Honeycomb type noise power generation system and method

Abstract

The invention relates to the technical field of noise power generation, in particular to a honeycomb type noise power generation system and method, and the system comprises a honeycomb power generation device which comprises a plurality of honeycomb power generation units, is used for carrying out the noise reduction of the noise of a target noise region, and generates an alternating current based on the noise sound wave vibration after the noise reduction; and the intelligent control system generates a control instruction of the honeycomb power generation device according to the initial noise intensity by using a mapping model of noise distribution and generated power, so that the honeycomb power generation device generates power based on the control instruction, and performs fault diagnosis according to the power generation data of each honeycomb power generation unit. Therefore, the problems that in the prior art, a scheme for generating power through noise vibration is simple in structure, low in efficiency, lack of intelligent management and optimization and incapable of adapting to industrial scenes such as thermal power plants with complex noise sources, variable frequency spectrums and wide space are solved, and the energy utilization efficiency and the intelligent management level of the thermal power plants are improved.

Description

Technical Field

[0001] This application relates to the field of noise power generation technology, and in particular to a cellular noise power generation system and method. Background Technology

[0002] Thermal power plants are major energy consumers and environmental polluters, and are also typical high-noise industrial environments. The boilers, steam turbines, coal mills, fans, pumps, and high-pressure steam pipelines within the plant generate continuous, high-noise levels of 90-120 decibels during operation. Currently, the main methods for dealing with this noise are passive, such as installing sound insulation cotton and soundproofing panels. While these methods can reduce noise transmission to some extent, they have significant drawbacks: Energy waste: Sound insulation materials reduce noise by damping vibrations and absorbing sound waves. In essence, they convert ordered sound energy into disordered heat energy and dissipate it into the environment, resulting in a waste of energy.

[0003] Material consumption and secondary pollution: Traditional sound insulation cotton is mostly porous organic material, which is prone to aging, may be flammable, and will cause environmental problems after disposal.

[0004] Single function: It only has the function of noise reduction and cannot generate any economic benefits. It is a purely investment-based environmental protection project.

[0005] On the other hand, the Internet of Things and smart sensor technologies are increasingly used in the industrial field, but their power supply usually relies on wiring or batteries, which has problems such as high cost and inconvenient maintenance.

[0006] In existing technologies, there are some schemes that use the piezoelectric effect to generate electricity through vibration, but most of them are simple in structure, inefficient, and lack intelligent management and optimization, making them unsuitable for industrial scenarios such as thermal power plants with complex noise sources, variable spectrum, and large space. Summary of the Invention

[0007] This application provides a cellular noise power generation system and method to solve the problems of existing schemes that use piezoelectric effect for vibration power generation, which are mostly simple in structure, inefficient, and lack intelligent management and optimization, and cannot adapt to the complex noise sources, variable spectrum, and large space of industrial scenarios such as thermal power plants.

[0008] The first aspect of this application provides a cellular noise power generation system, comprising the following steps: a cellular power generation device for receiving initial noise from a target noise area and reducing the initial noise, generating an alternating current based on the vibration of the reduced noise sound waves; the cellular power generation device includes a cellular housing and a piezoelectric power generation layer, the cellular housing covering the piezoelectric power generation layer to form multiple cellular power generation units; an energy management device electrically connected to each cellular power generation unit for rectifying, stabilizing, and storing the electrical energy generated by each cellular power generation unit; a sensor device for collecting the initial noise intensity and the power generation data of each cellular power generation unit; and an intelligent control system communicatively connected to the sensor device and the energy management device, for extracting the noise intensity distribution characteristics of the initial noise, and generating control commands for the cellular power generation device based on a pre-built mapping model of noise distribution and power generation, so that the cellular power generation unit generates power based on the control commands, and performs fault diagnosis based on the power generation data of each cellular power generation unit.

[0009] Optionally, the energy management device includes: a rectifier circuit, a maximum power point tracking circuit, and an energy storage unit, wherein the rectifier circuit is used to convert the generated alternating current into direct current; the energy storage unit is used to store the electrical energy output by the cellular power generation device; and the maximum power point tracking circuit is used to control the corresponding cellular power generation unit to output power according to the control command of the cellular power generation device output by the intelligent control system.

[0010] Optionally, the intelligent control system includes: a feature extraction module for identifying the noise intensity distribution characteristics of the initial noise; and an optimization control module for inputting the noise intensity distribution characteristics of the initial noise into a pre-built mapping model of noise distribution and power generation to obtain the target power generation of the cellular power generation device 100, and adjusting the power generation of the cellular power generation device 100 based on the target power generation.

[0011] Optionally, the intelligent control system further includes: a diagnostic module for comparing the current power generation of each cellular power generation unit with the power deviation of the target power generation efficiency; and an early warning module for issuing an abnormal warning when the power deviation of any cellular power generation unit exceeds a preset threshold.

[0012] Optionally, the early warning module includes: an acoustic alarm unit connected to the diagnostic module, which provides an acoustic alarm when the power deviation of any cellular power generation unit exceeds the preset threshold; and / or an optical alarm unit connected to the diagnostic module, which provides an optical alarm when the power deviation of any cellular power generation unit exceeds the preset threshold.

[0013] Optionally, the intelligent control system further includes an energy dispatching module, used to dispatch the electrical energy stored in the energy management device to supply power to the preset electrical equipment according to the preset target electrical energy demand of the electrical equipment.

[0014] Optionally, the structure of the piezoelectric power generation layer, from the outside to the inside, consists of a metal electrode layer, a piezoelectric ceramic layer, and a substrate electrode layer.

[0015] Optionally, the honeycomb cover adopts a hexagonal honeycomb structure.

[0016] A second aspect of this application provides a cellular noise power generation method, employing a cellular noise power generation system as described in any one of claims 1-8. The method includes the following steps: receiving initial noise from a target noise region and reducing the initial noise; generating an alternating current based on the vibration of the noise wave after noise reduction; extracting the noise intensity distribution characteristics of the initial noise; generating control commands for a cellular power generation device based on a pre-constructed mapping model of noise distribution and power generation according to the noise intensity distribution characteristics of the initial noise; enabling the cellular power generation device to generate power based on the control commands; and performing fault diagnosis based on the power generation data of each cellular power generation unit.

[0017] Optionally, before generating control commands for the cellular power generation device using a pre-built mapping model of noise distribution and power generation based on the initial noise intensity, the process includes: collecting noise intensity data and output voltage and current data at different times in the area corresponding to the cellular power generation device; removing invalid samples from the noise intensity data and output voltage and current data at different times in the area corresponding to the cellular power generation device to obtain corresponding training noise intensity distribution feature data and training voltage and current data at different times; and calculating the actual power generation of each cellular power generation unit based on the training voltage and current data at different times to obtain training power generation data at different times. Based on the noise intensity distribution characteristics and power generation data at different times, the optimal parameter combination datasets for different times are calibrated. A target neural network is constructed, and the optimal parameter combination datasets at different times are divided into training, validation, and test sets according to a certain ratio. The target neural network is trained using the training set, and the hyperparameters of the target neural network are adjusted using the validation set to obtain the trained neural network. The generalization ability of the trained neural network is evaluated using the test set, and when the generalization ability of the trained neural network meets the preset application conditions, the pre-constructed mapping model between noise distribution and power generation is obtained.

[0018] In the above embodiments, a cellular power generation device receives the initial noise from the target noise area and reduces it. Based on the vibration of the reduced noise sound waves, an alternating current is generated. The intelligent control system generates control commands for the cellular power generation device based on the noise intensity distribution characteristics of the initial noise using a pre-built mapping model between noise distribution and power generation. This enables the cellular power generation device to generate electricity based on the control commands, and fault diagnosis is performed based on the power generation data of each cellular power generation unit. This solves the problems of existing piezoelectric vibration power generation schemes, which are mostly simple in structure, inefficient, and lack intelligent management and optimization. They are unable to adapt to the complex noise sources, variable frequency spectrum, and vast space of industrial scenarios like thermal power plants. This improves the energy utilization efficiency and intelligent management level of thermal power plants, achieving proactive environmental noise reduction that "turns waste into treasure."

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a cellular noise power generation system according to an embodiment of this application; Figure 2 This is a schematic diagram of the specific structure of a cellular power generation device according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the structure and working principle of a cellular power generation device according to an embodiment of this application; Figure 4 This is an exploded view of the cellular power generation device and piezoelectric power generation layer according to an embodiment of this application; Figure 5 This is an exploded view of the overall structure of a cellular noise power generation system according to an embodiment of this application; Figure 6 This is a schematic flowchart of a power generation method for a cellular noise power generation system according to an embodiment of this application; Figure 7 This is a flowchart of a cellular noise power generation method according to a specific embodiment of this application; Figure 8 This is a schematic flowchart of a cellular noise power generation method according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] To address the shortcomings of existing piezoelectric vibration power generation solutions mentioned in the background section, which are mostly simple in structure, inefficient, and lack intelligent management and optimization, making them unsuitable for industrial environments like thermal power plants with complex noise sources, variable frequency spectra, and vast spaces, this application provides a cellular noise power generation system. In this system, a cellular power generation device receives initial noise from a target noise area and reduces it. Based on the vibration of the reduced noise sound waves, an alternating current is generated. An intelligent control system generates control commands for the cellular power generation device using a pre-built mapping model of noise distribution and power generation, based on the noise intensity distribution characteristics of the initial noise. This enables the cellular power generation device to generate electricity based on the control commands, and fault diagnosis is performed based on the power generation data of each cellular power generation unit. This solves the problems of existing piezoelectric vibration power generation solutions, which are mostly simple in structure, inefficient, lack intelligent management and optimization, and are unsuitable for industrial environments like thermal power plants with complex noise sources, variable frequency spectra, and vast spaces. It improves the energy utilization efficiency and intelligent management level of thermal power plants, achieving proactive environmental noise reduction that "turns waste into treasure."

[0023] Specifically, Figure 1 This is a schematic diagram of a cellular noise power generation system provided in an embodiment of this application.

[0024] like Figure 1 As shown, the cellular noise power generation system 10 includes: A cellular power generation device 100 is used to receive initial noise from a target noise area and reduce the initial noise. Based on the vibration of the reduced noise sound waves, an alternating current is generated. The cellular power generation device 100 includes a cellular housing and a piezoelectric power generation layer. The cellular housing covers the piezoelectric power generation layer to form multiple cellular power generation units. An energy management device 200 is electrically connected to each cellular power generation unit and is used to rectify, regulate, and store the electrical energy generated by each cellular power generation unit. A sensor device 300 is used to collect the initial noise intensity and the power generation data of each cellular power generation unit. An intelligent control system 400 is communicatively connected to the sensor device 300 and the energy management device 200, respectively. It is used to extract the noise intensity distribution characteristics of the initial noise and, based on the noise intensity distribution characteristics of the initial noise, use a pre-built mapping model between noise distribution and power generation to generate control commands for the cellular power generation device, enabling the cellular power generation device to generate power based on the control commands, and to perform fault diagnosis based on the power generation data of each cellular power generation unit.

[0025] In some embodiments, the structure of the piezoelectric power generation layer, from the outside to the inside, consists of a metal electrode layer, a piezoelectric ceramic layer, and a substrate electrode layer.

[0026] In some embodiments, the honeycomb cover adopts a hexagonal honeycomb structure.

[0027] Cellular power generation device 100 Figure 2 As shown, the honeycomb enclosure adopts a hexagonal honeycomb structure. Piezoelectric ceramic sheets or piezoelectric polymer films are bonded, coated, or embedded on the inner wall or core support structure of each individual honeycomb cell, forming a piezoelectric power generation layer. A schematic diagram of the structure and working principle of the honeycomb power generation device is shown below. Figure 3 As shown, this forms multiple cellular power generation units, such as Figure 4 As shown, the structure of the piezoelectric power generation layer consists of a metal electrode layer, a piezoelectric ceramic layer, and a substrate electrode layer from the outside to the inside. The cellular power generation device 100 receives the initial noise of the target noise area and reduces the initial noise. Based on the vibration of the noise sound wave after noise reduction, an alternating current is generated.

[0028] The sensor device 300 is set up near key noise sources in the factory area, deploying multiple noise sensors to form a network, and monitors the initial noise intensity at different locations in real time, as well as the power generation data of each cellular power generation unit.

[0029] The intelligent control system 400 receives the initial noise intensity and power generation data of each cellular power generation unit from the sensor device 300, uses a pre-built mapping model of noise distribution and power generation to predict the target power generation of the cellular power generation device 100 under different noise conditions, and generates control commands for the cellular power generation device 100, which are then sent to the energy management device 200. The energy management device 200 adjusts the power generation of the cellular power generation device 100 according to the control commands of the cellular power generation device.

[0030] Optionally, in some embodiments, the energy management device 200 includes: a rectifier circuit, a maximum power point tracking circuit, and an energy storage unit, wherein the rectifier circuit is used to convert the alternating current generated by the cellular power generation device 100 into direct current; the energy storage unit is used to store the electrical energy output by the cellular power generation device 100; and the maximum power point tracking circuit is used to control the corresponding cellular power generation unit to output power according to the control instructions of the cellular power generation device 100 output by the intelligent control system 400.

[0031] The energy management device 200 rectifies the alternating current generated by the piezoelectric material and stores the electrical energy in an energy storage unit, such as a supercapacitor or a small lithium battery, through a high-efficiency DC-DC conversion circuit with maximum power point tracking.

[0032] Optionally, in some embodiments, the intelligent control system 400 includes: a feature extraction module for identifying the noise intensity distribution characteristics of the initial noise; and an optimization control module for inputting the noise intensity distribution characteristics of the initial noise into a pre-built mapping model of noise distribution and power generation to obtain the target power generation of the cellular power generation device 100, and adjusting the power generation of the cellular power generation device 100 based on the target power generation.

[0033] Specifically, the feature extraction module identifies the noise intensity distribution characteristics of the initial noise, inputs the noise intensity distribution characteristics of the initial noise into a pre-built mapping model of noise distribution and power generation, thereby obtaining the target power generation of the cellular power generation device 100 under the initial noise, and sends the target power generation of the cellular power generation device 100 to the energy management device 200, which controls the power generation of the cellular power generation device 100 according to the target power generation.

[0034] Furthermore, this application can also adjust the number of activated cellular power generation units according to the target power generation of the cellular power generation device 100.

[0035] Optionally, in some embodiments, the intelligent control system 400 further includes: a diagnostic module for comparing the current power generation of each cellular power generation unit with the power deviation of the target power generation efficiency; and an early warning module for issuing an abnormal warning when the power deviation of any cellular power generation unit exceeds a preset threshold.

[0036] The preset threshold can be a threshold set by the user, a threshold obtained through a limited number of experiments, or a threshold obtained through a limited number of computer simulations; no specific limitation is made here.

[0037] The diagnostic module of the intelligent control system 400 detects that the power deviation between the current power generation of any cellular power generation unit and the target power generation efficiency is greater than a preset threshold, indicating that the power generation of the cellular power generation unit is abnormal.

[0038] Optionally, in some embodiments, the warning module includes: an acoustic alarm unit connected to the diagnostic module, which provides an acoustic alarm when the power deviation of any cellular power generation unit exceeds a preset threshold; and / or an optical alarm unit connected to the diagnostic module, which provides an optical alarm when the power deviation of any cellular power generation unit exceeds a preset threshold.

[0039] Understandably, the early warning module of the intelligent control system 400 is connected to the diagnostic module. If the power deviation of any cellular power generation unit exceeds a preset threshold, the early warning module will issue an acoustic alarm and / or an optical alarm to alert that any cellular power generation unit is abnormal.

[0040] Optionally, in some embodiments, the intelligent control system 400 further includes an energy dispatching device for dispatching the energy stored in the energy management device 200 to supply power to the preset electrical equipment according to the preset target demand for electrical energy of the electrical equipment.

[0041] The energy dispatching device dispatches the energy stored in the energy management device 200 to power the preset electrical equipment based on the target power demand of the preset electrical equipment, such as IoT sensors, monitoring equipment, indicator lights, etc. It can also prioritize powering the most important monitoring equipment and intelligently charge the energy storage device when there is surplus power.

[0042] Specifically, the cellular power generation device 100 uses a biomimetic honeycomb structure as its basic unit. The honeycomb structure possesses extremely high specific strength and excellent mechanical stability, effectively converting the pressure of sound waves into deformation of the structural walls. High-performance piezoelectric power generation materials (such as PZT piezoelectric ceramics or PVDF piezoelectric films) are bonded or embedded into the inner wall of each hexagonal honeycomb using a special process. When noise sound waves act on the enclosure, they cause violent vibrations in the honeycomb walls, thereby driving the piezoelectric material to generate alternating current. This design transforms the traditional "absorption" of sound energy into "collection" of sound energy.

[0043] Specifically, such as Figure 5 and Figure 6 As shown, the cellular power generation device 100 is installed in the boiler feed pump, coal mill, fan, steam turbine, high-pressure steam pipeline and valve area of ​​a thermal power plant to form a sound barrier or soundproof cover.

[0044] Each cellular power generation unit is connected to an energy management device 200, which is responsible for rectifying the alternating current generated by the piezoelectric material and storing the electrical energy in a supercapacitor or small lithium battery through a high-efficiency DC-DC conversion circuit with maximum power point tracking. This is the key to achieving efficient energy harvesting.

[0045] Multiple noise sensors are deployed near key noise sources in the factory area to form a network, which monitors the sound pressure level and spectral characteristics of the initial noise intensity at different locations in real time, as well as the power generation data of each cellular power generation unit.

[0046] The intelligent control system 400 receives the initial noise intensity and power generation data of each cellular power generation unit from the sensor device 300. Using a pre-built mapping model of noise distribution and power generation, it predicts the target power generation of the cellular power generation device 100 under different noise conditions. For example, when the coal mill starts and generates specific low-frequency noise, the system will automatically adjust the circuit load of the cellular power generation unit in the corresponding area so that it always works near the maximum power output point.

[0047] The intelligent control system 400 continuously learns the normal power output range of each cellular power generation unit. Once the power generation efficiency of a cellular power generation unit drops abnormally, the system will immediately identify and locate the faulty cellular power generation unit, issue an early warning to the maintenance personnel, and achieve predictive maintenance.

[0048] The energy dispatching device dispatches the energy stored in the energy management device 200 to power preset electrical equipment, such as IoT sensors, monitoring equipment, and indicator lights, based on the target power demand of the electrical equipment. It can also prioritize powering the most important monitoring equipment and intelligently charge the energy storage device when there is surplus power.

[0049] To enable those skilled in the art to further understand the power generation method of the cellular noise power generation system according to the embodiments of this application, the following detailed description is provided in conjunction with specific embodiments, such as... Figure 7 As shown.

[0050] After the system starts up, it collects two types of data simultaneously: noise intensity and power generation performance data of the cellular power generation unit.

[0051] Noise intensity distribution characteristics are obtained by feature extraction. The target power generation of the cellular power generation unit is calculated and predicted by the mapping model between noise distribution and power generation. Control commands are generated based on the target power generation and sent to the energy management device. The energy management device adjusts the power generation of the cellular power generation unit to achieve dynamic optimization of power generation efficiency.

[0052] The power generation efficiency of each cellular power generation unit in the cellular power generation device is evaluated based on the target power generation. If the power generation of a certain cellular power generation unit is determined to be abnormal, the faulty cellular power generation unit is located and a maintenance alarm is sent, while the data acquisition cycle is triggered.

[0053] The coal mill room in a thermal power plant will be used as an example for explanation.

[0054] A soundproof wall composed of the honeycomb power generation panels described in this invention is constructed around the coal mill to form a honeycomb power generation device.

[0055] Install 3-5 noise sensors in different locations within the computer room to transmit data back to the intelligent control system in real time.

[0056] The intelligent control system determines the working status of the coal mill (such as startup, full load, shutdown, etc.) based on the received noise data. During the startup phase, the noise spectrum is dominated by low frequencies, and the system adjusts the circuit parameters of the corresponding power generation units on the soundproof wall to make them more sensitive to low-frequency vibrations.

[0057] The generated electrical energy is stored in a supercapacitor in the energy management unit, which powers the vibration and temperature sensors installed on the coal mill. This sensor data is then transmitted wirelessly to the intelligent control system.

[0058] One day, the intelligent control system detected a 30% drop in power generation in a certain area of ​​the sound barrier. The system diagnostic module determined that several cell units in that area might have experienced piezoelectric element detachment due to long-term vibration, and immediately sent an alarm to the maintenance personnel, pinpointing the panel that needed repair.

[0059] Through this system, the area not only achieved noise level compliance, but also achieved energy self-sufficiency for a set of key monitoring equipment and reduced maintenance costs.

[0060] In summary, the beneficial effects of this invention are as follows: (1) While achieving effective noise reduction, the waste noise energy is converted into useful electrical energy, thus achieving the unity of environmental protection and energy conservation.

[0061] (2) The honeycomb structure not only provides a huge surface area for installing piezoelectric materials, but its excellent mechanical properties also make it more robust and durable than traditional sound insulation panels.

[0062] (3) Intelligent and efficient: The introduction of artificial intelligence technology enables the system to adapt to complex and ever-changing working environments and always maintain a high-efficiency power generation state, which is something that traditional passive devices cannot achieve.

[0063] (4) Empowering the Internet of Things: The generated electricity can directly power various sensors deployed in the factory area, solving the "last mile" problem of power supply for Internet of Things devices and promoting the intelligent upgrading of thermal power plants.

[0064] (5) Low-cost operation and maintenance: The fault diagnosis function greatly reduces the cost of manual inspection and realizes the self-monitoring and maintenance of the system.

[0065] The cellular noise power generation system proposed in this application receives initial noise from a target noise area using a cellular power generation device and reduces the initial noise. Based on the vibration of the reduced noise sound waves, an alternating current is generated. An intelligent control system generates control commands for the cellular power generation device using a pre-built mapping model of noise distribution and power generation, based on the noise intensity distribution characteristics of the initial noise. This enables the cellular power generation device to generate electricity based on the control commands, and fault diagnosis is performed based on the power generation data of each cellular power generation unit. This solves the problems of existing piezoelectric vibration power generation schemes, which are mostly simple in structure, inefficient, and lack intelligent management and optimization. They are unable to adapt to the complex noise sources, variable frequency spectra, and vast spaces of industrial environments like thermal power plants. This system improves the energy utilization efficiency and intelligent management level of thermal power plants, achieving proactive environmental noise reduction that "turns waste into treasure."

[0066] Next, the cellular noise power generation method according to the embodiments of this application is described with reference to the accompanying drawings.

[0067] Figure 8 This is a schematic diagram of a cellular noise power generation method according to an embodiment of this application.

[0068] like Figure 8 As shown, this cellular noise power generation method uses the aforementioned cellular noise power generation system, and the method includes the following steps: In step S801, the initial noise of the target noise region is received and the initial noise is denoised. An alternating current is generated based on the vibration of the denoised noise sound wave.

[0069] In step S802, the noise intensity distribution characteristics of the initial noise are extracted, and control commands for the cellular power generation device are generated based on the noise intensity distribution characteristics of the initial noise using a pre-built mapping model of noise distribution and power generation. This enables the cellular power generation device to generate electricity based on the control commands, and fault diagnosis is performed based on the power generation data of each cellular power generation unit.

[0070] Specifically, the sensor device collects the original noise signal of the target area. These signals include environmental background noise, interference signals, etc. The cellular power generation device receives the original noise signal, performs noise reduction, and converts the noise-reduced sound wave vibration into alternating current through piezoelectric effect or electromagnetic induction device, completing the first step of conversion from sound energy to electrical energy.

[0071] The intelligent control system extracts the initial noise intensity distribution characteristics based on the intensity of the raw noise signal collected by the sensor devices. These characteristics are then input into a pre-built mapping model between noise distribution and power generation to predict the optimal power generation of the cellular power generation unit. Based on the predicted optimal power, voltage and current regulation commands are generated and sent to the corresponding cellular power generation unit. Real-time data collection of actual power generation (voltage, current, and power) from each cellular power generation unit is performed, compared with the predicted optimal power, and abnormal cellular power generation units are identified and alarms are triggered.

[0072] Optionally, in some embodiments, before generating control commands for the cellular power generation device using a pre-built mapping model of noise distribution and power generation based on the initial noise intensity, the process includes: collecting noise intensity data and output voltage and current data at different times in the area corresponding to the cellular power generation device; removing invalid samples from the noise intensity data and output voltage and current data at different times in the area corresponding to the cellular power generation device to obtain corresponding training noise intensity distribution feature data and training voltage and current data at different times, calculating the actual power generation of each cellular power generation unit based on the training voltage and current data at different times to obtain training power generation data at different times, and calibrating the optimal parameter combination dataset at different times based on the training noise intensity distribution feature data and training power generation data at different times; constructing a target neural network, dividing the optimal parameter combination dataset at different times into a training set, a validation set, and a test set proportionally, training the target neural network using the training set, and adjusting the hyperparameters of the target neural network using the validation set to obtain the trained neural network; evaluating the generalization ability of the trained neural network using the test set, and obtaining the pre-built mapping model of noise distribution and power generation when the generalization ability of the trained neural network meets the preset application conditions.

[0073] The process of constructing the mapping model is crucial for achieving intelligent control, and it consists of the following five stages: 1. Collect the noise intensity distribution characteristics at the location of the cellular power generation device, and the real-time output voltage and current. The collection frequency is set according to the application scenario.

[0074] 2. Remove abnormal values ​​caused by sensor malfunctions (such as voltage / current being 0 or negative), filter noise peaks caused by sudden environmental changes (such as sudden impact sounds), and calculate the actual power generation of each unit.

[0075] 3. Input layer: Noise intensity distribution characteristics. Output layer: Outputs the target power output of the cellular power generation device, and uses mean square error (MSE) to measure the difference between the target power output and the actual power output.

[0076] 4. Divide the preprocessed data into training, validation, and test sets in a 7:1:2 ratio. During training, use the training set to optimize network weights and minimize the loss function. Use the validation set to monitor model overfitting and adjust hyperparameters such as the learning rate and regularization coefficient. Stop training when the validation set loss no longer decreases for 5 consecutive epochs to avoid overfitting.

[0077] 5. Calculate the root mean square error (RMSE) of the model using the test set. The default application condition is RMSE < 5%.

[0078] Finally, the trained model is deployed to the controller to achieve real-time inference.

[0079] According to the cellular noise power generation method proposed in this application, a cellular power generation device receives the initial noise from a target noise area and reduces the initial noise. Based on the vibration of the reduced noise sound waves, an alternating current is generated. The intelligent control system generates control commands for the cellular power generation device based on the noise intensity distribution characteristics of the initial noise and a pre-built mapping model between noise distribution and power generation. This enables the cellular power generation device to generate electricity based on the control commands, and fault diagnosis is performed based on the power generation data of each cellular power generation unit. This solves the problems of existing piezoelectric vibration power generation schemes, which are mostly simple in structure, inefficient, and lack intelligent management and optimization. They are unable to adapt to the complex noise sources, variable frequency spectrum, and vast space of industrial scenarios such as thermal power plants. This method improves the energy utilization efficiency and intelligent management level of thermal power plants, achieving proactive environmental noise reduction that "turns waste into treasure."

[0080] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0082] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be specifically implemented in any computer program product for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer program product" can be any means that can contain, store, communicate, propagate, or transmit a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection having one or N wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM). Furthermore, the computer program product can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0084] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer program product, and when executed, it includes one or a combination of the steps of the method embodiments.

[0086] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer program product.

[0087] The computer program product mentioned above may be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A cellular noise power generation system, characterized in that, include: A cellular power generation device is used to receive initial noise from a target noise area and reduce the initial noise. An alternating current is generated based on the vibration of the noise sound wave after the noise reduction. The cellular power generation device includes a cellular cover and a piezoelectric power generation layer. The cellular cover covers the piezoelectric power generation layer to form multiple cellular power generation units. An energy management device, electrically connected to each cellular power generation unit, is used to rectify, regulate, and store the electrical energy generated by each cellular power generation unit. A sensor device is used to collect initial noise intensity and power generation data of each cellular power generation unit; The intelligent control system is communicatively connected to the sensor device and the energy management device, respectively. It is used to extract the noise intensity distribution characteristics of the initial noise, and generate control commands for the cellular power generation device based on the noise intensity distribution characteristics of the initial noise using a pre-built mapping model of noise distribution and power generation. This enables the cellular power generation device to generate electricity based on the control commands, and performs fault diagnosis based on the power generation data of each cellular power generation unit.

2. The cellular noise power generation system according to claim 1, characterized in that, The energy management device includes: The circuit consists of a rectifier circuit, a maximum power point tracking circuit, and an energy storage unit. The rectifier circuit is used to convert the generated alternating current into direct current. The energy storage unit is used to store the electrical energy output by the cellular power generation device; The maximum power point tracking circuit is used to control the corresponding cellular power generation unit to output power according to the control command of the cellular power generation device output by the intelligent control system.

3. The cellular noise generation system according to claim 2, characterized in that, The intelligent control system includes: The feature extraction module is used to identify the noise intensity distribution characteristics of the initial noise; An optimization control module is used to input the noise intensity distribution characteristics of the initial noise into a pre-built mapping model of noise distribution and power generation to obtain the target power generation of the cellular power generation device, and adjust the power generation of the cellular power generation device based on the target power generation.

4. The cellular noise power generation system according to claim 3, characterized in that, The intelligent control system also includes: A diagnostic module is used to compare the current power generation of each cellular power generation unit with the power deviation of the target power generation efficiency; The early warning module is used to issue an abnormal warning when the power deviation of any cellular power generation unit exceeds a preset threshold.

5. The cellular noise power generation system according to claim 4, characterized in that, The early warning module includes: An acoustic alarm unit is connected to the diagnostic module. When the power deviation of any cellular power generation unit exceeds the preset threshold, the acoustic alarm unit will issue an acoustic alarm reminder. And / or, an optical alarm unit, which is connected to the diagnostic module, provides an optical alarm reminder when the power deviation of any cellular power generation unit exceeds the preset threshold.

6. The cellular noise power generation system according to claim 5, characterized in that, The intelligent control system also includes: The energy dispatch module is used to dispatch the electrical energy stored in the energy management device to supply power to the preset electrical equipment according to the preset target electrical energy demand of the electrical equipment.

7. The cellular noise power generation system according to claim 1, characterized in that, The structure of the piezoelectric power generation layer, from the outside to the inside, consists of a metal electrode layer, a piezoelectric ceramic layer, and a substrate electrode layer.

8. The cellular noise power generation system according to claim 1, characterized in that, The honeycomb cover adopts a hexagonal honeycomb structure.

9. A method for generating noise using a cellular network, characterized in that, The cellular noise power generation system as described in any one of claims 1-8, wherein the method comprises: Receive the initial noise of the target noise region, reduce the initial noise, and generate an alternating current based on the vibration of the noise sound wave after the noise reduction; Based on the noise intensity distribution characteristics of the initial noise, a pre-built mapping model between noise distribution and power generation is used to generate control commands for the cellular power generation device, enabling the cellular power generation device to generate electricity based on the control commands, and to perform fault diagnosis based on the power generation data of each cellular power generation unit.

10. The method according to claim 9, characterized in that, Before generating control commands for the cellular power generation device based on the initial noise intensity using a pre-built mapping model of noise distribution and power generation, the process includes: Collect noise intensity data and output voltage and current data at different times in the area corresponding to the cellular power generation device; Invalid samples are removed from the noise intensity distribution feature data and output voltage and current data of the area corresponding to the cellular power generation device at different times to obtain the corresponding noise intensity distribution feature data and voltage and current data to be trained at different times. Based on the voltage and current data to be trained at different times, the actual power generation of each cellular power generation unit is calculated to obtain the power generation data to be trained at different times. Based on the noise intensity distribution feature data and power generation data to be trained at different times, the optimal parameter combination dataset at different times is calibrated. Construct a target neural network by dividing the dataset of optimal parameter combinations at different times into a training set, a validation set, and a test set in proportion. Train the target neural network using the training set and adjust the hyperparameters of the target neural network using the validation set to obtain the trained neural network. The generalization ability of the trained neural network is evaluated using the test set, and when the generalization ability of the trained neural network meets the preset application conditions, the pre-constructed mapping model of noise distribution and power generation is obtained.

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