A remote operation and maintenance control method for an organic electroluminescent device

Abstract

The present invention relates to the field of organic electroluminescent operation and maintenance technology. The present invention provides a remote operation and maintenance control method for an organic electroluminescent device, which aims to solve the problem in the prior art that the collected data cannot be effectively identified, resulting in wrong operation and maintenance decisions. The present invention includes obtaining monitoring data and equipment information, comparing standard and real-time equipment information to identify missing data, using a deep learning model to train the operation simulation model of the missing equipment and all equipment, and generating an operation and maintenance strategy by comparing the real-time monitoring task with the simulation data. The present invention improves the accuracy of operation and maintenance data analysis and the correctness of decision-making through four steps: data acquisition and matching, data comparison and missing identification, model training and data simulation, and real-time monitoring and strategy generation, reduces operation and maintenance costs, and enhances the adaptability and scalability of the system.

Description

Technical Field

[0001] The present invention relates to the field of organic electroluminescent operation and maintenance technology, and in particular to a remote operation and maintenance control method for an organic electroluminescent device. Background Art

[0002] The existing technology for operation and maintenance of organic electroluminescent technology has made significant progress. With the widespread application of organic electroluminescent technology in smart phones, televisions, car displays, head-mounted displays, industrial control equipment displays, transparent displays, wearable displays and other fields, the operation and maintenance technology for organic electroluminescent technology devices is also constantly developing.

[0003] The use of organic electroluminescent operation and maintenance technology mainly includes the following steps. First, use an electroluminescent detector to conduct regular inspections of organic electroluminescent technology equipment. By applying current to the organic electroluminescent technology equipment to make it glow, and capturing and analyzing the luminescent image, defects and non-uniformities in the components can be identified. Secondly, use intelligent operation and maintenance technology such as infrared imaging cameras carried by drones to remotely monitor organic electroluminescent technology equipment, monitor the operating status of the equipment in real time, and promptly discover and deal with abnormal situations. At the same time, combined with data analysis technology, the operation and maintenance data of organic electroluminescent technology equipment is collected, analyzed and processed to optimize operation and maintenance strategies and improve operation and maintenance efficiency.

[0004] In the actual use of remote operation and maintenance of organic electroluminescent devices, there are the following technical pain points. In the actual operation and maintenance process of organic electroluminescent devices, organic electroluminescent devices are damaged due to unstable voltage and high temperature. When the organic power generation devices are damaged, the collected data will also be inaccurate. Remote operation and maintenance of organic electroluminescent devices is based on data analysis of the collected data. When the collected data is wrong, the existing organic electroluminescent device operation and maintenance cannot effectively identify the collected data, resulting in erroneous decisions made by the operation and maintenance data analysis. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a remote operation and maintenance control method for an organic electroluminescent device, which solves the problem that the existing operation and maintenance of organic electroluminescent devices cannot effectively identify the collected data, resulting in erroneous decisions made by the operation and maintenance data analysis.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides a remote operation and maintenance control method for an organic electroluminescent device, comprising:

[0008] Step S101, acquiring monitoring data, organic electroluminescent device equipment information, and a correspondence between the monitoring data and the organic electroluminescent device equipment information, wherein the monitoring data includes real-time monitoring data and historical monitoring data, determining the organic electroluminescent device equipment in real-time operation according to the real-time monitoring data, matching the organic electroluminescent device equipment in real-time operation according to the organic electroluminescent device equipment information and the correspondence between the monitoring data and the organic electroluminescent device equipment information, and obtaining standard organic electroluminescent device equipment information corresponding to the organic electroluminescent device equipment in real-time operation;

[0009] Step S102, comparing the standard organic electroluminescent device equipment information with the organic electroluminescent device equipment information collected in real time to obtain an information data comparison result, the information data comparison result includes no missing data and missing data, if there is missing data, then matching the corresponding relationship between the missing data information and the organic electroluminescent device equipment information to obtain the missing organic electroluminescent device equipment;

[0010] Step S103, retrieving historical data of the organic electroluminescent device equipment missing from the database, training a deep learning model based on the historical data of the organic electroluminescent device equipment missing from the database, and obtaining an operation simulation model of the missing organic electroluminescent device equipment;

[0011] Step S104, retrieving organic electroluminescent devices in a database according to the equipment information of the organic electroluminescent devices, obtaining historical data of each organic electroluminescent device, and using the historical data of each organic electroluminescent device to train a deep learning model in sequence, to obtain an operation simulation model of each organic electroluminescent device;

[0012] Step S105, receiving the real-time monitoring task, substituting the real-time monitoring task into the operation simulation model of each organic electroluminescent device, obtaining the operation simulation data of each organic electroluminescent device, comparing the operation simulation data of each organic electroluminescent device with the real-time monitoring data, obtaining the real-time missing organic electroluminescent device data, simulating the real-time missing organic electroluminescent device data using the missing organic electroluminescent device equipment operation simulation model, obtaining the missing organic electroluminescent device equipment simulation data, matching the missing organic electroluminescent device equipment simulation data in a preset operation and maintenance strategy library, and obtaining the real-time organic electroluminescent device operation and maintenance strategy.

[0013] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S101 comprises:

[0014] Collecting real-time operating data of organic electroluminescent device equipment, the operating data of organic electroluminescent device equipment includes voltage, current, brightness and temperature;

[0015] The collected real-time operation data of the organic electroluminescent device device is preprocessed, and the operation status of the real-time organic electroluminescent device device is identified by using the preprocessed real-time operation data of the organic electroluminescent device device in combination with the normal operating parameter range of the organic electroluminescent device device. The identification process includes comparing the real-time operation data of the organic electroluminescent device device with a preset threshold value to determine whether the organic electroluminescent device device is in a normal operating state.

[0016] According to the result of device status recognition, the organic electroluminescent device devices in real-time operation are screened out.

[0017] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S101 comprises:

[0018] Extracting the operating status characteristics of the organic electroluminescent device equipment from the real-time monitoring data, the operating status characteristics of the organic electroluminescent device equipment including the real-time value of voltage, the real-time value of current, the real-time value of brightness and the real-time value of temperature;

[0019] Calling a stored correspondence table between organic electroluminescent device equipment information and monitoring data, wherein the correspondence table between organic electroluminescent device equipment information and monitoring data includes an equipment identifier, equipment type, equipment specification, and a mapping relationship between the organic electroluminescent device equipment and the monitoring data;

[0020] Match the extracted real-time monitoring data features with the acquired device information correspondence, and use a fuzzy matching algorithm to find matching device information in the device information correspondence table based on the feature values ​​in the real-time monitoring data;

[0021] When device information matching the real-time monitoring data is matched, the standard organic electroluminescent device device information corresponding to the device is obtained from the corresponding relationship table or database. The standard organic electroluminescent device device information includes the standard operating parameter range, performance indicators and maintenance requirements of the device.

[0022] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, said step S102 comprises:

[0023] Extracting a standard operating voltage range, a standard operating current range, a standard brightness range, and a standard temperature range from standard organic electroluminescent device equipment information;

[0024] Real-time collection of voltage, current, brightness and temperature data of the currently running organic electroluminescent device equipment;

[0025] Compare the real-time collected data with the extracted standard data, compare the real-time voltage with the standard operating voltage range, compare the real-time current with the standard operating current range, compare the real-time brightness with the standard brightness range, and compare the real-time temperature with the standard temperature range;

[0026] Based on the comparison results, an information data comparison report is generated, which includes parameters within the standard range, parameters outside the standard range, and parameters below the standard range. Parameters outside or below the standard range are marked as missing data, and the organic electroluminescent device equipment information corresponding to the missing data is recorded.

[0027] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, said step S103 comprises:

[0028] Retrieving the historical data of the missing organic electroluminescent device equipment from the database, the historical data of the missing organic electroluminescent device equipment including the time series data of the voltage parameter, current parameter, brightness parameter and temperature parameter of the equipment during the past operation;

[0029] According to the missing characteristics of the organic electroluminescent device equipment and the amount of historical data, the data is matched in the preset model knowledge base, and the deep learning model is obtained for training;

[0030] After the training is completed, the simulated data output by the model is compared with the actual historical data. If the model performance meets the preset qualification requirements, the model is saved as a simulation model for the missing organic electroluminescent device equipment operation.

[0031] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S104 comprises:

[0032] According to the equipment information of the organic electroluminescent device, the historical data of each organic electroluminescent device is retrieved from the database, and the historical data of each organic electroluminescent device includes the time series data of the voltage parameter, current parameter, brightness parameter, temperature parameter, fault record and maintenance record of the device during operation;

[0033] For each organic electroluminescent device, a corresponding deep learning model is matched in the model knowledge base for training. The selection of the model should be based on the characteristics of the data and the requirements of the task. For each device, its corresponding historical data is used to train an independent model to obtain the operation simulation model of each organic electroluminescent device.

[0034] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, said step S104 comprises:

[0035] Retrieving actual operation data of each organic electroluminescent device within a verification period from a database to obtain verification data;

[0036] Matching the verification data with each organic electroluminescent device operation simulation model, inputting the verification data into each organic electroluminescent device operation simulation model according to the matching result, and obtaining simulated output data after running the model, wherein the simulated output data includes the device operation state predicted by the model according to the input data;

[0037] The simulation data output by the model is compared with the actual verification data, and the accuracy of the model prediction is evaluated by calculating the error index. Based on the comparison results, the performance of each organic electroluminescent device running simulation model is evaluated. The performance of each organic electroluminescent device running simulation model includes the accuracy, stability and generalization ability of the model;

[0038] If the performance of the model meets the preset qualification requirements, the model is considered to be effective and will be used for subsequent real-time monitoring and operation and maintenance strategy formulation. If the performance of the model does not meet the requirements, the model needs to be re-optimized and trained.

[0039] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, said step S105 comprises:

[0040] Receive a real-time monitoring task, which includes the requirement for real-time monitoring of the organic electroluminescent device equipment, and the requirement for real-time monitoring of the organic electroluminescent device equipment includes the monitoring time range and monitoring parameters;

[0041] According to the received real-time monitoring task, the corresponding real-time monitoring data is obtained, and the real-time monitoring data is substituted into the operation simulation model of each organic electroluminescent device. The operation simulation model of each organic electroluminescent device generates the operation simulation data of each organic electroluminescent device under the real-time monitoring task by running the simulation model. The operation simulation data of the organic electroluminescent device under the real-time monitoring task includes the simulation values ​​of voltage parameters, current parameters, brightness parameters and temperature parameters.

[0042] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, said step S105 comprises:

[0043] Acquire and parse the simulation data of the missing organic electroluminescent device, and extract parameter features from the simulation data, wherein the parameter features include a simulation value of a voltage feature, a simulation value of a current feature, a simulation value of a brightness feature, and a simulation value of a temperature feature;

[0044] The analog values ​​of the voltage characteristic, the current characteristic, the brightness characteristic, and the temperature characteristic are compared with the standard operating parameter range to identify abnormal or deviating parameters.

[0045] Furthermore, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, said step S105 comprises:

[0046] Access the preset operation and maintenance strategy library, which includes operation and maintenance strategies for different abnormal situations and device states;

[0047] The extracted parameter features and abnormal information are matched with the strategies in the operation and maintenance strategy library to obtain the most appropriate operation and maintenance measures. According to the matching results, a real-time operation and maintenance strategy for the missing organic electroluminescent device equipment is generated;

[0048] The real-time operation and maintenance strategy of the missing organic electroluminescent device equipment includes the maintenance steps of the organic electroluminescent device, the resources required for the maintenance of the organic electroluminescent device, and the expected effect of the maintenance of the organic electroluminescent device.

[0049] Beneficial effects of the present invention:

[0050] By comparing standard organic electroluminescent device equipment information with real-time collected equipment information, the present invention can identify missing or abnormal parts in the information data, and perform data simulation based on a deep learning model, thereby making up for the data missing and improving the accuracy of operation and maintenance data analysis, which helps to avoid making operation and maintenance decisions based on erroneous or incomplete data and improve the reliability of decisions.

[0051] The present invention can quickly generate operation simulation data for each organic electroluminescent device by combining real-time monitoring tasks and operation simulation models, and compare them with real-time monitoring data to promptly detect abnormal equipment status. At the same time, combined with the preset operation and maintenance strategy library, the system can automatically generate real-time operation and maintenance strategies, provide clear guidance and support for operation and maintenance personnel, and enhance the real-time and effectiveness of operation and maintenance decisions. By constructing a personalized operation simulation model for each organic electroluminescent device, the present invention can learn the operating laws of the equipment based on historical data and predict the future operating status of the equipment. This helps operation and maintenance personnel to discover potential problems in advance, take preventive measures, and avoid equipment failures, thereby improving operation and maintenance efficiency.

[0052] Since the present invention can perform data simulation and anomaly detection based on deep learning models, it reduces the need for manual intervention and reduces operation and maintenance costs. At the same time, by discovering and solving equipment problems in advance, it avoids downtime and maintenance costs caused by equipment failures, further reducing operation and maintenance costs.

[0053] The method of the present invention is not only applicable to current organic electroluminescent devices, but also has good adaptability and scalability. With the introduction of new equipment and changes in operation and maintenance requirements, the system can adapt to new environments and requirements by updating historical data and optimizing deep learning models, maintaining long-term effectiveness and accuracy.

[0054] In summary, the present invention significantly improves the efficiency and accuracy of remote operation and maintenance of organic electroluminescent devices through a series of innovative steps and methods, reduces operation and maintenance costs, and provides strong technical support for the development of related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0056] Figure 1 A schematic diagram of a remote operation and maintenance control method for an organic electroluminescent device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. 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. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0058] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0059] The present invention provides a remote operation and maintenance control method for an organic electroluminescent device, comprising:

[0060] Step S101, acquiring monitoring data, organic electroluminescent device equipment information, and a correspondence between the monitoring data and the organic electroluminescent device equipment information, wherein the monitoring data includes real-time monitoring data and historical monitoring data, determining the organic electroluminescent device equipment in real-time operation according to the real-time monitoring data, matching the organic electroluminescent device equipment in real-time operation according to the organic electroluminescent device equipment information and the correspondence between the monitoring data and the organic electroluminescent device equipment information, and obtaining standard organic electroluminescent device equipment information corresponding to the organic electroluminescent device equipment in real-time operation;

[0061] In step S101, the purpose of the present invention is to obtain monitoring data, device information and the corresponding relationship between the organic electroluminescent device, and determine the real-time running device and its corresponding standard device information through this information. The specific steps and process are as follows:

[0062] The real-time monitoring data includes key operating parameters such as real-time voltage, current, brightness, temperature, etc. of the organic electroluminescent device currently in operation.

[0063] Historical monitoring data records the operating status of the equipment over a period of time and is used for subsequent data analysis and model training.

[0064] The organic electroluminescent device equipment information obtained includes the equipment identifier (such as serial number, model, etc.), equipment type, equipment specifications, etc., which are used to uniquely identify and distinguish different devices.

[0065] Obtain the correspondence between monitoring data and equipment information, record the specific equipment information corresponding to each monitoring data point, and improve the ability to accurately associate monitoring data with equipment.

[0066] Based on the real-time monitoring data, the system can identify which devices are currently running by checking whether the device's voltage, current and other parameters are non-zero or within the preset threshold range. For each device running in real time, the system matches the device's standard operating parameter range, performance indicators, maintenance requirements and other standard information from the pre-stored device information library based on its device information and the corresponding relationship between the monitoring data and the device information.

[0067] Step S101 improves the system's ability to perform subsequent data comparison and analysis based on the standard information of the equipment, thereby making accurate operation and maintenance decisions.

[0068] Step S102, comparing the standard organic electroluminescent device equipment information with the organic electroluminescent device equipment information collected in real time to obtain an information data comparison result, the information data comparison result includes no missing data and missing data, if there is missing data, then matching the corresponding relationship between the missing data information and the organic electroluminescent device equipment information to obtain the missing organic electroluminescent device equipment;

[0069] In step S102, the purpose of the present invention is to compare the standard organic electroluminescent device device information with the real-time collected device information to identify the missing parts in the information data and further determine the specific device corresponding to the missing data. The following is a detailed analysis of this step:

[0070] First, the standard organic electroluminescent device equipment information obtained in step S101 is compared with the equipment information collected in real time, including comparing key operating parameters such as voltage, current, brightness, temperature, etc. of the equipment.

[0071] The comparison results are divided into two categories: no missing data and missing data. No missing data means that the real-time collected data is consistent with the standard data or is within an acceptable range; missing data means that the real-time collected data lacks certain key information or the information deviates significantly from the standard data.

[0072] If there is missing data, the data will be further analyzed to determine the specific content that is missing (such as the voltage value at a certain point in time, the current parameters of a certain device, etc.). The correspondence between the missing data information and the organic electroluminescent device device information is matched. This involves looking up a mapping table or database to determine the specific device to which the missing data corresponds.

[0073] Through the above matching process, the system can accurately identify which devices have missing data and record the relevant information of these devices (such as device identifier, model, location, etc.).

[0074] The importance of step S102 is that it can promptly discover problems in data collection, such as sensor failure, data transmission interruption, etc., so as to avoid subsequent analysis based on incomplete or erroneous data. At the same time, by determining the specific device with missing data, the system can take targeted measures (such as replacing sensors, repairing data transmission links, etc.) to make up for the missing data and improve the accuracy and effectiveness of operation and maintenance control.

[0075] Step S103, retrieving historical data of the organic electroluminescent device equipment missing from the database, training a deep learning model based on the historical data of the organic electroluminescent device equipment missing from the database, and obtaining an operation simulation model of the missing organic electroluminescent device equipment;

[0076] In step S103, the purpose of the present invention is to train a deep learning model using the historical data of the missing organic electroluminescent device device, so as to obtain a simulation model that can simulate the operating state of the device. The following is a detailed analysis of this step:

[0077] First, the historical data of the missing organic electroluminescent device equipment is retrieved from the database. The historical data of the missing organic electroluminescent device equipment includes the time series data of the key operating parameters of the equipment such as voltage, current, brightness, temperature, etc. over the past period of time, as well as fault records and maintenance records.

[0078] Before training a deep learning model, historical data needs to be preprocessed, including data cleaning (such as removing outliers, filling missing values, etc.), data normalization or standardization (to improve the ability to compare and analyze data of different dimensions), and other steps.

[0079] According to the characteristics of the missing organic electroluminescent device equipment and the amount of historical data, the system matches a suitable deep learning model from the preset model knowledge base for training. The selection of the model should be based on the characteristics of the data and the requirements of the task to improve the accuracy and efficiency of the model.

[0080] The selected deep learning model is trained using the preprocessed historical data, and the training process involves multiple iterations, in which the model minimizes the prediction error by continuously adjusting its internal parameters.

[0081] After the training is completed, the system compares the simulated data output by the model with the actual historical data to evaluate the performance of the model. If the model performance meets the preset qualification requirements (such as the prediction error is within an acceptable range), it is saved as a simulation model for running the missing organic electroluminescent device device. Otherwise, the system needs to reselect the model or adjust the training parameters for retraining.

[0082] The importance of step S103 lies in the ability to simulate the operating status of the missing organic electroluminescent device device through a deep learning model, thereby compensating for the problem caused by the missing data. This helps the system to conduct subsequent analysis and decision-making based on a more complete data set, improving the accuracy and efficiency of operation and maintenance control. At the same time, by continuously updating and optimizing the model, the system can adapt to changes in device status and the arrival of new data, maintaining its long-term effectiveness and accuracy.

[0083] Step S104, retrieving organic electroluminescent devices in a database according to the equipment information of the organic electroluminescent devices, obtaining historical data of each organic electroluminescent device, and using the historical data of each organic electroluminescent device to train a deep learning model in sequence, to obtain an operation simulation model of each organic electroluminescent device;

[0084] In step S104, the present invention constructs an operation simulation model for each organic electroluminescent device through the following steps to improve the accuracy and efficiency of remote operation and maintenance control:

[0085] Based on the device information of the organic electroluminescent device, the system retrieves the historical data of each device from the database. The historical data of each device includes the time series data of parameters such as voltage, current, brightness, temperature, etc. during the operation of the device, as well as past fault records and maintenance records.

[0086] Preprocess the retrieved historical data to improve the quality and consistency of the data. The preprocessing steps include data cleaning (such as removing noise, filling missing values), data standardization or normalization, etc., to facilitate subsequent model training.

[0087] For each organic electroluminescent device, the system matches a suitable deep learning model in the preset model knowledge base according to the characteristics of its data and task requirements. Different devices require different types of models for training due to their different operating characteristics, working environments or historical data volumes.

[0088] The selected deep learning model is trained using the preprocessed historical data. During the training process, the model learns the operating laws and characteristics of the equipment by adjusting its internal parameters to minimize the prediction error.

[0089] After training is complete, the system verifies the accuracy of the model by retrieving actual operating data from the database during the validation period. This validation data is input into the model, and the model outputs a prediction result, which is then compared with the actual data to evaluate the performance of the model.

[0090] Based on the comparison results, the system evaluates the performance of each organic electroluminescent device operation simulation model, including the accuracy, stability and generalization ability of the model. If the model performance meets the preset qualification requirements, it will be saved as a valid operation simulation model for subsequent real-time monitoring and operation and maintenance strategy formulation. If the model performance does not meet the requirements, the model needs to be re-optimized and trained.

[0091] Through step S104, the present invention constructs a personalized operation simulation model for each organic electroluminescent device. These models can learn the operation rules of the device based on historical data and predict the future operation status of the device. When the real-time monitoring data is missing or abnormal, the system can use these simulation models to complete the data or detect anomalies, thereby improving the accuracy and efficiency of operation and maintenance control. In addition, as new data continues to accumulate, these models can also be continuously learned and optimized to adapt to changes in device status and challenges in new environments.

[0092] Step S105, receiving the real-time monitoring task, substituting the real-time monitoring task into the operation simulation model of each organic electroluminescent device, obtaining the operation simulation data of each organic electroluminescent device, comparing the operation simulation data of each organic electroluminescent device with the real-time monitoring data, obtaining the real-time missing organic electroluminescent device data, simulating the real-time missing organic electroluminescent device data using the missing organic electroluminescent device equipment operation simulation model, obtaining the missing organic electroluminescent device equipment simulation data, matching the missing organic electroluminescent device equipment simulation data in a preset operation and maintenance strategy library, and obtaining the real-time organic electroluminescent device operation and maintenance strategy.

[0093] In step S105, the method of the present invention generates a real-time operation and maintenance strategy for an organic electroluminescent device through a series of sophisticated operations, and the specific steps are as follows:

[0094] Receive a real-time monitoring task, which specifies which organic electroluminescent device equipment needs to be monitored in real time, as well as specific requirements such as the monitoring time range and parameters.

[0095] The real-time monitoring task is substituted into the operation simulation model of each organic electroluminescent device. These models are trained based on historical data in step S104 and can simulate the operation status of the device under specific conditions. Each operation simulation model generates corresponding operation simulation data according to the requirements of the real-time monitoring task. These data include simulation values ​​of key parameters such as voltage, current, brightness, temperature, etc., reflecting the expected operation status of the device under the real-time monitoring task.

[0096] The system compares the operating simulation data of each organic electroluminescent device with the real-time monitoring data. Through this step, the system can identify which devices have missing or abnormal real-time monitoring data.

[0097] During the comparison process, if it is found that the real-time monitoring data of a certain device is missing or obviously abnormal, the system will identify the device as a real-time missing electroluminescent device and extract the specific missing or abnormal data.

[0098] For real-time missing organic electroluminescent devices, the system uses the missing organic electroluminescent device device operation simulation model obtained in step S103 to perform data simulation. This step can simulate reasonable values ​​of missing or abnormal data based on historical data and the learning ability of the model.

[0099] The system inputs the simulation data of the missing organic electroluminescent device equipment into the preset operation and maintenance strategy library for matching. The operation and maintenance strategy library contains operation and maintenance strategies for different abnormal situations and equipment status, and the system can automatically match the most appropriate operation and maintenance measures. Based on the matching results, the system generates real-time operation and maintenance strategies for the missing organic electroluminescent device equipment. These strategies include specific maintenance steps, required maintenance resources, and expected maintenance effects, etc., providing clear guidance and support for operation and maintenance personnel.

[0100] Through step S105, the method of the present invention can realize real-time monitoring of organic electroluminescent devices and generation of dynamic operation and maintenance strategies. Even if there is a lack or abnormality in the real-time monitoring data, effective operation and maintenance strategies can be quickly generated with the support of the running simulation model and the operation and maintenance strategy library to improve the stable operation and efficient maintenance of the equipment.

[0101] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S101 includes:

[0102] Collecting real-time operating data of organic electroluminescent device equipment, the operating data of organic electroluminescent device equipment includes voltage, current, brightness and temperature;

[0103] The collected real-time operation data of the organic electroluminescent device device is preprocessed, and the operation status of the real-time organic electroluminescent device device is identified by using the preprocessed real-time operation data of the organic electroluminescent device device in combination with the normal operating parameter range of the organic electroluminescent device device. The identification process includes comparing the real-time operation data of the organic electroluminescent device device with a preset threshold value to determine whether the organic electroluminescent device device is in a normal operating state.

[0104] According to the result of device status recognition, the organic electroluminescent device devices in real-time operation are screened out.

[0105] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S101 includes:

[0106] Extracting the operating status characteristics of the organic electroluminescent device equipment from the real-time monitoring data, the operating status characteristics of the organic electroluminescent device equipment including the real-time value of voltage, the real-time value of current, the real-time value of brightness and the real-time value of temperature;

[0107] Calling a stored correspondence table between organic electroluminescent device equipment information and monitoring data, wherein the correspondence table between organic electroluminescent device equipment information and monitoring data includes an equipment identifier, equipment type, equipment specification, and a mapping relationship between the organic electroluminescent device equipment and the monitoring data;

[0108] Match the extracted real-time monitoring data features with the acquired device information correspondence, and use a fuzzy matching algorithm to find matching device information in the device information correspondence table based on the feature values ​​in the real-time monitoring data;

[0109] When device information matching the real-time monitoring data is matched, the standard organic electroluminescent device device information corresponding to the device is obtained from the corresponding relationship table or database. The standard organic electroluminescent device device information includes the standard operating parameter range, performance indicators and maintenance requirements of the device.

[0110] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S102 includes:

[0111] Extracting a standard operating voltage range, a standard operating current range, a standard brightness range, and a standard temperature range from standard organic electroluminescent device equipment information;

[0112] Real-time collection of voltage, current, brightness and temperature data of the currently running organic electroluminescent device equipment;

[0113] Compare the real-time collected data with the extracted standard data, compare the real-time voltage with the standard operating voltage range, compare the real-time current with the standard operating current range, compare the real-time brightness with the standard brightness range, and compare the real-time temperature with the standard temperature range;

[0114] Based on the comparison results, an information data comparison report is generated, which includes parameters within the standard range, parameters outside the standard range, and parameters below the standard range. Parameters outside or below the standard range are marked as missing data, and the organic electroluminescent device equipment information corresponding to the missing data is recorded.

[0115] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S103 includes:

[0116] Retrieving the historical data of the missing organic electroluminescent device equipment from the database, the historical data of the missing organic electroluminescent device equipment including the time series data of the voltage parameter, current parameter, brightness parameter and temperature parameter of the equipment during the past operation;

[0117] According to the missing characteristics of the organic electroluminescent device equipment and the amount of historical data, the data is matched in the preset model knowledge base, and the deep learning model is obtained for training;

[0118] After the training is completed, the simulated data output by the model is compared with the actual historical data. If the model performance meets the preset qualification requirements, the model is saved as a simulation model for the missing organic electroluminescent device equipment operation.

[0119] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S104 includes:

[0120] According to the equipment information of the organic electroluminescent device, the historical data of each organic electroluminescent device is retrieved from the database, and the historical data of each organic electroluminescent device includes the time series data of the voltage parameter, current parameter, brightness parameter, temperature parameter, fault record and maintenance record of the device during operation;

[0121] For each organic electroluminescent device, a corresponding deep learning model is matched in the model knowledge base for training. The selection of the model should be based on the characteristics of the data and the requirements of the task. For each device, its corresponding historical data is used to train an independent model to obtain the operation simulation model of each organic electroluminescent device.

[0122] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S104 includes:

[0123] Retrieving actual operation data of each organic electroluminescent device within a verification period from a database to obtain verification data;

[0124] Matching the verification data with each organic electroluminescent device operation simulation model, inputting the verification data into each organic electroluminescent device operation simulation model according to the matching result, and obtaining simulated output data after running the model, wherein the simulated output data includes the device operation state predicted by the model according to the input data;

[0125] The simulation data output by the model is compared with the actual verification data, and the accuracy of the model prediction is evaluated by calculating the error index. Based on the comparison results, the performance of each organic electroluminescent device running simulation model is evaluated. The performance of each organic electroluminescent device running simulation model includes the accuracy, stability and generalization ability of the model;

[0126] If the performance of the model meets the preset qualification requirements, the model is considered to be effective and will be used for subsequent real-time monitoring and operation and maintenance strategy formulation. If the performance of the model does not meet the requirements, the model needs to be re-optimized and trained.

[0127] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S105 includes:

[0128] Receive a real-time monitoring task, which includes the requirement for real-time monitoring of the organic electroluminescent device equipment, and the requirement for real-time monitoring of the organic electroluminescent device equipment includes the monitoring time range and monitoring parameters;

[0129] According to the received real-time monitoring task, the corresponding real-time monitoring data is obtained, and the real-time monitoring data is substituted into the operation simulation model of each organic electroluminescent device. The operation simulation model of each organic electroluminescent device generates the operation simulation data of each organic electroluminescent device under the real-time monitoring task by running the simulation model. The operation simulation data of the organic electroluminescent device under the real-time monitoring task includes the simulation values ​​of voltage parameters, current parameters, brightness parameters and temperature parameters.

[0130] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S105 includes:

[0131] Acquire and parse the simulation data of the missing organic electroluminescent device, and extract parameter features from the simulation data, wherein the parameter features include a simulation value of a voltage feature, a simulation value of a current feature, a simulation value of a brightness feature, and a simulation value of a temperature feature;

[0132] The analog values ​​of the voltage characteristic, the current characteristic, the brightness characteristic, and the temperature characteristic are compared with the standard operating parameter range to identify abnormal or deviating parameters.

[0133] Specifically, the organic electroluminescent device remote operation and maintenance control method provided by the present invention, step S105 includes:

[0134] Access the preset operation and maintenance strategy library, which includes operation and maintenance strategies for different abnormal situations and device states;

[0135] The extracted parameter features and abnormal information are matched with the strategies in the operation and maintenance strategy library to obtain the most appropriate operation and maintenance measures. According to the matching results, a real-time operation and maintenance strategy for the missing organic electroluminescent device equipment is generated;

[0136] The real-time operation and maintenance strategy of the missing organic electroluminescent device equipment includes the maintenance steps of the organic electroluminescent device, the resources required for the maintenance of the organic electroluminescent device, and the expected effect of the maintenance of the organic electroluminescent device.

[0137] The present invention solves the problem that the existing organic electroluminescent device operation and maintenance cannot effectively identify the collected data, thereby causing errors in operation and maintenance data analysis and decision-making through a series of steps. The specific solution is as follows:

[0138] Determine the real-time running organic electroluminescent device equipment based on the real-time monitoring data, and match the standard organic electroluminescent device equipment information corresponding to the real-time running equipment through the correspondence between the equipment information and the monitoring data. Compare the standard equipment information with the real-time collected equipment information. Identify the missing data in the information data comparison results and match the specific equipment with the missing data. By comparing standard data and real-time data, the system can promptly detect data missing or abnormalities, which is the key to avoiding making decisions based on wrong data.

[0139] Retrieve the historical data of missing devices, train the deep learning model, and obtain the operation simulation model of the missing devices. All equipment model training: retrieve the historical data of each organic electroluminescent device, train the deep learning model separately, and obtain the operation simulation model of each device. Using the deep learning model to simulate the operation of the equipment can fill in the missing data, so that the operation and maintenance system can make relatively accurate predictions and decisions even when the data is incomplete.

[0140] Substitute the real-time monitoring task into the simulation model of each device to generate simulation data. Compare the simulation data with the real-time monitoring data, identify the missing real-time data, and use the simulation model of the missing device to simulate the data. Finally, match the simulation data in the preset operation and maintenance strategy library to obtain the real-time operation and maintenance strategy. By comparing the real-time monitoring and simulation data, the system can detect and handle abnormal situations in a timely manner. At the same time, combined with the preset operation and maintenance strategy library, it automatically generates appropriate operation and maintenance measures, thereby avoiding the problem of making decisions based on wrong data.

[0141] The present invention effectively solves the problem that the existing organic electroluminescent device operation and maintenance cannot effectively identify the collected data through four steps: data acquisition and matching, data comparison and missing identification, model training and data simulation, and real-time monitoring and strategy generation, thereby improving the accuracy of operation and maintenance data analysis and the correctness of decision-making.

[0142] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if the modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include the modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A remote operation and maintenance control method for an organic electroluminescent device, characterized in that: include: Step S101, acquiring monitoring data, organic electroluminescent device equipment information, and a correspondence between the monitoring data and the organic electroluminescent device equipment information, wherein the monitoring data includes real-time monitoring data and historical monitoring data, determining the organic electroluminescent device equipment in real-time operation according to the real-time monitoring data, matching the organic electroluminescent device equipment in real-time operation according to the organic electroluminescent device equipment information and the correspondence between the monitoring data and the organic electroluminescent device equipment information, and obtaining standard organic electroluminescent device equipment information corresponding to the organic electroluminescent device equipment in real-time operation; Step S102, comparing the standard organic electroluminescent device equipment information with the organic electroluminescent device equipment information collected in real time to obtain an information data comparison result, the information data comparison result includes no missing data and missing data, if there is missing data, then matching the corresponding relationship between the missing data information and the organic electroluminescent device equipment information to obtain the missing organic electroluminescent device equipment; Step S103, retrieving historical data of the organic electroluminescent device equipment missing from the database, training a deep learning model based on the historical data of the organic electroluminescent device equipment missing from the database, and obtaining an operation simulation model of the missing organic electroluminescent device equipment; Step S104, retrieving organic electroluminescent devices in a database according to the equipment information of the organic electroluminescent devices, obtaining historical data of each organic electroluminescent device, and using the historical data of each organic electroluminescent device to train a deep learning model in sequence, to obtain an operation simulation model of each organic electroluminescent device; Step S105, receiving the real-time monitoring task, substituting the real-time monitoring task into the operation simulation model of each organic electroluminescent device, obtaining the operation simulation data of each organic electroluminescent device, comparing the operation simulation data of each organic electroluminescent device with the real-time monitoring data, obtaining the real-time missing organic electroluminescent device data, simulating the real-time missing organic electroluminescent device data using the missing organic electroluminescent device equipment operation simulation model, obtaining the missing organic electroluminescent device equipment simulation data, matching the missing organic electroluminescent device equipment simulation data in a preset operation and maintenance strategy library, and obtaining the real-time organic electroluminescent device operation and maintenance strategy.

2. The remote operation and maintenance control method of an organic electroluminescent device according to claim 1, characterized in that: The step S101 includes: Collecting real-time operating data of organic electroluminescent device equipment, the operating data of organic electroluminescent device equipment includes voltage, current, brightness and temperature; Preprocessing the collected real-time operation data of the organic electroluminescent device, using the preprocessed real-time operation data of the organic electroluminescent device in combination with the normal operating parameter range of the organic electroluminescent device to identify the operation state of the real-time organic electroluminescent device, wherein the identification process includes comparing the operation data of the real-time organic electroluminescent device with a preset threshold value to determine whether the organic electroluminescent device is in a normal operating state; According to the result of device status recognition, the organic electroluminescent device devices in real-time operation are screened out.

3. The remote operation and maintenance control method of an organic electroluminescent device according to claim 2, characterized in that: The step S101 includes: Extracting the operating status characteristics of the organic electroluminescent device equipment from the real-time monitoring data, the operating status characteristics of the organic electroluminescent device equipment including the real-time value of voltage, the real-time value of current, the real-time value of brightness and the real-time value of temperature; Calling a stored correspondence table between organic electroluminescent device equipment information and monitoring data, wherein the correspondence table between organic electroluminescent device equipment information and monitoring data includes an equipment identifier, equipment type, equipment specification, and a mapping relationship between the organic electroluminescent device equipment and the monitoring data; Match the extracted real-time monitoring data features with the acquired device information correspondence, and use a fuzzy matching algorithm to find matching device information in the device information correspondence table based on the feature values ​​in the real-time monitoring data; When device information matching the real-time monitoring data is matched, the standard organic electroluminescent device device information corresponding to the device is obtained from the corresponding relationship table or database. The standard organic electroluminescent device device information includes the standard operating parameter range, performance indicators and maintenance requirements of the device.

4. The remote operation and maintenance control method of an organic electroluminescent device according to claim 1, characterized in that: The step S102 includes: Extracting a standard operating voltage range, a standard operating current range, a standard brightness range, and a standard temperature range from standard organic electroluminescent device equipment information; Real-time collection of voltage, current, brightness and temperature data of the currently running organic electroluminescent device equipment; Compare the real-time collected data with the extracted standard data, compare the real-time voltage with the standard operating voltage range, compare the real-time current with the standard operating current range, compare the real-time brightness with the standard brightness range, and compare the real-time temperature with the standard temperature range; Based on the comparison results, an information data comparison report is generated, which includes parameters within the standard range, parameters outside the standard range, and parameters below the standard range. Parameters outside or below the standard range are marked as missing data, and the organic electroluminescent device equipment information corresponding to the missing data is recorded.

5. The remote operation and maintenance control method of an organic electroluminescent device according to claim 1, characterized in that: The step S103 includes: Retrieving the historical data of the missing organic electroluminescent device equipment from the database, the historical data of the missing organic electroluminescent device equipment including the time series data of the voltage parameter, current parameter, brightness parameter and temperature parameter of the equipment during the past operation; According to the missing characteristics of the organic electroluminescent device equipment and the amount of historical data, the data is matched in the preset model knowledge base, and the deep learning model is obtained for training; After the training is completed, the simulated data output by the model is compared with the actual historical data. If the model performance meets the preset qualification requirements, the model is saved as a simulation model for the missing organic electroluminescent device equipment operation.

6. The remote operation and maintenance control method of an organic electroluminescent device according to claim 1, characterized in that: The step S104 includes: According to the equipment information of the organic electroluminescent device, the historical data of each organic electroluminescent device is retrieved from the database, and the historical data of each organic electroluminescent device includes the time series data of the voltage parameter, current parameter, brightness parameter, temperature parameter, fault record and maintenance record of the device during operation; For each organic electroluminescent device, a corresponding deep learning model is matched in the model knowledge base for training. The selection of the model should be based on the characteristics of the data and the requirements of the task. For each device, its corresponding historical data is used to train an independent model to obtain the operation simulation model of each organic electroluminescent device.

7. The remote operation and maintenance control method of an organic electroluminescent device according to claim 6, characterized in that: The step S104 includes: Retrieving actual operation data of each organic electroluminescent device within a verification period from a database to obtain verification data; Matching the verification data with each organic electroluminescent device operation simulation model, inputting the verification data into each organic electroluminescent device operation simulation model according to the matching result, and obtaining simulated output data after running the model, wherein the simulated output data includes the device operation state predicted by the model according to the input data; The simulation data output by the model is compared with the actual verification data, and the accuracy of the model prediction is evaluated by calculating the error index. Based on the comparison results, the performance of each organic electroluminescent device running simulation model is evaluated. The performance of each organic electroluminescent device running simulation model includes the accuracy, stability and generalization ability of the model; If the performance of the model meets the preset qualification requirements, the model is considered to be effective and will be used for subsequent real-time monitoring and operation and maintenance strategy formulation. If the performance of the model does not meet the requirements, the model needs to be re-optimized and trained.

8. The remote operation and maintenance control method of an organic electroluminescent device according to claim 1, characterized in that: The step S105 includes: Receive a real-time monitoring task, which includes the requirement for real-time monitoring of the organic electroluminescent device equipment, and the requirement for real-time monitoring of the organic electroluminescent device equipment includes the monitoring time range and monitoring parameters; According to the received real-time monitoring task, the corresponding real-time monitoring data is obtained, and the real-time monitoring data is substituted into the operation simulation model of each organic electroluminescent device. The operation simulation model of each organic electroluminescent device generates the operation simulation data of each organic electroluminescent device under the real-time monitoring task by running the simulation model. The operation simulation data of the organic electroluminescent device under the real-time monitoring task includes the simulation values ​​of voltage parameters, current parameters, brightness parameters and temperature parameters.

9. The remote operation and maintenance control method of an organic electroluminescent device according to claim 8, characterized in that: The step S105 includes: Acquire and parse the simulation data of the missing organic electroluminescent device, and extract parameter features from the simulation data, wherein the parameter features include a simulation value of a voltage feature, a simulation value of a current feature, a simulation value of a brightness feature, and a simulation value of a temperature feature; The analog values ​​of the voltage characteristic, the current characteristic, the brightness characteristic, and the temperature characteristic are compared with the standard operating parameter range to identify abnormal or deviating parameters.

10. The remote operation and maintenance control method of an organic electroluminescent device according to claim 9, characterized in that: The step S105 includes: Access the preset operation and maintenance strategy library, which includes operation and maintenance strategies for different abnormal situations and device states; The extracted parameter features and abnormal information are matched with the strategies in the operation and maintenance strategy library to obtain the most appropriate operation and maintenance measures. According to the matching results, a real-time operation and maintenance strategy for the missing organic electroluminescent device equipment is generated; The real-time operation and maintenance strategy of the missing organic electroluminescent device equipment includes the maintenance steps of the organic electroluminescent device, the resources required for the maintenance of the organic electroluminescent device, and the expected effect of the maintenance of the organic electroluminescent device.

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