Fixed asset full-life-cycle intelligent dynamic management system based on data management

Abstract

The invention discloses a fixed asset full-life-cycle intelligent dynamic management system based on data management, and the system comprises a data collection module which obtains fixed asset full-life-cycle data; the information entropy enhancement module is used for generating fixed asset management data; the dynamic management module is used for establishing a fixed asset dynamic management model; the real-time monitoring module is used for dynamically adjusting the maintenance plan and resource configuration of the fixed assets; the mobile checking module is used for intelligently correcting the mobile checking data of which the error exceeds a threshold value; the automatic reconciliation module is used for storing an optimization result into a fixed asset resume database; and the decision support module is used for seamless connection and collaborative optimization of fixed asset dynamic management, mobile checking and automatic account checking processes. According to the invention, seamless connection and collaborative optimization of dynamic management, mobile checking and automatic account checking processes of fixed assets can be realized.

Description

Technical Field

[0001] The present invention relates to the field of asset management technology, and in particular to an intelligent dynamic management system for the entire life cycle of fixed assets based on data governance. Background Art

[0002] In modern enterprise management, fixed asset management is an important part of corporate financial management, operational efficiency improvement and asset security. The life cycle of fixed assets includes multiple links such as purchase, use, maintenance, inventory, depreciation and scrapping, involving multiple departments and complex management processes. With the continuous expansion of corporate asset scale and the increasing refinement of management needs, the traditional fixed asset management model has been unable to meet the requirements of efficient, accurate and intelligent management.

[0003] At present, fixed asset management mainly relies on traditional manual records, static database management and regular manual inventory. The traditional methods have the following technical defects: First, the data collection and storage methods are relatively scattered, and the lack of systematic management of fixed asset history data leads to data redundancy, incompleteness or untimely updates, affecting the accuracy of asset management. Second, most existing fixed asset management systems rely on fixed rules or static models for asset scheduling and maintenance, which are difficult to adapt to the dynamic changes in asset status, resulting in delayed maintenance plans and low asset utilization efficiency. Third, fixed asset inventory usually relies on manual inspections, and data reconciliation relies on financial accounting and manual review, which is prone to discrepancies between accounts and actuals due to human errors, affecting asset security and the accuracy of financial management.

[0004] To sum up, existing fixed asset management technologies have significant shortcomings in data governance, dynamic asset management, real-time monitoring, inventory verification, and intelligent decision support. Enterprises urgently need a technology that can realize intelligent dynamic management of fixed assets throughout their entire life cycle to improve the degree of automation in asset management, optimize maintenance scheduling strategies, and enhance asset operation efficiency and financial compliance. Summary of the Invention

[0005] The purpose of the present invention is to propose an intelligent dynamic management system for the entire life cycle of fixed assets based on data governance. The present invention can achieve seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory and automatic reconciliation processes.

[0006] The intelligent dynamic management system for the entire life cycle of fixed assets based on data governance according to the present invention includes the following modules:

[0007] The data collection module obtains data on the entire life cycle of fixed assets and stores it in the fixed asset history database;

[0008] The information entropy enhancement module pre-processes the fixed asset life cycle data, calculates the information entropy contribution of the fixed asset life cycle data, filters high-value data based on the set dynamic information entropy threshold, and generates fixed asset management data;

[0009] The dynamic management module uses the variant grey wolf optimization algorithm to establish a dynamic management model for fixed assets based on fixed asset management data;

[0010] The real-time monitoring module monitors the operating status, maintenance plan, and resource allocation of fixed assets in real time based on the dynamic management model of fixed assets, and dynamically adjusts the maintenance plan and resource allocation of fixed assets;

[0011] The mobile inventory module collects mobile inventory data and compares it with the data in the updated fixed asset history database, and intelligently corrects mobile inventory data with errors exceeding the threshold;

[0012] The automatic reconciliation module uses the variant grey wolf optimization algorithm to match and optimize data based on the data in the fixed asset history database, and stores the optimization results in the fixed asset history database;

[0013] The decision support module is based on the optimized fixed asset management data in the fixed asset history database, and provides seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory and automatic reconciliation processes.

[0014] A data governance-based intelligent dynamic management method for the entire life cycle of fixed assets, applied to a data governance-based intelligent dynamic management system for the entire life cycle of fixed assets, includes the following steps:

[0015] S1. Obtain fixed asset life cycle data, store it in a fixed asset history database, and use information entropy enhancement technology to preprocess the fixed asset life cycle data stored in the fixed asset history database to form fixed asset management data;

[0016] S2. Based on fixed asset management data, a dynamic fixed asset management model is established using the variant grey wolf optimization algorithm. This model is based on an intelligent optimization scheduling mechanism with fixed asset operating status, maintenance cycle, and usage frequency as input parameters.

[0017] S3. Apply the fixed asset dynamic management model to the fixed asset history database to monitor and dynamically evaluate the real-time status of fixed assets. Based on the fixed asset operating status and the output of the fixed asset dynamic management model, the fixed asset maintenance plan and resource allocation are automatically adjusted, and the adjustment results are fed back to the fixed asset history database in real time.

[0018] S4. Mobile inventory of fixed assets, collect current mobile inventory data, and compare the collected mobile inventory data with the updated data in the fixed asset history database to achieve real-time verification;

[0019] S5. Use the variant grey wolf optimization algorithm to automatically reconcile fixed asset management data, mobile inventory data, and related financial accounting data in the fixed asset history database. Through multi-source data matching, identify discrepancies between fixed asset status and accounting information, and intelligently correct discrepant fixed asset lifecycle data. Ultimately, feedback the correction results to update the fixed asset history database.

[0020] S6. Conduct a comprehensive analysis of the data in the fixed asset history database updated in steps S3, S4, and S5, and output intelligent decision-making support information for the full life cycle management of fixed assets, thereby achieving seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory, and automatic reconciliation processes.

[0021] Optionally, the S1 includes the following steps:

[0022] Obtain a data set covering the entire lifecycle of fixed assets. Each item in this data set includes several dimensions, corresponding to the asset's basic attributes, status changes, usage cycles, maintenance plans, historical inventory information, and financial accounting information. Each data item undergoes standardization and transformation to form a structured data set. For data items with missing values, a weighted average approach based on information entropy contribution is used to complete the missing values. The completed value is a combination of the weights of multiple similar data items along the information entropy dimension.

[0023] Based on the structured data set, a probability model is performed on each data item to evaluate its frequency of occurrence in the entire fixed asset life cycle data set. Then, its joint information entropy value is calculated in a probability-weighted form. The joint information entropy reflects the total effective information contained in the entire data set under the multi-dimensional structure.

[0024] On the basis of obtaining the joint information entropy, the information entropy contribution of each data item is calculated. The information entropy contribution is used to quantify the independence and information strength of the data item to the full life cycle information system, that is, the importance of the information to the asset management system.

[0025] Set a dynamic information entropy threshold. The dynamic information entropy threshold is calculated based on the mean of all information entropy contributions, and an adjustment term consisting of the standard deviation multiplied by the adjustment factor is added. The dynamic information entropy threshold is used as the judgment boundary to distinguish between high information value and low information value data items;

[0026] Data items whose information entropy contribution is lower than the dynamic information entropy threshold are classified as low-value data and uniformly included in the noise data set; data items whose information entropy contribution is higher than or equal to the threshold are classified into the high-value data set, so that the fixed asset management system only retains data with significant information value for subsequent modeling and analysis;

[0027] For each data item in the high-value data set, the rate of change of its information entropy in the historical sequence, namely the information entropy gradient, is calculated to determine the mutation characteristics of the data item in the time series. When the information entropy gradient of a data item is greater than the set mutation detection threshold, the system marks the data item as a mutation anomaly and performs correction processing based on its historical neighboring data;

[0028] The data set after excluding mutation anomalies is used as the final fixed asset management data set and stored in the fixed asset history database.

[0029] Optionally, S2 includes the following steps:

[0030] S21. Extract fixed asset operating status, maintenance cycle, and usage frequency data based on fixed asset management data, and set fixed asset dynamic management optimization goals, including minimizing asset maintenance costs, maximizing asset utilization efficiency, and optimizing resource consumption in asset scheduling;

[0031] S22. Construct a fixed asset dynamic management model based on the Mutated Gray Wolf Optimization Algorithm. The Mutated Gray Wolf Optimization Algorithm introduces a mutation strategy based on the standard Gray Wolf Optimization Algorithm, which enables the fixed asset management optimization process to have high search capability and global convergence. The Mutated Gray Wolf Optimization Algorithm uses a dual mutation mechanism, including adaptive step size mutation and dynamic leadership mutation.

[0032] The adaptive step size variation adaptively adjusts the search step size of the gray wolf individual at each iterative update, so that the gray wolf individual has a high exploration ability in the early stage and focuses on the search in the later stage. The adaptive step size variation formula is:

[0033] S t γ·(A α -A current )+μ·(A β -A current )+v·(A δ -A current )+λ·randn(0,1);

[0034] Among them, S t is the search step length of the tth iteration, A a , A β , A δThey represent the optimal fixed asset management strategy, the suboptimal fixed asset management strategy, and the candidate fixed asset management strategy, respectively. γ, μ, and v are step adjustment factors that are dynamically adjusted according to the number of iterations. λ randn (0, 1) is the Gaussian random variation term. A current is the current fixed asset management strategy set;

[0035] The dynamic leadership variation, in the Grey Wolf Optimization Algorithm, leads the individual fixed asset management strategy to the optimal A α Dynamic leadership variation is introduced to enable individual leaders to dynamically adjust according to their fitness:

[0036]

[0037] in, is the next generation leader individual, P(A) is the corresponding fitness function, and the comprehensive trade-off between asset maintenance cost, utilization efficiency and scheduling consumption is calculated;

[0038] S23. Based on the historical maintenance records and asset operating status data in the fixed asset history database, define the fitness function F(X) of the fixed asset management strategy:

[0039]

[0040] Among them, C maint，i is the maintenance cost of the i-th fixed asset, E util，i is the utilization efficiency of the i-th fixed asset, R alloc，i is the resource scheduling consumption of the i-th fixed asset, λ1, λ2, λ3 are the dynamically adjusted fitness weight coefficients;

[0041] After calculating the fitness function of the fixed asset management strategy, the population position is adjusted according to the fitness function ranking, and the population is dynamically adjusted according to the dual mutation mechanism of the mutant gray wolf optimization algorithm, so that the optimization process can adapt to different asset management scenarios;

[0042] S24. After the optimization iteration converges, generate the fixed asset dynamic management model A opt :

[0043] A opt =A α +ω1(A β -A α )+ω2(A δ -A α );

[0044] Among them, A opt is the dynamic management model of fixed assets, ω1 and ω2 are scheduling adjustment factors.

[0045] Optionally, S3 includes the following steps:

[0046] S31. Based on the dynamic management model of fixed assets A opt The fixed asset real-time status monitoring system is established based on the data from the fixed asset history database. The fixed asset real-time status monitoring system monitors the operating status, maintenance plan and resource allocation of fixed assets in real time. The monitoring data includes the current load status of fixed assets. asset , Current availability of fixed assets U asset and the fixed asset status change rate V change , define the real-time status data S of fixed assets monitor :

[0047] S monitor ={L asset ,U asset ,V change};

[0048] Among them, L asset Represents the current load status of fixed assets, U asset Represents the current availability of fixed assets:

[0049]

[0050] Among them, T avaliable is the current available time of fixed assets, T total is the total monitoring cycle time, V change Represents the rate of change of status of fixed assets:

[0051]

[0052] Among them, S current and S previous They represent the fixed asset status at the current and previous moments respectively, and Δt is the time interval;

[0053] S32. Dynamically evaluate the real-time status data of fixed assets monitored in step S31 and calculate the fixed asset health score H asset :

[0054] H asset =ω4L asset +ω5(1-U asset )+ω6V change ;

[0055] Among them, H asset is the fixed asset health status score, the higher the value, the more unstable the asset status, ω4, ω5, ω6 are dynamic weight coefficients;

[0056] S33. Based on the fixed asset health status score H asset, adopt dynamic maintenance adjustment mechanism to optimize the maintenance time T of fixed assets maint and maintain resource allocation R maint :

[0057]

[0058] in, is the adjusted maintenance time of fixed assets, is the originally set maintenance time, H th The trigger threshold for fixed asset maintenance. If H asset >H th , then maintenance is triggered immediately;

[0059] For maintenance resource allocation R maint , optimized based on the current status of fixed assets:

[0060]

[0061] in, For optimized maintenance resource allocation, is the maintenance resource allocation before optimization, β is the dynamic adjustment factor of maintenance resources;

[0062] S34. After adjusting the maintenance plan in step S33, further optimize the allocation of fixed asset resources and calculate the resource adjustment vector based on the variant gray wolf optimization algorithm

[0063]

[0064] Among them, λ4 and λ5 are resource adjustment factors, A current For the current fixed asset management strategy;

[0065] S35. Store the maintenance plan and resource allocation adjustment data optimized in steps S33 and S34 into the fixed asset history database.

[0066] Optionally, the S4 includes the following steps:

[0067] S41. Use mobile terminal equipment to conduct on-site inventory of fixed assets and collect current mobile inventory data D scan , including the location of fixed assets, fixed asset numbers, current status of fixed assets and usage of fixed assets;

[0068] S42. The mobile inventory data collected in step S41 is updated with the fixed asset status data stored in the fixed asset history database D after S3. record Compare and calculate the deviation of mobile inventory data:

[0069] D diff =Dscan -D record

[0070] Among them, D diff To count data deviations for mobile inventory;

[0071] If the mobile inventory data deviation D diff =0, the mobile inventory data status is determined to be normal;

[0072] If the mobile inventory data deviation D diff ≠0, the mobile inventory data status is determined to be abnormal;

[0073] S43. Calculate the error correction factor δ for fixed asset status data with mobile inventory data deviation asset :

[0074]

[0075] If the error correction factor δ of the mobile inventory data state asset >δ th , then the state deviation of the mobile inventory data exceeds the threshold δ th , triggering an exception flag;

[0076] S44. For mobile inventory data whose error correction factor exceeds the threshold, a dynamic weight adjustment mechanism is used to correct the mobile inventory data:

[0077] D corrected =ω7D record +ω8D scan ;

[0078] Among them, D corrected is the adjusted mobile inventory data, ω7 and ω8 are the data weight coefficients:

[0079]

[0080] Among them, the smaller the weight of ω7, the larger the deviation of the fixed asset status data, and the more correction is needed. asset for;

[0081] S45. Store the mobile inventory data adjusted in step S44 into the fixed asset history database and update the fixed asset management system.

[0082] Optionally, the S5 includes the following steps:

[0083] S51. Based on the fixed asset history database, extract fixed asset management data, mobile inventory data, and related financial accounting data to construct a fixed asset automatic reconciliation data set;

[0084] S52. Use the variant grey wolf optimization algorithm to perform multi-source matching of fixed asset lifecycle data, calculate the matching error between fixed asset management data, mobile inventory data, and financial accounting data, and construct a fitness function based on the data matching error.

[0085] S53. Based on the data matching error, a correction vector is generated to adjust the data differences between fixed asset management data, mobile inventory data, and financial accounting data. The correction vector is dynamically optimized based on the historical data trends to optimize the matching degree between fixed asset management data, mobile inventory data, and financial accounting data.

[0086] S54. Store the fixed asset life cycle data optimized in step S53 into the fixed asset history database and update the fixed asset management system.

[0087] The beneficial effects of the present invention are:

[0088] (1) The present invention introduces an information entropy enhanced data preprocessing algorithm in the data processing of the entire life cycle of fixed assets. By calculating the information entropy contribution of the fixed asset data throughout the entire life cycle, high-value data is screened out, and a dynamic information entropy threshold is set to optimize data storage and processing. By combining information entropy calculation and gradient detection, low-value data is effectively removed, abnormal data is identified and intelligent correction is performed, ensuring that the data input into the management system has high information content and accuracy, reducing redundant data storage occupancy, improving data processing efficiency, and reducing errors in fixed asset status assessment, making the system more accurate and reliable during data collection and processing.

[0089] (2) The present invention adopts the mutated gray wolf optimization algorithm and makes innovative improvements to the asset maintenance and scheduling optimization problems in the dynamic management of fixed assets. By introducing a dual mutation mechanism (adaptive step size mutation and dynamic leadership mutation), the optimization process has high search capability and global convergence. The adaptive step size mutation enhances the exploration capability in the early stage of optimization and improves the search accuracy of the global optimal solution. The dynamic leadership mutation can dynamically adjust the optimization target according to the changes in the fixed asset status and historical data, making the management strategy more flexible and accurate.

[0090] (3) Aiming at the common discrepancy between accounts and actual assets in the process of fixed asset management, the present invention proposes an automatic reconciliation mechanism based on the variant grey wolf optimization algorithm. Through multi-source data matching optimization, the management data, mobile inventory data and financial accounting data in the fixed asset history database are intelligently matched, and a correction vector is constructed based on data matching error analysis to perform intelligent correction on abnormal data. It can adaptively optimize the differences in data throughout the life cycle of fixed assets, making the reconciliation process more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0092] Figure 1 This is a flow chart of the intelligent dynamic management system for the entire life cycle of fixed assets based on data governance proposed in this invention. DETAILED DESCRIPTION

[0093] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0094] refer to Figure 1 , an intelligent dynamic management system for the entire life cycle of fixed assets based on data governance, including the following modules:

[0095] The data collection module obtains data on the entire life cycle of fixed assets and stores it in the fixed asset history database;

[0096] The information entropy enhancement module pre-processes the fixed asset life cycle data, calculates the information entropy contribution of the fixed asset life cycle data, filters high-value data based on the set dynamic information entropy threshold, and generates fixed asset management data;

[0097] The dynamic management module uses the variant grey wolf optimization algorithm to establish a dynamic management model for fixed assets based on fixed asset management data;

[0098] The real-time monitoring module monitors the operating status, maintenance plan, and resource allocation of fixed assets in real time based on the dynamic management model of fixed assets, and dynamically adjusts the maintenance plan and resource allocation of fixed assets;

[0099] The mobile inventory module collects mobile inventory data and compares it with the data in the updated fixed asset history database, and intelligently corrects mobile inventory data with errors exceeding the threshold;

[0100] The automatic reconciliation module uses the variant grey wolf optimization algorithm to match and optimize data based on the data in the fixed asset history database, and stores the optimization results in the fixed asset history database;

[0101] The decision support module is based on the optimized fixed asset management data in the fixed asset history database, and provides seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory and automatic reconciliation processes.

[0102] A data governance-based intelligent dynamic management method for the entire life cycle of fixed assets, applied to a data governance-based intelligent dynamic management system for the entire life cycle of fixed assets, includes the following steps:

[0103] S1. Obtain fixed asset life cycle data, store it in a fixed asset history database, and use information entropy enhancement technology to preprocess the fixed asset life cycle data stored in the fixed asset history database to form fixed asset management data;

[0104] S2. Based on fixed asset management data, a dynamic fixed asset management model is established using the variant grey wolf optimization algorithm. This model is based on an intelligent optimization scheduling mechanism with fixed asset operating status, maintenance cycle, and usage frequency as input parameters.

[0105] S3. Apply the fixed asset dynamic management model to the fixed asset history database to monitor and dynamically evaluate the real-time status of fixed assets. Based on the fixed asset operating status and the output of the fixed asset dynamic management model, the fixed asset maintenance plan and resource allocation are automatically adjusted, and the adjustment results are fed back to the fixed asset history database in real time.

[0106] S4. Mobile inventory of fixed assets, collect current mobile inventory data, and compare the collected mobile inventory data with the updated data in the fixed asset history database to achieve real-time verification;

[0107] S5. Use the variant grey wolf optimization algorithm to automatically reconcile fixed asset management data, mobile inventory data, and related financial accounting data in the fixed asset history database. Through multi-source data matching, identify discrepancies between fixed asset status and accounting information, and intelligently correct discrepant fixed asset lifecycle data. Ultimately, feedback the correction results to update the fixed asset history database.

[0108] S6. Conduct a comprehensive analysis of the data in the fixed asset history database updated in steps S3, S4, and S5, and output intelligent decision-making support information for the full life cycle management of fixed assets, thereby achieving seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory, and automatic reconciliation processes.

[0109] In this embodiment, S1 includes the following steps:

[0110] Obtain a data set covering the entire lifecycle of fixed assets. Each item in this data set includes several dimensions, corresponding to the asset's basic attributes, status changes, usage cycles, maintenance plans, historical inventory information, and financial accounting information. Each data item undergoes standardization and transformation to form a structured data set. For data items with missing values, a weighted average approach based on information entropy contribution is used to complete the missing values. The completed value is a combination of the weights of multiple similar data items along the information entropy dimension.

[0111] Based on the structured data set, a probability model is performed on each data item to evaluate its frequency of occurrence in the entire fixed asset life cycle data set. Then, its joint information entropy value is calculated in a probability-weighted form. The joint information entropy reflects the total effective information contained in the entire data set under the multi-dimensional structure.

[0112] On the basis of obtaining the joint information entropy, the information entropy contribution of each data item is calculated. The information entropy contribution is used to quantify the independence and information strength of the data item to the full life cycle information system, that is, the importance of the information to the asset management system.

[0113] Set a dynamic information entropy threshold. The dynamic information entropy threshold is calculated based on the mean of all information entropy contributions, and an adjustment term consisting of the standard deviation multiplied by the adjustment factor is added. The dynamic information entropy threshold is used as the judgment boundary to distinguish between high information value and low information value data items;

[0114] Data items whose information entropy contribution is lower than the dynamic information entropy threshold are classified as low-value data and uniformly included in the noise data set; data items whose information entropy contribution is higher than or equal to the threshold are classified into the high-value data set, so that the fixed asset management system only retains data with significant information value for subsequent modeling and analysis;

[0115] For each data item in the high-value data set, the rate of change of its information entropy in the historical sequence, namely the information entropy gradient, is calculated to determine the mutation characteristics of the data item in the time series. When the information entropy gradient of a data item is greater than the set mutation detection threshold, the system marks the data item as a mutation anomaly and performs correction processing based on its historical neighboring data;

[0116] The data set after excluding the mutation abnormal points is used as the final fixed asset management data set and stored in the fixed asset history database.

[0117] This embodiment uses information entropy enhancement technology to pre-process fixed asset life cycle data, which can effectively improve the quality and credibility of the data. Compared with traditional fixed asset life cycle data management methods, this embodiment uses information entropy theory to remove noise, detect outliers, complete data, and normalize data to ensure data integrity and consistency. By jointly calculating the information contribution of different data items and setting dynamic information entropy thresholds to filter key data, it not only reduces the impact of redundant information on system performance, but also improves the ability to identify high-value data. In addition, by identifying sudden abnormal data points through information entropy gradient detection and combining the entropy gain weighted interpolation method to complete missing values, the fixed asset life cycle data maintains high quality and high availability throughout the entire life cycle. This data processing method based on information entropy enhancement makes the data in the fixed asset resume database more reliable, providing a solid data foundation for subsequent optimization scheduling, dynamic management, and decision support, thereby improving the intelligence level and management efficiency of asset management.

[0118] In this embodiment, S2 includes the following steps:

[0119] S21. Extract fixed asset operating status, maintenance cycle, and usage frequency data based on fixed asset management data, and set fixed asset dynamic management optimization goals, including minimizing asset maintenance costs, maximizing asset utilization efficiency, and optimizing resource consumption in asset scheduling;

[0120] S22. Construct a fixed asset dynamic management model based on the Mutated Gray Wolf Optimization Algorithm. The Mutated Gray Wolf Optimization Algorithm introduces a mutation strategy based on the standard Gray Wolf Optimization Algorithm, which enables the fixed asset management optimization process to have high search capability and global convergence. The Mutated Gray Wolf Optimization Algorithm uses a dual mutation mechanism, including adaptive step size mutation and dynamic leadership mutation.

[0121] Adaptive step size mutation adaptively adjusts the search step size of the gray wolf individual at each iterative update, so that the gray wolf individual has high exploration ability in the early stage and focuses on search in the later stage. The adaptive step size mutation formula is:

[0122] S t =γ·(A α -A current )+μ·(A β -A current )+v·(A δ -A current )+λ·randn(0,1);

[0123] Among them, S t is the search step length of the tth iteration, A α , A β , A δThey represent the optimal fixed asset management strategy, the suboptimal fixed asset management strategy, and the candidate fixed asset management strategy, respectively. γ, μ, and ν are step adjustment factors, which are dynamically adjusted according to the number of iterations. λ·randn(0, 1) is the Gaussian random variation term. A current is the current fixed asset management strategy set;

[0124] Adaptive step size variation is used to control how individual gray wolves move in the search space. While the step size update in traditional gray wolf optimization algorithms is based solely on the position information of the three leaders, this method introduces a dynamic adjustment factor and a Gaussian random variation term into the calculation of the search step size, ensuring:

[0125] Stronger early exploration capabilities: In the early stages of optimization, the step size is larger, which makes the search range wider, can quickly cover the entire solution space, and find the potential global optimal solution.

[0126] Later searches are more refined: As the number of iterations increases, the step size factor converges dynamically, and the optimization process gradually converges to the optimal solution, improving the accuracy of the fixed asset management model.

[0127] Avoid local optimal traps: The Gaussian random variation term introduces a certain degree of randomness to ensure that the fixed asset management scheduling plan is dynamically adjusted among multiple possible solutions, thereby improving optimization stability.

[0128] Dynamic leadership variation, optimal A in the gray wolf optimization algorithm leading individual fixed asset management strategy α Dynamic leadership variation is introduced to enable individual leaders to dynamically adjust according to their fitness:

[0129]

[0130] in, is the next generation leader individual, P(A) is the corresponding fitness function, and the comprehensive trade-off between asset maintenance cost, utilization efficiency and scheduling consumption is calculated;

[0131] A dynamic leadership mutation mechanism is introduced to enable the leader individual to dynamically replace the suboptimal fixed asset management strategy and the candidate fixed asset management strategy when the fitness is poor, so as to prevent the management plan from becoming rigid due to local optimality in the search process, and ensure that the best maintenance and scheduling strategy can be obtained in different asset operating environments.

[0132] S23. Based on the historical maintenance records and asset operating status data in the fixed asset history database, define the fitness function F(X) of the fixed asset management strategy:

[0133]

[0134] Among them, C maint，i is the maintenance cost of the i-th fixed asset, Eutil，i is the utilization efficiency of the i-th fixed asset, R alloc，i is the resource scheduling consumption of the i-th fixed asset, λ1, λ2, λ3 are the dynamically adjusted fitness weight coefficients;

[0135] After calculating the fitness function of the fixed asset management strategy, the population position is adjusted according to the fitness function ranking, and the population is dynamically adjusted according to the dual mutation mechanism of the mutant gray wolf optimization algorithm, so that the optimization process can adapt to different asset management scenarios;

[0136] S24. After the optimization iteration converges, generate the fixed asset dynamic management model A opt :

[0137] A opt =A α +ω1(A β -A α )+ω2(A δ -A α );

[0138] Among them, A opt is the dynamic management model of fixed assets, ω1 and ω2 are scheduling adjustment factors.

[0139] This embodiment establishes a dynamic management model for fixed assets through a mutated gray wolf optimization algorithm. By introducing an adaptive step size mutation mechanism on the basis of the standard gray wolf optimization algorithm, the optimization process can dynamically adjust the search range to avoid falling into local optimality, thereby improving the adaptability of the fixed asset management strategy. In addition, the application of the dynamic leadership mutation mechanism enables the optimal solution to be dynamically adjusted according to historical optimization results during the search process, thereby improving the stability of the optimization results. In the fixed asset management process, it can comprehensively consider the multi-dimensional factors of the fixed asset operating status, maintenance cycle and frequency of use to achieve intelligent scheduling optimization. The effectiveness of the optimization scheme is calculated by the fitness function, so that the system can dynamically generate the optimal fixed asset management strategy. The dynamic management model of fixed assets can be adaptively adjusted throughout the life cycle of fixed assets, improve the intelligence level of asset management, reduce maintenance costs, and improve asset utilization efficiency and the rationality of scheduling resource allocation.

[0140] In this embodiment, S3 includes the following steps:

[0141] S31. Based on the dynamic management model of fixed assets A opt The fixed asset real-time status monitoring system is established based on the data from the fixed asset history database. The fixed asset real-time status monitoring system monitors the operating status, maintenance plan and resource allocation of fixed assets in real time. The monitoring data includes the current load status of fixed assets. asset , Current availability of fixed assets U assetand the fixed asset status change rate V change , define the real-time status data S of fixed assets monitor :

[0142] S monitor ={L asset ,U asset ,V change};

[0143] Among them, L asset Represents the current load status of fixed assets, U asset Represents the current availability of fixed assets:

[0144]

[0145] Among them, T available is the current available time of fixed assets, T total is the total monitoring cycle time, V change Represents the rate of change of status of fixed assets:

[0146]

[0147] Among them, S current and S previous They represent the fixed asset status at the current and previous moments respectively, and Δt is the time interval;

[0148] S32. Dynamically evaluate the real-time status data of fixed assets monitored in step S31 and calculate the fixed asset health score H asset :

[0149] H asset =ω4L asset +ω5(1-U asset )+ω6V change ;

[0150] Among them, H asset is the fixed asset health status score, the higher the value, the more unstable the asset status, ω4, ω5, ω6 are dynamic weight coefficients;

[0151] S33. Based on the fixed asset health status score H asset , adopt dynamic maintenance adjustment mechanism to optimize the maintenance time T of fixed assets maint and maintain resource allocation R maint :

[0152]

[0153] in, is the adjusted maintenance time of fixed assets, is the originally set maintenance time, Hth The trigger threshold for fixed asset maintenance. If H asset >H th , then maintenance is triggered immediately;

[0154] For maintenance resource allocation R maint , optimized based on the current status of fixed assets:

[0155]

[0156] in, For optimized maintenance resource allocation, is the maintenance resource allocation before optimization, β is the dynamic adjustment factor of maintenance resources;

[0157] S34. After adjusting the maintenance plan in step S33, further optimize the allocation of fixed asset resources and calculate the resource adjustment vector based on the variant gray wolf optimization algorithm

[0158]

[0159] Among them, λ4,λ5 are resource adjustment factors, A current For the current fixed asset management strategy;

[0160] S35. Store the maintenance plan and resource allocation adjustment data optimized in steps S33 and S34 into the fixed asset history database.

[0161] The real-time status monitoring and dynamic evaluation method of fixed assets proposed in this embodiment enables fixed asset management to shift from a static mode to a dynamic real-time optimization mode. It continuously monitors the operating status of assets based on the fixed asset history database and the fixed asset dynamic management model, and dynamically adjusts the maintenance plan based on the calculated health status score. By calculating the operating load, availability and status change rate of fixed assets, it can quickly detect abnormal changes in assets, and optimize maintenance plans and resource allocation based on the health status score. The dynamic evaluation method not only improves the accuracy of fixed asset management, but also effectively reduces the risk of asset failure and improves the utilization efficiency of fixed assets. By optimizing the triggering mechanism of the maintenance plan, the maintenance cycle is made more reasonable, thereby avoiding excessive or insufficient maintenance. It can intelligently allocate resources based on the dynamic changes of monitoring data, improve the intelligence level of fixed asset maintenance and management, improve resource utilization, and reduce management costs.

[0162] In this embodiment, S4 includes the following steps:

[0163] S41. Use mobile terminal equipment to conduct on-site inventory of fixed assets and collect current mobile inventory data D scan, including the location of fixed assets, fixed asset numbers, current status of fixed assets and usage of fixed assets;

[0164] S42. The mobile inventory data collected in step S41 is updated with the fixed asset status data stored in the fixed asset history database D after S3. record Compare and calculate the deviation of mobile inventory data:

[0165] D diff =D scan -D record

[0166] Among them, D diff To count data deviations for mobile inventory;

[0167] If the mobile inventory data deviation D diff =0, the mobile inventory data status is determined to be normal;

[0168] If the mobile inventory data deviation D diff ≠0, the mobile inventory data status is determined to be abnormal;

[0169] S43. Calculate the error correction factor δ for fixed asset status data with mobile inventory data deviation asset :

[0170]

[0171] If the error correction factor δ of the mobile inventory data state asset >δ th , then the state deviation of the mobile inventory data exceeds the threshold δ th , triggering an exception flag;

[0172] S44. For mobile inventory data whose error correction factor exceeds the threshold, a dynamic weight adjustment mechanism is used to correct the mobile inventory data:

[0173] D corrected =ω7D record +ω8D scan ;

[0174] Among them, D corrected is the adjusted mobile inventory data, ω7 and ω8 are the data weight coefficients:

[0175]

[0176] Among them, the smaller the weight of ω7, the larger the deviation of the fixed asset status data, and the more correction is needed. asset for;

[0177] S45. Store the mobile inventory data adjusted in step S44 into the fixed asset history database and update the fixed asset management system.

[0178] The fixed asset mobile inventory method proposed in this embodiment uses mobile terminal devices to conduct on-site inventory of fixed assets and compares them with the data in the fixed asset history database. It can effectively detect the status deviation of fixed assets, calculate error correction factors, perform intelligent analysis of abnormal data, and use a dynamic weight adjustment mechanism to correct data, thereby ensuring the accuracy of fixed asset status data.

[0179] In this embodiment, S5 includes the following steps:

[0180] S51. Based on the fixed asset history database, extract fixed asset management data, mobile inventory data, and related financial accounting data to construct a fixed asset automatic reconciliation data set;

[0181] S52. Use the variant grey wolf optimization algorithm to perform multi-source matching of fixed asset lifecycle data, calculate the matching error between fixed asset management data, mobile inventory data, and financial accounting data, and construct a fitness function based on the data matching error.

[0182] S53. Based on the data matching error, a correction vector is generated to adjust the data differences between fixed asset management data, mobile inventory data, and financial accounting data. The correction vector is dynamically optimized based on the historical data trends to optimize the matching degree between fixed asset management data, mobile inventory data, and financial accounting data.

[0183] S54. Store the fixed asset life cycle data optimized in step S53 into the fixed asset history database and update the fixed asset management system.

[0184] The fixed asset automatic reconciliation method based on the variant gray wolf optimization algorithm proposed in this embodiment can efficiently identify matching errors between fixed asset management data, mobile inventory data, and financial accounting data, and intelligently correct data discrepancies. Compared with traditional reconciliation methods that rely on manual intervention or simple threshold matching methods, this method adopts multi-source data fusion and adaptive weight optimization strategies to improve the accuracy and automation of reconciliation. By calculating the data matching error vector and dynamically adjusting the weights based on the error correction factor, the system can adaptively optimize the data matching weights based on the error trends of historical data and real-time inventory data, improving the reliability of data correction. In addition, the variant gray wolf optimization algorithm is used to optimize the reconciliation data, enabling the data correction strategy to dynamically balance global optimization and local fine-grained optimization, thereby improving the stability and efficiency of fixed asset reconciliation. Ultimately, this method ensures high consistency of fixed asset data throughout the entire life cycle between financial accounting, physical inventory, and management records, improving the intelligent level of fixed asset life cycle management, and reducing asset management risks caused by data mismatches.

[0185] In this embodiment, S6 specifically includes:

[0186] S61. Based on the fixed asset history database updated in steps S3, S4, and S5, extract the fixed asset full life cycle management data, including dynamic management adjustment data, mobile inventory correction data, and automatic reconciliation correction data. The above data covers the operating status, maintenance plan, inventory records, reconciliation results, and historical optimization information of the fixed assets.

[0187] S62. Calculate intelligent evaluation indicators for fixed asset lifecycle data. Based on fixed asset lifecycle data, calculate key intelligent evaluation indicators for fixed asset management, including:

[0188] Fixed asset utilization efficiency: measures the actual utilization of fixed assets;

[0189] Fixed asset maintenance quality score: Comprehensively evaluate the implementation of the maintenance plan;

[0190] Fixed asset inventory consistency: calculate the consistency between mobile inventory data and database record data;

[0191] Fixed asset reconciliation accuracy: measures the matching degree of automatic reconciliation data.

[0192] S63. Based on the intelligent evaluation indicators calculated in step S62, a multi-dimensional optimization analysis method is used to dynamically adjust the fixed asset management strategy. The information entropy enhancement technology is used to evaluate the impact of each indicator on fixed asset management, and optimization weights are set. This enables the system to propose the best optimization suggestions for problems such as low asset utilization efficiency, unreasonable maintenance plans, inaccurate inventory data, or abnormal reconciliation data.

[0193] S64. Based on the optimization analysis results, generate fixed asset intelligent decision support information, including:

[0194] Fixed asset maintenance optimization suggestions: Dynamically adjust maintenance cycles based on health status scores;

[0195] Fixed asset resource scheduling optimization plan: optimize the allocation of asset resources and improve asset utilization;

[0196] Adjustment of fixed asset inventory strategy: Optimize inventory frequency and methods to reduce data errors;

[0197] Fixed asset reconciliation data optimization plan: Improve automatic reconciliation weights and enhance the matching between accounts and actual assets.

[0198] S65. Feedback the fixed asset optimization decision information generated in step S64 to the fixed asset history database, so that the system can optimize the dynamic management, mobile inventory and automatic reconciliation strategies of fixed assets in real time based on the latest intelligent decision-making solutions. Ultimately, the entire process of fixed asset management can be made intelligent, automated and efficient, and the accuracy and intelligent decision-making level of fixed asset management throughout its entire life cycle can be improved.

[0199] Example 1:

[0200] In March 2024, Company A, a large manufacturing enterprise located in a certain province, faced many challenges in fixed asset management. The company's total existing fixed assets exceeded 35,000 items, covering multiple categories of production equipment, testing instruments, office facilities, and storage equipment. Among them were 850 CNC machine tools, 1,200 testing equipment, and 6,500 office terminals. Due to the company's heavy production tasks and frequent equipment use, the status, maintenance, inventory, and reconciliation of fixed assets have become management challenges.

[0201] In the past, the company managed its assets using manual records and regular Excel spreadsheet updates. However, a recent financial audit revealed a 5.6% discrepancy between the fixed asset lifecycle data on the books and the actual asset inventory, affecting over 1,960 fixed assets. Among these discrepancies were:

[0202] Maintenance records for 876 devices were missing or updated late, resulting in some equipment being maintained too early or too late;

[0203] The status information of 612 assets was not updated in a timely manner. Some of these assets had been transferred to other departments but were still recorded in the database under their original departments.

[0204] The inventory data for 472 pieces of equipment contained discrepancies, mainly due to manual entry errors, missing assets or unregistered changes.

[0205] In order to improve asset management efficiency, the company decided to adopt the fixed asset full life cycle intelligent dynamic management system of the present invention and carried out a pilot application for two months from March 10 to May 10.

[0206] On March 10, Company A officially launched the system to conduct data governance on the company's existing fixed asset full life cycle database. The system first connected multiple data sources including the enterprise resource planning system, manufacturing execution system, financial management system, and asset management database, and standardized the asset full life cycle data.

[0207] During the data cleaning process, the system used the information entropy enhancement algorithm to evaluate the data value and found that:

[0208] 15.2% of the data is redundant or duplicate, mainly from manual entry and historical data archiving. The system automatically filters and deletes invalid data;

[0209] 9.7% of data items have missing values, including asset status, maintenance records, and inventory history. The system uses entropy gain weighted interpolation to fill in the missing data;

[0210] There were 312 abnormal data items, including 168 abnormal asset status items and 144 financial record errors. The system automatically triggered correction suggestions and updated the database.

[0211] After data governance was completed, the integrity of the database increased from the original 85% to 98%, laying the foundation for subsequent asset management optimization.

[0212] In the process of optimizing asset management, the system intelligently optimizes the maintenance plan of fixed assets based on the mutant gray wolf optimization algorithm. During this process, the system monitors the workload, fault log, and maintenance records of CNC machine tools in real time, and dynamically evaluates the health status scores of different equipment.

[0213] On March 15, the system detected that the real-time load of machine tool number CNC-20240315-001 reached 97% (exceeding the equipment health threshold of 90%). The system automatically analyzed its historical data and found that the maintenance cycle of this machine tool is usually 180 days, but in the past 120 days, its failure rate has increased by 12.5%. The system recommends maintenance in advance.

[0214] After receiving the maintenance reminder, the manager arranged for an equipment engineer to inspect the equipment. They discovered that the spindle bearing was already slightly worn and would suffer serious damage within the next two weeks if it continued to operate. Ultimately, the equipment was shut down for three hours on March 16 for preventive maintenance, avoiding potential damage risks. Subsequent statistics showed that after using this system, the sudden failure rate of CNC machine tools decreased by 17.8% and maintenance costs were optimized by 12.7%.

[0215] Under the previous manual inventory model, the company needed 12 employees and took 15 days to complete an inventory, and there was an average discrepancy rate of 5.6% between accounts and actuals. To improve inventory efficiency, the company piloted a fixed asset mobile inventory module from April 1 to April 7.

[0216] On April 2, the system discovered a high-precision measuring instrument numbered STO-20240402-015 during a warehouse inventory. The storage location recorded in the database was Warehouse No. 1, but the actual scanning results showed that it was in Laboratory No. 3.

[0217] After further verification, it was found that the equipment was allocated to Laboratory No. 3 in January 2024, but the asset management system did not update the database in time, resulting in an abnormality in this inventory. The system automatically updated the storage location of the equipment to ensure data consistency.

[0218] On April 5th, the system detected eight servers in the office area as "missing" during inventory. Upon inspection, it was discovered that the servers had been relocated to a new data center due to an office relocation, but the financial system still recorded them at the original location, resulting in a discrepancy between the accounts and actuals. The system automatically generated an update suggestion, which the Finance Department approved and updated the database.

[0219] In the end, the inventory took only 3 days (80% shorter than traditional methods), and the matching rate between accounts and actuals increased to 99.2%.

[0220] On May 1, the company's finance department conducted quarterly fixed asset reconciliation, and the system used the automatic reconciliation module to match data.

[0221] The system first extracted data from the asset management database, financial system, and production system, and compared them, and found that a total of 612 assets had discrepancies between the accounts and the actual assets.

[0222] Of these, 412 items were due to the financial system not updating depreciation data, resulting in discrepancies between the book net asset value and the actual depreciation amount. The system automatically calculated the revised amount and submitted it for review;

[0223] 120 items were not updated due to transfers, so the system automatically corrected their affiliated department information;

[0224] 80 items were missing asset disposal information. After analyzing their usage records, the system recommended that the finance department confirm their scrapped status.

[0225] Ultimately, the reconciliation process took only 7 days (65% shorter than the traditional 20 days), and the reconciliation matching rate increased to 99.2%.

[0226] Comparison of system optimization effects:

[0227]

[0228]

[0229] Through the information entropy enhanced data governance, intelligent maintenance optimization, mobile inventory and automatic reconciliation functions of the present invention, Company A has significantly improved the intelligence level of fixed asset management, reduced maintenance costs, improved asset utilization efficiency, reduced discrepancies between accounts and actual assets, and significantly improved the operational management efficiency and accuracy of the company's fixed assets.

[0230] The present invention introduces an information entropy enhanced data preprocessing algorithm in the processing of fixed asset full life cycle data. By calculating the information entropy contribution of fixed asset full life cycle data, high-value data is screened out, and a dynamic information entropy threshold is set to optimize data storage and processing. By combining information entropy calculation and gradient detection, low-value data is effectively removed, abnormal data is identified and intelligent correction is performed, ensuring that the data input into the management system has high information content and accuracy, reducing redundant data storage occupancy, improving data processing efficiency, and reducing errors in fixed asset status assessment, making the system more accurate and reliable during data collection and processing.

[0231] The present invention adopts the mutated grey wolf optimization algorithm and makes innovative improvements to the asset maintenance and scheduling optimization problems in the dynamic management of fixed assets. By introducing a dual mutation mechanism (adaptive step size mutation and dynamic leadership mutation), the optimization process is endowed with high search capability and global convergence. The adaptive step size mutation enhances the exploration capability in the early stage of optimization and improves the search accuracy of the global optimal solution. The dynamic leadership mutation can dynamically adjust the optimization target according to the changes in the fixed asset status and historical data, making the management strategy more flexible and accurate.

[0232] Aiming at the common problem of discrepancies between accounts and actual assets in the process of fixed asset management, the present invention proposes an automatic reconciliation mechanism based on the variant grey wolf optimization algorithm. Through multi-source data matching optimization, the management data, mobile inventory data and financial accounting data in the fixed asset history database are intelligently matched, and a correction vector is constructed based on data matching error analysis to perform intelligent correction on abnormal data. It can adaptively optimize the differences in data throughout the entire life cycle of fixed assets, making the reconciliation process more accurate and efficient.

[0233] 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 intelligent dynamic management system for the entire life cycle of fixed assets based on data governance, characterized by: Includes the following modules: The data collection module obtains data on the entire life cycle of fixed assets and stores it in the fixed asset history database; The information entropy enhancement module pre-processes the fixed asset life cycle data, calculates the information entropy contribution of the fixed asset life cycle data, filters high-value data based on the set dynamic information entropy threshold, and generates fixed asset management data; The dynamic management module uses the variant grey wolf optimization algorithm to establish a dynamic management model for fixed assets based on fixed asset management data; The real-time monitoring module monitors the operating status, maintenance plan, and resource allocation of fixed assets in real time based on the dynamic management model of fixed assets, and dynamically adjusts the maintenance plan and resource allocation of fixed assets; The mobile inventory module collects mobile inventory data and compares it with the data in the updated fixed asset history database, and intelligently corrects mobile inventory data with errors exceeding the threshold; The automatic reconciliation module uses the variant grey wolf optimization algorithm to match and optimize data based on the data in the fixed asset history database, and stores the optimization results in the fixed asset history database; The decision support module is based on the optimized fixed asset management data in the fixed asset history database, and provides seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory and automatic reconciliation processes.

2. A method for intelligent dynamic management of the entire life cycle of fixed assets based on data governance, applied to the intelligent dynamic management system for the entire life cycle of fixed assets based on data governance according to claim 1, characterized in that: The steps include: S1. Obtain fixed asset life cycle data, store it in a fixed asset history database, and use information entropy enhancement technology to preprocess the fixed asset life cycle data stored in the fixed asset history database to form fixed asset management data; S2. Based on fixed asset management data, a dynamic fixed asset management model is established using the variant grey wolf optimization algorithm. This model is based on an intelligent optimization scheduling mechanism with fixed asset operating status, maintenance cycle, and usage frequency as input parameters. S3. Apply the fixed asset dynamic management model to the fixed asset history database to monitor and dynamically evaluate the real-time status of fixed assets. Based on the fixed asset operating status and the output of the fixed asset dynamic management model, the fixed asset maintenance plan and resource allocation are automatically adjusted, and the adjustment results are fed back to the fixed asset history database in real time. S4. Mobile inventory of fixed assets, collect current mobile inventory data, and compare the collected mobile inventory data with the updated data in the fixed asset history database to achieve real-time verification; S5. Use the variant grey wolf optimization algorithm to automatically reconcile fixed asset management data, mobile inventory data, and related financial accounting data in the fixed asset history database. Through multi-source data matching, identify discrepancies between fixed asset status and accounting information, and intelligently correct discrepant fixed asset lifecycle data. Ultimately, feedback the correction results to update the fixed asset history database. S6. Conduct a comprehensive analysis of the data in the fixed asset history database updated in steps S3, S4, and S5, and output intelligent decision-making support information for the full life cycle management of fixed assets, thereby achieving seamless connection and collaborative optimization of the dynamic management of fixed assets, mobile inventory, and automatic reconciliation processes.

3. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 2 is characterized in that: Said S1 comprises the following steps: Obtain a data set covering the entire life cycle of fixed assets. Each item in the data set includes several dimensions, corresponding to the basic attributes, status changes, usage cycles, maintenance plans, historical inventory information, and financial accounting information of the fixed assets. After standardization, each data item forms a structured data set. For data items with missing values, a weighted average method based on information entropy contribution is used to complete the missing values. The completed value is a combination of the weights of multiple similar data items in the information entropy dimension. Based on the structured data set, a probability model is performed on each data item to evaluate its frequency of occurrence in the entire fixed asset life cycle data set. Then, its joint information entropy value is calculated in a probability-weighted form. The joint information entropy reflects the total effective information contained in the entire data set under the multi-dimensional structure. On the basis of obtaining the joint information entropy, the information entropy contribution of each data item is calculated. The information entropy contribution is used to quantify the independence and information strength of the data item to the full life cycle information system, that is, the importance of the information to the asset management system. Set a dynamic information entropy threshold. The dynamic information entropy threshold is calculated based on the mean of all information entropy contributions, and an adjustment term consisting of the standard deviation multiplied by the adjustment factor is added. The dynamic information entropy threshold is used as the judgment boundary to distinguish between high information value and low information value data items; Data items whose information entropy contribution is lower than the dynamic information entropy threshold are classified as low-value data and uniformly included in the noise data set; data items whose information entropy contribution is higher than or equal to the threshold are classified into the high-value data set, so that the fixed asset management system only retains data with significant information value for subsequent modeling and analysis; For each data item in the high-value data set, the rate of change of its information entropy in the historical sequence, namely the information entropy gradient, is calculated to determine the mutation characteristics of the data item in the time series. When the information entropy gradient of a data item is greater than the set mutation detection threshold, the system marks the data item as a mutation anomaly and performs correction processing based on its historical neighboring data; The data set after excluding mutation anomalies is used as the final fixed asset management data set and stored in the fixed asset history database.

4. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 3 is characterized in that: The S2 comprises the following steps: S21. Extract fixed asset operating status, maintenance cycle, and usage frequency data based on fixed asset management data, and set fixed asset dynamic management optimization goals, including minimizing asset maintenance costs, maximizing asset utilization efficiency, and optimizing resource consumption in asset scheduling; S22. Construct a fixed asset dynamic management model based on the Mutated Gray Wolf Optimization Algorithm. The Mutated Gray Wolf Optimization Algorithm introduces a mutation strategy based on the standard Gray Wolf Optimization Algorithm, which enables the fixed asset management optimization process to have high search capability and global convergence. The Mutated Gray Wolf Optimization Algorithm uses a dual mutation mechanism, including adaptive step size mutation and dynamic leadership mutation. The adaptive step size variation adaptively adjusts the search step size of the gray wolf individual at each iterative update, so that the gray wolf individual has a high exploration ability in the early stage and focuses on the search in the later stage; The dynamic leadership variation is introduced into the optimization of the fixed asset management strategy of the leader individual in the gray wolf optimization algorithm, so that the leader individual can dynamically adjust according to the fitness; S23. Define the fitness function F(X) of the fixed asset management strategy based on the historical maintenance records and asset operation status data in the fixed asset history database; After calculating the fitness function of the fixed asset management strategy, the population position is adjusted according to the fitness function ranking, and the population is dynamically adjusted according to the dual mutation mechanism of the mutant gray wolf optimization algorithm, so that the optimization process can adapt to different asset management scenarios; S24. After the optimization iteration converges, generate the fixed asset dynamic management model A opt .

5. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 4 is characterized in that: The S3 includes the following steps: S31. Based on the dynamic management model of fixed assets A opt The fixed asset real-time status monitoring system is established based on the data from the fixed asset history database. The fixed asset real-time status monitoring system monitors the operating status, maintenance plan and resource allocation of fixed assets in real time. The monitoring data includes the current load status of fixed assets. asset , Current availability of fixed assets U asset and the fixed asset status change rate V change , define the real-time status data S of fixed assets monitor ; S32. Dynamically evaluate the real-time status data of fixed assets monitored in step S31 and calculate the fixed asset health score H asset ; S33. Based on the fixed asset health status score H asset , adopt dynamic maintenance adjustment mechanism to optimize the maintenance time T of fixed assets maint and maintain resource allocation R maint ; S34. After adjusting the maintenance plan in step S33, further optimize the allocation of fixed asset resources and calculate the resource adjustment vector based on the variant gray wolf optimization algorithm : Among them, λ4 and λ5 are resource adjustment factors, A current For the current fixed asset management strategy; S35. Store the maintenance plan and resource allocation adjustment data optimized in steps S33 and S34 into the fixed asset history database.

6. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 5 is characterized in that: The S4 comprises the following steps: S41. Use mobile terminal equipment to conduct on-site inventory of fixed assets and collect current mobile inventory data D scan , including the location of fixed assets, fixed asset numbers, current status of fixed assets and usage of fixed assets; S42. The mobile inventory data collected in step S41 is updated with the fixed asset status data stored in the fixed asset history database D after S3. record Compare and calculate the mobile inventory data deviation D diff ; S43. Calculate the error correction factor δ for fixed asset status data with mobile inventory data deviation asset ; S44. For mobile inventory data whose error correction factor exceeds the threshold, a dynamic weight adjustment mechanism is used to correct the mobile inventory data; S45. Store the mobile inventory data adjusted in step S44 into the fixed asset history database and update the fixed asset management system.

7. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 6 is characterized in that: The S5 comprises the following steps: S51. Based on the fixed asset history database, extract fixed asset management data, mobile inventory data, and related financial accounting data to construct a fixed asset automatic reconciliation data set; S52. Use the variant grey wolf optimization algorithm to perform multi-source matching of fixed asset lifecycle data, calculate the matching error between fixed asset management data, mobile inventory data, and financial accounting data, and construct a fitness function based on the data matching error. S53. Based on the data matching error, a correction vector is generated to adjust the data differences between fixed asset management data, mobile inventory data, and financial accounting data. The correction vector is dynamically optimized based on the historical data trends to optimize the matching degree between fixed asset management data, mobile inventory data, and financial accounting data. S54. Store the fixed asset life cycle data optimized in step S53 into the fixed asset history database and update the fixed asset management system.

8. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 6 is characterized in that: If the mobile inventory data deviation D diff = 0, the mobile inventory data is judged to be normal. If the mobile inventory data deviation D diff ≠0, the mobile inventory data status is determined to be abnormal.

9. The method for intelligent dynamic management of fixed assets throughout their life cycle based on data governance according to claim 6 is characterized in that: If the error correction factor δ of the mobile inventory data state is asset >δ th , then the state deviation of the mobile inventory data exceeds the threshold δ th , triggering the exception flag.

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