Intelligent management method and system for avionics equipment

Abstract

The invention discloses an intelligent management system for avionics equipment, which comprises a parameter acquisition module, a data analysis module, a decision support module and a data storage module, and is characterized in that the parameter acquisition module is used for collecting running state data of the avionics equipment in real time, and the data analysis module is used for receiving parameter data; and processing and analyzing the data by using a preset algorithm, and identifying the running state and potential problems of the equipment. The intelligent management method for the avionics equipment comprises the following steps: step 1, data acquisition; step 2, data analysis; step 3, decision support analysis; step 4, data storage; through automatic data acquisition and analysis, manual operation and intervention are reduced, the automation level of equipment management is improved, through intelligent decision support, maintenance and management strategies of equipment can be rapidly generated, the management period is shortened, the management cost is reduced, and the functions of state monitoring, fault diagnosis, service life prediction and the like of the equipment can be achieved. And the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of avionics equipment management, and specifically to an intelligent management method and system for avionics equipment. Background Art

[0002] With the rapid development of aviation technology, avionics equipment is more and more widely used on aircraft, the number and complexity of equipment are increasing continuously, and the complexity of equipment management and maintenance is also rising. However, the traditional management method of avionics equipment mainly relies on manual operation, with low management efficiency and easy to make mistakes. At the same time, due to the lack of effective intelligent management means, the maintenance and repair of equipment are often not timely, resulting in a relatively high equipment failure rate, seriously affecting aviation safety. Therefore, an intelligent management method and system for avionics equipment are needed to solve the proposed technical problems. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent management method and system for avionics equipment. Through automatic data collection and analysis, manual operation and intervention are reduced, and the automation level of equipment management is improved. At the same time, through intelligent decision support, maintenance and management strategies for equipment can be quickly generated, shortening the management cycle and management cost. Through intelligent fault warning and maintenance plan optimization, unnecessary maintenance operations and labor costs can be reduced. By predicting information such as the life and reliability of equipment, management work such as the repair plan and resource allocation of equipment can be reasonably planned. Through efficient data processing and analysis technologies, functions such as equipment status monitoring, fault diagnosis, and life prediction can be realized, reducing the repair cost and maintenance cost, so as to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: an intelligent management system for avionics equipment, including a parameter collection module, a data analysis module, a decision support module, and a data storage module. The parameter collection module is used to collect the operation status data of avionics equipment in real time. The data analysis module receives the parameter data and uses preset algorithms to process and analyze the data to identify the operation status and potential problems of the equipment. The decision support module generates equipment maintenance and management strategies according to the output of the data analysis module and provides them for equipment managers to refer to. The data storage module is used to store the collected data and the output results of the data analysis module and the decision support module.

[0005] The method for intelligent management of avionics equipment includes Step 1: Data acquisition; Step 2: Data analysis; Step 3: Decision support analysis; Step 4: Data storage;

[0006] In the above step 1, by installing sensors and devices for reading device parameters on the avionics equipment, information such as the operating status data, environmental data, and device parameters of the equipment is collected in real time;

[0007] In the above step 2, a preset algorithm is used to process and analyze the data. The data analysis module adopts big data analysis technology and machine learning algorithms to mine and process the collected data, identify the operating status and potential problems of the equipment. By analyzing the operating data and fault information of the equipment, the lifespan and reliability of the equipment can be predicted, potential faults and problems can be discovered in a timely manner, and decision-making support can be provided for the maintenance and repair of the equipment;

[0008] In the above step 3, according to the output of the data analysis module, the decision support module generates equipment maintenance and management strategies for the reference of equipment management personnel. The decision support module adopts technical means such as expert systems and rule engines. According to the data analysis results and the preset rule library, it automatically generates equipment maintenance plans, warning information, and fault handling solutions. Through the automatically generated management strategies, manual intervention and error rates can be reduced, and the efficiency and accuracy of equipment management can be improved;

[0009] In the above step 4, the data storage module adopts database technology to classify and store and manage the collected data and results. Through data storage and management, the data can be conveniently queried, analyzed, and traced, providing a reliable basis for the maintenance and management of the equipment.

[0010] Preferably, the parameter acquisition module also realizes automatic data acquisition through technical means such as sensor networks, RFID technology, and web crawlers. The collected data includes parameters such as the temperature, humidity, current, voltage, air pressure, and vibration of the equipment, as well as the operating status and fault information of the equipment.

[0011] Preferably, the data analysis module includes data preprocessing, feature extraction, and model analysis. Data preprocessing is responsible for cleaning, filtering, and sorting the original data to eliminate outliers, missing values, and duplicate data, etc., to ensure the quality and accuracy of the data. At the same time, data preprocessing will perform normalization processing on the data, converting data with different dimensions and magnitudes into a unified standard, which is convenient for subsequent feature extraction and model analysis.

[0012] Preferably, the feature extraction extracts feature information related to the equipment status and performance from the preprocessed data. These features include the operating parameters of the equipment, environmental factors, historical maintenance records, etc., which can reflect the operating status and performance of the equipment. Feature extraction uses algorithms for principal component analysis, wavelet transform, etc. to reduce the dimension and transform the original data, and extract key features to provide input for model analysis.

[0013] Preferably, the model analysis uses machine learning algorithms and statistical analysis methods to deeply analyze and mine the extracted features. By constructing classifiers, regression models, and clustering algorithms, the model analysis can perform tasks such as monitoring the operating status of equipment, fault diagnosis, and life prediction. The algorithms include support vector machines, neural networks, and decision trees. The results of the model analysis will provide a basis for the decision support module to generate maintenance and management strategies for the equipment.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] Through automated data collection and analysis, the present invention reduces manual operations and interventions, improving the automation level of equipment management. At the same time, through intelligent decision support, it can quickly generate maintenance and management strategies for equipment, shortening the management cycle and management costs. Through intelligent fault warning and maintenance plan optimization, it can reduce unnecessary maintenance operations and labor costs. By predicting information such as the life and reliability of equipment, it can reasonably plan management tasks such as equipment repair plans and resource allocation. Through efficient data processing and analysis technologies, it can achieve functions such as equipment status monitoring, fault diagnosis, and life prediction, reducing repair costs and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the system flow of the present invention;

[0017] Figure 2 is a schematic diagram of the process of the parameter acquisition module of the present invention;

[0018] Figure 3 is a schematic diagram of the process of the data analysis module of the present invention;

[0019] Figure 4 is a schematic diagram of the process of the decision support analysis module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", "front end", "rear end", "both ends", "one end", "the other end", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0022] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "provided with", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] Please refer to Figure 1 、 Figure 2 、 Figure 3 and Figure 4 , an embodiment provided by the present invention:

[0024] Embodiment 1

[0025] An intelligent management system for avionics equipment includes a parameter acquisition module, a data analysis module, a decision support module, and a data storage module. The parameter acquisition module is used to collect the operation status data of avionics equipment in real time. The data analysis module receives the parameter data and uses a preset algorithm to process and analyze the data to identify the operation status and potential problems of the equipment. The decision support module generates equipment maintenance and management strategies based on the output of the data analysis module and provides them for the reference of equipment management personnel. The data storage module is used to store the collected data and the output results of the data analysis module and the decision support module.

[0026] A method for intelligent management of avionics equipment includes Step 1: Data acquisition; Step 2: Data analysis; Step 3: Decision support analysis; Step 4: Data storage;

[0027] Among them, in the above Step 1, by installing sensors and devices for reading equipment parameters on avionics equipment, the operation status data, environmental data, equipment parameters and other information of the equipment are collected in real time;

[0028] In the second step above, a preset algorithm is used to process and analyze the data. The data analysis module adopts big data analysis technology and machine learning algorithms to mine and process the collected data, identify the operating status and potential problems of the device. By analyzing the operating data and fault information of the device, the lifespan and reliability of the device can be predicted, potential faults and problems can be discovered in a timely manner, providing decision-making support for the maintenance and repair of the device;

[0029] In the third step above, the decision support module generates device maintenance and management strategies based on the output of the data analysis module and provides them for the reference of device management personnel. The decision support module adopts technical means such as expert systems and rule engines. According to the data analysis results and the preset rule library, it automatically generates device maintenance plans, warning information, and fault handling solutions. Through the automatically generated management strategies, manual intervention and error rates can be reduced, and the efficiency and accuracy of device management can be improved;

[0030] In the fourth step above, the data storage module adopts database technology to classify, store, and manage the collected data and results. Through data storage and management, the data can be conveniently queried, analyzed, and traced, providing a reliable basis for the maintenance and management of the device.

[0031] Furthermore, the parameter acquisition module also realizes automatic data acquisition through technical means such as sensor networks, RFID technology, and web crawlers. The collected data includes parameters such as the temperature, humidity, current, voltage, air pressure, and vibration of the device, as well as the operating status and fault information of the device.

[0032] Furthermore, the data analysis module includes data preprocessing, feature extraction, and model analysis. Data preprocessing is responsible for cleaning, filtering, and organizing the original data to eliminate outliers, missing values, and duplicate data, etc., ensuring the quality and accuracy of the data. At the same time, data preprocessing normalizes the data, converting data with different dimensions and magnitudes into a unified standard, facilitating subsequent feature extraction and model analysis.

[0033] Furthermore, feature extraction extracts feature information related to the device status and performance from the preprocessed data. These features include the operating parameters of the device, environmental factors, historical maintenance records, etc., which can reflect the operating status and performance of the device. Feature extraction uses algorithms for principal component analysis, wavelet transform, etc. to reduce the dimension and transform the original data, extracting key features and providing input for model analysis.

[0034] Furthermore, the model analysis uses machine learning algorithms and statistical analysis methods to deeply analyze and mine the extracted features. By constructing classifiers, regression models, and clustering algorithms, the model analysis can perform tasks such as monitoring the operating status of the device, fault diagnosis, and life prediction. The algorithms include support vector machines, neural networks, and decision trees. The results of the model analysis will provide a basis for the decision support module to generate maintenance and management strategies for the device.

[0035] Embodiment 2

[0036] Through automated data collection and analysis, manual operations and interventions are reduced, and the automation level of device management is improved. At the same time, through intelligent decision support, maintenance and management strategies for the device can be quickly generated, shortening the management cycle and management costs. Through intelligent fault warning and maintenance plan optimization, unnecessary maintenance operations and labor costs can be reduced. By predicting information such as the life and reliability of the device, management tasks such as the repair plan and resource allocation of the device can be reasonably planned. Through efficient data processing and analysis technologies, functions such as device status monitoring, fault diagnosis, and life prediction can be achieved, reducing repair costs and maintenance costs.

[0037] Details not described in the present invention are all well-known technologies to those skilled in the art.

[0038] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified and equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An intelligent management system for avionics equipment, comprising a parameter acquisition module, a data analysis module, a decision support module, and a data storage module. The parameter acquisition module is used to collect the operation status data of avionics equipment in real time. The data analysis module receives the parameter data and uses a preset algorithm to process and analyze the data to identify the operation status and potential problems of the equipment. The decision support module generates equipment maintenance and management strategies based on the output of the data analysis module and provides them for the reference of equipment management personnel. The data storage module is used to store the collected data and the output results of the data analysis module and the decision support module.

2. A method for the intelligent management of avionics equipment, comprising Step 1: Data acquisition; Step 2: Data analysis; Step 3: Decision support analysis; Step 4: Data storage; characterized in that: In Step 1 above, by installing sensors and devices for reading equipment parameters on avionics equipment, the operation status data, environmental data, and equipment parameters of the equipment are collected in real time; In Step 2 above, a preset algorithm is used to process and analyze the data. The data analysis module adopts big data analysis technology and machine learning algorithms to mine and process the collected data, identify the operation status and potential problems of the equipment. By analyzing the operation data and fault information of the equipment, the lifespan and reliability of the equipment can be predicted, potential faults and problems can be discovered in a timely manner, and decision support for equipment maintenance and repair can be provided; In Step 3 above, through the decision support module, based on the output of the data analysis module, equipment maintenance and management strategies are generated and provided for the reference of equipment management personnel. The decision support module adopts technical means such as expert systems and rule engines, and automatically generates equipment maintenance plans, warning information, and fault handling solutions according to the data analysis results and a preset rule library. Through the automatically generated management strategies, manual intervention and error rates can be reduced, and the efficiency and accuracy of equipment management can be improved; In Step 4 above, the data storage module adopts database technology to classify and store and manage the collected data and results. Through data storage and management, data can be conveniently queried, analyzed, and traced, providing a reliable basis for equipment maintenance and management.

3. The method for intelligent management of avionics equipment according to claim 2, characterized in that: The parameter acquisition module also realizes automatic data acquisition through technical means such as sensor networks, RFID technology, and web crawlers. The collected data includes parameters such as the temperature, humidity, current, voltage, air pressure, and vibration of the equipment, as well as the operation status and fault information of the equipment.

4. The method for intelligent management of avionics equipment according to claim 2, wherein: The data analysis module includes data preprocessing, feature extraction, and model analysis. Data preprocessing is responsible for cleaning, filtering, and sorting the original data to eliminate outliers, missing values, and duplicate data, etc., to ensure the quality and accuracy of the data. At the same time, data preprocessing will perform normalization processing on the data, converting data with different dimensions and magnitudes into a unified standard to facilitate subsequent feature extraction and model analysis.

5. The method for intelligent management of avionics equipment according to claim 4, wherein: The feature extraction extracts feature information related to the device status and performance from the preprocessed data. These features include the operating parameters of the device, environmental factors, historical maintenance records, etc., which can reflect the operating status and performance of the device. The feature extraction uses algorithms for principal component analysis, wavelet transform, etc. to reduce the dimension and transform the original data, and extract key features to provide input for model analysis.

6. The method for intelligent management of avionics equipment according to claim 4, characterized in that: The model analysis uses machine learning algorithms and statistical analysis methods to deeply analyze and mine the extracted features. By constructing classifiers, regression models, and clustering algorithms, the model analysis can perform tasks such as monitoring the operating status of the device, fault diagnosis, and life prediction. The algorithms include support vector machines, neural networks, and decision trees. The results of the model analysis will provide a basis for the decision support module to generate maintenance and management strategies for the device.

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