MCP image enhancement high-sensitivity detection system

Abstract

The invention relates to the technical field of detection, and discloses an MCP image enhancement high-sensitivity detection system, which comprises a photoelectric cathode, which adopts a multi-quantum well structure and a surface plasma enhancement technology, is used for converting incident photons into primary electrons, and comprises a quantum well formed by gallium arsenide and gallium aluminum arsenide; a microchannel plate for amplifying primary electrons generated from the photocathode; the microchannel plate is coated with a superconducting thin film so as to reduce noise and improve electronic gain; various types of sensors include optical sensors, thermal imaging sensors, acoustic sensors, and chemical sensors. By adopting a multi-quantum well structure and a surface plasma enhanced photoelectric cathode, the spectral response and photoelectric conversion efficiency are remarkably improved, and the sensitivity and response speed of the system to non-specific wavelengths are enhanced. Meanwhile, the microchannel plate coated with the superconducting thin film effectively reduces the noise level, improves the signal definition and the image quality, and solves the high-gain limitation of a traditional system.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, in particular to an MCP image-enhanced high-sensitivity detection system. Background Art

[0002] The microchannel plate image-intensified high-sensitivity detection system is an advanced photoelectric detection technology widely used in astronomy, biomedical imaging, particle physics, and security inspection. By converting optical signals into electrical signals, the system can detect extremely low-intensity light sources or other types of radiation.

[0003] Existing high-sensitivity detection systems are usually based on traditional photocathode and microchannel plate technology. Traditional photocathodes usually use a single layer made of gallium arsenide material. When in use, the photocathode is the first component in the detection system to contact the light signal. Its basic principle is the photoelectric effect, that is, when photons hit the surface of the material, they can stimulate photoelectrons. The microchannel plate is a device full of precise micropores, and the inner wall of each micropore is coated with conductive material. When the primary electrons released by the photocathode enter these micropores, they will collide with the inner wall and trigger the release of more electrons, forming an electron avalanche, thereby amplifying the original electronic signal.

[0004] Although existing high-sensitivity detection systems convert optical signals into electrical signals through photocathodes and microchannel plates and can detect extremely low-intensity light sources or other types of radiation, they still have some shortcomings. Traditional photocathodes usually use single gallium arsenide, and their photoelectric conversion efficiency is limited by the light absorption range and efficiency of the material itself. This leads to insufficient sensitivity and response speed of the system within the non-specific wavelength range, and it cannot effectively detect weak light signals beyond the specific spectral range. Secondly, although microchannel plates can amplify electronic signals, their amplification process is usually accompanied by a high noise level, especially in scenarios requiring extremely high gain, which affects the clarity of the signal and the quality of the final image. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention provides an MCP image-intensified, high-sensitivity detection system. This system utilizes a multi-quantum well structure and a surface plasmon-enhanced photocathode, significantly improving spectral response and photoelectric conversion efficiency, while also enhancing the system's sensitivity and response speed to non-specific wavelengths. Furthermore, a superconducting thin film-coated microchannel plate effectively reduces noise levels, improving signal clarity and image quality, and overcoming the high-gain limitations of conventional systems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an MCP image enhancement high-sensitivity detection system, comprising:

[0007] A photocathode, using a multi-quantum well structure and surface plasmon enhancement technology to convert incident photons into primary electrons, wherein the photocathode includes a quantum well composed of gallium arsenide and gallium aluminum arsenide;

[0008] a microchannel plate for amplifying primary electrons generated from the photocathode;

[0009] superconducting thin film-coated microchannel plates to reduce noise and increase electronic gain;

[0010] Various types of sensors, including optical, thermal, acoustic, and chemical sensors;

[0011] a readout system comprising a quantum Fourier transform and a quantum machine learning algorithm for processing data obtained from the microchannel plate and a variety of sensors;

[0012] a data fusion module for integrating data from the multiple sensors;

[0013] Control and analysis systems are used to process and analyze the integrated data in real time and provide decision support.

[0014] Preferably, the energy band of the multi-quantum well structure is determined by the following formula:

[0015]

[0016] Among them, E n is the energy level in quantum yin, is the reduced Planck constant, m * is the effective mass of the electron, d is the quantum width, n is the quantum number, and V0 is the barrier height.

[0017] Preferably, the surface plasmon enhancement technology includes preparing metal nanoparticles on the surface of the photocathode to enhance the localized surface plasmon resonance effect.

[0018] Preferably, the superconducting thin film coated microchannel plate specifically includes the following steps and characteristics:

[0019] The channel walls inside the microchannel plate are coated with a superconducting film made of a superconducting material with a high critical temperature;

[0020] In the superconducting state, the electronic gain of the microchannel plate is calculated by the following formula:

[0021]

[0022] Where B represents the magnetic field, λ is the London penetration depth;

[0023] The preparation of superconducting thin films includes the following processes:

[0024] Using pulsed laser deposition technology to deposit superconducting thin films on the inner wall of the microchannel plate channel;

[0025] After deposition, an annealing process is performed to optimize the film's crystal structure and ensure it reaches a superconducting state;

[0026] The superconducting transition temperature and critical magnetic field of the film were evaluated in a low-temperature test facility, and the behavior of its resistance as a function of temperature and magnetic field was measured using a four-probe resistance measurement technique.

[0027] Preferably, the readout system comprises a quantum circuit for implementing a quantum Fourier transform, comprising the following steps:

[0028] Initialize the state: Initialize the input state |x> to an n-qubit superposition state, representing the input data;

[0029] Execution of the Quantum Fourier Transform: Apply the Quantum Fourier Transform to the input state |x>. The transformation is performed as follows;

[0030]

[0031] where |x> represents the quantum state, encoded by the integer x; n is the number of qubits, which determines the resolution and computational power of the system; |k> is the state in the quantum register, corresponding to the output of the transformation; is a complex exponential function representing the phase factor in the transformation, where i is the imaginary unit;

[0032] After the quantum Fourier transform, the quantum register is measured to obtain the output frequency domain data |k>;

[0033] The measurement results are parsed into frequency information for subsequent data processing and analysis.

[0034] Preferably, the quantum machine learning algorithm includes a quantum support vector machine, and its optimization problem is described by the following formula:

[0035]

[0036] Among them, θ is the parameter of the parameterized quantum circuit, y i is the label, ξ i is the slack variable and b is the bias term.

[0037] Preferably, the data fusion module applies a weighted fusion model to integrate multi-sensor data, and the weighted fusion model specifically includes the following steps:

[0038] Calculate the weighted sum of each sensor data to generate fused data X fused , the formula is as follows:

[0039]

[0040] Among them, X i represents the data of the i-th sensor, w i represents the weight based on the reliability and accuracy of the sensor, and the weight w i The calculation formula is:

[0041]

[0042] In the above formula, α i represents the reliability score of the i-th sensor, Represents the sum of all sensor reliability scores.

[0043] Preferably, the control and analysis system includes a convolutional neural network for processing the fused image data, specifically comprising the following steps:

[0044] Receive fused image data X from the data fusion module fused ;

[0045] The fused image data X fused The input is fed into a convolutional neural network, which is configured with multiple convolutional layers, activation layers, and pooling layers to extract image features.

[0046] The convolutional neural network outputs the image features and classification results Y, where the calculation of Y is given by the following relationship:

[0047] Y = CNN(X fused )

[0048] In the above formula, X fused represents the fused image data, which is a composite image obtained by weighting and processing data from multiple sensors; Y represents the output of the convolutional neural network, including image classification results, target detection boxes, and other related image features.

[0049] Preferably, the control and analysis system comprises a recursive neural network for processing time series data from a variety of sensors, specifically comprising the following steps:

[0050] Receive time series data from the data fusion module;

[0051] The time series data is fed into a recurrent neural network, which recursively processes the data through its multiple recurrent layers to extract time-dependent features.

[0052] Each step in the recurrent neural network is calculated as shown in the following formula to update the hidden state:

[0053] h t=σ(W ih x t +b ih +W hh h t-1 +b hh )

[0054] In the above formula: x t represents the input data at time t, h t represents the hidden state at time t, W ih Represents the weight matrix input to the hidden layer, W hh represents the weight matrix from hidden layer to hidden layer, b ih represents the bias term input to the hidden layer, b hh represents the bias term from hidden layer to hidden layer, σ represents the activation function, and the nonlinear function is the logistic function;

[0055] Use the trained recurrent neural network model to parse sequence data and output processing results, such as classification labels and predicted values.

[0056] Preferably, the data from the various sensors are preprocessed by the following steps:

[0057] Standardize the data of each sensor, including filtering, denoising and normalization;

[0058] Use timestamps and synchronization signals to ensure that all sensor data are aligned in time;

[0059] Perform spatial calibration to ensure that the sensor's measurement coordinates are consistent.

[0060] The present invention provides an MCP image enhancement high-sensitivity detection system. It has the following beneficial effects:

[0061] 1. The present invention significantly improves the photoelectric conversion efficiency of the photocathode by integrating the multi-quantum well structure and surface plasmon enhancement technology. The multi-quantum well structure enables the photocathode to have a higher absorptivity at specific wavelengths, while the surface plasmon technology further enhances the local field intensity of light and improves the efficiency of electron release. This combination not only solves the problem of insufficient sensitivity of the photocathode in the prior art, but also improves the overall response speed of the system by quickly and efficiently converting optical signals into electrical signals.

[0062] 2. The present invention improves electron gain and signal-to-noise ratio while reducing the noise level during system operation by using a microchannel plate coated with a superconducting thin film. The low scattering characteristics and high electron transmission efficiency of the superconducting film ensure efficient electron multiplication, which solves the shortcoming of traditional microchannel plates being easily affected by thermal noise under high gain conditions. It not only improves the accuracy of detection data, but also enhances the stability and reliability of the entire system under various environments.

[0063] 3. This invention significantly improves the speed and intelligence of data processing by introducing quantum Fourier transform and quantum machine learning algorithms. Quantum algorithms can complete complex data conversion and analysis tasks in a very short time. The application of this technology significantly optimizes the ability to extract key information from multi-sensor fusion data and enhances the application effect of the system in automatic monitoring, remote diagnosis and other aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] Please see the attached Figure 1 The embodiment of the present invention provides an MCP image enhancement high-sensitivity detection system, comprising:

[0067] The photocathode uses a multi-quantum well structure and surface plasmon enhancement technology to convert incident photons into primary electrons. The photocathode includes quantum wells composed of gallium arsenide and gallium aluminum arsenide. By utilizing the band gap characteristics of different materials, the photoelectric conversion efficiency is optimized. When photons are incident on the photocathode, they are absorbed by electrons in the quantum well structure, causing the electrons to transition from the valence band to the conduction band, thereby releasing photogenerated electrons. Surface plasmon enhancement technology further improves light absorption efficiency through localized surface resonance, increasing the detector's response speed and sensitivity, enabling it to operate under lower light conditions.

[0068] The microchannel plate is used to amplify the primary electrons generated by the photocathode. When the primary electrons strike the inner wall of the microchannel plate, they trigger the release of more electrons. These electrons repeatedly collide within the microchannel, and each collision may release more electrons, forming an electron avalanche, greatly enhancing the intensity of the electron flow. This improves the sensitivity of the entire detection system, enabling it to detect signals at extremely low light levels. This not only amplifies the signal, but also effectively reduces ratio noise through continuous electron impact and secondary emission.

[0069] Superconducting film-coated microchannel plates (MCPs) reduce noise and increase electron gain. Superconducting film is applied to the inner surface of the MCP to improve the efficiency of secondary electron emission. Primary electrons collide with the inner wall of the MCP coated with superconducting film, triggering the release of more electrons. These electrons then collide with other areas, creating a cascade amplification effect. The superconducting film reduces the material's resistance and thermal noise, thereby improving the signal-to-noise ratio and significantly increasing the electron gain and the overall sensitivity of the system, especially in high-frequency and low-light-intensity detection scenarios.

[0070] Various types of sensors, including optical sensors, thermal imaging sensors, acoustic sensors, and chemical sensors. Optical sensors provide high-resolution and high-precision image capture capabilities. Thermal imaging sensors enable the system to monitor and detect at night and in low-visibility conditions, enhancing the system's application flexibility in various environmental conditions. Acoustic sensors provide important data on environmental noise and other sound events, helping to enhance the system's ability to respond to acoustic events. Chemical sensors can provide real-time monitoring of harmful substances or pollutants in the environment, which is of great significance for protecting human health and environmental safety. The use of multiple sensors can enhance the practicality and flexibility of the system and provide more comprehensive data analysis and decision-making support capabilities.

[0071] The readout system, which includes quantum Fourier transform and quantum machine learning algorithms, is used to process data obtained from microchannel plates and various sensors. It leverages the parallelism and ultra-fast processing capabilities of quantum computing to quickly and efficiently analyze and interpret signals. The integrated quantum Fourier transform can quickly convert collected time-domain data into frequency-domain data, facilitating analysis of the signal's frequency components. The quantum machine learning algorithm uses the superposition and entanglement of quantum states to achieve rapid classification and learning of big data, significantly improving data processing speed and accuracy. It is particularly suitable for complex signal processing tasks requiring real-time or near-real-time analysis.

[0072] The data fusion module is used to integrate data from multiple sensors. It uses software algorithms to integrate data from optical, thermal imaging, acoustic, and chemical sensors to provide a comprehensive data view. Data fusion technology provides an accurate multi-dimensional data output by weighting the data from different sensors based on their reliability and accuracy. By integrating multi-source information, it improves data quality and the system's decision-making ability;

[0073] The control and analysis system is used to process and analyze the integrated data in real time and provide decision support. The real-time processing and efficient data analysis capabilities provide immediate decision support and enhance the system's adaptability and response speed in a dynamic environment.

[0074] In a preferred embodiment of the present invention, the energy band of the multi-quantum well structure is determined by the following formula:

[0075]

[0076] Among them, E n is the energy level in quantum yin, is the reduced Planck constant, m * is the effective mass of the electron, d is the quantum width, n is the quantum number, V0 is the barrier height, and molecular beam epitaxy technology is used to grow gallium arsenide and gallium aluminum arsenide layer by layer on the substrate. The uniformity of the quantum well and the clarity of the interface are ensured by controlling the growth rate and layer thickness. In these nanoscale quantum wells, electrons are confined in an extremely narrow space, causing their energy levels to be quantized. This quantization effect makes the behavior of electrons significantly different from that of macroscopic materials. By precisely controlling the width and material of the quantum well, the energy band structure of the quantum well can be adjusted to better match the energy of the incident light, thereby improving the absorption and photoelectric conversion efficiency of the photocathode at specific light wavelengths. At the same time, the use of the multi-quantum well structure also enhances the physical stability of the photocathode, reduces performance fluctuations caused by temperature changes, and enables the photocathode to maintain stable high-precision measurements during long-term operation.

[0077] In a preferred embodiment of the present invention, the surface plasmon enhancement technology includes preparing metal nanoparticles on the surface of the photocathode to enhance the localized surface plasmon resonance effect. The metal nanoparticles are prepared on the surface of the photocathode using chemical reduction, electrochemical deposition or nanoprinting technology. These methods can control the size, shape and distribution density of the nanoparticles. When light waves are irradiated on the metal nanoparticles, the free electrons in the metal interact with the light waves to produce localized surface plasmon resonance. By introducing localized surface plasmon resonance on the surface of the photocathode, light absorption at specific wavelengths can be significantly enhanced, thereby improving the photoelectric conversion efficiency. Due to its efficient light absorption and electron excitation process, the plasma-enhanced photocathode can achieve higher response speed and sensitivity.

[0078] In a preferred embodiment of the present invention, a superconducting thin film coated microchannel plate specifically includes the following steps and characteristics:

[0079] The channel walls inside the microchannel plate are coated with a superconducting film made of a superconducting material with a high critical temperature;

[0080] In the superconducting state, the electronic gain of the microchannel plate is calculated by the following formula:

[0081]

[0082] Where B represents the magnetic field, λ is the London penetration depth;

[0083] The preparation of superconducting thin films includes the following processes:

[0084] Using pulsed laser deposition technology to deposit superconducting thin films on the inner wall of the microchannel plate channel;

[0085] After deposition, an annealing process is performed to optimize the film's crystal structure and ensure it reaches a superconducting state;

[0086] The superconducting transition temperature and critical magnetic field of the film are evaluated in a low-temperature test device, and the four-probe resistance measurement technology is used to measure the behavior of its resistance as a function of temperature and magnetic field. Superconducting materials with high critical temperatures such as niobium or yttrium barium copper oxide are selected, and pulsed laser deposition technology is used to uniformly deposit the superconducting film on the inner wall of the channel of the microchannel plate. Annealing treatment is performed after deposition to optimize the crystal structure of the film to ensure that it achieves the best superconducting performance. The superconducting transition temperature and critical magnetic field of the film are evaluated using low-temperature testing equipment and four-probe resistance measurement technology. Through these measurements, the superconducting phase transition point of the film and its behavior under different magnetic fields can be determined. By utilizing superconducting films, and the low resistance and high secondary electron emission efficiency in the superconducting state, the cascade multiplication effect of electrons is greatly improved. The application of superconducting films reduces thermal noise and other forms of electron scattering, thereby improving signal clarity and overall signal-to-noise ratio.

[0087] In a preferred embodiment of the present invention, the readout system comprises a quantum circuit for implementing a quantum Fourier transform, comprising the following steps:

[0088] Initialize the state: Initialize the input state |x> to an n-qubit superposition state representing the input data. Use quantum logic gates and quantum state preparation techniques to ensure that each qubit is correctly initialized to reflect the state of the input data.

[0089] Execution of the Quantum Fourier Transform: Apply the Quantum Fourier Transform to the input state |x>. The transformation is performed as follows;

[0090]

[0091] where |x> represents the quantum state, encoded by the integer x; n is the number of qubits, which determines the resolution and computational power of the system; |k> is the state in the quantum register, corresponding to the output of the transformation; is a complex exponential function representing the phase factor in the transformation, where i is the imaginary unit. The quantum Fourier transform exploits quantum parallelism, allowing all possible input states to be processed simultaneously. Through superposition, large data sets can be processed in a single operation. The quantum Fourier transform can provide faster processing speeds when processing large data sets, and can also provide more accurate data analysis results when high-precision scientific research and complex signal processing are required;

[0092] After the quantum Fourier transform, the quantum register is measured to obtain the output frequency domain data |k>. Quantum measurement techniques, such as quantum state tomography, are used to ensure that accurate frequency information is obtained from the quantum register.

[0093] The measurement results are parsed into frequency information for subsequent data processing and analysis.

[0094] In a preferred embodiment of the present invention, the quantum machine learning algorithm includes a quantum support vector machine, and its optimization problem is described by the following formula:

[0095]

[0096] Among them, θ is the parameter of the parameterized quantum circuit, y i is the label, ξ i is a slack variable, b is a bias term, and parameterized quantum circuits are used to encode and process quantum states through the operation of quantum gates. These parameters θ are optimized during training to minimize the loss function and thus find the optimal position of the classification hyperplane. Each training data point i has a corresponding label y i , these labels indicate the classification of data points, and by introducing the slack variable ξ i , to allow flexible adjustment of classification boundaries, thereby tolerating certain classification errors. Quantum support vector machines utilize the superposition and entanglement capabilities of quantum states to effectively represent and process data in high-dimensional feature space. At the same time, information related to classification decisions is obtained by measuring quantum states. This information is used to adjust classification boundaries to achieve the best classification effect, significantly improving the speed of processing high-dimensional data. It is especially suitable for training large-scale data sets and complex models. The flexibility and powerful optimization capabilities of quantum support vector machines enable them to adapt to various complex classification problems.

[0097] In a preferred embodiment of the present invention, the data fusion module applies a weighted fusion model to integrate multi-sensor data. The weighted fusion model specifically includes the following steps:

[0098] Calculate the weighted sum of each sensor data to generate fused data X fused , the formula is as follows:

[0099]

[0100] Among them, X i represents the data of the i-th sensor, w i represents the weight based on the reliability and accuracy of the sensor, and the weight w i The calculation formula is:

[0101]

[0102] In the above formula, α i represents the reliability score of the i-th sensor, It represents the sum of the reliability scores of all sensors. By fusing the frequency information obtained by quantum Fourier transform with data from other sensors (such as optical, thermal imaging, acoustic, and chemical sensors), the system can parse and understand the environment from multiple dimensions, thereby providing more comprehensive data analysis and decision support. The fused data is more comprehensive, combining information from multiple dimensions such as time and frequency, which helps to improve the accuracy and reliability of the final decision.

[0103] In a preferred embodiment of the present invention, the control and analysis system includes a convolutional neural network for processing the fused image data, specifically comprising the following steps:

[0104] Receive fused image data X from the data fusion module fused ;

[0105] The fused image data X fused The input is fed into a convolutional neural network, which is configured with multiple convolutional layers, activation layers, and pooling layers to extract image features.

[0106] The convolutional neural network outputs the image features and classification results Y, where the calculation of Y is given by the following relationship:

[0107] Y = CNN(X fused )

[0108] In the above formula, X fused Represents the fused image data, which is a composite image obtained by weighting and processing data from multiple sensors; Y represents the output of the convolutional neural network after processing, including image classification results, target detection boxes, and other relevant image features. The fused image data collected in real time is input into the convolutional neural network model. The network processes the input data, extracts features through convolutional layers, activation layers, and pooling layers, and finally generates outputs in the fully connected layer. These outputs represent the classification and features of the image data. Through its multi-layer structure, it automatically learns and extracts important features from the image. Each convolutional layer uses multiple filters to capture the local connectivity characteristics and spatial hierarchy of the image. The activation function adds nonlinearity, enabling the network to learn complex patterns. By using the convolutional neural network model, the system can quickly and accurately process and analyze large amounts of fused image data. At the same time, the convolutional neural network model can automatically learn complex patterns and features from the data, reducing the need for expert intervention and manual feature engineering. This not only improves the operational efficiency of the system but also reduces maintenance costs.

[0109] In a preferred embodiment of the present invention, the control and analysis system includes a recurrent neural network for processing time series data from multiple sensors, specifically comprising the following steps:

[0110] Receive time series data from the data fusion module;

[0111] The time series data is fed into a recurrent neural network, which recursively processes the data through its multiple recurrent layers to extract time-dependent features.

[0112] Each step in the recurrent neural network is calculated as shown in the following formula to update the hidden state:

[0113] h t =σ(W ih x t +b ih +W hh h t-1 +b hh )

[0114] In the above formula: x t represents the input data at time t, h t represents the hidden state at time t, W ih Represents the weight matrix input to the hidden layer, W hh represents the weight matrix from hidden layer to hidden layer, b ih represents the bias term input to the hidden layer, b hh represents the bias term from hidden layer to hidden layer, σ represents the activation function, and the nonlinear function is the logistic function;

[0115] A trained recurrent neural network model is used to parse sequence data and output processing results, such as classification labels and predicted values. The recurrent neural network architecture is designed, and long short-term memory or gated recurrent units are selected to effectively handle long-term dependencies. Labeled time series datasets are used to train the recurrent neural network model. These data include time-dependent data from optical, thermal imaging, acoustic, and chemical sensors. The network is trained using a backpropagation algorithm and an appropriate optimizer to minimize prediction error, such as using mean squared error as a loss function. Real-time data is fed into the trained recurrent neural network model for processing. The network continuously updates information through recursive propagation of its internal state, providing immediate data analysis and prediction output. By continuously learning and integrating historical time series data, the recurrent neural network model is able to predict future events and states. Furthermore, the recurrent neural network model's ability to handle long-term dependencies makes it particularly suitable for processing complex and long-term data series, thereby improving overall data processing efficiency and effectiveness. Furthermore, the model can adjust its predictions based on real-time changes in input data, enabling the system to quickly adapt to and respond to environmental changes, enhancing its ability to operate in dynamic environments.

[0116] In a preferred embodiment of the present invention, data from multiple sensors are preprocessed by the following steps:

[0117] Standardize the data from each sensor, including filtering, denoising, and normalization. Filter the data collected by each sensor to remove noise and unnecessary frequency components. Apply denoising algorithms such as wavelet transform, median filtering, or machine learning-based denoising techniques to further eliminate random noise in the data. Finally, normalize the data to a uniform range or distribution to eliminate the impact of different sensors and ensure data consistency. Ensuring that data from different sensors is consistent in range and format is crucial for subsequent data analysis and fusion.

[0118] Use timestamps and synchronization signals to ensure that all sensor data is aligned in time. Implement timestamps on data from different sensors to ensure that the data is aligned in time. Before data fusion, the data needs to be adjusted to match the lowest sampling rate or align key time points.

[0119] Spatial calibration is performed to ensure that the measurement coordinates of the sensors are consistent, ensuring that the data from all sensors are spatially aligned, especially in applications involving position and distance measurements.

[0120] The preprocessed data has higher quality, which improves the accuracy of data analysis and the reliability of system decision-making. By normalizing and synchronizing various data, the system can better adapt to changing operating environments and different working conditions, improving the flexibility and robustness of the system. At the same time, the preprocessing step reduces the computational burden of subsequent processing stages, enabling the system to respond and process data more quickly.

[0121] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An MCP image intensification high sensitivity detection system, characterized in that: include: A photocathode, using a multi-quantum well structure and surface plasmon enhancement technology to convert incident photons into primary electrons, wherein the photocathode includes a quantum well composed of gallium arsenide and gallium aluminum arsenide; a microchannel plate for amplifying primary electrons generated from the photocathode; superconducting thin film-coated microchannel plates to reduce noise and increase electronic gain; Various types of sensors, including optical, thermal, acoustic, and chemical sensors; a readout system comprising a quantum Fourier transform and a quantum machine learning algorithm for processing data obtained from the microchannel plate and a variety of sensors; a data fusion module for integrating data from the multiple sensors; Control and analysis systems are used to process and analyze the integrated data in real time and provide decision support.

2. The MCP image intensification high-sensitivity detection system according to claim 1, characterized in that: The energy band of the multi-quantum well structure is determined by the following formula: Among them, E n is the energy level in quantum yin, is the reduced Planck constant, m * is the effective mass of the electron, d is the quantum width, n is the quantum number, and V0 is the barrier height.

3. The MCP image-enhanced high-sensitivity detection system according to claim 1, characterized in that: The surface plasmon enhancement technology includes preparing metal nanoparticles on the surface of the photocathode to enhance the localized surface plasmon resonance effect.

4. The MCP image intensification high-sensitivity detection system according to claim 1, characterized in that: The superconducting thin film coated microchannel plate specifically includes the following steps and characteristics: The channel walls inside the microchannel plate are coated with a superconducting film made of a superconducting material with a high critical temperature; In the superconducting state, the electronic gain of the microchannel plate is calculated by the following formula: Where B represents the magnetic field, λ is the London penetration depth; The preparation of superconducting thin films includes the following processes: Using pulsed laser deposition technology to deposit superconducting thin films on the inner wall of the microchannel plate channel; After deposition, an annealing process is performed to optimize the film's crystal structure and ensure it reaches a superconducting state; The superconducting transition temperature and critical magnetic field of the film were evaluated in a low-temperature test facility, and the behavior of its resistance as a function of temperature and magnetic field was measured using a four-probe resistance measurement technique.

5. The MCP image-enhanced high-sensitivity detection system according to claim 1, characterized in that: The readout system comprises a quantum circuit for implementing a quantum Fourier transform, comprising the following steps: Initialize the state: Initialize the input state |x> to an n-qubit superposition state, representing the input data; Execution of the Quantum Fourier Transform: Apply the Quantum Fourier Transform to the input state |x>. The transformation is performed as follows; where |x> represents the quantum state, encoded by the integer x; n is the number of qubits, which determines the resolution and computational power of the system; |k> is the state in the quantum register, corresponding to the output of the transformation; is a complex exponential function representing the phase factor in the transformation, where i is the imaginary unit; After the quantum Fourier transform, the quantum register is measured to obtain the output frequency domain data |k>; The measurement results are parsed into frequency information for subsequent data processing and analysis.

6. The MCP image intensification high-sensitivity detection system according to claim 1, characterized in that: The quantum machine learning algorithm includes a quantum support vector machine, and its optimization problem is described by the following formula: Where θ is the parameter of the parameterized quantum circuit, y i is the label, ξ i is the slack variable and b is the bias term.

7. The MCP image-enhanced high-sensitivity detection system according to claim 1, characterized in that: The data fusion module applies a weighted fusion model to integrate multi-sensor data. The weighted fusion model specifically includes the following steps: Calculate the weighted sum of each sensor data to generate fused data X fused , the formula is as follows: Among them, X i represents the data of the i-th sensor, w i represents the weight based on the reliability and accuracy of the sensor, and the weight w i The calculation formula is: In the above formula, α i represents the reliability score of the i-th sensor, Represents the sum of all sensor reliability scores.

8. The MCP image-enhanced high-sensitivity detection system according to claim 1, characterized in that: The control and analysis system includes a convolutional neural network for processing the fused image data, specifically comprising the following steps: Receive fused image data X from the data fusion module fused ; The fused image data X fused The input is fed into a convolutional neural network, which is configured with multiple convolutional layers, activation layers, and pooling layers to extract image features. The convolutional neural network outputs the image features and classification results Y, where the calculation of Y is given by the following relationship: Y=CNN(X fused ) In the above formula, X fused represents the fused image data, which is a composite image obtained by weighting and processing data from multiple sensors; Y represents the output of the convolutional neural network, including image classification results, target detection boxes, and other related image features.

9. The MCP image-enhanced high-sensitivity detection system according to claim 1, characterized in that: The control and analysis system includes a recurrent neural network for processing time series data from multiple sensors, specifically including the following steps: Receive time series data from the data fusion module; The time series data is fed into a recurrent neural network, which recursively processes the data through its multiple recurrent layers to extract time-dependent features. Each step in the recurrent neural network is calculated as shown in the following formula to update the hidden state: h t =σ(W ih x t +b ih +W hh h t-1 +b hh ) In the above formula: x t represents the input data at time t, h t represents the hidden state at time t, W ih Represents the weight matrix input to the hidden layer, W hh represents the weight matrix from hidden layer to hidden layer, b ih represents the bias term input to the hidden layer, b hh represents the bias term from hidden layer to hidden layer, σ represents the activation function, and the nonlinear function is the logistic function; Use the trained recurrent neural network model to parse sequence data and output processing results, such as classification labels and predicted values.

10. The MCP image-enhanced high-sensitivity detection system according to claim 1, characterized in that: The data from the various sensors are preprocessed through the following steps: Standardize the data of each sensor, including filtering, denoising and normalization; Use timestamps and synchronization signals to ensure that all sensor data are aligned in time; Perform spatial calibration to ensure that the sensor's measurement coordinates are consistent.

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