Energy-saving efficiency online detection method and system of elevator electric energy feedback device

Abstract

The invention relates to the technical field of elevator energy-saving efficiency detection, in particular to an energy-saving efficiency online detection method and system for an elevator electric energy feedback device, and the method comprises the steps: obtaining various electric power signals of each loop of an elevator in real time; obtaining the noise interference degree of each type of electric power signals in each time interval according to the fluctuation characteristics of each type of electric power signals of each loop in each time interval when subjected to noise interference and the measurement errors of each type of electric power signals of each loop in each time interval under different operation states of the elevator; and filtering parameter adjustment values of the various electric power signals in the various time intervals are obtained, so that the various electric power signals of the various loops in the various time intervals are filtered, and the energy-saving efficiency of the elevator electric energy feedback device in the various time intervals is obtained. According to the invention, the filtering parameters in each time interval are adaptively obtained, so that the filtering effect of the electric power signal is improved, and the energy-saving efficiency detection accuracy of the electric energy feedback device is improved.

Description

Technical Field

[0001] This application relates to the field of elevator energy efficiency testing technology, specifically to an online testing method and system for the energy efficiency of an elevator power feedback device. Background Technology

[0002] Energy feedback devices are primarily used in elevator systems during braking to recover the electrical energy generated during light-load upward movement, heavy-load downward movement, and leveling braking, allowing it to be returned to the power grid for use by other equipment. To accurately calculate the energy efficiency of these devices, the testing process requires collecting voltage and current signals from the traction elevator's power circuit, feedback circuit, and main power supply circuit. The energy efficiency of the power system feedback device and the overall system feedback device is then calculated based on these signals. Therefore, high accuracy in current and voltage data measurement is crucial. However, in actual measurements, data is subject to significant noise interference. Factors such as the high-frequency PWM control technology used in elevator inverters, rapid switching of power devices, and high-frequency switching actions can all cause deviations in the measurement results, affecting subsequent calculations.

[0003] Data filtering algorithms are typically used to process the acquired current and voltage data. However, traditional wavelet threshold filtering algorithms usually employ fixed filtering parameters (i.e., filtering thresholds) during the data filtering process. The acquired voltage and current signals are subject to varying noise levels at different times, and the interference levels also differ depending on the elevator's operating state. Therefore, filtering algorithms with fixed parameters may fail to effectively eliminate noise interference in the more noise-affected portions of the voltage and current signals, while the less noise-affected portions may lose their original characteristics. Ultimately, this results in poor filtering performance, which in turn affects the accuracy of energy efficiency detection for elevator energy feedback devices. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an online detection method and system for the energy efficiency of elevator energy feedback devices. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide an online detection method for the energy efficiency of an elevator energy feedback device, the method comprising the following steps: The voltage and current signals of each circuit of the elevator are acquired in real time, and the two types of signals are collectively referred to as power signals. Based on the degree of difference between various types of power signals in each circuit within each time interval, as well as the average level of peak and trough values, the total number of peaks, and the time interval between all adjacent peaks in various types of power signals in each circuit, the comprehensive disturbance coefficient of various types of power signals in each time interval is obtained. Based on the phase consistency and peak spacing of various power signals between each time interval and the previous time interval, the time interval between all adjacent troughs in all power signals of all circuits within each time interval, and the frequency magnitude of all peaks in the frequency domain of various power signals of all circuits within each time interval, the signal purity of various power signals in each time interval is obtained. Combined with the comprehensive disturbance coefficient of various power signals in each time interval, the noise interference of various power signals in each time interval is obtained. Then, the filter parameter adjustment values ​​of various power signals in each time interval are obtained. In this way, various power signals of each circuit in each time interval are filtered, and the energy-saving efficiency of the elevator power feedback device in each time interval is obtained.

[0005] Preferably, the formula for calculating the comprehensive disturbance coefficient of the various types of power signals in each time interval is as follows: In the formula, Let be the comprehensive disturbance coefficient of the current signal in the i-th time interval; Let be the mean of the cosine similarity between the current signals of any two loops within the i-th time interval; is the mean of all peak and valley values ​​in the current signals of all loops within the i-th time interval; exp() is an exponential function with the natural constant e as the base; These represent the total number of peaks in the current signals of all loops within the i-th time interval; Let be the mean of the time intervals between all adjacent peaks in the current signals of all loops within the i-th time interval; norm() is the normalization function.

[0006] Preferably, the formula for calculating the signal purity of the various types of power signals in each time interval is as follows: In the formula, Let represent the signal purity of the current signal in the i-th time interval; Let J be the PLV value of the j-th loop in the i-th time interval and its current signal in the previous time interval; J is the total number of loops. , These are the normalized results of the average peak interval for the i-th and (i-1)-th time intervals, respectively. These are the normalized mean values ​​of the peak widths of all loops within the i-th time interval; It is the mean of the normalized results of the average peak frequency of all loops in the i-th time interval; where the peak width of each loop in each time interval refers to the mean of the time interval between all adjacent troughs of each loop in each time interval.

[0007] Preferably, the average peak interval of each time interval refers to the mean of the peak intervals of all loops in each time interval; wherein, the peak interval of each loop refers to the mean of the time interval between all adjacent peaks in the current signal of each loop.

[0008] Preferably, the average peak frequency of each circuit within each time interval refers to the average frequency value corresponding to all peaks in the frequency domain data of the current signal of each circuit within each time interval.

[0009] Preferably, the noise interference of the various types of power signals in each time interval is positively correlated with the comprehensive disturbance coefficient and negatively correlated with the signal purity.

[0010] Preferably, the adjustment value of the filtering parameter for each type of power signal in each time interval refers to the product between the noise interference level of each type of power signal in each time interval and the preset maximum filtering threshold.

[0011] Preferably, the specific process of filtering various types of power signals of each circuit in each time interval is as follows: the various types of power signals of each circuit in each time interval are used as inputs to the wavelet threshold filtering algorithm, the filtering threshold is set as the filtering parameter adjustment value of the corresponding types of power signals in each time interval, and the output is the filtered various types of power signals of each circuit in each time interval.

[0012] Preferably, the process of obtaining the energy-saving efficiency of the elevator power feedback device in each time interval is as follows: based on the current signal and voltage signal of each circuit after filtering in each time interval, and combined with the pre-obtained elevator feedback energy-saving efficiency calculation formula, the energy-saving efficiency of the power system feedback device and the overall system feedback device in each time interval are calculated.

[0013] Secondly, embodiments of this application also provide an online energy efficiency detection system for an elevator energy feedback device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application addresses the problem of poor filtering performance caused by the use of fixed filtering parameters in traditional wavelet threshold filtering of current and voltage signal data. By analyzing the fluctuation characteristics of current and voltage signals under noise interference, a comprehensive disturbance coefficient is constructed, which can initially assess the degree of noise interference in the current and voltage signals. By analyzing the interference characteristics generated by elevator measurements of current and voltage signal data under different loads and operating conditions, a signal purity is constructed, which can further assess the purity of the current and voltage signals. Combined with the comprehensive disturbance coefficient, a noise interference degree is constructed, which can accurately assess the degree of noise interference in the current and voltage signals in each time interval. This allows for the adaptive acquisition of the filtering threshold in the wavelet threshold filtering algorithm, thereby accurately eliminating noise interference while ensuring the authenticity and usability of the current and voltage signal data, improving the accuracy of data filtering, and ultimately improving the accuracy of energy efficiency detection for elevator energy feedback devices. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of an online energy efficiency detection method for an elevator energy feedback device, provided in one embodiment of this application; Figure 2 This is a flowchart illustrating the acquisition of noise interference levels of various power signals in different time intervals, as provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an online energy efficiency detection method and system for an elevator energy feedback device proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the online energy efficiency detection method and system for an elevator energy feedback device provided in this application.

[0020] Please see Figure 1 The document illustrates a flowchart of an online energy efficiency detection method for an elevator energy feedback device according to an embodiment of this application. The method includes the following steps: Step 1: Acquire the voltage and current signals of each circuit of the elevator in real time, and refer to both types of signals as power signals.

[0021] First, intelligent sensors are used to collect various electrical signals from the traction elevator's power circuit, feedback circuit, and main power supply circuit in real time. In this embodiment, these electrical signals refer to voltage and current signals. The sampling frequency for all electrical signals is FkHz, and the entire detection time from the start of detection is divided into time intervals of T hours. In this embodiment, F is set to 20 and T is set to 0.1, but the implementer can choose the appropriate values ​​based on the actual situation.

[0022] The power circuit is the circuit from the inverter output to the traction motor, responsible for driving the elevator car up and down, and is the core link of energy consumption and regeneration; the feedback circuit is a dedicated circuit that inverts the regenerated electrical energy generated by the traction machine (when the DC bus voltage rises) back to the AC grid, replacing the traditional method of consuming energy through "braking resistors"; the main power supply circuit is the total input circuit that provides electrical energy to the entire elevator system from the building's power grid (three-phase 380V or single-phase 220V), including all electrical equipment such as the elevator control cabinet, inverter, lighting, fans, and door operators; To avoid the impact of dimensional differences on subsequent calculation and analysis, this embodiment uses maximum value normalization to normalize the current and voltage signals of each circuit to the range [-1, 1].

[0023] Since the interference experienced by current signals and voltage signals is similar, this embodiment will use the current signal as an example for processing and analysis.

[0024] Step 2: Based on the degree of difference between various types of power signals in each circuit within each time interval, as well as the average level of peak and trough values, the total number of peaks, and the time interval between all adjacent peaks in various types of power signals in each circuit, obtain the comprehensive disturbance coefficient of various types of power signals in each time interval.

[0025] For current signals acquired from different circuits, each circuit's current signal will be subject to certain interference. For example, in the power circuit, the rectifier-inverter module inside the frequency converter is in a high-frequency switching state, which will cause glitches and high-frequency noise in the current signal. Specifically, this manifests as waveform distortion in the current signal data, with significant differences in waveform shape. In the feedback circuit, when the dead time of the positive and negative half-cycles of the PWM rectifier is inconsistent, the feedback current contains 0.5%-1% DC bias, resulting in DC bias interference. The presence of a DC component in the feedback current causes the measured value to drift positively. Ordinary AC current transformers will... Due to unidirectional saturation of the magnetic core, even-order harmonic "bulges" appear on the secondary side, causing the measured value to drift in the positive direction, resulting in distortion of the output waveform. Specifically, the peaks and troughs in the current signal shift upwards as a whole. In the main power supply circuit, due to contactor bounce, switching of multiple devices, and the superposition and coupling of lightning / surge and inverter common-mode current on the same bus impedance, short-time pulse groups may be generated in the measured current signal data. Specifically, a cluster of closely arranged spike waveforms appears in the current waveform, resulting in an increase in the total number of current peaks in the time interval affected by the interference, and an overall reduction in the interval between peaks.

[0026] To characterize the above features, taking the current signal of each loop in the i-th time interval as an example, a peak and trough detection algorithm is used to obtain all peaks and troughs in the current signal data of the three loops in the i-th time interval. The peak and trough detection algorithm is a known technique, and the specific process will not be described in detail.

[0027] Based on the above analysis, as a preferred implementation, the comprehensive disturbance coefficient of various power signals in each time interval is obtained according to the degree of difference between various power signals in each circuit within each time interval, as well as the average level of peak and valley values, the total number of peaks, and the time interval between all adjacent peaks in various power signals in each circuit. This coefficient is used to characterize the degree of noise interference in various power signals in each time interval.

[0028] In this embodiment, the comprehensive disturbance coefficient of the current signal in the i-th time interval is denoted as... Its specific expression is: In the formula, Let be the comprehensive disturbance coefficient of the current signal in the i-th time interval; The mean of the cosine similarity between the current signals of any two loops in the i-th time interval is used to characterize the distortion of the current waveform of each loop when high-frequency noise with glitches occurs. is the average of all peak and valley values ​​in the current signal of all loops within the i-th time interval, used to characterize the overall upward shift of the peak and valley data of the current signal when DC bias interference occurs; exp() is an exponential function with the natural constant e as the base; These represent the total number of peaks in the current signals of all loops within the i-th time interval, used to characterize the total number of peaks in the current signal when a short-time pulse occurs. Let be the mean of the time intervals between all adjacent peaks in the current signals of all loops within the i-th time interval; norm() is the normalization function, designed to eliminate... To mitigate the influence of dimensions, this embodiment uses the tanh function for normalization. Normalize to the range (0,1). The calculation of cosine similarity is a well-known technique, and the specific process will not be described in detail.

[0029] in, The larger the value, the greater the waveform difference between the current signals of each loop in the i-th time interval, and the more significant the overall upward shift of the peaks and troughs of the current signals of each loop in this time interval, and the more likely short-term pulse groups are to appear. In this case, the current signal in this time interval is more significantly affected by the interference of each loop, and the more the filtering parameters need to be increased when filtering the current signal in this time interval in the future.

[0030] Step 3: Based on the phase consistency and peak spacing of various power signals between each time interval and the previous time interval, the time interval between all adjacent troughs of various power signals in all circuits within each time interval, and the frequency magnitude of all peaks of various power signals in all circuits within each time interval, obtain the signal purity of various power signals in each time interval. Combined with the comprehensive disturbance coefficient of various power signals in each time interval, obtain the noise interference of various power signals in each time interval. Then, obtain the filter parameter adjustment value of various power signals in each time interval. Filter the various power signals of each circuit in each time interval to obtain the energy-saving efficiency of the elevator power feedback device in each time interval.

[0031] Elevators typically operate in four different states: accelerating up / down, constant speed up / down, deceleration and braking, and zero-speed stop. The interference to current signal data varies depending on the elevator's operating state. For example, during constant speed up / down operation, motor rotor imbalance, gear meshing, bearing wear, or poor lubrication can cause periodic displacement or torque pulsation in the shaft. Displacement directly modulates the air gap magnetic permeability, while torque pulsation forces the inverter / motor to momentarily increase or decrease the electromagnetic torque. Both cause the stator current amplitude to fluctuate periodically at the same frequency. When the frequency components (rotation frequency, bearing characteristic frequency, meshing frequency) are collected by the current sensor, they will interfere with the peak value of the current signal data. Specifically, the peak value of the current signal data will fluctuate periodically in the time domain, resulting in a large difference in the peak interval between adjacent time intervals. When the elevator accelerates up and down or decelerates and brakes under different loads (no load, light load, half load, full load), the current phase will change significantly and there will be instantaneous large current surges. As a result, the current signal of each circuit will form short-term spikes in the time domain and multiple isolated high-frequency, high-energy peaks will appear in the frequency domain.

[0032] To characterize the above features, taking the current signals of the three loops in the i-th time interval as an example, the Fourier transform algorithm is used to convert the current signals of the three loops in the i-th time interval from the time domain to the frequency domain. The Fourier transform algorithm is a well-known technique, and its specific process will not be elaborated further. A peak and trough detection algorithm is used to obtain all peaks in the frequency domain data of the current signals of each loop in the i-th time interval. The average frequency value corresponding to all peaks in the frequency domain data of the current signals of each loop in the i-th time interval is recorded as the average peak frequency of each loop in the i-th time interval, which is used to characterize the average frequency level of the peaks of the current signals of each loop in the i-th time interval in the frequency domain.

[0033] In addition, all peaks and troughs of the current signal data of each loop in the time domain are obtained within the i-th time interval, and the average time interval between all adjacent peaks in the current signal of each loop is calculated and denoted as the peak interval of each loop in the i-th time interval. The average peak interval of all loops in the i-th time interval is denoted as the average peak interval of the i-th time interval, which is used to characterize the peak interval distance in the current signal within the i-th time interval. Furthermore, the average time interval between all adjacent troughs in each loop within the i-th time interval is denoted as the peak width of each loop.

[0034] Furthermore, to avoid the influence of dimensions on subsequent calculations, the average peak interval of each time interval, the average peak frequency of each loop, and the peak width of each loop are normalized. In this embodiment, the tanh function is used for normalization.

[0035] Based on the above analysis, as a preferred implementation, the signal purity of various power signals in each time interval is obtained according to the phase consistency and peak spacing of various power signals between each time interval and the previous time interval, the time interval between all adjacent troughs in various power signals of all circuits in each time interval, and the frequency magnitude of all peaks in various power signals of all circuits in each time interval in the frequency domain. This is used to characterize the overall purity of various power signals of all circuits in each time interval.

[0036] In this embodiment, the signal purity of the current signal in the i-th time interval is denoted as . Its specific expression is: In the formula, Let represent the signal purity of the current signal in the i-th time interval; Let J be the PLV value of the j-th loop in the i-th time interval and its current signal in the previous time interval; J is the total number of loops. , These are the normalized results of the average peak interval for the i-th and (i-1)-th time intervals, respectively. These are the normalized mean values ​​of the peak widths of all loops within the i-th time interval; This is the mean of the normalized peak frequencies of all loops within the i-th time interval. It should be noted that the PLV value between the two current signals is calculated using the PLV (Phase Locking Value) algorithm. The PLV algorithm is a well-known technique, and its specific process will not be elaborated further.

[0037] in, The larger the value, the greater the consistency of the current phase of each loop in the i-th time interval with the previous time interval, the smaller the difference between the peak intervals, that is, the more consistent the periodic fluctuations of the peaks, the less likely the current signal of each loop in the i-th time interval will have short-term spikes, and the lower the peak frequency in the frequency domain. This means that the current signal in the i-th time interval is less affected by noise and has a higher purity. When filtering the current signal in this time interval in the future, the filtering parameters can be appropriately reduced.

[0038] Furthermore, based on the comprehensive disturbance coefficient and signal purity of various power signals in each time interval, the noise interference degree of various power signals in each time interval is obtained. This is used to characterize the degree of adjustment of the filtering parameters when filtering various power signals in each time interval. The noise interference degree of various power signals in each time interval is positively correlated with the comprehensive disturbance coefficient and negatively correlated with the signal purity. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and the negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases). The flowchart for obtaining the noise interference degree of various power signals in each time interval is as follows: Figure 2 As shown.

[0039] Preferably, in this embodiment, the noise interference level of the current signal in the i-th time interval is denoted as... Its specific expression is: In the formula, Let be the noise interference level of the current signal in the i-th time interval; Let be the comprehensive disturbance coefficient of the current signal in the i-th time interval. Let represent the signal purity of the current signal in the i-th time interval; norm() is the normalization function, and in this embodiment, the tanh function is used for normalization.

[0040] Specifically, the more severe the noise interference in the current signal within the i-th time interval, the lower the purity of the current signal data. This indicates that the current signal data within this time interval is more likely to be affected by interference from different circuits or different elevator operating states under different loads. Consequently, the noise intensity in the current signal is higher, and a larger filtering parameter is needed to remove the noise. Conversely, the less interference the current signal data within this time interval is affected by, the higher the reliability of the current signal data. A smaller filtering parameter should be selected to process the current signal data to ensure the authenticity and usability of the data.

[0041] Furthermore, the more significant the noise interference in the current signal during the i-th time interval, the larger the filter threshold needs to be to remove the noise interference; conversely, the smaller the filter threshold should be to preserve the signal characteristics as much as possible. Therefore, the adjustment values ​​of the filter parameters for the current signal in each time interval are calculated as follows: In the formula, Let be the adjustment value of the filter parameters for the current signal in the i-th time interval. Let be the noise interference level of the current signal in the i-th time interval. Preset maximum filtering threshold ( The value range is 20~50A, and 40A is used in this embodiment. The implementer needs to adjust it according to the actual measured current signal data.

[0042] Similarly, the filter parameter adjustment values ​​for the current signal in all time intervals can be calculated using the above method.

[0043] The current signal data of each loop in each time interval is used as input to the wavelet threshold filtering algorithm. A soft thresholding method is adopted, and the filtering threshold is set to the adjusted value of the filtering parameter of the current signal in the corresponding time interval. The number of decomposition layers is set to 5. The output is the filtered current signal data of each loop in each time interval. At the same time, the voltage signal is filtered in the same way to obtain the filtered voltage signal data of each loop in each time interval.

[0044] Finally, based on the filtered current and voltage signals of each circuit in each time interval, and combined with the pre-obtained elevator feedback energy efficiency calculation formula, the energy efficiency of the power system feedback device and the overall system feedback device in each time interval are calculated. For the specific calculation process, please refer to the energy efficiency calculation method in the literature "Li Cuncen, Li Shunrong, Liu Songguo, et al. Design of elevator feedback power quality and feedback energy efficiency detection system [J]. China Test, 2015, 41(02):76-79+83". The specific process will not be repeated in this embodiment.

[0045] This completes the online detection method for the energy efficiency of elevator power feedback devices.

[0046] Based on the same inventive concept as the above method, this application embodiment also provides an online energy efficiency detection system for an elevator energy feedback device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described online energy efficiency detection methods for an elevator energy feedback device.

[0047] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0048] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0049] 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 principles of this application should be included within the protection scope of this application.

Claims

1. A method for online detection of the energy efficiency of an elevator energy feedback device, characterized in that, The method includes the following steps: The voltage and current signals of each circuit of the elevator are acquired in real time, and the two types of signals are collectively referred to as power signals. Based on the degree of difference between various types of power signals in each circuit within each time interval, as well as the average level of peak and trough values, the total number of peaks, and the time interval between all adjacent peaks in various types of power signals in each circuit, the comprehensive disturbance coefficient of various types of power signals in each time interval is obtained. Based on the phase consistency and peak spacing of various power signals between each time interval and the previous time interval, the time interval between all adjacent troughs in all power signals of all circuits within each time interval, and the frequency magnitude of all peaks in the frequency domain of various power signals of all circuits within each time interval, the signal purity of various power signals in each time interval is obtained. Combined with the comprehensive disturbance coefficient of various power signals in each time interval, the noise interference of various power signals in each time interval is obtained. Then, the filter parameter adjustment values ​​of various power signals in each time interval are obtained. In this way, various power signals of each circuit in each time interval are filtered, and the energy-saving efficiency of the elevator power feedback device in each time interval is obtained.

2. The online detection method for the energy-saving efficiency of an elevator energy feedback device as described in claim 1, characterized in that, The formula for calculating the comprehensive disturbance coefficient of various power signals in each time interval is as follows: In the formula, Let be the comprehensive disturbance coefficient of the current signal in the i-th time interval; Let be the mean of the cosine similarity between the current signals of any two loops within the i-th time interval; is the mean of all peak and valley values ​​in the current signals of all loops within the i-th time interval; exp() is an exponential function with the natural constant e as the base; These represent the total number of peaks in the current signals of all loops within the i-th time interval; Let be the mean of the time intervals between all adjacent peaks in the current signals of all loops within the i-th time interval; norm() is the normalization function.

3. The online detection method for the energy efficiency of an elevator energy feedback device as described in claim 1, characterized in that, The formulas for calculating the signal purity of various types of power signals in each time interval are as follows: In the formula, Let represent the signal purity of the current signal in the i-th time interval; Let J be the PLV value of the j-th loop in the i-th time interval and its current signal in the previous time interval; J is the total number of loops. , These are the normalized results of the average peak interval for the i-th and (i-1)-th time intervals, respectively. These are the normalized mean values ​​of the peak widths of all loops within the i-th time interval; It is the mean of the normalized results of the average peak frequency of all loops in the i-th time interval; where the peak width of each loop in each time interval refers to the mean of the time interval between all adjacent troughs of each loop in each time interval.

4. The online detection method for the energy-saving efficiency of an elevator energy feedback device as described in claim 3, characterized in that, The average peak interval of each time interval refers to the mean of the peak intervals of all loops in each time interval; where the peak interval of each loop refers to the mean of the time interval between all adjacent peaks in the current signal of each loop.

5. The online detection method for the energy efficiency of an elevator energy feedback device as described in claim 3, characterized in that, The average peak frequency of each circuit within each time interval refers to the average frequency value corresponding to all peaks in the frequency domain data of the current signal of each circuit within each time interval.

6. The online detection method for the energy-saving efficiency of an elevator energy feedback device as described in claim 1, characterized in that, The noise interference of various types of power signals in each time interval is positively correlated with the comprehensive disturbance coefficient and negatively correlated with the signal purity.

7. The online detection method for the energy efficiency of an elevator energy feedback device as described in claim 1, characterized in that, The filter parameter adjustment values ​​for various types of power signals in each time interval refer to the product between the noise interference level of various types of power signals in each time interval and the preset maximum filter threshold.

8. The online detection method for the energy efficiency of an elevator energy feedback device as described in claim 1, characterized in that, The specific process of filtering various types of power signals of each circuit in each time interval is as follows: the various types of power signals of each circuit in each time interval are used as inputs to the wavelet threshold filtering algorithm, the filtering threshold is set as the filtering parameter adjustment value of the corresponding types of power signals in each time interval, and the output is the filtered power signals of each circuit in each time interval.

9. The online detection method for the energy efficiency of an elevator energy feedback device as described in claim 1, characterized in that, The process of obtaining the energy-saving efficiency of the elevator power feedback device in each time interval is as follows: based on the current and voltage signals of each circuit after filtering in each time interval, and combined with the pre-obtained elevator feedback energy-saving efficiency calculation formula, the energy-saving efficiency of the power system feedback device and the overall system feedback device in each time interval are calculated.

10. An online energy efficiency monitoring system for an elevator energy feedback device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the online energy efficiency detection method for an elevator energy feedback device as described in any one of claims 1-9.

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