An asset value protection management monitoring method and device based on an industrial internet of things
By leveraging industrial IoT and multi-sensor collaborative data acquisition, combined with sliding window mean smoothing and piecewise multinomial regression models, the problems of lag and insufficient accuracy in asset preservation management have been solved, enabling real-time, accurate assessment and intelligent upgrading of asset preservation management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF LOGISTICS SCI & TECH ACAD OF SYST ENG ACAD OF MILITARY SCI
- Filing Date
- 2026-03-24
- Publication Date
- 2026-06-16
AI Technical Summary
Existing asset preservation management methods rely on manual inspections, which are lagging and inaccurate. They cannot capture the dynamic changes of asset technical indicators in real time, make it difficult to predict impairment trends in advance, and lack multi-dimensional data integration and analysis, resulting in inaccurate assessment results.
Based on the Industrial Internet of Things, a multi-sensor collaborative data acquisition system is built. Through sliding window mean smoothing and piecewise multinomial regression models, combined with weighting coefficients and the analytic hierarchy process, asset preservation assessment is carried out to achieve multi-dimensional and accurate assessment.
It achieves real-time and comprehensive asset preservation management, improves the accuracy and stability of assessment results, reduces manpower and time costs, and adapts to the assessment needs of different assets and industrial scenarios.
Smart Images
Figure CN122222384A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of industrial data processing and industrial Internet of Things (IIoT), specifically to an asset preservation management and monitoring method and device based on the IIoT. Background Technology
[0002] In the industrial production sector, assets such as electronic equipment and machinery are the core foundation for ensuring continuous production and maintaining stable capacity. Their operational status and value retention directly affect a company's production cost control, production efficiency improvement, and core competitiveness building. With the deepening of Industry 4.0, the scale of assets in industrial scenarios continues to expand and the technological complexity continues to increase. Traditional asset value retention management and monitoring methods are no longer sufficient to meet the needs of modern production.
[0003] Current asset preservation management largely relies on manual inspections and periodic maintenance. This involves manually recording asset operating parameters and relying on experience to judge the asset's wear and tear and preservation value. This method is not only costly in terms of manpower and time but also suffers from significant lag. It cannot capture the dynamic changes in asset technical indicators in real time, making it difficult to predict asset depreciation trends. Failure to promptly detect potential faults or escalating wear can lead to abnormal asset depreciation, increased maintenance costs, and even production interruptions, resulting in additional economic losses. Some companies have attempted to introduce sensors to collect asset status data, but most only achieve simple monitoring of single or a few indicators, lacking systematic integration and analysis of multi-dimensional technical indicators. Furthermore, existing monitoring methods process the collected data in a rather crude manner, failing to fully explore the underlying asset depreciation patterns, decay rates, and functional effectiveness reflected in the data. The assessment process often relies on single-dimensional numerical comparisons, ignoring the combined impact of indicator trends, decay characteristics, and functional adaptability. This results in inaccurate asset preservation assessments with limited reference value, failing to provide a scientific basis for precise asset maintenance and preservation strategy formulation.
[0004] Furthermore, the technical indicators of various assets in industrial settings suffer from differences in dimensions and fluctuations, and existing technologies lack effective data standardization and noise suppression methods, further affecting the reliability of assessment results. Therefore, there is an urgent need for a method that can achieve real-time collection and accurate analysis of multi-dimensional technical indicators based on the Industrial Internet of Things (IIoT) to comprehensively assess the asset preservation status. This would address the issues of lag, bias, and insufficient accuracy in existing technologies, providing efficient and scientific technical support for industrial asset preservation management. Summary of the Invention
[0005] This invention primarily addresses the problem of how to manage and monitor the preservation of fixed assets in real time based on the Industrial Internet of Things (IIoT). This invention discloses an asset preservation management and monitoring method and device based on the Industrial Internet of Things.
[0006] In a first aspect, this invention discloses an asset preservation management and monitoring method based on the Industrial Internet of Things (IIoT), comprising: S1, Obtain the set of standard technical indicator values for the asset; the set of standard technical indicator values includes the standard value for each technical indicator; S2, based on various sensors loaded on the Industrial Internet of Things (IIoT), performs status acquisition and processing on various technical indicators of the asset to obtain a set of technical indicator data sequences; the set of technical indicator data sequences includes data sequences for each type of technical indicator of the asset; the data sequences are obtained by collecting various technical indicators of the material at preset time intervals; each type of sensor is used to collect data on the corresponding type of technical indicator to obtain the corresponding data sequence; various sensors are connected through the Industrial Internet. S3, perform asset preservation assessment processing on the set of technical indicator data sequences and the set of standard technical indicator values to obtain the asset preservation assessment value.
[0007] The step of performing asset preservation assessment on the set of technical indicator data sequences and the set of standard technical indicator values to obtain the asset preservation assessment value includes: S31, based on the set of technical indicator data sequences and the set of standard technical indicator values, perform change evaluation processing to obtain the impairment effect evaluation value; S32, perform attenuation rate evaluation processing on the set of technical indicator data sequences to obtain the attenuation rate evaluation value; S33, Perform functional effect evaluation processing on the set of technical indicator data sequences and the set of standard technical indicator values to obtain functional effect evaluation values; S34, perform integrated calculation on the impairment effect assessment value, impairment rate assessment value and functional effect assessment value to obtain the asset preservation assessment value.
[0008] The process of performing change assessment based on the set of technical indicator data sequences and the set of standard technical indicator values to obtain a value assessment of the impairment effect includes: S311, For each type of technical indicator, the corresponding relative state change rate sequence is calculated based on the data sequence in the set of technical indicator data sequences and the standard value in the set of standard technical indicator values. S312, Perform sliding window mean smoothing on each relative state change rate sequence to obtain a smoothed state change trend sequence; S313, based on the preset weight coefficients of various technical indicators, performs state change evaluation calculations on all smoothed state change trend sequences to obtain the impairment effect evaluation value.
[0009] The calculation expression for the relative state change rate sequence is: in, This is a preset constant to prevent division by zero. and Let the standard value of the i-th type of technical indicator and the data sequence of each type of technical indicator be respectively represented in the context of... The value of time, M is the total number of time points. For the relative state change rate sequence of the i-th type of technical indicator, The value at time.
[0010] The expression for the state change evaluation calculation is as follows: , , , in, For the multiplicative factor of the i-th type of technical indicator, Let be the weighting coefficient of the i-th type of technical indicator. and Let be the mean and variance of the smoothed concentration change trend sequence of the i-th type of technical indicator, respectively. For the scale factor of the i-th type of technical indicator, For the smoothed concentration change trend sequence of the i-th type of technical indicator, The value of time, where k is the time index. This is the assessment value for the impairment effect, where N is the total number of technical indicators.
[0011] The process of evaluating the decay rate of the technical indicator data sequence set to obtain the depreciation rate evaluation value includes: S321, perform composite attenuation rate calculation on the data sequence of each type of technical indicator in the set of technical indicator data sequences to obtain the composite attenuation rate sequence of the technical indicators; S322, perform the first attenuation assessment calculation on the composite attenuation rate sequence of each technical indicator to obtain the corresponding rate assessment value; S323, perform a first weighted fusion of all rate assessment values to obtain the depreciation rate assessment value.
[0012] The calculation of the composite decay rate for each type of technical indicator data sequence in the set of technical indicator data sequences to obtain the composite decay rate sequence of the technical indicators includes: S3211, the set of technical indicator data sequences is represented as follows: The data sequence of the i-th type of technical indicator is represented as ; S3212 uses a piecewise multinomial regression model, fitting each data sequence separately to obtain the fitted polynomial. The local rate of change of the fitted polynomial at each time point was calculated. Its calculation expression is: ; This represents the local rate of change of the i-th type of technical indicator at the k-th time point; S3213, standardize the local rate of change to obtain the composite attenuation rate; S3214, using all the composite attenuation rates, construct the composite attenuation rate sequence of the technical indicators.
[0013] A second aspect of this invention discloses an asset preservation management and monitoring device based on the Industrial Internet of Things (IIoT), the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the asset preservation management and monitoring method based on the Industrial Internet of Things.
[0014] In a third aspect, the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a computer, are used to execute the asset preservation management and monitoring method based on the Industrial Internet of Things.
[0015] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the asset preservation management and monitoring method based on the Industrial Internet of Things.
[0016] The beneficial effects of this invention are as follows: This invention establishes a multi-sensor collaborative acquisition system based on the Industrial Internet of Things, enabling real-time and continuous acquisition of various technical indicators of assets. It breaks through the limitations of traditional manual inspection and single-indicator monitoring, and can comprehensively capture the dynamic changes in the technical status of assets during operation. This provides a complete and accurate data foundation for asset preservation assessment, effectively avoids assessment bias caused by missing or delayed data, and improves the real-time and comprehensiveness of asset preservation management.
[0017] In the data processing, this invention employs sliding window mean smoothing to suppress measurement noise fluctuations, eliminates dimensional differences between different technical indicators through dimensionless processing, and combines piecewise multinomial regression model to fit the data sequence and calculate local rate of change. This series of refined data processing methods effectively reduces the impact of interference factors on the evaluation results, further ensuring the reliability and stability of the evaluation results and ensuring that the evaluation results can truly reflect the actual value preservation level of the assets.
[0018] This invention, by pre-setting weight coefficients and supporting dynamic adjustment based on the analytic hierarchy process or user feedback online learning mechanism, can adapt to the assessment needs of different types of assets and different industrial scenarios. It has strong flexibility and versatility and can be widely used in the value preservation management and monitoring of various industrial assets such as electronic equipment and mechanical equipment. It eliminates the need to build separate assessment systems for different assets, reducing the cost of technology implementation and the threshold for use.
[0019] This invention deeply integrates industrial IoT technology with asset preservation assessment, constructing a complete technical solution from data collection and processing to comprehensive evaluation. It realizes the intelligent and precise upgrade of asset preservation management, significantly reducing manpower and time investment compared with traditional management methods, while improving the scientific nature of management decisions, and providing a brand-new technical path for intelligent asset management in the industrial field. Attached Figure Description
[0020] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation
[0021] To better understand the content of this invention, an embodiment is provided here.
[0022] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.
[0023] In a first aspect, this invention discloses an asset preservation management and monitoring method based on the Industrial Internet of Things (IIoT), comprising: S1, Obtain the set of standard technical indicator values for the asset; the set of standard technical indicator values includes the standard value for each technical indicator; S2, based on various sensors loaded on the Industrial Internet of Things (IIoT), performs status acquisition and processing on various technical indicators of the asset to obtain a set of technical indicator data sequences; the set of technical indicator data sequences includes data sequences for each type of technical indicator of the asset; the data sequences are obtained by collecting various technical indicators of the material at preset time intervals; each type of sensor is used to collect data on the corresponding type of technical indicator to obtain the corresponding data sequence; various sensors are connected through the Industrial Internet. S3, perform asset preservation assessment processing on the set of technical indicator data sequences and the set of standard technical indicator values to obtain the asset preservation assessment value.
[0024] The assets mentioned may be electronic equipment, mechanical equipment, etc.
[0025] This invention employs a layered and progressive evaluation logic to assess asset preservation from three dimensions: impairment effect, impairment rate, and functional effect. It does not simply rely on the comparison of single indicator values, but rather deeply explores the changing trends, decay patterns, and functional adaptability contained in the data sequence of technical indicators. This achieves a multi-dimensional and comprehensive assessment of the asset preservation status, significantly improving the accuracy and comprehensiveness of the assessment results. It can provide a scientific basis for enterprises to accurately grasp the asset loss status and formulate targeted preservation strategies.
[0026] The step of performing asset preservation assessment on the set of technical indicator data sequences and the set of standard technical indicator values to obtain the asset preservation assessment value includes: S31, based on the set of technical indicator data sequences and the set of standard technical indicator values, perform change evaluation processing to obtain the impairment effect evaluation value; S32, perform attenuation rate evaluation processing on the set of technical indicator data sequences to obtain the attenuation rate evaluation value; S33, Perform functional effect evaluation processing on the set of technical indicator data sequences and the set of standard technical indicator values to obtain functional effect evaluation values; S34, perform integrated calculation on the impairment effect assessment value, impairment rate assessment value and functional effect assessment value to obtain the asset preservation assessment value.
[0027] The process of performing change assessment based on the set of technical indicator data sequences and the set of standard technical indicator values to obtain a value assessment of the impairment effect includes: S311, For each type of technical indicator, the corresponding relative state change rate sequence is calculated based on the data sequence in the set of technical indicator data sequences and the standard value in the set of standard technical indicator values. S312, each relative state change rate sequence is smoothed by sliding window mean to suppress fluctuations caused by measurement noise, and a smoothed state change trend sequence is obtained. S313, based on the preset weight coefficients of various technical indicators (determined by a preset database), performs state change evaluation calculations on all smoothed state change trend sequences to obtain the impairment effect evaluation value.
[0028] The calculation expression for the relative state change rate sequence is: in, A preset constant to prevent division by zero (e.g.) ), and Let the standard value of the i-th type of technical indicator and the data sequence of each type of technical indicator be respectively represented in the context of... The value of time, M is the total number of time points. For the relative state change rate sequence of the i-th type of technical indicator, The value at time.
[0029] The relative state change rate calculation formula effectively avoids calculation failures caused by a baseline value being zero or close to zero by introducing a zero-prevention constant, ensuring the stability and continuity of the calculation process. Simultaneously, the use of a sine function to transform the difference between the actual and standard values of the indicator can reasonably compress the impact of outliers on the calculation results, making the obtained state change rate more closely reflect the actual operating state of the asset. This avoids the drawbacks of amplifying local fluctuations through single linear calculations, providing accurate basic data for subsequent impairment effect assessment and solving the problem of indicator difference calculations being easily interfered with by extreme values in existing technologies.
[0030] The expression for the state change evaluation calculation is as follows: , , , in, For the multiplicative factor of the i-th type of technical indicator, Let be the weighting coefficient of the i-th type of technical indicator. and Let be the mean and variance of the smoothed concentration change trend sequence of the i-th type of technical indicator, respectively. For the scale factor of the i-th type of technical indicator, For the smoothed concentration change trend sequence of the i-th type of technical indicator, The value of time, where k is the time index. This is the assessment value for the impairment effect, where N is the total number of technical indicators.
[0031] The state change assessment formula introduces a multiplicative factor, assigning differentiated weights to state change trend data at different times. This makes the impact of recent data on the assessment results more significant, aligning with the time-sensitive nature of asset state changes. It accurately captures recent changes in asset impairment trends, avoiding the reduction in assessment accuracy caused by outdated data from the past. Simultaneously, it combines the weighting coefficients of various technical indicators with the smoothed trend sequence for comprehensive calculation. This approach considers the varying degrees of impact of different indicators on asset preservation while offsetting measurement noise through prior smoothing. This achieves a comprehensive and accurate quantification of asset impairment effects, overcoming the shortcomings of existing technologies in considering indicator weights and data timeliness.
[0032] The process of evaluating the decay rate of the technical indicator data sequence set to obtain the depreciation rate evaluation value includes: S321, perform composite attenuation rate calculation on the data sequence of each type of technical indicator in the set of technical indicator data sequences to obtain the composite attenuation rate sequence of the technical indicators; S322, perform the first attenuation assessment calculation on the composite attenuation rate sequence of each technical indicator to obtain the corresponding rate assessment value; S323, perform a first weighted fusion of all rate assessment values to obtain the depreciation rate assessment value.
[0033] The expression for the first attenuation assessment calculation is: , in, and These are the mean and variance of a composite decay rate sequence for a given technical indicator. This is the rate evaluation value.
[0034] The first attenuation assessment formula uses the tangent function to calculate the mean and variance of the composite attenuation rate sequence. This effectively amplifies key information in the attenuation rate fluctuation characteristics, accurately distinguishes the differences in attenuation rates among different assets, and provides a clearer boundary between slow and rapid attenuation, avoiding the problem that traditional mean calculations cannot highlight the impact of attenuation fluctuations. The rate assessment value obtained through this formula accurately reflects the severity of the attenuation of asset technical indicators, providing highly identifiable basic data for subsequent comprehensive assessment of impairment rates and helping to accurately predict the pace of asset impairment.
[0035] The expression for the first weighted fusion is: , in, This is the rate of impairment assessment value. The weight value of the i-th technical indicator is preset.
[0036] The first weighted fusion method integrates the rate assessment values of various technical indicators by pre-setting indicator weights. It allocates weights according to the degree of influence of different indicators on the overall asset degradation, making the impairment rate assessment results more consistent with the actual loss logic of assets and avoiding the bias caused by a single indicator dominating the assessment results. This method has a simple and efficient calculation logic, and can quickly integrate multi-dimensional degradation rate data to form a unified impairment rate assessment value, solving the problems of difficult effective integration of multi-indicator degradation data and fragmented assessment results in existing technologies.
[0037] The calculation of the composite decay rate for each type of technical indicator data sequence in the set of technical indicator data sequences to obtain the composite decay rate sequence of the technical indicators includes: S3211, the set of technical indicator data sequences is represented as follows: The data sequence of the i-th type of technical indicator is represented as ; S3212 uses a piecewise polynomial regression model (order 2 or 3) to fit each data sequence separately, obtaining the fitted polynomial. The local rate of change of the fitted polynomial at each time point was calculated. Its calculation expression is: ; This represents the local rate of change of the i-th type of technical indicator at the k-th time point; S3213, standardize the local rate of change to obtain the composite attenuation rate; S3214, using all the composite attenuation rates, construct the composite attenuation rate sequence of the technical indicators; The expression for the standardized calculation is: , in, For composite attenuation rate, This is a preset weighting factor.
[0038] The standardized calculation formula for composite decay rate introduces a weighting factor, effectively avoiding calculation anomalies when the actual value of the indicator is zero or close to zero, ensuring the smoothness of the standardization process. Simultaneously, by combining the local rate of change with the actual value of the indicator at the corresponding time point for standardization, the influence of differences in the dimensions and numerical ranges of different technical indicators can be eliminated, making the decay rates of different types of indicators comparable. This lays the foundation for subsequent multi-indicator decay rate fusion evaluation and solves the problem of the inability to directly compare and analyze multi-dimensional decay data in existing technologies.
[0039] The piecewise multinomial regression model can be implemented using a piecewise linear regression model.
[0040] The process of performing functional performance evaluation on the set of technical indicator data sequences and the set of standard technical indicator values to obtain functional performance evaluation values includes: S331, representing the set of standard technical indicator values as ,in Indicates the first Standard values for similar technical indicators; S332, Obtain the i-th data sequence of the technical indicator data sequence set at the last sampling time. The value of Construct actual data vectors ; S333, calculate the exceedance index for each type of technical indicator: ,when This indicates that the technical indicator has been met. These are preset non-zero coefficients; The excess index calculation formula avoids calculation failure by setting a non-zero coefficient, and uses a maximum value function to define the excess range, quantifying only the portion exceeding the standard value. This accurately reflects the deviation of the actual indicator from the standard requirements, clearly defining the compliance status and avoiding the distortion of results caused by including compliance status in the impairment assessment. This formula can quickly screen out key excess indicators affecting asset preservation, focusing on core influencing factors for subsequent functional effect assessment, thus improving assessment efficiency and relevance.
[0041] S334, based on the exceedance index of all technical indicators, calculates the distance metric value. ; The expression for calculating the distance metric is: , in, The normalization factor for the i-th type of technical indicator (which can be taken as follows) (or the historical maximum value of the data sequence), used to eliminate dimensional differences, where N is the number of categories of the technical indicator; The distance metric calculation formula introduces a normalization factor to eliminate dimensional differences among various technical indicators, ensuring that deviation data from various indicators can be integrated and calculated within the same dimension. Simultaneously, it incorporates indicator weight coefficients to highlight the impact of key indicators on functional deviations, enabling the distance metric to accurately reflect the comprehensive degree of deviation across multiple indicators. By amplifying the impact of significantly deviating indicators through square root operations, it further enhances the sensitivity of the assessment results to asset functional anomalies, solving the problem of difficulty in comprehensively quantifying multi-indicator functional deviations in existing technologies, and providing a precise quantitative basis for functional performance evaluation.
[0042] S335, Perform functional deviation calculation on the set of technical indicator data sequences to obtain functional effect evaluation value; The functional effect evaluation value The calculation expression is: in, Let be the functional deviation component of the i-th technical indicator.
[0043] The functional effectiveness assessment formula uses a piecewise function to differentiate the functional deviation components between compliant and non-compliant states. A stable base score is assigned to compliant states, while an exponential function amplifies the impact of deviations in non-compliant states. This accurately reflects the non-linear impact of exceeding indicators on asset function, better aligning with the actual operational patterns of assets. Simultaneously, a weighted average is calculated using indicator weighting coefficients to comprehensively consider the contribution of various indicators to the overall asset function. This ensures that the functional effectiveness assessment value comprehensively and accurately reflects the effectiveness of asset function, avoiding misleading overall assessment conclusions due to abnormalities in a single indicator.
[0044] The expression for the fusion calculation process is: , The preset weighting coefficients and It can be dynamically adjusted through the Analytic Hierarchy Process (AHP) or an online learning mechanism based on user feedback. This is the asset preservation valuation.
[0045] The integrated computational processing method combines the three assessment results—impairment effect, impairment rate, and functional effect—through preset weighting coefficients. This achieves the organic fusion of multi-dimensional assessment data, taking into account the three core dimensions of the asset's current impairment status, the pace of impairment development, and functional effectiveness, forming a comprehensive asset preservation assessment value. This solves the problem of the one-sidedness of existing single-dimensional assessment technologies. Furthermore, it supports online dynamic adjustment of weighting coefficients through the analytic hierarchy process or user feedback, adapting to the assessment needs of different asset types and industrial scenarios. This enhances the versatility and flexibility of the solution, making the assessment results more aligned with practical application needs and providing a scientific basis for asset preservation management decisions.
[0046] This invention, by accurately assessing the asset's value preservation status, can predict asset depreciation trends and potential failure risks in advance, helping enterprises optimize maintenance plans, avoid abnormal asset depreciation caused by over-maintenance or untimely maintenance, effectively reduce asset repair and replacement costs, extend asset lifespan, improve asset utilization efficiency, and provide strong support for enterprises to achieve cost reduction and efficiency improvement and strengthen core competitiveness.
[0047] In all embodiments of the present invention, the variables involved in all computational expressions or mathematical functions have been dimensionlessized before computation.
[0048] In all embodiments of the present invention, the values of the independent variables in the input of all computational expressions or mathematical functions meet the reasonable requirements of the input range of the computational expressions or mathematical functions, and can ensure that the computational expressions or mathematical functions can be calculated smoothly without violating physical laws or mathematical rules.
[0049] A second aspect of this invention discloses an asset preservation management and monitoring device based on the Industrial Internet of Things (IIoT), the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the asset preservation management and monitoring method based on the Industrial Internet of Things.
[0050] In a third aspect, the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked by a computer, are used to execute the asset preservation management and monitoring method based on the Industrial Internet of Things.
[0051] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the asset preservation management and monitoring method based on the Industrial Internet of Things.
[0052] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.
Claims
1. A method for asset preservation management and monitoring based on the Industrial Internet of Things, characterized in that, include: S1, Obtain the set of standard technical indicator values for the asset; the set of standard technical indicator values includes the standard value of each technical indicator of the asset; S2, based on various sensors loaded on the Industrial Internet of Things (IIoT), performs status acquisition and processing on various technical indicators of the asset to obtain a set of technical indicator data sequences; the set of technical indicator data sequences includes data sequences for each type of technical indicator of the asset; the data sequences are obtained by collecting various technical indicators of the material at preset time intervals; each type of sensor is used to collect data on the corresponding type of technical indicator to obtain the corresponding data sequence; various sensors are connected through the Industrial Internet. S3, perform asset preservation assessment processing on the set of technical indicator data sequences and the set of standard technical indicator values to obtain the asset preservation assessment value.
2. The asset preservation management and monitoring method based on the Industrial Internet of Things as described in claim 1, characterized in that, The step of performing asset preservation assessment on the set of technical indicator data sequences and the set of standard technical indicator values to obtain the asset preservation assessment value includes: S31, based on the set of technical indicator data sequences and the set of standard technical indicator values, perform change evaluation processing to obtain the impairment effect evaluation value; S32, perform attenuation rate evaluation processing on the set of technical indicator data sequences to obtain the attenuation rate evaluation value; S33, Perform functional effect evaluation processing on the set of technical indicator data sequences and the set of standard technical indicator values to obtain functional effect evaluation values; S34, perform integrated calculation on the impairment effect assessment value, impairment rate assessment value and functional effect assessment value to obtain the asset preservation assessment value.
3. The asset preservation management and monitoring method based on the Industrial Internet of Things as described in claim 2, characterized in that, The process of performing change assessment based on the set of technical indicator data sequences and the set of standard technical indicator values to obtain a value assessment of the impairment effect includes: S311, For each type of technical indicator, the corresponding relative state change rate sequence is calculated based on the data sequence in the set of technical indicator data sequences and the standard value in the set of standard technical indicator values. S312, Perform sliding window mean smoothing on each relative state change rate sequence to obtain a smoothed state change trend sequence; S313, based on the preset weight coefficients of various technical indicators, performs state change evaluation calculations on all smoothed state change trend sequences to obtain the impairment effect evaluation value.
4. The asset preservation management and monitoring method based on the Industrial Internet of Things as described in claim 3, characterized in that, The calculation expression for the relative state change rate sequence is: in, This is a preset constant to prevent division by zero. and Represent the standard value of the i-th type of technical indicator and the data sequence of each type of technical indicator, respectively. The value of time, M is the total number of time points. For the relative state change rate sequence of the i-th type of technical indicator, The value at time.
5. The asset preservation management and monitoring method based on the Industrial Internet of Things as described in claim 3, characterized in that, The expression for the state change evaluation calculation is as follows: , , ， in, For the multiplicative factor of the i-th type of technical indicator, Let be the weighting coefficient of the i-th type of technical indicator. and Let be the mean and variance of the smoothed concentration change trend sequence of the i-th type of technical indicator, respectively. For the scale factor of the i-th type of technical indicator, For the smoothed concentration change trend sequence of the i-th type of technical indicator, The value of time, where k is the time index. This is the assessment value for the impairment effect, where N is the total number of technical indicators.
6. The asset preservation management and monitoring method based on the Industrial Internet of Things as described in claim 2, characterized in that, The process of evaluating the decay rate of the technical indicator data sequence set to obtain the depreciation rate evaluation value includes: S321, perform composite attenuation rate calculation on the data sequence of each type of technical indicator in the set of technical indicator data sequences to obtain the composite attenuation rate sequence of the technical indicators; S322, perform the first attenuation assessment calculation on the composite attenuation rate sequence of each technical indicator to obtain the corresponding rate assessment value; S323, perform a first weighted fusion of all rate assessment values to obtain the depreciation rate assessment value.
7. The asset preservation management and monitoring method based on the Industrial Internet of Things as described in claim 6, characterized in that, The calculation of the composite decay rate for each type of technical indicator data sequence in the set of technical indicator data sequences to obtain the composite decay rate sequence of the technical indicators includes: S3211, the set of technical indicator data sequences is represented as follows: The data sequence of the i-th type of technical indicator is represented as ; S3212 uses a piecewise multinomial regression model, fitting each data sequence separately to obtain the fitted polynomial. The local rate of change of the fitted polynomial at each time point was calculated. Its calculation expression is: ; This represents the local rate of change of the i-th type of technical indicator at the k-th time point; S3213, standardize the local rate of change to obtain the composite attenuation rate; S3214, using all the composite attenuation rates, construct the composite attenuation rate sequence of the technical indicators.
8. An asset preservation and monitoring device based on the Industrial Internet of Things, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the asset preservation management and monitoring method based on the Industrial Internet of Things as described in any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked by a computer, are used to execute the asset preservation management and monitoring method based on the Industrial Internet of Things as described in any one of claims 1 to 7.
10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the asset preservation management and monitoring method based on the Industrial Internet of Things as described in any one of claims 1 to 7.