Asset management system based on full life cycle

Abstract

The invention discloses an asset management system based on a full life cycle, relates to the technical field of asset management, and comprises centralized management, real-time monitoring, intelligent analysis and scientific decision of asset data. The asset management system based on the full life cycle comprises an asset information management module, an Internet of Things data acquisition module, a data analysis and intelligent maintenance module, an intelligent decision support module, a visual management module, a risk early warning and compliance management module and a block chain evidence storage and traceability module. Through the asset information management module, basic information of assets is intensively recorded, updated, audited and cleaned, data formats are unified, meanwhile, the Internet of Things data collection module collects asset operation data in real time, the data are subjected to cleaning, conversion, feature extraction and other processing in the big data analysis and intelligent maintenance module, and the data are stored in the storage module. Effective integration of multi-source data is achieved, the system further provides an intelligent analysis function, and deep mining is carried out based on the integrated data.

Description

Technical Field

[0001] The present invention relates to the technical field of asset management, and in particular to an asset management system based on the entire life cycle. Background Art

[0002] In today's era of rapid development of digitalization and informatization, the scale of enterprise operations continues to expand, and the scale and types of assets are growing sharply. As the core element of enterprise operations and development, the management level of assets is directly related to the economic benefits, operational efficiency and ability to achieve strategic goals of the enterprise. With the acceleration of the digital transformation process of enterprises, the demand for refined asset management is becoming more and more urgent. However, there are many problems with existing asset management. Data is scattered across different departments or systems, with large format differences and inconsistent standards, making it difficult to integrate and analyze, affecting the scientific nature of decision-making. Key information such as asset location and operating status cannot be obtained in real time. Assets are frequently damaged, lost or idle. Maintenance plans often rely on experience and do not take into account the actual situation of assets, resulting in excessive or insufficient maintenance, increasing costs and affecting production operations. There is a lack of scientific evaluation and standardized processes for asset scrapping. Information flow between departments is blocked, there is a lack of coordination mechanism, and asset management efficiency is low. Therefore, it is urgent to build an asset management system based on the entire life cycle. To this end, we propose an asset management system based on the entire life cycle. Summary of the Invention

[0003] The purpose of the present invention is to provide an asset management system based on the entire life cycle.

[0004] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: an asset management system based on the whole life cycle, including centralized management, real-time monitoring, intelligent analysis and scientific decision-making of asset data, the asset management system based on the whole life cycle includes an asset information management module, an Internet of Things data acquisition module, a data analysis and intelligent maintenance module, an intelligent decision support module, a visualization management module, a risk warning and compliance management module and a blockchain evidence storage and traceability module, the asset information management module provides data for other modules as a basis, the Internet of Things data acquisition module provides the collected data to the big data analysis and intelligent maintenance module, the visualization management module and the blockchain evidence storage and traceability module, the data The analysis and intelligent maintenance module obtains data from the module and provides the analysis results to the intelligent decision support module, the visualization management module and the blockchain evidence storage and traceability module. The intelligent decision support module makes decisions based on the data and analysis results of other modules and feeds back the decision results to other modules. The visualization management module obtains data from multiple modules and displays them. The risk warning and compliance management module obtains data from other modules and performs risk assessment and compliance monitoring, and at the same time feeds back warning and response information to other modules. The blockchain evidence storage and traceability module encrypts and stores data from the asset information management module, the Internet of Things data acquisition module and the big data analysis and intelligent maintenance module, and provides users with asset traceability query functions.

[0005] As a further solution of the present invention: the asset information management module includes asset information entry, asset information update, data review and cleanup, and data export management. The specific operation steps of the module are as follows:

[0006] When purchasing assets and putting them into storage, open the system asset information management interface and enter the asset name, model, specifications, purchase date, supplier, purchase price, using department and location of use in sequence. Click "Save" after completion. When the asset information changes, the system uses the asset number and name to search for the corresponding asset. After a successful review, click "Edit", modify the corresponding information and save. Every 2-3 weeks, arrange for staff to screen duplicate and erroneous data in the system, verify and supplement assets with missing information, and submit for review after completion.

[0007] As a further solution of the present invention: the Internet of Things data acquisition module includes sensor selection and installation, sensor connection configuration, data acquisition and preprocessing, and sensor maintenance and management. The specific operation steps of the module are as follows:

[0008] Select appropriate IoT sensors based on asset type and monitoring requirements, such as installing vibration sensors on equipment to monitor operating status. Install sensors at appropriate locations on assets, ensuring they are firm and do not affect normal asset operation. Connect sensors to the system gateway via wired or wireless means, and configure sensor parameters in the system background, including data collection frequency and transmission protocol. The sensor collects and uploads data at the set frequency. After receiving the data, the system performs preprocessing operations such as format conversion, denoising, and outlier removal. Check the sensor's appearance for damage every 2-3 weeks, check the sensor data transmission log in the system, and replace and repair it in a timely manner if any abnormality is found.

[0009] As a further solution of the present invention, the big data analysis and intelligent maintenance module includes data acquisition and processing, asset health assessment, maintenance plan generation, maintenance task execution and feedback, and model optimization. The specific operation steps of the module are as follows:

[0010] Set a data acquisition cycle in the system, and regularly extract data from the asset information management and IoT data acquisition modules for cleaning, conversion, and feature extraction. In the system analysis interface, select the asset range to be evaluated, run the asset health assessment model, and view the asset health score and remaining service life. The system generates maintenance task work orders based on the assessment results and preset rules, including maintenance type, time, and personnel arrangements. Maintenance personnel receive work orders on the system mobile and computer terminals, perform maintenance operations as required, and fill in and submit maintenance records in the system upon completion. Based on new data and actual maintenance results, adjust and optimize model parameters every quarter and retrain the model.

[0011] As a further solution of the present invention, the intelligent decision support module includes decision requirement input, data analysis and calculation, decision report generation, decision execution and recording, and decision effect review. The specific operation steps of the module are as follows:

[0012] Managers access the system's intelligent decision support module, input decision requirements at the corresponding function entrance, and use data analysis algorithms and models for calculations. The system generates a decision report based on the analysis results, presented in the form of charts and text. Managers view the report in the system and make decisions based on the report. At the same time, the system records information on the decision content, execution department, and execution time. After the decision has been executed for a period of time, the system collects relevant data on the decision execution, compares the expected and actual effects of the decision, and summarizes and records the comparison results.

[0013] As a further solution of the present invention: the visualization management module includes visualization display settings, real-time data update, icon report customization, and data export and sharing. The specific operation steps of the module are as follows:

[0014] In the system visualization management module, select the asset data indicators to be displayed according to the settings interface, including the geographical location and quantity of asset distribution, select the chart type, set the color and font style, and save. At the same time, the system regularly obtains real-time data from each data module and automatically updates the visualization charts and reports. Users can modify data filtering conditions and add data labels in the chart editing function according to their needs, adjust rows and columns and perform formula calculations on the report, find the data export function in the system visualization interface, select the export data range and format, and share the file after exporting.

[0015] As a further solution of the present invention, the risk warning and compliance management module includes risk indicator and compliance standard setting, real-time data monitoring, warning information processing, risk response and feedback, and indicator standard update. The specific operation steps of the module are as follows:

[0016] In the setting interface of the system risk warning and compliance management module, risk thresholds and compliance standards are set according to the enterprise risk preference requirements. The system continuously obtains data from the above modules, compares it with the set indicator standards, and triggers warnings in real time when abnormalities are found. After receiving the warning information, the relevant responsible persons view the detailed risk information in the system and conduct risk assessments. Based on the assessment results, the responsible persons formulate and implement risk response measures, record the content and implementation status of the measures in the system, regularly pay attention to changes in laws and policies and corporate business adjustments, and update risk indicators and compliance standards in the system in real time.

[0017] As a further solution of the present invention: the blockchain evidence storage and traceability module includes data on-chain evidence storage, asset traceability query, and data security and privacy protection. The specific operation steps of the module are as follows:

[0018] The basic asset information in the asset information management module, the real-time operating data of the Internet of Things data acquisition module, and the evaluation and maintenance records of the data analysis and intelligent maintenance module are automatically encrypted when generated and uploaded to the blockchain for evidence storage. Users use the system to enter the asset number and related information to query detailed information on the entire life cycle of the asset from procurement, use, maintenance to scrapping, and query the asset source and circulation process. Data security and privacy protection adopts the encryption algorithm and distributed storage technology of the blockchain. Users can access and query data after authorization.

[0019] As a further solution of the present invention: the data in the blockchain evidence storage and traceability module is stored on the blockchain after encryption processing of the basic asset information in the asset information management module, the real-time operation data of the Internet of Things data acquisition module, the evaluation of the data analysis and intelligent maintenance module and the maintenance record data. Specifically, the required data is obtained from the asset information management module, the Internet of Things data acquisition module and the data analysis and intelligent maintenance module in real time or regularly, the format of the collected data is unified and missing values ​​are processed, the data is encrypted using the RSA encryption algorithm, and a public key and a private key are generated. The public key is used to encrypt the data, and the private key is securely stored by the system for subsequent decryption. Where n is the number of samples, m is the number of features, each column of data is logarithmically transformed, and then Z-score normalized. Combined with nonlinear transformation, the logarithmic transformation formula is:

[0020] Y ij =ln(X ij +1)

[0021] Where i = 1,…,n, j = 1,…,m.

[0022] Z-score normalization:

[0023]

[0024] in,

[0025] Nonlinear transformation: A ij =tanh(Z ij ).

[0026] Data encryption introduces multiple random numbers and multi-round encryption based on traditional RSA encryption to generate k random numbers r1, r2, ..., r k , satisfying 1<r i <n, i=1,…,k;

[0027] First round of encryption:

[0028] Subsequent rounds of encryption (l=2,…,L):

[0029] Final encryption result: C=C L ;

[0030] When decrypting, first calculate the inverse element of each round, and then decrypt in the reverse order;

[0031] In the asset traceability query phase, based on the traditional decryption formula, the timestamp and data block number are combined for verification. Let the encrypted data be C, the private key be (d, n), the timestamp be t, the data block number be b, and the decrypted intermediate value M′ = C d mod n, verify the hash value H′ calc =Hash(M′‖t‖b), which is the hash value H′ recorded on the blockchain, including the timestamp and data block number record Compare and compare calc =H′ recored , data verification is completed.

[0032] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. The present invention uses the asset information management module to centrally input, update, review and clean up the basic information of assets, so as to achieve a unified data format. At the same time, the Internet of Things data acquisition module collects asset operation data in real time. These data are cleaned, converted and feature extracted in the big data analysis and intelligent maintenance module to achieve effective integration of multi-source data. The system also provides intelligent analysis function, which conducts in-depth mining based on the integrated data to provide a scientific basis for enterprise decision-making, changing the previous situation of relying on scattered and low-quality data for decision-making, and improving the accuracy and scientificity of decision-making. By utilizing the Internet of Things data acquisition module, various sensors, including vibration sensors, GPS positioning sensors, etc., are installed according to asset types and monitoring requirements to achieve real-time monitoring of asset location and operating status. Combined with the visual management module, the real-time asset data is displayed in intuitive charts and reports. Managers can grasp the asset dynamics at any time, discover abnormal situations in time and take measures, which can effectively reduce asset damage, loss and idleness and achieve accurate asset management.

[0034] 2. The present invention uses big data analysis and intelligent maintenance modules to regularly obtain data from asset information management and Internet of Things data collection modules, run asset health assessment models, and generate scientific maintenance plans based on the actual situation of assets. Maintenance personnel perform maintenance operations based on the work orders generated by the system and provide timely feedback. The system can also optimize the model based on new data and maintenance effects. This intelligent maintenance method avoids the drawbacks of empiricism, reasonably arranges maintenance tasks, reduces maintenance costs, and ensures the continuity of production operations. Through comprehensive analysis of data from the entire process of asset procurement to scrapping, including asset usage, maintenance records, health assessments, etc., scientific maintenance plans are made. At the same time, it standardizes the scrapping process, ensures the rational disposal of assets, reduces waste and loss risks, reduces adverse impacts on the environment, and ensures the compliance of enterprises in the asset disposal process. At the same time, the unified data platform can realize data sharing among various modules, and the intelligent decision support module provides a unified decision-making basis for different departments. The risk warning and compliance management module enables each department to understand the asset operation risks and compliance requirements in a timely manner. The blockchain evidence storage and traceability module ensures the credibility and traceability of the data. By combining the multi-round encryption RSA algorithm with the blockchain evidence storage, the data cannot be tampered with, and the maintenance cost is reduced through the dynamic health assessment model. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flowchart of the full life cycle asset management system in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following is a further description of specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0037] According to the attached Figure 1As shown, the present invention provides an asset management system based on the whole life cycle, including centralized management, real-time monitoring, intelligent analysis and scientific decision-making of asset data. The asset management system based on the whole life cycle includes an asset information management module, an Internet of Things data acquisition module, a data analysis and intelligent maintenance module, an intelligent decision support module, a visualization management module, a risk warning and compliance management module and a blockchain evidence storage and traceability module. The asset information management module provides data for other modules as a basis. The Internet of Things data acquisition module provides the collected data to the big data analysis and intelligent maintenance module, the visualization management module and the blockchain evidence storage and traceability module. The data analysis and intelligent maintenance module is based on the model. The intelligent decision support module makes decisions based on the data and analysis results of other modules, and feeds back the decision results to other modules. The visual management module obtains data from multiple modules and displays them. The risk warning and compliance management module obtains data from other modules and conducts risk assessment and compliance monitoring, and feeds back warning and response information to other modules. The blockchain evidence and traceability module encrypts and stores data from the asset information management module, the Internet of Things data collection module, and the big data analysis and intelligent maintenance module, and provides users with asset traceability query functions.

[0038] In one embodiment of the present invention, the asset information management module includes asset information entry, asset information update, data review and cleanup, and data import and export management. The specific operation steps of the module are as follows:

[0039] When purchasing assets and putting them into storage, open the system asset information management interface and enter the asset name, model, specifications, purchase date, supplier, purchase price, using department and location of use in sequence. Click "Save" after completion. When the asset information changes, the system uses the asset number and name to search for the corresponding asset. After a successful review, click "Edit", modify the corresponding information and save. Every 2-3 weeks, arrange for staff to screen duplicate and erroneous data in the system, verify and supplement assets with missing information, and submit for review after completion.

[0040] In one embodiment of the present invention, the IoT data acquisition module includes sensor selection and installation, sensor connection configuration, data acquisition and preprocessing, and sensor maintenance and management. The specific operation steps of the module are as follows:

[0041] Select appropriate IoT sensors based on asset type and monitoring requirements, such as installing vibration sensors on equipment to monitor operating status. Install sensors at appropriate locations on assets, ensuring they are firm and do not affect normal asset operation. Connect sensors to the system gateway via wired or wireless means, and configure sensor parameters in the system background, including data collection frequency and transmission protocol. The sensor collects and uploads data at the set frequency. After receiving the data, the system performs preprocessing operations such as format conversion, denoising, and outlier removal. Check the sensor's appearance for damage every 2-3 weeks, check the sensor data transmission log in the system, and replace and repair it in a timely manner if any abnormality is found.

[0042] In one embodiment of the present invention, the big data analysis and intelligent maintenance module includes data acquisition and processing, asset health assessment, maintenance plan generation, maintenance task execution and feedback, and model optimization. The specific operation steps of the module are as follows:

[0043] Set a data acquisition cycle in the system, and regularly extract data from the asset information management and IoT data acquisition modules for cleaning, conversion, and feature extraction. In the system analysis interface, select the asset range to be evaluated, run the asset health assessment model, and view the asset health score and remaining service life. The system generates maintenance task work orders based on the assessment results and preset rules, including maintenance type, time, and personnel arrangements. Maintenance personnel receive work orders on the system mobile and computer terminals, perform maintenance operations as required, and fill in and submit maintenance records in the system upon completion. Based on new data and actual maintenance results, adjust and optimize model parameters every quarter and retrain the model.

[0044] In one embodiment of the present invention, the intelligent decision support module includes decision requirement input, data analysis and calculation, decision report generation, decision execution and recording, and decision effect review. The specific operation steps of the module are as follows:

[0045] Managers access the system's intelligent decision support module, input decision requirements at the corresponding function entrance, and use data analysis algorithms and models for calculations. The system generates a decision report based on the analysis results, presented in the form of charts and text. Managers view the report in the system and make decisions based on the report. At the same time, the system records information on the decision content, execution department, and execution time. After the decision has been executed for a period of time, the system collects relevant data on the decision execution, compares the expected and actual effects of the decision, and summarizes and records the comparison results.

[0046] In one embodiment of the present invention, the visualization management module includes visualization display settings, real-time data updates, icon report customization, and data export and sharing. The specific operation steps of the module are as follows:

[0047] In the system visualization management module, select the asset data indicators to be displayed according to the settings interface, including the geographical location and quantity of asset distribution, select the chart type, set the color and font style, and save. At the same time, the system regularly obtains real-time data from each data module and automatically updates the visualization charts and reports. Users can modify data filtering conditions and add data labels in the chart editing function according to their needs, adjust rows and columns and perform formula calculations on the report, find the data export function in the system visualization interface, select the export data range and format, and share the file after exporting.

[0048] In one embodiment of the present invention, the risk warning and compliance management module includes risk indicator and compliance standard setting, real-time data monitoring, warning information processing, risk response and feedback, and indicator standard update. The specific operation steps of the module are as follows:

[0049] In the setting interface of the system risk warning and compliance management module, risk thresholds and compliance standards are set according to the enterprise risk preference requirements. The system continuously obtains data from the above modules, compares it with the set indicator standards, and triggers warnings in real time when abnormalities are found. After receiving the warning information, the relevant responsible persons view the detailed risk information in the system and conduct risk assessments. Based on the assessment results, the responsible persons formulate and implement risk response measures, record the content and implementation status of the measures in the system, regularly pay attention to changes in laws and policies and corporate business adjustments, and update risk indicators and compliance standards in the system in real time.

[0050] In one embodiment of the present invention, the blockchain evidence storage and traceability module includes data on-chain evidence storage, asset traceability query, and data security and privacy protection. The specific operation steps of the module are as follows:

[0051] The basic asset information in the asset information management module, the real-time operating data of the Internet of Things data acquisition module, and the evaluation and maintenance records of the data analysis and intelligent maintenance module are automatically encrypted when generated and uploaded to the blockchain for evidence storage. Users use the system to enter the asset number and related information to query detailed information on the entire life cycle of the asset from procurement, use, maintenance to scrapping, and query the asset source and circulation process. Data security and privacy protection adopts the encryption algorithm and distributed storage technology of the blockchain. Users can access and query data after authorization.

[0052] In one embodiment of the present invention: the data in the blockchain evidence and traceability module is stored on the blockchain after encryption processing of the basic asset information in the asset information management module, the real-time operation data of the Internet of Things data acquisition module, the evaluation of the data analysis and intelligent maintenance module and the maintenance record data. Specifically, the required data is obtained from the asset information management module, the Internet of Things data acquisition module and the data analysis and intelligent maintenance module in real time or regularly, the format of the collected data is unified and missing values ​​are processed, the data is encrypted using the RSA encryption algorithm, and a public key and a private key are generated. The public key is used to encrypt the data, and the private key is securely stored by the system for subsequent decryption. Where n is the number of samples, m is the number of features, each column of data is logarithmically transformed, and then Z-score normalized. Combined with nonlinear transformation, the logarithmic transformation formula is:

[0053] Y ij =ln(X ij +1)

[0054] Where i = 1,…,n, j = 1,…,m.

[0055] Z-score normalization:

[0056]

[0057] in,

[0058] Nonlinear transformation: A ij =tanh(Z ij ).

[0059] Data encryption introduces multiple random numbers and multi-round encryption based on traditional RSA encryption to generate k random numbers r1, r2, ..., r k , satisfying 1<r i <n, i=1,…,k;

[0060] First round of encryption:

[0061] Subsequent rounds of encryption (l=2,…,L):

[0062] Final encryption result: C=C L ;

[0063] When decrypting, first calculate the inverse element of each round, and then decrypt in the reverse order;

[0064] In the asset traceability query phase, based on the traditional decryption formula, the timestamp and data block number are combined for verification. Let the encrypted data be C, the private key be (d, n), the timestamp be t, the data block number be b, and the decrypted intermediate value M′ = C d mod n, verify the hash value H′ calc =Hash(M′‖t‖b), which is the hash value H′ recorded on the blockchain, including the timestamp and data block number record Compare and compare calc =H′ record , data verification is completed.

[0065] In one embodiment of the present invention, in the asset information management module, after submission and review, if batch import and export of data is required, select "Import / Export" in the system data management function, download the template, fill in the data, and then upload it, or select the export range and download the file. In the asset information entry process, detailed records are also made for the asset's technical parameters, warranty information, etc. When the asset information is updated, the system automatically records the update log, including the update time, updater, and comparison of information before and after the update. During data review and cleaning, data verification rules and algorithms are used to check the logical relationship of the data, such as the reasonableness of the purchase price and asset type. In terms of data export management, the import and export of files in multiple formats are supported, and a pre-check of the data format and content is performed during import. When entering information, the accuracy and completeness of the data are ensured to avoid entry errors or omission of key information. The asset number should be unique to facilitate asset identification and management. After the data is updated, relevant personnel are notified in a timely manner to ensure information synchronization. When conducting data review, the review process is strictly followed, and questionable data is verified in a timely manner. During the data import and export process, attention is paid to the security of the file to prevent data leakage.

[0066] In one embodiment of the present invention: In the IoT data acquisition module, during the sensor selection and installation phase, a detailed evaluation is conducted on the applicable scenarios of different types of sensors to ensure the rationality of sensor selection. When configuring sensor connections, a backup connection solution is set to cope with the situation where the main connection fails. During the data acquisition and preprocessing phase, analysis and prediction of data change trends are increased to discover potential problems in advance. In terms of sensor maintenance management, a sensor maintenance file is established to record maintenance time, maintenance content, replacement parts and other information. When installing sensors, attention is paid to the accuracy and stability of the installation position to avoid affecting the normal operation of assets and the accuracy of data collection. When configuring sensor parameters, reasonable settings are made based on the actual operation conditions and needs of the assets. Sensors are calibrated and maintained regularly to ensure data reliability. During the data acquisition process, attention is paid to the stability of data transmission, and data loss or abnormalities are handled in a timely manner.

[0067] In one embodiment of the present invention, in the intelligent decision support module, managers should describe the problem and goal in as much detail as possible when inputting decision requirements to avoid decision bias caused by vague requirements. When using data analysis algorithms and models, managers should understand their scope of application and limitations and analyze them in light of actual conditions. During the decision-making process, managers should closely monitor the implementation status and make timely adjustments. When reviewing the decision results, managers should objectively and impartially evaluate and summarize the lessons learned to provide reference for subsequent decisions.

[0068] In one embodiment of the present invention: In the blockchain evidence storage and traceability module, when data is uploaded to the chain for evidence storage, the data is subjected to multiple encryption and hash verification to ensure the integrity and non-tamperability of the data. In the asset traceability query function, a detailed traceability path and data explanation are provided to facilitate users to understand the asset circulation process. In terms of data security and privacy protection, a variety of encryption algorithms and access control technologies are used to ensure data security. At the same time, blockchain nodes are regularly maintained and upgraded to ensure the stable operation of the blockchain. Before data is uploaded to the chain, the authenticity and accuracy of the data are ensured. When querying asset traceability information, attention is paid to protecting the company's business secrets and privacy. When using blockchain technology, relevant laws, regulations and technical specifications are complied with, blockchain data is regularly backed up to prevent data loss, and when authorizing users to access data, user permissions are strictly reviewed to ensure data security.

[0069] Example 1

[0070] Equipment asset management for manufacturing enterprises,

[0071] background:

[0072] A manufacturing company has a large number of production equipment. To improve equipment management efficiency, reduce maintenance costs, and ensure production safety, it uses an asset management system based on the entire life cycle.

[0073] Implementation status of each module of the system:

[0074] Asset information management module,

[0075] Asset information entry: When purchasing new equipment and entering the warehouse, the operator opens the system asset information management interface and enters the equipment name, model, specifications, purchase date, supplier, purchase price, using department, use location, technical parameters and warranty information in detail;

[0076] Asset information update: When a device undergoes technical transformation or changes its user department, search for the corresponding device by asset number and name, click "Edit" to modify the corresponding information, and the system will automatically record the update log, such as the update time, updater, and comparison of information before and after the update;

[0077] Data review and cleanup: Assign dedicated personnel to review system data monthly, using data verification rules to check the rationality of the purchase price and equipment type. For high-precision processing equipment, the purchase price should be within a reasonable range. Missing or erroneous data will be verified and supplemented to ensure data accuracy and completeness;

[0078] Data import and export management: When an enterprise needs to import new device information in batches or export device usage reports, it selects "Import / Export" in the system data management function, downloads the template, fills in the data, and uploads it. The system pre-checks the data format and content before importing. When exporting, it selects the appropriate range and file format, such as CSV or Excel.

[0079] Internet of Things data acquisition module,

[0080] Sensor selection and installation: Based on the equipment type and monitoring requirements, vibration sensors, temperature sensors, etc. are installed on key equipment. Vibration sensors are installed on motors to monitor operating status. Ensure that the installation location is accurate and does not affect the normal operation of the equipment. At the same time, a detailed evaluation of the applicable scenarios of different types of sensors is conducted to ensure the selection of appropriate sensors.

[0081] Sensor connection configuration: Connect the sensor to the system gateway via a wired connection and set up a backup wireless connection solution. Configure the sensor parameters in the system background. The data collection frequency is once every 5 minutes, and the transmission protocol uses MQTT.

[0082] Data acquisition and preprocessing: The sensor collects and uploads data at a set frequency. After receiving the data, the system performs format conversion, noise reduction, outlier removal, and data trend analysis. By analyzing the temperature trend of the motor, potential overheating problems can be discovered in advance.

[0083] Sensor maintenance management: Establish sensor maintenance files, record maintenance time, maintenance content, replacement parts and other information, and regularly calibrate and maintain sensors to ensure data reliability;

[0084] Blockchain evidence storage and traceability module,

[0085] Data on-chain evidence storage: Basic equipment information in the asset information management module, real-time operating data from the IoT data acquisition module, and evaluation and maintenance records from the data analysis and intelligent maintenance module are automatically encrypted and hashed upon generation before being uploaded to the blockchain. The equipment's operating parameters and maintenance records are encrypted to ensure data integrity and non-tamperability.

[0086] Asset traceability query: When an enterprise needs to understand the full life cycle information of a certain device, the user enters the device number and related information, and the system provides a detailed traceability path and data explanation, clearly showing the entire process of the equipment from procurement, use, maintenance to scrapping;

[0087] Data security and privacy protection: We use a variety of encryption algorithms and access control technologies to ensure data security. We use the RSA encryption algorithm to encrypt data, and we implement strict user rights management. We regularly maintain and upgrade blockchain nodes to ensure the stable operation of the blockchain, and we regularly back up blockchain data to prevent data loss.

[0088] Example 2

[0089] Logistics enterprise vehicle asset management,

[0090] background:

[0091] A logistics company has many transport vehicles. In order to optimize vehicle scheduling, improve transportation efficiency and ensure vehicle safety, it introduced this asset management system based on the entire life cycle.

[0092] Implementation status of each module of the system:

[0093] Asset information management module,

[0094] Asset information entry: When a new vehicle is purchased and put into storage, detailed information such as vehicle brand, model, license plate number, purchase date, supplier, purchase price, fleet affiliation, and usage route must be entered. Also, the vehicle's technical parameters, including engine power and load capacity, must be recorded.

[0095] Asset information update: When a vehicle is repaired after an accident, the driver is changed, or the route is adjusted, the relevant information is updated in the system in a timely manner. The system automatically records the update log to facilitate subsequent inquiries and audits;

[0096] Data review and cleanup: Regularly assign dedicated personnel to review vehicle data, check the logical relationship of the data, including the rationality of vehicle load and transportation routes, clean up duplicate or erroneous data, and verify and supplement vehicles with missing information;

[0097] Data import and export management: When companies need to import new vehicle information in batches or export vehicle operation reports, they can use the system's data import and export functions to support the import and export of multiple file formats, including XML format. Data pre-checking is performed before import to ensure data accuracy.

[0098] Internet of Things data acquisition module,

[0099] Sensor selection and installation: Install GPS positioning sensors, fuel consumption sensors, and tire pressure sensors on vehicles. Select the sensor type and installation location based on the vehicle's characteristics and monitoring requirements to ensure they do not affect the vehicle's normal driving.

[0100] Sensor connection configuration: connect the sensor to the system gateway wirelessly, set up a backup connection plan, configure sensor parameters in the system background, set the GPS positioning data collection frequency to once per minute, and use the HTTP transmission protocol;

[0101] Data collection and preprocessing: Sensors collect and upload vehicle operating data in real time. The system converts the data format, removes noise, removes outliers, and analyzes data trend changes. By analyzing the trend of fuel consumption data, it can be determined whether the vehicle has abnormal fuel consumption.

[0102] Sensor maintenance management: establish sensor maintenance files, record sensor maintenance status, regularly inspect and calibrate sensors to ensure data accuracy and reliability;

[0103] Intelligent decision support module,

[0104] Decision demand input: Managers input decision demands into the system's intelligent decision support module to optimize vehicle scheduling to improve transportation efficiency, describing problems and goals in detail to avoid ambiguity in demands;

[0105] Data analysis and calculation: The system uses data analysis algorithms and models to analyze and calculate vehicle operation data, transportation task data, etc., and optimizes vehicle scheduling through linear programming algorithms;

[0106] Decision report generation: The system generates a decision report based on the analysis results, presented in charts and text, including comparative analysis charts and detailed descriptions of vehicle scheduling optimization plans;

[0107] Decision execution and recording: Managers make decisions based on the decision report and arrange vehicle dispatch. At the same time, they record information such as the decision content, execution department, and execution time in the system;

[0108] Decision-making effect review: After a period of time, the system collects relevant data on the decision-making process, compares the expected and actual results, and compares the transportation efficiency and cost before and after optimization, summarizing the lessons learned to provide reference for subsequent decisions.

[0109] Example 3:

[0110] Medical equipment asset management in medical institutions,

[0111] background:

[0112] A medical institution has a large number of advanced medical equipment. In order to ensure the normal operation of medical equipment, improve equipment utilization efficiency and ensure medical safety, this asset management system based on the entire life cycle is adopted.

[0113] Implementation status of each module of the system:

[0114] Asset information management module,

[0115] Asset information entry: When purchasing new medical equipment, the equipment name, model, specifications, purchase date, supplier, purchase price, user department, user personnel, technical parameters, and warranty information are fully entered. For a CT scanner, its scanning accuracy, scanning speed and other technical parameters are recorded in detail;

[0116] Asset information update: When the equipment undergoes software upgrades, parts replacement, or adjustment of the use department, the relevant information is updated in the system in a timely manner. The system automatically records the update log, making it easier for medical staff and management personnel to understand the changes of the equipment;

[0117] Data review and cleanup: regularly arrange for dedicated personnel to review medical equipment data, check the accuracy and completeness of the data, check whether the purchase price of the equipment is consistent with the market conditions, and verify and supplement missing or erroneous data;

[0118] Data import and export management: When the hospital needs to import new equipment information in batches or export equipment usage reports, the system's data import and export function is used. It supports import and export of multiple file formats, including PDF format. Data pre-checking is performed before importing to ensure data quality.

[0119] Data analysis and intelligent maintenance module,

[0120] Data acquisition and processing: The system sets a weekly data acquisition cycle, extracts data from the asset information management and IoT data collection modules, and cleans, converts, and extracts features from the data, including equipment usage time and number of failures.

[0121] Asset health assessment: Select the medical equipment to be assessed on the system analysis interface, run the asset health assessment model, evaluate the operating status of the CT scanner, and obtain the equipment's health score and remaining service life.

[0122] Maintenance plan generation: The system generates maintenance task work orders based on the assessment results and preset rules. For equipment with low health scores, regular maintenance and preventive maintenance are arranged. The work orders include the maintenance type, time, and personnel arrangements.

[0123] Maintenance task execution and feedback: Maintenance personnel receive work orders on the system's mobile and computer terminals, perform maintenance operations as required, and fill in and submit maintenance records in the system upon completion, including information such as replaced parts and maintenance process;

[0124] Model optimization: Based on new data and actual maintenance results, regularly adjust and optimize the parameters of the asset health assessment model, retrain the model, and improve the accuracy of the assessment;

[0125] Risk warning and compliance management module,

[0126] Risk indicator and compliance standard setting: In the system risk warning and compliance management module setting interface, set risk thresholds and compliance standards based on the medical institution's risk preferences and relevant regulatory requirements, and set failure rate thresholds and quality and safety standards for medical equipment;

[0127] Real-time data monitoring: The system continuously obtains data from each module, compares it with the set indicator standards, and monitors the failure rate of the CT scanner and the radiation dose of the equipment in real time. When the national standard limit is exceeded, an early warning is triggered and pushed to the hospital safety management department;

[0128] Warning information processing: After receiving the warning information, the relevant responsible person will view the detailed risk information in the system, conduct risk assessment, and evaluate the impact of equipment failure on medical services;

[0129] Risk response and feedback: Based on the assessment results, the responsible person formulates and implements risk response measures, arranges emergency repairs or spare equipment replacement, and records the content of the measures and the implementation status in the system;

[0130] Indicator and standard updates: Regularly monitor changes in regulations and policies and hospital business adjustments, and update risk indicators and compliance standards in the system in real time to ensure that medical device management meets the latest requirements.

[0131] It can be seen from the above three groups of embodiments that this asset management system based on the entire life cycle is applicable to different industries such as manufacturing, logistics, and medical care. It can target the asset characteristics of each industry, including production equipment in the manufacturing industry, transportation vehicles of logistics companies, medical equipment of medical institutions, etc., and achieve effective management through the collaborative operation of various modules to meet the diverse needs of different industries for asset information management, data collection, maintenance, decision support, etc.

[0132] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It is apparent to those skilled in the art that various changes, modifications, substitutions, and variations to these embodiments may be made without departing from the principles and spirit of the present invention, and these changes and modifications still fall within the scope of protection of the present invention.

[0133] Throughout the specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0134] The above contents are merely examples and explanations of the present invention. Any modifications, additions or replacements in similar ways made by those skilled in the art to the specific embodiments described shall fall within the scope of protection of the present invention as long as they do not deviate from the invention or exceed the scope defined by the claims.

[0135] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An asset management system based on the entire life cycle, including centralized management of asset data, real-time monitoring, intelligent analysis, and scientific decision-making, characterized by: The asset management system based on the entire life cycle includes an asset information management module, an Internet of Things data collection module, a data analysis and intelligent maintenance module, an intelligent decision support module, a visualization management module, a risk warning and compliance management module, and a blockchain evidence storage and traceability module. The asset information management module serves as a basis for providing data for other modules. The Internet of Things data collection module provides the collected data to the big data analysis and intelligent maintenance module, the visualization management module, and the blockchain evidence storage and traceability module. The data analysis and intelligent maintenance module obtains data from the module and provides the analysis results to the intelligent decision support module, the visualization management module, and the blockchain evidence storage and traceability module. The intelligent decision support module makes decisions based on the data and analysis results of other modules and feeds back the decision results to other modules. The visualization management module obtains data from multiple modules and displays them. The risk warning and compliance management module obtains data from other modules and performs risk assessment and compliance monitoring, and at the same time feeds back warning and response information to other modules. The blockchain evidence storage and traceability module encrypts and stores data from the asset information management module, the Internet of Things data collection module, and the big data analysis and intelligent maintenance module, and provides users with an asset traceability query function.

2. The asset management system based on the entire life cycle according to claim 1, characterized in that: The asset information management module includes asset information entry, asset information update, data review and cleanup, and data import and export management. The specific operation steps of the module are as follows: When purchasing and warehousing assets, open the system asset information management interface and enter the asset name, model, specifications, purchase date, supplier, purchase price, using department and location of use in sequence. Click "Save" after completion. When the asset information changes, the system uses the asset number and name to search for the corresponding asset. After a successful review, click "Edit", modify the corresponding information and save. Every 2-3 weeks, arrange for staff to screen duplicate and erroneous data in the system, verify and supplement assets with missing information, and submit for review after completion.

3. The asset management system based on the entire life cycle according to claim 2, characterized in that: The IoT data acquisition module includes sensor selection and installation, sensor connection configuration, data acquisition and preprocessing, and sensor maintenance and management. The specific operation steps of the module are as follows: Select appropriate IoT sensors based on asset type and monitoring requirements, such as installing vibration sensors on equipment to monitor operating status. Install sensors at appropriate locations on assets, ensuring they are firm and do not affect normal asset operation. Connect sensors to the system gateway via wired or wireless means, and configure sensor parameters in the system background, including data collection frequency and transmission protocol. The sensor collects and uploads data at the set frequency. After receiving the data, the system performs preprocessing operations such as format conversion, denoising, and outlier removal. Check the sensor's appearance for damage every 2-3 weeks, check the sensor data transmission log in the system, and replace and repair it in a timely manner if any abnormality is found.

4. The asset management system based on the entire life cycle according to claim 3, characterized in that: The big data analysis and intelligent maintenance module includes data acquisition and processing, asset health assessment, maintenance plan generation, maintenance task execution and feedback, and model optimization. The specific operation steps of the module are as follows: Set a data acquisition cycle in the system, and regularly extract data from the asset information management and IoT data acquisition modules for cleaning, conversion, and feature extraction. In the system analysis interface, select the asset range to be evaluated, run the asset health assessment model, and view the asset health score and remaining service life. The system generates maintenance task work orders based on the assessment results and preset rules, including maintenance type, time, and personnel arrangements. Maintenance personnel receive work orders on the system mobile and computer terminals, perform maintenance operations as required, and fill in and submit maintenance records in the system upon completion. Based on new data and actual maintenance results, adjust and optimize model parameters every quarter and retrain the model.

5. The asset management system based on the entire life cycle according to claim 4, characterized in that: The intelligent decision support module includes decision requirement input, data analysis and calculation, decision report generation, decision execution and recording, and decision effect review. The specific operation steps of the module are as follows: Managers access the system's intelligent decision support module, input decision requirements at the corresponding function entrance, and use data analysis algorithms and models for calculations. The system generates a decision report based on the analysis results, presented in the form of charts and text. Managers view the report in the system and make decisions based on the report. At the same time, the system records information on the decision content, execution department, and execution time. After the decision has been executed for a period of time, the system collects relevant data on the decision execution, compares the expected and actual effects of the decision, and summarizes and records the comparison results.

6. The asset management system based on the entire life cycle according to claim 5, characterized in that: The visualization management module includes visualization display settings, real-time data updates, icon report customization, and data export and sharing. The specific operation steps of the module are as follows: In the system visualization management module, select the asset data indicators to be displayed according to the settings interface, including the geographical location and quantity of asset distribution, select the chart type, set the color and font style, and save. At the same time, the system regularly obtains real-time data from each data module and automatically updates the visualization charts and reports. Users can modify data filtering conditions and add data labels in the chart editing function according to their needs, adjust rows and columns and perform formula calculations on the report, find the data export function in the system visualization interface, select the export data range and format, and share the file after exporting.

7. The asset management system based on the entire life cycle according to claim 6, characterized in that: The risk warning and compliance management module includes risk indicator and compliance standard setting, real-time data monitoring, warning information processing, risk response and feedback, and indicator standard update. The specific operation steps of the module are as follows: In the setting interface of the system risk warning and compliance management module, risk thresholds and compliance standards are set according to the enterprise risk preference requirements. The system continuously obtains data from the above modules, compares it with the set indicator standards, and triggers warnings in real time when abnormalities are found. After receiving the warning information, the relevant responsible persons view the detailed risk information in the system and conduct risk assessments. Based on the assessment results, the responsible persons formulate and implement risk response measures, record the content and implementation status of the measures in the system, regularly pay attention to changes in laws and policies and corporate business adjustments, and update risk indicators and compliance standards in the system in real time.

8. The asset management system based on the entire life cycle according to claim 7, characterized in that: The blockchain evidence storage and traceability module includes data on-chain evidence storage, asset traceability query, and data security and privacy protection. The specific operation steps of the module are as follows: The basic asset information in the asset information management module, the real-time operating data of the Internet of Things data acquisition module, and the evaluation and maintenance records of the data analysis and intelligent maintenance module are automatically encrypted when generated and uploaded to the blockchain for evidence storage. Users use the system to enter the asset number and related information to query detailed information on the entire life cycle of the asset from procurement, use, maintenance to scrapping, and query the asset source and circulation process. Data security and privacy protection adopts the encryption algorithm and distributed storage technology of the blockchain. Users can access and query data after authorization.

9. The asset management system based on the entire life cycle according to claim 8, characterized in that: The data on-chain evidence storage in the blockchain evidence storage and traceability module is to encrypt the basic asset information in the asset information management module, the real-time operation data of the Internet of Things data acquisition module, the evaluation of the data analysis and intelligent maintenance module, and the maintenance record data and store them on the blockchain. Specifically, the required data is obtained from the asset information management module, the Internet of Things data acquisition module and the data analysis and intelligent maintenance module in real time or regularly, the format of the collected data is unified and missing values ​​are processed, the data is encrypted using the RSA encryption algorithm, and a public key and a private key are generated. The public key is used to encrypt the data, and the private key is securely stored by the system for subsequent decryption. Where n is the number of samples, m is the number of features, each column of data is logarithmically transformed, and then Z-score normalized. Combined with nonlinear transformation, the logarithmic transformation formula is: Y ij =ln(X ij +1) Where i = 1,…,n, j = 1,…,m. Z-score normalization: in, Nonlinear transformation: A ij =tanh(Z ij ). Data encryption introduces multiple random numbers and multi-round encryption based on traditional RSA encryption to generate k random numbers r1, r2, ..., r k , satisfying 1<r i <n, i=1,…,k; First round of encryption: Subsequent rounds of encryption (l=2,…,L): Final encryption result: C=C L ; When decrypting, first calculate the inverse element of each round, and then decrypt in the reverse order; In the asset traceability query phase, based on the traditional decryption formula, the timestamp and data block number are combined for verification. Let the encrypted data be C, the private key be (d, n), the timestamp be t, the data block number be b, and the decrypted intermediate value M′ = C d mod n, verify the hash value H′ calc =Hash(M′||t||b), and the hash value H′ recorded on the blockchain containing the timestamp and data block number record Compare and compare calc =H′ record , data verification is completed.

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