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

Abstract

The present application discloses a low-voltage power distribution cabinet monitoring system and a communication control method thereof, belonging to the field of communication technology. A communication control method for a low-voltage power distribution cabinet monitoring system includes the following steps: pre-allocating a number of communication areas in the low-voltage power distribution cabinet, configuring a concentrator in each communication area, and connecting each concentrator to a CPU module via a signal line to form a main communication network; allocating a number of sensors to each concentrator, and each sensor communicating with its corresponding concentrator via ultrasonic signals to form a branch communication network; wherein the low-voltage power distribution cabinet area responsible for each concentrator is separated by a sound insulation structure; and each sensor in the same branch communication network is allocated a non-overlapping signal generation window. In the technical solution provided by the present application: a wired signal line is used to connect the CPU module and the fixed-position concentrator to construct the main communication network, ensuring high-quality transmission of the backbone communication link.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a low-voltage distribution cabinet monitoring system and a communication control method thereof. Background Art

[0002] Low-voltage distribution cabinets are critical power supply devices that distribute power to low-voltage equipment within a factory. In practice, they are often deployed in a modular configuration. To ensure the safe and stable operation of the power distribution system, the cabinets must be equipped with a variety of detection modules to monitor electrical parameters and environmental conditions, such as voltage, current, insulation resistance, and temperature, in real time, in key areas. This monitoring data is centrally collected and aggregated by the main controller (CPU module) within the distribution cabinet before being uploaded to the higher-level server system.

[0003] Because low-voltage distribution cabinets are densely packed with components and have diverse functions, a large number of specialized sensors must be deployed in various locations. To collect sensor data, traditional solutions rely on laying numerous low-frequency analog signal cables within the cabinet and connecting them to the main controller. This wired communication method has significant drawbacks:

[0004] High implementation cost: The routing and connection of a large number of signal lines, as well as the strict isolation design from high-voltage lines required 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 changes to the original wiring structure, making system upgrades and maintenance difficult and increasing costs.

[0006] To overcome the shortcomings of wired solutions, some technical solutions attempt to integrate low-frequency wireless communication modules on the sensor side to transmit data wirelessly to the main controller. However, the numerous high-voltage circuits (such as 220V and 380V AC power lines) within low-voltage switchgear generate strong electromagnetic fields that can severely interfere with short-range low-frequency wireless signals, preventing the main controller from reliably and accurately receiving sensor information.

[0007] In summary, the current sensor data communication network in low-voltage distribution cabinets faces two core challenges: the high cost of building communication links; and the weak ability of communication methods (especially wireless solutions) to resist strong electromagnetic interference. Summary of the Invention

[0008] The content of this application is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this application is not intended to identify key features or essential features of the technical solution for which protection is sought, nor is it intended to limit the scope of the technical solution for which protection is sought.

[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 the 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 sound insulation 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 signals for 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 suppressing ultrasonic interference: Soundproofing separates the areas served by each concentrator, effectively preventing ultrasonic signals from propagating and overlapping across a large area within the cabinet, thus avoiding inter-area acoustic noise interference. Sensors within the same area (i.e., within the same communication network) emit acoustic signals within non-overlapping signal generation windows, significantly reducing signal interference between sensors within the same area.

[0018] To address the issues of information collection delays caused by an excessive number of sensors under the control of a concentrator, and increased communication costs caused by an excessive number of concentrators, in some embodiments of the present application, the following steps are used to construct a primary 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 audio features of the low-voltage distribution cabinet under operating conditions and extract idle audio segments from the audio features;

[0021] S3: Use the idle audio band as the communication frequency band between the sensor and the concentrator, and calculate the information collection rate Q' of the concentrator based on 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'. The locations of sensors in the same communication group are divided into a communication area.

[0023] S5: A concentrator is set in the middle of each communication area, and all concentrators are connected to the CPU module through signal lines to form a main communication network.

[0024] In the technical solution provided in this application, the information collection rate of the information receiving end is calculated, and the number of sensors that the concentrator can control is determined based on the information collection rate, thereby determining the location and number of concentrators. In this way, the sensors under the concentrator can send information to the concentrator in a timely manner while minimizing the number of concentrators, thereby ensuring the timeliness of monitoring.

[0025] Furthermore, S2 includes the following steps:

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

[0027] S22: randomly extracting a number of audio frames from the audio information, and splicing the audio frames to obtain an original audio signal, where 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 using Fourier transform, and calculate the frequency energy E at each frequency m , m represents the frequency index;

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

[0030] In the technical solution provided in the present application, the sound information generated by the low-voltage switchgear at each stage during the 24-hour operation can be obtained by random extraction. By extracting these sound information, the sound range of the low-voltage switchgear can be obtained, and then the frequency conversion is performed using Fourier transform to convert the time domain information of the original audio signal into the frequency domain, so that the audio energy at different frequencies can be analyzed, and the frequency with low audio energy can be used as an idle audio segment. In this way, the sensor and the concentrator transmit signals in the idle audio segment, which can avoid reducing the impact of the noise generated during the operation of the low-voltage switchgear on the communication.

[0031] S3 includes the following steps:

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

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

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

[0035] .

[0036] In the technical solution provided in the present application, the channel acquisition rate of each channel is calculated separately for each channel, thereby being able to accurately calculate the theoretical information throughput capacity of the concentrator in an idle frequency band.

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

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

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

[0040] , where i=1, 2, …, L;

[0041] ; is the frequency resolution;

[0042] S322: Calculate channel response H;

[0043] ; represents the channel frequency response of the Lth 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, π represents pi;

[0046] ;

[0047] Among them, l prepresents the length of path p, represents the complex reflection coefficient, a0 represents the absorption coefficient, and l0 represents the reference length.

[0048] The paths mainly include two: a direct path and a reflected path. The path length can be measured in advance. In practice, the dimensions inside the low-voltage switchgear are very small compared to the speed of sound, so the direct path and the reflected path are set to fixed values ​​to reduce the amount of calculation. For example, the direct path is 0.5m and the reflected path is 1.2m.

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

[0050] ;

[0051] ;

[0052] in, represents the channel power gain, express The complex conjugate of , 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 represents the noise power spectrum density, each element in g represents the signal-to-noise ratio at the corresponding frequency point, g i represents the signal-to-noise ratio of the i-th frequency point;

[0056] S325: Calculate acquisition rate q k ;

[0057] ;

[0058] ;

[0059] ;

[0060] 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 of the i-th frequency point.

[0061] The core advantage of discretization in calculating channel capacity is that it transforms the complex continuous integral problem into a achievable discrete numerical calculation, and transforms the channel capacity formula into It is not only directly compatible with the measured channel data and avoids function fitting errors, but also can efficiently implement the optimal power allocation strategy.

[0062] Furthermore, 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 frame, N is the length of the signal frame, 0<n<N, and ψ represents the index of the interval.

[0066] S233: Signal frame x ψ [n] Perform discrete Fourier transform to obtain the spectrum X of each signal frame 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 X ψ The complex conjugate of [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 frequency spectrum, the energy of each frequency can be determined based on the power spectrum, thereby screening out frequency bands with lower energy. Frequency bands with lower energy have less interference information in practice and can avoid being affected by background noise when transmitting information.

[0072] When audio signals propagate in the air, they are interfered with, which causes audio signal distortion and the concentrator cannot accurately extract the corresponding audio signals.

[0073] Furthermore, in the same communication network, each sensor is assigned a non-overlapping signal generation window including the following steps:

[0074] Z1: Obtain all 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;

[0075] Among them, discrete information sensors send status information periodically, and continuous information sensors continuously send monitoring information to the outside world;

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

[0077] Z3: Based on the particle swarm algorithm, each sensor is assigned to a channel, and only one sensor in each channel transmits a signal in each time period.

[0078] The communication window of the discrete information sensor is embedded in the channel window of the continuous information sensor.

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

[0080] As a second aspect of the present application, the present application provides a low-voltage distribution cabinet monitoring system, which uses the aforementioned communication control method to control communication. The low-voltage distribution cabinet monitoring system includes:

[0081] The CPU module is connected to the cloud server signal to upload the monitoring information of the low-voltage distribution cabinet;

[0082] There are multiple concentrators, each of which is installed in each communication area of ​​the low-voltage distribution cabinet;

[0083] There are multiple sensors located at various positions of the low-voltage distribution cabinet for monitoring various information;

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

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

[0086] The low-voltage distribution cabinet area that each concentrator is responsible for is separated by a soundproof structure; each sensor within the same communication network is assigned a non-overlapping signal generation window.

[0087] The technical solution provided by this application utilizes a combination of wired and acoustic communication. This ensures that all monitored signals are fully transmitted to the CPU module via the signal line, thus ensuring monitoring accuracy. Furthermore, ultrasonic signals are not affected by the magnetic field generated within the low-voltage switchgear during transmission, reducing signal distortion. Furthermore, the information transmitted by the ultrasonic signal is relayed to the CPU module by the concentrator. Therefore, within each communication area, the overall amount of information transmitted by the ultrasonic signal is low, ensuring timely information delivery.

[0088] Further,

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

[0090] An encoder, used for encoding monitoring information;

[0091] a modulator, used for modulating the encoded monitoring information into an analog signal;

[0092] A transmitting transducer is used to convert the analog signal into an ultrasonic signal and send it to the signal receiving end of the concentrator;

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

[0094] A receiving transducer receives ultrasonic signals and converts the ultrasonic signals into analog signals;

[0095] Demodulator, which converts analog signals into digital signals;

[0096] Decoder converts digital signals into monitoring information.

[0097] In the technical solution provided in the present application, a pair of ultrasonic transducers are used for signal transmission, and the signal transmission is smooth, which can ensure good transmission effect in each communication area.

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

[0099] 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.

[0100] Sensor communication has strong anti-interference ability: The sensor and concentrator use ultrasonic signals for 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.

[0101] Effectively suppressing ultrasonic interference: Soundproofing separates the areas served by each concentrator, effectively preventing ultrasonic signals from propagating and overlapping across a large area within the cabinet, thus avoiding inter-area acoustic noise interference. Sensors within the same area (i.e., within the same communication network) emit acoustic signals within non-overlapping signal generation windows, significantly reducing signal interference between sensors within the same area. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The drawings constituting a part of this application are used to provide a further understanding of this application and make other features, purposes and advantages of this application more apparent. The drawings and descriptions of the exemplary embodiments of this application are used to explain this application and do not constitute an improper limitation on 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 that the elements and components are not necessarily drawn to scale.

[0104] In the attached figure:

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

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

[0107] Figure 3 The flowchart of the communication control method of the low-voltage distribution cabinet monitoring system. DETAILED DESCRIPTION

[0108] The following describes embodiments of the present application in more detail with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of protection of the present application.

[0109] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

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

[0111] Reference Figure 1, Example 1: The control system of the low-voltage distribution cabinet monitoring system includes a CPU module, a concentrator and a sensor. Among them, the CPU module is connected to the cloud server signal for uploading the monitoring information of the low-voltage distribution cabinet. The CPU module is the control system of the low-voltage distribution cabinet. There are multiple sensors, which are located at various positions of the low-voltage distribution cabinet for monitoring various information. The number, type and position of the sensors are not limited here. The sensors are set according to the monitoring requirements of the low-voltage distribution cabinet. For example, if the temperature inside the cabinet needs to be monitored, a temperature sensor is set in the low-voltage 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 distribution cabinet is divided into several communication zones based on the distribution of sensors. Sound insulation serves as a boundary between these zones. The specific placement of the insulation is not detailed here. The division of communication zones is primarily based on the number of sensors and the amount of information they upload.

[0113] A concentrator is set up in the center of each communication area. The concentrator is fixedly installed on the low-voltage distribution cabinet. The concentrator and the CPU module are connected by a signal line. The concentrator can realize signal connection with the CPU module through the signal line, thereby forming the main communication network.

[0114] Each sensor communicates with its corresponding concentrator via ultrasonic signals, forming a branch communication network. The signal transmission carrier in a branch communication network is acoustic signals, which have a low transmission rate and small transmission capacity. Therefore, the number of sensors in a branch communication network is limited by the communication capacity. In practice, each concentrator is assigned four sensors.

[0115] refer to Figure 2 The sensor's signal transmitter includes an encoder, a modulator, and a transmitting transducer. The encoder encodes the monitoring information; the modulator modulates the encoded monitoring information into an analog signal; and the transmitting transducer converts the analog signal into an ultrasonic signal and sends it to the concentrator's signal receiver.

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

[0117] In order to avoid mutual interference of acoustic signals, each sensor in the same communication network is assigned a non-overlapping signal generation window.

[0118] For example, there are two sensors in a communication network. The first sensor only transmits ultrasonic signals when the time is odd, and the second sensor only transmits ultrasonic signals when the time is even. In this way, the two sensors will emit ultrasonic signals alternately, but the emitted ultrasonic signals will not be transmitted in the communication area at the same time, avoiding crosstalk between the acoustic signals.

[0119] refer to Figure 3 , Example 2: A communication control method for a low-voltage distribution cabinet monitoring system includes:

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

[0121] Step 2: Allocate several sensors to each concentrator. Each sensor communicates with its corresponding concentrator via ultrasonic signals to form a branch communication network.

[0122] The low-voltage distribution cabinet area that each concentrator is responsible for is separated by a soundproof structure; each sensor within the same communication network is assigned a non-overlapping signal generation window.

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

[0124] 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.

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

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

[0127] When low-voltage distribution cabinets are operating, they produce current noise and cooling system noise. These noises are generally low-frequency and can easily interfere with and distort acoustic signals. To address this problem, we can obtain audio features of the low-voltage distribution cabinet during operation and extract idle audio segments from these features.

[0128] The idle audio band is the frequency band with low energy during the operation of the low-voltage distribution cabinet. The noise source inside the low-voltage distribution cabinet is stable, and the audio energy distribution of the noise is also stable. Avoid the audio bands where these noises occur. The remaining audio bands are channels with good communication capabilities.

[0129] S2 includes the following steps:

[0130] S21: Acquire audio information of the low-voltage distribution cabinet during normal operation. The length of the audio information is greater than 24 hours.

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

[0132] S22: randomly extracting a number of audio frames from the audio information, and splicing the audio frames to obtain an original audio signal, where the length of the original audio signal I(t) is less than 30 minutes.

[0133] Random sampling can reduce the length of audio information and the amount of data processing. In practice, 2-3 minutes of audio frames can be randomly sampled every hour to form the original raw audio signal I(t). Try to ensure that the original raw audio signal I(t) contains information from the entire working cycle.

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

[0135] S23 includes the following steps:

[0136] 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;

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

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

[0139] S233: Signal frame x ψ [n] Perform discrete Fourier transform to obtain the spectrum X of each signal frame 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],X ψ `[k] is X ψ The complex conjugate of [k].

[0144] Signal frame x ψ The frequency corresponding to [n] is m, so E ψ =E m .

[0145] S24: The frequency energy E m The frequency less than the preset frequency threshold E0 is regarded as the idle audio segment M.

[0146] S3: Use the idle audio band as the communication frequency band between the sensor and the concentrator, and calculate the information collection rate Q' of the concentrator based on the communication frequency band.

[0147] The information acquisition rate Q' is the maximum possible unilateral information transmission rate obtained after obtaining the idle audio segment. The specific calculation method is described in Example 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'. The locations of sensors in the same communication group are divided into a communication area.

[0149] S5: A concentrator is set in the middle of each communication area, and all concentrators are connected to the CPU module through signal lines to form a main communication network.

[0150] When dividing the communication area, the division is mainly based on the information transmission rate of the sensor and the location of the sensor. It is necessary to ensure that the sensors in the communication area are as concentrated as possible, and the sum of the information transmission rates of the sensors is less than Q'. The specific division process is not repeated here. When the information transmission rate Q of each sensor is known, i With the information collection rate Q', the communication area can be reasonably divided. After the communication area is divided, a concentrator is placed in the middle of the communication area to form the main communication network. The middle position is the center of the communication area and the physical location center.

[0151] Example 3: A method for calculating an information acquisition rate Q' is provided, comprising the following steps:

[0152] S3 includes the following steps:

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

[0154] Channel range u is the minimum frequency difference that can be identified. The smaller the channel range u, the greater the communication capacity and the higher the signal strength requirement. In this solution, the channel range u is 10kHz. The communication frequency of each channel is different, so the communication capacity of each channel must be calculated separately. In addition, 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 , k represents the index of the channel;

[0156] Channel acquisition rate q k The calculation process is as follows:

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

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

[0159] , where i=1, 2, …, L;

[0160] ; is the frequency resolution;

[0161] S322: Calculate 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 pi. In this application, the total number of paths AA is simplified to three based on the environment within the power distribution cabinet. The first path is the path where the sound wave reaches the concentrator after the least reflection, the second path is the path where the sound wave reaches the concentrator after the most reflections, and the third path is the path where the number of sound wave reflections is between the first and second paths. The number of paths needs to be measured after the communication system is established.

[0165] ;

[0166] Among them, 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] in, represents the channel power gain, express The complex conjugate of , 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 spectrum density, each element in g represents the signal-to-noise ratio at the corresponding frequency point, g i represents the signal-to-noise ratio of the i-th frequency point;

[0174] S325: Calculate acquisition rate q k ;

[0175] ;

[0176] ;

[0177] ;

[0178] 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 of the i-th frequency point.

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

[0180] .

[0181] The multipath refined modeling method proposed in this application accurately characterizes the multipath propagation effect during the channel acquisition rate calculation process, targeting the path length differences and signal linear superposition characteristics caused by multipath reflections when sound waves propagate in a complex medium environment. Given the physical property that the speed of sound is much lower than the speed of light, the delay differences caused by different propagation paths significantly affect signal transmission. This modeling method effectively enhances the accuracy of signal capacity calculation by mathematically describing the signal expansion characteristics in the time dimension. Specifically, it achieves high-fidelity simulation of key physical effects in real propagation environments at the mathematical level, including delay expansion (time domain dispersion) in the time domain and frequency selective fading (frequency domain fluctuation) in the frequency domain, ensuring that the system accurately describes the communication capacity in a complex multipath environment.

[0182] Example 4: Example 4 provides a specific method for allocating non-overlapping signal generation windows to sensors based on Example 2:

[0183] The steps of allocating non-overlapping signal generation windows to sensors in the same communication network include:

[0184] Z1: Obtain all 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, discrete information sensors send status information periodically, and continuous information sensors continuously send monitoring information to the outside.

[0186] Generally speaking, status information contains less information, only a 4-bit identifier and a 2-bit status symbol. Continuous signals, in addition to the identifier, also have more bits occupied by the numerical symbol.

[0187] For example, a discrete information sensor with 01 indicating an alarm and 10 indicating normal status would send a 6-bit code of 000101. The first two bits (00) represent the sensor's ID, the middle bit (01) indicates an alarm, and the final bit (01) marks the end of the signal. This is the minimum encoding of status information.

[0188] Monitoring information is different. In addition to including a 4-bit identifier, monitoring information also needs to include specific numerical information. After the decimal number is converted into binary, it needs to occupy a large number of coding positions.

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

[0190] In Example 3, the channel acquisition rate of each channel is calculated, and the channel acquisition rates can be directly arranged. Generally speaking, channels with low channel acquisition rates transmit status information, and channels with high channel acquisition rates transmit monitoring information.

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

[0192] Furthermore, Z3 includes the following steps:

[0193] Z31: construct channel allocation model D;

[0194] D={RE1, RE2, …, RE U},U=h d ;

[0195] ;

[0196] ; ; ;

[0197] Rd represents the sensor allocation matrix, Indicates that the first sensor is assigned to the first channel. Indicates that the first sensor is assigned to the hth channel; Indicates that the dth sensor is assigned to the 1st channel; Indicates that the dth sensor is assigned to the hth 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, and RE U represents the U-th sensor and channel allocation method, and U represents the total number of elements in D.

[0198] Each row in Rd selects an element to form d elements as a feasible solution, and the feasible solution corresponds to an element in D. The arrangement of elements in D is to gradually select new elements from Rd from top to bottom. For example: RE1 to RE 2， Only the first line Replace with , when the first row is completely replaced, the second row will be further adjusted, and the channel allocation model D can be obtained after the arrangement.

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

[0200] ;

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

[0202] , ;

[0203] Among them, q k represents the channel acquisition rate of the kth channel, represents the information upload rate of the wth sensor assigned to the kth channel, dk represents the total number of sensors assigned to the kth channel, wk represents the index of the sensor assigned to the kth channel, Indicates that the total information upload rate of the sensors allocated in the channel needs to be less than the channel acquisition rate. This means that the closer the total information upload rate of the sensors assigned to the channel is to the channel acquisition rate, the larger the function value will be. In this way, during iteration, the total information upload rate of the sensors will be guided as close to the channel acquisition rate as possible.

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

[0205] Each particle is updated as follows:

[0206] Z331: Calculate the objective function value of each particle in each iteration and select the three 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 three 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 random particle in the target particle group in the distribution model D, Indicates the first particle outside the target particle group The position of each particle in the distribution model D;

[0210] Z333: Particles outside the target particle group add their current positions to the distance vector V to complete one iteration. This iteration is continued until the maximum number of iterations is reached or the optimal solution of the particle swarm meets the preset threshold.

[0211] In this application, when iterating, all particles will consider the position of the particles in the three best positions, and will move closer to the three positions through the distance vector during the updating process. Therefore, the convergence efficiency of the model can be increased. When the particles outside the target particle group are closer to the particles in the target ion group, V is smaller, and the movement distance of the corresponding particles is small. When the particles outside the target particle group are farther away from the particles in the target ion group, V is larger, and the movement distance of the corresponding particles is large. In this way, the movement rate of the particles can be flexibly controlled to increase the convergence rate.

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

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

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

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

[0216] In this way, each sensor sends ultrasonic signals only within the signal generation window. Furthermore, within each time period T, the discrete information sensor sends ultrasonic information first, and then the continuous information sensor sends ultrasonic information. This alternation ensures that the signals do not interfere with each other and each sensor can transmit signals in a timely manner.

[0217] The above descriptions are merely some preferred embodiments of the present application and illustrate the technical principles employed. Those skilled in the art should understand that the scope of the invention described in the embodiments of the present application is not limited to technical solutions formed by specific combinations of the aforementioned technical features, but also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present application.

Claims

1. A communication control method for a low-voltage distribution cabinet monitoring system, characterized in that: The steps include: 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 the main communication network. Each concentrator is assigned a number of sensors, and each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network: The low-voltage distribution cabinet area that each concentrator is responsible for is separated by a sound insulation structure; Each sensor in the same communication network is assigned a non-overlapping signal generation window; The following steps are used to build the main communication network: 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; S2: Acquire audio features of the low-voltage distribution cabinet under operating conditions and extract idle audio segments from the audio features; S3: Use the idle audio band as the communication frequency band between the sensor and the concentrator, and calculate the information collection rate Q' of the concentrator based on the communication frequency band; 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'. The locations of sensors in the same communication group are divided into a communication area. S5: A concentrator is set in the middle of each communication area, and all concentrators are connected to the CPU module through signal lines to form the main communication network; S3 includes the following steps: S31: Obtain an idle audio segment M and set a minimum channel range u to generate h channels; h = M / u; S32: Calculate the channel acquisition rate q for each channel k , k represents the index of the channel; S33: According to the channel acquisition rate q k Calculate the information acquisition rate Q`; Channel acquisition rate q k The calculation process is as follows: S321: Obtain the lower limit of the operating frequency band of the channel f min and the upper limit of the working frequency band f max , extract L frequency points from it and calculate the discrete frequency point set F; F=[f1, f2, f i ,…,f L ], i represents the index of the frequency point, L represents the total number of frequency points; Where, i = 1, 2, ..., L; Δf is the frequency resolution; S322: Calculate channel response H; H=[H(f1),H(f2),…,H(f L )];H(f L ) represents the channel frequency response of the Lth frequency point; AA represents the total number of paths, τ p represents the path delay, represents the path gain, represents the reference path transfer function, π represents pi; Among them, l p represents the length of path p, Γ p represents the complex reflection coefficient, a0 represents the absorption coefficient, and l0 represents the reference length; S323: Calculate the channel gain set G; |H(f i )| 2 =H(f i )·H`(f i ); G=[|H(f1)| 2 ,|H(f2)| 2 ,…,|H(f L )| 2 ]; Among them, |H(f i )| 2 represents the channel power gain, H`(f i ) represents H(f i ), 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; g=[g1,g2,…,g L ]; N0 represents the noise power spectrum density, and each element in g represents the signal-to-noise ratio at the corresponding frequency point. i represents the signal-to-noise ratio of the i-th frequency point; S325: Calculate acquisition rate q k ; Among them, E s represents the transmission power, B represents the bandwidth, Δf represents the frequency resolution, is the actual signal-to-noise ratio, represents the communication capacity of the i-th frequency point.

2. The communication control method of the low-voltage distribution cabinet monitoring system according to claim 1, characterized in that: S2 includes the following steps: S21: Acquire audio information of the low-voltage distribution cabinet during normal operation. The length of the audio information is greater than 24 hours. S22: randomly extracting a number of audio frames from the audio information, and splicing the audio frames to obtain an original audio signal, where 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 using Fourier transform, and calculate the frequency energy E at each frequency m , m represents the frequency index; S24: The frequency energy E m The frequency less than the preset frequency threshold E0 is regarded as the idle audio segment M.

3. The communication control method of the low-voltage distribution cabinet monitoring system according to claim 2, characterized in that: S23 includes the following steps: 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; S232: Divide the discrete signal x[n] into ψ signal frames x ψ [n]; x m [n] = x[n+ψ*M], 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: Signal frame x ψ [n] Perform discrete Fourier transform to obtain the spectrum X of each signal frame ψ [k]; Where 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 The complex conjugate of [k].

4. The communication control method of the low-voltage distribution cabinet monitoring system according to claim 3, characterized in that: The steps of allocating non-overlapping signal generation windows to sensors in the same communication network include: Z1: Obtain all 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; Among them, discrete information sensors send status information periodically, and continuous information sensors continuously send monitoring information to the outside world; Z2: Obtain all channels of the same communication network, sort the channels in ascending order of channel acquisition rate, and generate a channel list; Z3: Based on the particle swarm algorithm, each sensor is assigned to a channel, and only one sensor in each channel transmits a signal in each time period. The communication window of the discrete information sensor is embedded in the channel window of the continuous information sensor.

5. A low voltage distribution cabinet monitoring system, characterized in that: The communication control method according to any one of claims 1 to 4 is used to control communication, and the low-voltage distribution cabinet monitoring system includes: The CPU module is connected to the cloud server signal to upload the monitoring information of the low-voltage distribution cabinet; There are multiple concentrators, each of which is installed in each communication area of ​​the low-voltage distribution cabinet; There are multiple sensors located at various positions of the low-voltage distribution cabinet for monitoring various information; Among them, each concentrator and CPU module are connected by signal lines to form the main communication network; Each sensor communicates with its corresponding concentrator through ultrasonic signals to form a branch communication network. The low-voltage distribution cabinet area responsible for each concentrator is separated by a sound insulation structure. Each sensor in the same branch communication network is assigned a non-overlapping signal generation window.

6. The low-voltage distribution cabinet monitoring system according to claim 5, characterized in that: The signal transmitting end of the sensor includes: An encoder, used for encoding monitoring information; a modulator, used for modulating the encoded monitoring information into an analog signal; A transmitting transducer 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 receives ultrasonic signals and converts the ultrasonic signals into analog signals; Demodulator, which converts analog signals into digital signals; Decoder converts digital signals into monitoring information.

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