Industrial park asset tracking management system and method thereof
By deploying sensors and deep learning networks on assets to analyze data, the inefficiency of traditional asset management is solved, enabling real-time and accurate asset monitoring and optimization, improving management efficiency and reducing resource waste.
Patent Information
- Application Number
- CN202511496071.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2026-01-02
AI Technical Summary
Traditional asset management methods rely on manual inspections and periodic checks, which are inefficient, prone to errors, wasteful of resources, and costly, and lack systematization and dynamic optimization.
Physical sensors are used to monitor the location and status of assets in real time. Combined with deep learning neural networks, stress and deformation data are analyzed to generate maintenance task orders and perform automated monitoring and optimization.
It achieves real-time and high-precision asset management, automated monitoring and optimization, reduces labor costs, improves management efficiency, promptly identifies problems, and avoids resource waste.
Smart Images
Figure CN121256708A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of asset tracking management, in particular to an industrial park asset tracking management system and method thereof. BACKGROUND
[0002] The traditional method usually relies on manual patrol or regular inspection to obtain asset location and physical state information, which has great lag and error, is low in efficiency, and is prone to error. The generation of the maintenance task list of the traditional method usually relies on manual input and judgment, which may have omissions or errors and is not systematic. The execution of the maintenance task of the traditional method is generally recorded and tracked by manual, and the feedback mechanism is single, lacking dynamic adjustment and optimization of the execution process. The traditional method optimizes asset management by relying on the experience of management personnel, and the optimization process is slow. Due to the reliance on manual operation and regular inspection, the traditional method may cause resource waste and high cost. SUMMARY
[0003] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art and provide an industrial park asset tracking management system and method thereof.
[0004] The technical solution adopted to solve the above technical problems is as follows: an industrial park asset tracking management method, comprising: locating a to-be-managed asset in an industrial park to obtain location data of the asset, and obtaining physical state information of the asset based on a physical sensor deployed on the asset, wherein the physical state information includes pressure data and deformation data; performing feature fusion analysis on the pressure data and the deformation data based on a pre-trained asset state recognition model to obtain a wear degree evaluation result of the asset; monitoring the asset based on the location data, and triggering a warning mechanism when detecting that the location of the asset exceeds a pre-determined regional boundary, wherein the warning mechanism includes sending an alarm record of a location trajectory to a management personnel; obtaining an asset file based on the alarm record and the wear degree evaluation result, and generating a maintenance task list based on the asset file, wherein the maintenance task list includes a location trajectory, a current wear state and a recommended maintenance measure of the asset; tracking and feeding back the execution of the maintenance task list to obtain an execution result, and optimizing the tracking management of the asset based on the execution result to obtain an optimization scheme, wherein the optimization scheme includes adjusting the monitoring range and updating parameters of the asset state recognition model.
[0005] Preferably, the location of the assets to be managed in the industrial park is positioned to obtain the location data of the assets, including: Real-time collection of location data based on RFID tags installed on the assets to obtain the location data; The RFID tags are wirelessly connected to the central management system to ensure the real-time transmission of the location data.
[0006] Preferably, the asset state recognition model includes a deep learning neural network that has been trained with a large number of asset wear samples and can accurately identify and analyze the stress data and deformation data of the assets to achieve accurate assessment of the wear degree of the assets.
[0007] Preferably, the stress data and deformation data are analyzed based on a pre-trained asset state recognition model to obtain the wear degree assessment result of the assets, including: Standardizing and preprocessing the stress data and deformation data to obtain preprocessed data; Inputting the preprocessed data into the deep learning neural network to obtain the wear characteristics of the assets; Numerical output of the wear characteristics based on the output layer of the deep learning neural network to obtain the wear degree assessment result.
[0008] Preferably, the assets are monitored based on the location data, including: Setting a predefined monitoring area and dividing the monitoring area into multiple sub-areas; Determining the current sub-area of the assets based on the location data, detecting the movement of the assets from the current sub-area to other sub-areas, and determining whether the other sub-areas are preset sensitive areas; If the other sub-areas are the sensitive areas, a warning mechanism is triggered immediately, and if the other sub-areas are not the sensitive areas, the assets are continuously monitored and the location changes of the assets are recorded.
[0009] Preferably, the asset file is obtained based on the alarm record and the wear degree assessment result, including: Retrieving asset information matching the alarm record and wear degree assessment result in the asset management database to obtain the asset file; The asset file includes the basic information, historical maintenance record, and current wear state of the assets.
[0010] Preferably, the maintenance task sheet is generated based on the asset file, including: Formulating a maintenance plan based on the basic information and current wear state of the asset file. generating the maintenance task list based on the maintenance plan, wherein the maintenance task list is sent to the relevant personnel in an electronic manner so that the maintenance task list can be quickly executed.
[0011] Preferably, the execution of the maintenance task list is tracked and feedback is provided to obtain an execution result, including: the relevant personnel fill in the execution details and feedback opinions after receiving and executing the maintenance task list; the system automatically collects and integrates the execution details and feedback opinions to obtain the execution result; the execution result is displayed through a visual interface so that the relevant personnel can intuitively understand the actual execution of the maintenance task.
[0012] Preferably, the tracking management of the asset is optimized based on the execution result to obtain an optimization scheme, including: obtaining the loss trend of the asset and the deficiencies of the tracking management based on the execution result; adjusting the monitoring strategy based on the deficiencies to enhance the monitoring strength of the asset; dynamically adjusting the training parameters of the asset state recognition model based on the loss trend to improve the accuracy of the loss evaluation.
[0013] The technical solution adopted to solve the above technical problems is: an industrial park asset tracking management system, which is applicable to the industrial park asset tracking management method and includes: a data acquisition unit for positioning the assets to be managed in the industrial park to obtain the position data of the assets, and obtaining the physical state information of the assets based on the physical sensors deployed on the assets, wherein the physical state information includes pressure data and deformation data; a loss evaluation unit for performing feature fusion analysis on the pressure data and deformation data based on a pre-trained asset state recognition model to obtain a loss degree evaluation result of the asset; a position monitoring unit for monitoring the asset based on the position data, and triggering a warning mechanism when detecting that the position of the asset exceeds the pre-determined area boundary, wherein the warning mechanism includes sending an alarm record of the position trajectory to the manager; a loss maintenance unit for obtaining an asset file based on the alarm record and the loss degree evaluation result, and generating a maintenance task list based on the asset file, wherein the maintenance task list includes the position trajectory, the current loss state and the recommended maintenance measures of the asset; The management optimization unit is used to track and provide feedback on the execution status of the maintenance task order to obtain the execution result, and to optimize the tracking management of the asset based on the execution result to obtain an optimization plan, wherein the optimization plan includes adjusting the monitoring scope and updating the parameters of the asset status identification model.
[0014] The beneficial effects of the present invention are as follows: (1) The present invention uses physical sensors to locate and monitor the status of assets in real time, which can obtain the current status of assets in a timely and accurate manner. This real-time and high-precision nature ensures that asset management is more scientific and effective; (2) The present invention proposes optimization schemes for asset status and monitoring scope by automatically analyzing the execution results, which can continuously adjust and optimize itself to improve management efficiency and effectiveness; (3) The present invention can greatly improve management efficiency, reduce labor costs and resource waste, and discover potential problems in a timely manner by using automated monitoring, intelligent early warning and data-driven decision support, thus avoiding high maintenance or replacement costs. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the overall method steps in one embodiment of the present invention; Figure 2 This is a schematic diagram of the overall system flow in one embodiment of the present invention; Attached reference numerals: 1. Data acquisition unit; 2. Loss assessment unit; 3. Location monitoring unit; 4. Loss maintenance unit; 5. Management optimization unit. Detailed Implementation
[0016] Example 1, as Figure 1 As shown, the present invention proposes an industrial park asset tracking and management method, comprising: S1. Locate the assets to be managed within the industrial park to obtain the asset location data. Obtain the physical status information of the assets based on the physical sensors deployed on the assets, including pressure data and deformation data. S2. Based on the pre-trained asset condition recognition model, feature fusion analysis is performed on stress data and deformation data to obtain the assessment results of the asset's degree of damage. S3. Monitor the range of assets based on location data. When the location of an asset is detected to be outside the predefined area boundary, an early warning mechanism is triggered. The early warning mechanism includes sending an alarm record of the location trajectory to the management personnel. S4. Obtain asset profiles based on alarm records and wear assessment results, and generate maintenance task orders based on asset profiles. The maintenance task orders include the location trajectory of the assets, their current wear status, and recommended maintenance measures. S5. Track and provide feedback on the execution of maintenance task orders to obtain execution results. Based on the execution results, optimize the tracking and management of assets to obtain optimization solutions. The optimization solutions include adjusting the monitoring scope and updating the parameters of the asset status identification model.
[0017] In this invention, physical sensors refer to sensors installed on assets to monitor their physical state in real time. Common physical sensors include temperature sensors, pressure sensors, deformation sensors, and vibration sensors. These sensors can acquire real-time data about the assets, helping to assess their usage and health status. Pressure data refers to numerical information recorded by sensors regarding the pressure exerted on the asset. For example, when the pressure on machinery, equipment, pipelines, and buildings exceeds the normal range, it may cause damage or malfunction. Pressure data can help predict asset damage or maintenance needs. Deformation data refers to data recorded by sensors regarding changes in the asset's shape, typically the deformation of an object under stress, heat, or other factors. Deformation data can reflect whether the asset is in normal working condition and whether deformation or damage has occurred. The wear and tear assessment result is an asset health assessment result derived from the asset's physical state information, such as pressure and deformation data. It can quantify the degree of wear or aging of the asset to help determine whether the asset needs maintenance or replacement. Asset range monitoring refers to real-time monitoring of the asset's location within a specific area. The system has several key components: **Control:** When an asset's location exceeds the preset monitoring range or boundary, the system automatically issues an alarm. **Alarm Log:** This refers to the system's detailed logs recording when an asset's location exceeds the predetermined range. It typically includes the alarm trigger time, asset location information, trajectory information, and other relevant data. These records are crucial for subsequent asset management and tracking. **Asset File:** This refers to the asset's historical record, including basic information, status assessment, degree of wear and tear, and maintenance records. It provides a documented management system for assets, helping managers understand their usage, health status, and maintenance history. **Maintenance Task Sheet:** This refers to a specific maintenance plan generated for an asset, including its wear and tear status, early warning records, and suggested maintenance measures. It provides maintenance personnel with necessary operational guidelines to effectively execute maintenance work. **Execution Result:** This refers to the feedback data after the maintenance task is completed, indicating the effectiveness of the maintenance work. The execution result helps evaluate the effectiveness of maintenance measures and whether further adjustments or optimizations are needed. **Optimization Plan:** This refers to specific measures to adjust and improve deficiencies in the asset management process, used to enhance the overall performance of the asset management system.
[0018] Example 2: The industrial park asset tracking and management method proposed in this invention, compared with Example 1, further includes: A1. Locate the assets to be managed within the industrial park to obtain asset location data, including: A2, real-time collection of location data based on RFID tags installed on assets to obtain location data; A3, the RFID tags are wirelessly connected to the central management system to ensure the real-time transmission of location data.
[0019] In this embodiment, the RFID tag refers to a device that uses radio wave technology to identify and track objects. The RFID tag usually contains a small chip and an antenna, which communicates with the RFID reader through radio waves. Each tag has a unique Internet of Things code, which can be used to track the location or status of assets. The central management system refers to a centralized software platform that collects, stores and manages data from various sensors, devices and tags. The RFID tag is connected to the central management system through wireless networks or other communication methods to ensure that the location information of the asset can be transmitted to the management system in a timely manner for real-time monitoring and data analysis.
[0020] In an optional embodiment, the asset state recognition model includes a deep learning neural network that has been trained on a large number of asset wear samples and can accurately identify and analyze stress data and deformation data of assets to accurately assess the degree of asset wear.
[0021] In an optional embodiment, the pre-trained asset state recognition model is used to analyze the features of the stress data and deformation data to obtain the asset wear degree evaluation results, including: B1, standardizing and preprocessing the stress data and deformation data to obtain preprocessed data; B2, inputting the preprocessed data into the deep learning neural network to obtain the wear characteristics of the asset; B3, numerically outputting the wear characteristics based on the output layer of the deep learning neural network to obtain the wear degree evaluation results.
[0022] It should be noted that standardized preprocessing refers to a method of data preprocessing, the purpose of which is to adjust the scale and range of data to make it suitable for machine learning models. In the standardization process, the mean of the data is adjusted to zero and the variance is adjusted to one, thereby eliminating the scale difference of different sensors or measurement data. The purpose of standardization is to enable the model to more effectively learn and process data with different features; the deep learning neural network is an algorithm that simulates the working of brain neurons and can process data and extract features through a multi-layer network structure. The deep learning neural network is used to analyze the stress data and deformation data of the asset, identify the damage features and perform damage assessment; the damage feature refers to the features extracted from the sensor data such as stress data and deformation data. These features are used to describe the health status, damage degree or aging condition of the asset. By analyzing these data through the deep learning neural network, the key factors affecting the damage or performance degradation of the asset can be extracted; the numerical output refers to the final result of the neural network, which is represented in the form of a numerical value. In the damage degree assessment, the deep learning neural network calculates the degree of damage according to the input data (such as stress and deformation information) and generates a numerical value through the output layer of the network. This numerical value usually represents the health status or damage degree of the asset.
[0023] In an optional embodiment, the range monitoring of the asset based on the location data comprises: C1, setting a predefined monitoring area and dividing the monitoring area into multiple sub-areas; C2, determining the sub-area where the asset is currently located based on the location data, detecting the movement of the asset from the current sub-area to other sub-areas, and determining whether the other sub-area is a preset sensitive area; C3, if the other sub-area is a sensitive area, triggering the early warning mechanism immediately, and if the other sub-area is not a sensitive area, continuing to monitor the range of the asset and recording the location changes of the asset.
[0024] It should be noted that the sub-area refers to a small area further divided from the predefined monitoring area. By dividing a large area into multiple sub-areas, more accurate management and monitoring can be achieved. For example, a large warehouse or factory can divide each different area or department into a sub-area. Each sub-area may contain different asset types or functions, which facilitates more detailed tracking and management of assets; the sensitive area refers to an area that needs special attention due to security, asset value or other risk reasons. For example, areas storing valuable items, flammable and explosive materials or having special security requirements. The assets in these areas may pose a greater security risk if they move abnormally or change position, so they need to be monitored more strictly.
[0025] In an optional embodiment, an asset profile is obtained based on the alarm record and the wear degree assessment result, including: D1, retrieving asset information matching the alarm record and the wear degree assessment result in an asset management database to obtain an asset profile; D2, the asset profile includes basic information, historical maintenance records, and current wear state of the asset.
[0026] It should be noted that the asset management database refers to a database system that stores all asset information, usually including detailed data of each asset, such as asset type, number, location, status, etc., which helps managers to keep track of the status and dynamics of assets in real time; the alarm record refers to the record generated when the system finds an anomaly during monitoring, which details the cause of the alarm, time, involved assets, and specific circumstances, and the alarm record helps subsequent tracking and analysis to determine whether there is a potential safety hazard or management problem.
[0027] In an optional embodiment, a maintenance task list is generated based on the asset profile, including: E1, developing a maintenance plan based on the basic information and current wear state of the asset profile; E2, generating a maintenance task list based on the maintenance plan, wherein the maintenance task list is sent to relevant personnel through electronic means to enable the maintenance task list to be executed quickly.
[0028] It should be noted that the maintenance plan is a series of predetermined maintenance activities developed based on the information in the asset profile, especially the wear state, and the maintenance plan aims to ensure that the asset is running in the best state and prevent production downtime due to equipment failure or damage.
[0029] In an optional embodiment, the execution of the maintenance task list is tracked and feedback is provided to obtain an execution result, including: F1, relevant personnel fill in execution details and feedback after receiving and executing the maintenance task list; F2, the system automatically collects and integrates the execution details and feedback to obtain the execution result; F3, the execution result is displayed through a visual interface to enable relevant personnel to intuitively understand the actual execution of the maintenance task.
[0030] It should be noted that the execution details refer to the information recorded by the relevant personnel after completing the maintenance task, including the time of executing the maintenance task, the materials used, the specific maintenance measures taken, the status of work completion, etc. The execution details help to ensure the transparency of the maintenance task and provide detailed records for subsequent review and optimization. The feedback refers to the evaluation and suggestions provided by the relevant personnel after completing the maintenance task, regarding the task execution process, equipment status, maintenance effect, and any possible challenges or problems. These opinions help managers understand the shortcomings in maintenance work and further improve the process or prevent similar problems from occurring. The visual interface refers to an interface that displays data in graphical, chart, or other image-based forms. The execution results of the maintenance task will be displayed through the visual interface, allowing relevant personnel to intuitively view the execution of the task.
[0031] In an optional embodiment, the tracking management of the asset is optimized based on the execution results to obtain an optimization scheme, including: G1, obtaining the loss trend of the asset and the shortcomings of the tracking management based on the execution results; G2, adjusting the monitoring strategy based on the shortcomings to enhance the monitoring intensity of the asset; G3, dynamically adjusting the training parameters of the asset state recognition model based on the loss trend to improve the accuracy of the loss assessment.
[0032] It should be noted that the loss trend refers to the pattern of gradual decline in the physical state or performance of an asset over time during use. It is usually revealed by long-term analysis of monitoring data (such as pressure, deformation, temperature, etc.) of the equipment or asset, which reveals the loss change law of a certain asset. Training parameters refer to key parameters used to adjust the performance of a model in the process of machine learning and data analysis. These parameters affect the learning speed, accuracy, and generalization ability of the model. Common training parameters include learning rate, batch size, and training times. By dynamically adjusting these parameters, the accuracy and adaptability of the asset state recognition model can be improved, thereby better assessing asset loss.
[0033] Embodiment three, as shown in Figure 2 The present application proposes an industrial park asset tracking management system, which is suitable for an industrial park asset tracking management method, including: A data acquisition unit 1 is used to position the assets to be managed in the industrial park to obtain the position data of the assets, and the physical state information of the assets is obtained based on the physical sensors deployed on the assets, wherein the physical state information includes pressure data and deformation data. The loss evaluation unit 2 is configured to perform feature fusion analysis on the pressure data and the deformation data based on the pre-trained asset state recognition model to obtain a loss degree evaluation result of the asset; The position monitoring unit 3 is configured to perform range monitoring on the asset based on the position data, and trigger a pre-warning mechanism when it is detected that the position of the asset exceeds a pre-defined region boundary, wherein the pre-warning mechanism comprises sending an alarm record of the position trajectory to a manager; The loss maintenance unit 4 is configured to obtain an asset archive based on the alarm record and the loss degree evaluation result, and generate a maintenance task list based on the asset archive, wherein the maintenance task list comprises the position trajectory, the current loss state and the recommended maintenance measures of the asset; The management optimization unit 5 is configured to track and feedback the execution of the maintenance task list to obtain an execution result, and optimize the tracking management of the asset based on the execution result to obtain an optimization scheme, wherein the optimization scheme comprises adjusting the monitoring range and updating the parameters of the asset state recognition model.
[0034] The embodiments of the present application are described in detail above with reference to the drawings, but the present application is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application.
Claims
1. An industrial park asset tracking management method, characterized in that, The method comprises the following steps: Positioning the assets to be managed in the industrial park to obtain position data of the assets, and obtaining physical state information of the assets based on physical sensors deployed on the assets, wherein the physical state information comprises pressure data and deformation data; Performing feature fusion analysis on the pressure data and the deformation data based on a pre-trained asset state recognition model to obtain a loss degree evaluation result of the assets; Performing range monitoring on the assets based on the position data, and triggering a warning mechanism when it is detected that the position of the assets exceeds a pre-defined regional boundary, wherein the warning mechanism comprises sending an alarm record of the position trajectory to a manager; Obtaining an asset archive based on the alarm record and the loss degree evaluation result, and generating a maintenance task list based on the asset archive, wherein the maintenance task list comprises the position trajectory, the current loss state and the recommended maintenance measures of the assets; Tracking and feeding back the execution of the maintenance task list to obtain an execution result, and optimizing the tracking management of the assets based on the execution result to obtain an optimization scheme, wherein the optimization scheme comprises adjusting the monitoring range and updating the parameters of the asset state recognition model.
2. The industrial park asset tracking management method of claim 1, wherein, Positioning the assets to be managed in the industrial park to obtain position data of the assets, comprising: Performing real-time collection of the position data based on RFID tags installed on the assets to obtain the position data; The RFID tags are wirelessly connected with a central management system to ensure instant transmission of the position data.
3. The industrial park asset tracking management method of claim 2, wherein, The asset state recognition model comprises a deep learning neural network, which is trained by a large number of asset loss samples and can accurately identify and analyze the pressure data and the deformation data of the assets to achieve accurate evaluation of the loss degree of the assets.
4. The method of claim 3, wherein, Performing feature fusion analysis on the pressure data and the deformation data based on a pre-trained asset state recognition model to obtain a loss degree evaluation result of the assets, comprising: Performing standardization preprocessing on the pressure data and the deformation data to obtain preprocessed data; Inputting the preprocessed data into the deep learning neural network to obtain loss features of the assets; Performing numerical output on the loss features based on the output layer of the deep learning neural network to obtain the loss degree evaluation result.
5. The method of claim 4, wherein, Performing range monitoring on the assets based on the position data, comprising: Setting a pre-defined monitoring area and dividing the monitoring area into multiple sub-areas; Determining the sub-area where the assets are currently located based on the position data, and detecting that the assets move from the current sub-area to other sub-areas, and determining whether the other sub-areas are pre-set sensitive areas; If the other sub-areas are the sensitive areas, a warning mechanism is triggered immediately, and if the other sub-areas are not the sensitive areas, the range monitoring on the assets is continued and the position changes of the assets are recorded.
6. The industrial park asset tracking management method of claim 5, wherein, Obtaining an asset archive based on the alarm record and the loss degree evaluation result, comprising: retrieving asset information matching the alarm record and the loss degree evaluation result from an asset management database to obtain an asset profile; the asset profile includes basic information, historical maintenance records, and current loss status of the asset.
7. The industrial park asset tracking management method of claim 6, wherein, generating a maintenance task list based on the asset profile, including: developing a maintenance plan based on the basic information and the current loss status of the asset profile; generating the maintenance task list based on the maintenance plan, wherein the maintenance task list is sent to relevant personnel in an electronic manner to enable quick execution of the maintenance task list.
8. The industrial park asset tracking management method of claim 7, wherein, tracking and feedback on the execution of the maintenance task list to obtain an execution result, including: the relevant personnel fill in the execution details and feedback opinions after receiving and executing the maintenance task list; the system automatically collects and integrates the execution details and feedback opinions to obtain the execution result; the execution result is displayed through a visual interface to enable the relevant personnel to intuitively understand the actual execution of the maintenance task.
9. The industrial park asset tracking management method of claim 8, wherein, optimizing the tracking management of the asset based on the execution result to obtain an optimization scheme, including: obtaining the loss trend of the asset and the shortcomings of the tracking management based on the execution result; adjusting the monitoring strategy based on the shortcomings to enhance the monitoring intensity of the asset; dynamically adjusting the training parameters of the asset state recognition model based on the loss trend to improve the accuracy of loss evaluation.
10. An industrial park asset tracking management system, which is suitable for the industrial park asset tracking management method of any one of claims 1-9, characterized in that, including: a data acquisition unit (1) for positioning the assets to be managed in an industrial park to obtain location data of the assets, and obtaining physical state information of the assets based on physical sensors deployed on the assets, wherein the physical state information includes pressure data and deformation data; a loss evaluation unit (2) for feature fusion analysis of the pressure data and deformation data based on a pre-trained asset state recognition model to obtain a loss degree evaluation result of the asset; a location monitoring unit (3) for range monitoring of the asset based on the location data, triggering a warning mechanism when detecting that the location of the asset exceeds the pre-defined regional boundary, wherein the warning mechanism includes sending an alarm record of the location trajectory to the manager; a loss maintenance unit (4) for obtaining an asset profile based on the alarm record and the loss degree evaluation result, and generating a maintenance task list based on the asset profile, wherein the maintenance task list includes the location trajectory, the current loss status, and the recommended maintenance measures of the asset; a management optimization unit (5) for tracking and feedback on the execution of the maintenance task list to obtain an execution result, and optimizing the tracking management of the asset based on the execution result to obtain an optimization scheme, wherein the optimization scheme includes adjusting the monitoring range and updating the parameters of the asset state recognition model.