Digital fixed asset electronic tag management system

Abstract

The invention discloses a digital fixed asset electronic tag management system, which relates to the technical field of digital management and comprises a tag management unit, an asset management unit, an information acquisition unit, a system management unit, an inventory management unit and a fault early warning unit. According to the information of the fixed assets, a unique corresponding electronic tag code can be generated, the situation of fixed asset confusion is avoided, meanwhile, image acquisition and processing are performed on the fixed assets, feature information in the image is extracted, the feature information is matched with features in a system image library, and a matching result is verified and evaluated; similar fixed assets are prevented from being stored and confused, the fixed asset management accuracy is improved, through feature extraction and evaluation of different fixed assets, a fault prediction model is established, possible faults of the fixed assets are predicted in advance, early warning information is sent out in time, and a maintenance plan is made. And the fault probability of the fixed assets in the production process is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of digital management, and in particular to a digital fixed asset electronic tag management system. Background Art

[0002] Fixed assets are an important part of the assets of enterprises and institutions, including buildings, equipment, vehicles, office furniture, etc. These assets are of high value and have a long service life. Effective management of them is of vital importance to ensuring asset security, improving asset utilization efficiency, reducing operating costs, and accurately performing financial accounting.

[0003] For example, the publication number CN113159636A, “A fixed asset management system based on precise positioning of the Internet of Things”, includes electronic tags, a platform system, and a fixed asset management system; the electronic tags are set on the fixed assets, the platform system scans the electronic tag information, and all fixed asset information is registered in the fixed asset management system, which can update the real-time information status of the fixed assets.

[0004] In the prior art, when conducting digital management of fixed assets, due to the similarity of the nail tips of some fixed assets, it is not convenient to quickly and accurately identify and put into storage the fixed assets, resulting in misjudgment after fixed asset identification, and the untimely update of information will lead to the inability to timely grasp the real-time situation of the assets. Fixed assets of different categories and uses have different service lives and failure-inducing risks. It is impossible to predict asset failures and repair them in time, which will lead to sudden failures during use and production interruptions. Summary of the invention

[0005] The purpose of the present invention is to provide a digital fixed asset electronic tag management system to solve the problem raised in the above background technology that untimely information update will lead to the inability to timely grasp the real-time status of assets, inability to timely predict asset failures and repair them, and production interruption.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: a digital fixed asset electronic tag management system, comprising a tag management unit, an asset management unit, an information collection unit, a system management unit, an inventory management unit and a fault warning unit; The tag management unit is used to generate and modify the electronic tags of fixed assets, and to manage the electronic tags of fixed assets throughout their life cycle, so as to realize the digital and refined management of fixed assets and improve the efficiency and accuracy of asset management; The asset management unit is used to centrally integrate and manage scattered asset information, update and correct asset status, break information silos, form a unified asset information database, and provide comprehensive data support for enterprise management; The information collection unit is used to identify the electronic tags of fixed assets and verify the accuracy of the data; The system management unit is used to manage system users, set user access rights, store traceability historical data, collect basic asset information, monitor asset status and environmental parameters in real time, collect image and video data, store traceability historical data, and realize data sharing and business collaboration with other systems, providing comprehensive and accurate data support for fixed asset management; The inventory management unit is used for asset entry registration, outbound management, inventory counting and reconciliation, inventory status monitoring, report generation and analysis, so as to achieve accurate control of fixed asset inventory and improve asset management efficiency and asset utilization; The fault warning unit is used to monitor asset operation data in real time, analyze and determine potential fault risks, issue warning information in a timely manner, and provide maintenance decision support to reduce the occurrence of faults, reduce maintenance costs, and ensure the normal operation of assets; The fault warning unit includes a fault prediction module, a depreciation warning module and a scrapping processing module; The fault prediction module is used to analyze the usage data and maintenance records of the asset, establish a fault prediction model, predict possible faults of the asset in advance, provide maintenance personnel with maintenance suggestions and plans, implement preventive maintenance, and reduce asset downtime; The depreciation warning module is used to set the asset depreciation threshold, and when the asset depreciation amount reaches the threshold, a warning is issued to remind the staff to handle it; The scrapping processing module is used to issue a scrapping warning when the asset reaches the scrapping age and meets the scrapping conditions, reminding the staff to carry out asset scrapping processing.

[0007] Preferably, the label management unit includes a label generation module and a label loss reporting module; The label generation module is used to generate a unique corresponding electronic label code according to the information of the fixed asset, and write the relevant information into the electronic label, wherein the relevant information includes the asset number, name, specification model and purchase date; The tag loss reporting module is used to report the electronic tags that are damaged, lost or need to be replaced, and re-bind the tags after replacing them with new ones to prevent the original tags from being used illegally, ensure the uniqueness of the fixed asset tags and avoid confusion.

[0008] Preferably, the asset management unit includes an asset entry module, an asset query module and an asset change module; The asset entry module is used to enter detailed information of fixed assets into the system, the detailed information including asset category, purchase price, using department and user; The asset query module is used to use partial information to perform asset query and quickly locate the required asset information; The asset change module records various change information of fixed assets during use, and the change information includes asset transfer, maintenance records and depreciation information.

[0009] Preferably, the information collection unit includes a tag identification module and a data verification module; The tag recognition module is used to read the electronic tag information on the fixed assets, collect and process images of the fixed assets, use the convolutional neural network algorithm to compare the collected images with the asset image library pre-stored in the system, automatically identify the fixed assets, and realize rapid identification and data collection of the assets; The data verification module is used to verify the collected data and check the correctness of the label coding and the integrity of the data.

[0010] Preferably, the image acquisition and processing of fixed assets includes the following steps: S1. The sensor detects the arrival of the fixed asset and automatically triggers the camera to take a picture of the fixed asset to obtain the image information of the fixed asset; S2, graying the collected color image and converting it into a grayscale image, using histogram equalization to adjust the grayscale histogram of the image, enhancing the contrast of the image and highlighting the key information in the image. Histogram equalization adjusts the grayscale histogram of the image to make the grayscale distribution of the image more uniform, thereby enhancing the contrast of the image; S3, converting the grayscale image into a binary image, determining a fixed threshold according to the overall grayscale distribution of the image, setting pixels greater than the threshold as the foreground color, and setting pixels less than the threshold as the background color; S4. Use convolutional neural network to input the image into the network. After the operation of convolution layer and pooling layer, the feature map of the image is automatically extracted. The extracted features are screened and optimized to remove redundant features and improve the quality of features and recognition efficiency. S5. Match the extracted fixed asset image features with the features in the system image library, verify and evaluate the matching results, output the corresponding fixed asset information after successful recognition, display the name, number, and location information of the fixed asset, and record the recognition results in the system database. The recognition results include image acquisition time, recognition time, and recognition accuracy.

[0011] Preferably, the system management unit includes a user management module, a rights management module, a user interaction module and a data backup module; The user management module is responsible for adding, deleting and setting permissions for users, and different users have corresponding operation permissions; The authority management module is used to define different roles for the system and assign corresponding functional permissions and data access permissions to each role; The user interaction module is used to provide an operation interface for asset information query, asset judgment, fault warning viewing and location tracking; The data backup module is used to regularly back up and store system data to prevent data loss, and can restore data in time when encountering system failures and data corruption.

[0012] Preferably, the inventory management unit includes an inbound management unit, an outbound management unit and an inventory counting module; The storage management unit is used to scan the electronic tags of the newly stored fixed assets, associate the asset information with the storage record, and update the inventory information; The outbound management unit is used to check the outbound fixed asset information, complete the outbound operation, and update the inventory status; The inventory counting module is used to regularly count inventory assets, quickly obtain the actual inventory status of assets by scanning tags, compare with system records, and generate inventory counting reports.

[0013] Preferably, the periodic inventory of inventory assets includes real-time positioning and tracking of fixed assets, regularly sending asset location information to the system, and displaying asset information in real time during inventory.

[0014] Preferably, the predicting possible failure of an asset comprises the following steps: A1. Extract the cumulative operating time and average daily operating time features from the original data, extract the fault frequency and the time interval from the last maintenance to the present from the maintenance records, and extract the average value and standard deviation features of the ambient temperature and humidity from the asset's environmental parameters; A2. Evaluate the importance of each feature for fault prediction, remove features with low relevance, reduce feature dimensions, reduce model complexity, improve model training efficiency, divide the preprocessed and feature-engineered data into training sets and test sets, use the training set to train the selected model, and adjust the model parameters so that it can learn the laws in the data and the relationship between features and faults; A3. Use the test set to evaluate the trained model, calculate the model's evaluation indicators, optimize the model based on the evaluation results, predict the probability and time of possible asset failure, and issue early warning information in a timely manner.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention can generate a unique corresponding electronic tag code based on the information of the fixed assets to avoid confusion of the fixed assets. At the same time, the fixed assets are imaged and processed, feature information in the image is extracted, and matched with features in the system image library. The matching results are verified and evaluated to avoid confusion when similar fixed assets are put into storage, thereby improving the accuracy of fixed asset management. By extracting and evaluating features of different fixed assets, a fault prediction model is established to predict possible faults of fixed assets in advance, issue early warning information in a timely manner, formulate maintenance plans, and reduce the probability of fixed asset failures during the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system block diagram of a digital fixed asset electronic tag management system of the present invention; Figure 2 This is a block diagram of an asset management unit of a digital fixed asset electronic tag management system of the present invention; Figure 3 A system management unit block diagram of a digital fixed asset electronic tag management system of the present invention; Figure 4 This is a block diagram of an inventory management unit of a digital fixed asset electronic tag management system of the present invention; Figure 5 The present invention is a block diagram of a fault warning unit of a digital fixed asset electronic tag management system.

[0017] In the figure: 1. Tag management unit; 11. Tag generation module; 12. Tag loss reporting module; 2. Asset management unit; 21. Asset entry module; 22. Asset query module; 23. Asset change module; 3. Information collection unit; 31. Tag identification module; 32. Data verification module; 4. System management unit; 41. User management module; 42. Authority management module; 43. User interaction module; 44. Data backup module; 5. Inventory management unit; 51. Inbound management unit; 52. Outbound management unit; 53. Inventory counting module; 6. Fault warning unit; 61. Fault prediction module; 62. Depreciation warning module; 63. Scrap processing module. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described 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.

[0019] Example: Refer to Figure 1 - Figure 5 As shown: a digital fixed asset electronic tag management system, including a tag management unit 1, an asset management unit 2, an information collection unit 3, a system management unit 4, an inventory management unit 5 and a fault warning unit 6; The tag management unit 1 is used to generate and modify the electronic tags of fixed assets, and to manage the electronic tags of fixed assets throughout their life cycle, so as to realize the digital and refined management of fixed assets and improve the efficiency and accuracy of asset management; The label management unit 1 includes a label generation module 11 and a label loss reporting module 12; The label generation module 11 is used to generate a unique corresponding electronic label code according to the information of the fixed asset, and write the relevant information into the electronic label, the relevant information including the asset number, name, specification model and purchase date; The tag loss reporting module 12 is used to report the electronic tags that are damaged, lost or need to be replaced, and re-bind them after replacing them with new ones. It not only binds the corresponding relationship between the new tags and the assets, but also retains the replacement records of the old and new tags to prevent the original tags from being used illegally, effectively ensure the uniqueness of the fixed asset tags, and effectively avoid confusion in the asset management process.

[0020] Asset management unit 2 is used to centrally integrate and manage scattered asset information, update and correct asset status, break information silos, form a unified asset information database, and provide comprehensive data support for enterprise management; The asset management unit 2 includes an asset entry module 21, an asset query module 22 and an asset change module 23; The asset entry module 21 is used to enter detailed information of fixed assets into the system, including asset category, purchase price, user department and user; The asset query module 22 is used to use partial information to query assets, quickly locate the required asset information, and provide a variety of query methods. Users can not only use incomplete information such as asset number, name, and user department to perform accurate or fuzzy queries, but also use combined queries to quickly locate the required asset information. The query results are presented in an intuitive list format. At the same time, according to user needs, detailed information of the assets can be displayed or only key information can be displayed to facilitate user screening; The asset change module 23 records various change information of fixed assets during use, including asset transfer, maintenance records and depreciation information. Each time a change is made, the operating system automatically records the change time, operator and approver to ensure the traceability of asset change information.

[0021] The information collection unit 3 is used to identify the electronic tags of fixed assets, verify the accuracy of data, and provide a reliable data source for asset management; The information collection unit 3 includes a tag identification module 31 and a data verification module 32; The tag recognition module 31 is used to read the electronic tag information on the fixed assets, collect and process images of the fixed assets, use the convolutional neural network algorithm to compare the collected images with the asset image library pre-stored in the system, automatically identify the fixed assets, and realize rapid identification and data collection of the assets; Image acquisition and processing of fixed assets includes the following steps: Step 1: The sensor detects the arrival of the fixed asset and automatically triggers the camera to take a picture of the fixed asset to obtain the image information of the fixed asset, ensuring that the collected image can fully reflect the characteristics of the asset; Step 2: Grayscale the collected color image and convert it into a grayscale image. Use histogram equalization to adjust the grayscale histogram of the image to enhance the contrast of the image and highlight the key information in the image. Histogram equalization adjusts the grayscale histogram of the image to make the grayscale distribution of the image more uniform. Step 3: Convert the grayscale image into a binary image, determine a fixed threshold value based on the overall grayscale distribution of the image, set pixels greater than the threshold value as the foreground color, and set pixels less than the threshold value as the background color, effectively avoiding inaccurate image segmentation caused by factors such as uneven illumination; Step 4: Use a convolutional neural network to input the image into the network. After the convolution layer and pooling layer operations, the feature map of the image is automatically extracted. The extracted features are screened and optimized to remove redundant features and improve the quality of features and recognition efficiency. Step 5: Match the extracted fixed asset image features with the features in the system image library, verify and evaluate the matching results, output the corresponding fixed asset information after successful recognition, display the name, number, and location information of the fixed asset, and record the recognition results in the system database. The recognition results include image acquisition time, recognition time, and recognition accuracy; The data verification module 32 is used to verify the collected data, check the correctness of the label coding and the integrity of the data, and also ensure the accuracy and consistency of the collected data by cross-checking with the existing data in the system.

[0022] The system management unit 4 is used to manage system users, set user access rights, store traceability historical data, collect basic asset information, monitor asset status and environmental parameters in real time, collect image and video data, store traceability historical data, and realize data sharing and business collaboration with other systems, providing comprehensive and accurate data support for fixed asset management; The system management unit 4 includes a user management module 41, a permission management module 42, a user interaction module 43 and a data backup module 44; The user management module 41 is responsible for adding, deleting and setting permissions for users, and assigning corresponding operation permissions to users according to their job responsibilities and business needs; The authority management module 42 is used to define different roles for the system, assign corresponding functional permissions and data access permissions to each role, ensure that users of different roles can only operate within their authorized scope, and ensure the security and confidentiality of system data; The user interaction module 43 is used to provide an operation interface for asset information query, asset judgment, fault warning viewing and location tracking; The data backup module 44 is used to regularly back up and store system data to prevent data loss. In the event of system failure or data corruption, data can be restored in a timely manner to minimize the impact of data loss on business operations.

[0023] Inventory management unit 5 is used for asset entry registration, outbound management, inventory counting and reconciliation, inventory status monitoring, report generation and analysis, so as to achieve accurate control of fixed asset inventory and improve asset management efficiency and asset utilization; The inventory management unit 5 includes an inbound management unit 51, an outbound management unit 52 and an inventory counting module 53; The storage management unit 51 is used to scan the electronic tags of the newly stored fixed assets, associate the asset information with the storage record, and update the inventory information; The outbound management unit 52 is used to check the outbound fixed asset information, complete the outbound operation, and update the inventory status; The inventory counting module 53 is used to regularly count inventory assets, quickly obtain the actual inventory status of assets by scanning tags, compare with system records, and generate inventory reports; Regular inventory of inventory assets includes real-time positioning and tracking of fixed assets, regularly sending asset location information to the system, and displaying asset information in real time during inventory.

[0024] The fault warning unit 6 is used to monitor the asset operation data in real time, analyze and determine the potential fault risks, issue warning information in time, and provide maintenance decision support to reduce the occurrence of faults, reduce maintenance costs, and ensure the normal operation of assets; The fault warning unit 6 includes a fault prediction module 61, a depreciation warning module 62 and a scrapping processing module 63; The fault prediction module 61 is used to analyze the usage data and maintenance records of the asset, establish a fault prediction model, predict possible faults of the asset in advance, provide maintenance personnel with maintenance suggestions and plans, implement preventive maintenance, and reduce asset downtime; Predicting possible failure of an asset involves the following steps: Step 1: Extract the cumulative running time and average daily running time features from the original data, extract the fault frequency and the time interval from the last maintenance to the present from the maintenance records, and extract the average value and standard deviation features of the ambient temperature and humidity from the asset's environmental parameters; Step 2: Evaluate the importance of each feature for fault prediction, remove features with low relevance, reduce feature dimensions, reduce model complexity, improve model training efficiency, divide the preprocessed and feature-engineered data into training sets and test sets, use the training set to train the selected model, and adjust the model parameters so that it can learn the laws in the data and the relationship between features and faults; Step 3: Use the test set to evaluate the trained model, calculate the model's evaluation indicators, optimize the model based on the evaluation results, predict the probability and time of possible asset failure, and issue early warning information in a timely manner.

[0025] The depreciation warning module 62 is used to set the asset depreciation threshold. When the asset depreciation amount reaches the threshold, an early warning is issued to remind the staff to handle it; The scrapping processing module 63 is used to issue a scrapping warning when an asset reaches the scrapping age and meets the scrapping conditions, reminding the staff to carry out asset scrapping processing.

[0026] The present invention can generate a unique corresponding electronic tag code based on the information of the fixed assets, and at the same time collect and process images of the fixed assets, extract feature information in the images, screen and optimize the features, remove redundant features, match the extracted fixed asset image features with the features in the system image library, verify and evaluate the matching results, and avoid confusion between similar fixed assets entering the warehouse. By regularly taking inventory of the fixed assets, the real-time positioning information of the asset location is sent to the system, and the real-time location information of the assets can be displayed during the inventory, the current location status of the fixed assets can be accurately located, feature extraction and evaluation can be performed on different fixed assets, a fault prediction model can be established, possible faults of fixed assets can be predicted in advance, early warning information can be issued in a timely manner, and a maintenance plan can be formulated. At the same time, the real-time status of the fixed assets in the warehouse is monitored, depreciation warnings and scrapping warnings can be issued, and staff can be reminded to handle them in a timely manner.

[0027] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital fixed asset electronic tag management system, characterized by: It includes a tag management unit (1), an asset management unit (2), an information collection unit (3), a system management unit (4), an inventory management unit (5) and a fault warning unit (6); The tag management unit (1) is used to generate and modify the electronic tags of fixed assets and to perform full life cycle management on the electronic tags of fixed assets; The asset management unit (2) is used to centrally integrate and manage dispersed asset information, and update and correct asset status; The information collection unit (3) is used to identify the electronic tags of fixed assets and verify the accuracy of the data; The system management unit (4) is used to manage system users, set user access rights, and store traceability history data; The inventory management unit (5) is used for asset entry registration, outbound management, inventory counting and reconciliation, inventory status monitoring, report generation and analysis; The fault warning unit (6) is used to monitor asset operation data in real time, analyze and determine potential fault risks, issue warning information in a timely manner, and provide maintenance decision support; The fault warning unit (6) comprises a fault prediction module (61), a depreciation warning module (62) and a scrapping processing module (63); The fault prediction module (61) is used to analyze the usage data and maintenance records of the asset, establish a fault prediction model, predict possible faults of the asset in advance, and provide maintenance personnel with maintenance suggestions and plans; the depreciation warning module (62) is used to set an asset depreciation threshold, and when the asset depreciation amount reaches the threshold, issue a warning to remind staff to handle it; the scrapping processing module (63) is used to issue a scrapping warning when the asset reaches the scrap age and meets the scrapping conditions, and remind staff to carry out asset scrapping processing.

2. A digital fixed asset electronic tag management system according to claim 1, characterized in that: The label management unit (1) comprises a label generation module (11) and a label loss reporting module (12); The label generation module (11) is used to generate a unique corresponding electronic label code according to the information of the fixed asset, and write the relevant information into the electronic label, wherein the relevant information includes the asset number, name, specification model and purchase date; The label loss reporting module (12) is used to report the loss of an electronic label that is damaged, lost or needs to be replaced, and to re-bind the label after replacing it with a new one.

3. A digital fixed asset electronic tag management system according to claim 1, characterized in that: The asset management unit (2) comprises an asset entry module (21), an asset query module (22) and an asset change module (23); The asset entry module (21) is used to enter detailed information of fixed assets into the system, wherein the detailed information includes asset category, purchase price, user department and user personnel; The asset query module (22) is used to use partial information to perform asset query and quickly locate required asset information; The asset change module (23) records various change information of fixed assets during use, and the change information includes asset transfer, maintenance records and depreciation information.

4. A digital fixed asset electronic tag management system according to claim 1, characterized in that: The information collection unit (3) comprises a tag identification module (31) and a data verification module (32); The tag recognition module (31) is used to read the electronic tag information on the fixed asset, collect and process the image of the fixed asset, use the convolutional neural network algorithm to compare the collected image with the asset image library pre-stored in the system, and automatically identify the fixed asset; The data verification module (32) is used to verify the collected data and check the correctness of the label coding and the integrity of the data.

5. The digital fixed asset electronic tag management system according to claim 1 is characterized by: The image acquisition and processing of fixed assets comprises the following steps: S1. The sensor detects the arrival of the fixed asset and automatically triggers the camera to take a picture of the fixed asset to obtain the image information of the fixed asset; S2, graying the collected color image, converting it into a grayscale image, and using histogram equalization to adjust the grayscale histogram of the image, thereby enhancing the contrast of the image and highlighting the key information in the image; S3, converting the grayscale image into a binary image, determining a fixed threshold according to the overall grayscale distribution of the image, setting pixels greater than the threshold as the foreground color, and setting pixels less than the threshold as the background color; S4. Use a convolutional neural network to input the image into the network. After the convolution layer and pooling layer operations, the feature map of the image is automatically extracted. The extracted features are screened and optimized to remove redundant features. S5. Match the extracted fixed asset image features with the features in the system image library, verify and evaluate the matching results, output the corresponding fixed asset information after successful recognition, display the name, number, and location information of the fixed asset, and record the recognition results in the system database. The recognition results include image acquisition time, recognition time, and recognition accuracy.

6. A digital fixed asset electronic tag management system according to claim 1, characterized in that: The system management unit (4) comprises a user management module (41), a rights management module (42), a user interaction module (43) and a data backup module (44); The user management module (41) is responsible for adding, deleting and setting permissions for users, and different users have corresponding operation permissions; The authority management module (42) is used to define different roles for the system and assign corresponding functional authority and data access authority to each role; The user interaction module (43) is used to provide an operation interface for asset information query, asset judgment, fault warning viewing and location tracking; The data backup module (44) is used to regularly back up and store system data.

7. The digital fixed asset electronic tag management system according to claim 1 is characterized by: The inventory management unit (5) comprises an inbound management unit (51), an outbound management unit (52) and an inventory counting module (53); The storage management unit (51) is used to scan the electronic tags of the newly stored fixed assets, associate the asset information with the storage record, and update the inventory information; The outbound management unit (52) is used to check the outbound fixed asset information, complete the outbound operation, and update the inventory status; The inventory counting module (53) is used to regularly count inventory assets, quickly obtain the actual inventory status of assets by scanning tags, compare with system records, and generate an inventory counting report.

8. A digital fixed asset electronic tag management system according to claim 7, characterized in that: The periodic inventory of inventory assets includes real-time positioning and tracking of fixed assets, regularly sending asset location information to the system, and displaying asset information in real time during inventory.

9. The digital fixed asset electronic tag management system according to claim 1, characterized in that: The method of predicting possible failure of an asset comprises the following steps: A1. Extract the cumulative operating time and average daily operating time features from the original data, extract the fault frequency and the time interval from the last maintenance to the present from the maintenance records, and extract the average value and standard deviation features of the ambient temperature and humidity from the asset's environmental parameters; A2. Evaluate the importance of each feature for fault prediction, remove features with low relevance, divide the preprocessed and feature-engineered data into training and test sets, use the training set to train the selected model, and adjust the model parameters; A3. Use the test set to evaluate the trained model, calculate the model's evaluation indicators, optimize the model based on the evaluation results, predict the probability and time of possible asset failure, and issue early warning information in a timely manner.

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