CN119001266A - AI-based LED large screen fault prediction and maintenance system - Google Patents
AI-based LED large screen fault prediction and maintenance system Download PDFInfo
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- CN119001266A CN119001266A CN202411008466.6A CN202411008466A CN119001266A CN 119001266 A CN119001266 A CN 119001266A CN 202411008466 A CN202411008466 A CN 202411008466A CN 119001266 A CN119001266 A CN 119001266A
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- 238000012423 maintenance Methods 0.000 title claims abstract description 49
- 238000012544 monitoring process Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 7
- 230000007613 environmental effect Effects 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000013135 deep learning Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 6
- 238000003745 diagnosis Methods 0.000 claims description 6
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- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims description 3
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- 238000000605 extraction Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000007637 random forest analysis Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 230000017525 heat dissipation Effects 0.000 claims 1
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09G—ARRANGEMENTS OR CIRCUITS FOR CONTROL OF INDICATING DEVICES USING STATIC MEANS TO PRESENT VARIABLE INFORMATION
- G09G3/00—Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes
- G09G3/006—Electronic inspection or testing of displays and display drivers, e.g. of LED or LCD displays
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M11/00—Testing of optical apparatus; Testing structures by optical methods not otherwise provided for
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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Abstract
The invention belongs to the technical field of LED display, in particular to an AI-based LED large screen fault prediction and maintenance system, which adopts the following scheme that the system comprises a sensor network, a data acquisition module, an AI analysis engine, a fault prediction model, an alarm and notification module and a remote control module, wherein the system comprises the following components: the sensor network is deployed at each key part of the LED large screen and is responsible for collecting environmental parameters of temperature, humidity, voltage and current and running state data; the data acquisition module is responsible for collecting data transmitted by the sensor network and carrying out preliminary processing and storage, and the invention comprises the following steps: the system realizes omnibearing monitoring, fault prediction and remote maintenance of the LED large screen through an integrated sensor network, a data acquisition module, an AI analysis model and a remote control module.
Description
Technical Field
The invention relates to the technical field of LED display, in particular to an AI-based LED large screen fault prediction and maintenance system.
Background
Along with the wide application of the LED large screen in a plurality of fields such as advertisements, performance, conferences and the like, the stability and the reliability of the LED large screen become the focus of attention of users.
However, the traditional maintenance mode is often dependent on manual inspection and maintenance after fault, and the mode is low in efficiency and difficult to discover and prevent potential faults in time, so that the invention provides an AI-based LED large screen fault prediction and maintenance system for solving the problems.
Disclosure of Invention
Based on the background technology, the traditional maintenance mode is often dependent on manual inspection and maintenance after faults, the mode is low in efficiency, and potential faults are difficult to discover and prevent in time.
The invention provides an AI-based LED large screen fault prediction and maintenance system, which comprises a sensor network, a data acquisition module, an AI analysis engine, a fault prediction model, an alarm and notification module and a remote control module, wherein the system comprises the following components:
The sensor network is deployed at each key part of the LED large screen and is responsible for collecting environmental parameters of temperature, humidity, voltage and current and running state data;
the data acquisition module is responsible for collecting data transmitted by the sensor network and performing preliminary processing and storage;
The AI analysis engine analyzes the collected data by using a machine learning or deep learning algorithm, identifies an abnormal mode and predicts potential fault points;
The fault prediction model is based on historical fault data and real-time monitoring data, and a prediction model is constructed to predict the occurrence probability and possible time of faults;
When the alarm and notification module detects potential faults or actual faults, maintenance personnel are notified through mails, short messages or APP pushing modes.
The remote control module allows maintenance personnel to conduct fault diagnosis, parameter adjustment or software upgrading operation on the LED large screen in a remote mode.
Preferably, the data acquisition and preprocessing: the system firstly collects various parameter data of the LED large screen through a sensor network, the data collection module carries out cleaning, denoising and normalization processing on the original data so as to improve the accuracy of subsequent analysis, and the processed data are transmitted to a server where an AI analysis engine is located through an Ethernet or wireless network.
Preferably, the feature extraction and selection: features useful for fault prediction are extracted from the preprocessed data using feature engineering methods. These characteristics may include temperature trend, voltage fluctuation range, humidity rate, current stability index, etc.
Preferably, the model is trained by: and selecting a proper machine learning or deep learning algorithm (such as random forest, gradient lifting tree, neural network and the like), and training a fault prediction model by utilizing historical fault data and real-time monitoring data. In the training process, the prediction precision and generalization capability of the model are improved through methods such as cross verification, super parameter tuning and the like.
Preferably, the real-time prediction: the trained model is deployed on an AI analysis engine server, receives new data from the data acquisition module in real time, and outputs a failure prediction result. The predicted outcome includes the probability of occurrence of the fault, the type of fault possible, the predicted time of occurrence, etc.
Preferably, the alarm and notification: when the model predicts potential faults, the AI analysis engine immediately triggers an alarm mechanism, and sends alarm information to maintenance personnel in a mode of short messages, mails or APP pushing and the like according to preset rules and priorities. Meanwhile, the system also generates a detailed fault analysis report, which comprises a fault prediction basis, a possible influence range, suggested maintenance measures and the like.
Preferably, the remote maintenance: after receiving the alarm information, maintenance personnel can log in to the AI analysis engine server through the remote control terminal to check the real-time running state and the fault analysis report of the LED large screen. According to the report content, maintenance personnel can perform remote fault diagnosis, parameter adjustment, software upgrading and other operations so as to eliminate potential faults or reduce the influence of the faults.
Preferably, the redundancy design: in the system design, measures such as redundant sensors, redundant data acquisition modules, redundant servers and the like are adopted to improve the reliability and fault tolerance of the system. When a certain component fails, the system can be automatically switched to the backup component, so that continuous operation of the system is ensured.
By the above mechanism: the system realizes omnibearing monitoring, fault prediction and remote maintenance of the LED large screen through an integrated sensor network, a data acquisition module, an AI analysis model and a remote control module.
The beneficial effects of the invention are as follows:
1. Maintenance efficiency is improved: through real-time monitoring and fault prediction, potential faults are found and solved in advance, and the downtime and maintenance cost are reduced;
2. Enhancing system stability: potential fault points are found and processed in time, the fault occurrence rate is reduced, and the overall stability and reliability of the LED large screen are improved;
3. The labor cost is reduced: the remote monitoring and maintenance functions are realized, and the field inspection times and personnel requirements are reduced;
4. And the user experience is improved: the LED large screen is ensured to normally operate at key time, and the user satisfaction and brand image are improved;
The invention provides an AI-based LED large screen fault prediction and maintenance system, and aims to provide an AI-based LED large screen fault prediction and maintenance system which realizes omnibearing monitoring, fault prediction and remote maintenance of an LED large screen through an integrated sensor network, a data acquisition module, an AI analysis model and a remote control module.
Drawings
FIG. 1 is a schematic diagram of a system architecture of an AI-based LED large screen fault prediction and maintenance system;
Fig. 2 is a schematic diagram of a workflow of an AI-based LED large screen fault prediction and maintenance system according to the present invention.
Detailed Description
The invention is further illustrated below in connection with specific embodiments.
1-2, An AI-based LED large screen fault prediction and maintenance system is provided in this embodiment, and includes a sensor network, a data acquisition module, an AI analysis engine, a fault prediction model, an alarm and notification module, and a remote control module:
The sensor network is deployed at each key part of the LED large screen and is responsible for collecting environmental parameters of temperature, humidity, voltage and current and running state data;
the data acquisition module is responsible for collecting data transmitted by the sensor network and performing preliminary processing and storage;
The AI analysis engine analyzes the collected data by using a machine learning or deep learning algorithm, identifies an abnormal mode and predicts potential fault points;
The fault prediction model is based on historical fault data and real-time monitoring data, and a prediction model is constructed to predict the occurrence probability and possible time of faults;
When the alarm and notification module detects potential faults or actual faults, maintenance personnel are notified through mails, short messages or APP pushing modes.
The remote control module allows maintenance personnel to conduct fault diagnosis, parameter adjustment or software upgrading operation on the LED large screen in a remote mode.
In this embodiment, data acquisition and preprocessing: the system firstly collects various parameter data of the LED large screen through a sensor network, the data collection module carries out cleaning, denoising and normalization processing on the original data so as to improve the accuracy of subsequent analysis, and the processed data are transmitted to a server where an AI analysis engine is located through an Ethernet or wireless network.
In this embodiment, feature extraction and selection: features useful for fault prediction are extracted from the preprocessed data using feature engineering methods. These characteristics may include temperature trend, voltage fluctuation range, humidity rate, current stability index, etc.
In this embodiment, model training: and selecting a proper machine learning or deep learning algorithm (such as random forest, gradient lifting tree, neural network and the like), and training a fault prediction model by utilizing historical fault data and real-time monitoring data. In the training process, the prediction precision and generalization capability of the model are improved through methods such as cross verification, super parameter tuning and the like.
In this embodiment, real-time prediction: the trained model is deployed on an AI analysis engine server, receives new data from the data acquisition module in real time, and outputs a failure prediction result. The predicted outcome includes the probability of occurrence of the fault, the type of fault possible, the predicted time of occurrence, etc.
In this embodiment, alarm and notification: when the model predicts potential faults, the AI analysis engine immediately triggers an alarm mechanism, and sends alarm information to maintenance personnel in a mode of short messages, mails or APP pushing and the like according to preset rules and priorities. Meanwhile, the system also generates a detailed fault analysis report, which comprises a fault prediction basis, a possible influence range, suggested maintenance measures and the like.
In this embodiment, remote maintenance: after receiving the alarm information, maintenance personnel can log in to the AI analysis engine server through the remote control terminal to check the real-time running state and the fault analysis report of the LED large screen. According to the report content, maintenance personnel can perform remote fault diagnosis, parameter adjustment, software upgrading and other operations so as to eliminate potential faults or reduce the influence of the faults.
In this embodiment, redundancy design: in the system design, measures such as redundant sensors, redundant data acquisition modules, redundant servers and the like are adopted to improve the reliability and fault tolerance of the system. When a certain component fails, the system can be automatically switched to the backup component, so that continuous operation of the system is ensured.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (8)
1. The utility model provides a LED large screen fault prediction system based on AI, includes sensor network, data acquisition module, AI analysis engine, fault prediction model, report to the police and inform module and remote control module, its characterized in that:
The sensor network is deployed at each key part of the LED large screen and is responsible for collecting environmental parameters of temperature, humidity, voltage and current and running state data;
the data acquisition module is responsible for collecting data transmitted by the sensor network and performing preliminary processing and storage;
The AI analysis engine analyzes the collected data by using a machine learning or deep learning algorithm, identifies an abnormal mode and predicts potential fault points;
The fault prediction model is based on historical fault data and real-time monitoring data, and a prediction model is constructed to predict the occurrence probability and possible time of faults;
When the alarm and notification module detects potential faults or actual faults, maintenance personnel are notified through mails, short messages or APP pushing modes.
The remote control module allows maintenance personnel to conduct fault diagnosis, parameter adjustment or software upgrading operation on the LED large screen in a remote mode.
2. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein the data acquisition and preprocessing: the temperature sensor, the humidity sensor, the voltage sensor and the current sensor with high precision and high reliability are selected and used and are respectively arranged at key parts such as a heat dissipation system, a power module, a control panel and a display module of the LED large screen, and the sensors are connected to the data acquisition module in a wired or wireless mode. The system firstly collects various parameter data of the LED large screen through a sensor network, the data collection module carries out cleaning, denoising and normalization processing on the original data so as to improve the accuracy of subsequent analysis, and the processed data is transmitted to a server where an AI analysis engine is located through an Ethernet or wireless network.
3. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein the feature extraction and selection: features useful for fault prediction are extracted from the preprocessed data using feature engineering methods. These characteristics may include temperature trend, voltage fluctuation range, humidity rate, current stability index, etc.
4. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein the model trains: and selecting a proper machine learning or deep learning algorithm (such as a random forest, a gradient lifting tree, a neural network and the like), training a fault prediction model by utilizing historical fault data and real-time monitoring data, and improving the prediction precision and generalization capability of the model by means of cross verification, super-parameter tuning and the like in the training process.
5. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein said real-time prediction: the trained model is deployed on an AI analysis engine server, receives new data from the data acquisition module in real time, and outputs a failure prediction result. The predicted outcome includes the probability of occurrence of the fault, the type of fault possible, the predicted time of occurrence, etc.
6. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein said alarm and notification: when the model predicts potential faults, the AI analysis engine immediately triggers an alarm mechanism, and sends alarm information to maintenance personnel in a mode of short messages, mails or APP pushing and the like according to preset rules and priorities. Meanwhile, the system also generates a detailed fault analysis report, which comprises a fault prediction basis, a possible influence range, suggested maintenance measures and the like.
7. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein the remote maintenance: after receiving the alarm information, maintenance personnel can log in to the AI analysis engine server through the remote control terminal to check the real-time running state and the fault analysis report of the LED large screen. According to the report content, maintenance personnel can perform remote fault diagnosis, parameter adjustment, software upgrading and other operations so as to eliminate potential faults or reduce the influence of the faults.
8. The AI-based LED large screen fault prediction and maintenance system of claim 1, wherein the redundancy design: in the system design, measures such as redundant sensors, redundant data acquisition modules, redundant servers and the like are adopted to improve the reliability and fault tolerance of the system. When a certain component fails, the system can be automatically switched to the backup component, so that continuous operation of the system is ensured.
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| CN202411008466.6A CN119001266A (en) | 2024-07-26 | 2024-07-26 | AI-based LED large screen fault prediction and maintenance system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN119181318A (en) * | 2024-11-25 | 2024-12-24 | 国鲸科技(广东横琴粤澳深度合作区)有限公司 | Organic electroluminescent driving circuit fault diagnosis method based on deep learning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN119181318A (en) * | 2024-11-25 | 2024-12-24 | 国鲸科技(广东横琴粤澳深度合作区)有限公司 | Organic electroluminescent driving circuit fault diagnosis method based on deep learning |
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