Burner cold flow field measurement system and method based on pressure sensor array

Abstract

The invention provides a combustor cold-state flow field measurement system and method based on a pressure sensor array, and belongs to the technical field of boiler combustor flow field measurement, and the system comprises the pressure sensor array which is used for measuring the pressure generated by gas sprayed out of a combustor nozzle and converting a pressure signal into an electric signal; the data acquisition module is used for acquiring an electric signal in real time by adopting a synchronous clock technology and performing analog-to-digital conversion on the electric signal to obtain a digital pressure data packet corresponding to the electric signal; the data processing module is used for receiving the digital pressure data packet, calculating flow field parameters based on the digital pressure data packet, obtaining flow field feature data, and extracting features in the flow field parameters based on the flow field feature data to obtain key flow field feature parameters; and the display module is used for displaying the key flow field characteristic parameters output in the structured data format in a preset display form. According to the scheme, the accuracy of cold flow field measurement of the boiler burner can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of boiler burner flow field measurement, and in particular to a burner cold flow field measurement system and method based on a pressure sensor array. Background Art

[0002] At present, boilers are key equipment for industrial production and energy conversion, and their internal flow field characteristics directly affect combustion efficiency and pollutant emissions. With the development of science and technology and the enrichment of testing methods, a variety of measurement technologies have been used to study the cold flow field characteristics of boiler burners, such as aerodynamic field test fireworks tracing technology, particle imaging velocimetry technology, and numerical simulation technology. Although these technologies provide certain information for production guidance and scientific research, they often have problems such as insufficient data accuracy.

[0003] Therefore, how to improve the accuracy of cold flow field measurement of boiler burners has become a problem to be solved. Summary of the invention

[0004] The purpose of the present application is to provide a burner cold flow field measurement system based on a pressure sensor array, which can solve the problem of low accuracy in cold flow field measurement of boiler burners in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a burner cold flow field measurement system based on a pressure sensor array, the system comprising: a pressure sensor array, a data acquisition module, a data processing module and a display module; The pressure sensor array is used to measure the pressure of the gas ejected from the burner nozzle, convert the pressure signal into an electrical signal, and output the electrical signal to the data acquisition module; The data acquisition module is used to collect electrical signals in real time using synchronous clock technology, and perform analog-to-digital conversion on the electrical signals to obtain a digital pressure data packet corresponding to the electrical signals, and output the digital pressure data packet to the data processing module; A data processing module is used to receive a digital pressure data packet, calculate flow field parameters based on the digital pressure data packet, obtain flow field characteristic data, extract features in the flow field parameters based on the flow field characteristic data, obtain key flow field characteristic parameters, and output the key flow field characteristic parameters to the display module in a structured data format; The display module is used to display the key flow field characteristic parameters output in a structured data format in a preset display form.

[0006] In a possible implementation manner of the first aspect, the pressure sensor array includes: n rows and m columns of independent driving units of pressure sensors, a grouping controller, and a plurality of mounting brackets; Each column of pressure sensor independent drive units is fixed on a mounting bracket. Each pressure sensor independent drive unit is used to measure the pressure generated by the gas ejected from the burner nozzle at its location and convert the pressure signal into an electrical signal. The group controller is used to control each pressure sensor independent drive unit to perform linear motion.

[0007] In a possible implementation manner of the first aspect, each pressure sensor independent drive unit includes: a pressure sensor, a motor, a coupling, a lead screw, a movable slider, and a guide rail; The first side of the lead screw is connected to the motor shaft through a coupling, the second side of the lead screw is connected to the moving slider through the internal thread of the moving slider, the pressure sensor is embedded in the moving slider, and the guide rail is set on the mounting bracket; the group controller is used to control the forward or reverse rotation of the motor shaft, the forward rotation of the motor shaft is converted into the forward movement of the pressure sensor, and the reverse rotation of the motor shaft is converted into the backward movement of the pressure sensor.

[0008] In a possible implementation of the first aspect, the data acquisition module includes: a data acquisition card, a signal conditioning device, an analog-to-digital converter, a synchronous clock device, and a high-speed cache memory; A data acquisition card is used to collect electrical signals; a synchronous clock device is used to enable the data acquisition card to simultaneously collect electrical signals output by the independent driving units of each pressure sensor; a signal conditioning device is used to perform signal conditioning on the electrical signals output by the independent driving units of each pressure sensor; an analog-to-digital converter is used to perform analog-to-digital conversion on each electrical signal after signal conditioning to obtain digital pressure data corresponding to each electrical signal, and to encode and mark each digital pressure data and integrate it into a digital pressure data packet; The high-speed cache memory is used to locally cache the remaining digitized pressure data after transmission to the data processing module. When the digitized pressure data in the cache reaches a preset data amount or a request from the data processing module is received, the digitized pressure data in the cache is transmitted to the data processing module via a high-speed data bus.

[0009] In a possible implementation of the first aspect, the signal conditioning device includes: an amplifier and a filter; the amplifier is used to enhance the signal amplitude of the electrical signal output by each independent driving unit of the pressure sensor; the filter is used to filter the electrical signals after the signal amplitude is enhanced.

[0010] In a possible implementation manner of the first aspect, the data processing module includes at least one server. When the data processing module includes multiple servers, the servers are connected via a switch. Each server is used to receive a digital pressure data packet, use computational fluid dynamics software to calculate flow field parameters based on the digital pressure data packet, obtain flow field characteristic data, and use data analysis and calculation software to extract features in the flow field parameters based on the flow field characteristic data to obtain key flow field characteristic parameters, and use data analysis and visualization processing software to output the key flow field characteristic parameters to the display module in a structured data format.

[0011] In a possible implementation of the first aspect, the flow field parameters include: a velocity field, a pressure field, and a turbulence intensity; and calculating the flow field parameters based on the digitized pressure data packet includes: The pressure information in the digital pressure data packet is extracted, and the pressure data at different collection points at different times are sorted and integrated based on the pressure information; the sorted and integrated pressure data is used as the input data of the fluid mechanics calculation model, and the discrete control equations of the finite volume method are used to iteratively solve the discretized equation group to obtain the velocity field, pressure field and turbulence intensity.

[0012] In a possible implementation of the first aspect, after the pressure data from different collection points at different times are sorted and integrated based on the pressure information, the data processing module is further used to: preprocess the sorted and integrated pressure data to obtain preprocessed pressure data.

[0013] In a possible implementation manner of the first aspect, preprocessing the sorted and integrated pressure data includes: The residual noise that has not been completely removed in the sorted and integrated pressure data is removed; the statistical analysis method is used to identify and eliminate the abnormal pressure data after the residual noise is removed; wherein, the abnormal pressure data is the pressure data that deviates from the normal pressure range; the polynomial fitting method is used to smooth the remaining normal pressure data.

[0014] In a possible implementation manner of the first aspect, after calculating the flow field parameters based on the digitized pressure data packet, the data processing module is further used to: The measured pressure value of each independent driving unit of the pressure sensor is compared with the predicted pressure value in the key flow field characteristic parameters of the corresponding acquisition point; when the error between each measured pressure value and the corresponding predicted pressure value exceeds the preset error range, the spatial geometric model is replaced, the mesh quality of the spatial geometric model is optimized, and the boundary conditions of the flow field are modified to recalculate the flow field parameters.

[0015] In a possible implementation of the first aspect, the key flow field characteristic parameters include flow velocity characteristic parameters, pressure characteristic parameters, and turbulence characteristic parameters; extracting features in the flow field parameters based on the flow field characteristic data to obtain the key flow field characteristic parameters includes: A multi-dimensional comprehensive analysis is performed based on the real-time operating condition data and historical operating condition data of the burner, external environment data and test data to obtain the flow field information. According to the flow field information, the characteristics of the velocity field, pressure field and turbulence intensity are extracted to obtain the velocity characteristic parameters, pressure characteristic parameters and turbulence characteristic parameters.

[0016] In a possible implementation manner of the first aspect, after extracting features in flow field parameters based on flow field feature data to obtain key flow field feature parameters, the data processing module is further used to: The characteristic vector composed of the key flow field characteristic parameters is input into the trained machine learning model, and the high-level features of the key flow field characteristic parameters are extracted based on the machine learning model using data analysis and calculation software; based on the machine learning model, the flow field parameters are optimized and corrected according to the high-level features and the ideal patterns and laws of the flow field parameters learned during the training process; the optimized and corrected flow field parameters are fed back to the machine learning model to update and improve the machine learning model.

[0017] In a possible implementation of the first aspect, after the key flow field characteristic parameters are output to the display module in a structured data format, the data processing module is also used to: store the intermediate data and result data in the data processing process into a database, and the intermediate data and result data are used to provide data support for subsequent historical data analysis, fault diagnosis and model verification.

[0018] In a possible implementation of the first aspect, key flow field characteristic parameters output in a structured data format are displayed in a preset display form, including: a velocity vector diagram of flow velocity characteristic parameters, a pressure cloud diagram of pressure characteristic parameters, and a streamline diagram of turbulence characteristic parameters in the form of two-dimensional graphics, three-dimensional graphics, charts, or animations.

[0019] In a second aspect, an embodiment of the present application provides a method for measuring a cold flow field of a burner based on a pressure sensor array. The method is implemented based on a cold flow field measurement system for a burner based on a pressure sensor array according to any one of the first aspects. The method includes: The pressure sensor array measures the pressure of the gas ejected from the burner nozzle, converts the pressure signal into an electrical signal, and outputs the electrical signal to the data acquisition module; Based on the data acquisition module, the synchronous clock technology is used to collect the electrical signal in real time, and the electrical signal is converted into analog to digital to obtain the digital pressure data packet corresponding to the electrical signal, and the digital pressure data packet is output to the data processing module; Based on the data processing module receiving the digital pressure data packet, calculating the flow field parameters according to the digital pressure data packet, obtaining the flow field characteristic data, extracting the characteristics in the flow field parameters according to the flow field characteristic data, obtaining the key flow field characteristic parameters, and outputting the key flow field characteristic parameters to the display module in a structured data format; The key flow field characteristic parameters output in a structured data format are displayed in a preset display form based on the display module.

[0020] In the solution of the present application, the pressure sensor array converts the pressure signal generated by the gas ejected from the burner into an electrical signal, the data acquisition module obtains a digital pressure data packet by performing analog-to-digital conversion on the electrical signal acquired by adopting the synchronous clock technology, the data processing module obtains the key flow field characteristic parameters by extracting the features in the flow field parameters calculated based on the digital pressure data packet, and the display module displays the key flow field characteristic parameters.

[0021] This application solution can accurately capture the details in the flow field, such as high-speed or micro-scale flow phenomena, without manual judgment, and is less subjective. At the same time, it has lower requirements for environmental conditions, such as the need to accurately control temperature and cleanliness. In addition, even if the burner ages and deforms after long-term combustion operation, it will not have a significant impact on the measurement results, thereby improving the accuracy of the cold flow field measurement of the boiler burner and achieving accurate measurement of the cold flow field of the burner, with strong ease of use and practicality.

[0022] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 It is a schematic diagram of the architecture of a combustor cold flow field measurement system based on a pressure sensor array provided in an embodiment of the present application; Figure 2 is a schematic diagram of a burner flow field in a furnace of a pressure sensor array provided in an embodiment of the present application; Figure 3 It is a schematic diagram of a burner nozzle direction observation pressure sensor array provided in an embodiment of the present application; Figure 4 is a schematic diagram of the structure of an independent driving unit of a pressure sensor provided in an embodiment of the present application; Figure 5It is a schematic diagram of the steps of a method for measuring a cold flow field of a burner based on a pressure sensor array provided in an embodiment of the present application; Figure 6 It is a flow chart of a method for measuring a cold flow field of a burner based on a pressure sensor array provided in an embodiment of the present application; Figure 7 It is a flowchart of data processing by the data processing module provided in the embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0026] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or photovoltaic components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, photovoltaic components and / or combinations thereof.

[0027] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0028] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0030] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0032] Boiler burner flow field measurement technology is a key technical means to achieve boiler performance optimization. By accurately analyzing the airflow distribution, velocity and turbulence characteristics in the burner area, it plays a significant role in improving combustion efficiency, reducing pollutant emissions and enhancing boiler operation stability. With the implementation of the "Action Plan for Green, Low-Carbon and High-Quality Development of Boilers" issued by the National Development and Reform Commission and other departments, boiler burner flow field measurement technology plays a vital role in promoting the transformation of the boiler industry towards a green and low-carbon direction.

[0033] This technology not only helps to improve energy efficiency and reduce energy consumption, but also has far-reaching significance for reducing the emission of pollutants such as nitrogen oxides, improving air quality, and achieving national energy policies and environmental protection goals. By adopting advanced boiler burner flow field measurement technology, researchers can deeply explore the cold operation flow field of boiler burners, and then optimize the burner design, promote efficient and clean combustion of pulverized coal, and meet the sustainable development of energy and environmental protection.

[0034] At present, boilers are key equipment for industrial production and energy conversion, and their internal flow field characteristics directly affect combustion efficiency and pollutant emissions. With the development of science and technology and the enrichment of testing methods, a variety of measurement technologies have been used to study the cold flow field characteristics of boiler burners, such as aerodynamic field test fireworks tracing technology, particle imaging velocimetry technology, and numerical simulation technology.

[0035] Among them, the fireworks tracer technology of aerodynamic field test is a technical means to track the movement of airflow by burning particles produced by fireworks. Although it provides an intuitive display of flow characteristics visually, it has some disadvantages and limitations. The fireworks tracer method may not accurately capture all the details of the flow field, especially for high-speed or micro-scale flow phenomena; the visual effect produced by the fireworks tracer method depends on the subjective judgment of the observer, and it is difficult to convert it into data that can be quantitatively analyzed. Different observers may come to different conclusions, which leads to a reduction in its accuracy and reliability in scientific research and engineering applications.

[0036] Particle imaging velocimetry measures the velocity distribution of the flow field by tracking the movement of tracer particles. It can provide detailed flow characteristics and has the advantages of non-contact and high dynamic responsiveness. However, particle imaging velocimetry has high requirements for environmental conditions (such as temperature, pressure, and cleanliness of optical windows). Therefore, this technology can be used as laboratory equipment to guide the development of new burners, but it is difficult to become a device to guide production in the complex environment of power plant boilers.

[0037] Numerical simulation technology is a low-cost and flexible research method. However, after long-term combustion operation, the burner will experience internal parts wear and nozzle deformation. In this case, the numerical simulation geometric boundary cannot be guaranteed, the initial conditions of the gas ejected from the nozzle cannot be determined, and the final simulation results may lose their true meaning.

[0038] Although these technologies provide certain information for production guidance and scientific research, they often have problems such as greater experimental risks, insufficient data accuracy, complex operating procedures, and limited testing conditions.

[0039] In response to the above-mentioned defects, an embodiment of the present application provides a burner cold flow field measurement system based on a pressure sensor array. The pressure sensor array converts the pressure signal generated by the gas ejected from the burner into an electrical signal. The data acquisition module obtains a digital pressure data packet by performing analog-to-digital conversion on the electrical signal acquired using synchronous clock technology. The data processing module obtains key flow field characteristic parameters by extracting features from the flow field parameters calculated based on the digital pressure data packet. The display module displays the key flow field characteristic parameters.

[0040] This application solution can accurately capture the details in the flow field, such as high-speed or micro-scale flow phenomena, without manual judgment, and is less subjective. At the same time, it has lower requirements for environmental conditions, such as the need to accurately control temperature and cleanliness. In addition, even if the burner ages and deforms after long-term combustion operation, it will not have a significant impact on the measurement results, thereby improving the accuracy of the cold flow field measurement of the boiler burner and achieving accurate measurement of the cold flow field of the burner, with strong ease of use and practicality.

[0041] The specific process implemented by this application is introduced below through specific embodiments.

[0042] See also Figure 1 , Figure 1 Schematic diagram of the structure of the burner cold flow field measurement system based on the pressure sensor array provided in the embodiment of the present application. Figure 1 As shown, the system includes: a pressure sensor array, a data acquisition module, a data processing module and a display module. The four parts work together to realize the observation of flow field characteristics.

[0043] In one embodiment, the pressure sensor array is used to measure the pressure of the gas ejected from the burner nozzle, and convert the pressure signal into an electrical signal, and output the electrical signal to the data acquisition module. The electrical signal can reflect the pressure changes at different positions of the flow field.

[0044] In one embodiment, the data acquisition module establishes communication with the pressure sensor array through a wireless or wired connection, and is used to use synchronous clock technology to collect the electrical signal output by the pressure sensor array in real time, and perform analog-to-digital conversion on the electrical signal, converting the analog signal into a digital signal to obtain a digitized pressure data packet corresponding to the electrical signal, and then output the digitized pressure data packet to the data processing module.

[0045] It should be noted that multiple pressure sensors in the pressure sensor array usually work simultaneously. In order to avoid time inconsistency in the data collected by the data acquisition module, synchronous clock technology is used to ensure that the collected data is pressure data at different positions at the same time, thereby improving the synchronization of the data acquisition process and making the time of collecting data consistent.

[0046] In one embodiment, the data processing module is the core part of the entire system, and is responsible for processing and analyzing the large amount of pressure data collected. The data processing module is used to continuously monitor and receive the digital pressure data packets sent by the data acquisition module, calculate the flow field parameters based on the digital pressure data packets, obtain the flow field characteristic data, and extract the characteristics of the flow field parameters based on the flow field characteristic data to obtain the key flow field characteristic parameters, and output the key flow field characteristic parameters to the display module in a structured data format for subsequent display and analysis.

[0047] In one embodiment, the display module is a display device, such as a display device. The display module is used to display the key flow field characteristic parameters output in a structured data format in a preset display form. After the display module obtains the flow field data obtained through analysis and calculation from the data processing module, the flow field data is displayed to the user in an intuitive and clear manner. Among them, the preset display form can be determined according to the specific situation in the actual application scenario, and is not specifically limited here.

[0048] See also Figure 2 , Figure 2 Schematic diagram of the burner flow field in the furnace of the pressure sensor array provided in the embodiment of the present application. Figure 2 As shown, the two sides of the burner nozzle (1) are water-cooled walls (2), and a row of pressure sensors in a pressure sensor array (3) are arranged at a preset distance from the burner nozzle (1). The burner nozzle (1) sprays gas, and the gas acts on the pressure sensor to generate pressure. Each pressure sensor senses the pressure change at its own position in real time, and converts the pressure signal into an electrical signal for output.

[0049] Among them, the preset distance can be determined according to the specific situation in the actual application scenario and is not specifically limited here.

[0050] See also Figure 3 , Figure 3 Schematic diagram of a burner nozzle direction observation pressure sensor array provided in an embodiment of the present application. Figure 3 As shown, the pressure sensor array (3) comprises: n rows and m columns of independent pressure sensor drive units (4), a grouping controller (not shown in the figure), and a plurality of mounting brackets (5).

[0051] Combination Figure 2 and Figure 3 In one embodiment, the pressure sensor array is composed of 15×15 high-precision, high-response speed pressure sensor independent drive units (4), and adjacent pressure sensor independent drive units (4) are arranged in a square at the outlet of the burner nozzle (1) with equal spacing in the horizontal and vertical directions. The mounting bracket (5) can avoid the horizontal and vertical deviation of the pressure sensor. The distance between adjacent pressure sensor independent drive units (4) can be 0.15m, so the maximum flow field area that can be accurately measured is 2.25×2.25m 2 .

[0052] Combination Figure 2 and Figure 3 In one embodiment, the flow field size is related to the size and type of the burner, the operating conditions, the test distance and other factors. In order to reasonably utilize the computing resources in the subsequent data processing process, the system provided in the embodiment of the present application can independently control some pressure sensors to work. The specific implementation process is 1.5×1.5m at a distance of 1m from the vertical line between the burner nozzle (1) and the water wall (2). 2 The two-dimensional flow field test is described as an example.

[0053] Combination Figure 2 and Figure 3 In some embodiments, each column of pressure sensor independent drive units (4) is fixed on a mounting bracket (5). Each pressure sensor independent drive unit (4) is used to measure the pressure generated by the gas ejected from the burner nozzle (1) at its location and convert the pressure signal into an electrical signal. The group controller is used to control each pressure sensor independent drive unit (4) to perform linear motion.

[0054] See also Figure 4 , Figure 4 is a schematic diagram of the structure of the pressure sensor independent drive unit (4) provided in the embodiment of the present application. Figure 4As shown, each pressure sensor independent drive unit (4) includes: a pressure sensor (9), a motor (10), a coupling (11), a lead screw (8), a movable slider (6) and a guide rail (7).

[0055] The motor (10) may be a stepper motor or a servo motor. The servo motor has high precision but is relatively expensive. In a preferred embodiment, the motor (10) may be a stepper motor.

[0056] In some embodiments, the first side of the lead screw (8) is connected to the motor shaft via a coupling (11), and the second side of the lead screw (8) is connected to the movable slider (6) via an internal thread of the movable slider (6). The pressure sensor (9) is embedded in the movable slider (6) and can be attached to the pressure sensor independent drive unit (4) to move forward and backward axially. The guide rail is arranged on the mounting bracket (5) to ensure the straightness and stability of the movement of the pressure sensor (9).

[0057] In some embodiments, the group controller is used to realize the forward or backward movement of the corresponding pressure sensor (9) by independently controlling the forward or reverse rotation of the rotating shaft of each motor (10), wherein the forward rotation of the rotating shaft of the motor (10) is converted into the forward movement of the pressure sensor (9), and the reverse rotation of the rotating shaft of the motor (10) is converted into the backward movement of the pressure sensor (9).

[0058] In some embodiments, the number of pressure sensors (9) put into operation can be adjusted according to the complexity of the flow field, so as to reasonably save computing resources and efficiently apply to flow fields with different types of burners, different air volumes and different measurement distances. When three-dimensional flow field measurement is required, different pressure sensors (9) can be controlled to move forward and backward respectively, and pressure tests can be performed in different sections to finally obtain the three-dimensional flow field of the area.

[0059] In some embodiments, when the boiler is shut down, the pressure sensor array (3) is installed 1.3 m in front of the burner nozzle (1). After installation, the sensor is calibrated using a standard pressure source to ensure the accuracy of the measurement. After calibration, the 10×10 motors (10) of the burner in the central area are controlled by the group controller to rotate forward, and the output shaft (motor shaft) of the motor (10) rotates forward to drive the screw (8) to rotate forward through the coupling (11). The moving slider (6) embedded with the pressure sensor (9) is connected to the screw (8) through an internal thread, and the forward motion of the screw (8) can be converted into the forward motion of the moving slider (6). The pressure sensor (9) moves forward 0.3 m with the moving slider (6) to reach the flow field test position.

[0060] In some embodiments, the movable slider (6) is coupled to the guide rail (7) provided on the mounting bracket (5) to work together to ensure the stability and linearity of the movement of the pressure sensor (9). If it is necessary to measure the three-dimensional flow field nearby during the test, the group controller can be used to control the motor (10) corresponding to the pressure sensor (9) at the interval position to reverse, so that the pressure sensor (9) at the interval position moves backward. At this time, the front and rear pressure sensors (9) can be layered to construct a three-dimensional flow field. The pressure sensor (9) senses the pressure changes at different positions in the flow field in real time, and converts the pressure signal into an electrical signal for output.

[0061] According to an implementation of the present application, the data acquisition module includes: a data acquisition card, a signal conditioning device, an analog to digital converter (A / D converter or ADC), a synchronous clock device and a high-speed cache memory.

[0062] In some embodiments, a data acquisition card is used to collect electrical signals output by a pressure sensor array, a synchronous clock device is used to enable the data acquisition card to simultaneously collect electrical signals output by each independent driving unit of the pressure sensor, and a signal conditioning device is used to perform signal conditioning on the electrical signals output by each independent driving unit of the pressure sensor.

[0063] In some embodiments, a high-precision master clock source can be set during the data collection process to provide a reference time for the entire system. The data collection module receives a clock signal from the master clock source. During the data collection process, each time a data point is collected, the data collection module records the precise collection time according to the time stamp of the master clock.

[0064] In this way, even if there are multiple pressure sensors working at the same time, data can be collected based on the same time base. Then, according to the data collection time, source location or other related attributes, the data is divided into different categories, and a coding system is determined using specific numbers and letters. According to the coding rules, a unique coding value is assigned to each data category.

[0065] In some embodiments, the analog-to-digital converter is used to perform analog-to-digital conversion on each electrical signal after signal conditioning to obtain digital pressure data corresponding to each electrical signal, and each digital pressure data is encoded and marked and then integrated into a digital pressure data packet. Encoding and marking each digital pressure data facilitates accurate identification of the acquisition time, source location and other important information of each data point in subsequent data processing.

[0066] In some embodiments, the cache memory is used to locally cache the digitized pressure data remaining after transmission to the data processing module (i.e., data that cannot be transmitted to the data processing module in a short time). When the digitized pressure data in the cache reaches a preset data amount or a request from the data processing module is received, the digitized pressure data in the cache is transmitted to the data processing module via a high-speed data bus.

[0067] Among them, when the digital signal encounters transmission bandwidth limitations, network congestion, or busy data processing modules during further transmission, which leads to data transmission delays, the data cannot be transmitted to the data processing module in a short time. The preset data volume can be determined according to the specific circumstances in the actual application scenario and is not specifically limited here. The high-speed data bus can be a data bus such as PCI Express (eripheral component interconnect express, a high-speed serial computer expansion bus standard) or Ethernet.

[0068] According to one embodiment of the present application, the signal conditioning device includes: an amplifier and a filter. The amplifier is used to amplify the electrical signal output by each independent driving unit of the pressure sensor to enhance its amplitude. The filter is used to filter the electrical signal after each signal amplitude enhancement to remove high-frequency noise and interference signals such as the electronic noise of the pressure sensor itself, the environmental electromagnetic interference noise, and the noise in the signal transmission process.

[0069] According to an embodiment of the present application, the data processing module includes at least one high-performance server. When the data processing module includes multiple servers, each server performs different operations respectively, and each server is connected through a switch to achieve high-speed data transmission and sharing.

[0070] In some embodiments, each server is used to receive a digitized pressure data packet, use computational fluid dynamics software to calculate flow field parameters based on the digitized pressure data packet, obtain flow field characteristic data, and use data analysis and calculation software to extract features in the flow field parameters based on the flow field characteristic data to obtain key flow field characteristic parameters, and use data analysis and visualization processing software to output the key flow field characteristic parameters in a structured data format to a display module.

[0071] The computational fluid dynamics software may be ANSYS-Flune and other software, which is a general CFD (computational fluid dynamics) software. The data analysis and calculation software may be MATLAB and other software, and the data analysis and visualization processing software may be Tecplot and other software.

[0072] According to an embodiment of the present application, the flow field parameters include: velocity field, pressure field and turbulence intensity. Calculating the flow field parameters based on the digitized pressure data packet may include the following steps: The pressure information in the digital pressure data package is extracted, and the pressure data of different acquisition points at different times are sorted and integrated based on the pressure information. The sorted and integrated pressure data is used as the input data of the fluid mechanics calculation model, and the discrete control equations of the finite volume method are used to iteratively solve the discretized equations to obtain the velocity field, pressure field and turbulence intensity.

[0073] In some embodiments, the digital pressure data packet is parsed according to the agreed coding rules to extract the pressure value and related timestamp, collection point identification and other information in the digital pressure data packet. After the timestamp in the pressure data is calibrated for time consistency, the pressure data at different collection points at different times are sorted and integrated in chronological order.

[0074] In some embodiments, the calculation of flow field parameters in the data processing module is carried out based on the computational fluid dynamics method, and pre-processing work such as geometric modeling and meshing is completed in advance according to the shape and size of the burner. When calculating the flow field parameters through computational fluid dynamics, the geometric model that needs to be calculated this time is established in advance according to geometric conditions such as the burner size, the layout range of the pressure sensor, and the layout dimension of the pressure sensor, and the geometric model is divided into a suitable type of high-quality grid. After the grid is imported into the CFD calculation software, a suitable physical model is selected according to the air volume corresponding to the operating condition of the burner, such as the k-epsilon (k-ε) model, the k-omega (k-ω) model, or the RSM model (Regime Shift Model, mechanism conversion model) and other models.

[0075] In some embodiments, reasonable boundary conditions and initial conditions are defined. At this time, the pressure data at different locations are also imported into the fluid mechanics calculation model as initial conditions through user-defined functions (UDF). Then, the discrete control equations of the finite volume method are used to iteratively solve the discrete equations until convergence, and the velocity field, pressure field, turbulence intensity and other flow field parameters are obtained through numerical solution.

[0076] The boundary conditions include: wall, free outlet, symmetry, pressure outlet, and mass flow outlet.

[0077] According to an implementation of the present application, after the pressure data from different collection points at different times are sorted and integrated based on the pressure information, the data processing module is further used to: preprocess the sorted and integrated pressure data to obtain preprocessed pressure data.

[0078] According to an implementation of the present application, the pressure data after sorting and integration is preprocessed, including: removing the residual noise that has not been completely removed in the pressure data after sorting and integration, using statistical analysis to identify and eliminate abnormal pressure data after the residual noise is removed, and using polynomial fitting to smooth the remaining normal pressure data. Among them, abnormal pressure data is pressure data that deviates from the normal pressure range, and the normal pressure range can be determined according to the specific situation in the actual application scenario, and is not specifically limited here.

[0079] In some embodiments, in order to prevent abnormal data from misleading subsequent calculations and analyses, a filtering algorithm is first used to remove the fluctuation noise caused by the complexity and uncertainty of the flow field itself, and then a statistical analysis method (such as the 3σ principle) is used to identify and remove abnormal data points that are significantly deviated from the normal range. Finally, a polynomial fitting method is used to smooth the data to reduce data fluctuations and improve the accuracy of flow field parameter calculations. After the data preprocessing is completed, it is saved as a file in a specific format and written to the shared memory, and the data can be read from the shared memory when calculating flow field parameters through computational fluid dynamics.

[0080] According to one embodiment of the present application, after calculating the flow field parameters based on the digitized pressure data packet, the data processing module is further used to: The measured pressure value of each independent driving unit of the pressure sensor is compared with the predicted pressure value in the key flow field characteristic parameters of the corresponding acquisition point. When the error between each measured pressure value and the corresponding predicted pressure value exceeds the preset error range, the spatial geometric model is replaced, the mesh quality of the spatial geometric model is optimized, and the boundary conditions of the flow field are modified to recalculate the flow field parameters.

[0081] In some embodiments, the data processing module compares the pressure value measured by the pressure sensor with the pressure value of the corresponding node (corresponding to the position of the pressure sensor array) calculated by fluid mechanics through data evaluation and analysis to evaluate whether the selection of the numerical calculation model is appropriate. If the final pressure numerical error between the two is not within a reasonable range, for example, more than 90% of the effective data errors are greater than or equal to 5%, the flow field parameters are recalculated by optimizing the grid quality, modifying the boundary conditions, or replacing the physical model until the pressure numerical error is within a reasonable range before the next step of data processing is performed.

[0082] According to an embodiment of the present application, the key flow field characteristic parameters include velocity characteristic parameters, pressure characteristic parameters and turbulence characteristic parameters. The key flow field characteristic parameters are obtained by extracting the characteristics of the flow field parameters based on the flow field characteristic data, including: A multi-dimensional comprehensive analysis is performed based on the real-time operating condition data and historical operating condition data of the burner, external environment data and test data to obtain the flow field information. According to the flow field information, the characteristics of the velocity field, pressure field and turbulence intensity are extracted to obtain the velocity characteristic parameters, pressure characteristic parameters and turbulence characteristic parameters.

[0083] In some embodiments, the burner flow field may be very complex, and it may be difficult to fully capture all key features by relying solely on CFD calculation results. Therefore, the final calculated data is combined with the operating conditions of the burner (such as primary air volume, secondary air volume, swirler blade angle, damper damper opening, etc.), external environmental data (such as ambient temperature, humidity and pressure, etc.), historical operating data and test data for a multi-dimensional comprehensive analysis.

[0084] Through comprehensive analysis, it is easier to obtain a comprehensive description of the flow field, and obtain information such as the size and position of the recirculation zone, the position of the vortex center, the velocity peak area, and the area with large pressure gradient. Based on the flow field information currently available, after in-depth understanding of the flow pattern of the burner, the key characteristic parameters in the flow field are extracted, such as velocity characteristic parameters (such as maximum velocity, minimum velocity, average velocity, and velocity gradient, etc.), pressure characteristic parameters (such as maximum pressure, minimum pressure, and average pressure, etc.), and turbulence characteristic parameters (such as turbulence intensity, turbulence dissipation rate, etc.).

[0085] According to an embodiment of the present application, after extracting features in flow field parameters based on flow field feature data to obtain key flow field feature parameters, the data processing module is further used to: The feature vector composed of key flow field characteristic parameters is input into the trained machine learning model, and the high-level features in the key flow field characteristic parameters are extracted based on the machine learning model using data analysis and calculation software. The flow field parameters are optimized and corrected based on the high-level features and the ideal patterns and laws of the flow field parameters learned during the training process based on the machine learning model. The optimized and corrected flow field parameters are fed back to the machine learning model to update and improve the machine learning model.

[0086] In some embodiments, the data processing module collects key feature data sets obtained after data evaluation and analysis, and performs standardization processing (such as normalization processing, etc.) on data of different dimensions and ranges. After data cleaning and preprocessing, key feature parameters, burner operating conditions, and external environmental data are combined into feature vectors and input into a pre-trained machine learning model. The machine learning model can be trained based on laboratory flow field experiments and public data sets of equipment manufacturers.

[0087] The machine learning model will automatically mine hidden, potential, and difficult to intuitively discover (i.e., nonlinear relationships that are beyond what can be discovered by traditional analysis based on physical principles) high-level features from the input data. Based on the extracted high-level features, the model will optimize and correct the flow field parameters according to the ideal patterns and laws of the flow field parameters learned during the training process to make them closer to the real and accurate situation. Finally, this data can be fed back to the machine learning model for further updates and improvements to improve the performance and optimization capabilities of the model.

[0088] Based on new measurement data, the machine learning model is continuously updated and improved to improve its adaptability and prediction accuracy to flow fields under different working conditions. The actual flow field situation may be affected by a variety of complex factors and has uncertainty. The development of machine learning models can better handle this uncertainty and predict results that are closer to the actual situation by learning from a large amount of data.

[0089] According to one embodiment of the present application, after the key flow field characteristic parameters are output to the display module in a structured data format, the data processing module is also used to: store the intermediate data and result data in the data processing process into a database, and the intermediate data and result data are used to provide data support for subsequent historical data analysis, fault diagnosis and model verification.

[0090] In one embodiment, the data processing flow of the data processing module is: data reception and sorting integration, data preprocessing, flow field parameter calculation, data evaluation and analysis, machine learning optimization, result output and storage. The specific implementation principles of each process have been described in the above embodiment and will not be repeated here.

[0091] According to one embodiment of the present application, key flow field characteristic parameters output in a structured data format are displayed in a preset display form, including: a velocity vector diagram of a flow velocity characteristic parameter, a pressure cloud diagram of a pressure characteristic parameter, and a streamline diagram of a turbulence characteristic parameter in the form of a two-dimensional graphic, a three-dimensional graphic, a chart, or an animation, so that the user can have a deep understanding of the characteristics of the burner flow field.

[0092] In some embodiments, the display module obtains the flow field data after analysis and calculation from the data processing module, and can use two-dimensional graphics, three-dimensional graphics, charts or animations to display the velocity vector diagram, pressure cloud diagram and streamline diagram of the flow field. At the same time, interactive functions are provided to users, such as zooming, translating or rotating graphics, so that users can observe the flow field from different angles. The generated graphics and related information are output to the user, and the user can adjust the output quality and frame rate of the graphics according to the resolution and performance of the display device to ensure a smooth display effect.

[0093] See also Figure 5 , Figure 5 Schematic diagram of the steps of the method for measuring the cold flow field of a burner based on a pressure sensor array provided in an embodiment of the present application. Figure 5 The burner cold flow field measurement system based on the pressure sensor array is shown in FIG. Figure 5 As shown, the method may include the following steps: S501, measuring the pressure of the gas ejected from the burner nozzle based on the pressure sensor array, converting the pressure signal into an electrical signal, and outputting the electrical signal to the data acquisition module.

[0094] S502, based on the data acquisition module, the synchronous clock technology is used to collect the electrical signal in real time, and the analog-to-digital conversion is performed on the electrical signal to obtain a digital pressure data packet corresponding to the electrical signal, and the digital pressure data packet is output to the data processing module.

[0095] S503, based on the data processing module receiving the digital pressure data packet, calculating the flow field parameters according to the digital pressure data packet, obtaining the flow field characteristic data, and extracting the characteristics in the flow field parameters according to the flow field characteristic data, obtaining the key flow field characteristic parameters, and outputting the key flow field characteristic parameters to the display module in a structured data format.

[0096] S504: Displaying the key flow field characteristic parameters output in a structured data format in a preset display form based on the display module.

[0097] It should be noted that the specific implementation principles of steps S501 to S504 have been described in the above embodiments and will not be repeated here.

[0098] See also Figure 6 , Figure 6 FIG. 1 is a flow chart of a method for measuring a cold flow field of a burner based on a pressure sensor array provided in an embodiment of the present application. Figure 6 As shown, the method may include the following steps: First, the pressure is measured based on the pressure sensor array; second, the data acquisition module performs signal reception, amplification and filtering, analog-to-digital conversion, data synchronization and encoding, data storage, and data output; third, the data processing module performs data reception and integration, data preprocessing, flow field parameter calculation, data evaluation and analysis, machine learning optimization, result output and storage; finally, the display module displays the data.

[0099] The pressure sensor array includes 15×15 independent drive units and group controllers, and each independent drive unit includes: a mounting bracket, a motor, a lead screw, a slider, a pressure sensor, and a guide rail. The specific implementation principle of this embodiment has been described in the above embodiment and will not be repeated here.

[0100] See also Figure 7 , Figure 7 Schematic diagram of the process of processing data by the data processing module provided in the embodiment of the present application. Figure 7 As shown, the process may include the following steps: First, the data acquisition module outputs data, then the data processing module receives and integrates data, preprocesses data, calculates flow field parameters based on computational fluid dynamics, evaluates and analyzes data, optimizes machine learning, outputs and stores results, and finally the display module inputs data.

[0101] Among them, data reception and integration of the data processing module include: data reception, data analysis, and data integration. Data preprocessing includes: noise removal, outlier processing, and data smoothing. Flow field parameter calculation based on computational fluid dynamics includes: flow field parameter calculation, numerical solution, pressure data initialization, and physical model selection. Data evaluation and analysis include: evaluation of calculation results, multi-dimensional comprehensive analysis, and extraction of feature parameters. Machine learning optimization includes: model parameter improvement, data optimization and correction, extraction of high-level features, input model, and data preparation. Result output and storage include: structured data output, storage of intermediate data, and result data.

[0102] It should be noted that the specific implementation principle of this embodiment has been described in the above embodiment and will not be repeated here.

[0103] The embodiment of the present application provides a burner cold flow field measurement system based on a pressure sensor array, in which the pressure sensor array converts the pressure signal generated by the gas ejected from the burner into an electrical signal, the data acquisition module obtains a digital pressure data packet by performing analog-to-digital conversion on the electrical signal acquired using a synchronous clock technology, the data processing module obtains key flow field characteristic parameters by extracting features from the flow field parameters calculated based on the digital pressure data packet, and the display module displays the key flow field characteristic parameters.

[0104] This application provides a technical route for the cold-running flow field of a boiler swirl burner. This route combines measured pressure values, computational fluid dynamics, and machine learning to achieve observation of flow field characteristics. This application can accurately capture details in the flow field, such as high-speed or micro-scale flow phenomena, without manual judgment, with less subjectivity, and at the same time, has lower requirements for environmental conditions, such as no need to accurately control temperature and cleanliness.

[0105] In addition, even if the burner ages and deforms after long-term combustion operation, it will not have a significant impact on the measurement results, thereby improving the accuracy of the cold flow field measurement of the boiler burner and achieving precise measurement of the cold flow field of the burner, with strong ease of use and practicality.

[0106] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0109] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A burner cold flow field measurement system based on a pressure sensor array, characterized in that: The system comprises: a pressure sensor array, a data acquisition module, a data processing module and a display module; The pressure sensor array is used to measure the pressure of the gas ejected from the burner nozzle, convert the pressure signal into an electrical signal, and output the electrical signal to the data acquisition module; The data acquisition module is used to acquire the electrical signal in real time by using a synchronous clock technology, and to perform analog-to-digital conversion on the electrical signal to obtain a digital pressure data packet corresponding to the electrical signal, and to output the digital pressure data packet to the data processing module; The data processing module is used to receive the digitized pressure data packet, calculate the flow field parameters based on the digitized pressure data packet, obtain flow field characteristic data, and extract the characteristics of the flow field parameters based on the flow field characteristic data to obtain key flow field characteristic parameters, and output the key flow field characteristic parameters to the display module in a structured data format; The display module is used to display the key flow field characteristic parameters output in a structured data format in a preset display form.

2. The burner cold flow field measurement system based on the pressure sensor array according to claim 1 is characterized in that: The pressure sensor array comprises: n rows and m columns of independent driving units of pressure sensors, a grouping controller and a plurality of mounting brackets; Each column of pressure sensor independent drive units is fixed on a mounting bracket. Each pressure sensor independent drive unit is used to measure the pressure generated by the gas ejected from the burner nozzle at its location and convert the pressure signal into an electrical signal. The group controller is used to control each pressure sensor independent drive unit to perform linear motion.

3. The burner cold flow field measurement system based on the pressure sensor array according to claim 2, characterized in that: Each pressure sensor independent driving unit includes: a pressure sensor, a motor, a coupling, a lead screw, a movable slider and a guide rail; The first side of the lead screw is connected to the motor shaft through a coupling, the second side of the lead screw is connected to the moving slider through the internal thread of the moving slider, the pressure sensor is embedded in the moving slider, and the guide rail is arranged on the mounting bracket; The group controller is used to control the forward or reverse rotation of the motor shaft. The forward rotation of the motor shaft is converted into the forward movement of the pressure sensor, and the reverse rotation of the motor shaft is converted into the backward movement of the pressure sensor.

4. The burner cold flow field measurement system based on the pressure sensor array according to claim 2, characterized in that: The data acquisition module includes: a data acquisition card, a signal conditioning device, an analog-to-digital converter, a synchronous clock device and a high-speed buffer memory; The data acquisition card is used to acquire the electrical signal; the synchronous clock device is used to enable the data acquisition card to simultaneously acquire the electrical signal output by each independent driving unit of the pressure sensor; the signal conditioning device is used to perform signal conditioning on the electrical signal output by each independent driving unit of the pressure sensor; The analog-to-digital converter is used to perform analog-to-digital conversion on each electrical signal after signal conditioning to obtain digital pressure data corresponding to each electrical signal, and to encode and mark each digital pressure data and integrate it into a digital pressure data packet; The high-speed cache memory is used to locally cache the remaining digitized pressure data after transmission to the data processing module. When the digitized pressure data in the cache reaches a preset data amount or a request from the data processing module is received, the digitized pressure data in the cache is transmitted to the data processing module via a high-speed data bus.

5. The burner cold flow field measurement system based on the pressure sensor array according to claim 4, characterized in that: The signal conditioning device includes: an amplifier and a filter; The amplifier is used to enhance the signal amplitude of the electrical signal output by each independent driving unit of the pressure sensor; the filter is used to filter the electrical signal after the signal amplitude is enhanced.

6. The burner cold flow field measurement system based on pressure sensor array according to claim 1, characterized in that: The data processing module includes at least one server. When the data processing module includes multiple servers, the servers are connected via switches. Each server is used to receive the digital pressure data packet, use computational fluid dynamics software to calculate flow field parameters based on the digital pressure data packet, obtain flow field characteristic data, and use data analysis and calculation software to extract features in the flow field parameters based on the flow field characteristic data to obtain key flow field characteristic parameters, and use data analysis and visualization processing software to output the key flow field characteristic parameters in a structured data format to the display module.

7. The burner cold flow field measurement system based on the pressure sensor array according to claim 1, characterized in that: The flow field parameters include: velocity field, pressure field and turbulence intensity; the flow field parameters are calculated based on the digitized pressure data packet, including: Extracting pressure information from the digital pressure data packet, and sorting and integrating pressure data from different collection points at different times based on the pressure information; The sorted and integrated pressure data are used as the input data of the fluid mechanics calculation model. The discrete control equations of the finite volume method are used to iteratively solve the discretized equation group to obtain the velocity field, pressure field and turbulence intensity.

8. The burner cold flow field measurement system based on the pressure sensor array according to claim 7, characterized in that: After sorting and integrating the pressure data of different collection points at different times based on the pressure information, the data processing module is further used to: The sorted and integrated pressure data are preprocessed to obtain preprocessed pressure data.

9. The burner cold flow field measurement system based on the pressure sensor array according to claim 8, characterized in that: Preprocess the sorted and integrated pressure data, including: Removing the residual noise that has not been completely removed from the sorted and integrated pressure data; Using statistical analysis methods, identifying and eliminating abnormal pressure data after removing residual noise; wherein the abnormal pressure data is pressure data that deviates from the normal pressure range; The remaining normal pressure data are smoothed using the polynomial fitting method.

10. The burner cold flow field measurement system based on the pressure sensor array according to claim 2, characterized in that: After calculating the flow field parameters based on the digitized pressure data packet, the data processing module is further used to: Compare the measured pressure value of each independent driving unit of the pressure sensor with the predicted pressure value in the key flow field characteristic parameters of the corresponding acquisition point; When the error between each measured pressure value and the corresponding predicted pressure value exceeds a preset error range, the spatial geometric model is replaced, the mesh quality of the spatial geometric model is optimized, and the boundary conditions of the flow field are modified to recalculate the flow field parameters.

11. The burner cold flow field measurement system based on pressure sensor array according to claim 7, characterized in that: The key flow field characteristic parameters include flow velocity characteristic parameters, pressure characteristic parameters and turbulence characteristic parameters; Extracting features from the flow field parameters based on the flow field feature data to obtain key flow field feature parameters includes: Based on the real-time and historical operating data of the burner, external environment data and test data, a multi-dimensional comprehensive analysis is performed to obtain flow field information; The characteristics of the velocity field, the pressure field and the turbulence intensity are extracted respectively according to the flow field information to obtain velocity characteristic parameters, pressure characteristic parameters and turbulence characteristic parameters.

12. The burner cold flow field measurement system based on pressure sensor array according to claim 1, characterized in that: After extracting the features in the flow field parameters based on the flow field characteristic data to obtain key flow field characteristic parameters, the data processing module is further used to: Inputting the characteristic vector formed by the key flow field characteristic parameters into the trained machine learning model, and using data analysis and calculation software to extract high-level features in the key flow field characteristic parameters based on the machine learning model; Based on the machine learning model, the flow field parameters are optimized and corrected according to the high-level features and the ideal patterns and laws of the flow field parameters learned during the training process; The optimized and corrected flow field parameters are fed back to the machine learning model to update and improve the machine learning model.

13. The burner cold flow field measurement system based on pressure sensor array according to claim 1, characterized in that: After outputting the key flow field characteristic parameters to the display module in a structured data format, the data processing module is further used to: The intermediate data and result data in the data processing process are stored in the database, and the intermediate data and result data are used to provide data support for subsequent historical data analysis, fault diagnosis and model verification.

14. The burner cold flow field measurement system based on the pressure sensor array according to claim 11, characterized in that: Display key flow field characteristic parameters output in a structured data format in a preset display form, including: The velocity vector diagram of the velocity characteristic parameter, the pressure cloud diagram of the pressure characteristic parameter and the streamline diagram of the turbulence characteristic parameter are displayed in the form of two-dimensional graphics, three-dimensional graphics, charts or animations.

15. A method for measuring the cold flow field of a burner based on a pressure sensor array, characterized in that: The method is implemented based on the combustor cold flow field measurement system based on the pressure sensor array according to any one of claims 1 to 14, and the method comprises: Measuring the pressure of the gas ejected from the burner nozzle based on the pressure sensor array, converting the pressure signal into an electrical signal, and outputting the electrical signal to the data acquisition module; Based on the data acquisition module, the electrical signal is collected in real time using the synchronous clock technology, and the electrical signal is converted into analog to digital to obtain a digital pressure data packet corresponding to the electrical signal, and the digital pressure data packet is output to the data processing module; Based on the data processing module receiving the digitized pressure data packet, calculating the flow field parameters according to the digitized pressure data packet, acquiring the flow field characteristic data, extracting the characteristics in the flow field parameters according to the flow field characteristic data, obtaining the key flow field characteristic parameters, and outputting the key flow field characteristic parameters to the display module in a structured data format; The key flow field characteristic parameters output in a structured data format are displayed in a preset display form based on the display module.

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