Equipment monitoring method and system based on image recognition

Abstract

The invention discloses an equipment monitoring method and system based on image recognition, and relates to the technical field of image recognition, and the method comprises the steps: obtaining real-time image data, carrying out the preprocessing of the real-time image data, and integrating a multi-level feature vector into a comprehensive feature vector through feature fusion; inputting the comprehensive feature vector into a classification model which is constructed based on depth features and environment dynamic adjustment and is enhanced in environment adaptability and generalization ability, monitoring the equipment state in real time by applying the classification model, calculating the abnormal probability of the equipment, and evaluating the state of the current equipment based on the probability that the equipment is in the abnormal state. And generating an equipment state monitoring report. According to the method, the real-time images are collected and processed, the monitoring efficiency is improved, features are extracted through the deep learning technology, the recognition capability is enhanced, after feature fusion, the model sensitivity is improved, anomaly detection is more accurate, the monitoring accuracy is ensured through a self-adaptive classifier, the equipment state is analyzed in real time, the health degree is evaluated, data support is provided for maintenance, and faults are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to an equipment monitoring method and system based on image recognition. Background Art

[0002] Traditional equipment monitoring methods rely on sensors to directly measure the physical parameters of equipment, such as vibration, temperature, and pressure. The rise of image recognition technology has provided a new perspective for equipment monitoring. Image recognition technology combined with deep learning algorithms, especially deep convolutional neural networks, can extract features from complex image data and realize non-contact monitoring of equipment operating status. This technological innovation not only broadens the scope of monitoring, but also improves the accuracy and efficiency of monitoring.

[0003] Most of the existing equipment monitoring methods based on image recognition ignore the influence of environmental factors, which leads to poor generalization and adaptability of the monitoring system. In different working environments, the same equipment may show different visual characteristics. If this is not taken into account, it will directly affect the accuracy of the monitoring results. Secondly, traditional monitoring systems often focus on feature extraction at a single level and fail to fully utilize the multi-level information in the image, which limits the system's ability to identify complex equipment status. Furthermore, existing technologies have limitations in real-time monitoring and anomaly detection, especially when dealing with dynamically changing environments and equipment parameters. It is difficult to respond quickly and accurately evaluate equipment status, resulting in monitoring delays and high false alarm rates. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an equipment monitoring method and system based on image recognition to solve the problems of poor environmental adaptability, single feature extraction and limited real-time monitoring.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an equipment monitoring method based on image recognition, which includes acquiring real-time image data and performing preprocessing; using a pre-trained deep convolutional neural network to perform deep feature extraction on the preprocessed image; integrating multi-level feature vectors into a comprehensive feature vector through feature fusion; inputting the comprehensive feature vector into a classification model with enhanced environmental adaptability and generalization capability, which is constructed based on deep features and dynamic adaptation to the environment; applying the classification model to monitor the equipment status in real time and calculate the abnormal probability of the equipment; based on the probability that the equipment is in an abnormal state, evaluating the current status of the equipment and generating an equipment status monitoring report.

[0008] As a preferred solution of the device monitoring method based on image recognition of the present invention, the specific steps of acquiring real-time image data and preprocessing are as follows:

[0009] Use industrial-grade high-frame-rate cameras and high-performance image acquisition cards to collect real-time image data;

[0010] The acquired original image data is size-standardized using a bilinear interpolation algorithm. After the size-standardized image, the color image is converted into a grayscale image using RGB to grayscale conversion, and the grayscale image is noise-filtered using a median filter.

[0011] As a preferred solution of the device monitoring method based on image recognition described in the present invention, the pre-trained deep convolutional neural network is used to extract deep features from the pre-processed image. The specific steps are as follows:

[0012] Set the input image I to pass through the convolution layer of the pre-trained VGG16 model for deep feature extraction;

[0013] The VGG16 model is used to extract deep features in different convolutional layers to obtain a multi-layer feature map set {F1, F2, ... F i}, where F i Represents the feature map produced by the i-th convolutional layer.

[0014] The multi-level feature vectors are integrated into a comprehensive feature vector by feature fusion, and the specific steps are as follows:

[0015] Generate query vector Q(i, j) and key vector K(k, l) from the feature graph through linear transformation;

[0016] Before starting, we need to generate the query vector Q(i, j) and key vector K(k, l) from the feature graph through linear transformation. Q and W K Use Glorot uniform initialization;

[0017] For each layer of feature maps, the attention weight matrix W is calculated, and the feature information at different positions is combined into a comprehensive feature vector using the attention mechanism;

[0018] The attention mechanism is used to combine feature information at different positions into a comprehensive feature vector.

[0019] The step of inputting the comprehensive feature vector into a classification model with enhanced environmental adaptability and generalization capability, which is constructed based on deep features and dynamic environmental adaptation, is as follows:

[0020] Define an adaptive dynamic weight adjustment function, G(F *i (i,j) E, θ G ) Using the radial basis function (RBF) kernel function as the basic building block, input the comprehensive feature vector into the adaptive dynamic weight adjustment function G(F * i (i,j) E, θ G ) Combining the information of the feature vector itself and the influence of environmental changes, by dynamically adjusting the weights inside the model, enhance the adaptability and generalization ability of the model;

[0021] Use the adaptive dynamic weight adjustment function G to adjust the weights in the classification model.

[0022] The application of the classification model to monitor the device status in real time and calculate the abnormal probability of the current device is as follows:

[0023] By considering the absolute differences in the changes of internal parameters of the device, environmental factor changes, and the dynamic evolution of features, accurately calculate the probability that the device is in an abnormal state;

[0024] Through the fixed percentile method, define an abnormal behavior threshold T. When the device is running, through the value of P ay value, compare the value of P ay calculated in real time with the set abnormal behavior threshold T. If P ay > T, it is determined that the device is in an abnormal state. If P ay < T, it is determined that the device is in a normal state;

[0025] As a preferred solution of the device monitoring method based on image recognition described in the present invention, wherein: based on the probability that the device is in an abnormal state, evaluate the current device status and generate a device status monitoring report, the specific steps are as follows:

[0026] Through the fixed percentile method, define an abnormal behavior threshold T. When the device is running, through the value of P ay value, compare the value of P ay calculated in real time with the set abnormal behavior threshold T. If P ay > T, it is determined that the device is in an abnormal state. If P ay < T, it is determined that the device is in a normal state;

[0027] Integrate the device status evaluation results, the abnormal state probability, and the environmental parameter change trend, conduct a comprehensive analysis, and formulate countermeasures based on the analysis results.

[0028] In the second aspect, the present invention provides an equipment monitoring system based on image recognition, including a data acquisition and preprocessing module, a deep feature extraction and fusion module, an environmental dynamic debugging module, an equipment status anomaly detection module, and an equipment status evaluation module. The data acquisition and preprocessing module is used to acquire real-time image data and preprocess the image. The deep feature extraction and fusion module is used to perform deep feature extraction on the preprocessed image, fuse feature vectors of different levels, and form a comprehensive feature vector. The environmental dynamic debugging module is used to automatically adjust model parameters according to different environmental conditions. The equipment status anomaly detection module is used to use a classification model to monitor the equipment status in real time and calculate the abnormal probability of the equipment in the current state. The equipment status evaluation module is used to evaluate the overall operating status of the current equipment according to the probability of the equipment abnormal state, and automatically generate a detailed equipment status monitoring report.

[0029] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the device monitoring method based on image recognition as described in the first aspect of the present invention is implemented.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the device monitoring method based on image recognition as described in the first aspect of the present invention is implemented.

[0031] The beneficial effects of the present invention are as follows: by acquiring and preprocessing real-time image data, the method can quickly adapt to the equipment operating environment and improve monitoring efficiency, and use deep convolutional neural networks to perform accurate feature extraction, thereby improving the recognition accuracy and reliability of equipment status. Through feature fusion, multi-level information is integrated, model sensitivity is enhanced, and the accuracy and timeliness of anomaly detection are improved. The fused feature vector is input into a classification model with self-adjustment capability, ensuring high-accuracy monitoring in different environments, real-time monitoring of equipment status and calculation of abnormal probability, achieving continuous monitoring of equipment health status, and timely discovery of potential problems. Finally, based on the probability of abnormal status, the equipment status is evaluated and a report is generated, which provides a scientific basis for equipment maintenance and reduces the risk of failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 This is a flow chart of the device monitoring method based on image recognition in Example 1.

[0034] Figure 2 This is a flow chart of the equipment monitoring system based on image recognition in Example 1. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0036] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a device monitoring method based on image recognition, comprising the following steps:

[0037] S1. Acquire real-time image data and perform preprocessing.

[0038] Furthermore, an industrial-grade high-frame-rate camera (which has excellent light adaptability and high-speed data transmission capability) is used in conjunction with a high-performance image acquisition card to ensure that the data transmission rate is not less than 60 frames and to collect real-time image data;

[0039] The acquired original image data was normalized to 224×224 pixels using a bilinear interpolation algorithm to keep the geometric shape of the image unchanged. The image after normalization was converted into a grayscale image using RGB to grayscale to reduce the amount of calculation. The grayscale image was filtered for noise using a 3×3 median filter.

[0040] It should also be noted that the image size is normalized: the target size is set to 640×480640×480 pixels, and the bilinear interpolation algorithm is used for scaling to keep the geometric shape of the image unchanged. The size-normalized image will be converted to a color space to further optimize the image quality to meet the input requirements of the deep learning model;

[0041] Grayscale conversion: Use the standard formula of RGB to grayscale to convert the color image into a grayscale image to reduce the amount of calculation. The expression is:

[0042] Y=0.299R+0.587G+0.114BY=0.299R+0.587G+0.114B;

[0043] Among them, Y is the gray value, R is the red in the original image, G is the green in the original image, and B is the blue in the original image;

[0044] The grayscale image will then undergo noise filtering to enhance the image clarity and contrast, creating better conditions for deep feature extraction;

[0045] The working principle of the median filter is to take a neighborhood of fixed size for each pixel in the image, and then replace the value of the central pixel with the median of all pixel values ​​in the neighborhood. Applying this operation to each pixel point according to the window size can effectively remove salt and pepper noise.

[0046] S2. Use the pre-trained deep convolutional neural network to extract deep features from the preprocessed image.

[0047] Furthermore, the input image I is set to be extracted through the convolution layer of the pre-trained VGG16 model for deep feature extraction, and the expression is:

[0048] F = Cv(I; W, b);

[0049] Among them, Cv represents the convolution operation, W represents the weight of the convolution kernel, b represents the bias of the convolution kernel, and F represents the feature map;

[0050] The VGG16 model is used to extract deep features in different convolutional layers to obtain a multi-layer feature map set {F1, F2, ... F i}, where F i Represents the feature map produced by the i-th convolutional layer.

[0051] It should also be noted that the process of deep feature extraction is essentially the process of converting an image into a feature map. In deep learning, the feature map F is usually a three-dimensional array with a shape of H′×W′×D, where H′ and W′ are the height and width of the feature map, and D is the depth or number of channels;

[0052] The VGG16 model extracts deep features from the input image layer by layer through multiple convolutional layers in its structure. The initial layer focuses on simple features such as edges, and subsequent layers capture more complex concepts. This process begins with preprocessing the image input, undergoes convolution, activation, and pooling to form a multi-layer feature map, and finally converges into an information-rich feature vector, which is suitable for tasks such as classification and detection. The feature fusion strategy enhances the model performance and adapts to specific application requirements.

[0053] S3. Integrate multi-level feature vectors into a comprehensive feature vector through feature fusion.

[0054] Furthermore, through linear transformation, the query vector Q(i, j) and key vector K(k, l) are generated from the feature map as follows:

[0055] Q(i,j)=F i (i,j)·W Q ;

[0056] K(k,l)=F i (k,l)·W K ;

[0057] Among them, W Q The weight matrix W used to generate the query vector Q(i, j) K The weight matrix, W, used to generate the key vector K(k, l) Q and W K The dimension of matches the number of channels of the feature map and can be obtained by random initialization or pre-training;

[0058] Before starting, we need to generate the query vector Q(i, j) and key vector K(k, l) from the feature graph through linear transformation. Q and W K Use Glorot uniform initialization;

[0059] For each layer of feature maps, the attention weight matrix W is calculated, and the attention mechanism is used to combine the feature information at different positions into a comprehensive feature vector, which is expressed as:

[0060]

[0061] Among them, d k is the dimension of the key vector K, which is usually determined by the number of channels of the feature map F, exp(Q(i, j)K(k', l')T) is the sum of the unnormalized attention scores calculated for all positions (k', l'), and W(i, j, k, l) represents the attention weight from position (i, j) to position (k, l);

[0062] The attention mechanism is used to combine feature information at different positions into a comprehensive feature vector, which is expressed as:

[0063]

[0064] Among them, F * i (i,j) It represents the attention weighted feature value at position (i, j) after the feature map of the i-th layer is processed by the attention mechanism.

[0065] It should also be noted that in the feature fusion process, the model first generates query and key vectors through specific linear transformations to capture the correlation between positions on the feature map. Then, the attention weight matrix is ​​calculated to quantify the importance relationship between different positions and achieve fine-tuning of global features. Finally, based on these weights, the model aggregates local features at each level to generate a comprehensive feature vector that integrates multi-level information, enhancing the model's ability to understand and represent complex patterns. This mechanism is particularly helpful in strengthening key features, suppressing irrelevant background information, and improving overall recognition accuracy.

[0066] S4. Input the comprehensive feature vector into a classification model that is built based on deep features and dynamic adaptation to the environment, with enhanced environmental adaptability and generalization ability.

[0067] Furthermore, we define an adaptive dynamic weight adjustment function, G(F * i (i,j) , E, θ G ), using the radial basis function RBF kernel function as the basic building block, the comprehensive feature vector is input into the adaptive dynamic weight adjustment function G(F * i (i,j) , E, θ G ), combining the information of the feature vector itself and the influence of environmental changes, the adaptability and generalization ability of the model are enhanced by dynamically adjusting the weights within the model. The expression is:

[0068]

[0069] Among them, F * i (i,j) represents the comprehensive feature vector obtained by the attention mechanism, E represents the environmental factor matrix, θ G is G(F * i (i,j) , E, θ G ) parameter set, w m is the comprehensive feature vector F * i (i,j) The weight coefficient, β m is the weight coefficient of the environmental factor matrix E, μ m is the reference point of the comprehensive eigenvector, V m is the reference point of the environmental factor matrix, γ is used to control the sensitivity of the RBF kernel to the distance between the input features, and η controls the distance between each element in the environmental factor matrix E and the reference point V m The distance sensitivity between them, M is the number of RBF units;

[0070] Parameters in the G function such as M, μ m , v m , w m , β m Etc., can be set through experience or learned through training.

[0071] The adaptive dynamic weight adjustment function G is used to adjust the weights in the classification model. The expression is:

[0072]

[0073] Among them, W(i,j) is the original weight of the classification model at (i, j), W n (i,j) is the updated weight.

[0074] It should also be noted that in the G(F i (i,j) , E, θ G ) function, the range of the output weight adjustment factor is between 0 and infinity. When the comprehensive feature vector and the environmental factor matrix highly match the reference point, the weight adjustment factor will be larger, indicating that these features and environmental conditions contribute significantly to the classification performance; conversely, the weight adjustment factor is smaller, meaning that the features or the environment have a smaller impact on the classification result.

[0075] S5. Apply the classification model to monitor the device status in real time and calculate the abnormal probability of the current device.

[0076] Furthermore, by considering the absolute differences in the changes of the internal parameters of the device, the changes in environmental factors, and the dynamic evolution of features, accurately calculate the probability that the device is in an abnormal state. The expression is:

[0077]

[0078] where, P ay (Y = 1|F * i (i,j) , E) represents the probability that the device is in an abnormal state given the comprehensive feature vector F * i (i,j) and the environmental factor matrix E. σ is the mapping function and b is the bias term;

[0079] By the fixed percentile method, define an abnormal behavior critical value T. When the device is running, through the value of P ay , compare the value of P ay calculated in real time with the set abnormal behavior critical value T. If P ay >T, it is determined that the device is in an abnormal state. If P ay <T, it is determined that the device is in a normal state;

[0080] It should also be noted that this formula combines three key aspects: weight change, environmental factor change, and feature change. It transforms them into the probability that the device is in an abnormal state through the log function, ensuring that the value range is between 0 and 1 for intuitive understanding. By introducing the log function, even when there are large changes in weights or features, it will not cause drastic fluctuations in the probability, enhancing the stability of the model. In addition, this formula considers the multi-dimensional characteristics of device status evaluation, improving the accuracy and reliability of prediction.

[0081] S6. Evaluate the current device status based on the probability of the device being in an abnormal state, and generate a device status monitoring report.

[0082] Furthermore, through the fixed percentile method, define an abnormal behavior threshold T. When the device is running, through the value of P ay The value of P ay calculated in real time is compared with the set abnormal behavior threshold T. If P ay > T, it is determined that the device is in an abnormal state. If P ay < T, it is determined that the device is in a normal state;

[0083] Integrate the device status evaluation results and the changing trend of environmental parameters in the abnormal state probability, conduct comprehensive analysis, and formulate countermeasures based on the analysis results.

[0084] It should also be noted that the fixed percentile method is a statistical method used to determine the outlier threshold in a dataset. Specifically, select a specific percentile as the critical point, such as the 95th percentile (which means 95% of the data is below this value), and this value is T. In the device monitoring scenario, if the calculated Pay metric exceeds this pre-set T value, it is considered that the current observed value deviates from the normal range, which may indicate an abnormal device state. This method effectively utilizes the data distribution characteristics to identify abnormalities without making assumptions about the specific distribution form of the data, so it has high flexibility and practicality.

[0085] This embodiment also provides a device monitoring system based on image recognition, including: a data acquisition and preprocessing module, a deep feature extraction and fusion module, an environmental dynamic adjustment module, a device status abnormal detection module, and a device status evaluation module. The data acquisition and preprocessing module is used to obtain real-time image data and preprocess the images. The deep feature extraction and fusion module is used to extract deep features from the preprocessed images, fuse feature vectors at different levels to form a comprehensive feature vector. The environmental dynamic adjustment module is used to automatically adjust model parameters according to different environmental conditions. The device status abnormal detection module is used to use a classification model to monitor the device status in real time and calculate the abnormal probability of the device in the current state. The device status evaluation module is used to evaluate the overall operating condition of the current device according to the probability of the device abnormal state and automatically generate a detailed device status monitoring report.

[0086] This embodiment also provides a computer device applicable to the situation of the device monitoring method based on image recognition, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the device monitoring method based on image recognition as proposed in the above embodiment.

[0087] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0088] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the device monitoring method based on image recognition as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0089] In summary, the present invention ensures high-quality and high-speed acquisition of real-time image data by using high-performance image acquisition hardware, optimizes image quality and reduces computational burden through image preprocessing technology, extracts deep features of images with the help of deep learning model VGG16, enhances feature richness and expressiveness, and uses feature fusion and attention mechanism to accurately locate key information in the image, significantly improving the environmental adaptability and classification accuracy of the model, and through adaptive dynamic weight adjustment, enables the model to respond to environmental changes in real time and maintain the stability of monitoring effects, calculates the probability of abnormal equipment status according to changes in features, environment and weights, realizes fault warning, systematically analyzes equipment status, automatically generates detailed monitoring reports, and guides maintenance decisions. This series of innovative designs greatly improves the accuracy, efficiency and adaptability of equipment monitoring, provides strong technical support for the health management and fault prevention of industrial equipment, and ensures the dual improvement of production safety and efficiency.

[0090] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a device monitoring method based on image recognition is provided.

[0091] Five different time periods are selected, and the accuracy of the traditional method and the accuracy of the method of the present invention, as well as the response time of the traditional method and the response time of the method of the present invention are set in each time period.

[0092] First, the acquired original image data is size-normalized by bilinear interpolation algorithm, then the RGB is converted to grayscale to reduce the computational burden, and the median filter is applied to remove noise.

[0093] Secondly, the pre-trained VGG16 model is used to perform deep feature extraction on the preprocessed images, and the feature vectors extracted by different convolutional layers are input into the attention mechanism to generate a comprehensive feature vector.

[0094] Next, the integrated feature vector is input into a classification model with an adaptive dynamic weight adjustment function, which is able to adjust its internal weights according to the dynamic changes of the environment and device status.

[0095] Finally, the classification model is applied to monitor the equipment status in real time, calculate the abnormal probability of the equipment, judge the equipment status according to the critical value T, and generate a monitoring report.

[0096] The details are shown in Table 1:

[0097] Table 1 Experimental record table

[0098]

[0099] Through the data analysis of the above table, it can be clearly seen that the present invention has high accuracy and fast response time. For example, in the morning period with low light conditions, the accuracy of the traditional method is 75%, while the method of the present invention reaches 92%, which significantly improves the accuracy of abnormality detection. At the same time, the response time of the present invention is reduced from 180 milliseconds to 120 milliseconds. The faster response time means that potential abnormal situations can be detected and handled more quickly. In addition, the improvement in the adaptive adjustment efficiency, from 5 times / hour to 7 times / hour, shows that the system can better adapt and optimize performance under different environmental conditions, ensuring the efficient operation of the monitoring system.

[0100] The image recognition-based equipment monitoring of the present invention can not only significantly improve the accuracy of abnormality detection, but also greatly shorten the response time of the system, especially the ability to quickly adapt to environmental changes in a short period of time, which reflects the efficiency and reliability of the present invention in the field of automated production line monitoring.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A device monitoring method based on image recognition, characterized in that: include, Acquire real-time image data and perform preprocessing; Use pre-trained deep convolutional neural network to extract deep features from pre-processed images; Integrate multi-level feature vectors into a comprehensive feature vector through feature fusion; The comprehensive feature vector is input into a classification model built based on deep features and dynamic adaptation of the environment, with enhanced environmental adaptability and generalization ability; Apply classification models to monitor equipment status in real time and calculate the probability of equipment abnormality; Based on the probability that the equipment is in an abnormal state, the current equipment status is evaluated and an equipment status monitoring report is generated.

2. The device monitoring method based on image recognition according to claim 1, characterized in that: The specific steps of acquiring real-time image data and preprocessing are as follows: Use industrial-grade high-frame-rate cameras and high-performance image acquisition cards to collect real-time image data; The acquired original image data is size-standardized using a bilinear interpolation algorithm. After the size-standardized image, the color image is converted into a grayscale image using RGB to grayscale conversion, and the grayscale image is noise-filtered using a median filter.

3. The device monitoring method based on image recognition according to claim 2, characterized in that: The pre-trained deep convolutional neural network is used to extract deep features from the pre-processed image. The specific steps are as follows: Set the input image I to pass through the convolution layer of the pre-trained VGG16 model for deep feature extraction; The VGG16 model is used to extract deep features in different convolutional layers to obtain a multi-layer feature map set {F1, F2, ... F i }, where F i Represents the feature map produced by the i-th convolutional layer.

4. The device monitoring method based on image recognition according to claim 3, characterized in that: The multi-level feature vectors are integrated into a comprehensive feature vector by feature fusion, and the specific steps are as follows: Generate query vector Q(i, j) and key vector K(k, l) from the feature graph through linear transformation; Before starting, we need to generate the query vector Q(i, j) and key vector K(k, l) from the feature graph through linear transformation. Q and W K Use Glorot uniform initialization; For each layer of feature maps, the attention weight matrix W is calculated, and the feature information at different positions is combined into a comprehensive feature vector using the attention mechanism; The attention mechanism is used to combine feature information at different positions into a comprehensive feature vector.

5. The device monitoring method based on image recognition according to claim 4, characterized in that: The step of inputting the comprehensive feature vector into a classification model with enhanced environmental adaptability and generalization capability, which is constructed based on deep features and dynamic environmental adaptation, is as follows: Define an adaptive dynamic weight adjustment function, G(F * i (i,j) , E, θ G ), using the radial basis function RBF kernel function as the basic building block, the comprehensive feature vector is input into the adaptive dynamic weight adjustment function G(F * i (i,j) , E, θ G ), combining the information of the feature vector itself and the influence of environmental changes, and dynamically adjusting the weights within the model to enhance the adaptability and generalization ability of the model; The adaptive dynamic weight adjustment function G is used to adjust the weights in the classification model.

6. The device monitoring method based on image recognition according to claim 5, characterized in that: The application classification model monitors the device status in real time and calculates the abnormal probability of the current device. The specific steps are as follows: By considering the absolute differences in the changes in internal parameters of the device, changes in environmental factors, and the dynamic evolution of characteristics, the probability of the device being in an abnormal state can be accurately calculated; By the fixed percentile method, an abnormal behavior threshold value T is defined. When the device is running, through the value of P ay , the real-time calculated value of P ay is compared with the set abnormal behavior threshold value T. If P ay >T, it is determined that the device is in an abnormal state. If P ay <T, it is determined that the device is in a normal state.

7. The device monitoring method based on image recognition according to claim 6, characterized in that: Based on the probability of the device being in an abnormal state, the current device state is evaluated and a device state monitoring report is generated. The specific steps are as follows: Through the fixed percentile method, an abnormal behavior threshold value T is defined. When the device is running, through the value of P ay , the real-time calculated value of P ay is compared with the set abnormal behavior threshold value T. If P ay >T, it is determined that the device is in an abnormal state. If P ay <T, it is determined that the device is in a normal state; Integrate the equipment status assessment results and the changing trends of abnormal status probability environmental parameters, conduct a comprehensive analysis, and formulate response measures based on the analysis results.

8. An equipment monitoring system based on image recognition, based on the equipment monitoring method based on image recognition according to any one of claims 1 to 7, characterized in that: Including data acquisition and preprocessing module, deep feature extraction and fusion module, environment dynamic debugging module, equipment status anomaly detection module, equipment status assessment module; The data acquisition and preprocessing module is used to acquire real-time image data and preprocess the image; The deep feature extraction and fusion module is used to extract deep features from the preprocessed image and fuse feature vectors at different levels to form a comprehensive feature vector; The environment dynamic debugging module is used to automatically adjust model parameters according to different environmental conditions; The device state anomaly detection module is used to use a classification model to monitor the device state in real time and calculate the abnormal probability of the device in the current state; The equipment status evaluation module is used to evaluate the overall operating status of the current equipment according to the probability of abnormal equipment status, and automatically generate a detailed equipment status monitoring report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the device monitoring method based on image recognition according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the device monitoring method based on image recognition according to any one of claims 1 to 7 are implemented.

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