A high-fidelity equipment simulation method and system based on a three-dimensional digital model

Abstract

This invention discloses a high-fidelity equipment simulation method and system based on a three-dimensional digital model, belonging to the field of digital equipment status monitoring technology. The method includes: preprocessing point cloud scanning data, performing registration, fitting, and model reconstruction based on an initial three-dimensional digital model to generate a three-dimensional geometric model of the equipment; constructing an infrared thermal distribution model based on infrared thermal imaging data and the three-dimensional geometric model; using a multi-loop closed-loop temperature control algorithm to differentiate and adjust the temperature of flexible heating elements in each heat source region to obtain infrared radiation characteristics; constructing an infrared emissivity benchmark database based on the divided heat source regions and infrared radiation characteristics, and forming a simulated outer surface layer using an infrared camouflage coating; constructing a radar feature benchmark database using the three-dimensional geometric model and a broadband radar test terminal, and retesting and verifying the simulated outer surface layer to obtain a multi-spectral inflatable simulated target. This invention can quickly meet the equipment requirements for large-scale camouflage and deception on the battlefield, possessing engineering application value and scalability.

Description

Technical Field

[0001] This invention relates to the field of digital monitoring technology for equipment status, and more specifically, to a highly realistic equipment simulation method and system based on a three-dimensional digital model. Background Technology

[0002] High-fidelity simulation equipment (i.e., inflatable simulation equipment) is developed to meet the application requirements of equipment simulation and deception. By developing equipment decoys that are highly consistent with real equipment in terms of appearance, infrared characteristics, radar echo characteristics, etc., a realistic battlefield camouflage effect can be achieved, realizing equipment simulation camouflage, improving target deception, and meeting the application requirements of simulation and deception in multiple scenarios.

[0003] However, existing simulation equipment technologies with shape-simulation capabilities only replicate the outline of real equipment at a 1:1 scale, achieving a basic level of deception in visible light reconnaissance scenarios. This technology only provides single-dimensional shape camouflage and lacks the ability to simulate infrared and radar echo characteristics, making it ill-suited for the multi-spectral reconnaissance environments of modern battlefields (such as infrared and radar reconnaissance). When the enemy employs infrared detection or radar scanning, these decoys will be quickly identified due to fundamental differences in infrared radiation characteristics and radar cross-section (RCS) compared to real equipment, completely negating their camouflage effect.

[0004] Another type of existing simulation technology involves simulating the shape and infrared characteristics of equipment. Based on shape simulation, it achieves infrared radiation characteristics consistent with real equipment by adding infrared heating devices and simulating heat source distribution. However, this type of technology lacks specific design for radar echo characteristics and cannot simulate key radar features of real equipment, such as radar cross section (RCS), echo waveform, and polarization characteristics. In modern battlefields, radar reconnaissance is a core means for the enemy to detect, identify, and lock onto equipment. These decoys will exhibit echo signals completely different from real equipment under radar detection, making them easily identified as false targets by enemy radar systems. This leads to the failure of deception and camouflage, failing to meet the deception requirements of multi-spectral battlefields and hindering the achievement of pre-set battlefield camouflage and tactical deception effects.

[0005] There are currently no effective solutions to the problems in the relevant technologies. Summary of the Invention

[0006] To address the problems in related technologies, this invention proposes a high-fidelity equipment simulation method and system based on a three-dimensional digital model, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] Firstly, this invention proposes a high-fidelity equipment simulation method based on a three-dimensional digital model, comprising: S1. Perform a full-dimensional scan of the inflatable simulation equipment, preprocess the point cloud data obtained from the scan, and use the initial three-dimensional digital model to register, fit and reconstruct the preprocessed point cloud data to obtain the three-dimensional geometric model of the equipment. S2. Obtain infrared thermal imaging data of the inflatable simulation equipment and construct an infrared thermal distribution model by combining the equipment's three-dimensional geometric model. S3. Using a multi-loop closed-loop temperature control algorithm, the flexible heating elements distributed in the heat source area are subjected to zoned temperature control, and the infrared radiation characteristics are simulated based on the control results. S4. Use an infrared thermal distribution model to divide the heat source region, and obtain the infrared emissivity benchmark database for each region based on the heat source region and infrared radiation characteristics. Combine this with the target infrared camouflage coating to obtain the simulated outer surface layer. S5. Using the equipment's three-dimensional geometric model and broadband radar test terminal, obtain the radar feature reference database for each region of the inflatable simulation equipment. Based on the radar feature reference database and the zonal coating conditions, retest and verify the simulated outer surface layer to obtain a multi-spectral inflatable simulation target, thereby realizing the simulation of the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment.

[0009] Furthermore, the inflatable simulation equipment is scanned in all dimensions. The point cloud data acquired by the scan is preprocessed, and the preprocessed point cloud data is registered, fitted, and reconstructed using the initial 3D digital model to obtain the equipment's 3D geometric model, including: S11. Using structured light scanning technology, perform a full-dimensional scan of the same type of inflatable simulation equipment to obtain full-dimensional scan point cloud data of the equipment; S12. Preprocess the point cloud data acquired by scanning to remove noise points and redundant points generated during the scanning process; S13. Using feature point matching and global registration, the point cloud data from multi-view scanning is stitched together into a complete global point cloud of the equipment, and the point cloud coordinate system is unified. S14. Convert the preprocessed discrete point cloud data into a continuous triangular mesh model to initially restore the equipment's outline. S15. Using 3D modeling software, construct an initial 3D digital model from the triangular mesh model, and perform registration, fitting, and model reconstruction on the initial 3D digital model. S16. Decompose the reconstructed three-dimensional digital model into planar cutting sheet drawings, and mark the size, crease line, welding position, splicing position and tolerance range of each preset material to obtain the equipment three-dimensional geometric model.

[0010] Furthermore, infrared thermal imaging data of the inflatable simulation equipment is acquired, and combined with the equipment's three-dimensional geometric model, an infrared thermal distribution model is constructed, including: S21. Use an infrared thermal imager to obtain measured infrared thermal imaging data of the inflatable simulation equipment. S22. Based on the three-dimensional geometric model of the equipment, use finite element thermal simulation software to draw thermal distribution cloud maps from infrared thermal imaging measured data; and construct a full-vehicle infrared characteristic thermal distribution model based on the thermal distribution cloud maps. S23. Using the infrared characteristic thermal distribution model, the key locations of typical heat sources of the inflatable simulation equipment are calibrated, and an infrared thermal distribution model is constructed based on the infrared radiation intensity, temperature distribution gradient, thermal radiation range and dynamic change law of the key locations of typical heat sources.

[0011] Furthermore, a multi-loop closed-loop temperature control algorithm is used to perform zoned temperature control on the flexible heating elements distributed in the heat source area, and the simulated infrared radiation characteristics are based on the control results, including: S31. Based on the corresponding position of the inflatable simulated target, a polyimide-based flexible heating film with a built-in thermistor temperature sensor and a polyvinyl chloride mesh composite material is bonded and laid on the substrate using thermally conductive adhesive to obtain a closed-loop control terminal with temperature feedback. S32. Utilize a multi-channel independent closed-loop heating controller to connect with each group of closed-loop control terminals with temperature feedback to construct a multi-loop closed-loop temperature control regulation circuit. S33. Using the step response method, the characteristics of each heating circuit are identified, the temperature response curve is obtained and the characteristic parameters are extracted to construct an inertial pure time delay model. S34. Using the Ziegler-Nichols critical proportional method, the initial tuning of the temperature control parameters is completed, the critical oscillation related parameters are obtained, and the basic control parameters of the preset loop are set. Based on the actual operating conditions of the temperature control terminal, and combined with the anti-integral saturation optimization logic and the derivative-first control structure, the closed-loop temperature control adjustment coefficients are optimized and corrected by region. S35. Based on the infrared thermal imaging time-series temperature data of the simulation equipment, generate a target temperature curve for adjustment, and suppress noise and set a temperature deviation dead zone through moving average filtering. S36. Based on the optimized multi-loop closed-loop temperature control logic, the flexible heating elements of each heat source are divided into zones for temperature control to obtain the infrared radiation characteristics of the simulated equipment.

[0012] Furthermore, using the Ziegler-Nichols critical proportional gain method, the initial tuning of the temperature control parameters is completed, critical oscillation-related parameters are obtained, and the basic control parameters of the preset loop are established. Based on the actual operating conditions of the temperature control terminal, and combined with anti-integral saturation optimization logic and derivative-first control structure, the closed-loop temperature control adjustment coefficients are optimized and corrected in different zones, including: S341. Initial tuning of temperature control parameters is performed using the Ziegler-Nichols critical proportionality method. S342. Based on the closed-loop critical experiment, the integral and derivative actions of the controller are removed, and pure proportional control is retained. The proportional coefficient is gradually increased until a stable constant amplitude oscillation occurs in the single-path heating film temperature control loop. The critical proportional coefficient and the critical oscillation period are recorded. S343. Input the critical proportionality coefficient and critical oscillation period into the Ziegler-Nichols critical proportionality method to calculate the initial parameters and obtain the integral coefficient and differential coefficient. S344. Based on the actual operating conditions of the temperature control terminal, the entire temperature operating range is divided into three sections: low temperature, normal temperature and high temperature, and the closed-loop temperature control adjustment coefficients of each section are finely adjusted. S345. Based on the anti-integral saturation, optimization logic and derivative-first control structure, when the control output reaches the preset power limit value, the integral accumulation operation is paused and the temperature feedback signal is differentiated; based on on-site debugging and iteration, the optimized and corrected multi-loop closed-loop temperature control adjustment coefficient is obtained.

[0013] Furthermore, based on the time-series temperature data from the infrared thermal imaging of the simulation equipment, a target temperature curve is generated for adjustment, and noise is suppressed through moving average filtering and a temperature deviation dead zone is set, including: S351. Using the communication link in the main control terminal, issue operating instructions for each working condition to the infrared heating controller to obtain infrared thermal imaging time-series temperature data; wherein, the operating instructions include all working conditions of equipment startup, operation and shutdown. S352. Generate the target temperature curve based on infrared thermal imaging time-series temperature data; S353. Using a closed-loop adaptive temperature control controller, the target temperature curve is tracked and controlled with the target temperature curve as the set value. S354. Use the moving average filtering method to suppress noise interference from the temperature sensor; S355. Based on the preset temperature deviation dead zone, when the temperature deviation is within the dead zone, the controller output remains unchanged.

[0014] Furthermore, an infrared thermal distribution model is used to divide the heat source region, and based on the heat source region and infrared radiation characteristics, an infrared emissivity benchmark database for each region is obtained. Combined with the target infrared camouflage coating, the simulated outer surface layer is obtained, which includes: S41. Based on the characteristics of the inflatable substrate, a heat-conducting and heat-equalizing layer is added between the heating film and the substrate; S42. Using an infrared emissivity measuring instrument, under external radiation shielding conditions, in-situ measurements are performed on the target area of ​​the simulation equipment to obtain infrared emissivity data for each area. S43. Construct an infrared emissivity benchmark database based on infrared emissivity data; S44. On the outer surface of the simulated target, an infrared camouflage coating matching the emissivity of the simulated equipment is sprayed; and combined with the temperature conduction characteristics of the heat-conducting layer, a uniform temperature field is formed on the outer surface of the simulated target to obtain the simulated outer surface layer.

[0015] Furthermore, using the equipment's three-dimensional geometric model and a broadband radar test terminal, a radar characteristic benchmark database for each region of the inflatable simulation equipment was obtained. Based on the radar characteristic benchmark database and the zoned coating conditions, the simulated outer surface layer was retested and verified to obtain a multi-spectral inflatable simulated target. This allows for the simulation of the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment, including: S51. Based on the three-dimensional geometric model of the equipment, the division of each region of the inflatable simulation equipment is obtained, including the strong reflection zone, the weak reflection zone and the wave absorption zone. S52. Using a broadband radar test terminal, the radar cross section, echo phase and scattering characteristics of various parts of the simulated equipment are measured to construct a regional radar characteristic benchmark database. S53. Based on the radar feature reference database, composite coatings are applied to different regions; S54. Apply the composite coating to the outer surface layer of the simulated target according to the partitioned and layered composite spraying. S55. Using a broadband radar test terminal, the radar cross section, echo phase and scattering characteristics of each zone of the simulated target are fully retested and verified. S56. Based on the retest verification results, generate radar features of each part of the simulated target and combine them with the simulated outer surface layer to obtain a multi-spectral inflatable simulated target.

[0016] Furthermore, based on a radar feature benchmark database, composite coatings are applied to different regions, including: S531. Based on the radar feature benchmark database and according to the radar scattering characteristics requirements of the strong reflection zone, weak reflection zone and the absorbing zone of the inflatable simulation equipment; S532. For areas with strong reflections, a composite coating is used with aluminum powder and copper powder as the core, combined with a polyurethane flexible resin matrix. S533, based on weak reflection and the microwave absorption zone, uses ferrite and carbon nanotubes as microwave absorbing fillers, combined with a composite coating of epoxy modified flexible resin matrix. S534. Based on the folding and adhesion properties of the composite coating and the polyvinyl chloride mesh fabric, a composite coating with radar characteristics in each region is obtained.

[0017] Secondly, the present invention also provides a high-fidelity equipment simulation system based on a three-dimensional digital model, comprising: The point cloud modeling and reconstruction module is used to perform full-dimensional scanning of the inflatable simulation equipment. It preprocesses the point cloud data obtained by scanning and uses the initial three-dimensional digital model to register, fit and reconstruct the preprocessed point cloud data to obtain the three-dimensional geometric model of the equipment. The infrared thermal field construction module is used to acquire infrared thermal imaging data of inflatable simulation equipment and, in combination with the equipment's three-dimensional geometric model, construct an infrared thermal distribution model. The flexible heat source control module is used to perform zoned temperature control of flexible heating elements distributed in the heat source area using a multi-loop closed-loop temperature control algorithm, and to simulate infrared radiation characteristics based on the control results. The infrared coating adaptation module is used to divide the heat source area using an infrared thermal distribution model, and obtain the infrared emissivity benchmark database of each area based on the heat source area and infrared radiation characteristics, and combine it with the target infrared camouflage coating to obtain the simulated outer surface layer. The multi-spectral feature simulation module is used to obtain a radar feature benchmark database for each region of the inflatable simulation equipment using the equipment's three-dimensional geometric model and broadband radar test terminal. Based on the radar feature benchmark database and the zoned coating conditions, the module retests and verifies the simulated outer surface layer to obtain a multi-spectral inflatable simulated target, thereby simulating the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment.

[0018] The beneficial effects of this invention are as follows: 1) This invention significantly enhances the deception effect through comprehensive high-fidelity camouflage. It simultaneously achieves high-fidelity simulation of the shape, radar echo, and infrared in three dimensions, which can perfectly counter the enemy's joint reconnaissance by multiple means such as optical reconnaissance, radar detection, and infrared thermal imaging. The simulated target's appearance, radar scattering characteristics, and infrared radiation characteristics deviate very little from the real equipment, effectively confusing the enemy's reconnaissance and guidance systems, inducing the enemy to make wrong attack decisions, and improving the battlefield survivability probability of our own inflatable simulation equipment, thus playing an efficient strategic camouflage and tactical deception role.

[0019] 2) This invention features a lightweight structure, making deployment and storage convenient and efficient. The overall design adopts a flexible inflatable structure with a coating / heat source module, eliminating the need for heavy rigid components and large transportation equipment and hoisting machinery, thus facilitating transportation. It inflates and unfolds quickly, completing the setup within 15 minutes, and deflates and folds rapidly during dismantling, resulting in a compact storage volume that facilitates mobile transfer and concealed deployment. This meets the practical needs of rapid battlefield mobility and temporary camouflage deployment, significantly reducing deployment and transportation costs.

[0020] 3) This invention features a flexible structure with strong adaptability, outstanding durability and practicality. The core simulation components (i.e., radar simulation coating and infrared heating module) are perfectly compatible with the flexible inflatable substrate. The coating has strong adhesion and excellent flexibility. It can withstand repeated folding and deformation of the inflatable structure without cracking, peeling, or performance degradation. It is resistant to high and low temperatures, wind and sand, and rain erosion, and can be deployed for a long time in complex battlefield environments. Moreover, the overall structure has no easily damaged rigid parts, resulting in an extremely low failure rate. It can be reused multiple times, significantly reducing the cost of use compared to disposable camouflage equipment and significantly improving its practicality in actual combat.

[0021] 4) This invention features a simple process, controllable cost, and suitability for mass production. It adopts mature processes such as coating spraying, flexible material heat sealing, and heating module integration, eliminating the need for complex machining and precision component assembly. The production process is simple, the raw material cost is low, and the overall cost is far lower than that of hard simulated targets and traditional inflatable targets with built-in reflectors. It can achieve mass production and standardization, and can quickly meet the equipment needs of large-scale camouflage and deception on the battlefield. It has extremely high engineering application value and promotion potential.

[0022] 5) This invention is highly versatile and customizable, adaptable to simulation of various types of equipment. This simulation design scheme has good versatility and customizability. It can adjust the coating, structural dimensions and heating source module parameters according to the shape, radar scattering characteristics and infrared radiation characteristics of different equipment models, and quickly customize the corresponding simulated target without redesigning the core production process. It is suitable for the camouflage simulation needs of various equipment, has a wide range of applications, and can meet the simulation camouflage tasks of multiple types of targets. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a high-fidelity equipment simulation method based on a three-dimensional digital model according to an embodiment of the present invention.

[0025] Figure 2 This is a principle block diagram of a high-fidelity equipment simulation system based on a three-dimensional digital model according to an embodiment of the present invention. Detailed Implementation

[0026] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0027] According to an embodiment of the present invention, a high-fidelity equipment simulation method and system based on a three-dimensional digital model is proposed.

[0028] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, a high-fidelity equipment simulation method based on a three-dimensional digital model according to an embodiment of the present invention includes: Step S1: Perform a full-dimensional scan of the inflatable simulation equipment, preprocess the point cloud data obtained from the scan, and use the initial three-dimensional digital model to register, fit and reconstruct the preprocessed point cloud data to obtain the three-dimensional geometric model of the equipment. Step S2: Obtain infrared thermal imaging data of the inflatable simulation equipment, and construct an infrared thermal distribution model in combination with the equipment's three-dimensional geometric model. Step S3: Using a multi-loop closed-loop temperature control algorithm, the flexible heating elements distributed in the heat source area are divided into zones for temperature control, and the infrared radiation characteristics are simulated based on the control results. Step S4: Divide the heat source region using the infrared thermal distribution model, and obtain the infrared emissivity benchmark database for each region based on the heat source region and infrared radiation characteristics. Combine this with the target infrared camouflage coating to obtain the simulated outer surface layer. Step S5: Using the three-dimensional geometric model of the equipment and the broadband radar test terminal, obtain the radar feature reference database of each region of the inflatable simulation equipment. Based on the radar feature reference database and the zonal coating conditions, retest and verify the simulated outer surface layer to obtain a multi-spectral inflatable simulation target, so as to realize the simulation of the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment.

[0029] In this optional embodiment, the inflatable simulation equipment is scanned in all dimensions. The point cloud data obtained from the scan is preprocessed, and the preprocessed point cloud data is registered, fitted, and reconstructed using the initial three-dimensional digital model to obtain the equipment's three-dimensional geometric model, including: S11. Using structured light scanning technology, perform a full-dimensional scan of the same type of inflatable simulation equipment to obtain full-dimensional scan point cloud data of the equipment; S12. Preprocess the point cloud data acquired by scanning to remove noise points and redundant points generated during the scanning process; S13. Using feature point matching and global registration, the point cloud data from multi-view scanning is stitched together into a complete global point cloud of the equipment, and the point cloud coordinate system is unified. S14. Convert the preprocessed discrete point cloud data into a continuous triangular mesh model to initially restore the equipment's outline. S15. Using 3D modeling software, construct an initial 3D digital model from the triangular mesh model, and perform registration, fitting, and model reconstruction on the initial 3D digital model. S16. Decompose the reconstructed three-dimensional digital model into planar cutting sheet drawings, and mark the size, crease line, welding position, splicing position and tolerance range of each preset material to obtain the equipment three-dimensional geometric model.

[0030] Specifically, the design steps for the high-fidelity equipment model are as follows: The core of the 1:1 PVC inflatable model target design is to completely solve the problems of rounded outlines, structural collapse, detail distortion, and low visual recognition of ordinary PVC inflatable products. Through full-process digital modeling, modular structural design, and refined molding process, the model achieves an appearance that is indistinguishable from real equipment in visual, aerial photography, and optical reconnaissance scenarios. The dimensional accuracy, three-dimensionality of the outline, and detail reproduction meet the standards of military high-fidelity camouflage decoys. It is fully compatible with the processing and deformation characteristics of PVC inflatable materials, taking into account both appearance fidelity and the practicality of the inflatable structure.

[0031] Dimensional accuracy: Using structured light scanning technology, the same model of high-simulation equipment is scanned in all dimensions with a scanning accuracy of ≤1mm, ensuring that no external details are missed.

[0032] Contour Restoration: Based on the scanned data, noise and redundant points generated during the scanning process (such as environmental interference and abnormal points caused by surface reflections on the equipment) are removed, while effective features are retained. The point cloud data from multi-view scans are stitched together into a complete global point cloud of the equipment through feature point matching / global registration. The coordinate system of the point cloud is unified, with the equipment's reference plane / core structure as the origin, ensuring consistent dimensional references for subsequent modeling. Discrete point cloud data is converted into a continuous triangular mesh model (i.e., a mesh model) to initially restore the equipment's external contour, providing surface references for subsequent precise modeling. A 1:1 three-dimensional digital model is constructed using 3D modeling software such as UG (Unigraphics) and SolidWorks, completely restoring the real equipment model. The three-dimensional model is decomposed into planar cutting sheet drawings, accurately marking the dimensions, crease lines, weld positions, and splicing positions of each PVC material piece, and indicating tolerance ranges, providing accurate basis for subsequent cutting and processing.

[0033] It perfectly replicates the sharp angles, lines, three-dimensional curvature, and height differences of real equipment. The overall shape and dimensions of the simulated target and its components have an error of ≤±1.5% compared to the real equipment prototype, while the error of key stress / visual core components (launcher, radar antenna, driver's cab, vehicle body edges, tire contours) is ≤±1%.

[0034] Detail Reproduction: Using specialized coloring inks, and adhering to the actual color patterns and color block boundaries, the product is precisely produced through screen printing. This results in high color saturation, controlled color difference within a reasonable range, strong ink adhesion, wear resistance, and colorfastness, without affecting the substrate's flexibility. It completely reproduces the surface texture of the substrate, as well as the shape and proportions of various accessories, achieving a visual detail reproduction rate of ≥98%.

[0035] Stable shape: After being inflated to a gauge pressure of about 5 kPa by an air pump, the shape and size do not show obvious deformation. It is wind-resistant and does not collapse or deform after being touched.

[0036] Material selection: All simulated shapes are made of PVC mesh fabric, which does not damage the airtightness or strength of the material, and does not affect the inflation, deflation, and folding storage functions.

[0037] In this optional embodiment, acquiring infrared thermal imaging data of the inflatable simulation equipment and constructing an infrared thermal distribution model by combining the equipment's three-dimensional geometric model includes: S21. Use an infrared thermal imager to obtain measured infrared thermal imaging data of the inflatable simulation equipment. S22. Based on the three-dimensional geometric model of the equipment, use finite element thermal simulation software to draw thermal distribution cloud maps from infrared thermal imaging measured data; and construct a full-vehicle infrared characteristic thermal distribution model based on the thermal distribution cloud maps. S23. Using the infrared characteristic thermal distribution model, the key locations of typical heat sources of the inflatable simulation equipment are calibrated, and an infrared thermal distribution model is constructed based on the infrared radiation intensity, temperature distribution gradient, thermal radiation range and dynamic change law of the key locations of typical heat sources.

[0038] Specifically, the key heat source locations are precisely matched and the heating film is deployed. Infrared thermal imagers are used to acquire infrared thermal imaging measurement data of the high-simulation equipment. Combined with the three-dimensional geometric model of the equipment, thermal distribution cloud maps are drawn using the finite element thermal simulation software ANSYS (ANSYS Asia Pacific Finite Element Analysis Software). A full-vehicle infrared characteristic thermal distribution model is established. The key locations of typical heat sources of the high-simulation equipment are accurately calibrated, including core heat-generating areas such as the engine, electronic equipment, launcher, and driver's cab. The infrared radiation intensity, temperature distribution gradient, thermal radiation range, and dynamic change law of each area are clarified.

[0039] In this optional embodiment, a multi-loop closed-loop temperature control algorithm is used to perform zoned temperature control on the flexible heating elements distributed in the heat source area, and the simulated infrared radiation characteristics are performed based on the control results, including: S31. Based on the corresponding position of the inflatable simulated target, a polyimide-based flexible heating film with a built-in thermistor temperature sensor and a polyvinyl chloride mesh composite material is bonded and laid on the substrate using thermally conductive adhesive to obtain a closed-loop control terminal with temperature feedback. S32. Utilize a multi-channel independent closed-loop heating controller to connect with each group of closed-loop control terminals with temperature feedback to construct a multi-loop closed-loop temperature control regulation circuit. S33. Using the step response method, the characteristics of each heating circuit are identified, the temperature response curve is obtained and the characteristic parameters are extracted to construct an inertial pure time delay model. S34. Using the Ziegler-Nichols critical proportional method, the initial tuning of the temperature control parameters is completed, the critical oscillation related parameters are obtained, and the basic control parameters of the preset loop are set. Based on the actual operating conditions of the temperature control terminal, and combined with the anti-integral saturation optimization logic and the derivative-first control structure, the closed-loop temperature control adjustment coefficients are optimized and corrected by region. S35. Based on the infrared thermal imaging time-series temperature data of the simulation equipment, generate a target temperature curve for adjustment, and suppress noise and set a temperature deviation dead zone through moving average filtering. S36. Based on the optimized multi-loop closed-loop temperature control logic, the flexible heating elements of each heat source are divided into zones for temperature control to obtain the infrared radiation characteristics of the simulated equipment.

[0040] In this optional embodiment, the Ziegler-Nichols critical proportional gain method is used to complete the initial tuning of the temperature control parameters, obtain the critical oscillation-related parameters, and preset the basic control parameters of the loop. Based on the actual operating conditions of the temperature control terminal, and combined with anti-integral saturation optimization logic and derivative-first control structure, the closed-loop temperature control adjustment coefficients are optimized and corrected in different areas, including: S341. Initial tuning of temperature control parameters is performed using the Ziegler-Nichols critical proportionality method. S342. Based on the closed-loop critical experiment, the integral and derivative actions of the controller are removed, and pure proportional control is retained. The proportional coefficient is gradually increased until a stable constant amplitude oscillation occurs in the single-path heating film temperature control loop. The critical proportional coefficient and the critical oscillation period are recorded. S343. Input the critical proportionality coefficient and critical oscillation period into the Ziegler-Nichols critical proportionality method to calculate the initial parameters and obtain the integral coefficient and differential coefficient. S344. Based on the actual operating conditions of the temperature control terminal, the entire temperature operating range is divided into three sections: low temperature, normal temperature and high temperature, and the closed-loop temperature control adjustment coefficients of each section are finely adjusted. S345. Based on the anti-integral saturation, optimization logic and derivative-first control structure, when the control output reaches the preset power limit value, the integral accumulation operation is paused and the temperature feedback signal is differentiated; based on on-site debugging and iteration, the optimized and corrected multi-loop closed-loop temperature control adjustment coefficient is obtained.

[0041] In this optional embodiment, a target temperature curve is generated and adjusted based on the infrared thermal imaging time-series temperature data of the simulation equipment, and noise is suppressed by moving average filtering and a temperature deviation dead zone is set, including: S351. Using the communication link in the main control terminal, issue operating instructions for each working condition to the infrared heating controller to obtain infrared thermal imaging time-series temperature data; wherein, the operating instructions include all working conditions of equipment startup, operation and shutdown. S352. Generate the target temperature curve based on infrared thermal imaging time-series temperature data; S353. Using a closed-loop adaptive temperature control controller, the target temperature curve is tracked and controlled with the target temperature curve as the set value. S354. Use the moving average filtering method to suppress noise interference from the temperature sensor; S355. Based on the preset temperature deviation dead zone, when the temperature deviation is within the dead zone, the controller output remains unchanged.

[0042] Specifically, at the corresponding location of the inflatable simulation target, a flexible heating film is laid on the PVC substrate using a high-temperature resistant, high-adhesion thermally conductive adhesive. The heating film is an ultra-thin, highly flexible PI (polyimide)-based electrothermal film with a built-in PT100 thermistor temperature sensor, enabling real-time, high-precision acquisition of the heating film surface temperature (e.g., temperature acquisition accuracy ±0.5℃), forming a closed-loop control unit with temperature feedback. The size and power of the heating film are perfectly matched to the physical size and thermal radiation range of the heat source of the reference inflatable simulation equipment, ensuring that the spatial distribution of infrared features is highly consistent with the reference inflatable simulation equipment, avoiding problems such as thermal zone misalignment and radiation range distortion.

[0043] The precise temperature control achieved by the 24-channel independent closed-loop heating controller is designed to meet the requirements of infrared feature simulation in multiple areas. Each controller corresponds to a set of heating film units with temperature feedback, forming 24 independent PID temperature control loops.

[0044] The controller incorporates a PID closed-loop control algorithm (i.e., a multi-loop closed-loop temperature control algorithm): A FOPDT model (i.e., inertial pure time delay model) is established. For a 24-channel independent heating film-heating system, the step response method is used to complete the inertial pure time delay (FOPDT, First Order Plus Dead Time) model identification. The implementation steps are as follows: Step excitation application: Apply step voltage inputs with amplitudes of 10%, 20%, 50%, and 75% of the rated power to a single heating circuit to prevent the system from entering the saturation region.

[0045] Response data acquisition: The surface temperature response curve of the heating film is synchronously acquired at a sampling frequency of 10Hz, and the full cycle data from the step application to the temperature stabilization is recorded.

[0046] Model parameter identification: Extracting the lag time τ (i.e., the time from the application of the step jump to the start of temperature rise) and the time constant from the temperature response curve. T (Time difference for temperature to rise from initial value to 63.2% of steady-state value), static gain K (i.e., steady-state temperature change / step input amplitude), establish the FOPDT model. : ; in, s This is the Laplace operator. This model can accurately characterize the dynamic characteristics of a heating system, such as thermal inertia (e.g., represented by the time constant T) and pure time delay (e.g., represented by the time delay τ), providing a theoretical basis for PID parameter tuning.

[0047] Parameter tuning and on-site optimization: The Ziegler-Nichols critical proportional gain method was used for initial PID parameter tuning, following the closed-loop constant amplitude oscillation experimental logic throughout. Specific implementation steps are as follows: First, start the closed-loop critical experiment. Cut off the integral and derivative actions of the controller and retain only the pure proportional control. Gradually increase the proportional coefficient until the single-path heating film temperature control loop shows stable constant amplitude oscillation. Record the critical proportional coefficient K_u and the critical oscillation period T_u at this time. The initial parameters were calculated by substituting them into the standard formula of this method, namely K_p=0.6K_u, T_i=0.5T_u, T_d=0.125T_u; the integral coefficient K_i and the differential coefficient K_d were derived simultaneously, and the initial parameters were preset, laying a theoretical foundation for subsequent debugging.

[0048] The initial parameters are based on theoretical benchmarks and require on-site closed-loop optimization under actual operating conditions of the temperature control system. For the system's full temperature operating range of -40℃ to 110℃, three zones are defined: low temperature (-40℃ to 0℃), normal temperature (0℃ to 40℃), and high temperature (40℃ to 110℃). Considering the differences in thermal resistance and heat capacity of the heating film under different temperatures, the parameters K_p, Ki, and K_d are fine-tuned for each zone. Anti-integral saturation optimization logic is added to address the characteristics of the temperature control system. When the control output reaches the power limit, the integral accumulation calculation is immediately paused to avoid excessive integration leading to temperature overshoot. A derivative-first structure is adopted, performing derivative processing only on the temperature feedback signal to avoid output shocks caused by step changes in the temperature setpoint. Finally, through on-site debugging and iteration, a steady-state temperature control accuracy of ±1℃ across the entire temperature range is achieved, meeting the dual requirements of system temperature control stability and response speed.

[0049] Temperature profile tracking simulation: A temperature profile tracking method is designed for all operating conditions, including equipment startup, operation, and shutdown. Operating conditions: Data is sent by the user to the infrared heating controller via the CAN bus (communication link). Temperature curve tracking: Based on the infrared thermal imaging temperature time-series data of the high-fidelity simulation equipment, a target temperature curve is generated. The PID controller (i.e., closed-loop adaptive temperature control) uses the target temperature curve as the setpoint and achieves dynamic tracking through PID control. The tracking error is ≤±1℃, 100% replicating the temperature change characteristics of the inflatable simulation equipment. Anti-interference design: A moving average filter (window length 5~10 sampling points) is used to suppress sensor noise. A deviation dead zone (±0.5℃) is set: when the temperature deviation is within the dead zone, the output remains unchanged, avoiding temperature fluctuations caused by frequent adjustments, while also suppressing ambient temperature interference.

[0050] In this optional embodiment, an infrared thermal distribution model is used to divide the heat source region, and based on the heat source region and infrared radiation characteristics, an infrared emissivity benchmark database for each region is obtained. Combined with the target infrared camouflage coating, the simulated outer surface layer is obtained, including: S41. Based on the characteristics of the inflatable substrate, a heat-conducting and heat-equalizing layer is added between the heating film and the substrate; S42. Using an infrared emissivity measuring instrument, under external radiation shielding conditions, in-situ measurements are performed on the target area of ​​the simulation equipment to obtain infrared emissivity data for each area. S43. Construct an infrared emissivity benchmark database based on infrared emissivity data; S44. On the outer surface of the simulated target, an infrared camouflage coating matching the emissivity of the simulated equipment is sprayed; and combined with the temperature conduction characteristics of the heat-conducting layer, a uniform temperature field is formed on the outer surface of the simulated target to obtain the simulated outer surface layer.

[0051] Specifically, the multi-dimensional optimization and adaptation of infrared feature simulation further enhances the realism of infrared feature simulation. This solution optimizes the bonding method between the heating film and the substrate and the heat radiation conduction based on the characteristics of the inflatable PVC mesh substrate: a heat-conducting layer with high thermal conductivity and low heat storage is added between the heating film and the PVC substrate to ensure that the heat generated by the heating film is uniformly and quickly conducted to the outer surface of the simulated target, effectively avoiding problems such as local overheating and uneven temperature, and laying a uniform temperature field foundation for accurate matching of infrared emissivity.

[0052] The infrared emissivity of the high-fidelity simulation equipment was uniformly measured using an infrared emissivity meter under conditions of external radiation shielding (i.e., the installation of a sunshade to isolate solar radiation and environmental reflections). In-situ measurements were performed on key areas of the equipment (i.e., the casing, piping, heat dissipation vents, etc.) to accurately acquire emissivity data in the 8-14μm thermal imaging core band, establishing a benchmark database for the infrared emissivity of the high-fidelity simulation equipment. Based on this benchmark data, a customized infrared camouflage coating was sprayed onto the PVC outer surface. This coating's infrared emissivity across the entire temperature range matched that of the high-fidelity simulation equipment, ensuring that the heat generated by the heating film radiated outwards with an infrared emissivity completely consistent with the high-fidelity simulation. This completely eliminated infrared feature distortion caused by differences in material emissivity, guaranteeing a high degree of consistency between the infrared thermal imaging characteristics of the simulated target and the high-fidelity simulation.

[0053] In this optional embodiment, a radar feature reference database for each region of the inflatable simulation equipment is obtained using a three-dimensional geometric model of the equipment and a broadband radar test terminal. Based on the radar feature reference database and the zonal coating conditions, the simulated outer surface layer is retested and verified to obtain a multi-spectral inflatable simulated target. This enables the simulation of the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment, including: S51. Based on the three-dimensional geometric model of the equipment, the division of each region of the inflatable simulation equipment is obtained, including the strong reflection zone, the weak reflection zone and the wave absorption zone. S52. Using a broadband radar test terminal, the radar cross section, echo phase and scattering characteristics of various parts of the simulated equipment are measured to construct a regional radar characteristic benchmark database. S53. Based on the radar feature reference database, composite coatings are applied to different regions; S54. Apply the composite coating to the outer surface layer of the simulated target according to the partitioned and layered composite spraying. S55. Using a broadband radar test terminal, the radar cross section, echo phase and scattering characteristics of each zone of the simulated target are fully retested and verified. S56. Based on the retest verification results, generate radar features of each part of the simulated target and combine them with the simulated outer surface layer to obtain a multi-spectral inflatable simulated target.

[0054] In this optional embodiment, applying a composite coating to different regions based on a radar feature reference database includes: S531. Based on the radar feature benchmark database and according to the radar scattering characteristics requirements of the strong reflection zone, weak reflection zone and the absorbing zone of the inflatable simulation equipment; S532. For areas with strong reflections, a composite coating is used with aluminum powder and copper powder as the core, combined with a polyurethane flexible resin matrix. S533, based on weak reflection and the microwave absorption zone, uses ferrite and carbon nanotubes as microwave absorbing fillers, combined with a composite coating of epoxy modified flexible resin matrix. S534. Based on the folding and adhesion properties of the composite coating and the polyvinyl chloride mesh fabric, a composite coating with radar characteristics in each region is obtained.

[0055] Specifically, radar echo characteristic simulation shows that the essence of radar echo is the scattering characteristics of the target on the incident radar wave. The radar scattering characteristics of real military equipment show significant regional differences and band correlations: radar antennas, transmitter metal structures, vehicle body corners, wheel hubs and other parts are strong reflection areas, while non-metallic parts of the vehicle body and stealth coating areas are weak reflection / absorption areas. Moreover, the scattering characteristics of different radar bands (i.e., mainstream bands such as L / S / C / X / Ku / Ka) are significantly different.

[0056] This invention addresses the radar characteristic simulation requirements of inflatable high-fidelity equipment by proposing a radar echo simulation method based on composite coating materials. First, a broadband radar testing system is used to measure the RCS, echo phase, and scattering characteristics of various parts of the high-fidelity equipment, establishing a regional radar characteristic benchmark database. For the benchmark data of different regions, the high-reflectivity zone composite coating uses aluminum / copper powder as the core filler, combined with a polyurethane flexible resin matrix; the low-reflectivity / absorbing zone composite coating uses ferrite / carbon nanotubes as the absorbing filler, combined with an epoxy-modified flexible resin matrix. All coatings are adapted to the folding and adhesion characteristics of PVC mesh fabric (polyvinyl chloride mesh fabric), employing a precise, layered spraying process to strictly control the coating thickness and parameters. Finally, using a radar testing system consistent with the benchmark data acquisition, under the same frequency band and attitude angle, the RCS, echo phase, and scattering characteristics of each region of the simulated target are fully remeasured, ensuring that the radar characteristics of each part of the simulated target are highly consistent with the high-fidelity equipment, while maintaining the shape fidelity of the inflatable structure.

[0057] Specifically, this invention addresses the infrared feature simulation requirements of inflatable high-fidelity equipment simulation targets by proposing a multi-dimensional infrared feature simulation method based on a zoned temperature-controlled heating film. By deploying a flexible heating film array with high-precision temperature feedback at key locations of typical heat sources corresponding to the high-fidelity equipment in the simulation target, combined with a 24-channel independent closed-loop heating controller, the method achieves precise control and dynamic simulation of the infrared radiation characteristics of each region of the simulation target. This enables the high reproduction of the infrared radiation characteristics of real equipment in scenarios such as infrared detection and thermal imaging reconnaissance, significantly improving the multi-band deception and simulation accuracy of inflatable simulation targets.

[0058] This invention breaks through the technical limitations of traditional single-shape simulation by integrating a multi-dimensional simulation design. It integrates three core simulation modules—high-fidelity shape simulation, radar echo feature simulation, and infrared feature simulation—into a single inflatable simulated target structure. It abandons the design concept of traditional discrete simulation components and achieves synchronous and accurate replication of three-dimensional features: shape, radar, and infrared. It fully matches the multi-spectral detection features of high-fidelity equipment and solves the technical problem of existing simulated targets having a single simulation dimension and being easily detected and identified by multiple means.

[0059] This invention utilizes a flexible substrate-adapted zoned radar echo simulation technology. Addressing the characteristics of flexible inflatable substrates such as PVC mesh fabric used in inflatable simulation targets, it pioneers a zoned differentiated radar simulation coating technology. This eliminates the need for external / internal metal reflective components such as rigid corner reflectors or Luneburg lenses. By partitioning the surface of the inflatable substrate according to the intensity of radar reflection from highly simulated equipment, and spraying a customized conductive and absorbing composite coating, it precisely controls the radar cross section (RCS) and echo phase at different locations. This perfectly adapts to the foldable and inflatable deformation requirements of flexible inflatable structures, overcoming the technical bottlenecks of poor compatibility and distorted shape inherent in traditional radar simulation components with flexible inflatable substrates.

[0060] This invention utilizes a lightweight infrared feature precision replication technology, eliminating the drawbacks of traditional infrared simulation that rely on high-power external heat sources, resulting in significant weight increase and distorted infrared feature distribution. Instead, it employs a low-heat-power, lightweight heating film module installed at the corresponding simulation location to accurately reproduce the infrared thermal imaging contour and temperature difference characteristics of the highly simulated equipment. At the same time, it does not significantly increase the overall weight of the inflatable target (i.e., the total weight of 24 heating films is ≤3kg), and does not affect its core performance of rapid inflation and portable storage.

[0061] This invention utilizes high-fidelity flexible molding and detail reproduction technology, employing high-strength, flexible, and airtight PVC material. Combined with three-dimensional cutting and thermal bonding processes, it achieves a 1:1 high-fidelity reproduction of the vehicle's overall shape, structural outline, radar antenna, transmitter, and body edges—key appearance details—of real equipment. The proportions, dimensional accuracy, and structural form are highly consistent with the actual equipment. Furthermore, the material possesses excellent flexibility and anti-aging properties, allowing for repeated folding and storage, and inflation and unfolding without wrinkles or deformation. The fidelity of the shape far exceeds that of traditional hard-surfaced and simple inflatable simulated targets, achieving indiscriminate camouflage at the visual reconnaissance level.

[0062] like Figure 2 As shown, according to another embodiment of the present invention, a high-fidelity equipment simulation system based on a three-dimensional digital model is also provided, comprising: The point cloud modeling and reconstruction module 1 is used to perform full-dimensional scanning of the inflatable simulation equipment, preprocess the point cloud data obtained by scanning, and use the initial three-dimensional digital model to perform registration, fitting and model reconstruction on the preprocessed point cloud data to obtain the three-dimensional geometric model of the equipment. Infrared thermal field construction module 2 is used to acquire infrared thermal imaging data of inflatable simulation equipment and, in combination with the equipment's three-dimensional geometric model, construct an infrared thermal distribution model. The flexible heat source control module 3 is used to perform zoned temperature control of the flexible heating elements distributed in the heat source area using a multi-loop closed-loop temperature control algorithm, and to simulate infrared radiation characteristics based on the control results. Infrared coating adaptation module 4 is used to divide the heat source area using an infrared thermal distribution model, and obtain the infrared emissivity benchmark database of each area based on the heat source area and infrared radiation characteristics, and combine it with the target infrared camouflage coating to obtain the simulated outer surface layer. The multi-spectral feature simulation module 5 is used to obtain the radar feature benchmark database of each region of the inflatable simulation equipment by using the three-dimensional geometric model of the equipment and the broadband radar test terminal. Based on the radar feature benchmark database and the partition coating conditions, the simulated outer surface layer is retested and verified to obtain the multi-spectral inflatable simulation target, so as to realize the simulation of the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-fidelity equipment simulation method based on a three-dimensional digital model, characterized in that, include: S1. Perform a full-dimensional scan of the inflatable simulation equipment, preprocess the point cloud data obtained from the scan, and use the initial three-dimensional digital model to register, fit and reconstruct the preprocessed point cloud data to obtain the three-dimensional geometric model of the equipment. S2. Obtain infrared thermal imaging data of the inflatable simulation equipment and construct an infrared thermal distribution model by combining the equipment's three-dimensional geometric model. S3. Using a multi-loop closed-loop temperature control algorithm, the flexible heating elements distributed in the heat source area are subjected to zoned temperature control, and the infrared radiation characteristics are simulated based on the control results. S4. Use an infrared thermal distribution model to divide the heat source region, and obtain the infrared emissivity benchmark database for each region based on the heat source region and infrared radiation characteristics. Combine this with the target infrared camouflage coating to obtain the simulated outer surface layer. S5. Using the equipment's three-dimensional geometric model and broadband radar test terminal, obtain the radar feature reference database for each region of the inflatable simulation equipment. Based on the radar feature reference database and the zonal coating conditions, retest and verify the simulated outer surface layer to obtain a multi-spectral inflatable simulation target, thereby realizing the simulation of the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment.

2. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 1, characterized in that, The process involves a full-dimensional scan of the inflatable simulation equipment, preprocessing the acquired point cloud data, and using an initial 3D digital model to register, fit, and reconstruct the preprocessed point cloud data to obtain a 3D geometric model of the equipment, including: S11. Using structured light scanning technology, perform a full-dimensional scan of the same type of inflatable simulation equipment to obtain full-dimensional scan point cloud data of the equipment; S12. Preprocess the point cloud data acquired by scanning to remove noise points and redundant points generated during the scanning process; S13. Using feature point matching and global registration, the point cloud data from multi-view scanning is stitched together into a complete global point cloud of the equipment, and the point cloud coordinate system is unified. S14. Convert the preprocessed discrete point cloud data into a continuous triangular mesh model to initially restore the equipment's outline. S15. Using 3D modeling software, construct an initial 3D digital model from the triangular mesh model, and perform registration, fitting, and model reconstruction on the initial 3D digital model. S16. Decompose the reconstructed three-dimensional digital model into planar cutting sheet drawings, and mark the size, crease line, welding position, splicing position and tolerance range of each preset material to obtain the equipment three-dimensional geometric model.

3. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 1, characterized in that, The process of acquiring infrared thermal imaging data of the inflatable simulation equipment and constructing an infrared thermal distribution model by combining the equipment's three-dimensional geometric model includes: S21. Use an infrared thermal imager to obtain measured infrared thermal imaging data of the inflatable simulation equipment. S22. Based on the three-dimensional geometric model of the equipment, use finite element thermal simulation software to draw thermal distribution cloud maps from infrared thermal imaging measured data; and construct a full-vehicle infrared characteristic thermal distribution model based on the thermal distribution cloud maps. S23. Using the infrared characteristic thermal distribution model, the key locations of typical heat sources of the inflatable simulation equipment are calibrated, and an infrared thermal distribution model is constructed based on the infrared radiation intensity, temperature distribution gradient, thermal radiation range and dynamic change law of the key locations of typical heat sources.

4. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 1, characterized in that, The method of using a multi-loop closed-loop temperature control algorithm to perform zoned temperature control on flexible heating elements distributed in the heat source area, and simulating infrared radiation characteristics based on the control results, includes: S31. Based on the corresponding position of the inflatable simulated target, a polyimide-based flexible heating film with a built-in thermistor temperature sensor and a polyvinyl chloride mesh composite material is bonded and laid on the substrate using thermally conductive adhesive to obtain a closed-loop control terminal with temperature feedback. S32. Utilize a multi-channel independent closed-loop heating controller to connect with each group of closed-loop control terminals with temperature feedback to construct a multi-loop closed-loop temperature control regulation circuit. S33. Using the step response method, the characteristics of each heating circuit are identified, the temperature response curve is obtained and the characteristic parameters are extracted to construct an inertial pure time delay model. S34. Using the Ziegler-Nichols critical proportional method, the initial tuning of the temperature control parameters is completed, the critical oscillation related parameters are obtained, and the basic control parameters of the preset loop are set. Based on the actual operating conditions of the temperature control terminal, and combined with the anti-integral saturation optimization logic and the derivative-first control structure, the closed-loop temperature control adjustment coefficients are optimized and corrected by region. S35. Based on the infrared thermal imaging time-series temperature data of the simulation equipment, generate a target temperature curve for adjustment, and suppress noise and set a temperature deviation dead zone through moving average filtering. S36. Based on the optimized multi-loop closed-loop temperature control logic, the flexible heating elements of each heat source are divided into zones for temperature control to obtain the infrared radiation characteristics of the simulated equipment.

5. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 4, characterized in that, The Ziegler-Nichols critical proportional method is used to complete the initial tuning of temperature control parameters, obtain critical oscillation related parameters, and preset the basic control parameters of the loop. Based on the actual operating conditions of the temperature control terminal, and combined with anti-integral saturation optimization logic and derivative-first control structure, the closed-loop temperature control adjustment coefficients are optimized and corrected in different zones, including: S341. Initial tuning of temperature control parameters is performed using the Ziegler-Nichols critical proportionality method. S342. Based on the closed-loop critical experiment, the integral and derivative actions of the controller are removed, and pure proportional control is retained. The proportional coefficient is gradually increased until a stable constant amplitude oscillation occurs in the single-path heating film temperature control loop. The critical proportional coefficient and the critical oscillation period are recorded. S343. Input the critical proportionality coefficient and critical oscillation period into the Ziegler-Nichols critical proportionality method to calculate the initial parameters and obtain the integral coefficient and differential coefficient. S344. Based on the actual operating conditions of the temperature control terminal, the entire temperature operating range is divided into three sections: low temperature, normal temperature and high temperature, and the closed-loop temperature control adjustment coefficients of each section are finely adjusted. S345. Based on the anti-integral saturation, optimization logic and derivative-first control structure, when the control output reaches the preset power limit value, the integral accumulation operation is paused and the temperature feedback signal is differentiated; based on on-site debugging and iteration, the optimized and corrected multi-loop closed-loop temperature control adjustment coefficient is obtained.

6. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 5, characterized in that, The process of generating a target temperature curve based on the infrared thermal imaging time-series temperature data from the simulation equipment for adjustment, and suppressing noise through moving average filtering and setting a temperature deviation dead zone includes: S351. Using the communication link in the main control terminal, issue operating instructions for each working condition to the infrared heating controller to obtain infrared thermal imaging time-series temperature data; wherein, the operating instructions include all working conditions of equipment startup, operation and shutdown. S352. Generate the target temperature curve based on infrared thermal imaging time-series temperature data; S353. Using a closed-loop adaptive temperature control controller, the target temperature curve is tracked and controlled with the target temperature curve as the set value. S354. Use the moving average filtering method to suppress noise interference from the temperature sensor; S355. Based on the preset temperature deviation dead zone, when the temperature deviation is within the dead zone, the controller output remains unchanged.

7. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 1, characterized in that, The process involves using an infrared thermal distribution model to divide the heat source region, and based on the heat source region and infrared radiation characteristics, obtaining an infrared emissivity benchmark database for each region. Combined with the target infrared camouflage coating, the simulated outer surface layer is obtained, comprising: S41. Based on the characteristics of the inflatable substrate, a heat-conducting and heat-equalizing layer is added between the heating film and the substrate; S42. Using an infrared emissivity measuring instrument, under external radiation shielding conditions, in-situ measurements are performed on the target area of ​​the simulation equipment to obtain infrared emissivity data for each area. S43. Construct an infrared emissivity benchmark database based on infrared emissivity data; S44. On the outer surface of the simulated target, an infrared camouflage coating matching the emissivity of the simulated equipment is sprayed; and combined with the temperature conduction characteristics of the heat-conducting layer, a uniform temperature field is formed on the outer surface of the simulated target to obtain the simulated outer surface layer.

8. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 1, characterized in that, The process involves using a three-dimensional geometric model of the equipment and a broadband radar test terminal to obtain a radar feature reference database for each region of the inflatable simulation equipment. Based on this database and the zoned coating conditions, the simulated outer surface layer is retested and verified to obtain a multi-spectral inflatable simulated target. This process aims to simulate the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment. S51. Based on the three-dimensional geometric model of the equipment, the division of each region of the inflatable simulation equipment is obtained, including the strong reflection zone, the weak reflection zone and the wave absorption zone. S52. Using a broadband radar test terminal, the radar cross section, echo phase and scattering characteristics of various parts of the simulated equipment are measured to construct a regional radar characteristic benchmark database. S53. Based on the radar feature reference database, composite coatings are applied to different regions; S54. Apply the composite coating to the outer surface layer of the simulated target according to the partitioned and layered composite spraying. S55. Using a broadband radar test terminal, the radar cross section, echo phase and scattering characteristics of each zone of the simulated target are fully retested and verified. S56. Based on the retest verification results, generate radar features of each part of the simulated target and combine them with the simulated outer surface layer to obtain a multi-spectral inflatable simulated target.

9. The high-fidelity equipment simulation method based on a three-dimensional digital model according to claim 1, characterized in that, The method of applying composite coatings to different regions based on a radar feature reference database includes: S531. Based on the radar feature reference database and according to the radar scattering characteristics requirements of the strong reflection zone, weak reflection zone and the absorbing zone of the inflatable simulation equipment; S532. For areas with strong reflections, a composite coating is used with aluminum powder and copper powder as the core, combined with a polyurethane flexible resin matrix. S533, based on weak reflection and the microwave absorption zone, uses ferrite and carbon nanotubes as microwave absorbing fillers, combined with a composite coating of epoxy modified flexible resin matrix. S534. Based on the folding and adhesion properties of the composite coating and the polyvinyl chloride mesh fabric, a composite coating with radar characteristics in each region is obtained.

10. A high-fidelity equipment simulation system based on a three-dimensional digital model, used to implement the high-fidelity equipment simulation method based on a three-dimensional digital model as described in any one of claims 1-9, characterized in that, include: The point cloud modeling and reconstruction module is used to perform full-dimensional scanning of the inflatable simulation equipment. It preprocesses the point cloud data obtained by scanning and uses the initial three-dimensional digital model to register, fit and reconstruct the preprocessed point cloud data to obtain the three-dimensional geometric model of the equipment. The infrared thermal field construction module is used to acquire infrared thermal imaging data of inflatable simulation equipment and, in combination with the equipment's three-dimensional geometric model, construct an infrared thermal distribution model. The flexible heat source control module is used to perform zoned temperature control of flexible heating elements distributed in the heat source area using a multi-loop closed-loop temperature control algorithm, and to simulate infrared radiation characteristics based on the control results. The infrared coating adaptation module is used to divide the heat source area using an infrared thermal distribution model, and obtain the infrared emissivity benchmark database of each area based on the heat source area and infrared radiation characteristics, and combine it with the target infrared camouflage coating to obtain the simulated outer surface layer. The multi-spectral feature simulation module is used to obtain a radar feature benchmark database for each region of the inflatable simulation equipment using the equipment's three-dimensional geometric model and broadband radar test terminal. Based on the radar feature benchmark database and the zoned coating conditions, the module retests and verifies the simulated outer surface layer to obtain a multi-spectral inflatable simulated target, thereby simulating the infrared thermal radiation and radar electromagnetic scattering characteristics of the simulation equipment.

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