Metasurface waveguide array and spectral sensing system based on silicon carbide substrate

Abstract

This invention relates to the field of optical sensing technology, and more particularly to a metasurface waveguide array and spectral sensing system based on a silicon carbide substrate. The system includes a coupling modulation module, a waveguide transmission module, and a spectral analysis module. The coupling modulation module achieves multi-physics coupling modulation through the nonlinear coupling superposition principle of thermo-optic and electro-optic effects. The waveguide transmission module selectively activates waveguide channel combinations based on a neural network prediction model and dynamically optimizes transmission parameters through an online reinforcement learning algorithm. The spectral analysis module performs environmental compensation correction on spectral feature parameters and outputs the corrected recognition results. This invention innovatively combines multi-physics coupling modulation, intelligent waveguide channel selection, and environmental adaptive compensation technology, solving the problems of insufficient accuracy and poor adaptability of traditional spectral sensing systems in complex environments, and achieving high-precision, adaptive spectral sensing.

Description

Technical Field

[0001] This invention relates to the field of optical sensing technology, and more particularly to metasurface waveguide arrays and spectral sensing systems based on silicon carbide substrates. Background Technology

[0002] Spectroscopic sensing technology is widely used in materials analysis, environmental monitoring, and biomedical testing, offering advantages such as non-contact operation, high precision, and simultaneous multi-parameter detection. With the rapid development of the Internet of Things (IoT) and intelligent sensing technologies, higher demands are being placed on the accuracy, adaptability, and integration of spectral sensing systems. Traditional spectral sensing systems mainly consist of a light source, optical elements, a detector, and a signal processing unit, often facing problems such as insufficient accuracy, poor environmental adaptability, and large system size in complex environments.

[0003] Existing spectral sensing systems mostly employ single-physical-field modulation methods, lacking multi-physical-field coupling modulation capabilities, making it difficult to achieve high-precision spectral control. Furthermore, traditional waveguide transmission structures typically employ fixed-channel designs or simple selection mechanisms, lacking intelligent channel selection and parameter optimization capabilities, making it difficult to adapt to complex and changing measurement environments. In addition, existing systems have limited compensation capabilities for environmental factors, and measurement accuracy often decreases when environmental parameters such as temperature and humidity change.

[0004] To address the aforementioned problems, this invention proposes a metasurface waveguide array and spectral sensing system based on a silicon carbide substrate, which solves the problems of insufficient accuracy and poor adaptability of traditional spectral sensing systems in complex environments. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a metasurface waveguide array and spectral sensing system based on a silicon carbide substrate. Through multi-physics field coupling modulation, intelligent waveguide channel selection, and environmental adaptive compensation technology, high-precision and adaptive spectral sensing is achieved, effectively solving the problems of insufficient accuracy and poor adaptability of traditional spectral sensing systems in complex environments.

[0006] The spectral sensing system includes a coupling modulation module, a waveguide transmission module, and a spectral analysis module, wherein:

[0007] The coupling modulation module is used to receive the incident light signal and perform phase modulation and amplitude modulation on the incident light signal through the multi-physics field coupling effect of thermal field modulation and electric field modulation to generate a modulated light signal.

[0008] The waveguide transmission module is used to receive the modulated optical signal output by the coupling modulation module, and output the separated spectral signal by selectively activating the waveguide channel combination and dynamic spectral separation.

[0009] The spectral analysis module is used to receive the separated spectral signal output by the waveguide transmission module, obtain the initial spectral pattern recognition result based on the separated spectral signal, and output the corrected spectral pattern recognition result through environmental compensation correction.

[0010] The coupling modulation module includes a metasurface waveguide array, a thermal modulation control unit, an electrical modulation control unit, and a coupling parameter calculation unit, wherein:

[0011] The metasurface waveguide array is composed of multiple metasurface modulation units arranged in an array on a silicon carbide substrate. Each metasurface modulation unit includes a thermal modulation structure and an electrical modulation structure.

[0012] The thermal modulation control unit is used to generate a temperature modulation amount according to the target spectrum modulation requirements, and provide a thermal modulation signal to the thermal modulation structure based on the temperature modulation amount, thereby achieving thermo-optic effect modulation by changing the local temperature.

[0013] The electrical modulation control unit is used to generate an electric field intensity according to the target spectral modulation requirements and the temperature modulation amount, and to provide an electrical modulation signal to the electrical modulation structure based on the electric field intensity, thereby achieving electro-optic effect modulation by applying an electric field.

[0014] The coupling parameter calculation unit is used to calculate the multi-physics field coupling modulation parameters based on the thermo-optic effect modulation and electro-optic effect modulation, and to generate the modulated optical signal through the nonlinear coupling superposition principle of the thermo-optic effect and electro-optic effect.

[0015] The coupling parameter calculation unit includes a thermo-optical calculation subunit, an electro-optical calculation subunit, a superposition calculation subunit, a parameter generation subunit, and a signal modulation subunit, wherein:

[0016] The thermo-optic calculation subunit is used to calculate the change in refractive index of the thermo-optic effect based on the temperature modulation amount generated by the thermal modulation control unit and the thermo-optic coefficient of the silicon carbide substrate.

[0017] The electro-optic calculation subunit is used to calculate the change in refractive index of the electro-optic effect based on the electric field intensity generated by the electro-modulation control unit and the electro-optic coefficient of the silicon carbide substrate.

[0018] The superposition calculation subunit is used to obtain the total refractive index modulation amount by vector superposition based on the thermo-optical effect refractive index change amount and the electro-optical effect refractive index change amount;

[0019] The parameter generation subunit is used to calculate the phase modulation depth and transmittance modulation coefficient based on the total refractive index modulation amount and the geometric parameters of the metasurface modulation unit, and to generate multiphysics field coupling modulation parameters.

[0020] The signal modulation subunit is used to control the metasurface waveguide array to perform phase modulation and amplitude modulation on the incident light signal according to the multi-physics field coupling modulation parameters, so as to generate a modulated light signal.

[0021] The coupling parameter calculation unit further includes a thermo-electric coupling compensation subunit, an electro-thermal coupling compensation subunit, a correction superposition subunit, and an optimization parameter generation subunit, wherein:

[0022] The thermoelectric coupling compensation subunit is used to establish a thermoelectric coupling effect model based on the temperature modulation amount, calculate the influence coefficient of temperature on the electro-optic coefficient of silicon carbide, and perform temperature compensation correction on the change in refractive index of the electro-optic effect based on the influence coefficient of the electro-optic coefficient to obtain the corrected change in refractive index of the electro-optic effect.

[0023] The electrothermal coupling compensation subunit is used to establish an electrothermal coupling effect model based on the electric field strength, calculate the influence of the electric field on the local temperature distribution, and perform electric field compensation correction on the thermo-optical effect refractive index change based on the influence of the local temperature distribution to obtain the corrected thermo-optical effect refractive index change.

[0024] The modified superposition subunit is used to obtain the modified total refractive index modulation amount by means of a coupled vector superposition algorithm based on the modified thermo-optical effect refractive index change amount and the modified electro-optical effect refractive index change amount.

[0025] The optimized parameter generation subunit is used to recalculate the phase modulation depth and transmittance modulation coefficient based on the corrected total refractive index modulation amount, and generate optimized multiphysics coupling modulation parameters.

[0026] The waveguide transmission module includes a waveguide array unit, a spectral separation unit, a path selection unit, and a transmission parameter optimization unit, wherein:

[0027] The waveguide array unit includes multiple parallel, non-uniformly periodically arranged waveguide channels for receiving the modulated optical signal and distributing the modulated optical signal to different waveguide channels for transmission, thereby obtaining the transmission optical signal of each waveguide channel.

[0028] The spectral separation unit, based on the differences in dispersion and mode coupling characteristics between the transmitted optical signal and the waveguide channel, achieves dynamic spectral separation through dynamic phase matching and spatial rearrangement, and outputs a separated spectral signal containing intensity distribution data for each wavelength.

[0029] The path selection unit is used to receive external spectral detection requirements, determine the target detection wavelength range and resolution requirements, selectively activate waveguide channel combinations through a neural network prediction model, and output a channel activation control signal.

[0030] The transmission parameter optimization unit is used to receive the separated spectral signal output by the spectral separation unit, calculate the spectral separation degree, crosstalk coefficient, transmission efficiency and wavelength resolution based on the light intensity distribution data of each wavelength in the separated spectral signal, and dynamically generate the optimized transmission parameter configuration through an online reinforcement learning algorithm to optimize the separated spectral signal.

[0031] The transmission parameter optimization unit includes a performance index calculation subunit, a deviation evaluation subunit, a parameter optimization subunit, and a feedback control subunit, wherein:

[0032] The performance index calculation subunit is used to calculate the spectral separation degree, crosstalk coefficient, transmission efficiency and wavelength resolution respectively based on the light intensity distribution data of each wavelength in the separated spectral signal, and generate multi-dimensional dynamic performance indexes.

[0033] The deviation assessment subunit is used to compare the multi-dimensional dynamic performance indicators with preset dynamic performance thresholds, calculate the real-time deviation and trend change rate of each indicator, and generate weighted dynamic performance assessment parameters.

[0034] The parameter optimization subunit is used to dynamically generate an optimized transmission parameter configuration based on the weighted dynamic performance evaluation parameters using a reinforcement learning algorithm. The optimized transmission parameter configuration includes structural parameter adjustment parameters and excitation condition adjustment parameters of the waveguide channel.

[0035] The feedback control subunit is used to output real-time feedback control commands based on the optimized transmission parameter configuration and the channel activation control signal, thereby optimizing the separated spectral signal.

[0036] The feedback control subunit includes a parameter extraction component, a channel activation component, a timing coordination component, an instruction generation component, and a signal optimization component, wherein:

[0037] The parameter extraction component is used to extract the target structural parameter adjustment amount of the waveguide channel based on the structural parameter adjustment parameters. The target structural parameter adjustment amount includes waveguide width adjustment amount, period spacing adjustment amount, and refractive index distribution adjustment amount.

[0038] The channel activation component is used to adjust parameters and channel activation control signals according to the excitation conditions, determine the waveguide channel combination to be activated and the corresponding excitation power allocation scheme, and generate channel selective activation instructions based on priority sorting.

[0039] The timing coordination component is used to generate a coordinated control sequence containing parameter adjustment timing and channel switching timing through timing synchronization processing, based on the target structural parameter adjustment amount and the channel selective activation instruction.

[0040] The instruction generation component is used to output real-time feedback control instructions according to the coordinated control sequence. The real-time feedback control instructions include real-time structural parameter adjustment instructions and channel activation state switching instructions.

[0041] The signal optimization component is used to optimize the separated spectral signal by dynamically reconstructing the geometry of the waveguide channel and adaptively optimizing the selectively activated waveguide channel combination according to the real-time feedback control command.

[0042] The spectral analysis module includes a photoelectric conversion unit, a feature extraction unit, a pattern recognition unit, and an environmental compensation unit, wherein:

[0043] The photoelectric conversion unit is used to convert the separated spectral signal output by the waveguide transmission module into an electrical signal;

[0044] The feature extraction unit is used to extract spectral feature parameters from the electrical signal;

[0045] The pattern recognition unit is used to obtain an initial spectral pattern recognition result based on the spectral feature parameters through pattern recognition.

[0046] The environmental compensation unit is used to extract environmental parameters, perform environmental compensation correction on the spectral feature parameters, and output the corrected spectral pattern recognition result.

[0047] The environmental compensation unit includes an environmental data acquisition subunit, an impact coefficient calculation subunit, and a compensation and correction subunit, wherein:

[0048] The environmental acquisition subunit is used to collect temperature and humidity parameters of the system's working environment and generate environmental parameters.

[0049] The impact coefficient calculation subunit is used to calculate the environmental impact coefficient based on the environmental parameters and the preset environmental response model.

[0050] The compensation and correction subunit is used to perform environmental compensation and correction on the spectral feature parameters based on the environmental impact coefficient, and output the corrected spectral pattern recognition result.

[0051] This invention provides a metasurface waveguide array based on a silicon carbide substrate, applied to the aforementioned spectral sensing system. The metasurface waveguide array based on the silicon carbide substrate comprises a silicon carbide substrate and a metasurface waveguide array, wherein:

[0052] The silicon carbide substrate is used to provide mechanical support and thermal conduction channels;

[0053] The metasurface waveguide array includes multiple metasurface modulation units, each of which includes a thermal modulation structure and an electrical modulation structure. The thermal modulation structure is used to receive thermally modulated signals, and the electrical modulation structure is used to receive electrically modulated signals.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] This invention achieves multi-physics field coupling modulation through the nonlinear coupling superposition principle of thermo-optic and electro-optic effects. It enables precise phase and amplitude modulation of incident light signals in complex spectral environments and dynamically optimizes spectral sensing parameters based on the modulation results, achieving high-precision extraction of spectral features. By introducing a neural network prediction model and online reinforcement learning algorithm, the waveguide transmission module can comprehensively consider the target detection wavelength range and resolution requirements, selectively activating the optimal waveguide channel combination, effectively avoiding insufficient spectral separation and crosstalk problems caused by fixed channel design. This scheme can adaptively adjust spectral feature parameters through an environmental compensation correction mechanism while ensuring spectral sensing accuracy, thereby improving the system's stability and reliability under varying environmental factors such as temperature and humidity. It is suitable for high-precision spectral sensing applications in complex and variable environments. Attached Figure Description

[0056] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0057] Figure 1 This is a main framework diagram of the spectral sensing system in an embodiment of the present invention;

[0058] Figure 2 This is an overall framework diagram of the coupling modulation module in an embodiment of the present invention;

[0059] Figure 3 This is an overall framework diagram of the waveguide transmission module in an embodiment of the present invention;

[0060] Figure 4 This is an overall framework diagram of the spectral analysis module in the embodiments of this application. Detailed Implementation

[0061] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof.

[0062] The term "and / or" in the following text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0063] Please see Figure 1 The main framework of the spectral sensing system provided by this invention includes a coupling modulation module, a waveguide transmission module, and a spectral analysis module. The coupling modulation module receives the incident light signal and, through the multi-physics field coupling effect of thermal and electric field modulation, modulates the phase and amplitude of the incident light signal to generate a modulated light signal. The waveguide transmission module receives the modulated light signal output from the coupling modulation module and, through selective activation of waveguide channel combinations and dynamic spectral separation, outputs a separated spectral signal. The spectral analysis module receives the separated spectral signal output from the waveguide transmission module, obtains an initial spectral pattern recognition result based on the separated spectral signal, and outputs a corrected spectral pattern recognition result through environmental compensation correction.

[0064] It is understood that the metasurface waveguide array in this application is not only a micro / nano structure optical device, but also a typical optical metamaterial. Metamaterials are electromagnetic response characteristics that are difficult to obtain from natural materials through artificially designed subwavelength periodic structures. The metasurface waveguide array used in this application is constructed on a silicon carbide substrate and consists of periodically arranged subwavelength modulation units. Each modulation unit integrates both thermal and electrical modulation structures, enabling precise modulation of the phase, amplitude, and other dimensions of the optical field through fine control of the local refractive index.

[0065] Unlike traditional waveguide or substrate materials, the metasurface subwavelength structure of this application can generate artificially modulated effects on equivalent dielectric parameters, including dynamic adjustment of the equivalent refractive index, rapid reconstruction of dispersion relations, and deep manipulation of local electromagnetic fields. This enables the structure to possess typical metamaterial properties, such as extraordinary refraction, strong local mode confinement, and programmable optical response. By coupling thermo-optic and electro-optic effects into the metasurface unit structure, this application further realizes dynamic reconfigurability under multiple physics fields in the metamaterial layer, making the metasurface no longer a static device, but a real-time tunable active metamaterial.

[0066] Please see Figure 2 The diagram illustrates the overall framework of the coupling modulation module in this embodiment. The direction of the arrows indicates the data flow direction; that is, the node at the start of the arrow outputs data, and the node at the point the arrow points receives the data output by the node at the start of the arrow. The coupling modulation module includes a metasurface waveguide array, a thermal modulation control unit, an electrical modulation control unit, and a coupling parameter calculation unit.

[0067] The metasurface waveguide array employs a periodically arranged subwavelength structure, consisting of multiple metasurface modulation units arranged in an array on a silicon carbide substrate. Each metasurface modulation unit includes a thermal modulation structure and an electrical modulation structure. In this embodiment, the thermal modulation structure includes a micro / nano-scale metal heater, which achieves rapid local temperature regulation through precise current control. The electrical modulation structure uses an electrode pair design, consisting of a top electrode and a bottom electrode. The thermal modulation structure and the electrical modulation structure are integrated on the surface of the silicon carbide substrate in a stacked manner.

[0068] The thermal modulation control unit is used to generate a temperature modulation amount according to the target spectrum modulation requirements, and provide a thermal modulation signal to the thermal modulation structure based on the temperature modulation amount, thereby achieving thermo-optic effect modulation by changing the local temperature.

[0069] The electro-modulation control unit is used to generate an electric field intensity according to the target spectral modulation requirements and temperature modulation amount, and to provide an electro-modulation signal to the electro-modulation structure based on the electric field intensity, thereby achieving electro-optic effect modulation by applying an electric field.

[0070] The coupling parameter calculation unit is used to calculate the multi-physics field coupling modulation parameters based on the thermo-optic effect modulation and the electro-optic effect modulation, and to generate the modulated optical signal through the nonlinear coupling superposition principle of the thermo-optic effect and the electro-optic effect.

[0071] For example, the specific working principle of the coupling modulation module is as follows: On one hand, the thermal modulation control unit calculates the temperature modulation amount based on the current temperature of the silicon carbide substrate obtained by the temperature sensor and the target spectral modulation requirements, generates a corresponding thermal modulation signal, and sends it to the thermal modulation structure in the metasurface waveguide array. After receiving the thermal modulation signal, the thermal modulation structure changes the local temperature through a micro-nano scale metal heater, and utilizes the thermo-optic effect of silicon carbide material to cause a change in refractive index, thereby achieving phase modulation of the incident light signal.

[0072] Specifically, temperature sensors based on thin-film thermopile arrays are positioned at key locations within the metasurface waveguide array to monitor the temperature distribution in real time and obtain the current temperature of the silicon carbide substrate. These key locations include the central region, edge regions, and transition regions with the largest thermal gradient of the thermal modulation structure. After receiving the current temperature of the silicon carbide substrate, the thermal modulation control unit performs digital filtering to eliminate noise interference and then compares it with the target spectral modulation requirements to obtain the comparison analysis results. The target spectral modulation requirements include at least the wavelength to be modulated, the modulation depth, and the modulation bandwidth. Based on the comparison analysis results, the difference between the current temperature and the temperature required to achieve the target spectral modulation is determined. Based on this difference, the wavelength to be modulated, the modulation depth, and the modulation bandwidth are converted into target temperature values ​​to achieve the desired spectral modulation effect using a preset thermo-optical effect model library. The temperature modulation amount is calculated based on the current temperature of the silicon carbide substrate and the target temperature value.

[0073] Furthermore, the thermal modulation control unit generates a corresponding pulse width modulation signal based on the temperature modulation amount using a pulse width modulation algorithm. This signal is then amplified by the driving circuit and sent to the thermal modulation structure within the metasurface waveguide array. Upon receiving the pulse width modulation signal, the micro / nano-scale metal heater within the thermal modulation structure generates Joule heating under current drive. This Joule heating diffuses to the surrounding silicon carbide substrate via thermal conduction, thereby altering the temperature of the silicon carbide material in a localized area around the thermal modulation structure. Because silicon carbide exhibits thermo-optical properties, the refractive index of the local silicon carbide material changes linearly with temperature. When the local temperature changes, the refractive index of that region changes accordingly, affecting the propagation characteristics of light waves passing through that region. This causes a phase shift in the propagation of light waves within the material, ultimately achieving phase modulation of the incident light signal.

[0074] On the other hand, the electro-modulation control unit calculates the electric field strength required to achieve the target modulation effect based on the target spectral modulation requirements and the temperature modulation amount provided by the thermal modulation control unit, using an electro-optic response calculation algorithm. It then sends the corresponding electro-modulation signal to the electro-modulation structure within the metasurface waveguide array. Upon receiving the electro-modulation signal, the electrode pairs within the electro-modulation structure establish a non-uniform electric field distribution in the silicon carbide substrate. Utilizing the inherent electro-optic effect of silicon carbide, a local refractive index change is induced, altering the propagation loss and scattering characteristics of light waves within the material to achieve amplitude modulation of the incident light signal.

[0075] Specifically, considering the temperature dependence of the electro-optic coefficient of silicon carbide material, the electro-modulation control unit first uses a preset electro-optic coefficient-temperature change relationship mapping table to query and interpolate the current temperature modulation amount to obtain the real-time electro-optic coefficient correction value. Then, the electro-modulation control unit couples this actual electro-optic coefficient with the amplitude modulation parameters in the target spectral modulation requirements, and uses the electro-optic effect response model to back-calculate the required electric field strength. The amplitude modulation parameters include modulation depth, modulation bandwidth, and target transmittance.

[0076] Furthermore, the electro-modulation control unit, based on the calculated electric field strength, converts the digital control signal into an analog voltage signal using a high-precision digital-to-analog converter. This voltage signal is then linearly amplified by a low-noise voltage signal amplifier circuit and sent as a differential signal to the corresponding electro-modulation structure in the metasurface waveguide array. Upon receiving the amplified voltage signal, the electrode pairs in the electro-modulation structure establish a stable electric field distribution between the electrodes. This electric field alters the refractive index distribution of the silicon carbide material within the electrode coverage area through an electro-optic effect. The spatial variation in refractive index directly modulates the propagation mode of the light wave in this region, changing the amplitude attenuation coefficient and phase matching condition of the light wave, thereby precisely controlling the intensity distribution of the transmitted light and ultimately achieving amplitude modulation of the incident light signal.

[0077] Finally, the coupling parameter calculation unit calculates the total refractive index modulation amount under thermo-optical coupling based on the nonlinear coupling relationship between the thermo-optical effect and the electro-optical effect. Based on the total refractive index modulation amount and the geometric parameters of the metasurface modulation unit, the phase modulation depth and transmittance modulation coefficient are calculated, generating multi-physics coupling modulation parameters. The geometric parameters of the metasurface modulation unit include at least the size, period, thickness, fill factor, and shape characteristics of the subwavelength structure of the metasurface waveguide array.

[0078] This embodiment achieves multi-physical field coupling modulation of the incident light signal through the synergistic effect of thermal field modulation and electric field modulation. Compared with the single physical field modulation method, it has the advantages of wide modulation range, high precision and strong flexibility, providing a high-quality modulated light signal foundation for subsequent spectral separation and identification.

[0079] The coupling parameter calculation unit includes a thermo-optical calculation subunit, an electro-optical calculation subunit, a superposition calculation subunit, a parameter generation subunit, and a signal modulation subunit.

[0080] The thermo-optical calculation subunit is used to calculate the change in refractive index due to the thermo-optical effect based on the temperature modulation amount generated by the thermal modulation control unit and the thermo-optical coefficient of the silicon carbide substrate. This change in refractive index due to the thermo-optical effect achieves a precise mapping from temperature modulation to refractive index change, improving the accuracy and controllability of thermo-optical modulation.

[0081] The electro-optic calculation subunit is used to calculate the change in refractive index due to the electro-optic effect based on the electric field intensity generated by the electro-modulation control unit and the electro-optic coefficient of the silicon carbide substrate. This change in refractive index due to the electro-optic effect enables rapid refractive index modulation, significantly improving the temporal resolution and dynamic range of the modulation.

[0082] The superposition calculation subunit is used to obtain the total refractive index modulation amount by vector superposition based on the refractive index changes due to the thermo-optic effect and the electro-optic effect. In this embodiment, the total refractive index modulation amount overcomes the limitations of a single modulation mechanism.

[0083] The parameter generation subunit is used to calculate the phase modulation depth and transmittance modulation coefficient based on the total refractive index modulation amount and the geometric parameters of the metasurface modulation unit, thereby generating multi-physics coupling modulation parameters. By converting the material refractive index change into practically applicable optical modulation parameters, the transformation from physical mechanism to functional realization is achieved, ensuring the system's high-precision control capability.

[0084] The signal modulation subunit is used to control the metasurface waveguide array to perform phase modulation and amplitude modulation on the incident light signal according to the multi-physics field coupling modulation parameters, so as to generate a modulated light signal.

[0085] For example, the specific working principle of the coupling parameter calculation unit is as follows: On one hand, the thermo-optical calculation subunit receives the temperature modulation amount provided by the thermal modulation control unit, and calculates the thermo-optical effect refractive index change through a multi-level thermo-optical effect calculation framework. Specifically, the multi-level thermo-optical effect calculation framework includes a temperature distribution simulation layer, a thermo-optical coefficient mapping layer, and a refractive index change calculation layer. The temperature distribution simulation layer, based on the spatiotemporal distribution characteristics of the thermal modulation signal and the thermal parameters of the silicon carbide substrate, solves the partial differential equation of thermal diffusion using a three-dimensional transient finite element heat conduction model to calculate the non-uniform three-dimensional temperature distribution of each metasurface modulation unit in the metasurface waveguide array. The thermo-optical coefficient mapping layer constructs a multi-dimensional database covering the thermo-optical coefficients of silicon carbide substrates under different temperature ranges, crystal orientations, and wavelengths, and uses a multivariate interpolation algorithm to obtain the real-time thermo-optical coefficients of the silicon carbide substrate under the current working conditions. The refractive index change calculation layer, combining the non-uniform temperature distribution and the corresponding thermo-optical coefficients, calculates the spatial refractive index change distribution caused by the thermo-optical effect in the entire metasurface waveguide array through the thermo-optical effect constitutive relationship, and obtains the thermo-optical effect refractive index change.

[0086] On the other hand, the electro-optic calculation subunit receives the electric field intensity provided by the electro-modulation control unit. By constructing a two-layer architecture of electric field distribution simulation and electro-optic response calculation, it accurately calculates the change in refractive index due to the electro-optic effect. This two-layer architecture includes an electric field distribution simulation layer and an electro-optic response calculation layer. The electric field distribution simulation layer, based on the geometric configuration parameters of the electrode pairs in the electro-modulation structure and the applied voltage amplitude and frequency characteristics, establishes a three-dimensional electromagnetic field numerical model using the finite difference time-domain method, calculating the non-uniform electric field intensity distribution and electric field vector direction distribution in the metasurface waveguide array. The electro-optic response calculation layer, considering the temperature dependence of the electro-optic coefficient of silicon carbide material, first uses a preset electro-optic coefficient temperature change relationship mapping table to query and interpolate the current temperature distribution to obtain the real-time electro-optic coefficient correction value. Then, based on the electro-optic coefficient characteristics of the silicon carbide substrate, combined with the geometric relationship between the direction of the electric field distribution and the crystal principal axis orientation, it calculates the refractive index change in each spatial region under the action of the electric field through the electro-optic effect constitutive relation, thus obtaining the change in refractive index due to the electro-optic effect.

[0087] Furthermore, the superposition calculation subunit calculates the total refractive index modulation amount through vector superposition based on the refractive index changes of the thermo-optic effect and the electro-optic effect. Specifically, the superposition calculation subunit first represents the refractive index change of the thermo-optic effect as a spatial vector distribution and the refractive index change of the electro-optic effect as a corresponding spatial vector distribution. Then, considering the interaction between the thermo-optic effect and the electro-optic effect, the vector superposition process is corrected by the coupling coefficient. The corrected refractive index change vector of the thermo-optic effect and the refractive index change vector of the electro-optic effect are then subjected to point-by-point vector superposition to obtain the total refractive index modulation amount considering the multi-physics coupling effect.

[0088] Next, the parameter generation subunit establishes an electromagnetic scattering theoretical model of the metasurface waveguide array based on the total refractive index modulation amount and the geometric parameters of the metasurface modulation unit, using a rigorous coupled-wave analysis method, and calculates the optical response data. The optical response data includes the transmission coefficient and reflection coefficient. The phase angle is extracted from the transmission coefficient, and the phase difference before and after modulation is calculated. Based on the phase difference before and after modulation, combined with the reference phase before modulation, the phase modulation depth is calculated through normalization processing. This parameter characterizes the metasurface waveguide array's ability to control the phase of the light wave. Based on the transmission coefficient, the transmittance is calculated through modulus squaring. Based on the transmittance, the transmittance difference before and after modulation is calculated. Based on the transmittance difference before and after modulation, combined with the reference transmittance before modulation, the transmittance modulation coefficient is calculated. This parameter characterizes the metasurface waveguide array's ability to control the intensity of the light wave. Based on the phase modulation depth and the transmittance modulation coefficient, multiphysics coupling modulation parameters are formed.

[0089] Finally, the signal modulation subunit controls the metasurface waveguide array to perform phase and amplitude modulation on the incident light signal according to the multi-physics coupling modulation parameters. Specifically, the signal modulation subunit converts the phase modulation depth and transmittance modulation coefficients into specific control parameters for the thermal and electrical modulation structures using a preset parameter mapping algorithm. These control parameters are then distributed to the corresponding metasurface modulation units via address addressing. Each metasurface modulation unit adjusts its thermal and electric field distributions in real time through its internal control system based on the received control parameters, thereby achieving phase and amplitude modulation of the light signal passing through that unit. Multiple metasurface modulation units work collaboratively to form spatial modulation of the entire incident light signal, generating a modulated light signal.

[0090] The coupling parameter calculation unit further includes a thermoelectric coupling compensation subunit, an electrothermal coupling compensation subunit, a correction superposition subunit, and an optimization parameter generation subunit.

[0091] The thermoelectric coupling compensation subunit is used to establish a thermoelectric coupling effect model based on the temperature modulation amount, calculate the influence coefficient of temperature on the electro-optic coefficient of silicon carbide, and perform temperature compensation correction on the change in refractive index of the electro-optic effect based on the influence coefficient of the electro-optic coefficient, thus obtaining the corrected change in refractive index of the electro-optic effect. The construction of the thermoelectric coupling effect model eliminates the electro-optic modulation instability caused by temperature fluctuations.

[0092] The electrothermal coupling compensation subunit is used to establish an electrothermal coupling effect model based on the electric field strength, calculate the influence of the electric field on the local temperature distribution, and perform electric field compensation correction on the thermo-optical effect refractive index change based on the influence of the local temperature distribution to obtain the corrected thermo-optical effect refractive index change.

[0093] The correction superposition subunit is used to obtain the corrected total refractive index modulation amount based on the corrected thermo-optical effect refractive index change and the corrected electro-optical effect refractive index change through a coupled vector superposition algorithm. The optimization parameter generation subunit is used to recalculate the phase modulation depth and transmittance modulation coefficient based on the corrected total refractive index modulation amount to generate optimized multiphysics coupling modulation parameters.

[0094] For example, the specific working principle of the coupling parameter calculation unit also includes: when the spectral sensing system detects a temperature change or an electric field intensity change exceeding a preset threshold, it activates the thermoelectric coupling compensation and electrothermal coupling compensation mechanisms.

[0095] On one hand, the thermoelectric coupling compensation subunit first queries the electro-optic coefficient values ​​at different temperatures through a temperature-electro-optic coefficient mapping database based on the temperature modulation amount, calculating the influence coefficient of temperature change on the electro-optic coefficient. Then, based on the influence coefficient, the thermoelectric coupling compensation subunit performs temperature compensation correction on the electro-optic effect refractive index change output by the electro-optic calculation subunit, obtaining the corrected electro-optic effect refractive index change. This step eliminates the unstable influence of temperature fluctuations on electro-optic modulation, improving the modulation accuracy of the system under temperature variation environments.

[0096] On the other hand, the electrothermal coupling compensation subunit calculates the Joule heating effect generated by the electric field in the silicon carbide substrate using the electrothermal coupling simulation engine based on the electric field strength generated by the electromodulation control unit. It analyzes the impact of this Joule heating effect on the local temperature distribution and calculates the local temperature distribution influence quantity. This local temperature distribution influence quantity represents the degree to which the additional heat caused by the applied electric field changes the material's temperature distribution, including temperature increment distribution and temperature gradient change data. Then, based on the local temperature distribution influence quantity, the electrothermal coupling compensation subunit performs electric field compensation correction on the thermo-optical effect refractive index change output by the thermo-optical calculation subunit, obtaining the corrected thermo-optical effect refractive index change quantity.

[0097] The correction superposition subunit receives the corrected electro-optic refractive index change from the thermo-electric coupling compensation subunit and the corrected thermo-optic refractive index change from the electro-thermal coupling compensation subunit. Using a multi-order coupled tensor calculation framework, the corrected total refractive index modulation is calculated. This multi-order coupled tensor calculation framework first converts the corrected electro-optic and thermo-optic refractive index changes into tensor form, then constructs a complete coupled equation containing first-order linear terms, second-order cross terms, and third-order higher-order terms. Solving this equation yields the corrected total refractive index modulation.

[0098] Furthermore, the parameter generation subunit, based on the corrected total refractive index modulation output by the correction and superposition subunit, quantifies the difference between the actual modulation effect and the target requirement by defining an objective function. The objective function includes a phase modulation target deviation term and a transmittance modulation target deviation term, representing the degree of deviation of the phase modulation and transmittance modulation from the target values, respectively. Then, under system hardware constraints, the phase modulation depth and transmittance modulation coefficient are iteratively optimized using a gradient descent algorithm to minimize the objective function value. The system hardware constraints include at least temperature modulation range, electric field strength limit, device response speed, and power consumption threshold.

[0099] Specifically, the parameter generation subunit first calculates the initial optical response parameters, including the initial phase modulation depth and the initial transmittance modulation coefficient, using a rigorous coupled-wave analysis method based on the corrected total refractive index modulation. Then, it constructs a parameter optimization space, using the initial parameters as the starting point for iteration, and sets the parameter variation step size and iteration termination condition. In each iteration, the parameter generation subunit changes the parameter values ​​through small perturbations, recalculates the optical response, and evaluates the change in the objective function value. Based on the objective function gradient information, it updates the parameter values ​​along the gradient descent direction, while simultaneously checking whether the updated parameters satisfy the system hardware constraints. If not, it maps the parameters back to the constraint space using a projection method. The iterative process continues until the preset convergence accuracy or maximum number of iterations is reached.

[0100] Finally, the parameter generation subunit obtains the parameter combination that minimizes the objective function value. This parameter combination includes the optimal phase modulation depth and the optimal transmittance modulation coefficient. Based on the parameter combination, and combined with the geometric and material properties of the metasurface modulation unit, the optimal thermal modulation control parameters and the optimal electrical modulation control parameters are calculated. These optimal control parameters are integrated into optimized multiphysics coupling modulation parameters and passed to the signal modulation subunit to achieve high-precision spectral modulation.

[0101] In this embodiment, through this multi-level compensation and optimization mechanism, the spectral sensing system can adapt to complex environmental changes, maintain high-precision spectral modulation performance, and take into account the accuracy of thermoelectric coupling and electrothermal coupling, providing a stable and reliable signal foundation for subsequent spectral separation and identification.

[0102] Please see Figure 3 The diagram shows the overall framework of the waveguide transmission module in this embodiment. The direction of the arrows indicates the data flow direction; that is, the node at the beginning of the arrow outputs data, and the node at the end of the arrow receives the data output by the node at the beginning. The waveguide transmission module includes a waveguide array unit, a spectral separation unit, a path selection unit, and a transmission parameter optimization unit.

[0103] The waveguide array unit includes multiple parallel, non-uniformly periodically arranged waveguide channels for receiving modulated optical signals and distributing the modulated optical signals to different waveguide channels for transmission, thereby obtaining the transmitted optical signals of each waveguide channel.

[0104] The spectral separation unit achieves dynamic spectral separation by dynamically matching phases and rearranging spatially based on the differences in dispersion and mode coupling characteristics between the transmitted optical signal and the waveguide channel, and outputs a separated spectral signal containing intensity distribution data for each wavelength.

[0105] The path selection unit is used to receive external spectral detection requirements, determine the target detection wavelength range and resolution requirements, selectively activate waveguide channel combinations through a neural network prediction model, and output channel activation control signals.

[0106] The transmission parameter optimization unit is used to receive the separated spectral signal output by the spectral separation unit, calculate the spectral separation degree, crosstalk coefficient, transmission efficiency and wavelength resolution based on the light intensity distribution data of each wavelength in the separated spectral signal, and dynamically generate the optimized transmission parameter configuration through an online reinforcement learning algorithm to optimize the separated spectral signal.

[0107] For example, the specific working principle of the waveguide transmission module is as follows: the waveguide transmission module receives the modulated optical signal from the coupling modulation module, and through the coordinated work of the waveguide array unit, the spectral separation unit, the path selection unit and the transmission parameter optimization unit, achieves efficient transmission and accurate separation of the modulated optical signal.

[0108] Specifically, the path selection unit receives external spectral detection requests and extracts the target detection wavelength range and resolution requirements from these requests through parameter analysis. Using these target detection wavelength range and resolution requirements as input parameters, a neural network prediction model calculates the fitness score of each waveguide channel for the current detection request, evaluating the channel's wavelength coverage and resolution capability. The neural network prediction model employs a multilayer perceptron structure. Its input layer receives parameters such as the target detection wavelength range and resolution requirements, and after nonlinear transformation processing through multiple hidden layers, the output layer generates the optimal waveguide channel combination scheme. The neural network prediction model is constructed based on the characteristic parameters of the waveguide channels, including transmission efficiency, dispersion characteristics, mode distribution, and crosstalk.

[0109] Furthermore, based on the fitness scores of each waveguide channel, an optimal waveguide channel combination scheme is generated through a combination optimization algorithm to determine the channel number, activation order, and power allocation ratio to be activated, and this information is converted into a standard format channel activation control signal and transmitted to the waveguide array unit.

[0110] On one hand, the waveguide array unit consists of multiple non-uniformly periodically arranged waveguide channels made of silicon carbide, each with unique geometry and material composition. The waveguide channels are designed using parametric modeling, achieving selective transmission and mode control of different wavelength optical signals by precisely controlling the waveguide width, height, bending radius, and periodic spacing. Upon receiving a channel activation control signal, the waveguide array unit selectively opens the entrance of a designated waveguide channel and closes unselected channels via a microelectromechanical switch array. Simultaneously, an adjustable beam splitter distributes the modulated optical signal to each activated waveguide channel according to a specified ratio. The activated waveguide channels simultaneously transmit the allocated modulated optical signal. Due to the different structural characteristics of each channel, different wavelength optical signals exhibit specific propagation modes, group velocities, and phase changes. The transmitted optical signals are collected and integrated to form a spatially distributed transmission optical signal, which is then transmitted to the spectral separation unit.

[0111] On the other hand, the spectral separation unit adopts a cascaded spectral separation structure, including three functional regions: a phase-matching region, a mode-conversion region, and a spatial rearrangement region. The phase-matching region, based on waveguide coupling theory, controls the coupling length and coupling coefficient between adjacent waveguides to ensure that optical signals of specific wavelengths meet the phase-matching condition, achieving selective energy transfer between different waveguides. This region contains multiple pairs of coupled waveguides optimized for different wavelength ranges, achieving initial separation of optical signals by adjusting waveguide spacing and geometric parameters. The mode-conversion region utilizes the principle of adiabatic mode conversion, converting optical signals of different wavelengths into specific propagation modes through a gradual design of waveguide geometric parameters. This region contains multiple segments of gradually changing waveguide structures, whose geometric parameters and refractive index distribution change according to specific rules, gradually converting the optical signal into the target mode during transmission and enhancing the distinguishability between different wavelength optical signals. The spatial rearrangement region guides optical signals of different modes to different spatial locations through a specially designed waveguide array geometry and refractive index gradient distribution. This region adopts a fan-shaped waveguide array structure, combined with grating couplers and microlens arrays, to form a separated spectral signal containing intensity distribution data for each wavelength.

[0112] The transmission parameter optimization unit calculates performance indicators such as spectral separation, crosstalk coefficient, transmission efficiency, and wavelength resolution in real time, compares them with preset thresholds, and generates weighted dynamic performance evaluation parameters. Based on these parameters, the reinforcement learning algorithm dynamically generates optimized transmission parameter configurations, including adjustments to the structural parameters of the waveguide channel and excitation conditions. Real-time optimization of the separated spectral signal is achieved through a feedback control module.

[0113] In this embodiment, the non-uniformly periodically arranged waveguide channels are fabricated using micro-nano fabrication techniques. First, silicon carbide crystal growth is precisely controlled on a substrate using chemical vapor deposition, followed by fine surface polishing to improve the material's surface quality. Then, high-precision photolithography and fine etching techniques are employed to fabricate waveguide channel structures with specific periods, shapes, and depths on the silicon carbide material, ultimately forming the non-uniformly periodically arranged waveguide channels within the waveguide array unit. Through this technical solution, the waveguide transmission module achieves efficient transmission and precise separation of modulated optical signals, providing high-quality spectral separation signals for subsequent spectral detection, effectively improving the system's detection accuracy and stability.

[0114] The transmission parameter optimization unit includes a performance index calculation subunit, a deviation evaluation subunit, a parameter optimization subunit, and a feedback control subunit.

[0115] The performance index calculation subunit calculates spectral separation, crosstalk coefficient, transmission efficiency, and wavelength resolution based on the intensity distribution data of each wavelength in the separated spectral signal, generating multi-dimensional dynamic performance indices. The deviation evaluation subunit compares the multi-dimensional dynamic performance indices with preset dynamic performance thresholds, calculating the real-time deviation and trend rate of change of each index, generating weighted dynamic performance evaluation parameters. The parameter optimization subunit dynamically generates optimized transmission parameter configurations using a reinforcement learning algorithm based on the weighted dynamic performance evaluation parameters. The optimized transmission parameter configuration includes waveguide channel structural parameter adjustment parameters and excitation condition adjustment parameters. In this embodiment, the reinforcement learning algorithm uses a deep Q-network structure, with a state space including the current performance index and waveguide channel parameters, and an action space including the adjustment amounts of structural parameters and excitation conditions. The feedback control subunit outputs real-time feedback control commands based on the optimized transmission parameter configuration and channel activation control signals, optimizing the separated spectral signal.

[0116] For example, the specific working principle of the transmission parameter optimization unit is as follows: First, the performance index calculation subunit receives the separated spectral signal output by the spectral separation unit, which contains light intensity distribution data for each wavelength. Based on the light intensity distribution data for each wavelength, the performance index calculation subunit calculates the spectral separation degree, crosstalk coefficient, transmission efficiency, and wavelength resolution using a multi-dimensional spectral performance analysis framework. Specifically, the spectral separation degree is evaluated by analyzing the spatial separation degree of different wavelength light signals, using the energy distribution overlap between adjacent wavelength channels as a quantitative indicator. The crosstalk coefficient is determined by calculating the proportion of energy from other wavelengths in the target wavelength channel, reflecting the channel isolation performance of the spectral sensing system. The transmission efficiency is measured by comparing the ratio of output light intensity to input light intensity, evaluating the system's energy loss. The wavelength resolution is determined by analyzing the spectral sensing system's ability to distinguish between similar wavelengths, expressed as the minimum resolvable wavelength difference. The spectral separation degree, crosstalk coefficient, transmission efficiency, and wavelength resolution together constitute a multi-dimensional dynamic performance index.

[0117] The deviation assessment subunit receives the multi-dimensional dynamic performance indicators, compares them with preset dynamic performance thresholds, calculates the real-time deviation and trend change rate of each indicator, and generates weighted dynamic performance assessment parameters. The preset dynamic performance thresholds are determined according to different application scenarios and performance requirements. The deviation assessment subunit calculates the real-time deviation between each indicator and its corresponding threshold based on the spectral separation, crosstalk coefficient, transmission efficiency, and wavelength resolution among the multi-dimensional dynamic performance indicators. Then, based on the real-time deviation, the deviation assessment subunit constructs a time series model through continuous sampling to analyze the trend of each indicator over time. The trend change rate is obtained by calculating the rate of change of the deviation within a specific time window and is used to characterize the dynamic change characteristics of the spectral sensing system's performance. Finally, based on the real-time deviation and the trend change rate, the deviation assessment subunit uses a weighted fusion algorithm to generate weighted dynamic performance assessment parameters that reflect the current performance status and development trend of the spectral sensing system, providing a decision-making basis for subsequent parameter optimization. The weighted fusion algorithm sets different weights for the sensitivity of each performance indicator according to different application scenarios, enabling the weighted dynamic performance assessment parameters to adapt to performance optimization needs under different working environments. When the working environment of the spectral sensing system changes, the weighted fusion algorithm can adaptively adjust the weight configuration of each index to ensure the accuracy and applicability of the weighted dynamic performance evaluation parameters.

[0118] Furthermore, the parameter optimization subunit optimizes the transmission parameters using a deep reinforcement learning algorithm based on the weighted dynamic performance evaluation parameters. The framework of the deep reinforcement learning algorithm includes state space construction, action space definition, reward function design, and policy network training. In state space construction, the state space consists of the multi-dimensional dynamic performance indicators and waveguide channel states, comprehensively representing the operating state of the spectral sensing system. The waveguide channel states are obtained by real-time acquisition of operating state information from the waveguide channels, including channel activation states and current values ​​of structural parameters. The structural parameters include waveguide width, period spacing, and refractive index distribution. In action space definition, the action space includes adjustments to the structural parameters of the waveguide channels and adjustments to excitation conditions. The excitation conditions include excitation power and phase. In reward function design, the reward function is determined based on the degree of improvement of the multi-dimensional dynamic performance indicators. By evaluating the changes in the weighted dynamic performance evaluation parameters before and after adjustment, a reward signal reflecting the system performance improvement is generated, encouraging the spectral sensing system to evolve towards higher performance. In policy network training, the policy network adopts a dual-network architecture, including an evaluation network and a target network. The evaluation network is responsible for generating the optimal action selection based on the current state; the target network provides a stable learning objective. Through an experience replay mechanism, the parameter optimization subunit randomly samples from historical optimization data to reduce sample correlation; through a delayed update mechanism of the target network, the stability of the learning process is improved, and drastic fluctuations during the optimization process are avoided.

[0119] In this embodiment, the spectral sensing system continuously acquires and stores empirical data during operation. The parameter optimization subunit continuously generates optimization strategies based on the empirical data. The optimization strategies include structural parameter adjustment parameters and excitation condition adjustment parameters of the waveguide channel, which constitute the optimized transmission parameter configuration.

[0120] The feedback control subunit receives the channel activation control signal output by the optimized transmission parameter configuration and path selection unit, and generates real-time feedback control commands through a multi-level control processing flow to ensure that the system smoothly transitions to the optimized state.

[0121] The feedback control subunit includes a parameter extraction component, a channel activation component, a timing coordination component, an instruction generation component, and a signal optimization component.

[0122] The parameter extraction component is used to extract the target structural parameter adjustment amount of the waveguide channel based on the structural parameter adjustment parameters. The target structural parameter adjustment amount includes waveguide width adjustment amount, period spacing adjustment amount, and refractive index distribution adjustment amount.

[0123] The channel activation component is used to adjust parameters and channel activation control signals according to the excitation conditions, determine the waveguide channel combination to be activated and the corresponding excitation power allocation scheme, and generate channel selective activation instructions based on priority sorting.

[0124] The timing coordination component is used to generate a coordinated control sequence containing parameter adjustment timing and channel switching timing through timing synchronization processing, based on the target structural parameter adjustment amount and channel selective activation command.

[0125] The instruction generation component is used to output real-time feedback control instructions based on the coordinated control sequence. The real-time feedback control instructions include instructions for real-time adjustment of structural parameters and instructions for switching channel activation states.

[0126] The signal optimization component is used to optimize the separated spectral signal by dynamically reconstructing the geometry of the waveguide channel and adaptively optimizing the selectively activated waveguide channel combination according to real-time feedback control commands.

[0127] For example, the specific working principle of the feedback control subunit is as follows: the parameter extraction component adjusts the parameters according to the structural parameters to extract the target structural parameter adjustment amount of the waveguide channel. Specifically, the parameter extraction component first receives the structural parameter adjustment parameters in the optimized transmission parameter configuration output by the parameter optimization subunit, and then converts the structural parameter adjustment parameters into specific physical quantity adjustment values ​​through a parameter parsing algorithm. The target structural parameter adjustment amount includes waveguide width adjustment, periodic spacing adjustment, and refractive index distribution adjustment. The waveguide width adjustment represents the change in the lateral dimension of the waveguide channel, which directly affects the distribution characteristics and transmission loss of the optical mode. The periodic spacing adjustment represents the change in the distance between adjacent periodic units in the periodic waveguide structure, which affects the photonic bandgap characteristics and wavelength selectivity of the waveguide. The refractive index distribution adjustment represents the change in the spatial distribution of refractive index in the waveguide channel, which affects the propagation path and mode characteristics of light in the waveguide.

[0128] Furthermore, the channel activation component adjusts the parameters based on the excitation conditions and the channel activation control signal to determine the waveguide channel combination to be activated and the corresponding excitation power allocation scheme, generating a channel selective activation command based on priority ranking. Specifically, the channel activation component first receives the excitation condition adjustment parameters from the optimized transmission parameter configuration output by the parameter optimization subunit, and the channel activation control signal output by the path selection unit. The channel activation component determines the optimal waveguide channel activation combination through a multi-objective optimization algorithm. The optimal waveguide channel activation combination meets the requirements of energy efficiency, spectral coverage, and signal-to-noise ratio. The energy efficiency requirement is to minimize the total energy consumption of activating waveguide channels while meeting system functions, avoiding unnecessary energy loss; the spectral coverage requirement is to ensure that the activated waveguide channel combination can cover the key spectral regions within the target wavelength range, ensuring the integrity of the spectral signal; the signal-to-noise ratio requirement is to ensure that the activated waveguide channel combination can provide sufficiently high signal quality, making the separated spectral signal identifiable and exceeding the minimum signal-to-noise ratio threshold set by the system. The channel activation component, by balancing these three requirements and under the constraint of limited system resources, selects the waveguide channel combination that can simultaneously meet the above requirements as the optimal activation scheme. For each selected activated waveguide channel, the channel activation component adjusts parameters according to the excitation conditions, calculates its corresponding excitation power allocation value and phase adjustment value, and forms a complete excitation scheme.

[0129] The channel activation component employs a priority-based channel selection strategy. Based on the waveguide channel's characteristic parameters, it assigns dynamic priorities to each channel and generates channel-selective activation commands. Higher-priority waveguide channels are activated first, especially under resource constraints, compared to lower-priority channels. The channel-selective activation command includes a channel identifier, activation status flag, excitation power value, and phase adjustment value.

[0130] The timing coordination component generates a coordinated control sequence containing parameter adjustment timing and channel switching timing based on the target structural parameter adjustment amount and the channel selective activation command through timing synchronization processing. Specifically, the timing coordination component receives the target structural parameter adjustment amount output by the parameter extraction component and the channel selective activation command output by the channel activation component, normalizes the target structural parameter adjustment amount and the channel selective activation command into a unified format of control operation commands, and identifies the dependencies and potential conflicts between different control operations by constructing a control operation dependency graph. Based on the constructed control operation dependency graph, the execution time point of each control operation command is determined through a timing optimization algorithm, generating parameter adjustment timing and channel switching timing. The parameter adjustment timing and channel switching timing are integrated to form a coordinated control sequence. Specifically, the execution order and time interval of waveguide width adjustment, period spacing adjustment, and refractive index distribution adjustment are determined based on the parameter adjustment timing, and the execution timing of waveguide channel activation state changes and excitation condition adjustments are determined based on the channel switching timing.

[0131] The instruction generation component outputs real-time feedback control instructions based on the coordinated control sequence. These real-time feedback control instructions include real-time structural parameter adjustment instructions and channel activation state switching instructions. Specifically, the instruction generation component first receives the coordinated control sequence output by the timing coordination component and converts it into control instructions that the hardware can directly recognize and execute. During the conversion process, the component first parses the control operation instructions in the coordinated control sequence and then generates binary instructions that conform to the hardware interface requirements according to the instruction set specification of the target hardware. Next, the instruction generation component classifies the binary instructions. On one hand, based on the coordinated control sequence, it extracts control operation instructions related to structural parameters and generates real-time structural parameter adjustment instructions. These real-time structural parameter adjustment instructions include waveguide width adjustment control instructions, period spacing adjustment control instructions, and refractive index distribution adjustment control instructions. On the other hand, based on the coordinated control sequence, it extracts control operation instructions related to channel activation and generates channel activation state switching instructions. These channel activation state switching instructions include channel activation instructions, channel deactivation instructions, excitation power adjustment instructions, and phase adjustment instructions. Subsequently, the instruction generation component performs redundancy checks, instruction merging, and adds execution confirmations to the real-time structural parameter adjustment instructions and channel activation state switching instructions, eliminating unnecessary duplicate instructions and merging similar instructions that can be executed in batches. Finally, the instruction generation component encapsulates the real-time structural parameter adjustment instructions and channel activation state switching instructions according to the execution sequence to generate real-time feedback control instructions.

[0132] The signal optimization component optimizes the separated spectral signal by dynamically reconstructing the waveguide channel geometry and adaptively optimizing the selectively activated waveguide channel combination, based on the real-time feedback control command. Specifically, firstly, the signal optimization component receives the real-time feedback control command output by the command generation component and simultaneously acquires the current system operating status feedback information, including the actual physical parameters of the waveguide channels, signal transmission quality indicators, and system resource utilization. The signal transmission quality indicators include spectral separation, crosstalk coefficient, transmission efficiency, and wavelength resolution; the system resource utilization includes channel activation status and power consumption. Next, the real-time feedback control command and status feedback information are preprocessed to obtain standardized real-time feedback control commands and standardized status feedback information. The data preprocessing includes data standardization, noise filtering, and feature extraction. Subsequently, a system performance model is constructed based on the characteristic parameters of the waveguide channels. The system performance model takes standardized real-time feedback control commands and standardized state feedback information as inputs, and spectral separation, crosstalk coefficient, transmission efficiency, and wavelength resolution as outputs. It performs optimization calculations through an adaptive optimization algorithm, determines the optimization direction based on the deviation between the current system performance indicators and the preset target values, makes small perturbations to the structural parameter adjustment amount and channel activation combination, and predicts the spectral performance changes under different perturbation conditions. It iteratively adjusts the parameters along the direction of improving spectral performance, and finally obtains the optimized real-time feedback control commands and optimized state feedback information.

[0133] Based on the optimized real-time feedback control commands, the microelectromechanical control unit (MEMS) dynamically reconstructs the waveguide channel's geometry. This involves adjusting the waveguide width to the optimized target value via a MEMS drive mechanism, adjusting the spacing between adjacent periodic units in the periodic waveguide structure to match the optimized periodic spacing parameters, and adjusting the refractive index distribution by changing the doping concentration within the waveguide channel or applying a specific electric field. During this adjustment process, the actual physical parameters of the waveguide channel are simultaneously acquired, and the optical detection unit monitors the mode distribution and phase changes of the optical signal. If a deviation between the actual parameters and the optimized target is detected, a compensation signal is generated to correct the actions of the MEMS control unit, ensuring that the waveguide channel's geometry meets the optimization requirements. Based on the channel activation state switching instruction in the optimized real-time feedback control command, and combined with the system resource utilization, the waveguide channel combination to be activated and the corresponding excitation power allocation scheme and phase adjustment value are determined according to the priority-based channel selection strategy. The channel priority is determined based on the transmission efficiency, dispersion characteristics, and crosstalk of the waveguide channel. High-priority channels are selectively activated by a microelectromechanical switch array, while unselected channels are deactivated. The excitation power and phase of the activated channel are precisely adjusted using a high-precision power control module and a phase modulation module. The power control module is based on a closed-loop feedback mechanism and adjusts the input power according to the detected channel output light intensity. The phase modulation module controls the phase of the optical signal based on the electro-optic effect or thermo-optic effect to ensure that the dynamic phase matching requirements are met and to reduce crosstalk between channels.

[0134] After completing the dynamic reconstruction and activation combination optimization of the waveguide channel geometry, the separated spectral signal is acquired in real time. The spectral separation degree, crosstalk coefficient, transmission efficiency, and wavelength resolution of the signal are calculated, and the calculation results are compared with preset thresholds to evaluate the optimization effect. If the evaluation results show that the preset requirements are not met, a mapping model between control command execution and spectral signal changes is constructed. The causes of deviations are analyzed and compensation adjustment commands are generated to correct the structural parameters or activation combinations of the waveguide channel, forming a closed-loop feedback control. At the same time, historical control data and corresponding spectral signal performance data are analyzed periodically. Historical control data includes real-time feedback control commands, optimized parameters, and system status feedback information. By analyzing and extracting control laws and system response characteristics, the adaptive control knowledge base is updated. During subsequent system operation, mature control strategies in the knowledge base can be directly called and fine-tuned in combination with real-time data to continuously improve control accuracy and response speed, achieving effective optimization of the separated spectral signal.

[0135] Please see Figure 4This is a diagram illustrating the overall architecture of the spectral analysis module in this embodiment. The arrows indicate the direction of data flow; that is, the node at the beginning of the arrow outputs data, and the node at the end of the arrow receives data output by the node at the beginning. The spectral analysis module includes a photoelectric conversion unit, a feature extraction unit, a pattern recognition unit, and an environmental compensation unit. The photoelectric conversion unit converts the separated spectral signal output from the waveguide transmission module into an electrical signal. The feature extraction unit extracts spectral feature parameters from the electrical signal. The pattern recognition unit obtains an initial spectral pattern recognition result based on the spectral feature parameters through pattern recognition. The environmental compensation unit extracts environmental parameters, performs environmental compensation correction on the spectral feature parameters, and outputs a corrected spectral pattern recognition result.

[0136] For example, the specific working principle of the spectral analysis module is as follows: First, the photoelectric conversion unit receives the separated spectral signal output by the waveguide transmission module, and converts the optical signals of different wavelength channels into corresponding electrical signals through a high-sensitivity photodiode array. Each photodiode corresponds to an optical signal within a specific wavelength range, and the conversion of the entire spectral range is achieved through parallel acquisition, outputting raw electrical signal data containing spectral intensity distribution information.

[0137] Furthermore, the feature extraction unit extracts the spectral peak position, peak intensity, half-maximum width (HWHM), and peak-to-valley ratio based on the raw electrical signal data output by the photoelectric conversion unit using digital signal processing algorithms. Specifically, the position of the spectral peak is identified through a peak detection algorithm, the peak intensity is obtained through integration, the HWHM is calculated through spectral line analysis, and the peak-to-valley ratio is obtained through signal-to-noise ratio (SNR) analysis.

[0138] Furthermore, the pattern recognition unit performs similarity matching on the spectral feature vector generated by the feature extraction unit using standard templates in a pre-established spectral feature library, obtaining a similarity matching result. Based on the similarity matching result, a multi-class support vector machine classification algorithm is used to calculate the similarity score between the spectral feature vector and the standard template. Based on the highest similarity score, the type classification and concentration range of the substance to be tested are determined, and an initial spectral pattern recognition result containing a substance identification label and confidence assessment is output.

[0139] Finally, the environmental compensation unit collects real-time temperature and humidity parameters of the system's operating environment. Based on these parameters and a preset environmental response model, it calculates the environmental impact coefficient. The environmental impact coefficient is then used to compensate and correct the spectral feature parameters, eliminating the interference of environmental changes on the recognition results, and outputting the corrected spectral pattern recognition result.

[0140] The environmental compensation unit includes an environmental acquisition subunit, an influence coefficient calculation subunit, and a compensation and correction subunit.

[0141] The environmental acquisition subunit is used to collect temperature and humidity parameters of the system's operating environment and generate environmental parameters. The influence coefficient calculation subunit is used to calculate the environmental influence coefficient based on the environmental parameters and a preset environmental response model. The compensation and correction subunit is used to perform environmental compensation and correction on the spectral feature parameters based on the environmental influence coefficient and output the corrected spectral pattern recognition result.

[0142] For example, the specific working principle of the environmental compensation unit is as follows: the environmental acquisition subunit monitors the temperature and humidity parameters of the spectral sensing system in real time through integrated temperature and humidity sensors distributed within the spectral sensing system. The temperature and humidity parameters are synchronously acquired through a high-speed sampling circuit, and after signal conditioning and digital processing, environmental parameters are generated. These environmental parameters include the real-time values, rates of change, and statistical characteristics of the temperature and humidity parameters.

[0143] Furthermore, the influence coefficient calculation subunit calculates the environmental influence coefficient based on the environmental parameters and a preset environmental response model. Specifically, based on the temperature parameter, a preset temperature response model is invoked, and a polynomial fitting algorithm is used to calculate the influence coefficients of temperature on spectral baseline drift and peak position shift; based on the humidity parameter, a preset humidity transmittance model is invoked, and an exponential function fitting algorithm is used to calculate the influence coefficient of humidity on spectral intensity attenuation. Simultaneously, based on the interaction model of temperature and humidity, a multiple linear regression algorithm is used to calculate the temperature-humidity coupling influence coefficient. These influence coefficients are then weighted and fused to generate the environmental influence coefficient. The environmental influence coefficient includes a baseline compensation coefficient, a peak position correction coefficient, and an intensity correction coefficient.

[0144] Furthermore, the compensation and correction subunit receives the environmental impact coefficient output by the influence coefficient calculation subunit and the spectral feature parameters output by the feature extraction unit. Based on the baseline compensation coefficient, the compensation and correction subunit performs compensation calculations on the spectral feature parameters to eliminate baseline drift caused by temperature changes. Specifically, based on the peak position correction coefficient, it performs correction calculations on the spectral peak positions in the spectral feature parameters to eliminate peak position shifts caused by environmental factors; and based on the intensity correction coefficient, it performs normalization calculations on the peak intensities in the spectral feature parameters to eliminate the intensity attenuation effects of environmental factors such as humidity.

[0145] Finally, the compensation and correction subunit integrates the spectral feature parameters that have undergone baseline compensation, peak position correction, and intensity correction to generate environmentally compensated corrected spectral feature parameters. Based on these corrected spectral feature parameters, the compensation and correction subunit re-performs spectral pattern recognition analysis using the pattern recognition algorithm of the pattern recognition unit, and outputs the environmentally compensated and optimized corrected spectral pattern recognition result.

[0146] This application also discloses a metasurface waveguide array based on a silicon carbide substrate, applied to the aforementioned spectral sensing system. The metasurface waveguide array based on the silicon carbide substrate comprises a silicon carbide substrate and a metasurface waveguide array, wherein:

[0147] The silicon carbide substrate is used to provide mechanical support and thermal conduction channels;

[0148] The metasurface waveguide array includes multiple metasurface modulation units, each of which includes a thermal modulation structure and an electrical modulation structure. The thermal modulation structure is used to receive thermally modulated signals, and the electrical modulation structure is used to receive electrically modulated signals.

[0149] Through the above technical solutions, the metasurface waveguide array and spectral sensing system based on silicon carbide substrate provided by the present invention innovatively combines multi-physics field coupling modulation, intelligent waveguide channel selection and environmental adaptive compensation technology, which solves the problems of insufficient accuracy and poor adaptability of traditional spectral sensing systems in complex environments, and realizes high-precision and adaptive spectral sensing.

[0150] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0152] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0153] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A spectroscopic sensing system, characterized by The spectrum sensing system comprises a coupling modulation module, a waveguide transmission module and a spectrum analysis module, wherein: The coupling modulation module is configured to receive an incident light signal, perform phase modulation and amplitude modulation on the incident light signal through the multi-physical field coupling effect of thermal field modulation and electric field modulation, and generate a modulated light signal; The waveguide transmission module is configured to receive the modulated light signal output by the coupling modulation module, and output a separated spectrum signal through selective activation of waveguide channel combination and dynamic spectrum separation; The spectrum analysis module is configured to receive the separated spectrum signal output by the waveguide transmission module, obtain an initial spectrum pattern recognition result according to the separated spectrum signal, and output a corrected spectrum pattern recognition result through environmental compensation correction; The coupling modulation module comprises a metasurface waveguide array, a thermal modulation control unit, an electric modulation control unit and a coupling parameter calculation unit, wherein: The metasurface waveguide array is composed of a plurality of metasurface modulation units arranged in an array on a silicon carbide substrate, and each metasurface modulation unit comprises a thermal modulation structure and an electric modulation structure; The thermal modulation control unit is configured to generate a temperature modulation amount according to a target spectrum modulation requirement, provide a thermal modulation signal to the thermal modulation structure based on the temperature modulation amount, and achieve thermal-optic effect modulation by changing the local temperature; The electric modulation control unit is configured to generate an electric field intensity according to the target spectrum modulation requirement and the temperature modulation amount, provide an electric modulation signal to the electric modulation structure based on the electric field intensity, and achieve electro-optic effect modulation by applying an electric field; The coupling parameter calculation unit is configured to calculate the multi-physical field coupling modulation parameters through the nonlinear coupling superposition principle of thermal-optic effect and electro-optic effect according to the thermal-optic effect modulation and the electro-optic effect modulation, and generate a modulated light signal.

2. The optical spectral sensing system of claim 1, wherein, The coupling parameter calculation unit comprises a thermal-optic calculation subunit, an electro-optic calculation subunit, a superposition calculation subunit, a parameter generation subunit and a signal modulation subunit, wherein: The thermal-optic calculation subunit is configured to calculate a thermal-optic effect refractive index change amount according to the temperature modulation amount generated by the thermal modulation control unit and in combination with the thermal-optic coefficient of the silicon carbide substrate; The electro-optic calculation subunit is configured to calculate an electro-optic effect refractive index change amount according to the electric field intensity generated by the electric modulation control unit and in combination with the electro-optic coefficient of the silicon carbide substrate; The superposition calculation subunit is configured to obtain a total refractive index modulation amount through vector superposition according to the thermal-optic effect refractive index change amount and the electro-optic effect refractive index change amount; The parameter generation subunit is configured to calculate a phase modulation depth and a transmittance modulation coefficient according to the total refractive index modulation amount and the geometric parameters of the metasurface modulation unit, and generate multi-physical field coupling modulation parameters; The signal modulation subunit is configured to control the metasurface waveguide array to perform phase modulation and amplitude modulation on the incident light signal according to the multi-physical field coupling modulation parameters, and generate a modulated light signal.

3. The optical spectral sensing system of claim 2, wherein, The coupling parameter calculation unit further comprises a thermal-electric coupling compensation subunit, an electric-thermal coupling compensation subunit, a correction superposition subunit and an optimized parameter generation subunit, wherein: The thermoelectric coupling compensation subunit is configured to establish a thermoelectric coupling effect model according to the temperature modulation amount, calculate a temperature effect coefficient of an electro-optic coefficient of silicon carbide, and perform temperature compensation and correction on the electro-optic effect refractive index variation amount according to the electro-optic coefficient effect coefficient, to obtain a corrected electro-optic effect refractive index variation amount; The electro-thermal coupling compensation subunit is configured to establish an electro-thermal coupling effect model according to the electric field intensity, calculate a local temperature distribution influence amount of an electric field, and perform electric field compensation and correction on the thermo-optic effect refractive index variation amount according to the local temperature distribution influence amount, to obtain a corrected thermo-optic effect refractive index variation amount; The correction superposition subunit is configured to obtain a corrected total refractive index modulation amount by using a coupling vector superposition algorithm according to the corrected thermo-optic effect refractive index variation amount and the corrected electro-optic effect refractive index variation amount. The optimized parameter generation subunit is configured to recalculate a phase modulation depth and a transmittance modulation coefficient according to the corrected total refractive index modulation amount, and generate optimized multi-physical field coupling modulation parameters.

4. The optical spectral sensing system of claim 1, wherein, The waveguide transmission module comprises a waveguide array unit, a spectrum separation unit, a path selection unit, and a transmission parameter optimization unit, wherein: The waveguide array unit comprises a plurality of non-uniformly periodically arranged waveguide channels arranged in parallel, and is configured to receive the modulated optical signal and distribute the modulated optical signal to different waveguide channels for transmission, to obtain transmission optical signals of the waveguide channels; The spectrum separation unit is configured to realize dynamic spectrum separation by dynamic phase matching and spatial rearrangement based on differences in dispersion characteristics and mode coupling characteristics of the transmission optical signals and the waveguide channels, and output separated spectrum signals containing wavelength intensity distribution data; The path selection unit is configured to receive external spectrum detection requirements, determine target detection wavelength ranges and resolution requirements, selectively activate a waveguide channel combination by using a neural network prediction model, and output a channel activation control signal; The transmission parameter optimization unit is configured to receive the separated spectrum signals output by the spectrum separation unit, calculate spectrum separation degrees, crosstalk coefficients, transmission efficiencies, and wavelength resolutions according to the wavelength intensity distribution data in the separated spectrum signals, dynamically generate optimized transmission parameter configurations by using an online reinforcement learning algorithm, and optimize the separated spectrum signals.

5. The optical spectral sensing system of claim 4, wherein, The transmission parameter optimization unit comprises a performance index calculation subunit, a bias evaluation subunit, a parameter optimization subunit, and a feedback control subunit, wherein: The performance index calculation subunit is configured to calculate spectrum separation degrees, crosstalk coefficients, transmission efficiencies, and wavelength resolutions respectively according to the wavelength intensity distribution data in the separated spectrum signals, and generate multi-dimensional dynamic performance indexes; The bias evaluation subunit is configured to compare the multi-dimensional dynamic performance indexes with preset dynamic performance thresholds, calculate real-time bias amounts and trend change rates of the indexes, and generate weighted dynamic performance evaluation parameters; The parameter optimization subunit is configured to generate an optimized transmission parameter configuration by using a reinforcement learning algorithm according to the weighted dynamic performance evaluation parameter, wherein the optimized transmission parameter configuration comprises a structural parameter adjustment parameter and an excitation condition adjustment parameter of the waveguide channel. The feedback control subunit is configured to output a real-time feedback control instruction by combining the optimized transmission parameter configuration and the channel activation control signal, so as to optimize the separated spectral signal.

6. The optical spectral sensing system of claim 5, wherein, The feedback control subunit comprises a parameter extraction component, a channel activation component, a timing coordination component, an instruction generation component, and a signal optimization component. The parameter extraction component is configured to extract a target structural parameter adjustment amount of the waveguide channel according to the structural parameter adjustment parameter, wherein the target structural parameter adjustment amount comprises a waveguide width adjustment amount, a period spacing adjustment amount, and a refractive index distribution adjustment amount. The channel activation component is configured to determine a combination of waveguide channels to be activated and a corresponding excitation power distribution scheme according to the excitation condition adjustment parameter and the channel activation control signal, and generate a channel selective activation instruction based on priority sorting. The timing coordination component is configured to generate a coordinated control sequence comprising a parameter adjustment timing and a channel switching timing by timing synchronization processing according to the target structural parameter adjustment amount and the channel selective activation instruction. The instruction generation component is configured to output a real-time feedback control instruction according to the coordinated control sequence, wherein the real-time feedback control instruction comprises a structural parameter real-time adjustment instruction and a channel activation state switching instruction. The signal optimization component is configured to optimize the separated spectral signal by dynamically reconstructing a geometric structure of the waveguide channel and adaptively optimizing a combination of selectively activated waveguide channels according to the real-time feedback control instruction.

7. The optical spectral sensing system of claim 1, wherein, The spectral analysis module comprises an optoelectronic conversion unit, a feature extraction unit, a pattern recognition unit, and an environment compensation unit. The optoelectronic conversion unit is configured to convert the separated spectral signal output by the waveguide transmission module into an electrical signal. The feature extraction unit is configured to extract a spectral feature parameter from the electrical signal. The pattern recognition unit is configured to obtain an initial spectral pattern recognition result by pattern recognition according to the spectral feature parameter. The environment compensation unit is configured to extract an environmental parameter, perform environment compensation correction on the spectral feature parameter, and output a corrected spectral pattern recognition result.

8. The optical spectral sensing system of claim 7, wherein, The environment compensation unit comprises an environment acquisition subunit, an influence coefficient calculation subunit, and a compensation correction subunit. The environment acquisition subunit is configured to acquire a temperature parameter and a humidity parameter of a system working environment, and generate an environmental parameter. The influence coefficient calculation subunit is configured to calculate an environmental influence coefficient according to the environmental parameter and a preset environmental response model. The compensation correction subunit is configured to output a corrected spectral pattern recognition result by performing environment compensation correction on the spectral feature parameter according to the environmental influence coefficient.

9. A metasurface waveguide array based on silicon carbide substrate, applied to a spectroscopic sensing system as claimed in any one of claims 1-8, characterized in that, The super surface waveguide array based on a silicon carbide substrate comprises a silicon carbide substrate and a super surface waveguide array. The silicon carbide substrate is configured to provide mechanical support and a heat conduction channel. The metasurface waveguide array comprises a plurality of metasurface modulation units, wherein each metasurface modulation unit comprises a thermal modulation structure for receiving a thermal modulation signal and an electrical modulation structure for receiving an electrical modulation signal.

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