Equipment state detection system and method of intelligent production system

Abstract

The invention discloses an equipment state detection system and method of an intelligent production system. The method comprises the following steps: acquiring state information of equipment to be detected; performing feature analysis processing on the state information of the to-be-detected equipment to obtain target state feature information; and carrying out detection analysis processing on the target state feature information to obtain target equipment operation state information. According to the invention, the detection efficiency and accuracy of the equipment state are improved, and the labor intensity of operators is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection, and in particular to an equipment status detection method and system for an intelligent production system. Background Art

[0002] When the intelligent production system of intelligent production security equipment is in continuous operation, the actual operating state of the equipment may deviate from the initial design due to factors such as internal parts wear and accumulated data processing errors. This deviation will not only accelerate the loss of equipment, but also reduce energy efficiency and accelerate parts wear. Therefore, early warning of the equipment operation status is crucial to prevent the equipment from being in an abnormal state for a long time. At present, the monitoring and maintenance of equipment mainly rely on manual inspection. This method lacks intelligence, has low detection accuracy and efficiency, and also increases the workload of technicians. Therefore, a device status detection system and method for an intelligent production system are provided to improve the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, and improve the level of intelligent production and production efficiency. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an equipment status detection system and method for an intelligent production system, which can realize intelligent detection of equipment operating status, improve accuracy and efficiency, and improve the level of intelligent production and production efficiency.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a device status detection method for an intelligent production system, the method comprising:

[0005] Get the status information of the device to be detected;

[0006] Performing feature analysis on the state information of the device to be detected to obtain target state feature information;

[0007] The target status characteristic information is detected, analyzed and processed to obtain the target device operation status information.

[0008] In the above technical solution, the feature analysis and processing of the state information of the device to be detected to obtain target state feature information includes:

[0009] Performing time-frequency analysis on the state information of the device to be detected to obtain first state characteristic information;

[0010] Performing feature extraction processing on the state information of the device to be detected to obtain second state feature information;

[0011] The first state characteristic information and the second state characteristic information are concatenated to obtain target state characteristic information.

[0012] In the above technical solution, the feature extraction process is performed on the state information of the device to be detected to obtain the second state feature information, including:

[0013] Performing data conversion processing on the device status information to be detected by using a data conversion model to obtain converted device status information;

[0014] Wherein, the data conversion model is:

[0015] MBZH = x1*ln(1+DJC / x2);

[0016] In the formula, MBZH represents the state information of the conversion device; DJC represents the state information of the device to be detected; x1 and x2 represent the first conversion coefficient and the second conversion coefficient respectively;

[0017] Performing filtering processing on the conversion device state information to obtain filtering device state information;

[0018] The target feature extraction model is used to perform feature extraction processing on the filtering device state information to obtain second state feature information.

[0019] In the above technical solution, the target feature extraction model includes a first extraction module, a second extraction module, a third extraction module, a fourth extraction module, a first pooling module, a second pooling module, a first connection module and a second connection module; wherein,

[0020] The model architectures of the second extraction module, the third extraction module, and the fourth extraction module are consistent;

[0021] The input end of the first extraction module is configured to receive the first model input information of the target feature extraction model, the output end of the first extraction module is connected to the input end of the second extraction module; the output end of the second extraction module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the third extraction module; the output end of the third extraction module is connected to the input end of the fourth extraction module; the output end of the fourth extraction module is connected to the input end of the second pooling module; the output end of the second pooling module is connected to the input end of the first connection module; the output end of the first connection module is connected to the input end of the second connection module; the output end of the second connection module is configured to output the first model output information of the target feature extraction model.

[0022] In the above technical solution, the first extraction module includes a first convolution unit, a second convolution unit, a first activation unit, a second activation unit, a first pooling unit and a second pooling unit; wherein,

[0023] The input end of the first convolution unit is configured as the first model input information of the target feature extraction model, the output end of the first convolution unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the second pooling unit; the output end of the second pooling unit is connected to the input end of the second extraction module;

[0024] The second extraction module includes a third convolution unit and a third activation unit; wherein,

[0025] The input end of the third convolution unit is connected to the output end of the second pooling unit; the output end of the third convolution unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the first pooling module.

[0026] In the above technical solution, the detection, analysis and processing of the target state characteristic information to obtain the target device operation state information includes:

[0027] Performing detection processing on the target state feature information to obtain target detection state information; the target detection state information includes a plurality of target detection state values ​​distributed in time series;

[0028] The target detection status information is compared and analyzed to obtain target device operation status information.

[0029] In the above technical solution, the device status detection method of the intelligent production system according to claim 6 is characterized in that the comparative analysis and processing of the target detection status information to obtain the target device operation status information includes:

[0030] Using a first state analysis table to perform table lookup conversion processing on the target detection state information to obtain first device operation state information; the first device operation state information includes a plurality of device operation state representations;

[0031] Performing statistical sorting processing on the device operation status representation in the first device operation status information to obtain the first device operation status information; the first device operation status information includes a plurality of device operation status numerical relationships distributed according to operation status priorities;

[0032] Based on the second status analysis table, the first device operating status information is subjected to table lookup analysis and processing in the operating status priority order to obtain the target device operating status information.

[0033] The present invention also provides an equipment status detection system for an intelligent production system, the system comprising:

[0034] An acquisition module is used to obtain the status information of the device to be detected;

[0035] A first processing module is used to perform feature analysis on the state information of the device to be detected to obtain target state feature information;

[0036] The second processing module is used to detect, analyze and process the target status feature information to obtain target device operation status information.

[0037] The present invention also provides an equipment status detection system for an intelligent production system.

[0038] A memory storing executable program code;

[0039] a processor coupled to the memory;

[0040] The processor calls the executable program code stored in the memory to execute the above-mentioned device status detection method of the intelligent production system.

[0041] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the equipment status detection method of the above-mentioned intelligent production system.

[0042] Due to the application of the above technical solution, the present invention has the following advantages compared with the prior art:

[0043] 1. The present invention improves the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through an intelligent detection method, improves the subsequent production level and production efficiency, and reduces the labor intensity of maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a schematic diagram of a scenario of an equipment status detection system of an intelligent production system provided by an embodiment of the present invention;

[0046] Figure 2 It is a flow chart of a device status detection method of an intelligent production system disclosed in an embodiment of the present invention;

[0047] Figure 3 It is a structural schematic diagram of an equipment status detection system of an intelligent production system disclosed in an embodiment of the present invention;

[0048] Figure 4 It is a structural schematic diagram of another equipment status detection system of an intelligent production system disclosed in an embodiment of the present invention;

[0049] Figure 5 It is a structural schematic diagram of a target feature extraction model disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0054] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0055] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0056] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0057] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further perform graphic processing so that computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0058] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0059] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are often used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In an embodiment of the present application, the large model may be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongwen Qianyi model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0060] The embodiments of the present application provide a method, system, computer device and computer-readable storage medium for detecting the equipment status of an intelligent production system, which are described in detail below.

[0061] See also Figure 1 , Figure 1Schematic diagram of a scene of an equipment status detection system of an intelligent production system provided in an embodiment of the present application. The equipment status detection system of the intelligent production system may include a computer device 100, and the computer device 100 is integrated with the equipment status detection system of the intelligent production system, such as Figure 1 Computer equipment in.

[0062] In the embodiment of the present application, the computer device 100 is mainly used to obtain the status information of the device to be detected;

[0063] Performing feature analysis on the state information of the device to be detected to obtain target state feature information;

[0064] The target status characteristic information is detected, analyzed and processed to obtain the target device operation status information.

[0065] It can improve the accuracy and efficiency of intelligent detection of equipment operation status in intelligent production systems through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0066] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0067] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0068] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the equipment status detection system of the intelligent production system can also include one or more other services, which are not specifically limited here.

[0069] In addition, if Figure 1 As shown, the equipment status detection system of the intelligent production system may further include a memory 200 for storing data, such as image data, location information, and the like.

[0070] It should be noted that Figure 1 The scenario diagram of the equipment status detection system of the intelligent production system shown is merely an example. The equipment status detection system and scenario of the intelligent production system described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the equipment status detection system of the intelligent production system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0071] The present invention discloses an equipment status detection system and method for an intelligent production system, which is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through an intelligent detection method, and improving the intelligent production level and production efficiency. Detailed descriptions are given below.

[0072] Embodiment 1

[0073] See also Figure 2 , Figure 2 1 is a flow chart of a method for detecting the equipment status of an intelligent production system disclosed in an embodiment of the present invention. Figure 2 The device status detection method of the intelligent production system described is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the device status detection method of the intelligent production system may include the following operations:

[0074] 101. Obtain status information of the device to be detected.

[0075] 102. Perform feature analysis on the state information of the device to be detected to obtain target state feature information.

[0076] 103. Detect, analyze and process the target status characteristic information to obtain the target device operation status information.

[0077] It should be noted that the above-mentioned device status information to be detected includes a number of device status values ​​to be detected distributed in chronological order according to the collection time, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned device status information to be detected is collected by the device operation sound of the intelligent production system, which is not limited in the embodiment of the present invention. Furthermore, the frequency of the above-mentioned collection time can be set by the user according to actual needs, which is not limited in the embodiment of the present invention.

[0078] It should be noted that the above-mentioned target equipment operating status information is used to perform intelligent health status monitoring and maintenance on the equipment of the intelligent production system, so as to ensure the normal operation of the equipment and reduce the problem of decreased production efficiency due to equipment failure, which is not limited in the embodiments of the present invention.

[0079] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0080] In an optional embodiment, in the above step 102, feature analysis processing is performed on the state information of the device to be detected to obtain target state feature information, including:

[0081] Performing time-frequency analysis on the state information of the device to be detected to obtain first state characteristic information;

[0082] Performing feature extraction processing on the state information of the device to be detected to obtain second state feature information;

[0083] The first state characteristic information and the second state characteristic information are concatenated to obtain target state characteristic information.

[0084] It should be noted that the time-frequency analysis processing of the device status information to be detected may be based on wavelet transform to extract features, and then normalize the audio feature information to obtain the features, which is not limited in the embodiment of the present invention.

[0085] It should be noted that the above-mentioned splicing processing of the first state feature information and the second state feature information is a fusion of two feature extraction methods based on traditional machine learning algorithms and deep learning algorithms, so as to strengthen the extraction of deep features of the state information of the equipment to be detected, which is more conducive to the accurate identification of the equipment state of the intelligent production system, and the embodiments of the present invention are not limited to this.

[0086] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0087] In another optional embodiment, feature extraction processing is performed on the state information of the device to be detected to obtain second state feature information, including:

[0088] The data conversion model is used to perform data conversion processing on the state information of the device to be detected to obtain the converted device state information;

[0089] The data conversion model is:

[0090] MBZH = x1*ln(1+DJC / x2);

[0091] In the formula, MBZH represents the state information of the conversion device; DJC represents the state information of the device to be detected; x1 and x2 represent the first conversion coefficient and the second conversion coefficient respectively;

[0092] Filtering the conversion device status information (device working sound) to obtain filtered device status information;

[0093] The target feature extraction model is used to perform feature extraction processing on the filtering device state information to obtain second state feature information.

[0094] It should be noted that the above filtering process for the conversion device status information is to perform low-frequency filtering and high-frequency filtering respectively, which can also be implemented based on a Mel triangle filter group, and the embodiment of the present invention does not limit this.

[0095] It should be noted that the first conversion coefficient and the second conversion coefficient may be set by the user, or may be a default value given by the system, or may be determined based on the average of historical coefficients, which is not limited in the embodiment of the present invention. Furthermore, the first conversion coefficient is a positive integer greater than 2000 and less than 3000, and the second conversion coefficient is a positive integer greater than 500 and less than 800, which is not limited in the embodiment of the present invention.

[0096] Furthermore, the data conversion processing of the device status information to be detected using the data conversion model is to convert the data into a specific frequency domain interval to facilitate subsequent more accurate feature extraction and recognition based on the audio data, and then compare and identify the corresponding audio with the audio in normal working state to judge the device status information, which is not limited to the embodiments of the present invention.

[0097] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0098] In yet another optional embodiment, Figure 5 As shown, the target feature extraction model includes a first extraction module, a second extraction module, a third extraction module, a fourth extraction module, a first pooling module, a second pooling module, a first connection module and a second connection module; wherein,

[0099] The model architectures of the second extraction module, the third extraction module, and the fourth extraction module are consistent;

[0100] The input end of the first extraction module is configured to receive the first model input information of the target feature extraction model, the output end of the first extraction module is connected to the input end of the second extraction module; the output end of the second extraction module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the third extraction module; the output end of the third extraction module is connected to the input end of the fourth extraction module; the output end of the fourth extraction module is connected to the input end of the second pooling module; the output end of the second pooling module is connected to the input end of the first connection module; the output end of the first connection module is connected to the input end of the second connection module; the output end of the second connection module is configured to output the first model output information of the target feature extraction model.

[0101] It should be noted that the above-mentioned first pooling module and the second pooling module are both constructed based on the maximum pooling layer to achieve dimensionality reduction processing of features, reduce the number of parameters, and improve the processing efficiency of the model, which is not limited in the embodiment of the present invention.

[0102] It should be noted that the first connection module and the second connection module are both constructed based on the fully connected layer, and their output dimensions are both 128, which is not limited in the embodiment of the present invention.

[0103] It should be noted that the above target feature extraction model can be obtained based on cross entropy loss training, which is not limited in the embodiment of the present invention.

[0104] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0105] In yet another optional embodiment, the first extraction module includes a first convolution unit, a second convolution unit, a first activation unit, a second activation unit, a first pooling unit, and a second pooling unit; wherein,

[0106] The input end of the first convolution unit is configured as the first model input information of the target feature extraction model, the output end of the first convolution unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the second pooling unit; the output end of the second pooling unit is connected to the input end of the second extraction module;

[0107] The second extraction module includes a third convolution unit and a third activation unit; wherein,

[0108] The input end of the third convolution unit is connected to the output end of the second pooling unit; the output end of the third convolution unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the first pooling module.

[0109] It should be noted that the above-mentioned first activation unit, second activation unit and third activation unit are all constructed based on the Relu activation function so that the target feature extraction model has a better linear representation ability, which is not limited in the embodiment of the present invention.

[0110] It should be noted that the convolution kernel sizes of the first convolution unit, the second convolution unit, and the third convolution unit are all 3×3. The use of a small convolution kernel can retain the characteristics of the original data, which is convenient for the subsequent analysis of the original characteristics of the data. The embodiment of the present invention does not limit this. Furthermore, the number of channels of the first convolution unit is 64, the number of channels of the second convolution unit is 128, the number of channels of the third convolution unit corresponding to the second extraction module and the third convolution unit corresponding to the third extraction module are both 256, and the number of channels of the third convolution unit corresponding to the fourth extraction module and the third convolution unit corresponding to the fifth extraction module are both 512, which is not limited in the embodiment of the present invention.

[0111] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0112] In an optional embodiment, the target state feature information is detected, analyzed and processed to obtain the target device operation state information, including:

[0113] Detecting and processing the target state feature information to obtain target detection state information; the target detection state information includes a number of target detection state values ​​distributed in time series;

[0114] Compare and analyze the target detection status information to obtain the target device operation status information.

[0115] It should be noted that the above-mentioned temporal sequence relationship when collecting the device status values ​​to be detected in the device status information to be detected represented by the time series distribution is not limited in the embodiment of the present invention.

[0116] In this optional embodiment, as an optional implementation manner, the above-mentioned detection and processing of the target state feature information to obtain the target detection state information includes:

[0117] Encoding and compressing the target state feature information to obtain compressed space representation information;

[0118] The compressed spatial representation information is reconstructed to obtain target detection status information.

[0119] It should be noted that the above-mentioned encoding and compression processing of the target state feature information to obtain the compressed space representation information is a nonlinear dimensionality reduction processing of the data, which can be implemented based on a feedforward neural network including at least 3 hidden layers, and the embodiments of the present invention are not limited thereto.

[0120] It should be noted that the above-mentioned data reconstruction processing of the compressed space representation information is to reconstruct the original input from the compressed low-dimensional features. When there is an abnormality in the data, there is a large difference between the reconstructed data and the original data, so that it is possible to determine whether there is an abnormality in the device based on this difference. The greater the difference, the more serious the abnormality, which is not limited in the embodiments of the present invention.

[0121] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0122] In another optional embodiment, the target detection state information is compared and analyzed to obtain the target device operation state information, including:

[0123] Using the first state analysis table to perform table lookup conversion processing on the target detection state information, to obtain first device operation state information; the first device operation state information includes a plurality of device operation state representations;

[0124] Performing statistical sorting processing on the device operation status representation in the first device operation status information to obtain the first device operation status information; the first device operation status information includes a plurality of device operation status numerical relationships distributed according to the operation status priority;

[0125] Based on the second status analysis table, the operation status information of the first device is looked up and analyzed in the order of the operation status priority to obtain the operation status information of the target device.

[0126] It should be noted that the first state analysis table represents the relationship between the target detection state value and the equipment operation state of the intelligent production system. Furthermore, the equipment operation state represents the equipment operation state of the intelligent production system at a certain moment, which is not limited in the embodiment of the present invention. Furthermore, the first state analysis table can be:

[0127] Target detection status value Equipment operating status indication (0.8,1] Level 3 abnormality (0.6,0.8] Secondary abnormality (0.5,0.6] Level 1 abnormality [0,0.5] Equipment is normal

[0128] It should be noted that the above statistical sorting processing of the device operation status representation in the first device operation status information is performed to obtain the first device operation status information by statistics according to the category of the device operation status representation, so as to obtain the number of device operation status representations of level 3 abnormality, level 2 abnormality, level 1 abnormality and normal equipment in turn, that is, to obtain the numerical relationship of the device operation status (such as level 3 abnormality-4, that is, there are 4 values ​​between (0.8,1]), which is not limited in the embodiment of the present invention. Furthermore, the above operation status priority is level 3 abnormality, level 2 abnormality, level 1 abnormality and normal equipment from high to low, which is not limited in the embodiment of the present invention.

[0129] It should be noted that the above-mentioned second state analysis table characterizes the relationship between the statistical value represented by the equipment operating state and the current operating state of the equipment, which is not limited by the embodiment of the present invention. Further, based on the second state analysis table, the first equipment operating state information is analyzed and processed in the order of operating state priority to find the relationship between the statistical value represented by the equipment operating state that meets the requirements and the current operating state of the equipment in the order of three-level abnormality, two-level abnormality, one-level abnormality and normal equipment, that is, first use the numerical relationship of the equipment operating state corresponding to the three-level abnormality to look up the table in the second state analysis table, if the state condition that meets the condition is found, the corresponding current operating state of the equipment is directly determined as the target equipment operating state information, otherwise the numerical relationship of the equipment operating state corresponding to the two-level abnormality is used, until the corresponding numerical relationship of the equipment operating state of the normal equipment is used (that is, the normal equipment is used as the target equipment operating state information at this time), which is not limited by the embodiment of the present invention. Further, the above-mentioned second state analysis table can be:

[0130] Status conditions Current operating status of the device The number of level 3 anomalies is greater than or equal to 1 Equipment failure The number of secondary anomalies is greater than or equal to 6 Equipment failure The number of secondary anomalies is greater than or equal to 3 Need maintenance The number of secondary anomalies is greater than or equal to 1 Perform a comprehensive overhaul at the next maintenance cycle The number of first-level anomalies is greater than or equal to 3 Perform a comprehensive overhaul at the next maintenance cycle other Equipment is normal

[0131] It should be noted that the relationship between the statistical value represented by the above-mentioned device operating status and the current operating status of the device is determined through big data association analysis, which is related to the device type and device operating time, and can be updated irregularly, and the embodiments of the present invention do not limit this.

[0132] It can be seen that implementing the equipment status detection method of the intelligent production system described in the embodiment of the present invention is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the level of intelligent production and production efficiency.

[0133] Embodiment 2

[0134] See also Figure 3 , Figure 3 Schematic diagram of the structure of an equipment status detection system of an intelligent production system disclosed in an embodiment of the present invention. Figure 3The described system can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the system may include:

[0135] The acquisition module 201 is used to acquire the status information of the device to be detected;

[0136] The first processing module 202 is used to perform feature analysis on the state information of the device to be detected to obtain target state feature information;

[0137] The second processing module 203 is used to detect, analyze and process the target state feature information to obtain the target device operation state information.

[0138] It can be seen that implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0139] In another optional embodiment, feature analysis is performed on the state information of the device to be detected to obtain target state feature information, including:

[0140] Performing time-frequency analysis on the state information of the device to be detected to obtain first state characteristic information;

[0141] Performing feature extraction processing on the state information of the device to be detected to obtain second state feature information;

[0142] The first state characteristic information and the second state characteristic information are concatenated to obtain target state characteristic information.

[0143] It can be seen that implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0144] In yet another optional embodiment, feature extraction processing is performed on the state information of the device to be detected to obtain second state feature information, including:

[0145] The data conversion model is used to perform data conversion processing on the state information of the device to be detected to obtain the converted device state information;

[0146] The data conversion model is:

[0147] MBZH = x1*ln(1+DJC / x2);

[0148] In the formula, MBZH represents the state information of the conversion device; DJC represents the state information of the device to be detected; x1 and x2 represent the first conversion coefficient and the second conversion coefficient respectively;

[0149] Filtering the conversion device state information to obtain filtering device state information;

[0150] The target feature extraction model is used to perform feature extraction processing on the filtering device state information to obtain second state feature information.

[0151] It can be seen that the implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0152] In yet another optional embodiment, Figure 5 As shown, the target feature extraction model includes a first extraction module, a second extraction module, a third extraction module, a fourth extraction module, a first pooling module, a second pooling module, a first connection module and a second connection module; wherein,

[0153] The model architectures of the second extraction module, the third extraction module, and the fourth extraction module are consistent;

[0154] The input end of the first extraction module is configured to receive the first model input information of the target feature extraction model, the output end of the first extraction module is connected to the input end of the second extraction module; the output end of the second extraction module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the third extraction module; the output end of the third extraction module is connected to the input end of the fourth extraction module; the output end of the fourth extraction module is connected to the input end of the second pooling module; the output end of the second pooling module is connected to the input end of the first connection module; the output end of the first connection module is connected to the input end of the second connection module; the output end of the second connection module is configured to output the first model output information of the target feature extraction model.

[0155] It can be seen that the implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0156] In yet another optional embodiment, Figure 5 As shown, the first extraction module includes a first convolution unit, a second convolution unit, a first activation unit, a second activation unit, a first pooling unit, and a second pooling unit; wherein,

[0157] The input end of the first convolution unit is configured as the first model input information of the target feature extraction model, the output end of the first convolution unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the second pooling unit; the output end of the second pooling unit is connected to the input end of the second extraction module;

[0158] The second extraction module includes a third convolution unit and a third activation unit; wherein,

[0159] The input end of the third convolution unit is connected to the output end of the second pooling unit; the output end of the third convolution unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the first pooling module.

[0160] It can be seen that the implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0161] In yet another optional embodiment, the target state feature information is detected, analyzed and processed to obtain the target device operation state information, including:

[0162] Detecting and processing the target state feature information to obtain target detection state information; the target detection state information includes a number of target detection state values ​​distributed in time series;

[0163] Compare and analyze the target detection status information to obtain the target device operation status information.

[0164] It can be seen that the implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0165] In yet another optional embodiment, the target detection state information is compared and analyzed to obtain the target device operation state information, including:

[0166] Using the first state analysis table to perform table lookup conversion processing on the target detection state information, to obtain first device operation state information; the first device operation state information includes a plurality of device operation state representations;

[0167] Performing statistical sorting processing on the device operation status representation in the first device operation status information to obtain the first device operation status information; the first device operation status information includes a plurality of device operation status numerical relationships distributed according to the operation status priority;

[0168] Based on the second status analysis table, the operation status information of the first device is looked up and analyzed in the order of the operation status priority to obtain the operation status information of the target device.

[0169] It can be seen that implementation Figure 3 The equipment status detection system of the intelligent production system described is beneficial to improving the intelligent detection accuracy and efficiency of the equipment operation status of the intelligent production system through intelligent detection methods, thereby improving the intelligent production level and production efficiency.

[0170] Embodiment 3

[0171] See also Figure 4 , Figure 4 Schematic diagram of the structure of another device status detection system of an intelligent production system disclosed in an embodiment of the present invention. Figure 4 The described system can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the system may include:

[0172] A memory 301 storing executable program codes;

[0173] a processor 302 coupled to the memory 301;

[0174] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the device status detection method of the intelligent production system described in the first embodiment.

[0175] Embodiment 4

[0176] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the device status detection method of the intelligent production system described in the first embodiment.

[0177] Embodiment 5

[0178] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the device status detection method of the intelligent production system described in the first embodiment.

[0179] The system embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative labor.

[0180] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0181] Finally, it should be noted that the equipment status detection system and method of an intelligent production system disclosed in the embodiment of the present invention only discloses the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for detecting equipment status of an intelligent production system, characterized in that: The method comprises: Get the status information of the device to be detected; Performing feature analysis on the state information of the device to be detected to obtain target state feature information; The target status characteristic information is detected, analyzed and processed to obtain the target device operation status information.

2. The device status detection method of the intelligent production system according to claim 1 is characterized in that: The performing feature analysis on the state information of the device to be detected to obtain target state feature information includes: Performing time-frequency analysis on the state information of the device to be detected to obtain first state characteristic information; Performing feature extraction processing on the state information of the device to be detected to obtain second state feature information; The first state characteristic information and the second state characteristic information are concatenated to obtain target state characteristic information.

3. The device status detection method of the intelligent production system according to claim 2 is characterized in that: The step of performing feature extraction processing on the state information of the device to be detected to obtain second state feature information includes: Using a data conversion model to perform data conversion processing on the device status information to be detected to obtain converted device status information; Wherein, the data conversion model is: MBZH = x1*ln(1+DJC / x2); In the formula, MBZH represents the state information of the conversion device; DJC represents the state information of the device to be detected; x1 and x2 represent the first conversion coefficient and the second conversion coefficient respectively; Filtering the conversion device state information to obtain filtering device state information; The target feature extraction model is used to perform feature extraction processing on the filtering device state information to obtain second state feature information.

4. The device status detection method of the intelligent production system according to claim 3 is characterized in that: The target feature extraction model includes a first extraction module, a second extraction module, a third extraction module, a fourth extraction module, a first pooling module, a second pooling module, a first connection module and a second connection module; wherein, The model architectures of the second extraction module, the third extraction module, and the fourth extraction module are consistent; The input end of the first extraction module is configured to receive the first model input information of the target feature extraction model, the output end of the first extraction module is connected to the input end of the second extraction module; the output end of the second extraction module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the third extraction module; the output end of the third extraction module is connected to the input end of the fourth extraction module; the output end of the fourth extraction module is connected to the input end of the second pooling module; the output end of the second pooling module is connected to the input end of the first connection module; the output end of the first connection module is connected to the input end of the second connection module; the output end of the second connection module is configured to output the first model output information of the target feature extraction model.

5. The device status detection method of the intelligent production system according to claim 4 is characterized in that: The first extraction module includes a first convolution unit, a second convolution unit, a first activation unit, a second activation unit, a first pooling unit, and a second pooling unit; wherein, The input end of the first convolution unit is configured as the first model input information of the target feature extraction model, the output end of the first convolution unit is connected to the input end of the first activation unit; the output end of the first activation unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the second convolution unit; the output end of the second convolution unit is connected to the input end of the second activation unit; the output end of the second activation unit is connected to the input end of the second pooling unit; the output end of the second pooling unit is connected to the input end of the second extraction module; The second extraction module includes a third convolution unit and a third activation unit; wherein, The input end of the third convolution unit is connected to the output end of the second pooling unit; the output end of the third convolution unit is connected to the input end of the third activation unit; the output end of the third activation unit is connected to the input end of the first pooling module.

6. The device status detection method of the intelligent production system according to claim 1 is characterized in that: The detecting, analyzing and processing the target state characteristic information to obtain the target device operation state information includes: Performing detection processing on the target state feature information to obtain target detection state information; the target detection state information includes a plurality of target detection state values ​​distributed in time series; The target detection status information is compared and analyzed to obtain target device operation status information.

7. The device status detection method of the intelligent production system according to claim 6 is characterized in that: The comparing and analyzing the target detection status information to obtain the target device operation status information includes: Using a first state analysis table to perform table lookup conversion processing on the target detection state information to obtain first device operation state information; the first device operation state information includes a plurality of device operation state representations; Performing statistical sorting processing on the device operation status representation in the first device operation status information to obtain the first device operation status information; the first device operation status information includes a plurality of device operation status numerical relationships distributed according to operation status priorities; Based on the second status analysis table, the first device operating status information is subjected to table lookup analysis and processing in the operating status priority order to obtain the target device operating status information.

8. An equipment status detection system for an intelligent production system, characterized in that: The system comprises: An acquisition module is used to obtain the status information of the device to be detected; A first processing module is used to perform feature analysis on the state information of the device to be detected to obtain target state feature information; The second processing module is used to detect, analyze and process the target status feature information to obtain target device operation status information.

9. An equipment status detection system for an intelligent production system, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the equipment status detection method of the intelligent production system according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the equipment status detection method of the intelligent production system according to any one of claims 1 to 7.

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