Quantum sensor multi-detector array fault diagnosis method and device
By combining the CatBoost model with an improved sparrow search algorithm, the problem of fault diagnosis in multi-detector arrays of quantum sensors was solved, achieving high-precision and real-time fault diagnosis, and improving the stability and reliability of the system.
Patent Information
- Application Number
- CN202510810970.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-17
- Publication Date
- 2025-11-11
AI Technical Summary
Existing technologies are insufficient for effectively diagnosing faults in multi-detector arrays of quantum sensors, especially in complex signals where it is difficult to extract fault features, resulting in poor diagnostic performance.
The CatBoost model is used for training, and an improved sparrow search algorithm combining genetic algorithm and particle swarm optimization algorithm is used for hyperparameter optimization. A fault diagnosis dataset is constructed through signal preprocessing and feature extraction to achieve high-precision fault diagnosis.
It achieves high-precision fault diagnosis, improves the operational stability and reliability of quantum sensor multi-detector arrays, meets the high efficiency requirements of real-time status assessment, and reduces the operational risks caused by equipment failure.
Smart Images
Figure CN120930819A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of quantum technology, and in particular to a method and apparatus for fault diagnosis of a multi-detector array of quantum sensors. Background Technology
[0002] With the rapid development of quantum technology, quantum sensors, due to their high sensitivity, high precision, and broad-spectrum response characteristics, have been widely used in quantum communication, quantum computing, and precision measurement. Especially in multi-detector array systems, quantum sensors, through collaborative work, can achieve high-resolution signal acquisition and processing in complex environments. However, current multi-detector arrays of quantum sensors face the challenge of difficult fault diagnosis in practical operation. Specifically, the signals generated by multi-detector arrays of quantum sensors are highly complex, containing nonlinear, multidimensional characteristics, and noise interference. Traditional fault diagnosis methods for multi-detector arrays of quantum sensors struggle to effectively extract fault features, resulting in poor diagnostic performance. Summary of the Invention
[0003] In a first aspect, embodiments of this disclosure provide a method for fault diagnosis of a quantum sensor multi-detector array, the method comprising:
[0004] Acquire raw signal data of a quantum sensor multi-detector array with labeled fault types;
[0005] Preprocess the raw signal data to construct a fault diagnosis dataset;
[0006] The CatBoost model was trained using a fault diagnosis dataset, and during the training process, an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm was used to optimize the hyperparameters of the CatBoost model.
[0007] Fault diagnosis of quantum sensor multi-detector arrays was performed using a trained CatBoost model.
[0008] In some possible implementations of the first aspect, the raw signal data of the quantum sensor multi-detector array labeled with fault types is obtained, including:
[0009] For each detection unit in the quantum sensor multi-detector array, the signal of the detection unit is acquired at a fixed sampling rate using a high-frequency sampling device;
[0010] The signal data collected from each detection unit are summarized and analyzed to generate raw signal data of the quantum sensor multi-detector array labeled with fault types.
[0011] In some possible implementations of the first aspect, the raw signal data is preprocessed to construct a fault diagnosis dataset, including:
[0012] Signal denoising and feature extraction are performed on the original signal data to obtain signal features;
[0013] Using signal features as sample features and their corresponding fault types as sample labels, samples are constructed, and an initial dataset is built based on these samples.
[0014] The initial dataset is augmented with a small number of fault type samples to obtain a fault diagnosis dataset.
[0015] In some possible implementations of the first aspect, the CatBoost model is trained using a fault diagnosis dataset, and during training, an improved sparrow search algorithm that integrates genetic algorithms and particle swarm optimization is used to optimize the hyperparameters of the CatBoost model, including:
[0016] The fault diagnosis dataset was divided into a training set and a validation set according to a preset ratio. The CatBoost model was trained using the training set, and the performance of the CatBoost model was evaluated using K-fold cross-validation. The hyperparameters of the CatBoost model were optimized using an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm.
[0017] Among the possible implementations of the first aspect, an improved sparrow search algorithm that integrates genetic algorithms and particle swarm optimization algorithms includes:
[0018] For explorers, the crossover operation of the genetic algorithm is combined to enhance the explorer's global search capability in the hyperparameter space;
[0019] For followers, a speed update mechanism of particle swarm optimization algorithm is introduced to accelerate the followers toward a better solution;
[0020] For the vigilant, the random walk characteristic of the vigilant is preserved, and the mutation operation of the genetic algorithm is used to avoid getting trapped in local optima;
[0021] The optimization objective is set to maximize the fault diagnosis accuracy of the CatBoost model on the validation set.
[0022] Among the possible implementations of the first aspect, genetic algorithms include:
[0023] Selection: The roulette wheel selection method is used to select high-quality individuals based on their fitness. Here, an individual is a combination of hyperparameters of the CatBoost model, and the fitness of an individual is the fault diagnosis accuracy of the CatBoost model on the validation set when the individual is selected.
[0024] Crossover: A single-point crossover occurs with a crossover probability of 0.8, generating a new individual;
[0025] Mutation: Individuals are randomly perturbed with a mutation probability of 0.1 to increase population diversity.
[0026] Among some possible implementations of the first aspect, particle swarm optimization algorithms include:
[0027] The position and velocity of an individual are updated based on its historical best position and global best position, where the individual is a combination of hyperparameters of the CatBoost model.
[0028] Among some possible implementations of the first aspect, the method also includes:
[0029] Pruning and quantization are performed on the trained CatBoost model.
[0030] Among some possible implementations of the first aspect, the method also includes:
[0031] The fault diagnosis results of the quantum sensor multi-detector array output by the trained CatBoost model are compared with the actual operating status of the quantum sensor multi-detector array. False alarms and / or missed alarms are recorded, and the parameters of the CatBoost model are updated accordingly.
[0032] Secondly, embodiments of this disclosure provide a fault diagnosis device for a quantum sensor multi-detector array, the device comprising:
[0033] The acquisition module is used to acquire raw signal data of the quantum sensor multi-detector array with labeled fault types;
[0034] The module is used to preprocess the raw signal data and build a fault diagnosis dataset;
[0035] The training module is used to train the CatBoost model using a fault diagnosis dataset, and during the training process, an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm is used to optimize the hyperparameters of the CatBoost model.
[0036] The diagnostic module is used to perform fault diagnosis on the quantum sensor multi-detector array using a trained CatBoost model.
[0037] Thirdly, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.
[0038] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.
[0039] Compared with the prior art, this disclosure has at least the following technical effects:
[0040] High-precision fault diagnosis: This disclosure adopts the CatBoost model, which makes full use of its powerful classification ability and modeling advantages for complex nonlinear features. It can effectively extract fault features from the signals of quantum sensor multi-detector arrays and achieve high-precision fault diagnosis even when samples are scarce.
[0041] Optimizing hyperparameter search efficiency: This disclosure improves the sparrow search algorithm by integrating genetic algorithm and particle swarm optimization, which takes into account both global search capability and fast convergence characteristics, avoids getting trapped in local optima, and greatly improves the hyperparameter optimization efficiency of CatBoost model.
[0042] Adapting to Sparse Sample Scenarios: To address the challenge of scarce fault samples in multi-detector arrays of quantum sensors, this disclosure effectively alleviates the overfitting problem and enhances the model's generalization ability on small sample datasets by leveraging the robustness of the CatBoost model and the precise hyperparameter tuning of the improved Sparrow Search algorithm.
[0043] High real-time performance: The improved sparrow search algorithm, combined with the fast convergence characteristics of particle swarm optimization, significantly reduces the computational complexity of hyperparameter optimization, enabling this method to meet the high efficiency requirements of real-time state assessment and fault diagnosis of quantum sensor multi-detector arrays.
[0044] Improved stability and reliability: This disclosure improves the operational stability and reliability of quantum sensor multi-detector arrays by accurately detecting faults and assessing their condition, promptly identifying potential faults and providing diagnostic results, and reducing the operational risks caused by equipment failures.
[0045] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description
[0046] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:
[0047] Figure 1A flowchart of a fault diagnosis method for a quantum sensor multi-detector array provided by an embodiment of the present disclosure is shown;
[0048] Figure 2 A structural diagram of a fault diagnosis device for a quantum sensor multi-detector array provided in an embodiment of this disclosure is shown.
[0049] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation
[0050] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0051] Furthermore, the term "and / or" in this article 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 existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0052] To address the problems in the background art, embodiments of this disclosure provide a method, apparatus, device, and storage medium for fault diagnosis of a quantum sensor multi-detector array. Specifically, the method involves acquiring raw signal data of the quantum sensor multi-detector array labeled with fault types; preprocessing the raw signal data to construct a fault diagnosis dataset; training a CatBoost model using the fault diagnosis dataset, and optimizing the hyperparameters of the CatBoost model during training using an improved sparrow search algorithm that integrates genetic algorithms and particle swarm optimization algorithms; and using the trained CatBoost model to perform fault diagnosis on the quantum sensor multi-detector array. In this way, combining CatBoost model training with hyperparameter optimization using the improved sparrow search algorithm can effectively extract fault features, achieving high-precision, real-time fault diagnosis, thereby improving the fault diagnosis effect.
[0053] The following detailed description, with reference to the accompanying drawings, illustrates a method, apparatus, device, and storage medium for fault diagnosis of a quantum sensor multi-detector array provided by the present disclosure through specific embodiments.
[0054] Figure 1A flowchart illustrating a fault diagnosis method for a quantum sensor multi-detector array provided by an embodiment of this disclosure is shown, such as... Figure 1 As shown, method 100 may include the following steps:
[0055] S110: Acquire raw signal data of the quantum sensor multi-detector array with labeled fault types.
[0056] In some embodiments, the quantum sensor multi-detector array includes multiple detection units (such as superconducting quantum interference devices or optical quantum sensors). For each detection unit in the quantum sensor multi-detector array, the signal of the detection unit can be acquired by a high-frequency sampling device at a fixed sampling rate (such as 10KHz). Then, the signal data of each detection unit is summarized and analyzed to generate the original signal data of the quantum sensor multi-detector array labeled with the fault type (such as normal, sensor failure, signal drift).
[0057] The original signal data may include, but is not limited to, multi-channel time-series signals (such as voltage, current, and phase signals), frequency characteristics (such as the spectral characteristics after Fourier transform), and noise data.
[0058] S120 preprocesses the raw signal data to construct a fault diagnosis dataset.
[0059] In some embodiments, signal denoising and feature extraction can be performed on the original signal data to obtain signal features. Then, the signal features are used as sample features, and their corresponding fault types are used as sample labels to construct samples. An initial dataset is constructed based on this. To ensure the class balance of the dataset, a few fault type samples are augmented on the initial dataset to obtain a fault diagnosis dataset.
[0060] Signal denoising can be achieved through wavelet transform algorithm, specifically using the Daubechies wavelet basis (db4), with a decomposition level of 5 layers. High-frequency noise is filtered out by soft thresholding while retaining the main features of the signal.
[0061] Feature extraction mainly includes extracting time-domain features (such as mean, variance, and peak value), frequency-domain features (such as dominant frequency and spectral energy), and statistical features (such as skewness and kurtosis). To reduce data dimensionality, Principal Component Analysis (PCA) is used to map high-dimensional features to a low-dimensional space, retaining 95% of the variance contribution rate, thus obtaining signal features suitable for the CatBoost model.
[0062] Sample augmentation primarily addresses the scarcity of fault samples in multi-detector arrays of quantum sensors. It employs the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to generate synthetic fault samples. Specifically, based on the K-nearest neighbor algorithm (K=5), new samples are generated by interpolation of a few fault types (such as sensor failure or signal drift), ensuring class balance in the dataset and thus improving the model's training performance in scenarios with limited sample sizes.
[0063] S130 uses a fault diagnosis dataset to train the CatBoost model, and during the training process, an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm is used to optimize the hyperparameters of the CatBoost model.
[0064] In some embodiments, the fault diagnosis dataset can be divided into a training set and a validation set according to a preset ratio (e.g., 8:2). The CatBoost model is trained using the training set, and the performance of the CatBoost model is evaluated using K-fold (e.g., 5-fold) cross-validation. An improved sparrow search algorithm that integrates genetic algorithms and particle swarm optimization is used to optimize the hyperparameters of the CatBoost model. In each iteration, the optimized hyperparameter combination is used to update the model parameters and minimize the model loss.
[0065] It is worth noting that the CatBoost model here is pre-built based on the CatBoost algorithm, which is a machine learning algorithm based on Gradient Boosting Decision Tree (GBDT). It enhances the model's ability to classify complex signals through automatic encoding of class features and ordered boosting, and is particularly suitable for processing high-dimensional and sparse sensor signal data.
[0066] When constructing the CatBoost model, its input is set as signal features, and its output is set as the fault category and its corresponding probability. The hyperparameters of the CatBoost model include the learning rate, tree depth, number of iterations, and L2 regularization coefficient (l2_leaf_reg). Here, the initial hyperparameters are set as follows: learning rate = 0.1, depth = 6, iterations = 1000, l2_leaf_reg = 3.0, and subsequently adjusted through optimization strategies. Optionally, the multiclass cross-entropy loss function is used as the objective function for optimizing the CatBoost model parameters, and its formula is shown below:
[0067]
[0068] In the formula, L represents the loss value, N represents the number of samples, M represents the number of fault categories, and y ij Indicates a label, p ij This represents the predicted probability of the fault category.
[0069] For example, the above-mentioned use of an improved sparrow search algorithm that integrates genetic algorithms and particle swarm optimization algorithms to optimize the hyperparameters of the CatBoost model may include:
[0070] Multiple sets of CatBoost model hyperparameter combinations are randomly generated. Each set of CatBoost model hyperparameter combinations is used as an individual to construct the initial population. The population size is set to 50, and the hyperparameter range is set to:
[0071] learning_rate∈[0.01,0.5];
[0072] depth∈[4,10];
[0073] iterations∈[500,2000];
[0074] l2_leaf_reg∈[1,10].
[0075] Genetic algorithms are used to enhance the global search capability of the population. Specific operations include:
[0076] Selection: The roulette wheel selection method is used to select high-quality individuals based on their fitness (i.e., the fault diagnosis accuracy of the CatBoost model on the validation set when the individual is adopted);
[0077] Crossover: A single-point crossover occurs with a crossover probability of 0.8, generating a new individual;
[0078] Mutation: Individuals are randomly perturbed with a mutation probability of 0.1 to increase population diversity.
[0079] Particle Swarm Optimization (PSO) is introduced to accelerate the population's iteration towards a high-quality solution through its fast convergence characteristic. Specifically, the individual's position and velocity are updated based on its historical best position (pbest) and global best position (gbest). This process can be represented by the following formula:
[0080] v i (t+1)=w·v i (t)+c1·r1·(pbest i -x i (t))+c2·r2·(gbest-x i(t))
[0081] In the formula, v i (t) represents the velocity vector of the i-th individual, x i (t) represents the current position vector of the i-th individual, w = 0.5 is the inertia weight, c1 = c2 = 1.5 is the learning factor, and r1, r2 ∈ [0, 1] are random numbers.
[0082] By incorporating the explorer, follower, and watchdog mechanism of the sparrow search algorithm with the global search capability of the genetic algorithm and the rapid iterative characteristics of the particle swarm optimization algorithm, a hybrid optimization strategy is formed to improve the optimization efficiency and accuracy of the hyperparameters of the CatBoost model, as detailed below:
[0083] For explorers, the crossover operation of the genetic algorithm is combined to enhance the explorer's global search capability in the hyperparameter space;
[0084] For followers, a speed update mechanism of particle swarm optimization algorithm is introduced to accelerate the followers toward a better solution;
[0085] For the vigilant, the random walk characteristic of the vigilant is preserved, and the mutation operation of the genetic algorithm is used to avoid getting trapped in local optima;
[0086] The optimization objective is set to maximize the fault diagnosis accuracy of the CatBoost model on the validation set. The termination condition for hyperparameter optimization is set to the number of iterations reaching 100 or the accuracy reaching the convergence condition (accuracy reaching 95% or accuracy change less than 0.001).
[0087] As an example, the optimal combination of hyperparameters chosen here is learning_rate = 0.08, depth = 7, iterations = 1200, l2_leaf_reg = 2.5.
[0088] S140 uses a trained CatBoost model to perform fault diagnosis on a quantum sensor multi-detector array.
[0089] In some embodiments, the trained CatBoost model can be deployed in a real-time monitoring system for a quantum sensor multi-detector array (such as NVIDIA Jetson or a GPU server). The system continuously collects signal data from the quantum sensor multi-detector array, preprocesses it, and inputs it into the trained CatBoost model, outputting the fault type and probability (such as normal, sensor failure, signal interference).
[0090] It is worth noting that, in addition to S110-S140, method 100 may also include: pruning and quantizing the trained CatBoost model (such as INT8 quantization) to reduce the inference time of the CatBoost model and ensure that the time for a single diagnosis is less than 100 milliseconds, thus meeting the real-time requirements.
[0091] Meanwhile, method 100 may also include: displaying the fault diagnosis results in a visual form, including a fault probability distribution map (showing the confidence level of various faults), a status assessment report (such as "Sensor 1 failed, confidence level 0.92"), and time series outlier annotations. Furthermore, the fault diagnosis results can also be transmitted to the monitoring system via a human-machine interface or an application programming interface (API).
[0092] Furthermore, method 100 may also include: comparing the fault diagnosis results of the quantum sensor multi-detector array output by the trained CatBoost model with the actual operating state of the quantum sensor multi-detector array, recording false alarms and / or missed alarms, and updating the parameters of the CatBoost model accordingly to continuously improve the accuracy of fault diagnosis.
[0093] In summary, this disclosure achieves at least the following technical effects:
[0094] High-precision fault diagnosis: This disclosure adopts the CatBoost model, which makes full use of its powerful classification ability and modeling advantages for complex nonlinear features. It can effectively extract fault features from the signals of quantum sensor multi-detector arrays and achieve high-precision fault diagnosis even when samples are scarce.
[0095] Optimizing hyperparameter search efficiency: This disclosure improves the sparrow search algorithm by integrating genetic algorithm and particle swarm optimization, which takes into account both global search capability and fast convergence characteristics, avoids getting trapped in local optima, and greatly improves the hyperparameter optimization efficiency of CatBoost model.
[0096] Adapting to Sparse Sample Scenarios: To address the challenge of scarce fault samples in multi-detector arrays of quantum sensors, this disclosure effectively alleviates the overfitting problem and enhances the model's generalization ability on small sample datasets by leveraging the robustness of the CatBoost model and the precise hyperparameter tuning of the improved Sparrow Search algorithm.
[0097] High real-time performance: The improved sparrow search algorithm, combined with the fast convergence characteristics of particle swarm optimization, significantly reduces the computational complexity of hyperparameter optimization, enabling this method to meet the high efficiency requirements of real-time state assessment and fault diagnosis of quantum sensor multi-detector arrays.
[0098] Improved stability and reliability: This disclosure improves the operational stability and reliability of quantum sensor multi-detector arrays by accurately detecting faults and assessing their condition, promptly identifying potential faults and providing diagnostic results, and reducing the operational risks caused by equipment failures.
[0099] The method 100 will be described in detail below with reference to a specific embodiment, as shown in the following figure:
[0100] This method is applied to a multi-detector array of quantum sensors containing 16 detector units (superconducting quantum interference devices)100. The signal sampling rate is set to 10 kHz, and the fault types include normal, sensor failure, and signal drift. The acquired raw signal data with labeled fault types are preprocessed to generate a fault diagnosis dataset containing 5000 normal samples and 500 fault samples, where each sample has a 20-dimensional feature dimension. The CatBoost model is trained using the fault diagnosis dataset, and an improved sparrow search algorithm combining genetic algorithm and particle swarm optimization is used to optimize the hyperparameters of the CatBoost model during training. The final optimal hyperparameters are: learning_rate = 0.08, depth = 7, iterations = 1200, l2_leaf_reg = 2.5. The trained CatBoost model achieves a classification accuracy of 96.5% on the validation set, which is superior to the traditional XGBoost model (accuracy 92.3%). Real-time diagnosis takes approximately 80 milliseconds, meeting the system requirements. The trained CatBoost model was then used to diagnose faults in the quantum sensor multi-detector array.
[0101] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.
[0102] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.
[0103] Figure 2 A structural diagram of a fault diagnosis device for a quantum sensor multi-detector array provided in an embodiment of this disclosure is shown, as follows: Figure 2 As shown, the device 200 may include:
[0104] The acquisition module 210 is used to acquire raw signal data of the quantum sensor multi-detector array with labeled fault types.
[0105] Module 220 is used to preprocess the raw signal data and build a fault diagnosis dataset.
[0106] Training module 230 is used to train the CatBoost model using a fault diagnosis dataset and to optimize the hyperparameters of the CatBoost model during the training process using an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm.
[0107] The diagnostic module 240 is used to perform fault diagnosis on the quantum sensor multi-detector array using a trained CatBoost model.
[0108] Understandable, Figure 2 Each module / unit in the illustrated device 200 has the ability to implement Figure 1 The functions of each step in method 100 shown, and their corresponding technical effects, will not be elaborated here for the sake of brevity.
[0109] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0110] like Figure 3 As shown, the electronic device 300 may include a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.
[0111] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0112] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).
[0113] The various embodiments described above can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0114] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0115] In the context of this disclosure, a computer-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0116] It should be noted that this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by the embodiments of this disclosure in executing the method. For the sake of brevity, these will not be elaborated here.
[0117] In addition, this disclosure also provides a computer program product including a computer program that implements method 100 when executed by a processor.
[0118] To provide interaction with a user, the embodiments described above can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0119] The embodiments described above can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0120] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.
[0121] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.
[0122] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A method for fault diagnosis of a multi-detector array in a quantum sensor, characterized in that, The method includes: Acquire raw signal data of a quantum sensor multi-detector array with labeled fault types; Preprocess the raw signal data to construct a fault diagnosis dataset; The CatBoost model was trained using a fault diagnosis dataset, and during the training process, an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm was used to optimize the hyperparameters of the CatBoost model. Fault diagnosis of quantum sensor multi-detector arrays was performed using a trained CatBoost model.
2. The method according to claim 1, characterized in that, The acquisition of raw signal data labeled with fault types from the quantum sensor multi-detector array includes: For each detection unit in the quantum sensor multi-detector array, the signal of the detection unit is acquired at a fixed sampling rate using a high-frequency sampling device; The signal data collected from each detection unit are summarized and analyzed to generate raw signal data of the quantum sensor multi-detector array labeled with fault types.
3. The method according to claim 1, characterized in that, The preprocessing of the raw signal data to construct the fault diagnosis dataset includes: Signal denoising and feature extraction are performed on the original signal data to obtain signal features; Using signal features as sample features and their corresponding fault types as sample labels, samples are constructed, and an initial dataset is built based on these samples. The initial dataset is augmented with a small number of fault type samples to obtain a fault diagnosis dataset.
4. The method according to claim 1, characterized in that, The method involves training the CatBoost model using a fault diagnosis dataset, and optimizing its hyperparameters during training using an improved sparrow search algorithm that integrates genetic algorithms and particle swarm optimization algorithms. This includes: The fault diagnosis dataset was divided into a training set and a validation set according to a preset ratio. The CatBoost model was trained using the training set, and the performance of the CatBoost model was evaluated using K-fold cross-validation. The hyperparameters of the CatBoost model were optimized using an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm.
5. The method according to claim 4, characterized in that, The improved sparrow search algorithm, which integrates genetic algorithm and particle swarm optimization algorithm, includes: For explorers, the crossover operation of the genetic algorithm is combined to enhance the explorer's global search capability in the hyperparameter space; For followers, a speed update mechanism of particle swarm optimization algorithm is introduced to accelerate the followers toward a better solution; For the vigilant, the random walk characteristic of the vigilant is preserved, and the mutation operation of the genetic algorithm is used to avoid getting trapped in local optima; The optimization objective is set to maximize the fault diagnosis accuracy of the CatBoost model on the validation set.
6. The method according to claim 5, characterized in that, The genetic algorithm includes: Selection: The roulette wheel selection method is used to select high-quality individuals based on their fitness. Here, an individual is a combination of hyperparameters of the CatBoost model, and the fitness of an individual is the fault diagnosis accuracy of the CatBoost model on the validation set when the individual is selected. Crossover: A single-point crossover occurs with a crossover probability of 0.8, generating a new individual; Mutation: Randomly perturb individuals with a mutation probability of 0.1 to increase population diversity.
7. The method according to claim 5, characterized in that, The particle swarm optimization algorithm includes: The position and velocity of an individual are updated based on its historical best position and global best position, where the individual is a combination of hyperparameters of the CatBoost model.
8. The method according to claim 1, characterized in that, The method further includes: Pruning and quantization are performed on the trained CatBoost model.
9. The method according to claim 1, characterized in that, The method further includes: The fault diagnosis results of the quantum sensor multi-detector array output by the trained CatBoost model are compared with the actual operating status of the quantum sensor multi-detector array. False alarms and / or missed alarms are recorded, and the parameters of the CatBoost model are updated accordingly.
10. A fault diagnosis device for a quantum sensor multi-detector array, characterized in that, The device includes: The acquisition module is used to acquire raw signal data of the quantum sensor multi-detector array with labeled fault types; The module is used to preprocess the raw signal data and build a fault diagnosis dataset; The training module is used to train the CatBoost model using a fault diagnosis dataset, and during the training process, an improved sparrow search algorithm that combines genetic algorithm and particle swarm optimization algorithm is used to optimize the hyperparameters of the CatBoost model. The diagnostic module is used to perform fault diagnosis on the quantum sensor multi-detector array using a trained CatBoost model.