operating conditions.
[0004] Based on this, the present invention proposes a method and device for monitoring elevator internal noise to solve the above technical problems. Summary of the Invention
[0005] The present invention describes a method and device for monitoring the internal noise of an elevator, which can accurately monitor the internal noise of an elevator.
[0006] According to a first aspect, the present invention provides a method for monitoring noise inside an elevator, the method being applied to a cloud located outside the elevator, the method comprising:
[0007] Obtain the current elevator's internal noise and operating speed data for a preset time period;
[0008] Extracting features of the internal noise and the running speed data respectively to obtain internal noise features and running speed data features in sequence;
[0009] Performing feature fusion on the internal noise and the operating speed data according to a delay impact factor to obtain a fused feature; wherein the delay impact factor is determined based on the operating speed data, the distance between the cloud and the current elevator, and the operating state coefficient of the current elevator;
[0010] The fused features are input into the elevator noise monitoring model to obtain the predicted noise of the current elevator.
[0011] According to a second aspect, the present invention provides an elevator internal noise monitoring device, comprising:
[0012] an acquiring unit configured to acquire internal noise and running speed data of the current elevator in a preset time period;
[0013] a first data processing unit configured to extract features from the internal noise and the running speed data respectively, and obtain internal noise features and running speed data features in sequence;
[0014] a second data processing unit configured to perform feature fusion on the internal noise and the operating speed data according to a delay impact factor to obtain a fused feature; wherein the delay impact factor is determined based on the operating speed data, a distance between the cloud and the current elevator, and an operating state coefficient of the current elevator;
[0015] The third data processing unit is configured to input the fusion feature into the elevator noise monitoring model to obtain the predicted noise of the current elevator.
[0016] In a third aspect, an embodiment of this specification further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.
[0017] In a fourth aspect, an embodiment of this specification further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method described in any embodiment of this specification.
[0018] According to the elevator internal noise monitoring method and device provided by the present invention, the cloud uses noise monitoring sensors inside the elevator to collect the current elevator's internal noise over a preset time period and directly obtain its operating speed data. Feature extraction is performed on the collected internal noise and operating speed data, respectively, to generate internal noise features and operating speed data features. Because the cloud is located outside the elevator, the signal is highly susceptible to interference during data transmission, resulting in unstable transmission and time delay. The present invention determines a delay impact factor based on the operating speed data, the distance between the cloud and the current elevator, and the current elevator's operating status coefficient. The greater the delay impact factor, the greater the proportion of operating speed data in the fused feature. Based on this, the internal noise and operating speed data are fused to generate a fused feature. This fused feature more accurately reflects the elevator's noise characteristics. This fused feature is input into the elevator noise monitoring model to obtain the predicted noise value for the current elevator. Through this configuration, the present invention effectively improves the accuracy of elevator internal noise monitoring, providing strong support for protecting people's health. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0020] Figure 1 A schematic flow chart of a method for monitoring elevator internal noise according to one embodiment is shown;
[0021] Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of this specification;
[0022] Figure 3 A schematic block diagram of an elevator interior noise monitoring device according to an embodiment is shown. DETAILED DESCRIPTION
[0023] The solution provided by the present invention is described below with reference to the accompanying drawings.
[0024] Figure 1 The flowchart of the elevator internal noise monitoring method according to one embodiment is shown. It is understood that the method can be executed by any device, equipment, platform, or equipment cluster with computing and processing capabilities. Figure 1 As shown, the method includes:
[0025] Step 100: Obtain the internal noise and running speed data of the current elevator in a preset time period;
[0026] Step 102: Extract features of the internal noise and running speed data respectively to obtain internal noise features and running speed data features in sequence;
[0027] Step 104: Fusing the internal noise and running speed data according to the delay impact factor to obtain a fused feature; wherein the delay impact factor is determined based on the running speed data, the distance between the cloud and the current elevator, and the operating status coefficient of the current elevator;
[0028] Step 106: Input the fused features into the elevator noise monitoring model to obtain the predicted noise of the current elevator.
[0029] In this embodiment, the cloud uses noise monitoring sensors inside the elevator to collect the elevator's internal noise over a preset time period and directly obtain its operating speed data. Feature extraction is performed on the collected internal noise and operating speed data, respectively, generating internal noise features and operating speed data features. Because the cloud is located outside the elevator, the signal is easily interfered with during data transmission, resulting in unstable transmission and delay. The present invention determines a delay impact factor based on the operating speed data, the distance between the cloud and the current elevator, and the current elevator's operating status coefficient. The greater the delay impact factor, the greater the proportion of operating speed data in the fused feature. Based on this, the internal noise and operating speed data are fused to generate a fused feature. This fused feature more accurately reflects the elevator's noise characteristics. This fused feature is input into the elevator noise monitoring model to obtain the predicted noise value for the current elevator. Through this configuration, the present invention effectively improves the accuracy of elevator internal noise monitoring, providing strong support for protecting people's health.
[0030] In one embodiment of the present invention, the delay impact factor is determined by the following formula:
[0031]
[0032] Where F is the delay influencing factor, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, t1 is the starting time point of the preset time period, t2 is the ending time point of the preset time period, u is the running speed data, d is the distance between the cloud and the current elevator, δ is the running status coefficient of the current elevator, α is the first reference coefficient, β is the second reference coefficient, c1 is the adjustment constant, and c is the speed of light.
[0033] In this embodiment, the faster the elevator runs, the longer it may take to transmit data to the cloud (assuming the data is ultimately transmitted to the cloud for processing). This is because high-speed operation generates more signal interference and transmission delays. The distance between the cloud and the current elevator affects: The greater the distance between the cloud and the elevator, the longer the physical path for signal transmission, and accordingly, the longer the latency. The transmission delay can be accurately calculated based on the theoretical propagation speed of the signal in the transmission medium (such as the propagation speed of wireless signals in air) and the actual measured distance between the cloud and the elevator. The impact of the current elevator's operating state: During the elevator's acceleration or deceleration phase, the elevator control system generates more control signal interactions. This complex signal interaction may affect the priority and timeliness of data transmission, thereby causing latency. The current elevator's operating state coefficient is the proportion of the time spent accelerating or decelerating the current elevator within a preset time period. This delay impact factor is calculated using a weighted algorithm, taking into account three factors: operating speed, distance between the cloud and the elevator, and the elevator's operating state. In order to improve the monitoring accuracy of elevator internal noise, technical personnel in this field can customize the first weight coefficient, the second weight coefficient and the third weight coefficient, the distance between the cloud and the current elevator, the first reference coefficient and the second reference coefficient according to the actual technical usage. The adjustment constant is 1000000. The operating status coefficient of the current elevator is the proportion of the time used for the current elevator to open and close the door and accelerate and decelerate in the preset time period.
[0034] In one embodiment of the present invention, after inputting the fused features into the elevator noise monitoring model to obtain the predicted noise of the current elevator, the method further includes:
[0035] When the delay impact factor is greater than the first preset factor, obtaining the external noise of the current elevator in a preset time period;
[0036] Determine the corrected noise based on the external noise and the elevator sound insulation coefficient;
[0037] The corrected noise and the predicted noise of the current elevator are weighted according to the ratio of the delay influence factor to determine the final predicted noise;
[0038] The delay impact factor ratio is determined according to the delay impact factor.
[0039] In this embodiment, when the delay impact factor is greater than the first preset factor, it means that the internal noise accounts for a very small proportion in the fusion feature. Therefore, it is necessary to introduce the external noise of the current elevator and recalculate. The corrected noise is determined by multiplying the external noise and the elevator sound insulation coefficient. The corrected noise and the predicted noise of the current elevator are weighted according to the ratio of the delay impact factor to determine the final predicted noise, so as to improve the monitoring accuracy of the elevator internal noise. The ratio of the delay impact factor is positively correlated with the delay impact factor. Since different elevators have different sound insulation coefficients, technical personnel in this field should customize the elevator sound insulation coefficient according to actual usage.
[0040] In one embodiment of the present invention, the final predicted noise is determined by the following formula:
[0041]
[0042] Where Z is the final prediction noise, is the ratio of the delay influencing factor, Y is the fixed factor, YC is the predicted noise of the current elevator, and XZ is the corrected noise.
[0043] In this embodiment, the elevator internal noise (the final predicted noise) can be accurately calculated using the above formula, and those skilled in the art can customize the fixed factor according to actual usage.
[0044] In one embodiment of the present invention, a neural network model can be used as the elevator noise monitoring model. Neural networks possess powerful nonlinear mapping capabilities, enabling accurate learning and analysis of complex data patterns. This characteristic makes them particularly advantageous in processing the information-rich noise data generated during elevator operation. They can effectively capture the hidden connections between noise signals and elevator operating conditions, enabling precise prediction and monitoring of elevator noise, providing strong support for timely detection of potential elevator faults and ensuring smooth operation.
[0045] In summary, this invention enables in-depth analysis and mining of various types of data during elevator operation, potentially enabling more efficient and accurate monitoring of elevator noise. Furthermore, by incorporating external data, such as building structure information and ambient noise data, it can comprehensively assess elevator noise from multiple dimensions, further improving the accuracy and comprehensiveness of monitoring and providing strong support for creating a quieter and more comfortable elevator environment.
[0046] The foregoing description describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0047] like Figure 2 、 Figure 3 As shown, the embodiment of this specification provides an elevator internal noise monitoring device. The device embodiment can be implemented by software, hardware, or a combination of software and hardware. From the hardware level, such as Figure 2 As shown in the figure, an elevator internal noise monitoring system provided by the embodiment of this specification is
[0048] A hardware architecture diagram of the electronic device where the device is located, except Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, it is formed by the CPU of the electronic device in which it is located reading the corresponding computer program in the non-volatile memory into the internal memory and running it.
[0049] like Figure 3 As shown, this embodiment provides an elevator internal noise monitoring device, the device comprising:
[0050] The acquisition unit 300 is configured to acquire the internal noise and running speed data of the current elevator in a preset time period;
[0051] The first data processing unit 302 is configured to extract features from the internal noise and the running speed data respectively, and obtain internal noise features and running speed data features in sequence;
[0052] The second data processing unit 304 is configured to perform feature fusion on the internal noise and the operating speed data according to a delay impact factor to obtain a fused feature; wherein the delay impact factor is determined based on the operating speed data, the distance between the cloud and the current elevator, and the operating state coefficient of the current elevator;
[0053] The third data processing unit 306 is configured to input the fused features into the elevator noise monitoring model to obtain the predicted noise of the current elevator.
[0054] In the embodiment of this specification, the first acquisition module 300 can be used to execute step 100 in the above method embodiment, the first data processing module 302 can be used to execute step 102 in the above method embodiment, the second acquisition module can be used to execute step 104 in the above method embodiment, and the second data processing module 306 can be used to execute step 106 in the above method embodiment.
[0055] In one embodiment of the present invention, the delay impact factor is determined by the following formula:
[0056]
[0057] Wherein, F is the delay influencing factor, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, t1 is the starting time point of the preset time period, t2 is the ending time point of the preset time period, u is the running speed data, d is the distance between the cloud and the current elevator, δ is the running status coefficient of the current elevator, α is the first reference coefficient, β is the second reference coefficient, c1 is the adjustment constant, and c is the speed of light.
[0058] In one embodiment of the present invention, after inputting the fused features into the elevator noise monitoring model to obtain the predicted noise of the current elevator, the method further includes:
[0059] When the time delay impact factor is greater than a first preset factor, obtaining the external noise of the current elevator in the preset time period;
[0060] Determining a corrected noise based on the external noise and the elevator sound insulation coefficient;
[0061] Performing weighted calculation on the corrected noise and the predicted noise of the current elevator according to the ratio of the time delay influence factor to determine the final predicted noise;
[0062] The delay impact factor ratio is determined according to the delay impact factor.
[0063] In one embodiment of the present invention, the final predicted noise is determined by the following formula:
[0064]
[0065] Where Z is the final prediction noise, is the ratio of the time delay influencing factor, Y is a fixed factor, YC is the predicted noise of the current elevator, and XZ is the corrected noise.
[0066] It should be understood that the structures illustrated in the embodiments of this specification do not constitute specific limitations on an elevator internal noise monitoring device. In other embodiments of this specification, an elevator internal noise monitoring device may include more or fewer components than illustrated, or may combine or separate certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
[0067] The information interaction, execution process, etc. between the modules in the above-mentioned device are based on the same concept as the method embodiments of this specification. For specific contents, please refer to the description in the method embodiments of this specification and will not be repeated here.
[0068] An embodiment of this specification further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, an elevator internal noise monitoring method in any embodiment of this specification is implemented.
[0069] An embodiment of the present specification further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes an elevator internal noise monitoring method according to any embodiment of the present specification.
[0070] Specifically, a system or device equipped with a storage medium can be provided, on which software program codes that implement the functions of any of the above-mentioned embodiments are stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program codes stored in the storage medium.
[0071] In this case, the program code read from the storage medium itself can realize the function of any one of the above embodiments, and thus the program code and the storage medium storing the program code constitute part of this specification.
[0072] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code can be downloaded from a server computer via a communication network.
[0073] In addition, it should be clear that the functions of any of the above embodiments can be achieved not only by executing the program code read by the computer, but also by enabling the operating system operating on the computer to complete part or all of the actual operations based on the instructions of the program code.
[0074] In addition, it can be understood that the program code read from the storage medium is written into a memory provided in an expansion board inserted into the computer or into a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above embodiments.
[0075] It should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.
[0076] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk, etc. Various media that can store program codes.
[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this specification, rather than to limit them. Although this specification has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of this specification.
Claims
1. A method for monitoring noise inside an elevator, characterized in that: The method is applied to a cloud, which is located outside the elevator, and includes: Obtain the current elevator's internal noise and operating speed data for a preset time period; Extracting features of the internal noise and the running speed data respectively to obtain internal noise features and running speed data features in sequence; Performing feature fusion on the internal noise and the operating speed data according to a delay impact factor to obtain a fused feature; wherein the delay impact factor is determined based on the operating speed data, the distance between the cloud and the current elevator, and the operating state coefficient of the current elevator; The fused features are input into the elevator noise monitoring model to obtain the predicted noise of the current elevator.
2. The method according to claim 1, characterized in that The delay influencing factor is determined by the following formula: Wherein, F is the delay influencing factor, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, t1 is the starting time point of the preset time period, t2 is the ending time point of the preset time period, u is the running speed data, d is the distance between the cloud and the current elevator, δ is the running status coefficient of the current elevator, α is the first reference coefficient, β is the second reference coefficient, c1 is the adjustment constant, and c is the speed of light.
3. The method according to claim 2, characterized in that After inputting the fused features into the elevator noise monitoring model to obtain the predicted noise of the current elevator, the method further includes: When the time delay impact factor is greater than a first preset factor, obtaining the external noise of the current elevator in the preset time period; Determining a corrected noise based on the external noise and the elevator sound insulation coefficient; Performing weighted calculation on the corrected noise and the predicted noise of the current elevator according to the ratio of the time delay influence factor to determine the final predicted noise; The delay impact factor ratio is determined according to the delay impact factor.
4. The method according to claim 3, characterized in that The final predicted noise is determined by the following formula: Where Z is the final prediction noise, is the ratio of the time delay influencing factor, Y is a fixed factor, YC is the predicted noise of the current elevator, and XZ is the corrected noise.
5. An elevator internal noise monitoring device, characterized in that: include: an acquiring unit configured to acquire internal noise and running speed data of the current elevator in a preset time period; a first data processing unit configured to extract features from the internal noise and the running speed data respectively, and obtain internal noise features and running speed data features in sequence; a second data processing unit configured to perform feature fusion on the internal noise and the operating speed data according to a delay impact factor to obtain a fused feature; wherein the delay impact factor is determined based on the operating speed data, a distance between the cloud and the current elevator, and an operating state coefficient of the current elevator; The third data processing unit is configured to input the fusion feature into the elevator noise monitoring model to obtain the predicted noise of the current elevator.
6. The device according to claim 5, characterized in that The delay influencing factor is determined by the following formula: Wherein, F is the delay influencing factor, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, t1 is the starting time point of the preset time period, t2 is the ending time point of the preset time period, u is the running speed data, d is the distance between the cloud and the current elevator, δ is the running status coefficient of the current elevator, α is the first reference coefficient, β is the second reference coefficient, c1 is the adjustment constant, and c is the speed of light.
7. The device according to claim 6, characterized in that After inputting the fused features into the elevator noise monitoring model to obtain the predicted noise of the current elevator, the method further includes: When the time delay impact factor is greater than a first preset factor, obtaining the external noise of the current elevator in the preset time period; Determining a corrected noise based on the external noise and the elevator sound insulation coefficient; Performing weighted calculation on the corrected noise and the predicted noise of the current elevator according to the ratio of the time delay influence factor to determine the final predicted noise; The delay impact factor ratio is determined according to the delay impact factor.
8. The device according to claim 7, characterized in that The final predicted noise is determined by the following formula: Where Z is the final prediction noise, is the ratio of the time delay influencing factor, Y is a fixed factor, YC is the predicted noise of the current elevator, and XZ is the corrected noise.
9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 7.