Factory production equipment remote monitoring method, system, equipment and medium

Abstract

The invention provides a factory production equipment remote monitoring method and system, equipment and a medium, and belongs to the technical field of production equipment monitoring. Acquiring equipment state data of the production equipment based on the equipment code of the production equipment; constructing equipment monitoring items of the full-life-cycle equipment state data of the production equipment; constructing a monitoring keyword group of the production equipment based on the equipment monitoring threshold value of the production equipment; constructing a production equipment operation life monitoring sequence set based on the equipment monitoring items and the monitoring keyword groups; and carrying out real-time monitoring on the full life cycle of the production equipment based on the production equipment operation life monitoring sequence set, and carrying out real-time early warning. According to the invention, the abnormal state or potential fault of the equipment can be positioned, thereby timely processing and improving the overall operation and maintenance efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of production equipment monitoring, and in particular relates to a remote monitoring system, equipment and medium for factory production equipment. Background Art

[0002] As the level of factory automation and intelligence continues to improve, in order to ensure the stable operation of production equipment and to detect abnormalities in a timely manner, it is necessary to remotely monitor production equipment. Remote monitoring generally achieves real-time monitoring, fault warning, and remote maintenance of equipment by remotely acquiring equipment status data, operating parameters and other information.

[0003] However, in related technologies, there are many types of equipment and the status data is complex and changeable, which makes it difficult to identify and obtain the status of the equipment; the monitoring items are complex to build and need to comprehensively consider the various attributes and production processes of the equipment; the setting of monitoring keyword groups is unreasonable, which can easily lead to poor monitoring effects or false alarms; it is difficult to make predictions based on the combination of equipment life and production process, and it is difficult to accurately judge the operating status and remaining life of the equipment. Summary of the invention

[0004] The present invention provides a remote monitoring method for factory production equipment, which realizes real-time monitoring of production equipment to carry out real-time early warning and improve the stability of production equipment operation.

[0005] Methods include: S101, obtaining the equipment code of the production equipment; S102, acquiring equipment status data of the production equipment based on the equipment code of the production equipment; S103, constructing equipment monitoring items for equipment status data of the entire life cycle of production equipment; S104, constructing a monitoring keyword group for the production equipment based on the equipment monitoring threshold of the production equipment; S105, constructing a production equipment operation life monitoring sequence set based on the equipment monitoring items and monitoring keyword groups; S106, based on the production equipment operation life monitoring sequence set, the entire life cycle of the production equipment is monitored in real time, and a real-time warning is issued.

[0006] It should be further explained that step S102 also includes: After obtaining the equipment code of the production equipment, the equipment status data of the production equipment is obtained based on the equipment code of the production equipment; the equipment status data includes address data, type of production equipment, temperature, humidity, pressure, gas concentration and image data.

[0007] It should be further explained that step S103 also includes: S301, constructing a device production process monitoring data set based on the device attribute data in the device attribute data; S302, defining attribute categories in equipment production attributes based on the equipment production process monitoring data set; S303. Based on the attribute categories in the equipment attribute data, define the relationship between the attribute categories and the production process.

[0008] It should be further explained that step S104 also includes: S1041, obtaining equipment status data of the production equipment based on the equipment monitoring threshold of the production equipment; S1042, constructing a target production equipment identification model based on the equipment status data of the production equipment; S1043, constructing a monitoring keyword group for the production equipment through a target production equipment identification model.

[0009] It should be further explained that step S104 also includes: Input the training set of equipment status data into the initial production equipment identification model to obtain the predicted production equipment identification result; Based on the predicted production equipment identification results and the actual production equipment identification results, the corresponding loss value is obtained; The network parameters in the initial production equipment identification model are updated based on the loss value until the initial production equipment identification model converges. When the number of iterations of the network parameters reaches a preset number, a trained production equipment identification model is obtained.

[0010] It should be further explained that step S105 also includes: S1051, performing equipment status analysis on equipment status data of production equipment based on monitoring keyword groups, and generating corresponding structured instance data; S1052: Based on the equipment monitoring items, the structured instance data is integrated to construct a production equipment operation life monitoring sequence set.

[0011] The present application also provides a remote monitoring system for factory production equipment, the system comprising: A code acquisition module is used to obtain the equipment code of the production equipment; A data acquisition module, which acquires the equipment status data of the production equipment based on the equipment code of the production equipment; Monitoring item construction module, used to construct equipment monitoring items for equipment status data throughout the life cycle of production equipment; A keyword building module builds monitoring keyword groups for production equipment based on the equipment monitoring thresholds of production equipment; The monitoring process configuration module builds a production equipment operation life monitoring sequence set based on equipment monitoring items and monitoring keyword groups; The real-time monitoring module monitors the entire life cycle of production equipment in real time based on the production equipment operation life monitoring sequence set, and issues real-time warnings.

[0012] It should be further explained that the system also includes: The early warning module is used to display the real-time monitoring equipment status data. When the early warning information is received, the early warning equipment is marked and the early warning is issued through SMS, sound, and highlighting. The equipment control module is used to control the production equipment by connecting to the intelligent switch and valve equipment; it is also used to shut down the production equipment that has been warned after receiving the warning information, and to enable the backup production equipment when there is a backup production equipment; Display module: used to display the equipment status data of factory equipment through charts, numbers, and monitoring videos.

[0013] According to another embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the factory production equipment remote monitoring method when executing the program.

[0014] According to another embodiment of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the factory production equipment remote monitoring method are implemented.

[0015] It can be seen from the above technical solutions that the present invention has the following advantages: The remote monitoring method for factory production equipment involved in this application can locate abnormal status or potential failures of equipment by acquiring equipment status data of production equipment in real time, and accurately analyzing them in combination with equipment monitoring items and monitoring keyword phrases, so as to deal with them in time. The monitoring sequence set facilitates the monitoring of equipment status and improves the overall operation and maintenance efficiency. The method can monitor the equipment status in real time, and when abnormalities or situations exceeding preset thresholds are found, it can issue an early warning to remind operation and maintenance personnel to take measures.

[0016] This application sets monitoring keyword groups so that the system can focus on parameters that have a greater impact on equipment safety and ensure that the equipment operates within a safe range. Through equipment status monitoring throughout the entire life cycle, equipment anomalies can be discovered and handled in a timely manner, and equipment stability can be prompted. The monitoring sequence set provides data support for equipment life prediction. Operation and maintenance personnel can reasonably arrange maintenance plans based on the operating status and trends of the equipment, thereby extending the overall service life of the equipment. The method can obtain real-time and accurate equipment status data, and operation and maintenance personnel can adjust production plans based on equipment status to avoid production delays or interruptions due to equipment failures. The remote monitoring method for factory production equipment in this embodiment improves operation and maintenance efficiency, enhances equipment safety, and extends equipment life. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 A flow chart of the remote monitoring method for factory production equipment; Figure 2 A flow chart of an embodiment of a remote monitoring method for factory production equipment; Figure 3 This is a schematic diagram of the remote monitoring system for factory production equipment; Figure 4 Schematic diagram of an electronic device. DETAILED DESCRIPTION

[0019] The factory production equipment remote monitoring method provided by this application can monitor the operating status of the production equipment. It can monitor and warn of abnormalities in the production equipment. In order to respond to warnings in a timely manner, this application also builds an early warning model to predict various possible situations in advance. In addition to traditional system interaction methods, this application supports artificial language command recognition and displays factory equipment data according to real-time requirements. This application can enhance the existing monitoring capabilities of the factory, monitor factory equipment in real time from multiple directions and dimensions, and improve early warning capabilities and response speed.

[0020] In the following, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0021] Hereinafter, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0023] See also Figure 1 The figure is a flow chart of a clutch friction plate surface temperature analysis method in a specific embodiment, the method comprising: S101, obtaining the equipment code of the production equipment.

[0024] In some embodiments, the device code may be an RFID code, an NFC code, or a QR code. The device code is a unique identification code of the production device, and can be edited by reference according to the production process, production control software, production control model, etc.

[0025] In some embodiments, the remote monitoring of production equipment may utilize TCP protocol or Modbus communication protocol for data communication.

[0026] S102, acquiring equipment status data of the production equipment based on the equipment code of the production equipment.

[0027] In some embodiments, after obtaining the equipment code of the production equipment through the above steps, the equipment status data of the production equipment can be obtained based on the equipment code of the production equipment. The equipment status data may include address data, type of production equipment, temperature, humidity, pressure, gas concentration, image data, etc.

[0028] In some embodiments, after obtaining the device status data of the production equipment, the device status data may be stored after being encrypted and desensitized.

[0029] In some embodiments, the key parameters in the production equipment status data and the equipment status data are encrypted to effectively prevent data leakage. The encryption method can use the RSA asymmetric encryption algorithm for encryption and decryption.

[0030] S103, constructing equipment monitoring items for equipment status data of the entire life cycle of production equipment.

[0031] In some embodiments, equipment status data for the entire life cycle of production equipment is constructed, and through real-time monitoring, stable operation of the production equipment is ensured and production efficiency is improved.

[0032] Specifically, by defining the equipment monitoring method, the operating status of the equipment can be monitored in real time to ensure the continuity and stability of the production process. Equipment failures can be discovered and warned in a timely manner to improve equipment utilization and production efficiency.

[0033] In this embodiment, equipment monitoring involves the entire life cycle of production equipment, including various stages such as equipment installation, commissioning, operation, and maintenance. Specific information may involve equipment name, model, installation location, and time of commissioning. Equipment monitoring items may include status information such as equipment start, stop, and operation, as well as key parameters such as temperature, pressure, speed, and vibration. Record equipment failure history, failure time, failure cause, repair time, and repair measures.

[0034] Select a preset monitoring method based on the monitoring target and scope. The monitoring method can collect device status data in real time and has data analysis, early warning and reporting functions.

[0035] In some embodiments, based on the equipment status data of the production equipment, the method for constructing equipment monitoring items of the equipment status data of the entire life cycle of the production equipment may include the following steps: Figure 2 As shown, S301. Construct a device production process monitoring data set based on the device attribute data in the device attribute data.

[0036] This embodiment is the basis for constructing equipment monitoring items. Equipment attribute data includes the name, model, rated power, rated voltage, production process, process requirements, etc. of the equipment. By analyzing the equipment attribute data, the equipment monitoring items and parameters that need to be monitored can be determined, thereby constructing an equipment production process monitoring data set. The equipment production process monitoring data set involves the data generated during the entire process of equipment production, and effective monitoring can be achieved.

[0037] S302: Based on the equipment production process monitoring data set, define attribute categories in the equipment production attributes.

[0038] Based on the construction of the equipment production process monitoring data set, the defined attribute categories include identifying the different stages and links in the equipment production process, as well as the specific operations and tasks involved in each stage and link. By clarifying the monitoring attribute categories and production processes, we can have a clearer understanding of the equipment production process.

[0039] S303. Based on the attribute categories in the equipment attribute data, define the relationship between the attribute categories and the production process.

[0040] In this embodiment, defining the relationship between attribute categories and production processes can reflect the status and behavior of equipment in the production process. For example, for the direction monitoring of production equipment in the processing process, attributes such as processing time, processing temperature, and processing pressure can be defined; for the quality inspection process, attributes such as inspection items, inspection standards, and inspection results can be defined. At the same time, defining the relationship between attribute categories, such as the relationship between processing time and production efficiency, the relationship between processing temperature and product quality, etc., makes equipment monitoring more targeted.

[0041] S104: construct a monitoring keyword group for the production equipment based on the equipment monitoring threshold of the production equipment.

[0042] In some embodiments, based on the equipment monitoring threshold of the production equipment, the method for constructing a monitoring keyword group of the production equipment may include the following steps: S1041, obtaining equipment status data of the production equipment based on the equipment monitoring threshold of the production equipment; S1042, constructing a target production equipment identification model based on the equipment status data of the production equipment; S1043, constructing a monitoring keyword group for the production equipment through a target production equipment identification model.

[0043] In some embodiments, the equipment status data of the production equipment may be obtained based on the operation data, operating procedures, and accident information related to the production equipment.

[0044] In some embodiments, an initial production equipment identification model may be trained based on the equipment status data to obtain a target production equipment identification model.

[0045] Building monitoring keyword groups based on equipment monitoring thresholds for production equipment is a systematic process that aims to accurately monitor production equipment through scientific methods. The following is a detailed explanation of the specific implementation process, implementation methods, and beneficial effects of each step you proposed: S1041: Based on the equipment monitoring threshold of the production equipment, obtain equipment status data of the production equipment.

[0046] This embodiment determines the monitoring threshold of the production equipment. The monitoring threshold is set according to the normal operation parameters, historical data and production requirements of the equipment. By monitoring the operation status of the equipment in real time, the actual operation data is compared with the monitoring threshold to obtain the equipment status data.

[0047] This embodiment uses hardware devices such as sensors and data collectors to monitor the operating status of the equipment in real time, and transmits the data to the monitoring system for processing and analysis.

[0048] It can be seen that by real-time monitoring of the equipment's operating status and obtaining equipment status data, equipment anomalies and failures can be discovered in a timely manner. At the same time, by analyzing the equipment status data, the equipment's operating parameters and maintenance strategies can be optimized to improve the equipment's operating efficiency and service life.

[0049] S1042: Construct a target production equipment identification model based on the equipment status data of the production equipment.

[0050] After acquiring the equipment status data, the equipment status data is processed and analyzed to build a target production equipment identification model. The production equipment identification model identifies the operating status and type of the equipment based on the equipment status data. For example, the analysis of the equipment vibration data can be used to identify whether the equipment is faulty or abnormal.

[0051] This embodiment can realize accurate identification and classification of equipment by constructing a target production equipment identification model, thus facilitating subsequent monitoring and management.

[0052] S1043: Construct a monitoring keyword group for production equipment through a target production equipment identification model.

[0053] After the target production equipment identification model is constructed, the model can be used to analyze and process the equipment status data, thereby constructing a monitoring keyword group for the production equipment. The keyword group can be customized and combined according to the operating status and type of the equipment to meet different monitoring requirements.

[0054] This embodiment converts the state data and type information of the device into keywords according to the result of the recognition model. At the same time, the keyword group can also be optimized and expanded by using technologies such as natural language processing to improve its expression ability and accuracy.

[0055] By constructing monitoring keyword groups for production equipment, accurate monitoring of equipment can be achieved. Keyword groups can be customized and combined according to actual needs, making it convenient for monitoring personnel to quickly understand the operating status and abnormal conditions of the equipment.

[0056] In some embodiments, a method for training an initial production equipment identification model based on equipment status data to obtain a target production equipment identification model may include: inputting a training set of equipment status data into the initial production equipment identification model to obtain a predicted production equipment identification result; Specifically, the initial production equipment identification model can adopt CNN, RNN, LSTM neural network model, etc. The preprocessed training set data is input into the initial production equipment identification model. The model performs forward propagation based on the input data to obtain the predicted production equipment identification result.

[0057] Based on the predicted production equipment identification results and the actual production equipment identification results, the corresponding loss value is obtained; In this embodiment, the loss value can measure the difference between the predicted result of the production equipment identification model and the actual result. The loss function can be calculated based on the mean square error. For the classification method, the cross entropy loss can be used. When calculating the loss value, the predicted result is compared with the actual production equipment identification result, and the loss value is calculated according to the selected loss function.

[0058] The network parameters in the initial production equipment identification model are updated based on the loss value until the initial production equipment identification model converges. When the number of iterations of the network parameters reaches a preset number, a trained production equipment identification model is obtained.

[0059] In some embodiments, a back propagation algorithm is used to calculate the gradient of the loss value to the model parameters. The model parameters are updated according to the gradient descent method to reduce the loss value.

[0060] The convergence judgment of the production equipment identification model in this embodiment can set a convergence threshold or a maximum number of iterations. During the training process, if the loss value is lower than the convergence threshold or the number of iterations reaches the maximum set value, the production equipment identification model is considered to have converged. It is also possible to judge whether the production equipment identification model is overfitting or underfitting by observing the change trend of the loss value on the training set and the validation set.

[0061] The production equipment identification model of this embodiment can use a convolutional neural network (CNN), a recurrent neural network (RNN) or a long short-term memory network (LSTM). The output of the neural network can be post-processed using a traditional machine learning model.

[0062] In this embodiment, a suitable production equipment identification model is selected according to the requirements of the factory's production equipment and the characteristics of the data.

[0063] The factory production equipment remote monitoring system of this embodiment inputs the training set of equipment status data into the initial production equipment identification model, and updates the model parameters based on the loss value between the predicted results and the actual results. Through continuous iterative training, the production equipment identification model can gradually learn the complex relationship between the equipment status data and the equipment identification, thereby improving the accuracy of the prediction of the equipment status. The production equipment identification model obtained through training can stably output reliable prediction results, thereby enhancing the reliability of the monitoring system. Based on accurate equipment status prediction, the factory can formulate a more reasonable maintenance strategy. For example, for equipment predicted to be about to fail, maintenance or replacement can be carried out in advance to avoid production interruptions and losses.

[0064] This embodiment can provide support for intelligent decision-making of the factory based on the trained production equipment identification model.

[0065] In some embodiments, after the trained production equipment identification model is obtained through the above steps, the trained production equipment identification model can also be verified and tuned using the verification set to obtain a target production equipment identification model. After obtaining the target production equipment identification model, the target production equipment identification model can be used to identify the production equipment of the production equipment, and the identified production equipment results can be used to construct a monitoring keyword group for the production equipment.

[0066] S105, constructing a production equipment operation life monitoring sequence set based on the equipment monitoring items and monitoring keyword groups.

[0067] In some embodiments, a production equipment operation life monitoring sequence set may be constructed based on equipment monitoring items and monitoring keyword groups.

[0068] Specifically, in some embodiments, the method for constructing a production equipment operation life monitoring sequence set based on equipment monitoring items and monitoring keyword groups may include the following steps: S1051, performing equipment status analysis on equipment status data of production equipment based on monitoring keyword groups, and generating corresponding structured instance data.

[0069] In this embodiment, according to the characteristics of the production equipment and the monitoring requirements, a group of keywords related to the equipment status is determined, such as "temperature", "vibration", "pressure", etc. The equipment status data is traversed and matched with the monitoring keyword group. This can be achieved through string matching, regular expressions or natural language processing technology. For the matched keywords, the corresponding values ​​or status information are extracted as the characteristics of the equipment status. The extracted features are organized into structured data according to a predefined format, such as JSON, XML, etc.

[0070] The structured instance data of this embodiment refers to data that expresses key information in the device status data in a structured manner, and may often include fields such as device identification, monitoring time, monitoring item name, and monitoring item value.

[0071] S1052: Based on the equipment monitoring items, the structured instance data is integrated to construct a production equipment operation life monitoring sequence set.

[0072] In some embodiments, the data of different monitoring items are integrated together to form a comprehensive data set containing multiple monitoring item values. The integrated data is organized into sequences in chronological order, and each sequence represents the operating status of a device over a period of time. For each production equipment, the above data alignment, monitoring item integration and sequence construction process are repeated to obtain the operating life monitoring sequence of the equipment. The monitoring sequences of all equipment are aggregated together to form a production equipment operating life monitoring sequence set.

[0073] This embodiment achieves comprehensive monitoring of the equipment operating status by integrating data from multiple monitoring items. The monitoring sequence set provides a basis for analyzing the changing trend of the equipment status over time. Based on the monitoring sequence set, a prediction model can be constructed to estimate the remaining life of the equipment. It can be seen that this embodiment can achieve real-time and accurate monitoring of the operating status of production equipment by parsing and structuring the equipment status data based on monitoring keyword groups, and fusing the structured instance data and constructing the monitoring sequence set based on the equipment monitoring items.

[0074] In some embodiments, after the structured instance data is obtained, the structured instance data can be fused based on the equipment monitoring items to construct a production equipment operation life monitoring sequence set.

[0075] In some embodiments, near real-time retrieval is achieved by indexing production equipment data. Different from direct query of production equipment data, multi-level relational retrieval function for relational data. In order to achieve more efficient and high-speed storage and retrieval function for relational data in production equipment operation life monitoring series, data is stored on the network instead of in relational tables, and multi-level query of relational data is achieved.

[0076] S106, based on the production equipment operation life monitoring sequence set, the entire life cycle of the production equipment is monitored in real time, and a real-time warning is issued.

[0077] In some embodiments, after obtaining the production equipment operation life monitoring sequence set through the above steps, the entire life cycle of the production equipment can be monitored in real time based on the production equipment operation life monitoring sequence set to provide real-time warning.

[0078] The remote monitoring method for factory production equipment involved in this embodiment can locate abnormal status or potential failures of equipment by acquiring equipment status data of production equipment in real time, and accurately analyzing them in combination with equipment monitoring items and monitoring keyword phrases, so as to handle them in time. The monitoring sequence set facilitates the monitoring of equipment status and improves the overall operation and maintenance efficiency. The method can monitor the equipment status in real time, and when abnormalities or situations exceeding preset thresholds are found, an early warning can be issued to remind the operation and maintenance personnel to take measures.

[0079] The setting of monitoring keyword groups enables the system to focus on parameters that have a greater impact on equipment safety and ensure that the equipment operates within a safe range. Through equipment status monitoring throughout the entire life cycle, equipment anomalies can be discovered and handled in a timely manner, and equipment stability can be prompted. The monitoring sequence set provides data support for equipment life prediction. Operation and maintenance personnel can reasonably arrange maintenance plans based on the operating status and trends of the equipment, thereby extending the overall service life of the equipment. The method can obtain real-time and accurate equipment status data, and operation and maintenance personnel can adjust production plans based on equipment status to avoid production delays or interruptions due to equipment failures. The remote monitoring method for factory production equipment in this embodiment improves operation and maintenance efficiency, enhances equipment safety, and extends equipment life.

[0080] The following is an embodiment of a remote monitoring system for factory production equipment provided in an embodiment of the present disclosure. This system and the remote monitoring method for factory production equipment in the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiment of the remote monitoring system for factory production equipment, reference can be made to the embodiment of the above-mentioned remote monitoring method for factory production equipment.

[0081] like Figure 3 As shown, the system includes: A code acquisition module is used to obtain the equipment code of the production equipment; A data acquisition module, which acquires the equipment status data of the production equipment based on the equipment code of the production equipment; Monitoring item construction module, used to construct equipment monitoring items for equipment status data throughout the life cycle of production equipment; A keyword building module builds monitoring keyword groups for production equipment based on the equipment monitoring thresholds of production equipment; The monitoring process configuration module builds a production equipment operation life monitoring sequence set based on equipment monitoring items and monitoring keyword groups; The real-time monitoring module monitors the entire life cycle of production equipment in real time based on the production equipment operation life monitoring sequence set, and issues real-time warnings.

[0082] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, a more specific factory production equipment remote monitoring system is provided.

[0083] The data acquisition module can also be used to access sensors, smart meters, and smart switches, and store the device status data transmitted through various communication protocols.

[0084] This embodiment can use historical equipment status data for model training, process and learn the labeled equipment status data, and can accurately identify natural language instructions in the factory environment based on relevant terminology, grammatical habits, etc.

[0085] In order to achieve accurate recognition of natural language instructions of large language models in factory environments, the following are specific steps for model training based on historical monitoring data. Select the GPT-3 language model for natural language tasks. Use the pre-trained Word2Vec word embedding model to convert text into vector representation. Build a production equipment recognition model based on the Transformer architecture. The production equipment recognition model consists of multiple encoder and decoder layers and can capture long-distance dependencies. Use the cross-entropy loss function to evaluate the accuracy of model predictions. For each word or phrase in the labeled data, calculate the difference between the probability distribution predicted by the model and the actual labeling. Divide the labeled data into a training set and a validation set. Among them, the training set is used for model training, and the model parameters are updated by the back propagation algorithm. Adjust the model structure or hyperparameters according to the evaluation results to improve performance. This embodiment can also realize interface docking with the factory monitoring system to ensure that the model can receive and process natural language instructions in real time. Regularly collect new monitoring data and annotate it. Deploy the model in the factory environment and continuously update it to maintain accuracy.

[0086] The early warning module provided in this embodiment can use a large screen to display the current operating status of various types of equipment. When early warning information is received, the equipment is marked. At the same time, early warnings are issued to relevant personnel through text messages, sounds, highlights, etc., to remind personnel to take action.

[0087] The equipment control module is used to control the production equipment by connecting to intelligent switches and valve equipment. It is also used to shut down the production equipment that has been warned after receiving the warning information, and to enable the backup production equipment when there is backup production equipment.

[0088] Display module: Display the operating status of factory equipment from multiple angles and dimensions through charts, numbers, monitoring videos, etc.

[0089] The system provided in this application can collect the operating status of production equipment in real time, and determine whether the equipment has a fault based on the current operating status of the production equipment, and predict and display the future status of the equipment.

[0090] like Figure 4 As shown, the present application also provides an electronic device, including a display unit, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the factory production equipment remote monitoring method when executing the program.

[0091] In the embodiment of the present application, the processor 101 can be implemented by using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0092] The display unit 102 is used to display information input by the user or information provided to the user. The display unit 102 may include a display panel, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0093] The memory 103 can be used to store software programs and various data. The memory 103 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0094] In addition, the electronic device includes some functional modules not shown, which will not be described in detail here.

[0095] The present application also provides a storage medium, in which a program product capable of implementing the remote monitoring method for factory production equipment of the present specification is stored.

[0096] The method includes: obtaining equipment codes of production equipment; obtaining equipment status data of production equipment based on the equipment codes of production equipment; constructing equipment monitoring items of equipment status data of the entire life cycle of production equipment; constructing monitoring keyword groups of production equipment based on equipment monitoring thresholds of production equipment; constructing a production equipment operation life monitoring sequence set based on equipment monitoring items and monitoring keyword groups; and conducting real-time monitoring of the entire life cycle of production equipment based on the production equipment operation life monitoring sequence set, and conducting real-time early warning.

[0097] In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of various exemplary implementations of the present disclosure described in the above “Exemplary Method” section of this specification.

[0098] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0099] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote monitoring method for factory production equipment, characterized in that: Methods include: S101, obtaining the equipment code of the production equipment; S102, acquiring equipment status data of the production equipment based on the equipment code of the production equipment; S103, constructing equipment monitoring items for equipment status data of the entire life cycle of production equipment; S104, constructing a monitoring keyword group for the production equipment based on the equipment monitoring threshold of the production equipment; S105, constructing a production equipment operation life monitoring sequence set based on the equipment monitoring items and monitoring keyword groups; S106, based on the production equipment operation life monitoring sequence set, the entire life cycle of the production equipment is monitored in real time, and a real-time warning is issued.

2. The remote monitoring method for factory production equipment according to claim 1, characterized in that: Step S102 also includes: After obtaining the equipment code of the production equipment, the equipment status data of the production equipment is obtained based on the equipment code of the production equipment; the equipment status data includes address data, type of production equipment, temperature, humidity, pressure, gas concentration and image data.

3. The remote monitoring method for factory production equipment according to claim 1, characterized in that: Step S103 also includes: S301, constructing a device production process monitoring data set based on the device attribute data in the device attribute data; S302, defining attribute categories in equipment production attributes based on the equipment production process monitoring data set; S303. Based on the attribute categories in the equipment attribute data, define the relationship between the attribute categories and the production process.

4. The remote monitoring method for factory production equipment according to claim 1, characterized in that: Step S104 also includes: S1041, obtaining equipment status data of the production equipment based on the equipment monitoring threshold of the production equipment; S1042, constructing a target production equipment identification model based on the equipment status data of the production equipment; S1043, constructing a monitoring keyword group for the production equipment through a target production equipment identification model.

5. The remote monitoring method for factory production equipment according to claim 4, characterized in that: Step S104 also includes: Input the training set of equipment status data into the initial production equipment identification model to obtain the predicted production equipment identification result; Based on the predicted production equipment identification results and the actual production equipment identification results, the corresponding loss value is obtained; The network parameters in the initial production equipment identification model are updated based on the loss value until the initial production equipment identification model converges. When the number of iterations of the network parameters reaches a preset number, a trained production equipment identification model is obtained.

6. The remote monitoring method for factory production equipment according to claim 1, characterized in that: Step S105 also includes: S1051, performing equipment status analysis on equipment status data of production equipment based on monitoring keyword groups, and generating corresponding structured instance data; S1052: Based on the equipment monitoring items, the structured instance data is integrated to construct a production equipment operation life monitoring sequence set.

7. A remote monitoring system for factory production equipment, characterized in that: The system is used to implement the remote monitoring method for factory production equipment according to any one of claims 1 to 6; the system comprises: A code acquisition module is used to obtain the equipment code of the production equipment; A data acquisition module, which acquires the equipment status data of the production equipment based on the equipment code of the production equipment; Monitoring item construction module, used to construct equipment monitoring items for equipment status data throughout the life cycle of production equipment; A keyword building module builds monitoring keyword groups for production equipment based on the equipment monitoring thresholds of production equipment; The monitoring process configuration module builds a production equipment operation life monitoring sequence set based on equipment monitoring items and monitoring keyword groups; The real-time monitoring module monitors the entire life cycle of production equipment in real time based on the production equipment operation life monitoring sequence set, and issues real-time warnings.

8. The factory production equipment remote monitoring system according to claim 7, characterized in that: Also includes: The early warning module is used to display the real-time monitoring equipment status data. When the early warning information is received, the early warning equipment is marked and the early warning is issued through SMS, sound, and highlighting; The equipment control module is used to control the production equipment by connecting to the intelligent switch and valve equipment; it is also used to shut down the production equipment that has been warned after receiving the warning information, and to enable the backup production equipment when there is a backup production equipment; Display module: used to display the equipment status data of factory equipment through charts, numbers, and monitoring videos.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the factory production equipment remote monitoring method as described in any one of claims 1 to 6 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the remote monitoring method for factory production equipment as claimed in any one of claims 1 to 6 are implemented.

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