A multi-channel graphene far-infrared pulse modulation method, system, electronic device and storage medium

Abstract

The application provides a multi-channel graphene far-infrared pulse modulation method and system, electronic equipment and storage medium, and relates to the technical field of far-infrared pulse modulation. The application captures the heat distribution data of the graphene heating unit through an integrated temperature sensor array, imports the heat distribution data into a thermal coupling analysis model, analyzes the non-uniform physical expansion, and generates an equivalent lens curvature distribution. Based on this curvature distribution, the path offset of the far-infrared light beam after passing through the deformed film is simulated, and a spatial distortion vector field is constructed. Taking the vector field as the input, a cooperative redistribution algorithm is run to calculate the optimal pulse energy adjustment value. According to the adjustment value, the pulse modulation instructions suitable for the hardware are generated and applied to each channel, thereby actively offsetting the light field non-uniformity introduced by the thermal-induced deformation, and the stability and consistency of the far-infrared radiation field pattern of the multi-channel graphene device can be improved.

Description

Technical Field

[0001] This application relates to the field of infrared pulse modulation technology, and in particular to a multi-channel graphene far-infrared pulse modulation method, system, electronic device and storage medium. Background Technology

[0002] In fields such as precision optics, biomedical imaging, and advanced manufacturing, the demand for precise control of far-infrared radiation sources is increasing. Especially in scenarios requiring high spatial resolution and dynamic response, such as achieving precise thermal stimulation of specific areas at the micro-nano scale or performing non-destructive testing in complex media, traditional far-infrared light sources often struggle to meet the stringent requirements of real-time, multi-channel, and independent modulation of light field intensity, phase, and propagation direction. Therefore, developing a new method to overcome the limitations of existing technologies and achieve high-precision dynamic control of far-infrared light fields has become an important direction for current technological development.

[0003] Currently, a common existing solution for the technical requirement of dynamic control of far-infrared light fields is to use a microelectromechanical system (MEMS)-based mirror array. This solution changes the reflection direction of the incident far-infrared beam by independently driving the tilt angle of the micro mirrors, thereby achieving preliminary adjustment of the spatial distribution of the light field. By integrating a large number of micromirror units, a programmable light field modulator can theoretically be constructed to adapt to the light field morphology requirements of different application scenarios.

[0004] However, the existing scheme based on microelectromechanical system (MEMS) mirror arrays has significant drawbacks. First, the mechanical inertia of the micromirrors limits their response speed, making it difficult to meet the requirements of high-frequency dynamic modulation. Second, the manufacturing process of mirror arrays is complex and costly, and they are susceptible to environmental vibrations and temperature changes, making it difficult to guarantee modulation accuracy and long-term stability. Furthermore, due to their reflection-based principle, they inherently suffer from losses in light energy utilization efficiency and struggle to achieve deep focusing or defocus control of the beam focal point. This constitutes a significant performance bottleneck in specific applications requiring high energy density or complex optical field patterns. Summary of the Invention

[0005] The purpose of this application is to provide a multi-channel graphene far-infrared pulse modulation method, system, electronic device and storage medium to solve the problem of insufficient stability and consistency of far-infrared radiation field patterns in existing multi-channel graphene devices.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a multi-channel graphene far-infrared pulse modulation method, comprising: During the operation of the multi-channel graphene device, thermal distribution data of the entire heating plane is acquired, which reflects local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths. The thermal distribution data is imported into a preset thermo-mechanical coupling analysis model. The thermo-mechanical coupling analysis model is used to analyze the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly. Based on the physical expansion, an equivalent lens curvature distribution that dynamically describes the changes in the micro-morphology of the film surface is generated. Based on the equivalent lens curvature distribution, the complex propagation path offset of the multi-channel far-infrared beam when passing through the deformed thermosensitive polymer film is simulated to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel. Using the spatial distortion vector field as the core input, a collaborative redistribution algorithm for pulse energy is run. The collaborative redistribution algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. Based on the optimal pulse energy adjustment value, a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device is generated, and the pulse modulation command sequence is applied to the corresponding channel to counteract the non-uniformity of the light field introduced by thermal deformation, so as to ensure that the output far-infrared radiation field pattern remains stable.

[0007] Optionally, the step of running a cooperative pulse energy redistribution algorithm with the spatial distortion vector field as the core input, wherein the cooperative redistribution algorithm aims to restore the overall uniformity of the far-field radiation pattern, and calculates the optimal pulse energy adjustment value for each independent channel, including: All the deflection information contained in the spatial distortion vector field is projected forward through the far-field propagation model to generate a predicted energy map that reflects the actual distribution of energy in each channel on the target plane. The predicted energy map is then superimposed and compared with a reference energy map that represents absolutely uniform radiation under ideal conditions, thereby locking in the abnormal energy distribution area. A contribution analysis model is established for any point in the energy distribution anomaly region. The energy of the initial emission channel converging at the point is analyzed in reverse using the contribution analysis model, and the sensitivity coefficient of the energy variation to the energy level in the energy distribution anomaly region is quantified. The objective of restoring the overall uniformity of the far-field radiation pattern is transformed into a multivariate collaborative optimization task. Based on the sensitivity coefficient, the multivariate collaborative optimization task iteratively adjusts the energy output weights of each channel until the difference between the predicted energy spectrum and the reference energy spectrum converges to the minimum. The set of energy output weights obtained at this time is the optimal pulse energy adjustment value.

[0008] Optionally, the step of generating a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device based on the optimal pulse energy adjustment value, and applying the pulse modulation command sequences to the corresponding channels to counteract the light field inhomogeneity introduced by thermal deformation and ensure that the output far-infrared radiation field pattern remains stable, includes: Based on the pre-calibrated device characteristic profile, the hardware drive parameter combination corresponding to the optimal pulse energy adjustment value is found. The device characteristic profile records in detail the precise mapping relationship between the expected energy output and physical quantities such as the pulse width and pulse amplitude of the underlying drive signal. The hardware driver parameters are combined and bound to the physical address in the multi-channel graphene device, and the bound information is compiled into standardized digital instructions according to the communication protocol specification of the hardware driver circuit. The standardized digital instructions are sent to the hardware driver circuit via a dedicated data bus. After receiving and parsing the standardized digital instructions, the hardware driver circuit immediately adjusts the electrical excitation signal applied to a specific channel to complete the closed-loop correction of the optical field distortion.

[0009] Optionally, the step of simulating the complex propagation path offset of a multi-channel far-infrared beam as it passes through a deformed thermosensitive polymer film based on the equivalent lens curvature distribution, to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel, includes: Each beam in the multi-channel far-infrared beam is abstracted into a beam of light composed of a large number of independent light rays that follow the principles of geometric optics, and an initial direction vector is assigned to the light beam to accurately describe the initial state of the beam before it enters the thermosensitive polymer film. Using the continuous three-dimensional surface defined by the curvature distribution of the equivalent lens as the optical refraction interface, the propagation trajectory of each independent ray in the light beam is tracked and calculated. By applying Snell's law to the incident and exit points of the independent ray and the continuous three-dimensional surface respectively, the new propagation direction vector of the independent ray after passing through the thin film is determined. By comparing the propagation direction vector with the initial direction vector, an angular difference value representing the degree of propagation path deflection is obtained, and the angular difference values ​​are collected to construct a spatial distortion vector field.

[0010] Optionally, the step of importing the heat distribution data into a preset thermo-mechanical coupling analysis model, and using the thermo-mechanical coupling analysis model to analyze the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly, and generating an equivalent lens curvature distribution that dynamically describes the changes in the microstructure of the film surface based on the physical expansion, includes: The heat distribution data is mapped onto a preset two-dimensional grid to establish a discretized analysis model for the surface of the thermosensitive polymer film. A material thermal expansion function is called for each node in the two-dimensional grid. Based on the specific temperature value of each node, the corresponding local expansion intensity is calculated using the analysis model. Based on the local expansion strength, the theoretical displacement of each node along the normal direction of the film is determined. At the same time, the heat flow gradient between adjacent nodes is converted into an elastic constraint force applied between the nodes to simulate the correction effect of the physical tension inside the material on the theoretical displacement, thereby obtaining a set of accurate displacement data that is close to the real physical deformation. The spatial point set defined by the precise displacement data is integrated and reconstructed using surface fitting technology into a continuous three-dimensional surface that can characterize the overall morphology of the thermosensitive polymer film after heating. The equivalent lens curvature distribution is then analyzed from the geometric features of the continuous three-dimensional surface.

[0011] Optionally, the objective of restoring the overall uniformity of the far-field radiation pattern is transformed into a multivariate collaborative optimization task. This task, based on the sensitivity coefficient, iteratively adjusts the energy output weights of each channel until the difference between the predicted energy spectrum and the reference energy spectrum converges to a minimum. The resulting set of energy output weights is the optimal pulse energy adjustment value, including: The difference between the predicted energy map and the reference energy map is defined as the objective function value with the energy output weights of all channels as independent variables. A reasonable optimization search interval is set for each energy output weight based on the sensitivity coefficient. The optimization search interval ensures the stable convergence of the adjustment process. In the multidimensional parameter space formed by the optimization search interval, a guided search strategy is adopted to perform an iterative adjustment process. In each iteration, the guided search strategy generates candidate energy output weight adjustment schemes and prioritizes the adjustment scheme that can reduce the objective function value the fastest as the update direction of this iteration. The guided search strategy is continuously executed and the update direction is optimized until the objective function value reaches the preset stable convergence threshold. At this point, the iterative adjustment process is terminated, and the currently locked set of energy output weights is formally determined as the optimal pulse energy adjustment value.

[0012] Optionally, the step of binding the hardware driving parameters to the physical address in the multi-channel graphene device, and compiling the bound information into standardized digital instructions according to the communication protocol specification of the hardware driving circuit, includes: An independent parameter mapping data structure is created for each channel of the multi-channel graphene device, and the hardware driving parameters are combined and filled into a dedicated data field in the parameter mapping data structure. At the same time, the physical address of the multi-channel graphene device is filled into a dedicated address field in the parameter mapping data structure. A fixed frame format is defined according to the communication protocol specification of the hardware driver circuit. The frame format includes a start sequence for identifying the start of the instruction, an addressing area for storing the address field, a payload area for carrying the data field, and a check sequence for ensuring transmission integrity. According to the requirements of the frame format, the start sequence, the addressing area, the payload area, and the check sequence are concatenated into a complete bit stream in a predetermined order, and standardized digital instructions are generated based on the bit stream.

[0013] Secondly, this application provides a multi-channel graphene far-infrared pulse modulation system, comprising: The capture module is used to acquire heat distribution data of the entire heating plane during the operation of the multi-channel graphene device. The heat distribution data reflects local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths. The analysis module is used to import the heat distribution data into a preset thermo-mechanical coupling analysis model. The thermo-mechanical coupling analysis model analyzes the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly, and generates an equivalent lens curvature distribution that dynamically describes the changes in the micro-morphology of the film surface based on the physical expansion. A construction module is used to simulate the complex propagation path offset of a multi-channel far-infrared beam when passing through a deformed thermosensitive polymer film, based on the equivalent lens curvature distribution, so as to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel. The calculation module is used to run a collaborative redistribution algorithm for pulse energy with the spatial distortion vector field as the core input. The collaborative redistribution algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. The countermeasure module is used to generate a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device according to the optimal pulse energy adjustment value, and apply the pulse modulation command sequence to the corresponding channel to counteract the light field inhomogeneity introduced by thermal deformation and ensure that the output far-infrared radiation field pattern remains stable.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of a multi-channel graphene far-infrared pulse modulation method as described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the multi-channel graphene far-infrared pulse modulation method described in the first aspect above.

[0016] The multi-channel graphene far-infrared pulse modulation method provided in this application captures heat distribution data of the entire heating plane during the operation of the multi-channel graphene device through a temperature sensor array tightly integrated into the graphene heating unit. This heat distribution data reflects local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths, enabling precise perception and quantification of the internal thermal state of the multi-channel graphene device, providing a reliable raw data foundation for subsequent deformation analysis. By importing the heat distribution data into a preset thermo-mechanical coupling analysis model, the model analyzes the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomalies. Based on this physical expansion, an equivalent lens curvature distribution dynamically describes the changes in the microstructure of the film surface, establishing a physical correlation between the graphene heat distribution and the optical deformation of the thermosensitive film. This transforms the thermal effect into quantifiable optical parameters, providing a physical basis for light field modulation. Furthermore, based on the equivalent lens curvature distribution, the complex propagation path offset of the multi-channel far-infrared beam as it passes through the deformed thermosensitive polymer film is simulated. This method constructs a spatial distortion vector field to quantify the degree and direction of optical field distortion in each channel. This allows for precise prediction of the impact of thermal deformation on the propagation path of far-infrared beams, transforming physical deformation into calculable optical distortion information and providing a quantitative indicator for optical field correction. By using the spatial distortion vector field as the core input, a collaborative redistribution algorithm for pulse energy is run. This algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. This enables an intelligent compensation strategy for optical field distortion, effectively overcoming the negative impact of thermal deformation on optical field uniformity through collaborative optimization of energy output in each channel. Furthermore, based on the optimal pulse energy adjustment value, a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device is generated and applied to the corresponding channels to counteract the optical field inhomogeneity introduced by thermal deformation, ensuring the stability of the output far-infrared radiation pattern. This provides an executable hardware control scheme, transforming theoretical calculations into actual physical operations, and achieving precise and active control of the far-infrared light field.

[0017] Furthermore, all deflection information contained in the spatial distortion vector field is projected forward through a far-field propagation model to generate a predicted energy map reflecting the actual distribution of energy in each channel on the target plane. This predicted energy map is then superimposed and compared with a reference energy map representing absolutely uniform radiation under ideal conditions, thereby identifying regions with abnormal energy distribution. Subsequently, for any point within these abnormal energy distribution regions, a contribution analysis model is established. This model is used to reverse-analyze the energy of the initial emission channel converging at that point, and the sensitivity coefficient of the energy variation to the energy level in the abnormal energy distribution region is quantified. The overall uniformity objective of restoring the far-field radiation pattern is transformed into a multivariate collaborative optimization task. Based on the sensitivity coefficient, this task iteratively adjusts the energy output weights of each channel until the difference between the predicted energy map and the reference energy map converges to a minimum. The resulting set of energy output weights represents the optimal pulse energy adjustment value. This process enables accurate diagnosis and intelligent compensation of far-infrared light field distortion. By quantifying the contribution of each channel to the distortion and performing collaborative optimization, the overall uniformity and stability of the far-field radiation pattern are ensured. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic flowchart of a multi-channel graphene far-infrared pulse modulation method provided in this application embodiment; Figure 2 A schematic diagram illustrating a specific implementation of a multi-channel graphene far-infrared pulse modulation method provided in this application embodiment; Figure 3 A schematic diagram illustrating a specific implementation of a multi-channel graphene far-infrared pulse modulation method provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of a multi-channel graphene far-infrared pulse modulation system provided in an embodiment of this application. Detailed Implementation

[0020] Existing far-infrared light field modulation schemes based on microelectromechanical system (MEMS) mirror arrays exhibit significant limitations when facing key performance indicators such as high-frequency dynamic response, adaptability to complex environments, and light energy utilization efficiency. Their inherent mechanical inertia, complex manufacturing processes, environmental sensitivity, and energy loss due to the reflective modulation principle make it difficult to meet the stringent requirements of high-precision, multi-channel dynamic modulation of far-infrared light fields in precision optics, biomedical imaging, and advanced manufacturing. This technological bottleneck is particularly evident in applications requiring fine-grained and independent modulation of light field intensity, phase, and propagation direction, necessitating a novel, non-mechanical, and efficient light field modulation method.

[0021] To address the aforementioned problems in existing technologies, this invention proposes a multi-channel graphene far-infrared pulse modulation method. This method captures heat distribution data using a temperature sensor array tightly integrated into the graphene heating unit, and analyzes the equivalent lens curvature distribution caused by thermal deformation of the thermosensitive polymer film using a thermo-coupling analysis model. Based on this, an optical tracing algorithm is used to simulate beam propagation path offset, constructing a spatial distortion vector field. A pulse energy collaborative redistribution algorithm is then run to calculate the optimal pulse energy adjustment value, generating and applying a pulse modulation command sequence. This invention ingeniously combines the thermal effect of graphene with the optical properties of thermosensitive materials, achieving non-mechanical, active control of the far-infrared light field. It fundamentally solves the problems of slow response speed, limited accuracy, susceptibility to environmental interference, and high energy loss associated with traditional mechanical control schemes, significantly improving the stability and consistency of the far-infrared radiation field and providing a more efficient and reliable solution for related applications.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The core of this application is to provide a multi-channel graphene far-infrared pulse modulation method, the flowchart of one specific implementation of which is shown below. Figure 1 As shown, the method includes: 101. During the operation of the multi-channel graphene device, the heat distribution data of the entire heating plane is obtained by a temperature sensor array tightly integrated into the graphene heating unit. The heat distribution data reflects the local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths. In the above scheme, a multi-channel graphene device refers to a graphene structure containing multiple independent working regions, each capable of independently generating heat and far-infrared radiation. The graphene heating unit is the specific region in the multi-channel graphene device responsible for generating heat. The temperature sensor array is a network of multiple miniature temperature sensors closely arranged on or near the surface of the graphene heating unit to monitor temperature. Thermal distribution data refers to the set of data collected by the temperature sensor array, reflecting the temperature variations at different locations on the graphene heating plane. Material heterogeneity refers to the slight differences in the physical or chemical properties that may exist in different regions of the graphene material itself. Differences in heat dissipation paths refer to the potential inconsistencies in the pathways or efficiencies by which different regions of the graphene heating unit dissipate heat. Localized temperature anomalies refer to situations where the temperature of certain regions on the graphene heating plane is significantly higher or lower than the average temperature of their surrounding areas, which is usually caused by the aforementioned material heterogeneity or differences in heat dissipation paths.

[0024] In this embodiment, firstly, during the continuous operation of the multi-channel graphene device, the temperature sensor array tightly integrated into the graphene heating unit continuously scans and monitors the heating plane. For example, each micro-temperature sensor in the array continuously measures the temperature at its location and converts this temperature information into an electrical signal that can be recognized by the system. Secondly, the system collects all the electrical signals transmitted back from the temperature sensor array and digitizes them to form a raw dataset containing the temperature values ​​of each discrete point on the heating plane. For example, these data points are arranged according to their actual physical location on the heating plane to construct a two-dimensional temperature map. Finally, by analyzing the two-dimensional temperature map, the system can identify areas where the temperature values ​​deviate significantly from the normal range; these deviating temperature values ​​are determined to be local temperature anomalies. For example, if the temperature of a certain area is much higher than the average temperature of its surrounding area, this may indicate that there are minor defects in the graphene material in that area, or that the heat dissipation channels beneath it are not sufficiently unobstructed, causing heat to accumulate there, thus reflecting the existence of material heterogeneity or differences in heat dissipation paths.

[0025] 102. Import the heat distribution data into a preset thermo-mechanical coupling analysis model. The thermo-mechanical coupling analysis model is used to analyze the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly. Based on the physical expansion, an equivalent lens curvature distribution that dynamically describes the changes in the micro-morphology of the film surface is generated. Optionally, such as Figure 2 As shown, step 102 may specifically include the following steps: 1021. Map the heat distribution data onto a preset two-dimensional grid to establish a discretized analysis model for the surface of the thermosensitive polymer film. Call a material thermal expansion function for each node in the two-dimensional grid. Calculate the corresponding local expansion intensity based on the specific temperature value of each node using the analysis model. 1022. Based on the local expansion strength, determine the theoretical displacement of each node along the normal direction of the film, and at the same time convert the heat flow gradient between adjacent nodes into an elastic constraint force applied between the nodes to simulate the correction effect of the physical tension inside the material on the theoretical displacement, thereby obtaining a set of accurate displacement data that is close to the real physical deformation. 1023. Using surface fitting technology, the spatial point set defined by the precise displacement data is integrated and reconstructed into a continuous three-dimensional surface that can characterize the overall morphology of the thermosensitive polymer film after heating, and the equivalent lens curvature distribution is analyzed from the geometric features of the continuous three-dimensional surface.

[0026] In the above scheme, the thermo-coupling analysis model is a computational framework used to simulate and predict the deformation of materials under temperature changes. Thermosensitive polymer films are thin films made of polymer materials that are sensitive to temperature changes. Their physical properties, especially volume or shape, change significantly and predictably with increasing temperature, typically manifesting as thermal expansion. Localized temperature anomalies refer to areas on the heating plane of a multi-channel graphene device where the temperature is significantly higher or lower than the average temperature of the surrounding area. These anomalies are the main cause of non-uniform deformation of the thermosensitive polymer film. Non-uniform physical expansion refers to the inconsistent degree and direction of expansion in different regions of the thermosensitive polymer film when affected by localized temperature anomalies, resulting in complex and irregular deformation of the film surface, rather than simple uniform expansion. The equivalent lens curvature distribution refers to optically representing the non-uniform deformation of the thermosensitive polymer film caused by thermal expansion as a lens with variable focal length or beam deflection capability. The curvature value at each point on its surface reflects the region's ability to focus or diverge incident light. This distribution dynamically describes the influence of changes in the film's surface microstructure on the optical field. A material thermal expansion function is a mathematical expression or lookup table that describes how the size or volume of a specific material changes with temperature. By inputting a temperature value, the expansion ratio or strength of the material at that temperature can be calculated. Local expansion strength refers to the minute dimensional increment of a thermosensitive polymer film at each discrete node of a two-dimensional grid due to temperature increase; it quantifies the degree of thermal expansion at that point. A continuous three-dimensional surface refers to a geometric surface reconstructed from a set of spatial points using surface fitting techniques, capable of completely and smoothly describing the overall morphology of the thermosensitive polymer film after heating.

[0027] In this embodiment, firstly, through step 1021, heat distribution data acquired from the temperature sensor array is received. This data records in detail the temperature information at various locations on the heating plane of the multi-channel graphene device. To accurately calculate the deformation of the thermosensitive polymer film, this continuous temperature data is digitized and meshed, mapping it onto a preset two-dimensional grid. This two-dimensional grid divides the film surface into countless tiny, independently analyzable regions or nodes. For each node on this grid, a pre-established material thermal expansion function is invoked. This function is pre-calibrated based on the physical properties of the thermosensitive polymer film material. It can accurately calculate the local expansion intensity of the node at the current temperature based on different input temperature values. For example, if the temperature of a node increases by 10 degrees, the material thermal expansion function will calculate that the point will expand by 0.05 micrometers along the normal direction, while another point that only increases by 5 degrees may only expand by 0.025 micrometers. This yields the initial expansion trend of each discrete point on the film surface.

[0028] Subsequently, in step 1022, based on the local expansion intensity of each node calculated in step 1021, the theoretical displacement of each node along the film normal is initially determined. This theoretical displacement assumes that each node expands independently, without considering their interactions. However, in the real physical world, the thermosensitive polymer film is a continuous whole, and adjacent regions restrain each other. Therefore, the elastic constraint force inside the film is further considered. Specifically, it analyzes the heat flow gradient between adjacent nodes, i.e., the direction and intensity of energy transfer caused by temperature differences, which generates internal stress. This heat flow gradient is transformed into an elastic constraint force applied between nodes. This force is like the physical tension inside the film, which corrects the previously calculated theoretical displacement and simulates the resistance of material molecules and the overall structure to deformation. Through this fine correction, a set of accurate displacement data that more closely approximates the actual physical deformation of the thermosensitive polymer can be obtained. These data reflect the actual deformation of the film after considering the integrity of the material's own structure, rather than a simple ideal expansion.

[0029] Finally, in step 1023, advanced surface fitting techniques are used to integrate and reconstruct the spatial point set defined by the precise displacement data obtained in step 1022. This precise displacement data is actually a series of discrete points with definite coordinates in three-dimensional space, which together depict the surface profile of the deformed film. Surface fitting techniques, such as least squares or spline interpolation, construct a smooth and continuous three-dimensional surface based on these discrete points. This continuous three-dimensional surface is like a high-precision digital model of the thermosensitive polymer film after thermal deformation, capable of completely and smoothly representing the overall morphology of the film after heating. From the geometric features of this continuous three-dimensional surface, especially the curvature of its surface, the equivalent lens curvature distribution can be accurately resolved, thereby quantifying the influence of film deformation on the propagation direction and focusing characteristics of incident light, providing accurate input for subsequent optical corrections.

[0030] In practical applications, such as long-range infrared surveillance, multi-channel graphene devices serve as infrared light sources, and the quality of the emitted infrared beam directly affects the clarity of the surveillance image and the accuracy of target tracking. When the graphene device is working, its surface temperature may experience localized temperature anomalies due to uneven current distribution or differences in heat dissipation, causing non-uniform deformation of the thermally sensitive polymer film above it. Step 1021 continuously receives thermal distribution data from the graphene device surface temperature sensor array and precisely maps this data onto a two-dimensional grid on the film surface. For example, if the surveillance detects a blurred infrared image in a certain area, it immediately analyzes the temperature data of the corresponding graphene unit and calculates the local expansion intensity of the film in that area based on a preset material thermal expansion function, thereby making a preliminary judgment on the degree of deformation.

[0031] Next, in step 1022, the theoretical displacement of each point on the thin film is initially determined based on the local expansion intensity calculated in step 1021. However, considering the continuity and elasticity of the thin film material, the heat flow gradient between adjacent regions is further calculated and converted into elastic constraint forces to correct these theoretical displacements. For example, if a high-temperature region of the thin film has a strong expansion trend, but the elastic constraint force of the surrounding low-temperature region is large, iterative calculations will be used to obtain more accurate displacement data that reflects the actual deformation of the thin film under internal tension, avoiding overestimation or underestimation of deformation.

[0032] Subsequently, in step 1023, advanced surface fitting technology is used to reconstruct the spatial point set defined by the precise displacement data obtained in step 1022 into a continuous three-dimensional surface model. This model accurately depicts the overall morphology of the thermosensitive polymer film after heating, including its uneven surface. For example, if the temperature in the central region of the film is too high, causing bulges, while the edge region is relatively flat, surface fitting technology can accurately reconstruct this complex surface shape. From this reconstructed three-dimensional surface, the equivalent lens curvature distribution can be accurately resolved, thereby quantifying the effect of film deformation on the focusing or diverging of the infrared beam, providing key parameters for subsequent optical compensation.

[0033] The overall scheme of step 102 described above can accurately capture and quantify the microscopic deformation of the thermosensitive polymer film caused by thermal effects in the multi-channel graphene device under operating conditions, and convert it into an equivalent lens curvature distribution that is crucial for the control of the infrared light field. This allows the system to dynamically grasp the actual optical characteristics of the infrared beam, providing a precise physical basis for subsequent light field distortion correction, thereby significantly improving the response speed and control accuracy of the far-infrared pulse modulation method, ensuring the stability and consistency of the infrared radiation field pattern, and thus optimizing the imaging quality and target tracking capability of the long-distance infrared monitoring system.

[0034] 103. Based on the equivalent lens curvature distribution, simulate the complex propagation path offset of the multi-channel far-infrared beam when passing through the deformed thermosensitive polymer film, so as to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel. Optionally, such as Figure 3 As shown, step 103 may specifically include the following steps: 1031. Each beam of light in the multi-channel far-infrared beam is abstracted into a beam of light composed of a large number of independent light rays that follow the principles of geometric optics, and an initial direction vector is assigned to the light beam to accurately describe the initial state of the beam before it enters the thermosensitive polymer film. 1032. Using the continuous three-dimensional surface defined by the curvature distribution of the equivalent lens as the optical refraction interface, the propagation trajectory of each independent ray in the light beam is tracked and calculated. By applying Snell's law to the incident and exit points of the independent ray and the continuous three-dimensional surface respectively, the new propagation direction vector of the independent ray after passing through the thin film is determined. 1033. By comparing the propagation direction vector with the initial direction vector, an angular difference value representing the degree of propagation path deflection is obtained, and the angular difference values ​​are collected to construct a spatial distortion vector field.

[0035] In the above scheme, the optical tracing algorithm is a calculation method based on the principles of geometric optics, which calculates the light path and light field distribution by simulating the refraction and reflection behavior of light when it propagates through different media interfaces. A multi-channel far-infrared beam refers to a collection of beams emitted by multiple independent light-emitting units, possessing specific spatial distribution and propagation characteristics in the far-infrared band. A light beam is an abstract concept used in optical tracing to simulate an actual light beam, composed of a large number of independent light rays with specific initial positions and directions, which collectively represent the propagation characteristics of the beam. The initial direction vector is a mathematical representation of the propagation direction of the light before it enters the thermosensitive polymer film. The propagation direction vector is a mathematical representation of the new propagation direction of the light after it passes through the thermosensitive polymer film. The spatial distortion vector field is a three-dimensional vector field where each vector represents the degree and direction of distortion of the light at a specific spatial location, comprehensively describing the overall optical distortion produced by the beam after passing through the deformed film.

[0036] In this embodiment, firstly, through step 1031, each beam of light in the multi-channel far-infrared beam is abstracted into a beam composed of a large number of independent light rays following the principles of geometric optics, and an initial direction vector is assigned to the light beam to accurately describe the initial state of the beam before entering the thermosensitive polymer film. Specifically, this abstraction process refers to digitally modeling the actual far-infrared beam in a computer. For each far-infrared light, it is considered to be composed of countless theoretical light rays, all of which propagate in straight lines and each has a definite starting position and direction. The initial direction of each light ray before it contacts the thermosensitive polymer film is precisely recorded. This is like setting a starting point and a path for each light ray, thereby constructing a highly detailed optical model in digital space, laying the foundation for subsequent optical calculations.

[0037] Next, in step 1032, the continuous three-dimensional surface defined by the equivalent lens curvature distribution is used as the optical refraction interface. The propagation trajectory of each independent ray in the light beam is tracked and calculated. By applying Snell's law at the incident and exit points of the independent ray and the continuous three-dimensional surface, the new propagation direction vector of the independent ray after passing through the thin film is determined. The core of this process is the tracking calculation and application of Snell's law. The thin film surface shape obtained through thermo-coupling analysis, i.e., the equivalent lens curvature distribution, is regarded as a virtual optical interface with a complex surface. When the simulated light comes into contact with this interface, Snell's law is applied at the incident point and exit point of the light, respectively, according to the angle of light incidence and the characteristics of the thin film material. Snell's law is a physical law describing the refraction law of light when propagating between different media. Through it, it is possible to accurately calculate how the propagation direction of each ray changes after passing through the thin film, thereby simulating the actual impact of thin film deformation on the light path.

[0038] Finally, in step 1033, the propagation direction vector of each individual ray in the light beam after passing through the thermosensitive polymer film is compared with the initial direction vector to construct the spatial distortion vector field of the multi-channel far-infrared beam. This comparison and construction is a crucial step. After each ray passes through the film and its new propagation direction is calculated, this new direction is compared with the ray's initial direction before it was affected by the film. If there is a difference, it means that the ray has been distorted by the film deformation. The degree and direction of this distortion are quantified and represented as a vector. By combining these distortion vectors of all the rays, a complete distortion map, or spatial distortion vector field, is formed. This map visually shows how the far-infrared beam is precisely distorted and dispersed in space after passing through the deformed film, providing accurate data for subsequent distortion correction.

[0039] In practical applications, in long-range infrared monitoring systems, when the system needs to perform high-precision infrared imaging of a specific area, firstly, through step 1031, the system activates its internal optical modeling module. This module meticulously decomposes each beam of far-infrared light reflected from the target area into millions of independent, virtual rays in the computer. Each ray is assigned a precise initial position and direction, and this information is stored in a digital ray database, like preparing a detailed departure list for each ray about to travel, ensuring complete capture of the original light field state.

[0040] Next, in step 1032, as these virtual rays pass through the thermosensitive polymer-controlled thin film inside the system in digital space, the system invokes its core optical tracking engine. This engine utilizes the microscopic deformation data of the thin film surface obtained through previous thermodynamic analysis, i.e., the equivalent lens curvature distribution, treating it as a complex optical interface. When each virtual ray contacts this virtual interface, the engine precisely applies Snell's law at the point of incidence and exit, based on the angle of incidence and the optical properties of the thin film material. In this way, the system can calculate precisely how the propagation direction of each ray will change after passing through the thin film, thus simulating the refraction effect of thin film deformation on the actual infrared beam.

[0041] Subsequently, in step 1033, the system performs a crucial comparative analysis process. It compares the new propagation direction of each virtual ray after passing through the control film with the initial direction recorded in step 1031. If any angular or directional difference is found, the system determines that the ray has been distorted by the film deformation. The system quantifies the degree and direction of this distortion and converts it into a spatial distortion vector. By aggregating these distortion vectors for all rays, the system constructs a comprehensive spatial distortion vector field that clearly depicts how the entire far-infrared beam is precisely distorted and dispersed in space after passing through the control film.

[0042] This constructed spatial distortion vector field, acting like a precise distortion map, is transmitted to the image processing unit of the monitoring system. Based on the information provided by this map, the image processing unit performs pixel-level reverse correction on the received infrared image. For example, if a portion of the image on the map is shifted to the left by a distance of micrometers due to light distortion, the system can precisely correct it to the right. This ensures that even in complex environments or at long distances, the monitoring system can output highly clear, distortion-free infrared images, greatly improving the accuracy of target recognition and tracking.

[0043] The overall scheme in step 103 described above can accurately simulate the complex optical behavior of a multi-channel far-infrared beam passing through a thermosensitive polymer film deformed by thermal effects, and quantify the resulting spatial distortion. This allows the system to accurately grasp the degree and direction of the light field distortion, providing key optical parameters for subsequent distortion correction. This ensures the precise pointing and energy distribution of the far-infrared beam in space, significantly improving the imaging quality and target recognition capability of the long-range infrared monitoring system in complex environments.

[0044] 104. Using the spatial distortion vector field as the core input, run the collaborative redistribution algorithm for pulse energy. The collaborative redistribution algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. Optionally, step 104 may specifically include the following steps: 1041. Project all the deflection information contained in the spatial distortion vector field forward through the far-field propagation model to generate a predicted energy map that reflects the actual distribution of energy in each channel on the target plane. Then, superimpose and compare the predicted energy map with a reference energy map that represents absolutely uniform radiation under ideal conditions to lock down the abnormal energy distribution area. 1042. Establish a contribution analysis model for any point in the energy distribution anomaly region, and use the contribution analysis model to reverse analyze the energy of the initial emission channel that converges at the point, and quantify the sensitivity coefficient of the energy change to the energy level of the energy distribution anomaly region. 1043. The objective of restoring the overall uniformity of the far-field radiation pattern is transformed into a multivariate collaborative optimization task. The multivariate collaborative optimization task is based on the sensitivity coefficient and iteratively adjusts the energy output weights of each channel until the difference between the predicted energy spectrum and the reference energy spectrum converges to the minimum. The set of energy output weights obtained at this time is the optimal pulse energy adjustment value.

[0045] Step 1043 may specifically include the following process: defining the difference between the predicted energy spectrum and the reference energy spectrum as the objective function value with the energy output weights of all channels as independent variables, and setting a reasonable optimization search interval for each energy output weight based on the sensitivity coefficient, wherein the optimization search interval ensures the stable convergence of the adjustment process; in the multidimensional parameter space constituted by the optimization search interval, a guided search strategy is adopted to perform a repeated iterative adjustment process, wherein the guided search strategy generates candidate energy output weight adjustment schemes in each iteration, and the adjustment scheme that can reduce the objective function value the fastest is selected as the update direction of this iteration; the guided search strategy is continuously executed and the update direction is optimized until the objective function value reaches the preset stable convergence threshold, at which point the repeated iterative adjustment process is terminated, and the currently locked set of energy output weights is formally determined as the optimal pulse energy adjustment value.

[0046] In the above scheme, the spatial distortion vector field refers to the set of parameters obtained through optical tracing algorithms, describing the degree and direction of deviation of the far-infrared beam's propagation direction relative to its initial direction after passing through the thermosensitive polymer film. The optimal pulse energy adjustment value refers to the amount of energy output that each far-infrared channel needs to adjust, calculated through a collaborative redistribution algorithm, to achieve optimal uniformity in the far-field radiation pattern. The far-field propagation model is a mathematical tool used to simulate the energy distribution of the beam on a distant target plane after propagation through space from the emitter. The predicted energy spectrum is the actual energy distribution image of the far-infrared beam on the target plane predicted based on the current beam distortion and the far-field propagation model. The reference energy spectrum is a preset, ideally defined uniform energy distribution image of the far-infrared beam on the target plane, serving as the optimization target. An abnormal energy distribution region refers to an area where there is a significant difference between the predicted and reference energy spectra, indicating that the energy distribution in this region does not meet the uniformity requirements. The contribution analysis model is a mathematical model used to analyze the degree of influence of the energy output of each independent far-infrared channel on its energy level in an abnormal energy distribution region. Energy output weight refers to the scaling factor used to adjust the pulse energy output of each independent channel in the collaborative redistribution algorithm.

[0047] In this embodiment, firstly, through step 1041, the beam spatial distortion vector field generated in the previous step is received. This field contains detailed information about the distortion of each ray in the beam by the thin film deformation. A far-field propagation model is used, which acts like a virtual telescope, accurately simulating how these rays will be distributed on a distant target plane based on the current deflection state of the light. Through this simulation process, a predicted energy map is generated, which visually shows the actual energy distribution of the current beam in the target area. Subsequently, this predicted energy map is compared with a pre-set reference energy map representing an ideal uniform state. This comparison process is like performing pixel-level overlay and difference analysis on two images, which can quickly and accurately identify which areas have energy distribution deviations from the ideal state, thereby precisely locating areas with abnormal energy distribution.

[0048] Subsequently, in step 1042, a contribution analysis model is established for these locked energy distribution anomaly regions. The key function of this model is reverse analysis and quantification of sensitivity. It can trace back to any point within the anomaly region and analyze which independent emission channels initially contributed the energy converging at that point. For example, if a dark area on the target plane lacks energy, the model analyzes which channels failed to effectively reach that area. Furthermore, the model quantifies the impact of energy variations in each initial emission channel on the energy level of the anomaly region, thereby calculating a sensitivity coefficient. This coefficient indicates the extent to which adjusting the energy of a particular channel affects the energy distribution of the target region; a larger coefficient indicates that the energy adjustment of that channel is more critical to improving the energy distribution of the region, providing data for subsequent precise adjustments.

[0049] Finally, step 1043 transforms the goal of restoring the overall uniformity of the far-field radiation pattern into a multivariate collaborative optimization task. This task, based on the sensitivity coefficients obtained in step 1042, uses an intelligent iterative optimization algorithm to repeatedly adjust the energy output weights of each independent channel. In each iteration, the energy output of each channel is fine-tuned based on the current energy distribution and sensitivity coefficients. Then, a new energy spectrum is predicted again using the far-field propagation model and compared with the reference energy spectrum. This process continues until the difference between the predicted and reference energy spectra converges to a minimum, achieving an acceptable level of uniformity. The resulting set of energy output weights represents the optimal pulse energy adjustment values ​​for achieving the overall uniformity of the far-field radiation pattern, ensuring optimal uniform coverage of the output far-infrared beam in the target area.

[0050] In practical applications, in long-range infrared surveillance systems, such as thermal imaging tracking scenarios for perimeter security, when the system detects areas of excessive brightness or darkness in the infrared image, making target identification difficult, firstly, through step 1041, the system uses previously acquired beam distortion information to simulate the energy distribution of these distorted beams at a long distance, such as a wall area hundreds of meters away, in a computer. This simulation result generates an actual energy distribution map, which may show that some parts of the wall have excessively strong infrared signals, for example, a signal strength of 100 units at one point, while other parts are too weak, for example, a signal strength of 20 units at another point. The system compares this actual energy distribution map with a preset, ideal, uniform energy distribution map, for example, aiming for an infrared signal strength of 60 units at all points in the wall area, thereby accurately identifying which areas have infrared signal strengths deviating from expectations, such as a corner where the infrared signal is significantly weaker than other areas, or a central area where the signal is excessively strong.

[0051] Next, in step 1042, the system establishes an impact analysis model for these areas with abnormal infrared signals. For example, if a dark area in the wall has a signal strength of 20 units, caused by the deviation of light from transmission channels 3 and 7, the model will analyze that for every 1 unit increase in the energy of channel 3, the signal strength in that dark area will increase by 0.5 units; while for every 1 unit increase in the energy of channel 7, the signal strength in that dark area will increase by 0.8 units. These values ​​are the sensitivity coefficients, which tell the system which channel's energy adjustment is most effective in improving the signal strength of a specific area, thus providing a quantitative basis for subsequent precise adjustments.

[0052] Subsequently, through step 1043, the system transforms the goal of achieving uniform infrared signal intensity across the perimeter into a complex multi-channel energy collaborative optimization problem. Utilizing previously calculated sensitivity coefficients, the system employs an intelligent algorithm to repeatedly attempt to adjust the energy output ratio of each infrared emission channel. For example, the algorithm might first attempt to increase the energy of channel 3 by 5 units and channel 7 by 10 units, then re-simulate the energy distribution over a long distance and compare it to the ideal uniform distribution. If an area is found to be too bright or too dark, the algorithm will continue to fine-tune based on the sensitivity coefficients, for example, reducing the energy of channel 1 by 2 units. This process is repeated until the infrared signal intensity across the perimeter reaches the optimal uniformity, meaning the signal intensity at all points is close to 60 units, with no obvious bright spots or dark areas. At this point, the system obtains the optimal energy output value for each infrared channel, ensuring uniform and stable infrared coverage across the entire perimeter area.

[0053] The overall scheme in step 104 above can effectively compensate for the optical field distortion caused by thin film deformation during the propagation of the far-infrared beam, significantly improving the overall uniformity and stability of the far-field radiation pattern. By accurately calculating the optimal pulse energy adjustment value for each independent channel, this scheme ensures that far-infrared energy can be efficiently and uniformly projected onto the target area, thereby greatly optimizing the imaging quality and target recognition capability of the long-range infrared monitoring system. This enables it to provide clear and reliable infrared images even in complex environments, enhancing the system's adaptability and performance.

[0054] 105. Based on the optimal pulse energy adjustment value, generate a set of pulse modulation command sequences that are compatible with the hardware driving circuit of the multi-channel graphene device, and apply the pulse modulation command sequences to the corresponding channels to counteract the non-uniformity of the light field introduced by thermal deformation, and ensure that the output far-infrared radiation field pattern remains stable.

[0055] Optionally, step 105 may specifically include the following steps: 1051. Based on the pre-calibrated device characteristic profile, find the hardware drive parameter combination corresponding to the optimal pulse energy adjustment value. The device characteristic profile records in detail the precise mapping relationship between the desired energy output and physical quantities such as the pulse width and pulse amplitude of the underlying drive signal. 1052. The hardware driver parameters are combined and bound to the physical address in the multi-channel graphene device, and the bound information is compiled into standardized digital instructions according to the communication protocol specification of the hardware driver circuit. Step 1052 may specifically include the following processes: creating an independent parameter mapping data structure for each channel of the multi-channel graphene device, and filling the hardware driving parameters into a dedicated data field in the parameter mapping data structure, while filling the physical address of the multi-channel graphene device into a dedicated address field in the parameter mapping data structure; defining a fixed frame format according to the communication protocol specification of the hardware driving circuit, wherein the frame format includes a start sequence for identifying the start of the instruction, an addressing area for storing the address field, a payload area for carrying the data field, and a check sequence for ensuring transmission integrity; according to the requirements of the frame format, concatenating the start sequence, the addressing area, the payload area, and the check sequence into a complete bit stream in a predetermined order, and compiling and generating standardized digital instructions based on the bit stream.

[0056] 1053. The standardized digital instructions are sent to the hardware driver circuit via a dedicated data bus. After the hardware driver circuit receives and parses the standardized digital instructions, it immediately adjusts the electrical excitation signal applied to a specific channel to complete the closed-loop correction of the optical field distortion.

[0057] In the above scheme, the optimal pulse energy adjustment value refers to the additional or reduced energy applied to each channel of the multi-channel graphene device to correct optical field inhomogeneity. This value is calculated based on the optical field distortion. The pulse modulation command sequence refers to a series of commands arranged in a specific order. These commands control the hardware driving circuit of the multi-channel graphene device, enabling it to output far-infrared light with a specific energy. The hardware driving circuit refers to the electronic circuit responsible for receiving the pulse modulation command sequence and converting it into an actual electrical excitation signal to drive the multi-channel graphene device. The device characteristic profile is a document detailing the energy output performance of the multi-channel graphene device under different driving parameters, establishing the correspondence between the desired energy output and the actual driving signal. The hardware driving parameter combination refers to a set of parameters used to control the hardware driving circuit, such as pulse duration and pulse intensity. These parameters collectively determine the energy output of the graphene device.

[0058] In this embodiment, firstly, through step 1051, based on the previously calculated optimal pulse energy adjustment value, the system consults a pre-prepared device characteristic profile. This profile is like a detailed instruction manual, recording the specific parameters that the hardware drive circuit needs to be set for different energy requirements, such as the pulse duration and pulse intensity. Using this profile, the system can find the hardware drive parameter combination that matches the required energy adjustment value, ensuring the accuracy of the energy output. For example, if it is calculated that a certain channel needs an increase of 10 units of energy, the system will look up the pulse width and pulse amplitude corresponding to an increase of 10 units of energy in the profile. Assuming it finds that the pulse width needs to be set to 10 microseconds and the pulse amplitude to 5 volts.

[0059] Subsequently, in step 1052, the found hardware driver parameter combinations are assigned to the corresponding physical addresses in the multi-channel graphene device. This is similar to labeling each graphene unit that needs adjustment with the driver parameters it should receive. Then, this information, containing physical addresses and driver parameters, is organized and encoded according to the communication protocol specifications of the hardware driver circuit, becoming a standardized digital instruction that the hardware can understand. For example, for a channel requiring increased power, its physical address is 001, corresponding to a pulse width of 10 microseconds and a pulse amplitude of 5 volts. This information is packaged into a digital instruction packet conforming to the communication protocol, such as address 001, setting the pulse width to 10 and the pulse amplitude to 5.

[0060] Finally, through step 1053, these compiled standardized digital instructions are sent to the hardware driver circuit of the multi-channel graphene device via a dedicated data bus. Upon receiving these instructions, the hardware driver circuit immediately parses them and adjusts the electrical excitation signal applied to specific channels according to the instruction content. This adjustment is instantaneous, thus achieving rapid and precise correction of optical field distortion. For example, after receiving an instruction from address 001 to set the pulse width to 10 and the pulse amplitude to 5, the hardware driver circuit immediately sends an electrical signal of 5 volts lasting 10 microseconds to the graphene unit at physical address 001, causing it to emit far-infrared light of corresponding energy, thereby canceling out the optical field inhomogeneity previously caused by thermal deformation.

[0061] In a practical application, in a long-range infrared monitoring scenario, suppose a thermal imaging tracking system is monitoring an area. Due to fluctuations in ambient temperature, the system detects slight blurring in a localized area of ​​the infrared image. This indicates that the corresponding graphene device channel in that area is experiencing uneven light field output due to thermal deformation. Based on calculations from previous steps, it is determined that energy compensation is needed for channel A, with the optimal pulse energy adjustment being an increase of 0.03 units of energy. For channel B, a decrease of 0.02 units of energy is required.

[0062] In step 1051, the system queries a pre-established device characteristic profile. This profile details the energy output characteristics of the graphene device under different driving parameters. For example, the profile might state that when the pulse width is 12 microseconds and the pulse amplitude is 6 volts, the energy output increases by 0.03 units; when the pulse width is 8 microseconds and the pulse amplitude is 3 volts, the energy output decreases by 0.02 units. Based on the calculated optimal pulse energy adjustment value, the system precisely locates the hardware driving parameter combination for channel A as [pulse width 12 microseconds, pulse amplitude 6 volts] and the hardware driving parameter combination for channel B as [pulse width 8 microseconds, pulse amplitude 3 volts] from the profile.

[0063] Subsequently, in step 1052, the system binds the found hardware driver parameter combinations to the corresponding physical addresses in the multi-channel graphene device. Assume the physical address of channel A is 0x01 and the physical address of channel B is 0x02. The system compiles this binding information into standardized digital instructions according to the communication protocol specifications of the hardware driver circuit. For example, it generates the instruction for channel A: "Address 0x01, set pulse width 12, pulse amplitude 6"; and the instruction for channel B: "Address 0x02, set pulse width 8, pulse amplitude 3". These instructions are encoded binary data packets, ensuring that the hardware can directly recognize and execute them.

[0064] Finally, through step 1053, these standardized digital instructions are sent to the hardware driver circuit via a dedicated high-speed data bus. Upon receiving the instructions, the hardware driver circuit immediately parses and executes them. For channel A, the driver circuit immediately applies a 6-volt electrical excitation signal lasting 12 microseconds; for channel B, it applies a 3-volt electrical excitation signal lasting 8 microseconds. This instantaneous adjustment allows for precise compensation of the graphene device's energy output, thereby offsetting the optical field inhomogeneity introduced by thermal deformation, resulting in clearer infrared monitoring images and significantly improved target tracking stability.

[0065] The overall scheme in step 105 above can accurately calculate and generate corresponding pulse modulation commands based on the optical field distortion. By adjusting the energy output of the multi-channel graphene device, it can effectively counteract the optical field inhomogeneity caused by thermal deformation, ensuring that the far-infrared radiation field pattern remains stable and consistent, thereby significantly improving the imaging quality and target recognition capability of the long-distance infrared monitoring system.

[0066] The following is a complete embodiment for steps 101-105: In step 101, within a long-range infrared monitoring system, when a multi-channel graphene device is operating to emit an infrared beam, a densely packed array of miniature temperature sensors on its surface continuously collects temperature data from each heat-generating unit. For example, at a certain moment, the sensor array detects a temperature of 45.2 degrees Celsius in the central region of the device, while the temperature in the edge region is 40.1 degrees Celsius, resulting in a localized temperature difference of 5.1 degrees Celsius. This reflects a localized temperature anomaly due to material properties or uneven heat dissipation.

[0067] In step 102, the captured heat distribution data, such as the temperature differences mentioned above, is immediately input into a pre-established thermo-mechanical coupling analysis model. This model accurately calculates the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene device due to the absorption of this heat, based on the temperature distribution of the graphene device. For example, the model analysis shows that the film expands by 1.5 micrometers in the central region, while the edge region expands by only 0.8 micrometers. Based on this expansion data, the model further generates an equivalent lens curvature distribution that dynamically describes the changes in the microstructure of the film surface. For example, the equivalent radius of curvature is 200 millimeters in the central region and 250 millimeters in the edge region, indicating that the film surface has formed a concave lens-like deformation.

[0068] In step 103, using the equivalent lens curvature distribution obtained in step 102, the system invokes an optical tracing algorithm. This algorithm abstracts the multi-channel far-infrared beam into a series of rays and simulates the complex propagation paths of these rays as they pass through a deformed thermosensitive polymer film. For example, rays that should originally propagate in a straight line may deflect by 0.02 degrees after passing through the deformed film. By tracing all rays, the system can quantify the degree of distortion and the direction of offset of the optical field in each channel, thereby constructing a spatial distortion vector field. For example, a distortion vector of a certain channel (0.02 degrees, upward to the right) indicates that the beam has shifted 0.02 degrees to the upward to the right.

[0069] In step 104, using the spatial distortion vector field constructed in step 103 as the core input, the system runs a cooperative pulse energy redistribution algorithm. This algorithm aims to restore the overall uniformity of the far-field radiation pattern. Through iterative calculations, it determines the optimal pulse energy adjustment value for each individual channel. For example, if the beam in a certain channel is shifted 0.02 degrees to the upper right, the algorithm might calculate that the pulse energy of that channel needs to be increased by 0.05 units to compensate for the inhomogeneity of the light field; while another channel might need to have its energy reduced by 0.03 units. These adjustment values ​​are precisely calculated to achieve optimal uniformity in the output infrared radiation pattern.

[0070] In step 105, based on the optimal pulse energy adjustment value calculated in step 104, the system generates a set of pulse modulation instruction sequences adapted to the hardware driving circuit of the multi-channel graphene device. For example, for a channel requiring an increase of 0.05 units of energy, the system generates an instruction with a pulse width of 10 microseconds and a pulse amplitude of 5 volts, based on the device characteristic profile. These instruction sequences are sent to the corresponding hardware driving circuit via a dedicated data bus. After receiving and parsing the instructions, the hardware driving circuit immediately adjusts the electrical excitation signal applied to the specific channel. For example, it applies an electrical signal with a duration of 10 microseconds and an intensity of 5 volts to the graphene unit, thereby completing the closed-loop correction of the optical field distortion and ensuring that the far-infrared radiation field pattern remains stable.

[0071] The multi-channel graphene far-infrared pulse modulation method provided in this application effectively overcomes the problem of light field inhomogeneity caused by thermal effects in multi-channel graphene devices in actual working environments by sensing the thermal distribution of the device, accurately modeling thermally induced deformation, quantifying light field distortion, intelligently calculating energy compensation, and achieving precise hardware-level control. This enables long-distance infrared monitoring systems to continuously output stable and consistent infrared radiation patterns, thereby significantly improving their imaging clarity, target recognition capability, and tracking accuracy in complex environments, ensuring the reliability and effectiveness of monitoring tasks.

[0072] Figure 4 This is a schematic diagram illustrating a specific embodiment of a multi-channel graphene far-infrared pulse modulation system provided in this application. (Refer to...) Figure 4 The system may include: The capture module 41 is used to capture the heat distribution data of the entire heating plane through a temperature sensor array tightly integrated into the graphene heating unit during the operation of the multi-channel graphene device. The heat distribution data reflects the local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths. The analysis module 42 is used to import the heat distribution data into a preset thermo-mechanical coupling analysis model, analyze the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly, and generate an equivalent lens curvature distribution that dynamically describes the changes in the micro-morphology of the film surface based on the physical expansion. Module 43 is used to simulate the complex propagation path offset of a multi-channel far-infrared beam when passing through a deformed thermosensitive polymer film based on the equivalent lens curvature distribution, so as to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel. The calculation module 44 is used to run a collaborative redistribution algorithm for pulse energy with the spatial distortion vector field as the core input. The collaborative redistribution algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. The countermeasure module 45 is used to generate a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device according to the optimal pulse energy adjustment value, and apply the pulse modulation command sequence to the corresponding channel to counteract the light field inhomogeneity introduced by thermal deformation and ensure that the output far-infrared radiation field pattern remains stable.

[0073] The multi-channel graphene far-infrared pulse modulation system of this application embodiment is used to implement the aforementioned multi-channel graphene far-infrared pulse modulation method. Therefore, the specific implementation of the multi-channel graphene far-infrared pulse modulation system can be found in the embodiment section of the multi-channel graphene far-infrared pulse modulation method above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0074] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described multi-channel graphene far-infrared pulse modulation methods.

[0075] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described multi-channel graphene far-infrared pulse modulation methods.

[0076] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0077] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the multi-channel graphene far-infrared pulse modulation method.

[0078] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0079] The foregoing has provided a detailed description of a multi-channel graphene far-infrared pulse modulation method, system, electronic device, and storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A multi-channel graphene far-infrared pulse modulation method, characterized in that, include: During the operation of the multi-channel graphene device, thermal distribution data of the entire heating plane is acquired, which reflects local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths. The thermal distribution data is imported into a preset thermo-mechanical coupling analysis model. The thermo-mechanical coupling analysis model is used to analyze the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly. Based on the physical expansion, an equivalent lens curvature distribution that dynamically describes the changes in the micro-morphology of the film surface is generated. Based on the equivalent lens curvature distribution, the complex propagation path offset of the multi-channel far-infrared beam when passing through the deformed thermosensitive polymer film is simulated to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel. Using the spatial distortion vector field as the core input, a collaborative pulse energy redistribution algorithm is run. This algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. Specifically, this includes: projecting all deflection information contained in the spatial distortion vector field forward using a far-field propagation model to generate a predicted energy map reflecting the actual energy distribution of each channel on the target plane; and superimposing and comparing this predicted energy map with a reference energy map representing absolutely uniform radiation under ideal conditions to pinpoint areas of abnormal energy distribution. A contribution analysis model is established at any point in the abnormal energy distribution area. The energy of the initial emission channel converging at the point is then analyzed back through the contribution analysis model, and the sensitivity coefficient of the energy variation to the energy level of the abnormal energy distribution area is quantified. The overall uniformity objective of restoring the far-field radiation pattern is transformed into a multivariate collaborative optimization task. Based on the sensitivity coefficient, the multivariate collaborative optimization task iteratively adjusts the energy output weights of each channel until the difference between the predicted energy spectrum and the reference energy spectrum converges to the minimum. The set of energy output weights obtained at this point is the optimal pulse energy adjustment value. Based on the optimal pulse energy adjustment value, a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device is generated, and the pulse modulation command sequence is applied to the corresponding channel to counteract the non-uniformity of the light field introduced by thermal deformation, so as to ensure that the output far-infrared radiation field pattern remains stable.

2. The method according to claim 1, characterized in that, The step of generating a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device based on the optimal pulse energy adjustment value, and applying the pulse modulation command sequences to the corresponding channels to counteract the optical field inhomogeneity introduced by thermal deformation and ensure that the output far-infrared radiation field pattern remains stable includes: Based on the pre-calibrated device characteristic profile, the hardware drive parameter combination corresponding to the optimal pulse energy adjustment value is found. The device characteristic profile records in detail the precise mapping relationship between the expected energy output and physical quantities such as the pulse width and pulse amplitude of the underlying drive signal. The hardware driver parameters are combined and bound to the physical address in the multi-channel graphene device, and the bound information is compiled into standardized digital instructions according to the communication protocol specification of the hardware driver circuit. The standardized digital instructions are sent to the hardware driver circuit via a dedicated data bus. After receiving and parsing the standardized digital instructions, the hardware driver circuit immediately adjusts the electrical excitation signal applied to a specific channel to complete the closed-loop correction of the optical field distortion.

3. The method according to claim 1, characterized in that, The method of simulating the complex propagation path offset of a multi-channel far-infrared beam as it passes through a deformed thermosensitive polymer film, based on the equivalent lens curvature distribution, to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel, includes: An initial direction vector is set for each beam in the multi-channel far-infrared beam to describe the initial state of the beam before it enters the thermosensitive polymer film; Using the continuous three-dimensional surface defined by the curvature distribution of the equivalent lens as the optical refraction interface, the propagation trajectory of each independent ray in the light beam is tracked and calculated. By applying Snell's law to the incident and exit points of the independent ray and the continuous three-dimensional surface respectively, the new propagation direction vector of the independent ray after passing through the thin film is determined. By comparing the propagation direction vector with the initial direction vector, an angular difference value representing the degree of propagation path deflection is obtained, and the angular difference values ​​are collected to construct a spatial distortion vector field.

4. The method according to claim 1, characterized in that, The process involves importing the heat distribution data into a preset thermo-mechanical coupling analysis model. This model analyzes the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from localized temperature anomalies. Based on this physical expansion, an equivalent lens curvature distribution dynamically describing the changes in the film's surface microstructure is generated, including: The heat distribution data is mapped onto a preset two-dimensional grid to establish a discretized analysis model for the surface of the thermosensitive polymer film. A material thermal expansion function is called for each node in the two-dimensional grid. Based on the specific temperature value of each node, the corresponding local expansion intensity is calculated using the analysis model. Based on the local expansion intensity, the theoretical displacement of each node along the normal direction of the film is determined. At the same time, the heat flow gradient between adjacent nodes is converted into an elastic constraint force applied between the nodes to simulate the correction effect of the physical tension inside the material on the theoretical displacement, so as to obtain a set of accurate displacement data close to the real physical deformation. The spatial point set defined by the precise displacement data is integrated and reconstructed using surface fitting technology into a continuous three-dimensional surface that can characterize the overall morphology of the thermosensitive polymer film after heating. The equivalent lens curvature distribution is then analyzed from the geometric features of the continuous three-dimensional surface.

5. The method according to claim 1, characterized in that, The objective of restoring the overall uniformity of the far-field radiation pattern is transformed into a multivariate collaborative optimization task. This task, based on the sensitivity coefficient, iteratively adjusts the energy output weights of each channel until the difference between the predicted energy spectrum and the reference energy spectrum converges to a minimum. The resulting set of energy output weights represents the optimal pulse energy adjustment values, including: The difference between the predicted energy map and the reference energy map is defined as the objective function value with the energy output weights of all channels as independent variables, and an optimization search interval is set for each energy output weight according to the sensitivity coefficient. The optimization search interval ensures the stable convergence of the adjustment process. In the multidimensional parameter space formed by the optimization search interval, a guided search strategy is adopted to perform an iterative adjustment process. In each iteration, the guided search strategy generates candidate energy output weight adjustment schemes and prioritizes the adjustment scheme that can reduce the objective function value the fastest as the update direction of this iteration. The guided search strategy is continuously executed and the update direction is optimized until the objective function value reaches the preset stable convergence threshold. At this point, the iterative adjustment process is terminated, and the currently locked set of energy output weights is formally determined as the optimal pulse energy adjustment value.

6. The method according to claim 2, characterized in that, The step of combining the hardware driver parameters and binding them to the physical address in the multi-channel graphene device, and compiling the bound information into standardized digital instructions according to the communication protocol specification of the hardware driver circuit, includes: An independent parameter mapping data structure is created for each channel of the multi-channel graphene device, and the hardware driving parameters are combined and filled into a dedicated data field in the parameter mapping data structure. At the same time, the physical address of the multi-channel graphene device is filled into a dedicated address field in the parameter mapping data structure. A fixed frame format is defined according to the communication protocol specification of the hardware driver circuit. The frame format includes a start sequence for identifying the start of the instruction, an addressing area for storing the address field, a payload area for carrying the data field, and a check sequence for ensuring transmission integrity. According to the requirements of the frame format, the start sequence, the addressing area, the payload area, and the check sequence are concatenated into a complete bit stream in a predetermined order, and standardized digital instructions are generated based on the bit stream.

7. A multi-channel graphene far-infrared pulse modulation system, used to implement the multi-channel graphene far-infrared pulse modulation method as described in any one of claims 1 to 6, characterized in that, include: The capture module is used to acquire heat distribution data of the entire heating plane during the operation of the multi-channel graphene device. The heat distribution data reflects local temperature anomalies caused by material heterogeneity and differences in heat dissipation paths. The analysis module is used to import the heat distribution data into a preset thermo-mechanical coupling analysis model. The thermo-mechanical coupling analysis model analyzes the non-uniform physical expansion of the thermosensitive polymer film attached to the graphene due to the absorption of excess heat from the local temperature anomaly, and generates an equivalent lens curvature distribution that dynamically describes the changes in the micro-morphology of the film surface based on the physical expansion. A construction module is used to simulate the complex propagation path offset of a multi-channel far-infrared beam when passing through a deformed thermosensitive polymer film, based on the equivalent lens curvature distribution, so as to construct a spatial distortion vector field for quantifying the degree of optical field distortion and offset direction of each channel. The calculation module is used to run a collaborative redistribution algorithm for pulse energy with the spatial distortion vector field as the core input. The collaborative redistribution algorithm aims to restore the overall uniformity of the far-field radiation pattern and calculates the optimal pulse energy adjustment value for each independent channel. The countermeasure module is used to generate a set of pulse modulation command sequences adapted to the hardware driving circuit of the multi-channel graphene device according to the optimal pulse energy adjustment value, and apply the pulse modulation command sequence to the corresponding channel to counteract the light field inhomogeneity introduced by thermal deformation and ensure that the output far-infrared radiation field pattern remains stable.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the multi-channel graphene far-infrared pulse modulation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the multi-channel graphene far-infrared pulse modulation method as described in any one of claims 1 to 6.

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