Low-voltage power distribution cabinet monitoring system and communication control method thereof

Abstract

The invention discloses a low-voltage power distribution cabinet monitoring system and a communication control method thereof, and belongs to the technical field of communication, and the communication control method of the low-voltage power distribution cabinet monitoring system comprises the following steps: a plurality of communication areas are distributed in a low-voltage power distribution cabinet in advance, each communication area is provided with a concentrator, each concentrator is connected with a CPU module through a signal line, and the CPU module is connected with a central processing unit (CPU); a main communication network is formed; a plurality of sensors are distributed to each concentrator, and each sensor communicates with the corresponding concentrator through an ultrasonic signal to form a branch communication network; wherein a low-voltage power distribution cabinet area in charge of each concentrator is separated by a sound insulation structure; the sensors in the same communication network are assigned non-overlapping signal generation windows. According to the technical scheme provided by the invention, the CPU module is connected with the concentrator at a fixed position through the wired signal line to construct the main communication network, so that high-quality transmission of a main communication link is ensured.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and more particularly, to a monitoring system for low-voltage distribution cabinets and its communication control method. Background Art

[0002] A low-voltage distribution cabinet is a key power supply device that distributes electrical energy to low-voltage electrical equipment within a plant area and is often deployed in a combined manner in practice. To ensure the safe and stable operation of the power distribution system, a variety of detection modules need to be configured inside the cabinet to continuously monitor electrical parameters such as voltage, current, insulation resistance, and temperature in key areas, as well as the environmental status. These monitoring data need to be centrally collected and summarized and processed by the main controller (CPU module) built into the distribution cabinet, and finally uploaded to the superior server system.

[0003] Due to the dense internal components and diverse functions of low-voltage distribution cabinets, a large number of dedicated sensors distributed at different positions need to be arranged. To achieve the acquisition of sensor data, traditional solutions rely on laying a large number of low-frequency analog signal cables inside the cabinet and connecting them to the main controller. This wired communication method has significant drawbacks:

[0004] High implementation cost: The laying and connection of a large number of signal cables, as well as the strict isolation design from high-voltage lines that must be carried out to ensure safety, significantly increase the cabinet material cost and construction complexity.

[0005] Poor scalability and flexibility: Changes in different application scenarios or monitoring requirements (such as adding sensors) often require modifying the original wiring structure, resulting in difficult and costly system upgrade and maintenance.

[0006] To overcome the deficiencies of the wired solution, some technical solutions attempt to integrate low-frequency wireless communication modules at the sensor end and transmit data to the main controller wirelessly. However, there are a large number of strong electrical circuits inside low-voltage switch cabinets (such as 220V and 380V AC power supply lines), and the strong electromagnetic fields generated during their operation will cause serious interference to the low-frequency wireless signals in the vicinity, resulting in the main controller being unable to receive sensor information reliably and accurately.

[0007] In summary, the current sensor data communication network inside low-voltage distribution cabinets mainly faces two core challenges: high cost of constructing the communication link; weak anti-strong electromagnetic interference ability of the communication method (especially the wireless solution). Summary of the Invention

[0008] This section of the application is used to briefly introduce concepts that will be described in detail in the subsequent Detailed Description section. This section of the application is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0009] As a first aspect of the present application, in order to solve the technical problem of low existing communication efficiency, the present application provides a communication control method for a low-voltage distribution cabinet monitoring system, comprising the following steps:

[0010] Several communication areas are allocated in advance in the low-voltage distribution cabinet. Each communication area is equipped with a concentrator. Each concentrator is connected to the CPU module through a signal line to form a main communication network.

[0011] A number of sensors are allocated to each concentrator, and each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network;

[0012] The low-voltage distribution cabinet area that each concentrator is responsible for is separated by a soundproof structure;

[0013] Each sensor in the same communication network is assigned a non-overlapping signal generation window.

[0014] In the technical solution provided in this application:

[0015] The main communication network is reliable and stable: The CPU module and the fixed-position concentrator are connected by wired signal lines to build the main communication network, ensuring high-quality transmission of the backbone communication link without the need for subsequent line reconstruction.

[0016] Sensor communication has strong anti-interference ability: The sensor and concentrator use ultrasonic signal communication. Compared with wireless communication, ultrasonic communication is not affected by electromagnetic interference generated by strong current in the cabinet, and the communication quality is better.

[0017] Effectively suppress ultrasonic interference: Use sound insulation structures to separate the areas that each concentrator is responsible for, effectively preventing ultrasonic signals from spreading and overlapping over a large area in the cabinet, and avoiding sound wave noise interference between areas. Sensors in the same area (i.e., in the same communication network) emit sound wave signals in non-overlapping signal generation windows, significantly reducing signal interference between sensors in the area.

[0018] In order to solve the problem that too many sensors under the control of the concentrator lead to information collection delay, and too many concentrators lead to increased communication costs, in some embodiments of the present application, the following steps are adopted to build a main communication network;

[0019] S1: Obtain all sensors in the low-voltage distribution cabinet and determine the information transmission rate Q of each sensor ih ;Q ih represents the information transmission rate of the ih-th sensor;

[0020] S2: Acquire the audio features of the low-voltage distribution cabinet under operating conditions, and extract the idle audio segments from the audio features;

[0021] S3: Use the idle audio segment as the communication frequency band between the sensor and the concentrator, and calculate the information acquisition rate Q` of the concentrator according to the communication frequency band;

[0022] S4: Divide each sensor into n communication groups according to the information transmission rate. The sum of the information transmission rates of all sensors in each communication group is less than Q`. One communication area is divided for the sensors in the same communication group;

[0023] S5: Set a concentrator at the middle position of each communication area, and connect all the concentrators to the CPU module through signal lines to form a main communication network.

[0024] In the technical solution provided by this application, the information acquisition rate of the information receiving end is calculated, and the number of sensors that the concentrator can subordinate is judged according to the information acquisition rate, so as to determine the position and number of the concentrators. In this way, on the basis of minimizing the number of concentrators as much as possible, the sensors subordinate to the concentrators can send information to the concentrators in time, ensuring the timeliness of monitoring.

[0025] Further, S2 includes the following steps:

[0026] S21: Obtain the audio information of the low-voltage power distribution cabinet during normal operation. The length of the audio information is greater than 24h;

[0027] S22: Randomly extract several audio frames from the audio information, and splice the audio frames to obtain the original audio signal. The length of the original audio signal I(t) is less than 30 minutes;

[0028] S23: Convert the original audio signal I(t) into a frequency-domain signal by Fourier transform, and calculate the frequency energy E at each frequency. m , where m represents the index of the frequency;

[0029] S24: Use the frequency with the frequency energy E m less than the preset frequency threshold E0 as the idle audio segment M.

[0030] In the technical solution provided by this application, through the random extraction method, the sound information generated at each stage during the 24-hour operation of the low-voltage switch cabinet can be obtained. By extracting this sound information, the sound range of the low-voltage switch cabinet can be obtained, and then Fourier transform is used for frequency conversion to convert the time-domain information of the original audio signal into the frequency domain, so as to analyze the audio energy at different frequencies. Furthermore, the frequency with low audio energy can be used as the idle audio segment. In this way, when the sensor and the concentrator perform signal transmission in the idle audio segment, it can avoid reducing the influence of the noise generated during the operation of the low-voltage switch cabinet on the communication.

[0031] S3 includes the following steps:

[0032] S31: Obtain the idle audio segment M, set the minimum channel range u, and generate h channels, where h = M / u;

[0033] S32: Calculate the channel acquisition rate q for each channel k , where k represents the index of the channel;

[0034] S33: Calculate the information acquisition rate Q` according to the channel acquisition rate q k ;

[0035] .

[0036] In the technical solution provided by this application, for each channel, the channel acquisition rate of each channel is calculated separately, so that the theoretical information throughput capacity of the concentrator in the idle frequency band can be accurately calculated.

[0037] Furthermore, the calculation process of the channel acquisition rate q k is as follows:

[0038] S321: Obtain the lower limit f min and the upper limit f max of the working frequency band of the channel, extract L frequency points therefrom, and calculate the discrete frequency point set F;

[0039] F = [f1, f2, f i , …, f L , where i represents the index of the frequency point and L represents the total number of frequency points;

[0040] , where i = 1, 2, …, L;

[0041] ; is the frequency resolution;

[0042] S322: Calculate the channel response H;

[0043] ; represents the channel frequency response of the L-th frequency point;

[0044] ;

[0045] AA represents the total number of paths, represents the path delay, represents the path gain, represents the reference path transfer function, and π represents pi;

[0046] ;

[0047] where, l pDenotes the length of path p. Denotes the complex reflection coefficient, a0 denotes the absorption coefficient, and l0 denotes the reference length.

[0048] The path mainly includes two parts, one is the direct path and the other is the reflection path. The path length can be measured in advance. In practice, the size inside the low-voltage switchgear is very small compared to the speed of sound. Therefore, the direct path and the reflection path are respectively set to fixed values to reduce the computational amount. For example, the direct path is 0.5m and the reflection path is 1.2m.

[0049] S323: Calculate the channel gain set G;

[0050] ;

[0051] ;

[0052] Among them, Denotes the channel power gain, Denotes The complex conjugate of, and each element in G represents the channel power gain at the corresponding frequency point;

[0053] S324: Calculate the signal-to-noise ratio set g of the channel gain;

[0054] ;

[0055] , N0 denotes the noise power spectral density, and each element in g represents the signal-to-noise ratio at the corresponding frequency point. g i Denotes the signal-to-noise ratio at the i-th frequency point;

[0056] S325: Calculate the acquisition rate q k ;

[0057] ;

[0058] ;

[0059] ;

[0060] Among them, E s Denotes the transmit power, B denotes the bandwidth, Is the frequency resolution, Is the actual signal-to-noise ratio, Denotes the communication capacity at the i-th frequency point.

[0061] The core advantage of discretely calculating the channel capacity is that it transforms the complex continuous integration problem into an achievable discrete numerical calculation. By frequency sampling, the channel capacity formula is transformed into , not only directly compatible with the measured channel data, avoiding the function fitting error, but also efficiently implementing the optimal power allocation strategy such as

[0062] Further, S23 includes the following steps:

[0063] S231: Set the initial sampling rate f s , convert the original audio signal I(t) into a discrete signal x[n], where n is the index of the sampling point;

[0064] S232: Divide the discrete signal x[n] into ψ signal frames x ψ [n];

[0065] , where M is the overlapping length of the signal frames, N is the length of the signal frames, 0 < n < N, and ψ represents the index of the interval.

[0066] S233: Perform discrete Fourier transform on the signal frame x ψ [n] to obtain the spectrum X ψ [k];

[0067] ;

[0068] where k represents the frequency index and e represents the natural constant;

[0069] S234: Calculate the frequency energy E of each spectrum ψ ;

[0070] E ψ =|X ψ [k]| 2 =X ψ [k]×X ψ `[k], X ψ `[k] is the complex conjugate of X ψ [k].

[0071] In this application, by converting the audio signal from the time domain to the frequency domain and then calculating the power spectrum of each spectrum, the energy of each frequency can be determined according to the power spectrum, so as to screen out the frequency bands with lower energy. The frequency bands with lower energy have less interference information in practice and can avoid being affected by background noise when transmitting information.

[0072] When the audio signal propagates in the air, it will be interfered, resulting in the distortion of the audio signal, and the concentrator cannot accurately extract the corresponding audio signal.

[0073] Further, for the same communication network, the steps for allocating non-overlapping signal generation windows to each sensor include the following:

[0074] Z1: Obtain all sensors in the same communication network, and classify them into discrete information sensors and continuous information sensors according to the information transmitted by the sensors;

[0075] Among them, the discrete information sensors send status information at regular intervals, and the continuous information sensors continuously send monitoring information outward;

[0076] Z2: Obtain all channels of the same communication network, arrange the channels in ascending order according to the channel acquisition rate, and generate a channel list;

[0077] Z3: Based on the particle swarm optimization algorithm, allocate each sensor into a channel respectively, and only one sensor transmits signals in each time period among the sensors under each channel;

[0078] Among them, the communication window of the discrete information sensor is embedded into the channel window of the continuous information sensor.

[0079] In the technical solution provided by this application, there is only 1 sound source in each time window of each channel, so there will be no interference of audio signals in the same frequency band, which increases the stability of signal transmission. At the same time, the sensors are divided into discrete information sensors and continuous information sensors, and the communication window of the discrete information sensor is embedded into the channel window of the continuous information sensor, so that discrete information and continuous information can be alternately sent to the concentrator, ensuring the timeliness of information transmission.

[0080] As the second aspect of this application, this application provides a low-voltage power distribution cabinet monitoring system, which controls communication by using the foregoing communication control method. The low-voltage power distribution cabinet monitoring system includes:

[0081] A CPU module, which is signal-connected to the cloud server and is used to upload the monitoring information of the low-voltage power distribution cabinet;

[0082] Concentrators, multiple of them are respectively arranged in each communication area of the low-voltage power distribution cabinet;

[0083] Sensors, multiple of them are respectively located at each position of the low-voltage power distribution cabinet and are used to monitor various information;

[0084] Among them, each concentrator and the CPU module are connected by signal lines to form a main communication network;

[0085] Each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network;

[0086] Among them, the areas of the low-voltage power distribution cabinet responsible for each concentrator are separated by sound insulation structures; the sensors in the same branch communication network are assigned non-overlapping signal generation windows.

[0087] In the technical solution provided by this application, a combination of wired communication and acoustic wave communication is adopted. On the one hand, it can ensure that various monitored signals can be completely transmitted to the CPU module through the signal line, guaranteeing the accuracy of monitoring. On the other hand, ultrasonic signals will not be affected by the magnetic field generated inside the low-voltage switchgear during transmission, reducing signal distortion. Moreover, the information transmitted by ultrasonic signals is relayed and sent to the CPU module by the concentrator. Therefore, in each communication area, the total amount of information transmitted by ultrasonic signals is not high, ensuring the timeliness of information transmission.

[0088] Further,

[0089] The signal transmitting end of the sensor includes:

[0090] An encoder for encoding the monitored information;

[0091] A modulator for modulating the encoded monitored information into an analog signal;

[0092] A transmitting transducer for converting the analog signal into an ultrasonic signal and sending it to the signal receiving end of the concentrator;

[0093] The signal receiving end of the concentrator includes:

[0094] A receiving transducer for receiving the ultrasonic signal and converting the ultrasonic signal into an analog signal;

[0095] A demodulator for converting the analog signal into a digital signal;

[0096] A decoder for converting the digital signal into monitored information.

[0097] In the technical solution provided by this application, a pair of ultrasonic transducers are used for signal transmission, and the signal transmission is stable, ensuring good transmission effects in each communication area.

[0098] In the technical solution provided by this application:

[0099] The main communication network is reliable and stable: A main communication network is constructed by connecting the CPU module and the concentrator with a fixed position through a wired signal line, ensuring high-quality transmission of the main communication link and eliminating the need for subsequent line reconstruction.

[0100] The sensor communication has strong anti-interference ability: The sensor and the concentrator communicate using ultrasonic signals. Compared with wireless communication methods, ultrasonic communication is not affected by electromagnetic interference generated by strong electric currents inside the cabinet, resulting in better communication quality.

[0101] Effectively suppress ultrasonic interference: Use a sound insulation structure to separate the areas responsible for each concentrator, effectively preventing the large-area propagation and superposition of ultrasonic signals in the cabinet, and avoiding acoustic noise interference between areas. Sensors within the same area (i.e., within the same communication network) emit acoustic signals in non-overlapping signal generation windows, significantly reducing signal interference between sensors within the area. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The drawings forming a part of this application are used to provide a further understanding of this application, making other features, objectives, and advantages of this application more obvious. The schematic embodiments and descriptions of the drawings of this application are used to explain this application and do not constitute an improper limitation of this application.

[0103] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the elements and components are not necessarily drawn to scale.

[0104] In the drawings:

[0105] Figure 1 is a schematic structural diagram of a low-voltage power distribution cabinet monitoring system.

[0106] Figure 2 is a schematic structural diagram of the signal receiving end of the concentrator and the signal transmitting end of the sensor.

[0107] Figure 3 is a flowchart of the communication control method for the low-voltage power distribution cabinet monitoring system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0108] The embodiments of this application will be described in more detail below with reference to the drawings. Although some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand this application. It should be understood that the drawings and embodiments of this application are only for exemplary purposes and are not used to limit the protection scope of this application.

[0109] In addition, it should also be noted that for ease of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0110] This application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0111] Refer to Figure 1, Embodiment 1: The control system of the low-voltage power distribution cabinet monitoring system includes a CPU module, a concentrator, and sensors. Among them, the CPU module is signal-connected to the cloud server and is used to upload the monitoring information of the low-voltage power distribution cabinet. The CPU module is the control system of the low-voltage power distribution cabinet. Multiple sensors are provided and are respectively located at various positions of the low-voltage power distribution cabinet for monitoring various information. The quantity, type, and position of the sensors are not limited here, and the sensors are set according to the monitoring requirements of the low-voltage power distribution cabinet. For example, if the temperature inside the cabinet needs to be monitored, a temperature sensor is set inside the low-voltage power distribution cabinet; if the current of the distribution bus needs to be monitored, a current sensor is set on the distribution bus.

[0112] The low-voltage power distribution cabinet is divided into several communication areas according to the distribution of the sensors, and there is sound insulation cotton as a boundary between the communication areas. The specific layout method of the sound insulation cotton will not be elaborated here. The division of the communication areas is mainly based on the quantity of the sensors and the information volume uploaded by the sensors.

[0113] One concentrator is set at the center of each communication area. The concentrator is fixedly installed on the low-voltage power distribution cabinet. The concentrator and the CPU module are connected by a signal line. The concentrator can be signal-connected to the CPU module through the signal line, thereby forming a main communication network.

[0114] Each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network. The signal transmission carrier in the branch communication network is the acoustic wave signal. The transmission rate of the acoustic wave signal is low and the transmission capacity is small. Therefore, the quantity of sensors in the branch communication network is limited by the communication capacity. In practice, 4 sensors are allocated to each concentrator.

[0115] Reference Figure 2 , The signal transmitting end of the sensor includes: an encoder, a modulator, and a transmitting transducer. The encoder is used to encode the monitoring information; the modulator is used to modulate the encoded monitoring information into an analog signal; the transmitting transducer is used to convert the analog signal into an ultrasonic signal and send it to the signal receiving end of the concentrator.

[0116] The signal receiving end of the concentrator includes: a receiving transducer, a demodulator, and a decoder. The receiving transducer receives the ultrasonic signal and converts the ultrasonic signal into an analog signal; the demodulator converts the analog signal into a digital signal; the decoder converts the digital signal into monitoring information.

[0117] To avoid mutual interference of acoustic wave signals, each sensor in the same branch communication network is allocated non-overlapping signal generation windows.

[0118] For example, there are 2 sensors in a branch communication network. The first sensor emits ultrasonic signals only when the time is odd, and the second sensor emits ultrasonic signals only when the time is even. Thus, these 2 sensors will alternately emit ultrasonic signals, but the emitted ultrasonic signals will not be transmitted in the communication area simultaneously, avoiding crosstalk of acoustic signals.

[0119] Reference Figure 3 , Embodiment 2: A communication control method for a low-voltage power distribution cabinet monitoring system includes:

[0120] Step 1: Allocate several communication areas in the low-voltage power distribution cabinet in advance. Each communication area is configured with 1 concentrator, and each concentrator is connected to the CPU module through a signal line to form a main communication network;

[0121] Step 2: Allocate several sensors to each concentrator, and each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network;

[0122] Among them, the areas of the low-voltage power distribution cabinet responsible for each concentrator are separated by a sound insulation structure; the sensors in the same branch communication network are allocated non-overlapping signal generation windows.

[0123] Due to communication capacity limitations, the number of sensors under each concentrator cannot be too large. Therefore, the present application adopts the following scheme to construct the main communication network:

[0124] S1: Obtain all the sensors in the low-voltage power distribution cabinet and determine the information transmission rate Q of each sensor ih ; Q ih represents the information transmission rate of the ih-th sensor.

[0125] The content that each sensor needs to upload is fixed and will not change. For example, a temperature sensor always measures temperature data and then sends the temperature data to the CPU module, and will not suddenly convert the temperature data into humidity data during operation. Therefore, the information transmission Q of each sensor sending information to the concentrator i is a fixed value.

[0126] S2: Obtain the audio characteristics under the operating conditions of the low-voltage power distribution cabinet and extract the idle audio segments from the audio characteristics.

[0127] When the low-voltage power distribution cabinet is working, there will be current noise and noise of the cooling system. These noises generally belong to low-frequency noises, which are likely to affect the acoustic signals, interfere with the acoustic signals, and cause the acoustic signals to be distorted. Therefore, the audio characteristics under the operating conditions of the low-voltage power distribution cabinet can be obtained, and the idle audio segments can be extracted from the audio characteristics.

[0128] The idle audio segment refers to the frequency band with relatively low energy during the operation of the low-voltage power distribution cabinet. The noise sources inside the low-voltage power distribution cabinet are stable, and the audio energy distribution of the noise is also stable. By avoiding the audio segments where these noises are located, the remaining audio segments are channels with better communication capabilities.

[0129] S2 includes the following steps:

[0130] S21: Obtain the audio information of the low-voltage power distribution cabinet during normal operation, and the length of the audio information is greater than 24h.

[0131] The complete working cycle of the low-voltage power distribution cabinet is 24 hours. Obtaining 24-hour audio information can obtain a sufficient number of data samples.

[0132] S22: Randomly extract several audio frames from the audio information, splice the audio frames to obtain the original audio signal, and the length of the original audio signal I(t) is less than 30 minutes.

[0133] By means of random sampling, the length of the audio information can be reduced, and the amount of data processing can be decreased. In practice, 2 - 3 minutes of audio frames can be randomly sampled every hour to form the original audio signal I(t). Try to make the original audio signal I(t) contain the information of the entire working cycle.

[0134] S23: Convert the original audio signal I(t) into a frequency-domain signal by Fourier transform, and calculate the frequency energy E at each frequency m , where m represents the index of the frequency.

[0135] S23 includes the following steps:

[0136] S231: Set the initial sampling rate f s , and convert the original audio signal I(t) into a discrete signal x[n], where n is the index of the sampling point;

[0137] S232: Divide the discrete signal x[n] into ψ signal frames x ψ [n];

[0138] , where M is the overlapping length of the signal frames, N is the length of the signal frames, 0 < n < N, and ψ represents the index of the interval;

[0139] S233: Perform discrete Fourier transform on the signal frame x ψ [n] to obtain the spectrum X ψ [k];

[0140] ;

[0141] where k represents the frequency index and e represents the natural constant;

[0142] S234: Calculate the frequency energy E of each spectrum ψ ;

[0143] E ψ =|X ψ [k]| 2 =X ψ [k]×X ψ `[k], where X ψ `[k] is the complex conjugate of X ψ [k].

[0144] For the signal frame x ψ [n], the corresponding frequency is m, so E ψ =E m .

[0145] S24: Take the frequencies with frequency energy E m less than the preset frequency threshold E0 as the idle audio segment M.

[0146] S3: Use the idle audio segment as the communication frequency band between the sensor and the concentrator, and calculate the information acquisition rate Q` of the concentrator according to the communication frequency band.

[0147] The information acquisition rate Q` is the maximum possible one-way information transmission rate obtained after obtaining the idle audio segment. The specific calculation method is described in Embodiment 3.

[0148] S4: Divide each sensor into n communication groups according to the information transmission rate. The sum of the information transmission rates of all sensors in each communication group is less than Q`. Divide 1 communication area from the positions of the sensors in the same communication group;

[0149] S5: Set a concentrator at the middle position of each communication area, and connect all the concentrators to the CPU module through signal lines to form a main communication network.

[0150] When dividing the communication area, it is mainly divided according to the information transmission rate of the sensor and the position of the sensor. It is necessary to ensure that the positions of the sensors in the communication area are as concentrated as possible, and then the sum of the information transmission rates of the sensors is less than Q`. The specific division process will not be elaborated here. Given the information transmission rate Q i and the information acquisition rate Q` of each sensor, the communication area can be reasonably divided. After dividing the communication area, arrange a concentrator in the middle of the communication area to form a main communication network. The middle position is the center position of the communication area, which is the physical position center.

[0151] Embodiment 3: Provides a calculation method for the information acquisition rate Q`, including the following steps:

[0152] S3 includes the following steps:

[0153] S31: Obtain the idle audio segment M, set the minimum channel range u, and generate h channels, where h = M / u;

[0154] The channel range u is the lowest frequency difference that can be recognized. The smaller the channel range u, the greater the communication capacity, and the higher the requirement for the signal strength. In this solution, the channel range u is 10 kHz. Since the communication frequencies of each channel are different, the communication capacity of each channel needs to be calculated separately. When different channels are transmitting signals, the anti-interference ability is stronger, and different channels can transmit signals simultaneously.

[0155] S32: Calculate the channel acquisition rate q for each channel k , where k represents the index of the channel;

[0156] The calculation process of the channel acquisition rate q k is as follows:

[0157] S321: Obtain the lower limit fmin and upper limit fmax of the working frequency band of the channel, extract L frequency points therefrom, and calculate the discrete frequency point set F;

[0158] F = [f1, f2, f i , …, f L , where i represents the index of the frequency point and L represents the total number of frequency points;

[0159] , where i = 1, 2, …, L;

[0160] ; is the frequency resolution;

[0161] S322: Calculate the channel response H;

[0162] ; represents the channel frequency response of the Lth frequency point;

[0163] ;

[0164] AA represents the total number of paths, represents the path delay, represents the path gain, represents the reference path transfer function, and π represents the pi; in this application, the total number of paths AA is simplified to 3 according to the environment in the power distribution cabinet. The first path is the path where the sound wave reaches the concentrator with the least reflection, the second path is the path where the sound wave reaches the concentrator after a certain number of reflections, and the third path is the path where the number of sound wave reflections is between the first and the second. The number of paths needs to be actually measured after the communication system is constructed;

[0165] ;

[0166] where, l p represents the length of path p, represents the complex reflection coefficient, a0 represents the absorption coefficient, and l0 represents the reference length;

[0167] S323: Calculate the channel gain set G;

[0168] ;

[0169] ;

[0170] where, represents the channel power gain, represents 's complex conjugate, and each element in G represents the channel power gain at the corresponding frequency point;

[0171] S324: Calculate the signal-to-noise ratio set g of the channel gain;

[0172] ;

[0173] , N0 represents the noise power spectral density, each element in g represents the signal-to-noise ratio at the corresponding frequency point, and g i represents the signal-to-noise ratio at the i-th frequency point;

[0174] S325: Calculate the acquisition rate q k ;

[0175] ;

[0176] ;

[0177] ;

[0178] where, E s represents the transmit power, B represents the bandwidth, is the frequency resolution, is the actual signal-to-noise ratio, represents the communication capacity at the i-th frequency point.

[0179] S33: Calculate the information acquisition rate Q` according to the channel acquisition rate q k

[0180] .

[0181] The multipath refinement modeling method proposed in this application accurately depicts the multipath propagation effect in the process of calculating the channel acquisition rate in view of the path length difference and signal linear superposition characteristics caused by multipath reflection when sound waves propagate in a complex medium environment. Given the physical characteristic that the speed of sound is much lower than the speed of light, the time delay difference caused by different propagation paths significantly affects signal transmission. This modeling method effectively enhances the accuracy of signal capacity calculation through the mathematical description of the signal expansion characteristics in the time dimension. Specifically, it realizes the high-fidelity simulation of the key physical effects in the real propagation environment at the mathematical level, including delay spread (time domain dispersion) in the time domain and frequency selective fading (frequency domain fluctuation) in the frequency domain, ensuring the accurate description of the communication capacity of the system in a complex multipath environment.

[0182] Embodiment 4: Embodiment 4 provides a specific method for sensors to be assigned non-overlapping signal generation windows on the basis of Embodiment 2:

[0183] The steps for each sensor in the same communication network to be assigned non-overlapping signal generation windows are as follows:

[0184] Z1: Obtain all the sensors in the same communication network and divide them into discrete information sensors and continuous information sensors according to the information transmitted by the sensors;

[0185] Among them, the discrete information sensors send status information regularly, and the continuous information sensors continuously send monitoring information outward.

[0186] Generally speaking, the amount of information in the status information is small, only including a 4-bit identifier and a 2-bit status symbol. In addition to the identifier, the continuous signal occupies more bit positions for the numerical symbol.

[0187] For example, in a certain discrete information sensor, 01 means alarm and 10 means normal. In the 6-bit code 000101 it sends, the first two bits "00" represent its own number, the middle "01" represents the existence of an alarm, and the last "01" represents the end mark. This is the minimum coding of the status information.

[0188] The monitoring information is different. In addition to including a 4-bit identifier, the monitoring information also needs to include specific numerical information. After converting the decimal numbers into binary, a large number of coding positions are required.

[0189] Z2: Obtain all the channels of the same communication network, arrange the channels in ascending order according to the channel acquisition rate, and generate a channel list. ​

[0190] In Embodiment 3, the channel acquisition rate of each channel is calculated, and the channel acquisition rate can be directly arranged. Generally, the channel with a small channel acquisition rate transmits the channel transmission state information, and the channel with a large channel acquisition rate transmits the monitoring information.

[0191] Z3: Based on the particle swarm optimization algorithm, each sensor is respectively assigned to the channel, and only one sensor transmits the signal in each time period among the sensors under each channel.

[0192] Further, Z3 includes the following steps:

[0193] Z31: Construct the channel allocation model D;

[0194] D = {RE1, RE2,..., RE U}, U = h d ;

[0195] ;

[0196] ; ; ;

[0197] Rd represents the sensor allocation matrix, means that the first sensor is allocated to the first channel, means that the first sensor is allocated to the h-th channel; means that the d-th sensor is allocated to the first channel; means that the d-th sensor is allocated to the h-th channel, d represents the total number of sensors, h represents the total number of channels, and each element in D indicates a sensor and channel allocation method. RE1 represents the first sensor and channel allocation method, RE2 represents the second sensor and channel allocation method, RE U represents the U-th sensor and channel allocation method, and U represents the total number of elements in D.

[0198] Select one element from each row in Rd to form d elements as a feasible solution, and the feasible solution corresponds to one element in D. The arrangement method of the elements in D is to gradually select new elements from top to bottom in Rd. For example: RE1 to RE 2， Only the in the first row is replaced with . After all the elements in the first row are replaced, the second row will be further adjusted. After such arrangement, the channel allocation model D can be obtained.

[0199] Z32: Set the initial population size NT, the maximum number of iterations Tmax, and the objective function f(x);

[0200] ;

[0201] where x is the input of the objective function f(x), z is the index of the channel, and ST z is an intermediate parameter;

[0202] , ;

[0203] where q k represents the channel acquisition rate of the k-th channel, represents the information upload rate of the w-th sensor allocated in the k-th channel, dk represents the total number of sensors allocated in the k-th channel, and wk represents the index of the sensor allocated in the k-th channel, indicates that the total information upload rate of the sensors allocated in the channel needs to be less than the channel acquisition rate, indicates that the closer the total information upload rate of the sensors allocated in the channel is to the channel acquisition rate, the larger the function value; thus, during iteration, the total information upload rate of the sensors will be guided to be close to the channel acquisition rate as much as possible.

[0204] Z33: Randomly generate particles in the allocation model D with the same scale as the initial population. The particles diffuse based on the global optimal solution and the local optimal solution, and the calculation formula for the fitness value of each particle is the objective function;

[0205] The update method for each particle is as follows:

[0206] Z331: Calculate the objective function value of each particle during each iteration, and select the 3 particles with the highest fitness values as the target particle group;

[0207] Z332: Calculate the distance vector V between the remaining particles outside the target particle group and the 3 particles in the target particle group respectively;

[0208] ;

[0209] where b is a random number between 0 and 2, F represents the position of a randomly selected particle in the target particle group in the allocation model D, represents the position of the -th particle outside the target particle group in the allocation model D;

[0210] Z333: The particles outside the target particle group add the current position to the distance vector V to complete one iteration, and continue to perform multiple iterations until the maximum number of iterations is reached, or the optimal solution of the particle swarm meets the preset threshold.

[0211] In this application, during iteration, all particles will consider the positions of the particles at the three best positions, and will approach these 3 positions through the distance vector during the update process. Therefore, the convergence efficiency of the model can be increased. When the distance between the particles outside the target particle group and the particles inside the target ion group is relatively close, V is smaller, and the moving distance of the corresponding particle is small. When the distance between the particles outside the target particle group and the particles inside the target ion group is relatively far, V is larger, and the moving distance of the corresponding particle is large. In this way, the moving speed of the particles can be flexibly controlled, and the convergence speed can be increased.

[0212] Z34: For the sensors in each channel, set a time period T`, and assign a time window T to each sensor wk ;

[0213] , where T wk represents the size of the time window assigned to the w-th sensor in the k-th channel, represents the information upload rate of the w-th sensor assigned in the k-th channel, and q k represents the channel acquisition rate of the k-th channel.

[0214] In each channel, the size of the time window T wk assigned to each sensor is related to its own information upload rate. If the information upload rate is large, the corresponding time window is large.

[0215] Z35: During each time period T, arrange the time windows of the discrete information sensors in front of the continuous information sensors to form a time window sequence. The time window of each sensor is its corresponding signal generation window.

[0216] In this way, each sensor only sends ultrasonic signals within the signal generation window. Furthermore, during each time period T, it is always the discrete information sensors that send ultrasonic information first, and then the continuous information sensors send ultrasonic information. In this way, it alternates, ensuring that the signals do not interfere with each other, and each sensor can transmit signals in a timely manner.

[0217] The above description is only some preferred embodiments of this application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the embodiments of this application.

Claims

1. A communication control method for a low-voltage power distribution cabinet monitoring system, characterized in that It includes the following steps: Pre - allocate several communication areas in the low - voltage power distribution cabinet. Each communication area is equipped with 1 concentrator, and each concentrator is connected to the CPU module through a signal line to form a main communication network; Allocate several sensors to each concentrator. Each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network: Among them, the areas of the low - voltage power distribution cabinet responsible for each concentrator are separated by sound - insulation structures; Each sensor within the same branch communication network is assigned non - overlapping signal generation windows; Construct the main communication network by the following steps; S1: Obtain all sensors within the low-voltage power distribution cabinet and determine the information transmission rate Q of each sensor ih ; Q ih represents the information transmission rate of the ih-th sensor; S2: Obtain the audio features under the operating condition of the low - voltage power distribution cabinet, and extract the idle audio segments from the audio features; S3: Use the idle audio segments as the communication frequency bands between the sensors and the concentrators, and calculate the information acquisition rate Q` of the concentrators according to the communication frequency bands; S4: Divide all sensors into n communication groups according to the information transmission rate. The sum of the information transmission rates of all sensors in each communication group is less than Q`. One communication area is divided for the sensors in the same communication group; S5: Set a concentrator at the middle position of each communication area, and connect all the concentrators to the CPU module through signal lines to form a main communication network.

2. The communication control method of the low-voltage power distribution cabinet monitoring system according to claim 1, wherein: S2 includes the following steps: S21: Obtain the audio information of the low - voltage power distribution cabinet during normal operation, and the length of the audio information is greater than 24h; S22: Randomly extract several audio frames from the audio information, splice the audio frames to obtain the original audio signal, and the length of the original audio signal I(t) is less than 30 minutes; S23: Convert the original audio signal I(t) into a frequency-domain signal by Fourier transform, and calculate the frequency energy E at each frequency. Here, \(E_m = \sum_{n=0}^{N-1} |I(n)e^{-j\frac{2\pi}{N}mn}|^2\), where m represents the index of the frequency; m , m represents the index of the frequency; Note: The formula \(E_m = \sum_{n=0}^{N-1} |I(n)e^{-j\frac{2\pi}{N}mn}|^2\) is added to the translation of to make the expression more complete. If this is not allowed, please adjust according to the actual requirements. S24: Take the frequency energy E m with a frequency less than the preset frequency threshold E0 as the idle audio segment M.

3. The communication control method of the low-voltage power distribution cabinet monitoring system according to claim 2, characterized in that: S3 includes the following steps: S31: Obtain the idle audio segment M, and set the minimum channel range u to generate h channels; h = M / u; S32: Calculate the channel acquisition rate q for each channel k , where k represents the index of the channel S33: Calculate the information acquisition rate Q` according to the channel acquisition rate q k ​ 。 4. The communication control method of the low - voltage power distribution cabinet monitoring system according to claim 3, wherein: Channel acquisition rate q k The calculation process is as follows: S321: Obtain the lower limit f of the operating frequency band of the channel min and the upper limit f of the operating frequency band max , extract L frequency points therefrom, and calculate the discrete frequency point set F; F = [f1, f2, f i , …, f L , where i represents the index of the frequency point and L represents the total number of frequency points; , where i = 1, 2, …, L; ; is the frequency resolution; S322: Calculate the channel response H; ; represents the channel frequency response at the L-th frequency point; ； AA represents the total number of paths, represents the path delay, represents the path gain, represents the reference path transfer function, and π represents pi; ； where l p represents the length of path p, represents the complex reflection coefficient, a0 represents the absorption coefficient, and l0 represents the reference length; S323: Calculate the channel gain set G; ； ； Among them, represents the channel power gain, represents the complex conjugate of, and each element in G represents the channel power gain at the corresponding frequency point; S324: Calculate the signal - to - noise ratio set g of the channel gain; ； , where \(N_0\) represents the noise power spectral density, and each element in \(g\) represents the signal-to-noise ratio at the corresponding frequency point, and \(g\) i represents the signal-to-noise ratio at the \(i\)-th frequency point; S325: Calculate the acquisition rate q k ; ； ； ; Among them, E s represents the transmission power, B represents the bandwidth, is the frequency resolution, is the actual signal-to-noise ratio, represents the communication capacity at the i-th frequency point.

5. The communication control method of the low - voltage power distribution cabinet monitoring system according to claim 3, wherein: S23 includes the following steps: S231: Set the initial sampling rate f s , and convert the original audio signal I(t) into a discrete signal x[n], where n is the index of the sampling point; S232: Divide the discrete signal x[n] into ψ signal frames x ψ [n]; , where M is the overlap length of the signal frame, N is the length of the signal frame, 0 < n < N, and ψ represents the index of the interval; S233: Perform discrete Fourier transform on the signal frame x ψ [n] to obtain the spectrum X ψ [k] of each signal frame; ； Among them, k represents the frequency index, and e represents the natural constant; S234: Calculate the frequency energy E of each spectrum ψ ; E ψ = |X ψ [k]| 2 = X ψ [k] × X ψ `[k], X ψ `[k] is X ψ [k]'s complex conjugate.

6. The communication control method of the low-voltage power distribution cabinet monitoring system according to claim 5, characterized in that: The steps for each sensor within the same branch communication network to be assigned non - overlapping signal generation windows include: Z1: Obtain all the sensors in the same branch communication network, and divide them into discrete - information sensors and continuous - information sensors according to the information transmitted by the sensors; Among them, the discrete - information sensors send status information regularly, and the continuous - information sensors continuously send monitoring information; Z2: Obtain all the channels in the same branch communication network, arrange the channels in ascending order of the channel acquisition rate to generate a channel list; Z3: Based on the particle swarm algorithm, allocate each sensor to the channels respectively, and only one sensor transmits signals in each time period among the sensors under each channel; Among them, the communication window of the discrete - information sensors is embedded in the channel window of the continuous - information sensors.

7. A low-voltage power distribution cabinet monitoring system, characterized in that Control the communication by using the communication control method described in any one of claims 1 - 6. The low - voltage power distribution cabinet monitoring system includes: The CPU module is signal-connected to the cloud server and is used to upload the monitoring information of the low-voltage power distribution cabinet; There are multiple concentrators, which are respectively set in each communication area of the low-voltage power distribution cabinet; There are multiple sensors, which are respectively located at various positions of the low-voltage power distribution cabinet and are used to monitor various information; Among them, each concentrator and the CPU module are connected by signal lines to form a main communication network; Each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network; Among them, the areas of the low-voltage power distribution cabinet responsible for each concentrator are separated by a sound insulation structure; the sensors in the same branch communication network are assigned non-overlapping signal generation windows.

8. The low-voltage power distribution cabinet monitoring system according to claim 7, characterized in that, The signal transmitting end of the sensor includes: An encoder, which is used to encode the monitoring information; A modulator, which is used to modulate the encoded monitoring information into an analog signal; A transmitting transducer, which is used to convert the analog signal into an ultrasonic signal and send it to the signal receiving end of the concentrator; The signal receiving end of the concentrator includes: A receiving transducer, which receives the ultrasonic signal and converts the ultrasonic signal into an analog signal; A demodulator, which converts the analog signal into a digital signal; A decoder, which converts the digital signal into monitoring information.

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