CN-121980324-A - Method, system, equipment and medium for digitally and visually monitoring flow field in factory building

CN121980324ACN 121980324 ACN121980324 ACN 121980324ACN-121980324-A

Abstract

The application belongs to the technical field of flow field monitoring, and particularly relates to a method, a system, equipment and a medium for digitally and visually monitoring a flow field in a factory building; the method comprises the steps of calculating flow field speed gradient based on initial flow field distribution data, comparing the flow field speed gradient with a preset first speed gradient threshold value and a preset second speed gradient threshold value, dividing a monitoring area based on a comparison result, arranging a sensor network, collecting actual flow field data in real time, comparing the actual flow field data with the initial flow field distribution data, optimizing the initial flow field distribution data, fusing the optimized flow field distribution data and the actual flow field data, and generating and dynamically updating a three-dimensional visual image. The application improves the accuracy of monitoring the flow field of the factory building.

Inventors

HE PENG
Yao Niyu
LUO ZHIHAO
WU ZHIYONG
FENG MIAO
LIU CHENYONG
TIAN ZHEN

Assignees

中国航发湖南动力机械研究所

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (10)

1. A digitalized visual monitoring method for the flow field in factory is characterized by that, The method comprises the following steps: establishing a multi-mode flow field simulation model according to a plant structure and equipment layout of a target plant, and fusing simulation results of the multi-mode flow field simulation model based on an initial multi-turbulence model fusion frame to obtain initial flow field distribution data; Calculating a flow field velocity gradient based on initial flow field distribution data, comparing the flow field velocity gradient with a preset first velocity gradient threshold value and a preset second velocity gradient threshold value, dividing monitoring areas based on comparison results, and determining the type, the number and arrangement coordinate positions of a sensor network according to the flow field complexity and the spatial change rate of the gradients of each monitoring area, wherein the sensor network at least comprises a three-dimensional ultrasonic anemometer for measuring flow velocity, an infrared thermal image sensor array for measuring temperature distribution and a laser scattering particle counter for measuring air particulate concentration; The method comprises the steps of collecting actually measured flow field data of the sensor network in real time, comparing the actually measured flow field data with the initial flow field distribution data, carrying out sensor data fusion and space-time calibration based on residual errors of comparison results, and correcting model parameters in an initial multi-turbulence model fusion frame by adopting a data assimilation algorithm so as to optimize the initial flow field distribution data and obtain optimized flow field distribution data; And fusing the optimized flow field distribution data with the actually measured flow field data, generating and dynamically updating three-dimensional visual images of the flow field in the factory building through a multi-mode visual engine, and synchronously displaying flow field modes and key parameters of each monitoring area, wherein the key parameters at least comprise a real-time wind speed vector, turbulence intensity, a temperature gradient cloud picture and concentration distribution of particles with specific particle sizes.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises, Establishing a computational fluid dynamics simulation model according to a plant structure and equipment layout of a target plant, wherein the computational fluid dynamics simulation model specifically comprises the following steps: Determining actual space layout data based on the plant structure and the equipment layout, dividing a plurality of subareas based on the actual space layout, and configuring grid density for the subareas; And constructing a multi-mode flow field simulation model based on the divided subareas and the grid density corresponding to the subareas, wherein different turbulence models are configured for different subareas to perform coupling calculation.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises, The method adopts an initial multi-turbulence model fusion frame to simulate the multi-mode flow field, and specifically comprises the following steps: integrating sub-region simulation outputs of different turbulence models in the multi-mode flow field simulation model to obtain an output set; And carrying out data fusion on the output set based on a preset fusion rule, and obtaining initial flow field distribution data based on a fusion result.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises, Dividing the monitoring areas based on the comparison result, and determining the type, the number and the arrangement coordinate positions of the sensor network according to the flow field complexity and the spatial change rate of the gradient of each monitoring area, wherein the method specifically comprises the following steps: Dividing a flow field velocity gradient into a core parameter monitoring area when the flow field velocity gradient is larger than the first velocity gradient threshold value, distributing the three-dimensional ultrasonic anemometer around a key disturbance source at a first preset interval, and distributing the infrared thermal image sensor array at a second preset interval for capturing dynamic flow field parameters with high space-time resolution; Dividing the flow field velocity gradient into a transition zone monitoring network when the flow field velocity gradient is between the first velocity gradient threshold value and the second velocity gradient threshold value, and linearly adjusting the distribution density of the three-dimensional ultrasonic anemometer and the laser scattering particle counter according to the numerical value and the direction change rate of the local velocity gradient; and when the flow field speed gradient is smaller than the second speed gradient threshold, dividing the flow field speed gradient into boundary layer characteristic monitoring areas, and arranging sensor chains comprising the three-dimensional ultrasonic anemometer and the infrared thermal image sensor array at increasing intervals along the normal direction of the inner wall of a factory building or the surface of large equipment.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises, Comparing the measured data with the initial flow field distribution data, wherein the method specifically comprises the following steps: comparing the actually measured flow field data acquired by the sensor network in real time with the initial flow field distribution data corresponding to the space position and time to calculate a local error; When the local error exceeds a preset error threshold, triggering the initial multi-turbulence model fusion frame to adjust the turbulence model calculation weight of the corresponding area in the simulation model, and obtaining an adjustment weight.
6. The method of claim 5, wherein the step of determining the position of the probe is performed, Optimizing initial flow field distribution data based on comparison results specifically comprises the following steps: Updating the initial multi-turbulence model fusion frame based on the adjustment weight to obtain an updated multi-turbulence model fusion frame; and re-fusing simulation results of the multi-mode flow field simulation model in the corresponding area according to the updated multi-turbulence model fusion frame to generate the optimized flow field distribution data.
7. The method of claim 1, wherein the step of determining the position of the substrate comprises, Generating and dynamically updating a three-dimensional visual image of a flow field in a factory building through a multi-mode visual engine, wherein the three-dimensional visual image comprises the following specific steps: According to the dividing result of the monitoring area, correspondingly dividing the flow field visualization area into a core area, a transition area, a steady-state area and a boundary layer area, wherein the three-dimensional flow field morphology of the core area and the transition area is directly driven by the actual measurement data of the sensor of the corresponding monitoring area and is rendered and updated in real time, and the three-dimensional flow field morphology of the boundary layer area is driven and updated according to the simulation result in the optimized flow field distribution data; based on the flow field data of the core region, the transition region and the boundary layer region, generating a continuous flow field form of the steady-state region through an interpolation algorithm, and realizing three-dimensional visual display.
8. A digitalized visual monitoring system for the flow field in factory is characterized in that, The system comprises: The simulation module is used for establishing a multi-mode flow field simulation model according to the plant structure and equipment layout of the target plant, and fusing simulation results of the multi-mode flow field simulation model based on the initial multi-turbulence model fusion frame to obtain initial flow field distribution data; The sensor arrangement module is used for calculating a flow field speed gradient based on initial flow field distribution data, comparing the flow field speed gradient with a preset first speed gradient threshold value and a preset second speed gradient threshold value, dividing monitoring areas based on comparison results, and determining the type, the number and arrangement coordinate positions of a sensor network according to the flow field complexity and the spatial change rate of the gradient of each monitoring area, wherein the sensor network at least comprises a three-dimensional ultrasonic anemometer for measuring flow speed, an infrared thermal image sensor array for measuring temperature distribution and a laser scattering particle counter for measuring air particulate concentration; The data optimization module is used for collecting the actually measured flow field data of the sensor network in real time, comparing the actually measured flow field data with the initial flow field distribution data, carrying out sensor data fusion and space-time calibration based on residual errors of comparison results, and adopting a data assimilation algorithm to correct model parameters in an initial multi-turbulence model fusion frame so as to optimize the initial flow field distribution data and obtain optimized flow field distribution data; The visualization module is used for fusing the optimized flow field distribution data and the actually measured flow field data, generating and dynamically updating three-dimensional visual images of the flow field in the factory building through the multi-mode visual engine, and synchronously displaying the flow field modes and key parameters of each monitoring area, wherein the key parameters at least comprise a real-time wind speed vector, turbulence intensity, a temperature gradient cloud picture and concentration distribution of particles with specific particle sizes.
9. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus; a memory for storing a computer program; the processor is used for realizing the steps of the method for digitally and visually monitoring the flow field in the factory building according to any one of claims 1to 7 when executing the program stored in the memory.
10. A computer storage medium, wherein a computer program is stored in the computer storage medium, and when the computer program is executed by a processor, the computer program realizes the steps of the method for digitally and visually monitoring a fluid field in a factory building according to any one of claims 1 to 7.

Description

Method, system, equipment and medium for digitally and visually monitoring flow field in factory building Technical Field The application belongs to the technical field of flow field monitoring, and particularly relates to a method, a system, equipment and a medium for digital visual monitoring of a flow field in a factory building. Background The flow field monitoring of the traditional industrial factory building generally relies on experience to arrange measuring points, and has the following defects that 1) simulation and physical monitoring are disjointed, a model cannot be dynamically adjusted according to real-time data, 2) the measuring point arrangement lacks a multi-level optimization strategy, so that critical area coverage is insufficient or redundancy is caused, 3) a single turbulence model is difficult to adapt to complex working condition change, simulation precision is limited, and 4) a visualization technology is static, so that real-time synchronous updating of a flow field mode cannot be realized. Therefore, there is a need for a comprehensive solution that incorporates multi-modal simulation, intelligent site placement, closed loop optimization, and real-time visualization. Disclosure of Invention In order to solve the problems, the application provides a digital visual monitoring method, a system, equipment and a medium for a factory building internal flow field, which adopt a multi-mode simulation and actual measurement data fusion technology, realize simulation precision closed-loop optimization by dynamically adjusting turbulence model weights, and simultaneously combine an intelligent sensor arrangement strategy and a three-dimensional visual engine, so that the problems of disconnection of simulation and monitoring, unreasonable measuring point arrangement, poor model adaptability and visual lag in the traditional method are solved, and the real-time performance and accuracy of factory building flow field monitoring can be improved. In a first aspect, the present application provides a method for digitally and visually monitoring a flow field in a factory building, the method comprising: establishing a multi-mode flow field simulation model according to a plant structure and equipment layout of a target plant, and fusing simulation results of the multi-mode flow field simulation model based on an initial multi-turbulence model fusion frame to obtain initial flow field distribution data; Calculating a flow field velocity gradient based on initial flow field distribution data, comparing the flow field velocity gradient with a preset first velocity gradient threshold value and a preset second velocity gradient threshold value, dividing monitoring areas based on comparison results, and determining the type, the number and arrangement coordinate positions of a sensor network according to the flow field complexity and the spatial change rate of the gradients of each monitoring area, wherein the sensor network at least comprises a three-dimensional ultrasonic anemometer for measuring flow velocity, an infrared thermal image sensor array for measuring temperature distribution and a laser scattering particle counter for measuring air particulate concentration; The method comprises the steps of collecting actually measured flow field data of the sensor network in real time, comparing the actually measured flow field data with the initial flow field distribution data, carrying out sensor data fusion and space-time calibration based on residual errors of comparison results, and correcting model parameters in an initial multi-turbulence model fusion frame by adopting a data assimilation algorithm so as to optimize the initial flow field distribution data and obtain optimized flow field distribution data; And fusing the optimized flow field distribution data with the actually measured flow field data, generating and dynamically updating three-dimensional visual images of the flow field in the factory building through a multi-mode visual engine, and synchronously displaying flow field modes and key parameters of each monitoring area, wherein the key parameters at least comprise a real-time wind speed vector, turbulence intensity, a temperature gradient cloud picture and concentration distribution of particles with specific particle sizes. Further, the method comprises the steps of, Establishing a computational fluid dynamics simulation model according to a plant structure and equipment layout of a target plant, wherein the computational fluid dynamics simulation model specifically comprises the following steps: Determining actual space layout data based on the plant structure and the equipment layout, dividing a plurality of subareas based on the actual space layout, and configuring grid density for the subareas; And constructing a multi-mode flow field simulation model based on the divided subareas and the grid density corresponding to the subareas, wherein different turbulence models are config