Equipment infrared characteristic modeling method based on data multiplexing and multi-dimensional dynamic environment coupling technology

Abstract

The invention discloses an equipment infrared characteristic modeling method based on a data multiplexing and multi-dimensional dynamic environment coupling technology. The method comprises the following steps: constructing a material infrared attribute library; constructing an environment temperature and humidity change function, predicting the real-time temperature and humidity of a simulation scene based on geographic positions, time characteristics and historical meteorological data, and calculating the infrared radiation intensity of the equipment surface in combination with material infrared attribute library data; through a data cache multiplexing mechanism, storing target data generated in a simulation process and a calculation result in a preset format, and preferentially verifying and calling effective cache data in subsequent simulation; a visual detection model is established, infrared radiation intensity is converted into a gray level image, noise superposition and fuzzy processing are carried out, real infrared lens characteristics are simulated, and an equipment infrared characteristic image is output. According to the method, the fidelity of equipment infrared modeling and the real-time performance of infrared simulation are improved by establishing the material attribute library and caching and multiplexing data.

Description

Technical Field

[0001] The present invention relates to the technical field of infrared modeling and simulation, and particularly to a method for modeling the infrared characteristics of equipment based on data reuse and multi-dimensional dynamic environment coupling technology. Background Art

[0002] In the field of space-based earth target detection, the acquisition of infrared image data faces many challenges. For the demand of intelligent detection equipment for the lack of algorithm training data in target detection, a relatively accurate infrared scene simulation method is required to realize the simulation output of infrared simulation images and provide highly realistic simulation images for algorithm training.

[0003] Infrared images are not only affected by the thermal radiation characteristics of the target itself, but also restricted by various factors such as atmospheric conditions and environmental temperature, making the data collected each time unique and non-replicable. Secondly, due to the complexity and high-precision requirements of infrared imaging technology, even the data obtained through simulation means is difficult to meet the needs of large-scale model training in terms of quality and quantity. Currently, infrared simulation mainly faces the following two problems:

[0004] One is the insufficient fidelity of the generated simulation images. The key to the fidelity of the infrared simulation system lies in calculating the infrared radiation characteristics of the targets in the scene, that is, obtaining the surface temperature distribution of each part of the targets in the scene. This involves complex heat transfer knowledge and infrared radiation theory, and equations need to be established and solved. In the actual environment, there are various heat exchange methods (such as radiation, conduction, convection, etc.), and these factors affect each other, making it difficult to ensure the accuracy of radiation calculation.

[0005] The other is the insufficient real-time performance of the generated simulation images. The infrared simulation system needs to process a large amount of data and complex calculation processes (such as 3D modeling, radiation calculation, etc.), which consume a large amount of computing resources, and an unreasonable algorithm structure will also affect the real-time performance. Summary of the Invention

[0006] To solve the above technical problems existing in the prior art, the present invention proposes a method for modeling the infrared characteristics of equipment based on data reuse and multi-dimensional dynamic environment coupling technology, and by establishing a material attribute library and data caching and reuse, the purpose of improving the fidelity of equipment infrared modeling and the real-time performance of infrared simulation is achieved.

[0007] To achieve the above purpose, the present invention provides a method for modeling the infrared characteristics of equipment based on data reuse and multi-dimensional dynamic environment coupling technology, including:

[0008] Construct a material infrared attribute library;

[0009] Construct an environmental temperature and humidity change function, predict the real-time temperature and humidity of the simulation scenario based on geographical location, time characteristics and historical meteorological data, and calculate the infrared radiation intensity on the surface of the equipment in combination with the data in the material infrared property library;

[0010] Through a data cache reuse mechanism, store the target data and calculation results generated during the simulation process in a preset format, and preferentially verify and call the valid cache data in subsequent simulations;

[0011] Establish a visualization detection model, convert the infrared radiation intensity into a grayscale image, perform superposition noise and blur processing, simulate the characteristics of a real infrared lens, and output an infrared characteristic image of the equipment.

[0012] Preferably, the material infrared property library stores the thermal properties and optical properties of several surface materials of the equipment. The thermal properties include the thermal conductivity, specific heat capacity, thermal diffusivity, and emissivity of the material; the optical properties include the reflectivity, transmittance, and spectral absorption coefficient of the material.

[0013] Preferably, constructing the material infrared property library includes:

[0014] Obtain the attribute data of the material;

[0015] Perform outlier detection on the attribute data, store the detected data in a preset format, and read and call it in the target order.

[0016] Preferably, the environmental temperature and humidity change function is established through statistical regression analysis. The input parameters include longitude, latitude, timestamp, and weather conditions, and the output result is the predicted real-time temperature and humidity value.

[0017] Preferably, calculating the infrared radiation intensity on the surface of the equipment specifically is:

[0018]

[0019] In the formula, ε is the emissivity of the surface material of the equipment; λ1 and λ2 are both wavelength ranges; c1 is the first radiation constant; c2 is the second radiation constant; T is the absolute temperature of the equipment surface.

[0020] Preferably, the data cache reuse mechanism includes adding metadata tags to the cache data, and verifying the integrity and parameter matching of the cache data before each simulation. If the cache is invalid or missing, recalculate and store the new data;

[0021] Among them, the metadata includes timestamp, longitude and latitude, material name, and infrared band.

[0022] Preferably, during the simulation through the data cache reuse mechanism, it further includes: regularly eliminating old cache data, deleting the data with the lowest usage frequency or the longest storage time, and optimizing the utilization rate of the storage space.

[0023] Preferably, converting the infrared radiation intensity into a grayscale image specifically is:

[0024]

[0025] In the formula, G i represents the grayscale value corresponding to a certain point on the equipment target displayed on the screen; r is a constant related to the equivalent value of the infrared ambient light; E is the radiation intensity of a certain point on the equipment target; E max is the maximum value of the radiation intensity in this scene; E min is the minimum value of the radiation intensity in this scene.

[0026] Preferably, the three-dimensional model of the equipment is constructed by modeling software. The surface material of the three-dimensional model corresponds to the number in the material infrared attribute library, and the material number is stored through the Alpha channel of the pixel.

[0027] Compared with the prior art, the present invention has the following advantages and technical effects:

[0028] The present invention can determine the input parameters of the infrared characteristics of the equipment in different scenarios through data combination according to the data in the material attribute library; can predict the environmental temperature and humidity information for different moments, simulate the infrared radiation intensity in the real scene; can enhance the resource utilization efficiency through data caching and reuse, reduce repeated and complex calculations, and improve the real-time performance of generating simulation images. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0030] Figure 1 is a flowchart of a method for modeling the infrared characteristics of equipment based on data reuse and multi-dimensional dynamic environment coupling technology according to an embodiment of the present invention;

[0031] Figure 2 is a flowchart of constructing a material infrared attribute library according to an embodiment of the present invention;

[0032] Figure 3 is a flowchart of simulating using a data cache reuse mechanism according to an embodiment of the present invention;

[0033] Figure 4 is the first infrared characteristic image output by the method according to an embodiment of the present invention;

[0034] Figure 5 This is the second infrared characteristic image output by the method of the embodiment of the present invention. Specific Embodiments

[0035] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0036] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] The present invention proposes an equipment infrared characteristic modeling method based on data multiplexing and multi-dimensional dynamic environment coupling technology, as Figure 1 including:

[0038] Construct a material infrared attribute library;

[0039] Construct an environmental temperature and humidity change function, predict the real-time temperature and humidity of the simulation scene based on geographical location, time characteristics and historical meteorological data, and calculate the infrared radiation intensity on the surface of the equipment in combination with the data in the material infrared attribute library;

[0040] Through a data cache multiplexing mechanism, store the target data and calculation results generated during the simulation process in a preset format, and preferentially verify and call the valid cached data in subsequent simulations;

[0041] Establish a visualization detection model, convert the infrared radiation intensity into a grayscale image, perform superposition noise and blur processing, simulate the characteristics of a real infrared lens, and output an equipment infrared characteristic image.

[0042] In this embodiment, by constructing a material attribute library to simulate the thermal and optical properties of materials, etc., the equipment details in complex scenarios are increased; by the method of a dynamic environment real-time update mechanism, the dynamic fidelity of infrared simulation is improved; by the data cache and multiplexing method, the real-time performance of simulation calculation is improved; by establishing a visualization detection model, the imaging effect of an infrared payload is simulated; finally, an equipment infrared characteristic image is output.

[0043] Furthermore, the material infrared attribute library stores the thermal properties and optical properties of several kinds of equipment surface materials. The thermal properties include the thermal conductivity, specific heat capacity, thermal diffusion coefficient, and emissivity of the material; the optical properties include the reflectivity, transmittance, and spectral absorption coefficient of the material.

[0044] The material infrared attribute library is a data set, which includes detailed attribute information of various materials in various infrared bands. These attributes are divided into two categories: thermal properties and optical properties.

[0045] In this embodiment, the thermal properties include the thermal conductivity, specific heat capacity, thermal diffusivity, emissivity, etc. of the material. The thermal conductivity describes the ability of the material to conduct heat. The specific heat capacity represents the amount of heat absorbed or released by a unit mass of a substance when its temperature is raised or lowered by a unit degree. The thermal diffusivity represents the rate of heat propagation in the material. The emissivity is the ability of the material surface to emit heat in the form of radiation, which is the relative value compared with a black body, and the color of the material will also affect the emissivity of the material.

[0046] The optical properties include the reflectivity, transmittance, spectral absorption coefficient, etc. of the material. The reflectivity is the proportion of the incident light wave reflected by the material surface. The transmittance is the proportion of the light wave passing through the material without being absorbed or reflected. The spectral absorption coefficient represents the absorption rate varying with the wavelength.

[0047] Furthermore, constructing the infrared property library of the material includes:

[0048] Directly measure the properties of the material through an optical measurement device, or obtain property data by referring to reference documents, data manuals or industry standards;

[0049] Perform outlier detection on the obtained property data, store the detected data in JSON format, and read and call it in the order of material, wavelength band, thermal property, and optical property.

[0050] Specifically, there are two methods to obtain the infrared properties of the material: one is to directly measure the properties of the material using professional thermal and optical measurement devices (such as infrared spectrometers, thermal conductivity testers, etc.); the other is to refer to existing research documents, data manuals or industry standards, which contain property data of a large number of common materials. Store the obtained property data in JSON format and read and call it in the order of material, wavelength band, thermal property, and optical property.

[0051] Sort out the determined material property data, remove duplicate, incorrect or invalid data points, and perform outlier detection on the experimentally obtained data to ensure the accuracy and reliability of the data.

[0052] Such as Figure 2 , store the sorted data in a JSON data structure, design the top-level object of the "materials" field, and each "materials" item corresponds to a material, including sub-fields such as "name" (name), "properties" (properties), etc. Under the "properties" field, it is further divided into thermal properties and optical properties, including "λ" (thermal conductivity), "c" (specific heat capacity), "a" (thermal diffusivity), "ε" (emissivity), "ρ" (reflectivity), "δ" (transmittance).

[0053] Write a script for reading material property library data in C# language to quickly retrieve and read the required property data according to conditions such as material name and wavelength band.

[0054] Based on the three-dimensional dimension data of the ground equipment target, use modeling software to build a 1:1 model of the ground equipment target (such as a tank, a loading vehicle, etc.).

[0055] Use the a channel in the equipment model pixel storage array color.rgba to store the equipment material number at the corresponding pixel position, and establish the correspondence between the three-dimensional model of the equipment target and the material infrared property library.

[0056] Furthermore, the environmental temperature and humidity change function is established by statistical regression analysis. The input parameters include longitude, latitude, timestamp, and weather conditions, and the output result is the predicted real-time temperature and humidity value.

[0057] Specifically, the environmental temperature and humidity change function is used to estimate the environmental temperature and humidity at a future moment. This model is constructed based on factors such as historical data, geographical location, and time characteristics.

[0058] In this embodiment, by inputting the information of a certain location at a certain moment, the temperature and humidity output information under this input is obtained. Combining the temperature and humidity information and the material property library data, the infrared radiation intensity of the equipment is calculated. The infrared radiation intensity is a physical quantity that describes the transmission characteristics of infrared radiation energy and reflects the strength of the energy radiated by an object.

[0059] Specifically, it includes:

[0060] Obtain historical temperature and humidity data at different longitude and latitude locations from a weather station or a weather database. This data includes longitude, latitude, weather, timestamp, temperature value, and humidity value;

[0061] Use statistical regression analysis to analyze the data and find the changing trends of temperature and humidity with longitude, latitude, time date, and weather;

[0062] Based on the data analysis results, construct temperature and humidity change functions at different longitudes and latitudes to predict the real-time environmental temperature and humidity in the simulation scenario;

[0063] According to the real-time environmental temperature and humidity, thermal conductivity, specific heat capacity, thermal diffusivity, reflectivity, transmittance, and the simulation infrared wavelength band, calculate the real-time temperature of the equipment target in the scenario;

[0064] Calculate the surface radiation intensity of the equipment target according to the Planck formula:

[0065]

[0066] Wherein, ε is the emissivity of the surface material of the equipment; λ1 and λ2 are both wavelength ranges, with the unit of μm; c1 is the first radiation constant, c1 = 3.7418×10 4 (W·μm 4 / cm 2 );c2 is the second radiation constant, c2 = 1.4388×10 4 (μm·K); T is the absolute temperature of the equipment surface.

[0067] Furthermore, the data cache reuse mechanism includes adding metadata tags to the cached data, and verifying the integrity and parameter matching of the cached data before each simulation. If the cache is invalid or missing, recalculate and store the new data;

[0068] Among them, the metadata includes timestamp, longitude and latitude, material name and infrared band.

[0069] Specifically, the data cache reuse mechanism saves the target data and results to the storage medium in real time during the simulation process. Before the next simulation, first check whether there is relevant cached data. If there is relevant data, verify its validity. If the cached data is valid and meets the requirements, directly call these data for further processing, otherwise perform a new simulation calculation and save the new results to the storage medium for subsequent reuse.

[0070] Among them, the target data and results include the environmental temperature and humidity at a future moment, the infrared radiation intensity of different materials in different infrared bands, etc. The data cache reuse mechanism can reduce the calculation time, improve the resource utilization rate, and speed up the infrared simulation analysis speed.

[0071] The flowchart is as shown in the appendix Figure 3 as follows:

[0072] Step 1: Convert the calculated environmental temperature and humidity data, infrared radiation intensity data, etc. into the JSON data format. Add metadata to each cached data for subsequent data retrieval and verification, including timestamp, longitude and latitude, material, infrared band, etc. Save the formatted data and metadata to the storage medium.

[0073] Step 2: Before the next simulation, first query the metadata in the storage medium, verify whether the metadata parameters in the cached data match the current simulation requirements, and check whether the cached data is complete and not accidentally modified or damaged.

[0074] Step 3: If the cached data passes the validity verification, directly call these data for subsequent processing. If the cached data is invalid or does not exist, a new simulation calculation is required, and the results are saved in the storage medium according to Step 1 for subsequent reuse.

[0075] Step 4: As the number of simulation times increases, more and more cached data will be generated, occupying a large amount of storage space. It is necessary to eliminate some old data to free up space, regularly delete the least frequently used old data and invalid data to keep the cache system healthy and efficient.

[0076] Furthermore, a visualization detection model is established to convert the infrared radiation intensity of the equipment into infrared brightness information, and noise and blurring processing are superimposed, and finally an infrared characteristic image of the equipment is output.

[0077] The visualization detection model includes infrared brightness conversion, noise superposition, blurring processing, etc. The infrared brightness information is obtained by converting the infrared radiation intensity into grayscale. The noise superposition is realized by using a noise generation script, and the blurring processing of the detection model is realized by using a blurring generation script to complete the output display of the infrared characteristic image of the equipment.

[0078] Specifically, it includes:

[0079] The calculated infrared radiation intensity of the equipment needs to be converted into the screen grayscale value, and the conversion from radiation intensity to grayscale is completed according to the following formula:

[0080]

[0081] In the formula, G i represents the grayscale value corresponding to a certain point on the equipment target displayed on the screen; r is a constant related to the equivalent value of the infrared ambient light, and the value range is 0 ≤ r < 1; E is the radiation intensity of a certain point on the equipment target; E max is the maximum value of the radiation intensity in this scene; E min is the minimum value of the radiation intensity in this scene. In this embodiment, the radiation intensity E is mapped to the grayscale value range of 0 to 255 to form a grayscale image.

[0082] Use the screen post-processing script to superimpose noise and blurring processing on the display device to simulate the characteristics of the infrared lens in a real infrared scene.

[0083] Combine the grayscale information of the three-dimensional model of the equipment target with the screen noise, etc. to complete the visualization detection model, and finally output the infrared characteristic image of the equipment. Figure 4 - Figure 5 It is a short-wave infrared simulation image of a street scene at 15:00 in the afternoon of January at a location with longitude: 116.242300 and latitude: 40.067968.

[0084] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An infrared characteristic modeling method for equipment based on data reuse and multi-dimensional dynamic environment coupling technology, characterized in that Including: Construct an infrared property library of materials; Construct an environmental temperature and humidity change function, predict the real-time temperature and humidity of the simulation scenario based on geographical location, time characteristics and historical meteorological data, and calculate the infrared radiation intensity on the surface of the equipment in combination with the data in the material infrared property library; Through the data cache reuse mechanism, store the target data and calculation results generated during the simulation in a preset format, and preferentially verify and call the valid cache data in subsequent simulations; Establish a visual detection model, convert the infrared radiation intensity into a grayscale image, perform noise and blur processing, simulate the characteristics of a real infrared lens, and output an infrared characteristic image of the equipment.

2. The infrared characteristic modeling method of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 1, characterized in that, The material infrared property library stores the thermal properties and optical properties of several equipment surface materials. The thermal properties include the thermal conductivity, specific heat capacity, thermal diffusivity, and emissivity of the material; the optical properties include the reflectivity, transmittance, and spectral absorption coefficient of the material.

3. A method for modeling the infrared characteristics of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 2, characterized in that, Constructing the material infrared property library includes: Obtain the property data of the material; Perform outlier detection on the property data, store the detected data in a preset format, and read and call it in the target order.

4. A method for modeling the infrared characteristics of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 1, characterized in that The environmental temperature and humidity change function is established by statistical regression analysis. The input parameters include longitude, latitude, timestamp, and weather conditions, and the output result is the predicted real-time temperature and humidity value.

5. A method for modeling the infrared characteristics of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 1, characterized in that, Specifically, calculating the infrared radiation intensity on the surface of the equipment is: In the formula, ε is the emissivity of the equipment surface material; λ1 and λ2 are both wavelength ranges; c1 is the first radiation constant; c2 is the second radiation constant; T is the absolute temperature of the equipment surface.

6. The infrared characteristic modeling method of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 1, characterized in that The data cache reuse mechanism includes adding metadata tags to the cache data, and verifying the integrity and parameter matching of the cache data before each simulation. If the cache is invalid or missing, recalculate and store the new data; Among them, the metadata includes timestamp, longitude and latitude, material name, and infrared band.

7. An infrared characteristic modeling method for equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 6, characterized in that During the simulation through the data cache reuse mechanism, it also includes: regularly eliminating old cache data, deleting the data with the lowest usage frequency or the longest storage time, and optimizing the storage space utilization rate.

8. A method for modeling the infrared characteristics of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to claim 1, characterized in that, Specifically, converting the infrared radiation intensity into a grayscale image is: Where G i represents the grayscale value corresponding to the display of a certain point on the equipment target on the screen; r is a constant related to the equivalent value of infrared ambient light; E is the radiation intensity of a certain point on the equipment target; E max is the maximum value of the radiation intensity in this scene; E min is the minimum value of the radiation intensity in this scene.

9. A method for modeling the infrared characteristics of equipment based on data multiplexing and multi-dimensional dynamic environment coupling technology according to any one of claims 1-8, characterized in that, The three-dimensional model of the equipment is constructed by modeling software. The surface material of the three-dimensional model corresponds to the number in the material infrared property library, and the material number is stored through the Alpha channel of the pixel.

Images