Motor production workshop collaborative management system and method

Abstract

The invention relates to the field of intelligent management, in particular to a motor production workshop collaborative management system and method. The system comprises a task allocation module used for generating a production plan according to an order demand and existing resources, splitting the production plan into production tasks, and allocating the production tasks to operators of each production line; the progress tracking module is used for uploading production progress data through the data acquisition terminal deployed at each station and generating a real-time production progress chart based on the production progress data; the plan adjustment module is used for giving an alarm and generating a production plan adjustment prompt in response to the detected equipment fault; and the performance evaluation module is used for recording working hours and task completion conditions of each operator and generating a performance evaluation report. In this way, an enterprise can be helped to keep flexibility and high efficiency in a complex production environment.

Description

Technical Field

[0001] The present application relates to the field of intelligent management, and specifically to a motor production workshop collaborative management system and method. Background Art

[0002] In modern manufacturing, especially in the field of motor production, with the ever-changing market demand and the surge in order volume, production efficiency and flexibility have become key factors in corporate competitiveness. The customized production model requires factories to be able to respond quickly to customer needs while maintaining high-quality production standards.

[0003] Modern motor production often involves multiple processes and production lines, and each link needs to cooperate with each other to complete the production of the motor. However, in the traditional motor production workshop management system, it usually relies on manual operation and experience judgment, lacking intelligent data analysis and decision support. This makes it difficult to adjust and optimize the motor production plan and difficult to deal with emergencies. For example, when faced with production equipment failure problems, it is often difficult to discover and deal with them in time, resulting in production interruptions and losses. In addition, the existing motor production workshop management system lacks information sharing between the various production links, resulting in poor collaboration between departments and serious information islands, which makes the adjustment of production plans and task allocation complicated and reduces overall production efficiency.

[0004] Therefore, a motor production workshop collaborative management system is desired. Summary of the invention

[0005] This application is made in consideration of the above problems. One object of this application is to provide a motor production workshop collaborative management system and method.

[0006] The embodiment of the present application provides a motor production workshop collaborative management system, which includes:

[0007] A task allocation module is used to generate a production plan based on order requirements and existing resources, split the production plan into production tasks, and then allocate the production tasks to operators of each production line;

[0008] A progress tracking module, used to upload production progress data through a data acquisition terminal deployed at each workstation, and generate a real-time production progress chart based on the production progress data;

[0009] A plan adjustment module, for responding to detection of equipment failure, issuing an alarm and generating a production plan adjustment prompt;

[0010] The performance evaluation module is used to record the working hours and task completion status of each operator and generate a performance evaluation report.

[0011] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the plan adjustment module includes:

[0012] An equipment thermal infrared image acquisition unit, used to acquire a thermal infrared image of the monitored equipment acquired by the data acquisition terminal;

[0013] An equipment component image region division unit, used for performing image region division on the thermal infrared image based on equipment components to obtain a set of unit region images of thermal infrared components;

[0014] A thermal infrared component unit area position coding unit, used for using a position coding function to perform position coding on each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of device component position coding vectors;

[0015] The device component thermal distribution feature extraction unit is used to extract thermal distribution features of each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of device component thermal distribution feature maps;

[0016] A device component thermal distribution feature enhancement unit, used for respectively performing a feature space structure consistency self-attention cross-channel enhancement module on each device component thermal distribution feature map in the set of device component thermal distribution feature maps to obtain a set of device component thermal distribution enhancement feature maps;

[0017] A location information equipment thermal distribution feature fusion unit, used to fuse each corresponding set of equipment component thermal distribution enhancement feature maps and equipment component position encoding vectors in the set of equipment component thermal distribution enhancement feature maps and the set of equipment component position encoding vectors to obtain a set of equipment component thermal distribution feature vectors containing location information;

[0018] A device global component object granular temperature distribution characterization unit, used for inputting the set of device component thermal distribution feature vectors containing position information into a feature aggregation network based on feature energy local significance gating to obtain a device global component object granular temperature distribution feature vector;

[0019] The equipment fault detection unit is used to input the equipment global component object granular temperature distribution feature vector into a classifier-based equipment detector to obtain a detection result, wherein the detection result is used to indicate whether the monitored equipment has a fault.

[0020] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the equipment component thermal distribution feature extraction unit is used to:

[0021] Each thermal infrared component unit area image in the set of thermal infrared component unit area images is respectively passed through a device part thermal distribution feature extractor based on a hole convolutional neural network model to obtain a set of thermal distribution feature maps of the device parts.

[0022] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the equipment component thermal distribution feature enhancement unit includes:

[0023] A layer normalization subunit, used for performing layer normalization on the device component thermal distribution characteristic map to obtain a normalized device component thermal distribution characteristic map;

[0024] A point convolution processing subunit, used for performing point convolution processing on the normalized device component thermal distribution feature map to obtain a device component thermal distribution state channel context association representation feature map;

[0025] A convolutional encoding subunit, configured to perform convolutional encoding on the device component thermal distribution state channel context association representation feature map to obtain a device component thermal distribution state space context association representation feature map;

[0026] A copying subunit, used for copying the equipment component thermal distribution state space context association representation feature map to obtain the equipment component thermal distribution state backup space context association representation feature map;

[0027] The first characteristic shape reshaping subunit is used to perform characteristic shape reshaping on the equipment component thermal distribution state channel context association representation feature map, the equipment component thermal distribution state space context association representation feature map and the equipment component thermal distribution state backup space context association representation feature map to obtain an equipment component thermal distribution state channel context association representation feature matrix, an equipment component thermal distribution state space context association representation feature matrix and an equipment component thermal distribution state backup space context association representation feature matrix;

[0028] A covariance matrix calculation subunit, used to calculate a cross-channel cross-covariance matrix of thermal distribution states of equipment components between a channel context association representation feature matrix of thermal distribution states of equipment components and a spatial context association representation feature matrix of thermal distribution states of equipment components;

[0029] An activation subunit, used for activating the cross-channel cross-covariance matrix of the thermal distribution state of the equipment components using a Softmax function to obtain a global interactive attention matrix of the thermal distribution state features of the equipment components;

[0030] A product calculation subunit, used for calculating the product between the equipment component thermal distribution state backup space context association representation feature matrix and the equipment component thermal distribution state feature global interactive attention matrix to obtain the equipment component thermal distribution state attention enhancement feature representation matrix;

[0031] The second characteristic shape reshaping subunit is used to perform characteristic shape reshaping on the attention enhancement feature representation matrix of the thermal distribution state of the equipment component to obtain the equipment component thermal distribution enhancement feature map.

[0032] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the covariance matrix calculation subunit is used to:

[0033] Calculating matrix multiplication between the channel context association representation feature matrix of the thermal distribution state of the equipment component and the spatial context association representation feature matrix of the thermal distribution state of the equipment component to obtain a channel-spatial context association feature matrix of the thermal distribution state of the equipment component;

[0034] The position-wise division between the channel-space contextual association feature matrix of the thermal distribution state of the device components and the hyperparameters is calculated to obtain the cross-channel cross-covariance matrix of the thermal distribution state of the device components.

[0035] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the location information device thermal distribution feature fusion unit is used to:

[0036] Performing global mean pooling processing on each device component thermal distribution enhancement feature map in the set of device component thermal distribution enhancement feature maps to obtain a set of device component thermal distribution enhancement feature vectors;

[0037] Each corresponding set of equipment component position encoding vectors and equipment component thermal distribution enhancement feature vectors in the set of equipment component position encoding vectors and the set of equipment component thermal distribution enhancement feature vectors are fused separately to obtain the set of equipment component thermal distribution feature vectors containing position information.

[0038] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the equipment global component object granularity temperature distribution characterization unit includes:

[0039] a characteristic energy level coefficient calculation subunit, configured to determine the characteristic energy level coefficient of each equipment component thermal distribution feature vector containing position information in the set of equipment component thermal distribution feature vectors containing position information based on the maximum value, minimum value, mean value and variance of each equipment component thermal distribution feature vector containing position information, so as to obtain a sequence of equipment component thermal distribution characteristic energy level coefficients containing position information;

[0040] A local neighborhood mean calculation subunit is used to determine the scale of the local neighborhood of the thermal distribution of the equipment component containing the location information, use the scale of the local neighborhood of the thermal distribution of the equipment component containing the location information as the mean calculation range, and respectively calculate the local neighborhood mean of each thermal distribution characteristic energy level coefficient of the equipment component containing the location information to obtain a sequence of thermal distribution characteristic energy level significance descriptors of the equipment component containing the location information;

[0041] A mask subunit, used for inputting the sequence of equipment component thermal distribution feature energy level significance descriptors containing position information into a mask module based on a gating function to obtain a mask-gated probabilistic sequence of equipment component thermal distribution feature energy level significance descriptors containing position information;

[0042] The weighted sum calculation subunit is used to calculate the position-weighted sum of the set of thermal distribution feature vectors of the equipment components containing the position information by using the sequence of the mask-gated probabilistic energy level significance descriptors of the equipment components containing the position information as the sequence of weights to obtain the global component object granularity temperature distribution feature vector of the equipment.

[0043] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the characteristic energy level coefficient calculation subunit is used to:

[0044] Calculating the maximum value, minimum value, average value and variance of the thermal distribution feature vector of the equipment component containing the position information to obtain the maximum value of the thermal distribution of the equipment component containing the position information, the minimum value of the thermal distribution of the equipment component containing the position information, the average value of the thermal distribution of the equipment component containing the position information and the variance of the thermal distribution of the equipment component containing the position information;

[0045] Calculate the sum of the thermal distribution variance of the equipment component containing the position information and the hyperparameter to obtain a first local energy statistical factor of thermal distribution of the equipment component containing the position information;

[0046] Calculating the difference between the maximum value of the thermal distribution of the equipment component containing the position information and the average value of the thermal distribution of the equipment component containing the position information to obtain the maximum deviation value of the thermal distribution of the equipment component containing the position information;

[0047] Calculating the difference between the minimum value of the thermal distribution of the equipment component containing the position information and the average value of the thermal distribution of the equipment component containing the position information to obtain a minimum deviation value of the thermal distribution of the equipment component containing the position information;

[0048] Calculating the square sum of the maximum deviation value of the thermal distribution of the equipment component containing the position information and the minimum deviation value of the thermal distribution of the equipment component containing the position information and adding the sum to the hyperparameter to obtain a second local energy statistical factor of the thermal distribution of the equipment component containing the position information;

[0049] The division between the first local energy statistical factor of thermal distribution of equipment components containing position information and the second local energy statistical factor of thermal distribution of equipment components containing position information is calculated to obtain a characteristic energy level coefficient of thermal distribution of equipment components containing position information.

[0050] For example, according to the motor production workshop collaborative management system of an embodiment of the present application, the mask subunit is used to:

[0051] Taking the negative number of each device component thermal distribution feature energy level significance descriptor containing location information in the sequence of device component thermal distribution feature energy level significance descriptors containing location information as an exponential power, calculating the exponential function value with the natural constant e as the base to obtain a sequence of device component thermal distribution feature energy level class support significance descriptors containing location information;

[0052] Calculating the positional sum of the sequence of the equipment component thermal distribution feature energy level class support significant descriptors containing the position information and a constant 1 to obtain a linearly modulated sequence of the equipment component thermal distribution feature energy level class support significant descriptors containing the position information;

[0053] Calculating the inverse of each linear modulation device component thermal distribution feature energy level class support significance descriptor in the sequence of device component thermal distribution feature energy level class support significance descriptors containing linear modulation position information to obtain a sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing position information;

[0054] Performing masking processing on each device component thermal distribution gated probabilistic feature energy level significance descriptor containing position information in the sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing position information to obtain the sequence of masked gated probabilistic device component thermal distribution feature energy level significance descriptors containing position information;

[0055] Among them, performing masking processing on each device component thermal distribution gated probabilistic feature energy level significance descriptor containing location information in the sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information to obtain the sequence of masked gated probabilistic device component thermal distribution feature energy level significance descriptors containing location information, including:

[0056] In response to the device component thermal distribution gated probabilistic feature energy level significance descriptor containing the location information being greater than a predetermined threshold, the original value of the device component thermal distribution gated probabilistic feature energy level significance descriptor containing the location information is taken, otherwise it is set to 0.

[0057] The embodiment of the present application also provides a motor production workshop collaborative management method, which includes:

[0058] Generate a production plan based on order requirements and existing resources, split the production plan into production tasks, and assign the production tasks to operators of each production line;

[0059] Uploading production progress data through a data acquisition terminal deployed at each workstation, and generating a real-time production progress chart based on the production progress data;

[0060] In response to detecting equipment failure, an alarm is issued and a production plan adjustment prompt is generated;

[0061] Record the working hours and task completion of each operator and generate a performance evaluation report.

[0062] According to the motor production workshop collaborative management system and method of the embodiments of the present application, more intelligent and collaborative motor production management can be achieved through information interaction among the task assignment module, progress tracking module, plan adjustment module and performance evaluation module. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application are briefly introduced below. Obviously, the drawings described below only relate to some embodiments of the present application, and are not intended to limit the present application.

[0064] Figure 1 A schematic diagram of the application architecture of the motor production workshop collaborative management system in an embodiment of the present application is shown;

[0065] Figure 2 It shows a schematic diagram of the structure of the motor production workshop collaborative management system in an embodiment of the present application;

[0066] Figure 3 It shows a schematic diagram of the structure of the plan adjustment module of the motor production workshop collaborative management system in an embodiment of the present application;

[0067] Figure 4 A flowchart showing a method for collaborative management of a motor production workshop in an embodiment of the present application is shown; and

[0068] Figure 5 An application scenario diagram of the motor production workshop collaborative management system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0069] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present application.

[0070] The terms used in this specification are those common terms currently widely used in the art in consideration of the functions of the present application, but these terms may vary according to the intentions of those skilled in the art, precedents, or new technologies in the art. In addition, specific terms may be selected, and in this case, their detailed meanings will be described in the detailed description of the present application. Therefore, the terms used in the specification should not be understood as simple names, but rather as a general description based on the meaning of the terms and the present application.

[0071] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0072] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0073] Figure 1 A schematic diagram of the application architecture of the motor production workshop collaborative management system in an embodiment of the present application is shown, including a server 100 and a terminal device 200.

[0074] The terminal device 200 and the server 100 can be connected via the Internet to achieve mutual communication. Optionally, the above-mentioned Internet uses standard communication technology and / or protocol. The Internet is usually the Internet, but it can also be any network, including but not limited to any combination of a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a dedicated network or a virtual private network. In some embodiments, the data exchanged through the network is represented by technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0075] The server 100 can provide various network services for the terminal device 200, wherein the server 100 can be a single server, a server cluster consisting of several servers, or a cloud computing center. Specifically, the server 100 may include a processor 110 (Center Processing Unit, CPU), a memory 120, an input device 130, and an output device 140, etc. The input device 130 may include a keyboard, a mouse, a touch screen, etc., and the output device 140 may include a display device, such as a liquid crystal display (Liquid Crystal Display, LCD), a cathode ray tube (Cathode Ray Tube, CRT), etc.

[0076] The memory 120 may include a read-only memory (ROM) and a random access memory (RAM), and provides the processor 110 with program instructions and data stored in the memory 120. In the embodiment of the present application, the memory 120 may be used to store the corresponding method of the motor production workshop collaborative management system in the embodiment of the present application.

[0077] The processor 110 calls the program instructions stored in the memory 120, and the processor 110 is used to execute the steps of any motor production workshop collaborative management method in the embodiments of the present application according to the obtained program instructions.

[0078] In addition, the application architecture diagram in the embodiment of the present application is intended to more clearly illustrate the technical solution in the embodiment of the present application, and does not constitute a limitation on the technical solution provided in the embodiment of the present application. Of course, for other application architectures and business applications, the technical solution provided in the embodiment of the present application is also applicable to similar problems.

[0079] The following is a non-restrictive description of the motor production workshop collaborative management system provided according to at least one embodiment of the present application through several examples or embodiments. As described below, different features in these specific examples or embodiments can be combined with each other without conflicting with each other to obtain new examples or embodiments, and these new examples or embodiments also fall within the scope of protection of the present application.

[0080] It should be understood that production planning is the basis for ensuring a smooth production process. In the motor production workshop, a reasonable production plan can effectively balance resource allocation, equipment utilization and production efficiency. By real-time monitoring and adjusting the production plan, enterprises can minimize production delays and ensure on-time delivery and motor product quality.

[0081] Based on this, in the technical solution of this application, a motor production workshop collaborative management system is proposed, such as Figure 2 As shown, it includes: a task allocation module 810, which is used to generate a production plan according to order requirements and existing resources, and after splitting the production plan into production tasks, the production tasks are allocated to operators of each production line; a progress tracking module 820, which is used to upload production progress data through a data acquisition terminal deployed at each workstation, and generate a real-time production progress chart based on the production progress data; a plan adjustment module 830, which is used to respond to the detection of equipment failure, issue an alarm and generate a production plan adjustment prompt; a performance evaluation module 840, which is used to record the working hours and task completion status of each operator, and generate a performance evaluation report. In this motor production workshop collaborative management system, more intelligent and collaborative motor production management can be achieved through the information interaction of the task allocation module 810, the progress tracking module 820, the plan adjustment module 830 and the performance evaluation module 840.

[0082] In particular, in the above-mentioned motor production workshop collaborative management system, considering that equipment failure is one of the main factors affecting production efficiency, it is necessary to conduct real-time and effective detection of equipment failures in the motor production workshop, so as to timely discover potential failures and issue alarms, help operators take quick measures, and adjust production plans to ensure the quality of motor products while enabling the motor production workshop to complete production on time. In this way, more intelligent motor production workshop collaborative management can be achieved, helping enterprises maintain flexibility and efficiency in complex production environments.

[0083] Among them, Figure 3 As shown, the plan adjustment module 830 includes: an equipment thermal infrared image acquisition unit 831, which is used to obtain the thermal infrared image of the monitored equipment acquired by the data acquisition terminal; an equipment component image area division unit 832, which is used to divide the thermal infrared image based on the image area of ​​the equipment component to obtain a set of thermal infrared component unit area images; a thermal infrared component unit area position encoding unit 833, which is used to use a position encoding function to position encode each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of equipment component position encoding vectors; an equipment component thermal distribution feature extraction unit 834, which is used to extract thermal distribution features of each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of equipment component thermal distribution feature maps; an equipment component thermal distribution feature enhancement unit 835, which is used to thermally encode each equipment component in the set of equipment component thermal distribution feature maps. The distribution feature maps are respectively passed through the feature space structure consistency self-attention cross-channel enhancement module to obtain a set of device component thermal distribution enhancement feature maps; a position information device thermal distribution feature fusion unit 836 is used to fuse each group of corresponding device component thermal distribution enhancement feature maps and device component position encoding vectors in the set of device component thermal distribution enhancement feature maps and the set of device component position encoding vectors to obtain a set of device component thermal distribution feature vectors containing position information; a device global component object granularity temperature distribution characterization unit 837 is used to input the set of device component thermal distribution feature vectors containing position information into a feature aggregation network based on feature energy local significance gating to obtain a device global component object granularity temperature distribution feature vector; a device fault detection unit 838 is used to input the device global component object granularity temperature distribution feature vector into a classifier-based device detector to obtain a detection result, and the detection result is used to indicate whether the monitored device has a fault.

[0084] Specifically, in the plan adjustment module 830, first, a thermal infrared image of the monitored device collected by the data acquisition terminal is obtained. Next, considering that the thermal infrared image contains temperature distribution characteristic information about each component of the monitored device, the temperature distribution of these different equipment components can better reflect which components have abnormal conditions. Based on this, in the technical solution of the present application, the thermal infrared image is divided into image regions based on equipment components to obtain a set of unit area images of thermal infrared components. By dividing the thermal infrared image into regions based on equipment components, the thermal distribution information of the entire device can be refined to each specific component. This refinement enables subsequent analysis to be performed on each component, making it easier to identify which components have abnormal thermal distribution.

[0085] Furthermore, in order to be able to associate the thermal distribution characteristics of different equipment components with specific component positions, thereby improving the accuracy of equipment fault detection and facilitating more accurate subsequent equipment fault maintenance, in the technical solution of the present application, a position encoding function is further used to position encode each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of equipment component position encoding vectors. Specifically, by using a position encoding function to encode each thermal infrared component unit area image, it is helpful to combine the position information of each component with its thermal characteristics, which helps to quickly locate the problem component during analysis and fault detection, and improve the efficiency of troubleshooting.

[0086] Then, each thermal infrared component unit area image in the set of thermal infrared component unit area images is subjected to feature mining in the equipment part thermal distribution feature extractor based on the void convolutional neural network model, so as to extract the thermal distribution feature information about the equipment components in each thermal infrared part unit area image, thereby obtaining a set of thermal distribution feature maps of the equipment components.

[0087] Correspondingly, the equipment component thermal distribution feature extraction unit 834 is used to: respectively pass each thermal infrared component unit area image in the set of thermal infrared component unit area images through the equipment part thermal distribution feature extractor based on the void convolutional neural network model to obtain a set of thermal distribution feature maps of the equipment components.

[0088] It should be understood that since each device component thermal distribution feature map in the set of the device component thermal distribution feature maps contains the thermal distribution semantics and feature information about each device component, the device component thermal distribution feature map not only contains the key features of the thermal distribution of each device component, but also contains some irrelevant noise interference and redundant information, which will contribute less to the subsequent equipment fault detection task. Based on this, in the technical solution of the present application, each device component thermal distribution feature map in the set of the device component thermal distribution feature map is further processed through a feature space structure consistency self-attention cross-channel enhancement module to obtain a set of device component thermal distribution enhancement feature maps. The feature space structure consistency self-attention cross-channel enhancement module is designed to retain the structure of the feature space, which means that during the processing, the overall layout and relationship of the device component thermal distribution feature map will not be destroyed. This is particularly important for the understanding of the thermal distribution feature maps, because these feature maps reflect the thermal state and mutual relationship of each component of the equipment. Therefore, through the processing of the feature space structure consistency self-attention cross-channel enhancement module, the spatial structure of the thermal distribution features of the equipment components can be retained while using the interaction of feature channels to capture channel contextual association information, so that the enhanced thermal distribution feature map of the equipment components after feature enhancement can fuse the channel association information between feature structures in units of feature space structure while retaining the feature space structure to take into account the robustness and richness of feature expression. It is worth mentioning that through cross-channel interaction, the module can capture the contextual association information between the channels of the thermal distribution features of equipment components. This information is crucial to understanding the mutual influence between equipment components, especially in fault detection.

[0089] Correspondingly, the equipment component thermal distribution feature enhancement unit 835 includes: a layer normalization subunit, which is used to perform layer normalization on the equipment component thermal distribution feature map to obtain a normalized equipment component thermal distribution feature map; a point convolution processing subunit, which is used to perform point convolution processing on the normalized equipment component thermal distribution feature map to obtain an equipment component thermal distribution state channel context association representation feature map; a convolution encoding subunit, which is used to perform convolution encoding on the equipment component thermal distribution state channel context association representation feature map to obtain an equipment component thermal distribution state space context association representation feature map; a copying subunit, which is used to copy the equipment component thermal distribution state space context association representation feature map to obtain an equipment component thermal distribution state backup space context association representation feature map; a first feature shape reshaping subunit, which is used to perform feature shape reshaping on the equipment component thermal distribution state channel context association representation feature map, the equipment component thermal distribution state space context association representation feature map and the equipment component thermal distribution state backup space context association representation feature map to obtain an equipment component thermal distribution state channel context association representation feature map. A context-related representation feature matrix, a device component thermal distribution state space context-related representation feature matrix and a device component thermal distribution state backup space context-related representation feature matrix; a covariance matrix calculation subunit, used to calculate the device component thermal distribution state cross-channel cross-covariance matrix between the device component thermal distribution state channel context-related representation feature matrix and the device component thermal distribution state space context-related representation feature matrix; an activation subunit, used to activate the device component thermal distribution state cross-channel cross-covariance matrix using a Softmax function to obtain a device component thermal distribution state feature global interactive attention matrix; a product calculation subunit, used to calculate the product between the device component thermal distribution state backup space context-related representation feature matrix and the device component thermal distribution state feature global interactive attention matrix to obtain a device component thermal distribution state attention enhancement feature representation matrix; a second feature shape reshaping subunit, used to perform feature shape reshaping on the device component thermal distribution state attention enhancement feature representation matrix to obtain a device component thermal distribution enhancement feature map.

[0090] Among them, the covariance matrix calculation subunit is used to: calculate the matrix multiplication between the channel context association representation feature matrix of the thermal distribution state of the equipment components and the spatial context association representation feature matrix of the thermal distribution state of the equipment components to obtain the channel-spatial context association feature matrix of the thermal distribution state of the equipment components; calculate the positional division between the channel-spatial context association feature matrix of the thermal distribution state of the equipment components and the hyperparameters to obtain the cross-channel cross-covariance matrix of the thermal distribution state of the equipment components.

[0091] In a specific example, the device component thermal distribution feature enhancement unit 835 is used to: process each device component thermal distribution feature map in the set of the device component thermal distribution feature maps respectively through the feature space structure consistency self-attention cross-channel enhancement module with the following self-attention feature enhancement formula to obtain the set of the device component thermal distribution enhancement feature maps; wherein the self-attention feature enhancement formula is:

[0092] F ln =Layer Normalization(F i )

[0093] F Q =Conv 1×1 (F ln )

[0094] F K =Conv 3×3 (Conv 1×1 (F ln ))

[0095] F V =Copy(F K )

[0096] M Q =Reshape(F Q )

[0097] M K =Reshape(F K )

[0098] M V =Reshape(F V )

[0099]

[0100]

[0101] Among them, F i is the thermal distribution feature map of the equipment component, Layer Normalization(·) represents the layer normalization operation on the feature map, F ln To normalize the thermal distribution feature map of equipment components, Conv 1×1 (·) is the point convolution operation, F Q The channel context association representation feature map of the thermal distribution state of the equipment components, Conv 3×3 (·) is a dilated convolution operation with a convolution kernel of 3×3, F K is the feature map of the equipment component thermal distribution state space context association representation, Copy(·) is the copy operation, F VThe backup space context association represents the feature map of the thermal distribution state of the equipment components. Reshape(·) represents the feature shape reshaping process. Q is the channel context association representation feature matrix of the thermal distribution state of the equipment components, M K is the feature matrix of the equipment component thermal distribution state space context association representation, M V The backup space context association represents the feature matrix of the thermal distribution state of the equipment components. is matrix multiplication, θ is a hyperparameter, softmac(·) is the softmax activation function, M a is the global interactive attention matrix of the thermal distribution state characteristics of the equipment components, F a It is the thermal distribution enhancement characteristic diagram of each equipment component in the set of thermal distribution enhancement characteristic diagrams of the equipment components.

[0102] Then, after performing global mean pooling processing on each device component heat distribution enhancement feature map in the set of the device component heat distribution enhancement feature map to obtain a set of device component heat distribution enhancement feature vectors, each set of corresponding device component position encoding vectors and device component heat distribution enhancement feature vectors in the set of the device component position encoding vectors and the set of the device component heat distribution enhancement feature vectors are fused to obtain a set of device component heat distribution feature vectors containing position information. It should be understood that global mean pooling converts high-dimensional feature maps into low-dimensional feature vectors by calculating the global mean of each feature matrix along the channel dimension of each device component heat distribution enhancement feature map. This dimensionality reduction process not only reduces the computational complexity, but also retains important heat distribution feature information of the device components. In addition, since the device component position encoding vector contains the position information of each component in the overall device. Combining this information with the heat distribution enhancement feature vector can provide context for the feature vector, so that subsequent processing can better understand the positional relationship and heat distribution transfer between components, thereby better identifying the heat distribution of the device components and fault detection.

[0103] Correspondingly, the position information equipment thermal distribution feature fusion unit 836 is used to: perform global mean pooling processing on each equipment component thermal distribution enhancement feature map in the set of equipment component thermal distribution enhancement feature maps to obtain a set of equipment component thermal distribution enhancement feature vectors; and respectively fuse each group of corresponding equipment component position encoding vectors and equipment component thermal distribution enhancement feature vectors in the set of equipment component position encoding vectors and the set of equipment component thermal distribution enhancement feature vectors to obtain the set of equipment component thermal distribution feature vectors containing position information.

[0104] Furthermore, since each of the device component thermal distribution feature vectors containing location information in the set of device component thermal distribution feature vectors containing location information contains semantic features of thermal distribution of each device component containing location information, and the overall state and fault condition of the device are usually jointly determined by the local states of multiple components, this is crucial for the subsequent device fault detection task. Based on this, in the technical solution of the present application, the set of device component thermal distribution feature vectors containing location information is further input into a feature aggregation network based on feature energy local significance gating to obtain a device global component object granularity temperature distribution feature vector. The feature aggregation network based on feature energy local significance gating can quantify the energy distribution of each device component thermal distribution feature vector containing location information, thereby identifying the most important part for subsequent fault detection tasks in a specific scenario. In this way, it is possible to automatically focus on those with significance (i.e., features that have a greater impact on the overall device state) during the aggregation process of the thermal distribution features of each device component containing location information, and dynamically adjust the weights of different features to use the gating mechanism to highlight important features and suppress redundant information during the feature aggregation process, thereby improving the effectiveness of feature aggregation. In this way, the model's ability to understand and process data, as well as its generalization ability, can be improved, thereby improving its stability and reliability in detecting equipment faults in complex environments.

[0105] Accordingly, the device global component object granular temperature distribution characterization unit 837 includes: a characteristic energy level coefficient calculation subunit, which is used to determine the characteristic energy level coefficient of each device component thermal distribution feature vector containing location information based on the maximum value, minimum value, mean value and variance of each device component thermal distribution feature vector containing location information in the set of device component thermal distribution feature vectors containing location information to obtain a sequence of device component thermal distribution characteristic energy level coefficients containing location information; a local neighborhood mean value calculation subunit, which is used to determine the scale of the local neighborhood of the thermal distribution of the device component containing location information, and use the scale of the local neighborhood of the thermal distribution of the device component containing location information as the mean value calculation range to calculate each of the device components containing location information. The local neighborhood mean of the equipment component thermal distribution characteristic energy level coefficients is used to obtain a sequence of equipment component thermal distribution characteristic energy level significance descriptors containing position information; a mask subunit is used to input the sequence of equipment component thermal distribution characteristic energy level significance descriptors containing position information into a mask module based on a gating function to obtain a mask-gated probabilistic sequence of equipment component thermal distribution characteristic energy level significance descriptors containing position information; a weighted sum calculation subunit is used to use the mask-gated probabilistic sequence of equipment component thermal distribution characteristic energy level significance descriptors containing position information as a sequence of weights, and calculate the position-weighted sum of the set of equipment component thermal distribution feature vectors containing position information to obtain the equipment global component object granularity temperature distribution feature vector.

[0106] Wherein, the characteristic energy level coefficient calculation subunit is used to: calculate the maximum value, minimum value, average value and variance of the thermal distribution characteristic vector of the equipment component containing position information to obtain the maximum value of the thermal distribution of the equipment component containing position information, the minimum value of the thermal distribution of the equipment component containing position information, the average value of the thermal distribution of the equipment component containing position information and the variance of the thermal distribution of the equipment component containing position information; calculate the sum of the variance of the thermal distribution of the equipment component containing position information and the hyperparameter to obtain the first local energy statistical factor of the thermal distribution of the equipment component containing position information; calculate the difference between the maximum value of the thermal distribution of the equipment component containing position information and the average value of the thermal distribution of the equipment component containing position information to obtain the equipment component containing position information The maximum deviation value of thermal distribution; calculating the difference between the minimum value of thermal distribution of equipment components containing position information and the average value of thermal distribution of equipment components containing position information to obtain the minimum deviation value of thermal distribution of equipment components containing position information; calculating the sum of the squares of the maximum deviation value of thermal distribution of equipment components containing position information and the minimum deviation value of thermal distribution of equipment components containing position information and adding the sum to the hyperparameter to obtain a second local energy statistical factor of thermal distribution of equipment components containing position information; calculating the division between the first local energy statistical factor of thermal distribution of equipment components containing position information and the second local energy statistical factor of thermal distribution of equipment components containing position information to obtain the characteristic energy level coefficient of thermal distribution of equipment components containing position information.

[0107] The mask subunit is used to: use the negative number of each device component thermal distribution feature energy level significance descriptor containing position information in the sequence of device component thermal distribution feature energy level significance descriptors containing position information as an exponential power, calculate the exponential function value with the natural constant e as the base to obtain a sequence of device component thermal distribution feature energy level class support significance descriptors containing position information; calculate the position-based sum between the sequence of device component thermal distribution feature energy level class support significance descriptors containing position information and the constant 1 to obtain a linear modulation sequence of device component thermal distribution feature energy level class support significance descriptors containing position information; calculate the linear Modulate the inverse of each linear modulation in a sequence of device component thermal distribution feature energy level class supporting significance descriptors containing location information to obtain a sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information; mask each device component thermal distribution gated probabilistic feature energy level significance descriptor containing location information in the sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information to obtain the sequence of masked gated probabilistic feature energy level significance descriptors of device component thermal distribution containing location information. Among them, each device component thermal distribution gated probabilistic feature energy level significance descriptor containing location information in the sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information is masked to obtain the sequence of masked gated probabilistic device component thermal distribution feature energy level significance descriptors containing location information, including: in response to the device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information being greater than a predetermined threshold, taking the original value of the device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information, otherwise setting it to 0.

[0108] In a specific example, the device global component object granular temperature distribution characterization unit 837 is used to: input the set of device component thermal distribution feature vectors containing location information into the feature aggregation network based on feature energy local significance gating to process with the following feature aggregation formula to obtain the device global component object granular temperature distribution feature vector; wherein the feature aggregation formula is:

[0109] X={x1,x2,...,x k ,...,x n}

[0110]

[0111] w si =mask(w i )

[0112]

[0113] Y={y1,y2,...,y k ,...,y n},y i =w si ·x i

[0114]

[0115] Where X is the set of thermal distribution feature vectors of the equipment components containing position information, x i is the ith device component thermal distribution feature vector containing position information in the set of device component thermal distribution feature vectors containing position information, x k and x n are respectively the kth and nth thermal distribution feature vectors of the equipment components containing position information in the set of thermal distribution feature vectors of the equipment components containing position information, μ i is the mean value of the thermal distribution feature vector of the i-th equipment component containing position information, σ i 2 is the variance of the thermal distribution feature vector of the i-th equipment component containing position information, ε is a hyperparameter, max(x i ) represents the maximum value of the thermal distribution feature vector of the i-th equipment component containing position information, min(x i ) represents the minimum value of the thermal distribution feature vector of the i-th equipment component containing position information, e i is the characteristic energy level coefficient of the thermal distribution characteristic vector of the i-th equipment component containing position information, Num r Indicated by the characteristic energy level coefficient e i The number of characteristic energy coefficients in the local neighborhood centered at q is the characteristic energy level coefficient of the qth equipment component thermal distribution characteristic vector containing position information, e ci is the characteristic energy level significance descriptor of the thermal distribution feature vector of the i-th equipment component containing position information, w i is the gated probabilistic feature energy level significance descriptor of the thermal distribution feature vector of the i-th equipment component containing position information, mask(·) is the masking operation, θ is the predetermined threshold, w si is the mask-gated probabilistic feature energy level significance descriptor of the thermal distribution feature vector of the i-th equipment component containing location information, y iis the significantly enhanced device component thermal distribution feature vector containing location information corresponding to the i-th device component thermal distribution feature vector containing location information, n is the number of feature vectors in the set of significantly enhanced device component thermal distribution feature vectors containing location information, and f is the device global component object granularity temperature distribution feature vector.

[0116] Furthermore, the global component object granular temperature distribution feature vector of the device is input into the classifier-based device detector to obtain the detection result, which is used to indicate whether the monitored device has a fault. In other words, the global component temperature distribution aggregation characteristics of the device are used for classification processing to detect equipment faults in the motor production workshop, so as to timely discover potential faults and issue alarms, help operators take quick measures, and adjust production plans to ensure the quality of motor products while enabling the motor production workshop to complete production on time. In this way, more intelligent collaborative management of motor production workshops can be achieved, helping enterprises maintain flexibility and efficiency in complex production environments.

[0117] It should be understood that the role of the classifier is to use the given categories and known training data to learn classification rules and classifiers, and then classify (or predict) unknown data. Logistic regression and SVM are often used to solve binary classification problems. For multi-class classification problems, logistic regression or SVM can also be used, but multiple binary classifications are required to form a multi-classification. However, this is prone to errors and is not efficient. Commonly used multi-classification methods include the Softmax classification function.

[0118] In a preferred example, when the set of device component position encoding vectors and the set of device component thermal distribution enhancement feature vectors respectively express image semantic position encoding features and image semantic space self-attention channel distribution features based on the local image semantic space domain of the thermal infrared image, the feature aggregation network based on feature energy local significance gating will perform distribution aggregation based on the local image semantic space domain feature energy significance under the global image semantic space domain, so that the obtained device global component object granularity temperature distribution feature vector has insufficient fine-grained semantic aggregation expression due to energy significance aggregation differences. Therefore, it is expected to improve the detail semantic aggregation expression effect of the device global component object granularity temperature distribution feature vector based on image semantic representation differences.

[0119] In a preferred example, inputting the equipment global component object particle size temperature distribution feature vector into a classifier-based equipment detector to obtain a detection result includes:

[0120] Calculating the sum of the absolute values ​​of each eigenvalue of the device global component object granularity temperature distribution eigenvector to obtain a first device global component object granularity temperature distribution and a modulation value, and calculating the square root of the sum of the squares of each eigenvalue of the device global component object granularity temperature distribution eigenvector to obtain a second device global component object granularity temperature distribution and a modulation value;

[0121] After performing point subtraction on the device global component object granularity temperature distribution feature vector and the second device global component object granularity temperature distribution and modulation value, respectively perform point multiplication with the number of eigenvalues ​​of the device global component object granularity temperature distribution feature vector and the inverse of the first device global component object granularity temperature distribution and modulation value, and take the inverse of each eigenvalue to obtain the first device global component object granularity temperature distribution phase conversion vector;

[0122] After point-wise subtraction of the device global component object granularity temperature distribution feature vector from the first device global component object granularity temperature distribution and modulation value, point-wise multiplication is performed with the square root of the number of eigenvalues ​​of the device global component object granularity temperature distribution feature vector and the reciprocal of the second device global component object granularity temperature distribution and modulation value, and the reciprocal of each eigenvalue is taken to obtain the second device global component object granularity temperature distribution phase conversion vector;

[0123] Subtract the dot product vector of the first device global component object granularity temperature distribution phase conversion vector from the second device global component object granularity temperature distribution phase conversion vector and the weighted hyperparameter to obtain an optimized device global component object granularity temperature distribution feature vector;

[0124] The optimized equipment global component object particle size temperature distribution feature vector is input into a classifier-based equipment detector to obtain a detection result.

[0125] Here, the optimization of the equipment global component object particle size temperature distribution feature vector V is expressed as:

[0126]

[0127] v i ∈V∈R n

[0128] Where V is the granular temperature distribution feature vector of the global component object of the equipment, R represents a real number set, and v i represents the eigenvalue of the ith position of the global component object granularity temperature distribution feature vector of the device, n represents the number of eigenvalues ​​of the global component object granularity temperature distribution feature vector of the device, α represents the global component object granularity temperature distribution and modulation value of the first device, β represents the global component object granularity temperature distribution and modulation value of the second device, ⊙ represents the point multiplication by position, Indicates positional subtraction, (·) ⊙-1 Represents the inverse of each eigenvalue of the calculated eigenvector, V1 represents the phase transition vector of the first device global component object granularity temperature distribution, V2 represents the phase transition vector of the second device global component object granularity temperature distribution, ω represents the weighted hyperparameter, and V' represents the optimized device global component object granularity temperature distribution eigenvector.

[0129] Accordingly, in the preferred example, the difference of the characteristic value of the global component object granular temperature distribution feature vector relative to the feature set of the global component object granular temperature distribution feature vector of the device is used as the semantic change intensity information, and the phase-like conversion corresponding to the position-based intensity modulation is performed through different and modulation representation forms, so that the aggregation enhancement of the semantic change phase perception can improve the axial aggregation receptive field along the feature aggregation direction by performing a spatial translation operation based on alternating stacking under the scale balance of the vector set of the global component object granular temperature distribution feature vector of the device, thereby improving the perception effect of the aggregate semantics of the global component object granular temperature distribution feature vector for the detail semantic change, so as to improve the expression effect of the global component object granular temperature distribution feature vector of the device, and improve the accuracy of the detection result obtained by the device detector based on the classifier. In this way, the equipment failure of the motor production workshop can be effectively detected in real time, so as to timely discover potential failures and issue alarms, help operators to take measures quickly, and adjust the production plan, so as to ensure the quality of motor products while enabling the motor production workshop to complete production on time.

[0130] Based on the above embodiments, see Figure 4 As shown in FIG. 1 , a flow chart of a motor production workshop collaborative management method in an embodiment of the present application is shown. For example, the motor production workshop collaborative management method can be executed by a server, which can be Figure 1 The server 100 shown in FIG. Figure 4 As shown, the motor production workshop collaborative management method according to the embodiment of the present application includes the following steps: S510, generating a production plan according to order requirements and existing resources, splitting the production plan into production tasks, and then assigning the production tasks to operators of each production line; S520, uploading production progress data through a data acquisition terminal deployed at each workstation, and generating a real-time production progress chart based on the production progress data; S530, in response to detecting an equipment failure, issuing an alarm and generating a production plan adjustment prompt; S540, recording the working hours and task completion status of each operator, and generating a performance evaluation report.

[0131] Here, those skilled in the art will appreciate that the specific operations of each step in the above motor production workshop collaborative management method have been described in detail above. Figure 2 to Figure 3 The motor production workshop collaborative management system 800 has been introduced in detail, and therefore, its repeated description will be omitted.

[0132] Figure 5 FIG. 1 is an application scenario diagram of a motor production workshop collaborative management system according to an embodiment of the present application. Figure 5 As shown, in this application scenario, first, the thermal infrared image of the monitored device (for example, Figure 5 D), and then, the thermal infrared image is input to a server (for example, Figure 5 In S) shown in , the server can use the motor production workshop collaborative management algorithm to process the thermal infrared image to obtain a detection result indicating whether there is a fault in the monitored equipment.

[0133] Based on the above embodiment, another exemplary embodiment of an electronic device is also provided in the embodiment of the present application. In some possible implementations, the electronic device in the embodiment of the present application may include a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the steps of the motor production workshop collaborative management method in the above embodiment when executing the program.

[0134] For example, in the case of electronic equipment Figure 1 Taking the server 100 in the example as an example, the processor in the electronic device is the processor 110 in the server 100, and the memory in the electronic device is the memory 120 in the server 100.

[0135] The embodiment of the present application also provides a computer-readable storage medium having computer-executable instructions stored thereon. When the computer-executable instructions are executed by a processor, the motor production workshop collaborative management method according to the embodiment of the present application described with reference to the above figures can be executed. The computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0136] The embodiment of the present application also provides a computer program product or computer program, which includes computer executable instructions, and the computer executable instructions are stored in a computer readable storage medium. The processor of the computer device reads the computer executable instructions from the computer readable storage medium, and the processor executes the computer executable instructions, so that the computer device executes the motor production workshop collaborative management method according to the embodiment of the present application.

[0137] Those skilled in the art will appreciate that the contents disclosed in this application may be subject to various variations and improvements. For example, the various devices or components described above may be implemented by hardware, or by software, firmware, or a combination of some or all of the three.

[0138] In addition, although the present application makes various references to certain units in the system according to embodiments of the present application, any number of different units can be used and run on the client and / or server. The units are only illustrative, and different aspects of the system and method can use different units.

[0139] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present application is not limited to any particular form of combination of hardware and software.

[0140] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0141] The above is an explanation of the present application and should not be considered as limiting thereof. Although several exemplary embodiments of the present application have been described, those skilled in the art will readily appreciate that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present application.

Claims

1. A motor production workshop collaborative management system, characterized in that: include: A task allocation module is used to generate a production plan based on order requirements and existing resources, split the production plan into production tasks, and then allocate the production tasks to operators of each production line; A progress tracking module, used to upload production progress data through a data acquisition terminal deployed at each workstation, and generate a real-time production progress chart based on the production progress data; A plan adjustment module, for responding to detection of equipment failure, issuing an alarm and generating a production plan adjustment prompt; The performance evaluation module is used to record the working hours and task completion status of each operator and generate a performance evaluation report.

2. The motor production workshop collaborative management system according to claim 1, characterized in that: The plan adjustment module includes: An equipment thermal infrared image acquisition unit, used to acquire a thermal infrared image of the monitored equipment acquired by the data acquisition terminal; An equipment component image region division unit, used for performing image region division on the thermal infrared image based on equipment components to obtain a set of unit region images of thermal infrared components; A thermal infrared component unit area position coding unit, used for using a position coding function to perform position coding on each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of device component position coding vectors; The device component thermal distribution feature extraction unit is used to extract thermal distribution features of each thermal infrared component unit area image in the set of thermal infrared component unit area images to obtain a set of device component thermal distribution feature maps; A device component thermal distribution feature enhancement unit, used for respectively performing a feature space structure consistency self-attention cross-channel enhancement module on each device component thermal distribution feature map in the set of device component thermal distribution feature maps to obtain a set of device component thermal distribution enhancement feature maps; A location information equipment thermal distribution feature fusion unit, used to fuse each corresponding set of equipment component thermal distribution enhancement feature maps and equipment component position encoding vectors in the set of equipment component thermal distribution enhancement feature maps and the set of equipment component position encoding vectors to obtain a set of equipment component thermal distribution feature vectors containing location information; A device global component object granular temperature distribution characterization unit, used for inputting the set of device component thermal distribution feature vectors containing position information into a feature aggregation network based on feature energy local significance gating to obtain a device global component object granular temperature distribution feature vector; The equipment fault detection unit is used to input the equipment global component object granular temperature distribution feature vector into a classifier-based equipment detector to obtain a detection result, wherein the detection result is used to indicate whether the monitored equipment has a fault.

3. The motor production workshop collaborative management system according to claim 2, characterized in that: The equipment component thermal distribution feature extraction unit is used to: Each thermal infrared component unit area image in the set of thermal infrared component unit area images is respectively passed through a device part thermal distribution feature extractor based on a hole convolutional neural network model to obtain a set of thermal distribution feature maps of the device parts.

4. The motor production workshop collaborative management system according to claim 3, characterized in that: The equipment component thermal distribution characteristic enhancement unit comprises: A layer normalization subunit, used for performing layer normalization on the device component thermal distribution characteristic map to obtain a normalized device component thermal distribution characteristic map; A point convolution processing subunit, used for performing point convolution processing on the normalized device component thermal distribution feature map to obtain a device component thermal distribution state channel context association representation feature map; A convolutional encoding subunit, configured to perform convolutional encoding on the device component thermal distribution state channel context association representation feature map to obtain a device component thermal distribution state space context association representation feature map; A copying subunit, used for copying the equipment component thermal distribution state space context association representation feature map to obtain the equipment component thermal distribution state backup space context association representation feature map; The first characteristic shape reshaping subunit is used to perform characteristic shape reshaping on the equipment component thermal distribution state channel context association representation feature map, the equipment component thermal distribution state space context association representation feature map and the equipment component thermal distribution state backup space context association representation feature map to obtain an equipment component thermal distribution state channel context association representation feature matrix, an equipment component thermal distribution state space context association representation feature matrix and an equipment component thermal distribution state backup space context association representation feature matrix; A covariance matrix calculation subunit, used to calculate a cross-channel cross-covariance matrix of thermal distribution states of equipment components between a channel context association representation feature matrix of thermal distribution states of equipment components and a spatial context association representation feature matrix of thermal distribution states of equipment components; An activation subunit, used for activating the cross-channel cross-covariance matrix of the thermal distribution state of the equipment components using a Softmax function to obtain a global interactive attention matrix of the thermal distribution state features of the equipment components; A product calculation subunit, used for calculating the product between the equipment component thermal distribution state backup space context association representation feature matrix and the equipment component thermal distribution state feature global interactive attention matrix to obtain the equipment component thermal distribution state attention enhancement feature representation matrix; The second characteristic shape reshaping subunit is used to perform characteristic shape reshaping on the attention enhancement feature representation matrix of the thermal distribution state of the equipment component to obtain the equipment component thermal distribution enhancement feature map.

5. The motor production workshop collaborative management system according to claim 4, characterized in that: The covariance matrix calculation subunit is used for: Calculating matrix multiplication between the channel context association representation feature matrix of the thermal distribution state of the equipment component and the spatial context association representation feature matrix of the thermal distribution state of the equipment component to obtain a channel-spatial context association feature matrix of the thermal distribution state of the equipment component; The position-wise division between the channel-space contextual association feature matrix of the thermal distribution state of the device components and the hyperparameters is calculated to obtain the cross-channel cross-covariance matrix of the thermal distribution state of the device components.

6. The motor production workshop collaborative management system according to claim 5, characterized in that: The location information device thermal distribution feature fusion unit is used to: Performing global mean pooling processing on each device component thermal distribution enhancement feature map in the set of device component thermal distribution enhancement feature maps to obtain a set of device component thermal distribution enhancement feature vectors; Each corresponding set of equipment component position encoding vectors and equipment component thermal distribution enhancement feature vectors in the set of equipment component position encoding vectors and the set of equipment component thermal distribution enhancement feature vectors are fused separately to obtain the set of equipment component thermal distribution feature vectors containing position information.

7. The motor production workshop collaborative management system according to claim 6, characterized in that: The device global component object particle size temperature distribution characterization unit includes: a characteristic energy level coefficient calculation subunit, configured to determine the characteristic energy level coefficient of each equipment component thermal distribution feature vector containing position information in the set of equipment component thermal distribution feature vectors containing position information based on the maximum value, minimum value, mean value and variance of each equipment component thermal distribution feature vector containing position information, so as to obtain a sequence of equipment component thermal distribution characteristic energy level coefficients containing position information; A local neighborhood mean calculation subunit is used to determine the scale of the local neighborhood of the thermal distribution of the equipment component containing the location information, use the scale of the local neighborhood of the thermal distribution of the equipment component containing the location information as the mean calculation range, and respectively calculate the local neighborhood mean of each thermal distribution characteristic energy level coefficient of the equipment component containing the location information to obtain a sequence of thermal distribution characteristic energy level significance descriptors of the equipment component containing the location information; A mask subunit, used for inputting the sequence of equipment component thermal distribution feature energy level significance descriptors containing position information into a mask module based on a gating function to obtain a mask-gated probabilistic sequence of equipment component thermal distribution feature energy level significance descriptors containing position information; The weighted sum calculation subunit is used to calculate the position-weighted sum of the set of thermal distribution feature vectors of the equipment components containing the position information by using the sequence of the mask-gated probabilistic energy level significance descriptors of the equipment components containing the position information as the sequence of weights to obtain the global component object granularity temperature distribution feature vector of the equipment.

8. The motor production workshop collaborative management system according to claim 7, characterized in that: The characteristic energy level coefficient calculation subunit is used for: Calculating the maximum value, minimum value, average value and variance of the thermal distribution feature vector of the equipment component containing the position information to obtain the maximum value of the thermal distribution of the equipment component containing the position information, the minimum value of the thermal distribution of the equipment component containing the position information, the average value of the thermal distribution of the equipment component containing the position information and the variance of the thermal distribution of the equipment component containing the position information; Calculate the sum of the thermal distribution variance of the equipment component containing the position information and the hyperparameter to obtain a first local energy statistical factor of thermal distribution of the equipment component containing the position information; Calculating the difference between the maximum value of the thermal distribution of the equipment component containing the position information and the average value of the thermal distribution of the equipment component containing the position information to obtain the maximum deviation value of the thermal distribution of the equipment component containing the position information; Calculating the difference between the minimum value of the thermal distribution of the equipment component containing the position information and the average value of the thermal distribution of the equipment component containing the position information to obtain a minimum deviation value of the thermal distribution of the equipment component containing the position information; Calculating the square sum of the maximum deviation value of the thermal distribution of the equipment component containing the position information and the minimum deviation value of the thermal distribution of the equipment component containing the position information and adding the sum to the hyperparameter to obtain a second local energy statistical factor of the thermal distribution of the equipment component containing the position information; The division between the first local energy statistical factor of thermal distribution of equipment components containing position information and the second local energy statistical factor of thermal distribution of equipment components containing position information is calculated to obtain a characteristic energy level coefficient of thermal distribution of equipment components containing position information.

9. The motor production workshop collaborative management system according to claim 8, characterized in that: The mask subunit is used for: Taking the negative number of each device component thermal distribution feature energy level significance descriptor containing location information in the sequence of device component thermal distribution feature energy level significance descriptors containing location information as an exponential power, calculating the exponential function value with the natural constant e as the base to obtain a sequence of device component thermal distribution feature energy level class support significance descriptors containing location information; Calculating the positional sum of the sequence of the equipment component thermal distribution feature energy level class support significant descriptors containing the position information and a constant 1 to obtain a linearly modulated sequence of the equipment component thermal distribution feature energy level class support significant descriptors containing the position information; Calculating the inverse of each linear modulation device component thermal distribution feature energy level class support significance descriptor in the sequence of device component thermal distribution feature energy level class support significance descriptors containing linear modulation position information to obtain a sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing position information; Performing masking processing on each device component thermal distribution gated probabilistic feature energy level significance descriptor containing position information in the sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing position information to obtain the sequence of masked gated probabilistic device component thermal distribution feature energy level significance descriptors containing position information; Among them, performing masking processing on each device component thermal distribution gated probabilistic feature energy level significance descriptor containing location information in the sequence of device component thermal distribution gated probabilistic feature energy level significance descriptors containing location information to obtain the sequence of masked gated probabilistic device component thermal distribution feature energy level significance descriptors containing location information, including: In response to the device component thermal distribution gated probabilistic feature energy level significance descriptor containing the location information being greater than a predetermined threshold, the original value of the device component thermal distribution gated probabilistic feature energy level significance descriptor containing the location information is taken, otherwise it is set to 0.

10. A motor production workshop collaborative management method, characterized in that: include: Generate a production plan based on order requirements and existing resources, split the production plan into production tasks, and assign the production tasks to operators of each production line; Uploading production progress data through a data acquisition terminal deployed at each workstation, and generating a real-time production progress chart based on the production progress data; In response to detecting equipment failure, an alarm is issued and a production plan adjustment prompt is generated; Record the working hours and task completion of each operator and generate a performance evaluation report.

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