A non-contact electronic device ultrafast electromagnetic fault detection and identification device and method based on diamond NV color centers

Abstract

The present application relates to quantum magnetic sensing and intelligent detection technical field, specifically a kind of non-contact electronic equipment ultrafast electromagnetic fault detection identification device and identification method based on diamond NV color center.This identification device includes efficient excitation microstrip array antenna module, electromagnetic fault detection identification main control board, laser modulation and coupling interface unit, photoelectric detection control interface unit, laser heat management copper-based heat dissipation assembly, voltage follower circuit control switch unit, pixel-level quantum magnetic sensing unit group, laser control circuit mainboard and photoelectric detection control mainboard;Wherein each pixel-level quantum magnetic sensing unit group includes three narrow linewidth laser excitation source, three diamond NV color center quantum magnetic sensitive element, three narrow-band optical filter and three photoelectric detector.The identification device has the characteristics of fast detection speed, high sensitivity, strong real-time, low power consumption and no need to contact the measured target, etc., can effectively improve the efficiency and accuracy of complex electronic equipment electromagnetic fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of quantum magnetic sensing and intelligent detection technology, specifically a non-contact ultrafast electromagnetic fault detection and identification device and method for electronic devices based on diamond NV color centers. Background Technology

[0002] As electronic devices evolve towards higher integration and reliability, rapid detection and identification of circuit faults have become critical requirements. Existing detection methods suffer from drawbacks such as susceptibility to damage and low efficiency. While non-contact methods offer the advantage of being non-destructive, magnetic field detection systems based on Hall effect or magnetoresistive sensors generally employ a von Neumann architecture, requiring a separate "sensing-acquisition-storage-processing" process. This results in issues such as large response delays, high power consumption, and severe data redundancy, making it difficult to meet real-time requirements. Furthermore, traditional magnetic sensors suffer from slow response speeds and limited sensitivity adjustment capabilities, hindering the direct mapping and computational fusion of neural network models at the sensing level. Therefore, there is an urgent need for an electromagnetic fault detection and identification technology that can integrate magnetic field sensing and information processing at the physical level, possessing adjustable response characteristics and ultra-fast response capabilities. Summary of the Invention

[0003] To address the problems of slow response speed, high power consumption, and large data processing delays caused by the separation of sensing and calculation in existing circuit fault detection systems, this invention provides a non-contact ultrafast electromagnetic fault detection and identification device and method for electronic devices based on diamond NV centers. By constructing a hardware architecture that integrates magnetic field sensing and neural network model calculation, it achieves rapid acquisition and parallel processing of electromagnetic fault information, thereby improving detection efficiency and system real-time performance.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a non-contact electronic device for ultrafast electromagnetic fault detection and identification based on diamond NV color centers, comprising a high-efficiency excitation microstrip array antenna module, an electromagnetic fault detection and identification main control board, a laser modulation and coupling interface unit, a photoelectric detection and control interface unit, a voltage follower circuit control switch unit, a pixel-level quantum magnetic sensing unit group, a laser control circuit main board, and a photoelectric detection control main board;

[0005] The high-efficiency excitation microstrip array antenna module is attached to the surface of the electromagnetic fault detection and identification main control board. A microwave signal input interface is provided on one side of the high-efficiency excitation microstrip array antenna module for injecting external microwave signals. The electromagnetic fault detection and identification main control board integrates a pixel-level quantum magnetic sensing unit group arranged in a two-dimensional array. The pixel-level quantum magnetic sensing unit group includes three pixel units distributed in a triangular space. Each pixel unit includes a diamond NV color center quantum magnetic sensing element, a narrow-linewidth laser excitation source, a narrow-band optical filter, and a photodetector, thus forming a multi-channel quantum magnetic sensing array. The high-efficiency excitation microstrip array antenna module transmits microwave signals to each pixel unit through a constructed feeding network. A pixel-level quantum magnetic sensing unit group is provided; the electromagnetic fault detection and identification main control board is also distributed with a laser modulation and coupling interface unit, a photoelectric detection and control interface unit, and a voltage follower circuit control switch unit; the electromagnetic fault detection and identification main control board is fixed with the laser control circuit main board and the photoelectric detection and control main board, the laser modulation and coupling interface unit and the photoelectric detection and control interface unit are electrically connected to the laser control circuit main board and the photoelectric detection and control main board, respectively, the laser control circuit main board uniformly controls the driving current of the narrow linewidth laser excitation source in the electromagnetic fault detection and identification main control board, and the photoelectric detection and control main board controls the bias and gain parameters of the photodetector in the electromagnetic fault detection and identification main control board.

[0006] Furthermore, it also includes a laser thermal management copper-based heat dissipation component, which is distributed on the electromagnetic fault detection and identification main control board; the laser thermal management copper-based heat dissipation component surrounds the narrow linewidth laser excitation source in the pixel-level quantum magnetic sensing unit group; it is used to perform thermal management on multiple narrow linewidth laser excitation sources; through the synergistic effect of the temperature control module and the copper-based heat dissipation structure, the operating temperature of the narrow linewidth laser excitation source is stabilized within a preset range to suppress thermal drift and prevent device performance degradation.

[0007] Furthermore, in this identification device, the high-efficiency excitation microstrip array antenna module, the electromagnetic fault detection and identification main control board, the laser control circuit main board, and the photoelectric detection control main board are vertically distributed from top to bottom.

[0008] Furthermore, in the pixel unit, the narrow-linewidth laser excitation source is located on the side of the diamond NV color center quantum magnetic sensitive element, and the diamond NV color center quantum magnetic sensitive element, narrow-band optical filter and photodetector are arranged vertically in sequence.

[0009] Furthermore, based on the spatial distribution of the diamond NV center quantum magnetic sensitive element, the opening positions are designed on the high-efficiency excitation microstrip array antenna module, and the antenna feed network and input impedance matching characteristics are designed around the opening positions to form a high-efficiency radiation distribution area for the antenna, so as to achieve high-efficiency microwave excitation of the diamond NV center quantum magnetic sensitive element. The diamond NV center quantum magnetic sensitive element in the pixel-level quantum magnetic sensing unit group is installed at the opening position.

[0010] Furthermore, the electromagnetic fault detection and identification main control board constructs a corresponding hardware neural network model architecture at the circuit level based on the spatial distribution characteristics of the target magnetic field formed by the electromagnetic fault and the neural network model, so as to realize the parameterized mapping of the neural network model to the hardware circuit. The electromagnetic fault detection and identification main control board forms an arrayed sensing plane for magnetic field information acquisition and processing, which is used to spatially discretize the target magnetic field and complete the parallel identification of the target magnetic field.

[0011] The high-efficiency excitation microstrip array antenna module is designed based on the offline training results of the neural network model built in the electromagnetic fault detection and identification main control board. The offline training results are characterized as the weight parameter mapping relationship for different target magnetic fields. The opening positions in the high-efficiency excitation microstrip array antenna module are configured with corresponding feed networks and input impedance parameters according to the weight parameter mapping relationship, so as to achieve the matching and control of the microwave excitation frequency and field strength distribution required for the Zeeman splitting process of diamond NV color centers.

[0012] The laser modulation and coupling interface unit and the photoelectric detection and control interface unit are electrically connected to the laser control circuit motherboard and the photoelectric detection control motherboard, respectively, forming a weight parameter writing and control channel. The host computer configures the photoelectric response parameters of each pixel unit and, together with the voltage follower circuit control switch unit, adjusts the output polarity of the photodetector to realize the mapping of the neural network model weight parameters in the hardware.

[0013] A non-contact ultrafast electromagnetic fault detection and identification method for electronic devices based on diamond NV centers is implemented through the following steps:

[0014] S1 parameter configuration stage: The magnetic field formed by the electromagnetic fault is used as the target magnetic field. The field distribution pattern of the target magnetic field is used as the training target for offline training of the neural network model. The weight parameters of the corresponding target magnetic field are obtained. The gain and bias parameters of each photodetector are set through the laser modulation and coupling interface unit, the photoelectric detection and control interface unit and the voltage follower circuit control switch unit to complete the initial mapping of the weight parameters of the neural network model in the hardware.

[0015] S2 Preheating Stage: After the device is powered on, it enters the preheating process to bring the device to a stable working state;

[0016] S3 Magnetic Field Sensing and Signal Acquisition Stage: Under the combined action of a narrow-linewidth laser excitation source and a high-efficiency excitation microstrip array antenna module, each diamond NV color center quantum magnetic sensitive element responds to the target magnetic field of electromagnetic faults during the actual operation of electronic equipment, and generates a fluorescence signal through optical detection of magnetic resonance effect. The fluorescence signal is filtered by a narrow-band optical filter and then converted into an electrical signal by a photodetector.

[0017] S4 Parallel Computing Stage: The photodetectors are divided into three groups according to the pre-defined neural network model connection relationship. The photodetector of the first pixel unit in all pixel-level quantum magnetic sensing unit groups is divided into the first group, the photodetector of the second pixel unit in all pixel-level quantum magnetic sensing unit groups is divided into the second group, and the photodetector of the third pixel unit in all pixel-level quantum magnetic sensing unit groups is divided into the third group. Matrix operations on the target magnetic field are performed, and the calculation formula is as follows: , , In the formula, This refers to the numbering of the pixel-level quantum magnetic sensing unit group. For the first Magnetic field responsivity of a pixel-level quantum magnetic sensing unit group For the first The weight parameters of the first pixel unit in a pixel-level quantum magnetic sensing unit group. For the first The weight parameters of the second pixel unit in a pixel-level quantum magnetic sensing unit group For the first The weight parameters of the third pixel unit in a pixel-level quantum magnetic sensing unit group The output of the first group of photodetectors is a superimposed electrical signal obtained by superimposing currents based on Kirchhoff's current law. The output of the second set of photodetectors is a superimposed electrical signal obtained by superimposing currents based on Kirchhoff's current law. The output of the third group of photodetectors is a superimposed electrical signal obtained by superimposing currents based on Kirchhoff's current law.

[0018] S5 Result Judgment Stage: Based on the three sets of superimposed electrical signals, corresponding coded outputs are constructed, and binary coded result mappings corresponding to different target magnetic fields (e.g., 100, 010, 001, 110...) are constructed, realizing ultrafast electromagnetic fault detection and identification of electronic equipment.

[0019] This identification device enables non-contact, real-time detection of electromagnetic fault signals generated during the operation of electronic equipment. By acquiring the spatial distribution information of the magnetic field in the target area, it achieves rapid extraction and identification of electromagnetic fault features. The identification utilizes a quantum magnetic sensing array to synchronously sense electromagnetic signals and performs parallel computation within a hardware neural network model, directly outputting the identification result corresponding to the electromagnetic fault type. This achieves integrated processing of electromagnetic fault detection, classification, and judgment. The device features high detection speed, high sensitivity, strong real-time performance, low power consumption, and no contact with the target, effectively improving the efficiency and accuracy of electromagnetic fault diagnosis in complex electronic equipment. Attached Figure Description

[0020] Figure 1 This is a structural diagram of the electromagnetic fault detection and identification device described in this invention.

[0021] Figure 2 This is a side view of the electromagnetic fault detection and identification device described in this invention.

[0022] Figure 3 This is a schematic diagram of the main control board structure for electromagnetic fault detection and identification as described in this invention.

[0023] Figure 4 This is a schematic diagram of the pixel-level quantum magnetic sensing unit group of the present invention. Figure 1 , that is Figure 1 A magnified view of a portion of the image.

[0024] Figure 5 This is a schematic diagram of the pixel-level quantum magnetic sensing unit group of the present invention. Figure 2 , that is Figure 3 A magnified view of a portion of the image.

[0025] Figure 6 This is a graph showing the sensitivity test results of the pixel-level quantum magnetic sensing unit group of this invention.

[0026] Figure 7 This is a graph showing the output current test results corresponding to the three types of electromagnetic faults of this invention.

[0027] In the diagram: 1-High-efficiency excitation microstrip array antenna module, 2-Electromagnetic fault detection and identification main control board, 3-Laser modulation and coupling interface unit, 4-Photoelectric detection and control interface unit, 5-Laser thermal management copper-based heat dissipation component, 6-Voltage follower circuit control switch unit, 7-Pixel-level quantum magnetic sensing unit group, 8-Laser control circuit main board, 9-Photoelectric detection control main board, 10-Narrow linewidth laser excitation source, 11-Diamond NV color center quantum magnetic sensitive element, 12-Narrow band optical filter, 13-Photodetector, 14-High-speed backplane connector. Detailed Implementation

[0028] A non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers includes a high-efficiency excitation microstrip array antenna module 1, an electromagnetic fault detection and identification main control board 2, a laser modulation and coupling interface unit 3, a photoelectric detection and control interface unit 4, a laser thermal management copper-based heat dissipation component 5, a voltage follower circuit control switch unit 6, a pixel-level quantum magnetic sensing unit group 7, a laser control circuit main board 8, and a photoelectric detection control main board 9; wherein each pixel-level quantum magnetic sensing unit group 7 includes three narrow-linewidth laser excitation sources 10, three diamond NV center quantum magnetic sensitive elements 11, three narrow-band optical filters 12, and three photodetectors 13.

[0029] The high-efficiency excitation microstrip array antenna module 1 is attached to the surface of the electromagnetic fault detection and identification main control board 2. According to the spatial distribution of the diamond NV center quantum magnetic sensitive element 11, the corresponding antenna opening position is designed on the high-efficiency excitation microstrip array antenna module 1, and the feed network and input impedance matching characteristics are designed around the opening position so that the opening position corresponds to the high-efficiency radiation distribution area of ​​the antenna, so as to achieve high-efficiency microwave excitation of the diamond NV center quantum magnetic sensitive element 11. A microwave signal input interface is provided on one side of the high-efficiency excitation microstrip array antenna module 1 for the injection of external microwave excitation signal.

[0030] The electromagnetic fault detection and identification main control board 2 integrates a pixel-level quantum magnetic sensing unit group 7 arranged in an eight×eight two-dimensional array. The pixel-level quantum magnetic sensing unit group 7 includes three pixel units distributed in a triangular space, named A, B, and C respectively. Pixel unit A is set as the first pixel unit, pixel unit B as the second pixel unit, and pixel unit C as the third pixel unit. Each pixel unit contains a diamond NV color center quantum magnetic sensing element 11, a narrow linewidth laser excitation source 10, a narrowband optical filter 12, and a photodetector 13, thereby forming a multi-channel quantum magnetic sensing array with an overall eight×eight×three structure.

[0031] The electromagnetic fault detection and identification main control board 2 is equipped with a laser modulation and coupling interface unit 3, a photoelectric detection and control interface unit 4, and sixty-four voltage follower circuit control switch units 6. The laser modulation and coupling interface unit 3 and the photoelectric detection and control interface unit 4 each include three interfaces A, B, and C corresponding to different pixel areas. The electromagnetic fault detection and identification main control board 2 is also equipped with sixteen laser thermal management copper-based heat dissipation components 5. The electromagnetic fault detection and identification main control board 2 has openings at four opposite corners and is fixed to the laser control circuit main board 8 and the photoelectric detection control main board 9 by copper pillar screws.

[0032] The laser control circuit motherboard 8 is connected to the electromagnetic fault detection and identification main control board 2 via a high-speed backplane connector 14, and uniformly controls the driving current of the narrow linewidth laser excitation source 10 in the electromagnetic fault detection and identification main control board 2.

[0033] The photoelectric detection control motherboard 9 is connected to the electromagnetic fault detection and identification main control board 2 via a high-speed backplane connector 14, and adjusts the bias and gain parameters of the photoelectric detector 13 in the electromagnetic fault detection and identification main control board 2.

[0034] The high-efficiency excitation microstrip array antenna module 1, electromagnetic fault detection and identification main control board 2, laser control circuit main board 8, and photoelectric detection control main board 9 of the non-contact electronic device based on diamond NV color center are arranged vertically from top to bottom and are fixed vertically by high-speed backplane connector 14 and copper pillar screws at four diagonal opening positions.

[0035] The pixel-level quantum magnetic sensing unit group 7 consists of three narrow-linewidth laser excitation sources 10, three diamond NV center quantum magnetic sensing elements 11, three narrow-band optical filters 12, and three photodetectors 13 arranged in a triangular space. Each group of narrow-linewidth laser excitation sources 10, diamond NV center quantum magnetic sensing elements 11, narrow-band optical filters 12, and photodetectors 13 constitutes a pixel unit. In the pixel unit, the narrow-linewidth laser excitation source 10 is located on the side of the diamond NV center quantum magnetic sensing element 11, and the diamond NV center quantum magnetic sensing element 11, narrow-band optical filters 12, and photodetectors 13 are arranged longitudinally in sequence.

[0036] The electromagnetic fault detection and identification main control board 2 is based on the spatial distribution characteristics of the target magnetic field formed by the electromagnetic fault and the neural network model. It constructs a corresponding hardware neural network model architecture at the circuit level to realize the parameterized mapping of the neural network model to the hardware circuit. The electromagnetic fault detection and identification main control board 2 forms an arrayed sensing plane for magnetic field information acquisition and processing, which is used to spatially discretize the target magnetic field and complete the parallel identification of the target magnetic field at the eight×eight pixel level.

[0037] The high-efficiency excitation microstrip array antenna module 1 is structurally designed based on the offline training results of the neural network model built in the electromagnetic fault detection and identification main control board 2. The offline training results represent the weight parameter mapping relationship for different target magnetic fields. The opening positions in the microstrip array antenna module are configured with corresponding feed networks and input impedance parameters according to the weight parameter mapping relationship, so as to achieve matching and control of the microwave excitation frequency and field strength distribution required for the Zeeman splitting process of diamond NV color centers.

[0038] The laser modulation and coupling interface unit 3 and the photoelectric detection and control interface unit 4 are electrically connected to the laser control circuit motherboard 8 and the photoelectric detection control motherboard 9, respectively, forming a weight parameter writing and control channel. The upper computer configures the photoelectric response parameters of each pixel unit and, together with the voltage follower circuit control switch unit 6, adjusts the output polarity of the photodetector 13 to realize the mapping of the neural network model weight parameters in the hardware.

[0039] The laser thermal management copper-based heat dissipation component 5 surrounds the 192 narrow-linewidth laser excitation sources 10 in the device and is used to perform thermal management on the multiple narrow-linewidth laser excitation sources 10. Through the synergistic effect of the temperature control module and the copper-based heat dissipation structure, the operating temperature of the narrow-linewidth laser excitation source 10 is stabilized within a preset range to suppress thermal drift and prevent device performance degradation.

[0040] The high-efficiency excitation microstrip array antenna module 1 has a center frequency of 2.87 GHz ± 50 MHz; the narrow-linewidth laser excitation source 10 is a GH15130C8C laser diode; the diamond NV center quantum magnetic sensitive element 11 is a 1 mm × 1 mm × 0.6 mm finished diamond NV center with an NV concentration of 1.1 ppm; the narrow-band optical filter 12 is a 600 nm-800 nm high-transmittance filter; and the photodetector 13 is a PD15-22B / TR8 silicon PIN photodetector.

[0041] Example 1

[0042] Example 1 provides a magnetic sensitivity testing method for a non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers, including the following steps:

[0043] (1) Place the identification device inside the calibrated magnetic shielding cylinder and supply it with a stable power supply. Apply a bias magnetic field through an external coil. Randomly select a pixel-level quantum magnetic sensing unit group 7 in the identification device and measure its output signal according to the formula. (Current / Frequency) Obtain the corresponding demodulated signal slope data ;

[0044] (2) Under zero-input magnetic field conditions, the output signal of the selected pixel-level quantum magnetic sensing unit group 7 is continuously acquired for a period of not less than 1 hour, and the background noise time-domain signal of the magnetic field signal is obtained as follows: The collected data is stored and then solved using Fourier transform. power spectral density ,Right now ,in The number of samples, Sampling rate; To take the absolute value;

[0045] (3) The collected data is processed according to the magnetic sensitivity calculation formula of pixel-level quantum magnetic sensing unit group 7. ,in For demodulated signal slope data, For the electron gyromagnetic ratio of the NV color center ( =28.02MHz / mT), to obtain the magnetic sensitivity of the corresponding pixel-level quantum magnetic sensing unit group 7. The results were used to evaluate the magnetic field detection performance of the identification device.

[0046] Example 2

[0047] Example 2 provides an offline training algorithm for a fault classification neural network model of a non-contact electronic device for ultrafast electromagnetic fault detection and identification based on diamond NV centers, including the following steps:

[0048] (1) Construct a fault classification neural network model with the same scale and connection method as the eight×eight×three magnetic sensor array of the identification device, and use it to perform classification and identification training on the target magnetic field pattern to obtain the weight parameters that the identification device needs to deploy.

[0049] (2) Three classic letter patterns are used as the target magnetic field patterns corresponding to electromagnetic faults, namely "Z", "T" and "V". The three classic letter patterns are encoded in eight × eight pixels by binary and used as training samples to input into the fault classification neural network model.

[0050] (3) The fault classification neural network model is trained offline based on the above training samples, and the change of the mean square error loss function with the training rounds is recorded during the training process.

[0051] (4) After completing 100 training cycles, obtain the loss function value in the convergence state and the trend of the recognition accuracy under the corresponding training rounds to evaluate the convergence performance and classification accuracy of the fault classification neural network model, and finally obtain the weight parameters and theoretical output results required for the recognition device to realize the recognition function.

[0052] Example 3

[0053] This embodiment provides a method for testing the fault identification output characteristics of a non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers, including the following steps:

[0054] (1) Build a fault detection test platform, communicate with the host computer through the laser modulation and coupling interface unit 3, adjust the laser parameters to excite each pixel unit, connect an external microwave source to the high-efficiency excitation microstrip array antenna module 1 and drive the device at a fixed frequency, and use the photoelectric detection and control interface unit 4 and voltage follower circuit to control the switch unit 6 to assign the weight parameters obtained from offline training to the recognition device, thereby maintaining the normal operation of the recognition device.

[0055] (2) Construct an eight×eight fault simulation circuit board. By making a one-to-one correspondence between the coil of the fault simulation circuit board and the pixel-level quantum magnetic sensing unit group 7 in spatial position, and energizing the coil in a specific area, a magnetic field pattern with different spatial distribution (magnetic field strength of 2Gs) is generated to simulate the three types of electromagnetic fault targets trained offline.

[0056] (3) During the fault simulation process, the output current signals of the three outputs of the identification device are synchronously acquired and recorded using an oscilloscope;

[0057] (4) Compare the output current of each channel under different fault conditions, analyze the theoretical output channel characteristics of the corresponding fault type under the offline training results, and obtain the correspondence of the final output results;

[0058] (5) Based on the output current distribution results, evaluate the current differentiation and correspondence between different fault types, and verify the identification ability of the identification device to identify multiple types of fault signals.

[0059] Figure 6 The image shows the sensitivity test results of the pixel-level quantum magnetic sensing unit group 7 in Example 1. Specifically, within the frequency range of 1–10 Hz, the average noise spectral density of the pixel unit is approximately 1.1 nT / Hz. 1 / 2 This indicates that the identification device has good application potential in weak magnetic field detection.

[0060] Figure 7 The figure shows the output current test results for the three types of electromagnetic faults in Example 2. The output currents corresponding to different fault types exhibit clear separation characteristics between channels, and the average deviation between the output current of the identification device and the fitted curve is approximately 23.6 μA, indicating that the identification device has good fault identification capability.

[0061] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers, characterized in that: It includes a high-efficiency excitation microstrip array antenna module (1), an electromagnetic fault detection and identification main control board (2), a laser modulation and coupling interface unit (3), a photoelectric detection and control interface unit (4), a voltage follower circuit control switch unit (6), a pixel-level quantum magnetic sensing unit group (7), a laser control circuit main board (8), and a photoelectric detection control main board (9). The high-efficiency excitation microstrip array antenna module (1) is attached to the surface of the electromagnetic fault detection and identification main control board (2). A microwave signal input interface is provided on one side of the high-efficiency excitation microstrip array antenna module (1) for injecting external microwave excitation signals. The electromagnetic fault detection and identification main control board (2) integrates a pixel-level quantum magnetic sensing unit group (7) arranged in a two-dimensional array. The pixel-level quantum magnetic sensing unit group (7) includes three pixel units distributed in a triangular space. Each pixel unit includes a diamond NV color center quantum magnetic sensing element (11), a narrow linewidth laser excitation source (10), a narrow band optical filter (12), and a photodetector (13). The high-efficiency excitation microstrip array antenna module (1) transmits microwave signals to each pixel-level quantum magnetic sensing unit group (7) through the constructed feeding network. The electromagnetic fault detection and identification main control board (2) is also equipped with a laser modulation and coupling interface unit (3), a photoelectric detection and control interface unit (4), and a voltage follower circuit control switch unit (6). The electromagnetic fault detection and identification main control board (2) is fixed with the laser control circuit main board (8) and the photoelectric detection control main board (9). The laser modulation and coupling interface unit (3) and the photoelectric detection and control interface unit (4) are electrically connected to the laser control circuit main board (8) and the photoelectric detection control main board (9), respectively. The laser control circuit main board (8) uniformly controls the driving current of the narrow linewidth laser excitation source (10) in the electromagnetic fault detection and identification main control board (2). The photoelectric detection control main board (9) controls the bias and gain parameters of the photodetector (13) in the electromagnetic fault detection and identification main control board (2).

2. The non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers according to claim 1, characterized in that: It also includes a laser thermal management copper-based heat dissipation component (5), and the laser thermal management copper-based heat dissipation component (5) is distributed on the electromagnetic fault detection and identification main control board (2); the laser thermal management copper-based heat dissipation component (5) surrounds the narrow linewidth laser excitation source (10) in the pixel-level quantum magnetic sensing unit group (7).

3. A non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers according to claim 1 or 2, characterized in that: The high-efficiency excitation microstrip array antenna module (1), the electromagnetic fault detection and identification main control board (2), the laser control circuit main board (8) and the photoelectric detection control main board (9) are distributed vertically from top to bottom.

4. The non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers according to claim 3, characterized in that: In the pixel unit, the narrow linewidth laser excitation source (10) is located on the side of the diamond NV color center quantum magnetic sensitive element (11), and the diamond NV color center quantum magnetic sensitive element (11), narrow band optical filter (12) and photodetector (13) are arranged vertically in sequence.

5. The non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers according to claim 3, characterized in that: Based on the spatial distribution of the diamond NV center quantum magnetic sensitive element (11), the opening position is designed on the high-efficiency excitation microstrip array antenna module (1), and the feed network and input impedance matching characteristics are designed around the opening position so that the opening position forms an efficient radiation distribution area of ​​the antenna, so as to achieve efficient microwave excitation of the diamond NV center quantum magnetic sensitive element (11). The diamond NV center quantum magnetic sensitive element (11) in the pixel-level quantum magnetic sensing unit group (7) is installed at the opening position.

6. The non-contact ultrafast electromagnetic fault detection and identification device for electronic devices based on diamond NV centers according to claim 5, characterized in that: The electromagnetic fault detection and identification main control board (2) is based on the spatial distribution characteristics of the target magnetic field formed by the electromagnetic fault and the neural network model. It constructs a corresponding hardware neural network model architecture at the circuit level to realize the parameterized mapping of the neural network model to the hardware circuit. The electromagnetic fault detection and identification main control board (2) forms an arrayed sensing plane for magnetic field information acquisition and processing, which is used to spatially discretize the target magnetic field and complete the parallel identification of the target magnetic field. The high-efficiency excitation microstrip array antenna module (1) is designed based on the offline training results of the neural network model built in the electromagnetic fault detection and identification main control board (2). The offline training results are characterized as the weight parameter mapping relationship for different target magnetic fields. The opening positions in the high-efficiency excitation microstrip array antenna module (1) are configured with corresponding feed networks and input impedance parameters according to the weight parameter mapping relationship, so as to achieve the matching and control of the microwave excitation frequency and field strength distribution required for the Zeeman splitting process of diamond NV color center. The laser modulation and coupling interface unit (3) and the photoelectric detection control interface unit (4) are electrically connected to the laser control circuit motherboard (8) and the photoelectric detection control motherboard (9) respectively, forming a weight parameter writing control channel. The upper computer configures the photoelectric response parameters of each pixel unit, and cooperates with the voltage follower circuit control switch unit (6) to adjust the output polarity of the photodetector (13), thereby realizing the mapping of the neural network model weight parameters in the hardware.

7. A non-contact ultrafast electromagnetic fault detection and identification method for electronic devices based on diamond NV centers, characterized in that: This method is implemented using the following steps: S1 parameter configuration stage: The magnetic field formed by the electromagnetic fault is used as the target magnetic field. The field distribution pattern of the target magnetic field is used as the training target for offline training of the neural network model. The weight parameters of the corresponding target magnetic field are obtained. The gain and bias parameters of each photodetector (13) are set through the laser modulation and coupling interface unit (3), the photoelectric detection control interface unit (4) and the voltage follower circuit control switch unit (6) to complete the initial mapping of the weight parameters of the neural network model in the hardware. S2 Preheating Stage: After the device is powered on, it enters the preheating process to bring the device to a stable working state; S3 Magnetic Field Sensing and Signal Acquisition Stage: Under the combined action of the narrow linewidth laser excitation source (10) and the high-efficiency excitation microstrip array antenna module (1), each diamond NV color center quantum magnetic sensitive element (11) responds to the target magnetic field of electromagnetic faults during the actual operation of electronic equipment, and generates a fluorescence signal through optical detection of magnetic resonance effect. The fluorescence signal is filtered by the narrow band optical filter (12) and then converted into an electrical signal by the photodetector (13). S4 Parallel Computing Stage: The photodetector (13) is divided into three groups according to the connection relationship of the preset neural network model. The photodetector of the first pixel unit in all pixel-level quantum magnetic sensing unit groups (7) is divided into the first group, the photodetector of the second pixel unit in all pixel-level quantum magnetic sensing unit groups (7) is divided into the second group, and the photodetector of the third pixel unit in all pixel-level quantum magnetic sensing unit groups (7) is divided into the third group. The matrix operation of the target magnetic field is realized, and the operation formula is as follows: , , In the formula, This refers to the numbering of the pixel-level quantum magnetic sensing unit group. For the first Magnetic field responsivity of a pixel-level quantum magnetic sensing unit group For the first The weight parameters of the first pixel unit in a pixel-level quantum magnetic sensing unit group. For the first The weight parameters of the second pixel unit in a pixel-level quantum magnetic sensing unit group For the first The weight parameters of the third pixel unit in a pixel-level quantum magnetic sensing unit group The output of the first group of photodetectors is a superimposed electrical signal obtained by superimposing currents based on Kirchhoff's current law. The output of the second set of photodetectors is a superimposed electrical signal obtained by superimposing currents based on Kirchhoff's current law. The output of the third group of photodetectors is a superimposed electrical signal obtained by superimposing currents based on Kirchhoff's current law. S5 Result Judgment Stage: Based on the three sets of superimposed electrical signals, corresponding coded outputs are constructed, and binary coded result mappings corresponding to different target magnetic fields are built, realizing ultrafast electromagnetic fault detection and identification of electronic equipment.

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