An online monitoring device for high-voltage switchgear based on the Internet of Things

Abstract

The present invention relates to an online monitoring device for a high-voltage switch cabinet based on the Internet of Things. By setting an acquisition module, various trigger mechanisms and monitoring parameters in the switch cabinet are collected. When the monitoring parameters meet the trigger mechanism or receive a control signal from a control center or an automatic control device, the audio data generated when various protection mechanisms are in effect are collected, and the audio data is matched with reference audio features to determine whether the protection mechanism is triggered and whether it is triggered correctly. Finally, the results are verified by collecting the monitoring parameters again, thereby automatically monitoring various active or passive mechanisms in the switch cabinet. When the mechanism fails, it can be discovered in time to avoid the switch cabinet from being unable to work normally or malfunctioning due to the failure of the mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of switch cabinet monitoring, and in particular to an online monitoring device for a high-voltage switch cabinet based on the Internet of Things. Background Art

[0002] High-voltage switchgear is an electrical product used in power systems. It plays a key role in switching, controlling or protecting in power generation, transmission, distribution, power conversion and consumption.

[0003] In addition to the response mechanism of the corresponding control center or automatic control device, there are many self-protection mechanisms in the high-voltage switchgear. For example, when the temperature rises, the cooling fan starts; when the current in the high-voltage switchgear exceeds the rated value, the protection relay will perform overcurrent protection, etc. These active or passive mechanisms ensure that the switchgear can be effectively protected in various situations to ensure the normal operation of the switchgear. However, in the prior art, there is no technical solution to monitor these mechanisms online. Summary of the invention

[0004] In view of this, an object of the present invention is to provide an online monitoring device for a high-voltage switch cabinet based on the Internet of Things to solve the problem that the protection mechanism of the switch cabinet cannot be monitored in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An online monitoring device for a high-voltage switch cabinet based on the Internet of Things of the present invention comprises:

[0007] An acquisition module is used to obtain the values ​​of monitoring parameters in the switch cabinet and to obtain the triggering conditions of the protection mechanism of the switch cabinet;

[0008] A trigger judgment module, used to judge whether the value of the monitoring parameter meets the trigger condition of the protection mechanism;

[0009] An audio collection module, configured to collect audio data in the switch cabinet with a duration equal to a target value when a control signal is received from a control center or an automatic control device, or when the value of the monitoring parameter meets the triggering condition of the protection mechanism;

[0010] A matching module, used to match the audio data with a pre-built reference audio feature, wherein the reference audio feature is a feature of a reference audio generated when the control signal or the component action of the protection mechanism is executed;

[0011] A first monitoring module, configured to determine that a component corresponding to the control signal or protection mechanism completes an action when the audio data matches a pre-built reference audio feature;

[0012] The second monitoring module is used to re-acquire the value of the monitoring parameter in the switch cabinet after the components corresponding to the control signal or protection mechanism complete the action, and determine whether the value of the re-acquired monitoring parameter meets the triggering condition of the protection mechanism. If not, it is determined that the control signal or the protection mechanism is effective.

[0013] In an embodiment of the present application, the reference audio feature includes a reference amplitude, a reference fundamental frequency, and a reference frequency distribution feature, and matching the audio data with the pre-constructed reference audio feature includes:

[0014] Extracting the amplitude, fundamental frequency and frequency distribution characteristics of the audio data;

[0015] Match the amplitude of the audio data with the reference amplitude, match the fundamental frequency of the audio data with the reference fundamental frequency, and match the frequency distribution characteristics of the audio data with the reference frequency distribution characteristics; and when the amplitude of the audio data matches the reference amplitude, the fundamental frequency of the audio data matches the reference fundamental frequency, and the frequency distribution characteristics of the audio data match the reference frequency distribution characteristics, determine that the audio data matches a pre-constructed reference audio feature.

[0016] In an embodiment of the present application, the method for constructing the reference audio feature includes:

[0017] Obtain reference audio of various components working in experimental scenarios;

[0018] The amplitude, fundamental frequency and frequency distribution characteristics of the reference audio are extracted, and the amplitude of the reference audio is used as the reference amplitude, the fundamental frequency of the reference audio is used as the reference fundamental frequency, and the frequency distribution characteristics of the reference audio are used as the reference frequency distribution characteristics.

[0019] In one embodiment of the present application, extracting the amplitude, fundamental frequency, and frequency distribution characteristics of the reference audio or audio data includes:

[0020] Perform analog-to-digital conversion on audio data or reference audio to obtain an audio digital waveform;

[0021] Performing background sound filtering on the audio digital waveform to obtain an intermediate waveform;

[0022] Extracting a target section from the intermediate waveform, and performing a fast Fourier transform on the digital waveform of the target section to obtain a characteristic spectrum diagram;

[0023] The average amplitude and fundamental frequency of the target segment are extracted, and the average amplitude of the target segment is used as the amplitude of the audio data; and the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency in the characteristic spectrum diagram are extracted; and the frequency distribution characteristics are constructed based on the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency.

[0024] In one embodiment of the present application, extracting the target segment in the intermediate waveform includes:

[0025] Sliding along the intermediate waveform based on a pre-constructed sliding window, and calculating the average amplitude of the sampling points in the sliding window during each sliding;

[0026] When the average amplitude of the sampling points in the sliding window is greater than or equal to a preset amplitude threshold, the waveform in the sliding window is taken as a target segment.

[0027] In one embodiment of the present application, the frequency distribution feature constructed based on the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency includes:

[0028] Calculate the signal strength A corresponding to each harmonic frequency i The ratio A of the signal strength A0 corresponding to the fundamental frequency i / A0, where i is the serial number of the harmonic frequency;

[0029] A frequency distribution feature or a reference frequency distribution feature is constructed based on the fundamental frequency, each harmonic frequency and the ratio corresponding to each harmonic frequency.

[0030] In an embodiment of the present application, matching the amplitude of the audio data with the reference amplitude, and matching the fundamental frequency of the audio data with the reference fundamental frequency, includes:

[0031] Calculate the amplitude A of the audio data D With the reference amplitude A R The first deviation rate P1 is calculated, and the fundamental frequency F of the audio data is calculated. D With the reference fundamental frequency F R The second deviation rate P2, wherein the mathematical expressions of the first deviation rate P1 and the second deviation rate P2 are respectively:

[0032]

[0033] The first deviation rate P1 and the second deviation rate P2 are respectively compared with the preset deviation rate threshold; when the first deviation rate P1 is less than the deviation rate threshold, it is determined that the amplitude of the audio data matches the reference amplitude; when the second deviation rate P2 is less than the deviation rate threshold, it is determined that the fundamental frequency of the audio data matches the reference fundamental frequency.

[0034] In an embodiment of the present application, matching the frequency distribution feature of the audio data with the reference frequency distribution feature includes:

[0035] Sort the fundamental frequency and harmonic frequency of the audio data based on signal strength to obtain a frequency sequence;

[0036] Calculate the signal strength A of each frequency in the frequency sequence n The signal strength A of the corresponding frequency in the reference frequency distribution characteristic m The deviation rate P3, Wherein, n is the frequency number in the frequency sequence, and m is the number of the corresponding frequency in the reference frequency distribution feature;

[0037] When the deviation rate P3 is less than the preset deviation rate threshold, the amplitude of the corresponding position of the frequency sequence is 0; when the deviation rate P3 is greater than or equal to the preset deviation rate threshold, the amplitude of the corresponding position of the frequency sequence is 1, and a binary sequence is obtained;

[0038] The value of the binary sequence is compared with a preset matching threshold, and when the value of the binary sequence is less than the preset matching threshold, it is determined that the frequency distribution feature of the audio data matches the reference frequency distribution feature.

[0039] In one embodiment of the present application, the monitoring parameters include temperature and humidity inside the switch cabinet, dust concentration, voltage of the busbar and the outgoing line, and current of the busbar and the outgoing line.

[0040] In an embodiment of the present application, the protection mechanism includes:

[0041] When the temperature and humidity exceed the preset temperature and humidity range, the fan is triggered to turn on;

[0042] When the dust concentration exceeds the preset concentration threshold, the fan is triggered to turn on;

[0043] The voltage of the busbar and the outgoing line exceeds the preset overvoltage protection value, triggering the overvoltage protection relay to operate;

[0044] The current of the busbar and the outgoing line exceeds the preset overcurrent protection value, triggering the overcurrent protection relay to operate.

[0045] The beneficial effects of the present invention are as follows: an online monitoring device for a high-voltage switch cabinet based on the Internet of Things of the present invention collects various trigger mechanisms and monitoring parameters in the switch cabinet by setting an acquisition module, and when the monitoring parameters meet the trigger mechanism or receive a control signal from a control center or an automatic control device. The audio data generated when various protection mechanisms are in effect is collected, and the audio data is matched with the reference audio features to determine whether the protection mechanism is triggered and whether it is triggered correctly. Finally, the results are verified by collecting the monitoring parameters again, thereby automatically monitoring various active or passive mechanisms in the switch cabinet. When the mechanism fails, it can be discovered in time to avoid the switch cabinet from being unable to work normally or malfunctioning due to the failure of the mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0047] Figure 1 This is an application scenario diagram of an online monitoring device for a high-voltage switch cabinet based on the Internet of Things in one embodiment of the present invention;

[0048] Figure 2 This is a structural diagram of an online monitoring device for a high-voltage switch cabinet based on the Internet of Things in one embodiment of the present invention;

[0049] Figure 3 The present invention is a flowchart of a method for intelligently monitoring the health status of a switch cabinet in one embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0051] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0052] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0053] Figure 1 This is an application scenario diagram of an online monitoring device for high-voltage switchgear based on the Internet of Things in this application, such as Figure 1 As shown, in the present application, a main control device 110 , a microphone 120 and a variety of sensors 130 are provided in the switch cabinet.

[0054] The main control device 110 controls the various sensors 130 in the switch cabinet to collect monitoring data and obtain control instructions from the control center or automatic control device (such as a PLC host). Based on the collected monitoring data and control instructions, it is determined what action the switch cabinet will perform next. The microphone 120 is used to collect audio data in the switch cabinet, and the main control device 110 analyzes and processes the audio data to determine whether the active or passive mechanism in the switch cabinet is executed.

[0055] Finally, the main control device 110 returns the monitoring result to the cloud server 140 for storage to achieve online monitoring.

[0056] Figure 2 The structure of an online monitoring device for a high-voltage switch cabinet based on the Internet of Things is shown in an embodiment of the present application, such as Figure 2 As shown: An online monitoring device for a high-voltage switch cabinet based on the Internet of Things in this embodiment includes:

[0057] The acquisition module 210 is used to obtain the values ​​of the monitoring parameters in the switch cabinet and obtain the triggering conditions of the protection mechanism of the switch cabinet; wherein the monitoring parameters include the internal temperature and humidity of the switch cabinet, the dust concentration, the voltage of the busbar and the outgoing line, and the current of the busbar and the outgoing line.

[0058] Correspondingly, the acquisition module 210 includes a temperature and humidity sensor for collecting temperature and humidity, a dust concentration sensor for collecting dust concentration, a voltage transformer for detecting the voltage of the bus and the outgoing line, and a current transformer for collecting the current of the bus and the outgoing line. Among them, the temperature and humidity sensor and the dust concentration sensor can be directly connected to the main control device, and the current transformer and the voltage transformer are connected to the main control device through the electric energy metering IC.

[0059] A trigger determination module 220, configured to determine whether the value of the monitoring parameter satisfies the trigger condition of the protection mechanism;

[0060] In this application, there are various passive protection mechanisms in the switch cabinet, such as:

[0061] (1) When the temperature and humidity exceed the preset temperature and humidity range, the fan is triggered to turn on;

[0062] (2) The dust concentration exceeds the preset concentration threshold, triggering the fan to turn on;

[0063] (3) The voltage of the busbar and the outgoing line exceeds the preset overvoltage protection value, triggering the overvoltage protection relay to operate;

[0064] (4) The current of the busbar and the outgoing line exceeds the preset overcurrent protection value, triggering the overcurrent protection relay to operate.

[0065] Correspondingly, the triggering conditions of the protection mechanism include:

[0066] (1) The temperature and humidity exceed the preset temperature and humidity range;

[0067] (2) The dust concentration exceeds the preset concentration threshold;

[0068] (3) The voltage of the busbar and the outgoing line exceeds the preset overvoltage protection value;

[0069] (4) The current of the busbar and the outgoing line exceeds the preset overcurrent protection value.

[0070] The audio collection module 230 is used to collect audio data with a duration of a target value in the switch cabinet when a control signal is received from a control center or an automatic control device, or when the value of the monitoring parameter meets the triggering condition of the protection mechanism;

[0071] In addition to the passive protection mechanism, the present application also monitors the active control mechanism. When the control center or automatic control device sends a control signal to the switch cabinet, the present application analyzes the control signal to obtain the execution components corresponding to the control signal. In addition, the present application implants a protection mechanism program inside the main control device, so it can determine whether to trigger a specific protection mechanism and the execution components of the specific protection mechanism through the values ​​of the collected monitoring parameters.

[0072] Different components produce different sounds when in action, for example, fans and relays produce different sounds. Therefore, this application collects audio data with a target duration to determine whether the mechanism is effective.

[0073] A matching module 240, configured to match the audio data with a pre-built reference audio feature, wherein the reference audio feature is a feature of a reference audio generated when the control signal or the component action of the protection mechanism is executed;

[0074] In the application, the audio data is verified by pre-building reference audio features. Each component records a piece of audio data in advance when performing different actions. Then the corresponding audio features are collected to obtain the reference audio features. When matching, the corresponding reference audio features can be found for matching.

[0075] In an embodiment of the present application, the method for constructing the reference audio feature includes:

[0076] (1) Obtain reference audio when various components are working in the experimental scenario; for example, reference audio when the relay is disconnected, reference audio when the fan is started and at each gear of the fan, audio when an arc is generated, etc.

[0077] (2) Extracting the amplitude, fundamental frequency and frequency distribution characteristics of the reference audio, and taking the amplitude of the reference audio as the reference amplitude, the fundamental frequency of the reference audio as the reference fundamental frequency, and the frequency distribution characteristics of the reference audio as the reference frequency distribution characteristics.

[0078] In this application, the loudness of the audio is determined by the amplitude, the pitch of the audio is determined by the fundamental frequency, and the timbre of the audio is determined by the frequency distribution characteristics. When the same component performs the same action, the audio generated each time is similar. Therefore, the characteristics of the audio data can be matched with the reference audio characteristics. Therefore, the matching process of this application is as follows;

[0079] (1) extracting the amplitude, fundamental frequency and frequency distribution characteristics of the audio data;

[0080] (2) matching the amplitude of the audio data with the reference amplitude, matching the fundamental frequency of the audio data with the reference fundamental frequency, and matching the frequency distribution characteristics of the audio data with the reference frequency distribution characteristics; and when the amplitude of the audio data matches the reference amplitude, the fundamental frequency of the audio data matches the reference fundamental frequency, and the frequency distribution characteristics of the audio data matches the reference frequency distribution characteristics, determining that the audio data matches a pre-constructed reference audio feature.

[0081] This application performs matching from three aspects: loudness, pitch and timbre. When all three aspects match, it means that the audio data matches the reference audio feature.

[0082] In one embodiment of the present application, extracting the amplitude, fundamental frequency, and frequency distribution characteristics of the reference audio or audio data includes:

[0083] (1) Performing analog-to-digital conversion on audio data or reference audio to obtain an audio digital waveform; wherein the audio digital waveform may be a PCM (Pulse Code Modulation) waveform.

[0084] (2) Filtering the background sound of the audio digital waveform to obtain an intermediate waveform; the present application can filter the audio digital waveform to filter out low-frequency data, thereby filtering out the low-frequency background sound and obtaining an intermediate waveform for subsequent processing.

[0085] (3) extracting a target segment from the intermediate waveform, and performing a fast Fourier transform on the digital waveform of the target segment to obtain a characteristic spectrum diagram;

[0086] Due to the hysteresis of the execution of components, the collected audio data may contain invalid segments. In order to effectively analyze the audio generated by the components, the present application takes the target segment in the intermediate waveform, and then performs FFT transformation based on the target segment to obtain a characteristic spectrum diagram.

[0087] Specifically, the extraction process of the target segment is as follows:

[0088] (3-1) Slide along the intermediate waveform based on a pre-constructed sliding window, and calculate the average amplitude of the sampling points in the sliding window during each sliding; wherein the width of the sliding window is approximately 0.5 s.

[0089] (3-2) When the average amplitude of the sampling points in the sliding window is greater than or equal to a preset amplitude threshold, the waveform in the sliding window is taken as the target segment.

[0090] If the average amplitude of the sampling points in the sliding window is greater than or equal to the preset amplitude threshold, it means that there is a pulse signal with a higher amplitude in the sliding window, which is the segment corresponding to the audio generated when the components required in this application are activated.

[0091] (4) extracting the average amplitude and fundamental frequency of the target segment, and taking the average amplitude of the target segment as the amplitude of the audio data; and extracting the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency in the characteristic spectrum diagram; and constructing a frequency distribution feature based on the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency.

[0092] Among them, the average amplitude is the average value of the amplitudes of all sampling points. The fundamental frequency can be directly obtained by analyzing the characteristic spectrum, that is, finding the frequency corresponding to the position with the highest energy level in the characteristic spectrum, which is the fundamental frequency. The fundamental frequency is related to the pitch and determines the basic tone of the sound. However, the timbre is not only determined by the fundamental frequency, because even if two sounds have the same fundamental frequency (that is, the same pitch), their timbre may be completely different. Harmonics are integer multiples of the fundamental frequency, and their energy gradually decreases, but they still make a unique contribution to the timbre.

[0093] In this embodiment, the extracted fundamental frequency, amplitude and frequency distribution characteristics may be the fundamental frequency, amplitude and frequency distribution characteristics of the audio data extracted on site, or may be the reference fundamental frequency, reference amplitude and reference frequency distribution characteristics of the reference audio used as the matching template.

[0094] Specifically, the frequency distribution characteristics constructed based on the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency include:

[0095] (4-1) Calculate the signal strength A corresponding to each harmonic frequency i The ratio A of the signal strength A0 corresponding to the fundamental frequency i / A0, where i is the serial number of the harmonic frequency;

[0096] (4-2) A frequency distribution feature or a reference frequency distribution feature is constructed based on the fundamental frequency, each harmonic frequency, and the ratio corresponding to each harmonic frequency.

[0097] The present application constructs a frequency distribution feature or a reference frequency distribution feature based on each harmonic frequency of the fundamental frequency and the ratio corresponding to each harmonic frequency, so as to reflect the timbre characteristics of the audio.

[0098] After the above features are extracted, the matching process includes:

[0099] In an embodiment of the present application, matching the amplitude of the audio data with the reference amplitude, and matching the fundamental frequency of the audio data with the reference fundamental frequency, includes:

[0100] (1) Calculate the amplitude A of the audio data D With the reference amplitude A R The first deviation rate P1 is calculated, and the fundamental frequency F of the audio data is calculated. D With the reference fundamental frequency F R The second deviation rate P2, wherein the mathematical expressions of the first deviation rate P1 and the second deviation rate P2 are respectively:

[0101]

[0102] (2) Compare the first deviation rate P1 and the second deviation rate P2 with a preset deviation rate threshold value respectively; when the first deviation rate P1 is smaller than the deviation rate threshold value, determine that the amplitude of the audio data matches the reference amplitude; when the second deviation rate P2 is smaller than the deviation rate threshold value, determine that the fundamental frequency of the audio data matches the reference fundamental frequency.

[0103] In this application, for amplitude and fundamental frequency, this application analyzes the difference between the audio data and the reference audio by calculating the deviation rate. If the difference is small, it means that the two match. If the difference is large, it means that they do not match.

[0104] In an embodiment of the present application, matching the frequency distribution feature of the audio data with the reference frequency distribution feature includes:

[0105] (1) sorting the fundamental frequency and harmonic frequency of the audio data based on signal strength to obtain a frequency sequence;

[0106] (2) Calculate the signal strength A of each frequency in the frequency sequence n The signal strength A of the corresponding frequency in the reference frequency distribution characteristic m The deviation rate P3, Wherein, n is the frequency number in the frequency sequence, and m is the number of the corresponding frequency in the reference frequency distribution feature;

[0107] (3) When the deviation rate P3 is less than a preset deviation rate threshold, the amplitude of the corresponding position of the frequency sequence is 0; when the deviation rate P3 is greater than or equal to the preset deviation rate threshold, the amplitude of the corresponding position of the frequency sequence is 1, and a binary sequence is obtained;

[0108] (4) Comparing the value of the binary sequence with a preset matching threshold, and when the value of the binary sequence is less than the preset matching threshold, determining that the frequency distribution feature of the audio data matches the reference frequency distribution feature.

[0109] For the matching of the frequency distribution characteristics with the reference frequency distribution characteristics, it is not possible to directly use the deviation calculation. Therefore, the present application sorts the signals according to the signal strength from high to low (generally the strength corresponding to the fundamental frequency, the strength corresponding to the second harmonic, the strength corresponding to the third harmonic, etc.), and then calculates the deviation rate one by one.

[0110] For example, the frequency sequence is ABCDE, and the corresponding reference frequency distribution feature is abcde. If the intensities corresponding to the five frequencies all have small deviations, then the corresponding output binary array is 00000, indicating that the two match. If there is a frequency with a large deviation, such as B and b, the binary array 01000 is output, and this value is used as the deviation between the two. And the closer the frequency is to the front, the higher the weight. Therefore, the present application uses a simple encoding method to determine the similarity of multiple frequencies. Thereby, the frequency distribution feature can be quickly matched with the reference frequency distribution feature.

[0111] A first monitoring module 250, configured to determine that the control signal or the component corresponding to the protection mechanism completes an action when the audio data matches a pre-built reference audio feature;

[0112] In this application, if it is determined that the switch cabinet performs a specific action, and the audio generated by the subsequent execution of the action matches the audio template corresponding to the specific action, it means that the control signal or the component corresponding to the protection mechanism has completed the action. Otherwise, it means that the mechanism fails and the alarm information is sent to the target personnel for reminder.

[0113] The second monitoring module 260 is used to re-acquire the value of the monitoring parameter in the switch cabinet after the component corresponding to the control signal or the protection mechanism completes the action, and judge whether the value of the re-acquired monitoring parameter meets the triggering condition of the protection mechanism. If not, it is determined that the control signal or the protection mechanism is effective.

[0114] Finally, if the action is executed, but the value of the corresponding monitoring parameter does not return to the normal value, or does not reach the expected value, then it can be determined that the mechanism execution order is not effective, which may also cause the switch cabinet to malfunction. At this time, the alarm information is sent to the target personnel for reminder.

[0115] The present invention provides an online monitoring device for a high-voltage switch cabinet based on the Internet of Things. By setting an acquisition module, various trigger mechanisms and monitoring parameters in the switch cabinet are collected. When the monitoring parameters meet the trigger mechanism or receive a control signal from a control center or an automatic control device, the audio data generated when various protection mechanisms are in effect are collected, and the audio data is matched with reference audio features to determine whether the protection mechanism is triggered and whether it is triggered correctly. Finally, the results are verified by collecting the monitoring parameters again, thereby automatically monitoring various active or passive mechanisms in the switch cabinet. When the mechanism fails, it can be discovered in time to avoid the switch cabinet from not being able to work properly or malfunctioning due to the failure of the mechanism.

[0116] like Figure 3 As shown, the present application also provides an online monitoring method for a high-voltage switch cabinet based on the Internet of Things, comprising:

[0117] S310, obtaining the value of the monitoring parameter in the switch cabinet, and obtaining the triggering condition of the protection mechanism of the switch cabinet;

[0118] S320, determining whether the value of the monitoring parameter satisfies the triggering condition of the protection mechanism;

[0119] S330, when a control signal is received from a control center or an automatic control device, or when the value of the monitoring parameter meets the triggering condition of the protection mechanism, collecting audio data in the switch cabinet with a duration equal to a target value;

[0120] S340, matching the audio data with a pre-constructed reference audio feature, wherein the reference audio feature is a feature of a reference audio generated when the control signal or the component action of the protection mechanism is executed;

[0121] S350, when the audio data matches a pre-built reference audio feature, determining that a component corresponding to the control signal or protection mechanism completes an action;

[0122] S360, after the components corresponding to the control signal or protection mechanism complete the action, re-acquire the values ​​of the monitoring parameters in the switch cabinet, and determine whether the values ​​of the re-acquired monitoring parameters meet the triggering conditions of the protection mechanism. If not, determine that the control signal or the protection mechanism is effective.

[0123] The present invention provides an online monitoring method for a high-voltage switch cabinet based on the Internet of Things. By setting an acquisition module, various trigger mechanisms and monitoring parameters in the switch cabinet are collected. When the monitoring parameters meet the trigger mechanism or receive a control signal from a control center or an automatic control device, the audio data generated when various protection mechanisms are in effect is collected, and the audio data is matched with reference audio features to determine whether the protection mechanism is triggered and whether it is triggered correctly. Finally, the results are verified by collecting the monitoring parameters again, thereby automatically monitoring various active or passive mechanisms in the switch cabinet. When the mechanism fails, it can be discovered in time to avoid the switch cabinet from not being able to work properly or malfunctioning due to the failure of the mechanism.

[0124] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented, wherein the method is the execution logic of this system.

[0125] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0126] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0127] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0128] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0129] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0130] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0131] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art according to the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0132] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An online monitoring device for high-voltage switch cabinet based on the Internet of Things, characterized in that: include: An acquisition module is used to obtain the values ​​of monitoring parameters in the switch cabinet and obtain the triggering conditions of the protection mechanism of the switch cabinet; A trigger judgment module, used to judge whether the value of the monitoring parameter meets the trigger condition of the protection mechanism; An audio collection module, configured to collect audio data in the switch cabinet with a duration equal to a target value when a control signal is received from a control center or an automatic control device, or when the value of the monitoring parameter meets the triggering condition of the protection mechanism; A matching module is used to match the audio data with a pre-constructed reference audio feature, wherein the reference audio feature is a feature of a reference audio generated when the control signal or the component action of the protection mechanism is executed; the reference audio feature includes a reference amplitude, a reference fundamental frequency, and a reference frequency distribution feature, and matching the audio data with the pre-constructed reference audio feature includes: extracting the amplitude, fundamental frequency, and frequency distribution feature of the audio data; matching the amplitude of the audio data with the reference amplitude, matching the fundamental frequency of the audio data with the reference fundamental frequency, and matching the frequency distribution feature of the audio data with the reference frequency distribution feature; and matching the amplitude of the audio data with the reference amplitude. When the fundamental frequency of the audio data matches the reference fundamental frequency and the frequency distribution characteristics of the audio data match the reference frequency distribution characteristics, it is determined that the audio data matches the pre-constructed reference audio characteristics; the method for constructing the reference audio characteristics includes: obtaining the reference audio when various components are working in the experimental scenario; extracting the amplitude, fundamental frequency and frequency distribution characteristics of the reference audio, and using the amplitude of the reference audio as the reference amplitude, the fundamental frequency of the reference audio as the reference fundamental frequency and the frequency distribution characteristics of the reference audio as the reference frequency distribution characteristics; constructing the frequency distribution characteristics based on the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency, including: calculating the signal strength corresponding to each harmonic frequency The signal strength corresponding to the fundamental frequency Ratio ,in, is the serial number of the harmonic frequency; constructing a frequency distribution feature or a reference frequency distribution feature based on the fundamental frequency, each harmonic frequency and the ratio corresponding to each harmonic frequency; matching the frequency distribution feature of the audio data with the reference frequency distribution feature, including: sorting the fundamental frequency and the harmonic frequency of the audio data based on the signal strength to obtain a frequency sequence; calculating the signal strength of each frequency in the frequency sequence The signal strength of the corresponding frequency in the reference frequency distribution feature Deviation rate , ,in, is the frequency sequence number in the frequency sequence, is the serial number of the corresponding frequency in the reference frequency distribution characteristics; in the deviation rate When the deviation rate is less than the preset threshold, the amplitude of the corresponding position of the frequency sequence is 0; when the deviation rate is When the value of the binary sequence is greater than or equal to the preset deviation rate threshold, the amplitude of the corresponding position of the frequency sequence is 1 to obtain a binary sequence; the value of the binary sequence is compared with the preset matching threshold, and when the value of the binary sequence is less than the preset matching threshold, it is determined that the frequency distribution characteristics of the audio data match the reference frequency distribution characteristics; A first monitoring module, configured to determine that a component corresponding to the control signal or protection mechanism completes an action when the audio data matches a pre-built reference audio feature; The second monitoring module is used to re-acquire the value of the monitoring parameter in the switch cabinet after the components corresponding to the control signal or protection mechanism complete the action, and determine whether the value of the re-acquired monitoring parameter meets the triggering condition of the protection mechanism. If not, it is determined that the control signal or the protection mechanism is effective.

2. According to the Internet of Things-based high-voltage switch cabinet online monitoring device of claim 1, it is characterized in that: Extracting the amplitude, fundamental frequency and frequency distribution characteristics of the reference audio or audio data, including: Perform analog-to-digital conversion on audio data or reference audio to obtain an audio digital waveform; Performing background sound filtering on the audio digital waveform to obtain an intermediate waveform; Extracting a target section from the intermediate waveform, and performing a fast Fourier transform on the digital waveform of the target section to obtain a characteristic spectrum diagram; The average amplitude and fundamental frequency of the target segment are extracted, and the average amplitude of the target segment is used as the amplitude of the audio data; and the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency in the characteristic spectrum diagram are extracted; and the frequency distribution characteristics are constructed based on the signal strength corresponding to the fundamental frequency and the signal strength corresponding to the harmonic frequency.

3. According to the Internet of Things-based high-voltage switch cabinet online monitoring device of claim 2, it is characterized in that: Extracting a target segment from the intermediate waveform comprises: Sliding along the intermediate waveform based on a pre-constructed sliding window, and calculating the average amplitude of the sampling points in the sliding window during each sliding; When the average amplitude of the sampling points in the sliding window is greater than or equal to a preset amplitude threshold, the waveform in the sliding window is taken as a target segment.

4. According to the Internet of Things-based high-voltage switch cabinet online monitoring device of claim 1, it is characterized in that: Matching the amplitude of the audio data with the reference amplitude, and matching the fundamental frequency of the audio data with the reference fundamental frequency, comprises: Calculate the amplitude of the audio data With the reference amplitude The first deviation rate , and calculate the fundamental frequency of the audio data With the reference fundamental frequency The second deviation rate , wherein the first deviation rate and the second deviation rate The mathematical expressions are: The first deviation rate and the second deviation rate The first deviation rate is compared with the preset deviation rate threshold. When the amplitude of the audio data matches the reference amplitude, the second deviation rate When it is less than the deviation rate threshold, it is determined that the fundamental frequency of the audio data matches the reference fundamental frequency.

5. According to the Internet of Things-based high-voltage switch cabinet online monitoring device of claim 1, it is characterized in that: The monitoring parameters include the temperature and humidity inside the switch cabinet, dust concentration, the voltage of the busbar and the outgoing line, and the current of the busbar and the outgoing line.

6. According to the Internet of Things-based high-voltage switch cabinet online monitoring device of claim 5, it is characterized in that: The protection mechanism includes: When the temperature and humidity exceed the preset temperature and humidity range, the fan is triggered to turn on; When the dust concentration exceeds the preset concentration threshold, the fan is triggered to turn on; The voltage of the busbar and the outgoing line exceeds the preset overvoltage protection value, triggering the overvoltage protection relay to operate; The current of the busbar and the outgoing line exceeds the preset overcurrent protection value, triggering the overcurrent protection relay to operate.

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