Electricity approaching detection method and system for overhead working truck

Abstract

The invention relates to the technical field of near-electricity detection, in particular to a near-electricity detection method and system for an overhead working truck, and the method comprises the steps: collecting voltage data at each moment at a preset position of the overhead working truck, obtaining an elimination sequence, carrying out the fitting of the elimination sequence of each period through the combination of a preset power frequency, and obtaining a near-electricity detection result; obtaining a first feature according to the change condition of the difference feature between the actual numerical value and the fitting result in the adjacent period; determining a second feature and a third feature according to the fitting result and the change condition of the voltage data on the frequency domain feature; analyzing the change of the high-frequency frequency domain energy of the voltage data between adjacent periods, and determining a fourth characteristic; and determining a fifth characteristic according to the distance characteristic between the preset positions and the amplitude difference when the frequency is the power frequency, determining the near-electricity detection confidence coefficient of each period, and carrying out near-electricity detection on the overhead working truck. The invention aims to improve the accuracy of near-electricity risk detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of proximity detection, in particular to a proximity detection method and system for aerial work vehicles. BACKGROUND

[0002] When the insulating arm type aerial work vehicle is working on the outdoor wall surface, the arm support needs to be stretched to the position below the working position to facilitate the workers to work. If there are high-voltage transmission lines, transformers and other live equipment around the working position, the workers will be affected by the power frequency electric field generated by such equipment. If the workers are exposed to the power frequency electric field for a long time or contact the high-intensity power frequency electric field, it will affect the health of the workers, and even cause electric shock accidents due to electric field breakdown or indirect contact, which will endanger the life safety of the workers. Therefore, it is very important to implement effective proximity detection for the insulating arm type aerial work vehicle.

[0003] In the existing proximity detection methods, such as image recognition and laser ranging methods, the detection position is relatively single. The power frequency electric field strength will be affected by the distance. The farther the distance, the lower the electric field strength. At this time, in different fixed positions in the insulating arm type aerial work vehicle, there will be a certain spatial distribution law due to the distance difference from the electric field source. At the same time, the traditional method usually triggers an alarm only when the electric field strength at the detection point exceeds the preset fixed threshold. If the proximity characteristics of a single position are directly used as the basis for judgment, the electric field strength of the single position may be lower than the alarm threshold due to attenuation, which will affect the proximity detection result, and then misjudge the position with proximity risk as a position without risk, resulting in harm to the workers. SUMMARY

[0004] In view of the above, it is necessary to provide a proximity detection method and system for aerial work vehicles to solve the above problems.

[0005] The first aspect of the present application provides a proximity detection method for aerial work vehicles, which comprises: collecting voltage data at each time at preset positions of the aerial work vehicle; For each position, analyze the overall fluctuation of the voltage data in the preset period to obtain the elimination sequence, combine the preset power frequency, fit the elimination sequence of each period, and obtain the first feature of the voltage data in each period according to the difference characteristics between the actual value and the fitting result and the change of the adjacent period. Determine the second feature, the third feature of the voltage data in each period according to the change of the fitting result and the frequency domain characteristics of the voltage data; analyze the change of the high-frequency energy of the voltage data between adjacent periods to determine the fourth feature of the voltage data in each period; and determine the fifth feature of the voltage data in each period according to the distance characteristics between the preset positions and the amplitude difference when the frequency is the power frequency. Determine the near electric detection confidence of each cycle by comprehensively obtaining all features of the voltage data of each cycle, and perform near electric detection on the aerial work platform.

[0006] The mean value of all voltage data in each cycle is calculated, denoted as voltage mean value. The difference between each voltage data of each cycle and the voltage mean value is calculated, and the sequence composed of all difference values is denoted as elimination sequence.

[0007] The first feature of the voltage data of each cycle is obtained, specifically: The fitting formula of the voltage data with respect to time is obtained by fitting the data in the elimination sequence, and the fitting voltage data sequence composed of the fitting voltage data obtained by substituting the time of each cycle into the fitting formula is obtained. The sum of squares of the difference values of the data at the same time in the elimination sequence and the fitting voltage data sequence is calculated. The difference between the sum of squares of difference values obtained in each cycle and the previous cycle is calculated to determine the first feature of the voltage data of each cycle.

[0008] The process of determining the second feature and the third feature of the voltage data of each cycle is as follows: The amplitude value and the phase angle value of the elimination sequence of each cycle at the power frequency are obtained, denoted as first amplitude value and first phase angle value, respectively. The fitting parameters in the fitting formula of the voltage data of each cycle are denoted as , , respectively. The second amplitude value is denoted as , and its formula form is ; the second phase angle value is denoted as , and its formula form is: , wherein represents the inverse tangent trigonometric function. The difference between the first amplitude value and the second amplitude value obtained in each cycle is denoted as the second feature of the voltage data of each cycle. The difference between the first phase angle value and the second phase angle value obtained in each cycle is denoted as the third feature of the voltage data of each cycle.

[0009] The high frequency energy of the voltage data is specifically the energy value greater than the upper quartile in the frequency domain of the elimination sequence of each cycle.

[0010] The fourth feature of the voltage data of each cycle is determined, specifically: Calculate the mean of all high-frequency energy obtained in each cycle, and the difference between each cycle and the mean obtained in the last cycle as the fourth feature of the voltage data of each cycle.

[0011] The fifth feature of the voltage data of each cycle is determined, and the specific process is as follows: For each cycle, the amplitude difference of the voltage data between any two positions at the power frequency is obtained, and the amplitude difference is divided by the distance between the two positions to obtain the edge weight between the two positions. Based on the edge weight between all two-combined positions, a weighted undirected graph is constructed in combination with the spatial distribution of the positions to obtain an edge weight matrix. The cumulative sum of the edge weight of each position and the rest of the positions is constructed into a diagonal matrix, and the difference between the diagonal matrix and the edge weight matrix is obtained to obtain the graph Laplacian matrix. The eigenvalues of the graph Laplacian matrix are arranged from small to large, and the difference between the second eigenvalue obtained in each cycle and the last cycle is calculated to obtain the fifth eigenvalue of each cycle.

[0012] The specific formula for determining the near-electricity detection confidence of each cycle is as follows: ; wherein, represents the near-electricity detection confidence of the i th cycle. , represents the third feature and the fifth feature of the i th cycle, respectively. represents the hyperbolic tangent function. represents the difference complexity of the i th cycle. , , represents the first feature, the second feature, and the fourth feature of the i th cycle, respectively; e represents the natural constant. represents the circular constant.

[0013] The specific process of the near-electricity detection of the aerial work platform is as follows: the near-electricity detection confidence of a preset number of cycles in the safe position is calculated by the 3 sigma principle to obtain an upper limit value corresponding to 3 sigma as a near-electricity judgment threshold; when the near-electricity detection confidence of each cycle of the position to be detected is greater than the preset near-electricity judgment threshold, it is judged that there is a near-electricity risk; otherwise, there is no near-electricity risk.

[0014] In the second aspect, the embodiments of the present application also provide a near-electricity detection system for an aerial work platform, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above aspects are implemented.

[0015] The present application has at least the following beneficial effects: ​The embodiment of the present application first collects voltage data at each time at a preset position, analyzes voltage fluctuation of the position in a preset period, and eliminates periodic interference; the voltage data of each period is fitted to eliminate the difference between the sequence and the actual measurement result, and the sequence removes the power frequency and periodic noise in the voltage data, so that the remaining signal is more pure, which is convenient for identifying abnormal fluctuation or mutation. By eliminating the background interference signal, the data more truly reflects the nature of voltage fluctuation, which helps to more accurately identify and analyze potential voltage abnormalities; based on the difference between the fitting result and the actual measurement result, a first feature of the periodic voltage data is obtained, by analyzing the difference between the actual measurement data and the fitting data, the mutation or deviation of the voltage signal is identified, which helps to detect possible power abnormalities or dangers near the power source. This analysis based on periodic changes can quickly provide feedback when any abnormality occurs, supporting immediate response; by fitting the result and the change of the voltage data in the frequency domain, the second and third features are extracted, the frequency domain analysis can reveal more subtle fluctuations in the voltage signal, especially high-frequency noise, transient changes, etc. These are usually early signs of abnormal operation, failure or potential proximity to the power source of electrical equipment. The second and third features provide a more comprehensive perspective than simple time domain analysis, which helps to accurately judge the stability and reliability of the voltage; the change of the energy of the high-frequency part of the voltage signal in the adjacent period is analyzed to extract the fourth feature. The high-frequency part usually represents the transient behavior of electrical equipment or the peak when a fault occurs. Analyzing its change can detect abnormalities in the electrical system in advance. The characteristics of high-frequency signal changes can reveal electromagnetic interference or electrical noise, helping to predict the interference that may be caused when electrical equipment approaches the power source; according to the distance characteristics between the preset positions and the amplitude difference at the power frequency, the fifth feature of the voltage data is extracted. By analyzing the voltage amplitude difference at different positions, the uniformity of the voltage in space can be evaluated, and the voltage fluctuation caused by distance and current transmission effect can be revealed. This feature helps to identify voltage non-uniformity or potential proximity to the power source in the system, further optimizes power distribution and line layout, and reduces the risk of power equipment approaching failure or danger; finally, all features of each period voltage data are comprehensively analyzed to determine the confidence of the near-electricity detection; through multi-dimensional feature analysis, the relationship between voltage fluctuation and equipment safety can be comprehensively and accurately evaluated, the error caused by single feature judgment is reduced, and stronger protection is provided for the safe operation of the aerial work platform, helping the staff to early warn potential dangers, so as to take timely measures to avoid accidents. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A step flowchart of a near-electricity detection method for an aerial work platform is provided for an embodiment of the present application. Figure 2 A position diagram of a near-electricity detection sensor is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, the words "example" and "exemplary" are used to mean serving as an example, instance, or illustration. Any implementation described as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. Rather, use of the terms "example" and "exemplary" is intended to present concepts in a concrete manner.

[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 belongs. The terminology used in the description of the application herein is for describing particular embodiments only and is not intended to be limiting of the application.

[0019] In addition, it should be pointed out that the terms "first", "second" in the present application and the drawings are used to distinguish similar objects, and are not intended to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flowchart includes one or more steps for implementing the method, and the execution order of the steps can be interchanged with each other without departing from the scope of the present application, and some steps can also be deleted.

[0020] 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 belongs.

[0021] The specific scheme of the near electric detection method and system for the aerial work platform provided by the present application will be specifically described below in combination with the drawings.

[0022] Please refer to Figure 1 which shows a step flowchart of a near electric detection method for an aerial work platform provided by an embodiment of the present application, and the method includes the following steps: Step one: collecting voltage data at each time at a preset position of the aerial work platform.

[0023] In the present application, the position diagram of the near electric detection sensor is as shown in Figure 2As shown, in order to ensure the construction safety of the insulating arm type aerial work platform in the live environment, five electric field induction type electric field detection sensors are respectively installed at the four corners (sensors ABCD) of the workbench of the insulating arm type aerial work platform and the junctions (sensor E) of the workbench and the boom, the electric field detection sensor can sense the electric field intensity emitted by the power frequency electric field and convert it into voltage data proportional to the electric field intensity, and the distance between sensor E and sensor ABCD is kept the same during installation, which is beneficial to subsequent spatial electric field analysis. After completing the installation of the electric field detection sensor, start collecting voltage data, in this embodiment, the collection interval is set to 1s, the collection period is 20s, and the voltage data generated by the electric field intensity around the workbench during the construction process is collected in real time.

[0024] It should be noted that, in order to realize accurate fusion of multiple sensor data, time delay synchronization needs to be performed on multiple sensors. In this embodiment: a unified UTC time reference and high-precision second pulse signal are provided for each sensor node based on GPS, ensuring that the time stamps of the data collected by each channel are consistent. After completing sensor installation and clock synchronization, start collecting voltage data. This embodiment sets the collection interval to 1 second, and takes 20 seconds as a complete collection period, and continuously monitors the voltage data generated by the electric field intensity around the workbench in real time during the entire construction process.

[0025] During the collection of voltage data, periodic vibration of the sensor may cause certain noise in the collected voltage data, therefore, the comb filter is used for filtering in this embodiment to eliminate harmonics, pulse interference, noise and the like in the voltage data. The filtering algorithms are all known technologies, and the specific calculation methods are not described again.

[0026] Step two: for each position, analyze the overall fluctuation of the voltage data in the preset period, combine the preset power frequency, and fit the voltage data in each period, according to the difference between the actual value and the fitting result, and the change of the adjacent period, to obtain the first feature of each period voltage data; according to the change of the fitting result and the frequency domain feature of the voltage data, determine the second feature, the third feature of each period voltage data; analyze the change of the high frequency energy of the voltage data between adjacent periods, and determine the fourth feature of each period voltage data; according to the distance feature between the preset positions and the amplitude difference when the frequency is the power frequency, determine the fifth feature of each period voltage data.

[0027] The strength of the power frequency electric field varies with time in a sinusoidal law and gradually attenuates with the increase of the propagation distance. Therefore, for a single sensor, the voltage data obtained in each acquisition cycle also varies in a sinusoidal law. As the construction advances, if the workbench gradually moves towards the source of the power frequency electric field, the amplitude of the voltage data in adjacent cycles will increase, and the waveform similarity of the voltage data sequence in the same cycle will decrease. As the distance from the source of the power frequency electric field decreases, the power frequency electric field is less disturbed in the propagation process, so the high-frequency components of the voltage data in adjacent cycles will relatively decrease, and the energy will be more concentrated on the power frequency fundamental wave.

[0028] For multiple spatially distributed sensors, the voltage data of multiple sensors in the same cycle will have certain differences due to different sensor positions, and will satisfy a spatial topological structure in space. If the distance between the workbench and the source of the power frequency electric field decreases, the spatial topological structure of multiple sensors in adjacent cycles will change. After all sensors are affected by the decrease in distance, the topological relationship between the signals of each sensor will be restored to stability in a new equilibrium state, reflecting the dynamic response and structural adaptability of the system to the change in the distance of the field source.

[0029] Based on the above analysis, for a single sensor, since the voltage data obtained in each acquisition cycle varies in a sinusoidal law, taking the voltage data sequence of the ith cycle as an example, the mean value of all voltage data in the voltage data sequence of the ith cycle is calculated, denoted as the voltage mean value; the difference between each voltage data in the voltage data sequence of the ith cycle and the voltage mean value is calculated to eliminate the direct current component, and the sequence after eliminating the direct current component is obtained, which is referred to as the elimination sequence, denoted as .

[0030] as input. In this embodiment, the power frequency is set to 50 Hz, and the least squares method is used for fitting, and the fitting formula is: wherein, represents the data of the ith cycle elimination sequence at time t, and is a parameter to be fitted, represents the preset power frequency, represents the time t, and it is worth noting that t here needs to be converted into relative time. In this embodiment, the sampling interval is set to 1 second. For the voltage data sequence of the ith cycle, the relative time corresponding to each voltage data will start from 1 and increase in sequence according to the sampling order, until the relative time of the last voltage data in the cycle reaches N, where N represents the total number of voltage data in the cycle.

[0031] The fitting of and ​After that, the fitting formula is used to calculate the fitting voltage data sequence of the i th cycle at each time point: The fitting voltage data corresponding to each time point is calculated and denoted as The sequence is calculated. The sum of squares of the difference between the actual collected voltage data and the fitting voltage data at all same time points in is denoted as , which is used to judge the difference between the fitting data and the actual data. Similarly, the sum of squares of the difference between the actual collected voltage data and the fitting voltage data at all same time points in the i-1 th cycle is calculated and denoted as The smaller the distance between the near electric sensor and the source of the power frequency electric field, the smaller the interference on the voltage data, and the smaller the difference between the fitting data and the actual data. Therefore, the first feature of the voltage data sequence of the i th cycle is represented by . In particular, the first feature needs to be greater than 0 to meet the condition of moving towards the source of the power frequency electric field. If the value is less than or equal to 0, the possible reasons are that the workbench has not moved, the workbench has moved in the opposite direction of the source of the power frequency electric field, or there is no power frequency electric field around the workbench. Therefore, when is less than or equal to 0, the value of the first feature is 0.

[0032] Further, the smaller the distance between the near electric sensor and the source of the power frequency electric field, the smaller the interference on the voltage data, and the smaller the difference between the real amplitude and phase and the fitted amplitude and phase at the same frequency. Therefore, using the time-frequency conversion algorithm, in this embodiment, the Fourier transform is adopted, and in other embodiments, the wavelet transform algorithm can be used to convert the elimination sequence of the i th cycle to the frequency domain to obtain the first amplitude value and the first phase angle value at the preset frequency, which is 50 Hz in this embodiment. After fitting and using the least squares method, the second amplitude value and the second phase angle value of the fitting are calculated, i.e.: , The difference between the first amplitude value and the second amplitude value obtained in each cycle is calculated to obtain the second feature of the voltage data sequence of the i th cycle. The difference between the first phase angle value and the second phase angle value obtained in each cycle is calculated to obtain the third feature of the voltage data sequence of the i th cycle.

[0033] The square of the modulus of each frequency component in the frequency domain of the cancellation sequence of the i-th cycle is calculated to obtain the frequency energy in the frequency domain, and the frequency energy greater than the upper quartile is taken as the high-frequency frequency energy; the mean of all high-frequency frequency energies is calculated and denoted as , and the i-1-th cycle corresponding to the cancellation of the direct current component is calculated , and - The fourth feature of the voltage data sequence of the i-th cycle is represented, and the fourth feature is similar to the first feature, - When less than or equal to 0, the value of the fourth feature is 0.

[0034] Further, for multiple near-electricity detection sensors, a spatial topology structure will be satisfied in space. Due to the different distances of each sensor, the spatial topology structure will change as the distance from the power frequency electric field source decreases. Therefore, each near-electricity detection sensor is taken as a vertex (vertex set), and each two sensors are taken as an edge (edge set), and the edge weight is the signal amplitude correlation of the two sensors, for example: , The edge formed by the A sensor and the B sensor is represented, and the edge weight at the i-th cycle is represented; 、 The amplitudes of the A sensor and the B sensor at the i-th cycle when the frequency is the power frequency are represented, respectively. A 5x5 (number of sensors) all-zero matrix is initialized, and all edge weights are sequentially filled into the initialized matrix, and the diagonal elements are set to zero to obtain the edge weight matrix W:

[0035] The diagonal line of the edge weight matrix W is all 0 elements. It should be understood that the edge weight matrix is a symmetric matrix.

[0036] Then, a diagonal matrix P is defined, and the diagonal elements of the diagonal matrix P are the degrees of each vertex. As an example, the degree of the first vertex is the sum of all edge weights calculated by the first vertex and other vertices. At this time, the diagonal elements of the edge weight matrix are 0, and the non-diagonal elements are the edge weights of adjacent vertices; the diagonal elements of the diagonal matrix P are the degrees of each vertex, and the non-diagonal elements are 0. The difference between the diagonal matrix P and each corresponding position element in the edge weight matrix W is obtained to obtain the graph Laplacian matrix O:

[0037] wherein 、 、 , , respectively, the sum of all edge weights calculated between the vertex corresponding to the sensor A and other vertices.

[0038] Taking the graph Laplacian matrix O in the i th cycle as an example, the graph Laplacian matrix O in the i th cycle is taken as input, and the graph Laplacian matrix O is decomposed by using the Jacobi eigenvalue algorithm, and the second smallest eigenvalue is taken as the feature of the i th cycle. The second smallest eigenvalue of the graph Laplacian matrix can express the algebraic connectivity in the matrix, which can represent the connection tightness of the graph. If the sensor is close to the source of the power frequency electric field, the edge weight will become larger, and the algebraic connectivity will become larger, and the second smallest eigenvalue will become larger. Therefore, the feature of the i-1 th cycle is calculated, and the difference between the feature of the i th cycle and the feature of the i-1 th cycle is taken as the fifth feature of the i th cycle. When the difference is less than or equal to 0, the value of the fifth feature is 0.

[0039] Step three: all the features obtained by synthesizing the voltage data of each cycle are used to determine the near electric detection confidence of each cycle, and the near electric detection of the aerial work platform is performed.

[0040] The first, second, third, fourth and fifth features of the i th cycle are combined to construct the near electric detection confidence of the i th cycle, and the specific formula is:

[0041]

[0042] wherein, represents the near electric detection confidence of the i th cycle; , respectively, the third feature and the fifth feature of the i th cycle; represents the hyperbolic tangent function; represents the difference complexity of the i th cycle; , , respectively, the first feature, the second feature and the fourth feature of the i th cycle; e represents the natural constant; represents the circular constant.

[0043] The calculation principle of the difference complexity is as follows: is used to describe the difference between the predicted voltage data and the actual voltage data; is used to describe the overall change of the high frequency energy in the frequency domain corresponding to the voltage data sequence of the adjacent cycle. The closer the working position is to the source of the power frequency electric field, the smaller the interference on the voltage data collected by the near electric sensor, and the smaller the high frequency energy. Therefore, is smaller, is larger; The absolute value of the difference between the predicted amplitude value and the actual amplitude value is used to depict that the closer the working position is to the power frequency electric field source, the smaller the interference on the voltage data collected by the near electric sensor is.

[0044] The near electric detection confidence calculation principle is that the dimensional data is linearly weighted, and then multiplied by the normalized data. The change between the predicted phase angle value and the actual phase angle value is used to depict that the closer the working position is to the power frequency electric field source, the smaller the interference on the voltage data collected by the near electric sensor is. The second smallest feature of the Laplacian matrix corresponding to the voltage data sequence collected by all sensors in adjacent periods is used to depict that the closer the working position is to the power frequency electric field source, the larger the second smallest feature is. The closer the near electric detection confidence is, the closer the i-th period is to the power frequency electric field source compared with the i-1-th period.

[0045] The detection result of the near electric detection method includes two cases of no risk and risk, and the specific detection process is as follows: first, collect 20 periods of induction data at a safe working position, calculate the near electric detection confidence of all periods according to the scheme, calculate the near electric judgment threshold according to the 3 sigma principle, and specifically: calculate the upper limit value corresponding to 3 sigma as the near electric judgment threshold through the 3 sigma principle of the near electric detection confidence of a preset number of periods at the safe position; then, for the position to be detected, continuously collect the induction data of each period and calculate the near electric detection confidence, and judge whether the near electric detection confidence of the current period exceeds the threshold value, if not, the detection result is no risk, if it exceeds the threshold value, the detection result is risk, and the alarm system will issue an alarm.

[0046] Based on the same inventive concept as the above method, the embodiment of the present application also provides a near electric detection system for a high-altitude working vehicle, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the methods in the above near electric detection method for a high-altitude working vehicle.

[0047] ​​The computer program product of the present application can be a computer program implemented on one or more computers. The program instructions can be stored on a computer-readable medium, such as a floppy disk, CD-ROM, and the like. The computer program product can also include computer programs that are transmitted over a network via, for example, telephone line, LAN, wireless instrument, or others. Accordingly, the computer program product of the present application can be an article of manufacture including a computer usable medium having computer readable program code means distributed therein. The computer readable program code means is means for causing a computer to operate in a specific and predefined manner. The present application can also be embodied in a computer readable medium including transitory signals. Accordingly, the present application can be a product, an article of manufacture, and / or a machine.

[0048] It is apparent that the present application is not limited to the details of the foregoing exemplary embodiments, and thus modifications and / or improvements can be made thereto without departing from the scope of the present application. Accordingly, no matter from which point of view, the foregoing embodiments of the present application should be considered as exemplary and non-limiting; any modification of the technical solutions described in the foregoing embodiments, or equivalent replacement of some of the technical features, without changing the nature of the corresponding technical solutions, should be included in the scope of protection of the present application.

Claims

1. A proximity detection method for aerial work platforms, characterized in that, The method includes the following steps: Voltage data were collected at preset locations on the aerial work platform at various times. For each location, the overall fluctuation of voltage data over a preset period is analyzed to obtain an elimination sequence. This elimination sequence is then fitted to a preset power frequency. Based on the variation of the difference between the actual value and the fitted result in adjacent periods, the first feature of the voltage data for each period is obtained. The second and third features of the voltage data for each period are determined based on the variation of the fitted result and the voltage data in the frequency domain. The fourth feature of the voltage data for each period is determined by analyzing the variation of the high-frequency energy of the voltage data between adjacent periods. Finally, the fifth feature of the voltage data for each period is determined based on the distance between preset locations and the amplitude difference at the power frequency. By combining all the features obtained from the voltage data of each cycle, the confidence level of the proximity detection for each cycle is determined, and proximity detection is performed on the aerial work platform vehicle.

2. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, The elimination sequence is obtained by: calculating the mean of all voltage data in each period, denoted as the voltage mean; The difference between each voltage data point in each period and the voltage mean is calculated, and the sequence of all differences is denoted as the elimination sequence.

3. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, The first feature for obtaining the voltage data of each cycle is specifically: The data in the eliminated sequence are fitted to obtain the fitting formula of voltage data with respect to time. The fitting formula is then substituted into the time of each cycle to obtain the fitted voltage data sequence composed of the fitted voltage data. Calculate the sum of squares of the differences between the eliminated sequence and the fitted voltage data sequence at all identical times; Calculate the difference between the sum of squared differences obtained in each cycle and the previous cycle to determine the first characteristic of the voltage data in each cycle.

4. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, The process of determining the second and third features of the voltage data for each cycle is as follows: Obtain the amplitude and phase angle values ​​of the elimination sequence at the power frequency for each cycle, and denot them as the first amplitude value and the first phase angle value, respectively. The fitting parameters in the fitting formula for each period of voltage data are denoted as follows: , ; The second amplitude value is denoted as Its formula is as follows ; The second phase angle value is denoted as... Its formula is as follows: ,in Represents the arctangent trigonometric function; The difference between the first amplitude value and the second amplitude value obtained in each cycle is denoted as the second feature of the voltage data in each cycle. The difference between the first phase angle value and the second phase angle value obtained in each cycle is denoted as the third feature of the voltage data for each cycle.

5. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, Specifically, the high-frequency domain energy of the voltage data is the frequency domain energy of the elimination sequence in each period that is greater than the energy value of the upper quartile.

6. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, The fourth feature for determining the voltage data of each cycle is specifically: The mean value of all high-frequency domain energies obtained in each cycle is calculated, and the difference between the mean value obtained in each cycle and the mean value obtained in the previous cycle is used as the fourth feature of the voltage data in each cycle.

7. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, The fifth feature for determining the voltage data of each cycle is specifically: For each cycle, the amplitude difference of voltage data between any two locations at the power frequency is obtained, and divided by the distance between the two locations, the edge weight between the two locations is obtained. Based on the edge weights between all pairwise combinations of positions, and combined with the spatial distribution of positions, a weighted undirected graph is constructed to obtain the edge weight matrix; The sum of the edge weights of each position and the other positions is used to construct a diagonal matrix. The difference between the diagonal matrix and the edge weight matrix is ​​used to obtain the graph Laplacian matrix. Arrange the eigenvalues ​​of the graph Laplacian matrix in ascending order, calculate the difference between the second eigenvalue obtained in each period and the eigenvalue of the previous period, and obtain the fifth eigenvalue of each period.

8. The proximity detection method for aerial work platforms as described in claim 1, characterized in that, The specific formula for determining the near-electrical detection confidence level for each cycle is as follows: ; ;in, This represents the near-electrical detection confidence level for the i-th cycle; , Let these represent the third and fifth features of the i-th period, respectively. Represents the hyperbolic tangent function; Indicate the difference complexity of the i-th cycle; , , Let represent the first, second, and fourth characteristics of the i-th period, respectively; e represents the natural constant. It represents pi (π).

9. A proximity detection method for aerial work platforms as described in claim 1, characterized in that, The specific process for performing near-electricity detection on the aerial work platform is as follows: the confidence level of near-electricity detection for a preset number of cycles at the safe position is used to calculate the upper limit value corresponding to 3σ using the 3σ principle, which is then used as the near-electricity judgment threshold; when the confidence level of near-electricity detection for each cycle at the position to be detected is greater than the preset near-electricity judgment threshold, it is determined that there is a near-electricity risk. Otherwise, there is no risk of near-electricity.

10. A proximity detection system for aerial work platforms, 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 method as described in any one of claims 1-9.

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