A data-governed, intelligent, dynamic management system for the entire lifecycle of fixed assets.

Abstract

This invention discloses a data governance-based intelligent dynamic management system for the entire lifecycle of fixed assets. The system includes: a data acquisition module for acquiring data throughout the entire lifecycle of fixed assets; an information entropy enhancement module for generating fixed asset management data; a dynamic management module for establishing a dynamic management model for fixed assets; a real-time monitoring module for dynamically adjusting maintenance plans and resource allocation for fixed assets; a mobile inventory module for intelligently correcting mobile inventory data with errors exceeding a threshold; an automatic reconciliation module for storing optimization results in a fixed asset history database; and a decision support module for seamless integration and collaborative optimization of the dynamic management, mobile inventory, and automatic reconciliation processes for fixed assets. This invention enables seamless integration and collaborative optimization of the dynamic management, mobile inventory, and automatic reconciliation processes for fixed assets.

Description

Technical Field

[0001] This invention relates to the field of asset management technology, and in particular to a data-governed, intelligent, dynamic management system for the entire lifecycle of fixed assets. Background Technology

[0002] In modern enterprise management, fixed asset management is an important component of corporate financial management, operational efficiency improvement, and asset security. The life cycle of fixed assets includes multiple stages such as purchase, use, maintenance, inventory, depreciation, and disposal, involving multiple departments and complex management processes. As the scale of corporate assets continues to expand and management needs become increasingly refined, traditional fixed asset management models are no longer able to meet the requirements of efficient, accurate, and intelligent management.

[0003] Currently, fixed asset management mainly relies on traditional methods such as manual recording, static database management, and periodic manual inventory checks. These traditional methods have the following technical drawbacks: First, data collection and storage are fragmented, 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, making it difficult to adapt to dynamic changes in asset status, resulting in delayed maintenance plans and low asset utilization efficiency. Third, fixed asset inventory checks usually rely on manual inspections, and data reconciliation depends on financial accounting and manual review, which is prone to discrepancies between records and actual assets due to human error, affecting asset security and the accuracy of financial management.

[0004] In summary, 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 achieve intelligent and dynamic management of fixed assets throughout their entire lifecycle to improve the automation level of asset management, optimize maintenance and scheduling strategies, and enhance asset operation efficiency and financial compliance. Summary of the Invention

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

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

[0007] The data acquisition module acquires data on the entire lifecycle of fixed assets and stores it in the fixed asset history database;

[0008] The information entropy enhancement module preprocesses the fixed asset lifecycle data and calculates the information entropy contribution of the fixed asset lifecycle data. Based on the set dynamic information entropy threshold, it filters high-value data and generates fixed asset management data.

[0009] The dynamic management module uses the mutated gray 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 fixed asset dynamic management model, 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 Mutant 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, based on optimized fixed asset management data from the fixed asset history database, seamlessly integrates and collaboratively optimizes the dynamic management, mobile inventory, and automatic reconciliation processes of fixed assets.

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

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

[0016] S2. Based on fixed asset management data, a dynamic management model for fixed assets is established using the mutant gray wolf optimization algorithm. A dynamic management model for fixed assets with an intelligent optimization scheduling mechanism is constructed, taking the 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 operation status of fixed assets and the output of the fixed asset dynamic management model, automatically adjust the maintenance plan and resource allocation of fixed assets, and update the adjustment results to the fixed asset history database in real time.

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

[0019] S5. The mutated gray wolf optimization algorithm is used to automatically reconcile the fixed asset management data, mobile inventory data and related financial accounting data in the fixed asset history database. The algorithm identifies the differences between the fixed asset status and accounting information through multi-source data matching, and intelligently corrects the fixed asset life cycle data with differences. Finally, the correction results are fed back to update the fixed asset history database.

[0020] S6. Perform comprehensive analysis on the data in the updated fixed asset history database in steps S3, S4 and S5, and output intelligent decision support information for the whole life cycle management of fixed assets, so as to achieve seamless connection and collaborative optimization of the dynamic management, mobile inventory and automatic reconciliation process of fixed assets.

[0021] Optionally, S1 includes the following steps:

[0022] A complete lifecycle dataset of fixed assets is obtained. Each data item in the dataset includes several dimensions, corresponding to the basic attributes, status changes, usage period, maintenance plan, historical inventory information, and financial accounting information of the fixed asset. After standardization and transformation, each data item forms a structured dataset. For data items with missing values, a weighted average method based on information entropy contribution is used to complete the dataset. The completed value is a combination of the weights of multiple similar data items on the information entropy dimension.

[0023] Based on the structured dataset, a probabilistic model is performed on each data item to assess its frequency of occurrence in the entire fixed asset lifecycle dataset. Then, its joint information entropy value is calculated in a probabilistic weighted form. The joint information entropy reflects the total amount of effective information contained in the entire dataset under a multidimensional structure.

[0024] Based on the obtained 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 intensity of the data item to the whole life cycle information system, that is, the importance of the information to the asset management system.

[0025] A dynamic information entropy threshold is set. The dynamic information entropy threshold is calculated by adding an adjustment term consisting of the standard deviation multiplied by a regulation factor to the mean of all information entropy contributions. The dynamic information entropy threshold is used as a judgment boundary to distinguish between data items with high information values ​​and low information values.

[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 noisy dataset; data items whose information entropy contribution is higher than or equal to the threshold are classified as high-value datasets, so that the fixed asset management system retains only data with significant information value for subsequent modeling and analysis.

[0027] For each data item in the high-value dataset, the rate of change of its information entropy in the historical sequence is calculated, i.e., the information entropy gradient, which is used 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 in combination with its historical neighboring data.

[0028] The dataset after excluding mutation outliers 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. Based on fixed asset management data, extract data on fixed asset operating status, maintenance cycle and usage frequency, and set dynamic management optimization objectives for fixed assets, including minimizing asset maintenance costs, maximizing asset utilization efficiency and optimizing resource consumption for 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 on the basis of the standard gray wolf optimization algorithm, so that the fixed asset management optimization process has high search capability and global convergence. The mutated gray wolf optimization algorithm adopts a dual mutation mechanism, including adaptive step size mutation and dynamic leadership mutation.

[0032] The adaptive step-size mutation adaptively adjusts the search step size of the gray wolf individual during each iteration update, enabling the gray wolf individual to have high exploration ability in the early stage and focus on searching in the later stage. The adaptive step-size mutation formula is as follows:

[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 Let A be the search step size for the t-th iteration. a A β A δLet A represent the optimal, suboptimal, and candidate solutions of the fixed asset management strategy, respectively. γ, μ, and v are step size adjustment factors, dynamically adjusted based on the number of iterations. λ·randn(0,1) is the Gaussian random variation term. current This is the current set of fixed asset management strategies;

[0035] The dynamic leadership mutation, in the gray wolf optimization algorithm, optimizes the fixed asset management strategy of the leader individual. α The introduction of dynamic leadership variation allows individual leaders to dynamically adjust based on fitness:

[0036]

[0037] in, For the next generation of leaders, P(A) is the corresponding fitness function, which calculates the comprehensive trade-off between asset maintenance costs, utilization efficiency, and scheduling costs.

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

[0039]

[0040] Among them, C maint，i For the maintenance cost of the i-th fixed asset, E util，i For the utilization efficiency of the i-th fixed asset, R alloc，i Let λ1, λ2, and λ3 be the resource scheduling consumption of the i-th fixed asset, and let λ1, λ2, and λ3 be 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, a dynamic management model A for fixed assets is generated. opt :

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

[0044] Among them, A opt This is a dynamic management model for fixed assets, where ω1 and ω2 are scheduling and adjustment factors.

[0045] Optionally, S3 includes the following steps:

[0046] S31. Based on the dynamic management model of fixed assets A opt A real-time status monitoring system for fixed assets is established using data from the fixed asset history database. This system monitors the operational status, maintenance plans, and resource allocation of fixed assets in real time. The monitoring data includes the current load status L of the fixed assets. asset Current availability of fixed assets U asset and the rate of change of fixed assets 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 U represents the current load status of fixed assets. asset Represents the current availability of fixed assets:

[0049]

[0050] Among them, T avaliable T represents the current available time of a fixed asset. total V represents the total monitoring period. change Represents the rate of change of the condition of fixed assets:

[0051]

[0052] Among them, S current and S previous These represent the current and previous fixed asset statuses, respectively, with Δt being 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 status score H. asset :

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

[0055] Among them, H asset The health status of fixed assets is scored, with higher values ​​indicating more unstable asset status. ω4, ω5, and ω6 are dynamic weighting coefficients.

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

[0057]

[0058] in, This refers to the adjusted maintenance time for fixed assets. For the originally scheduled maintenance time, H th For fixed asset maintenance trigger threshold, if H asset >H th If so, maintenance will be triggered immediately;

[0059] For maintaining resource allocation R maint Optimize based on the current state of fixed assets:

[0060]

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

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

[0063]

[0064] Where λ4 and λ5 are resource adjustment factors, A current This is the current fixed asset management strategy;

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

[0066] Optionally, S4 includes the following steps:

[0067] S41. Conduct on-site inventory of fixed assets using mobile terminal devices and collect current mobile inventory data. scan This includes the location of fixed assets, fixed asset number, current status of fixed assets, and usage of fixed assets;

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

[0069] D diff =Dscan -D record

[0070] Among them, D diff For deviations in moving inventory count data;

[0071] If the moving inventory data deviation is D diff =0, then the status of the moving inventory data is considered normal;

[0072] If the moving inventory data deviation is D diff If the value is not equal to 0, the movement inventory data status is determined to be abnormal.

[0073] S43. For fixed asset status data with deviations in moving inventory counts, calculate the error correction factor δ. asset :

[0074]

[0075] If the error correction factor δ for the status of the moving inventory data asset >δ th If the status deviation of the moving inventory data exceeds the threshold δ, then... th This triggers an exception flag;

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

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

[0078] Among them, D corrected For the adjusted moving inventory data, ω7 and ω8 are data weighting coefficients:

[0079]

[0080] Among them, a smaller ω7 weight indicates a larger deviation in the fixed asset status data, requiring a greater degree of correction. δ 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, 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 an automatic fixed asset reconciliation dataset;

[0084] S52. The Mutant Gray Wolf Optimization Algorithm is used 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, generate a correction vector to adjust the data differences between fixed asset management data, mobile inventory data and financial accounting data, and dynamically optimize the correction vector in combination with the historical trend of data change, so as to optimize the matching degree between fixed asset management data, mobile inventory data and financial accounting data.

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

[0087] The beneficial effects of this invention are:

[0088] (1) This 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 data of the entire life cycle of fixed assets, 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 intelligently corrected, ensuring that the data input into the management system has high information content and accuracy, reducing redundant data storage and improving data processing efficiency. At the same time, it reduces the error in the fixed asset status assessment, making the system more accurate and reliable in the data collection and processing process.

[0089] (2) This invention adopts the mutated gray wolf optimization algorithm, which makes innovative improvements to the asset maintenance and scheduling optimization problem 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. Adaptive step size mutation enhances the exploration capability in the early stage of optimization and improves the search accuracy of the global optimal solution, while dynamic leadership mutation can dynamically adjust the optimization target according to the changes in the status of fixed assets and historical data, making the management strategy more flexible and accurate.

[0090] (3) In view of the common problem of discrepancy between accounts and actual assets in the process of fixed asset management, this invention proposes an automatic reconciliation mechanism based on the mutant gray 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. Based on the data matching error analysis, a correction vector is constructed to intelligently correct 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. Attached Figure Description

[0091] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0092] Figure 1 This is a flowchart of the intelligent dynamic management system for the entire lifecycle of fixed assets based on data governance proposed in this invention. Detailed Implementation

[0093] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0094] refer to Figure 1 The data-governed, data-driven, intelligent, dynamic management system for the entire lifecycle of fixed assets includes the following modules:

[0095] The data acquisition module acquires data on the entire lifecycle of fixed assets and stores it in the fixed asset history database;

[0096] The information entropy enhancement module preprocesses the fixed asset lifecycle data and calculates the information entropy contribution of the fixed asset lifecycle data. Based on the set dynamic information entropy threshold, it filters high-value data and generates fixed asset management data.

[0097] The dynamic management module uses the mutated gray 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 fixed asset dynamic management model, 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 Mutant 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, based on optimized fixed asset management data from the fixed asset history database, seamlessly integrates and collaboratively optimizes the dynamic management, mobile inventory, and automatic reconciliation processes of fixed assets.

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

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

[0104] S2. Based on fixed asset management data, a dynamic management model for fixed assets is established using the mutant gray wolf optimization algorithm. A dynamic management model for fixed assets with an intelligent optimization scheduling mechanism is constructed, taking the 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 operation status of fixed assets and the output of the fixed asset dynamic management model, automatically adjust the maintenance plan and resource allocation of fixed assets, and update the adjustment results to the fixed asset history database in real time.

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

[0107] S5. The mutated gray wolf optimization algorithm is used to automatically reconcile the fixed asset management data, mobile inventory data and related financial accounting data in the fixed asset history database. The algorithm identifies the differences between the fixed asset status and accounting information through multi-source data matching, and intelligently corrects the fixed asset life cycle data with differences. Finally, the correction results are fed back to update the fixed asset history database.

[0108] S6. Perform comprehensive analysis on the data in the updated fixed asset history database in steps S3, S4 and S5, and output intelligent decision support information for the whole life cycle management of fixed assets, so as to achieve seamless connection and collaborative optimization of the dynamic management, mobile inventory and automatic reconciliation process of fixed assets.

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

[0110] A complete lifecycle dataset of fixed assets is obtained. Each data item in the dataset includes several dimensions, corresponding to the basic attributes, status changes, usage period, maintenance plan, historical inventory information, and financial accounting information of the fixed asset. After standardization and transformation, each data item forms a structured dataset. For data items with missing values, a weighted average method based on information entropy contribution is used to complete the dataset. The completed value is a combination of the weights of multiple similar data items on the information entropy dimension.

[0111] Based on the structured dataset, a probabilistic model is performed on each data item to assess its frequency of occurrence in the entire fixed asset lifecycle dataset. Then, its joint information entropy value is calculated in a probabilistic weighted form. The joint information entropy reflects the total amount of effective information contained in the entire dataset under a multidimensional structure.

[0112] Based on the obtained 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 intensity of the data item to the whole life cycle information system, that is, the importance of the information to the asset management system.

[0113] A dynamic information entropy threshold is set. The dynamic information entropy threshold is calculated by adding an adjustment term consisting of the standard deviation multiplied by a regulation factor to the mean of all information entropy contributions. The dynamic information entropy threshold is used as a judgment boundary to distinguish between data items with high information values ​​and low information values.

[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 noisy dataset; data items whose information entropy contribution is higher than or equal to the threshold are classified as high-value datasets, so that the fixed asset management system retains only data with significant information value for subsequent modeling and analysis.

[0115] For each data item in the high-value dataset, the rate of change of its information entropy in the historical sequence is calculated, i.e., the information entropy gradient, which is used 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 in combination with its historical neighboring data.

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

[0117] This implementation method preprocesses fixed asset lifecycle data using information entropy enhancement technology, effectively improving data quality and reliability. Compared to traditional fixed asset lifecycle data management methods, this method uses information entropy theory to perform noise removal, outlier detection, data completion, and normalization, ensuring data integrity and consistency. By jointly calculating the information contribution of different data items using information entropy 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. Furthermore, it identifies abrupt and abnormal data points through information entropy gradient detection and uses entropy gain weighted interpolation to complete missing values, ensuring high quality and high availability of fixed asset lifecycle data throughout its entire lifecycle. This information entropy-enhanced data processing method makes the fixed asset history database more reliable, providing a solid data foundation for subsequent optimization scheduling, dynamic management, and decision support, thereby improving the intelligence and efficiency of asset management.

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

[0119] S21. Based on fixed asset management data, extract data on fixed asset operating status, maintenance cycle and usage frequency, and set dynamic management optimization objectives for fixed assets, including minimizing asset maintenance costs, maximizing asset utilization efficiency and optimizing resource consumption for 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 on the basis of the standard gray wolf optimization algorithm, so that the fixed asset management optimization process has high search capability and global convergence. The mutated gray wolf optimization algorithm adopts a dual mutation mechanism, including adaptive step size mutation and dynamic leadership mutation.

[0121] The adaptive step size mutation adaptively adjusts the search step size of individual gray wolves during each iteration, enabling them to have high exploration capabilities in the early stages and focus on searching in the later stages. The formula for adaptive step size mutation 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 Let A be the search step size for the t-th iteration. α A β A δLet A represent the optimal, suboptimal, and candidate solutions of the fixed asset management strategy, respectively. γ, μ, and ν are step size adjustment factors, dynamically adjusted based on the number of iterations. λ·randn(0,1) is the Gaussian random variation term. current This is the current set of fixed asset management strategies;

[0124] Adaptive step size mutation is used to control the movement of individual gray wolves in the search space. Traditional gray wolf optimization algorithms update step size based solely on the positions of the three leaders. This method, however, introduces a dynamic adjustment factor and a Gaussian random mutation term into the calculation of the search step size, ensuring that:

[0125] Stronger early-stage exploration capabilities: In the early stages of optimization, the larger step size allows for a wider search range, enabling rapid coverage of the entire solution space and the discovery of potential global optimal solutions.

[0126] More refined search in later stages: 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] To avoid the local optimum trap, the Gaussian random variation introduces a certain degree of randomness, ensuring that the fixed asset management and scheduling scheme is dynamically adjusted among multiple possible solutions, thereby improving the stability of optimization.

[0128] Dynamic leadership mutation, in the gray wolf optimization algorithm, the optimal fixed asset management strategy of the leader individual A α The introduction of dynamic leadership variation allows individual leaders to dynamically adjust based on fitness:

[0129]

[0130] in, For the next generation of leaders, P(A) is the corresponding fitness function, which calculates the comprehensive trade-off between asset maintenance costs, utilization efficiency, and scheduling costs.

[0131] A dynamic leadership mutation mechanism is introduced, which allows individual leaders to be dynamically replaced with suboptimal and candidate solutions for fixed asset management strategies when their fitness is poor. This prevents the management plan from becoming rigid due to local optima during the search process and ensures that the best maintenance and scheduling strategies can be obtained in different asset operating environments.

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

[0133]

[0134] Among them, C maint，i For the maintenance cost of the i-th fixed asset, Eutil，i For the utilization efficiency of the i-th fixed asset, R alloc，i Let λ1, λ2, and λ3 be the resource scheduling consumption of the i-th fixed asset, and let λ1, λ2, and λ3 be 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, a dynamic management model A for fixed assets is generated. opt :

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

[0138] Among them, A opt This is a dynamic management model for fixed assets, where ω1 and ω2 are scheduling and adjustment factors.

[0139] This implementation establishes a dynamic fixed asset management model using the mutated gray wolf optimization algorithm. By introducing an adaptive step-size mutation mechanism on top of the standard gray wolf optimization algorithm, the optimization process can dynamically adjust the search range, avoiding getting trapped in local optima and improving the adaptability of the fixed asset management strategy. Furthermore, the application of a dynamic leadership mutation mechanism allows the optimal solution to be dynamically adjusted based on historical optimization results during the search process, thereby improving the stability of the optimization results. In the fixed asset management process, multiple factors such as the fixed asset's operating status, maintenance cycle, and usage frequency can be comprehensively considered to achieve intelligent scheduling optimization. The effectiveness of the optimization scheme is calculated through a fitness function, enabling the system to dynamically generate the optimal fixed asset management strategy. The dynamic fixed asset management model can adaptively adjust throughout the entire lifecycle of fixed assets, improving the intelligence level of asset management, reducing maintenance costs, and enhancing asset utilization efficiency and the rationality of resource allocation.

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

[0141] S31. Based on the dynamic management model of fixed assets A opt A real-time status monitoring system for fixed assets is established using data from the fixed asset history database. This system monitors the operational status, maintenance plans, and resource allocation of fixed assets in real time. The monitoring data includes the current load status L of the fixed assets. asset Current availability of fixed assets U assetand the rate of change of fixed assets 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 U represents the current load status of fixed assets. asset Represents the current availability of fixed assets:

[0144]

[0145] Among them, T available T represents the current available time of a fixed asset. total V represents the total monitoring period. change Represents the rate of change of the condition of fixed assets:

[0146]

[0147] Among them, S current and S previous These represent the current and previous fixed asset statuses, respectively, with Δt being 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 status score H. asset :

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

[0150] Among them, H asset The health status of fixed assets is scored, with higher values ​​indicating more unstable asset status. ω4, ω5, and ω6 are dynamic weighting coefficients.

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

[0152]

[0153] in, This refers to the adjusted maintenance time for fixed assets. For the originally scheduled maintenance time, Hth For fixed asset maintenance trigger threshold, if H asset >H th If so, maintenance will be triggered immediately;

[0154] For maintaining resource allocation R maint Optimize based on the current state of fixed assets:

[0155]

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

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

[0158]

[0159] Where λ4 and λ5 are resource adjustment factors, A current This is the current fixed asset management strategy;

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

[0161] The real-time status monitoring and dynamic evaluation method for fixed assets proposed in this embodiment transforms fixed asset management from a static model to a dynamic real-time optimization model. Based on a fixed asset history database and a dynamic fixed asset management model, it continuously monitors the asset's operational status and dynamically adjusts maintenance plans according to the calculated health status score. By calculating the fixed asset's operating load, availability, and status change rate, it can quickly detect abnormal changes in assets and optimize maintenance plans and resource allocation based on the health status score. This dynamic evaluation method not only improves the accuracy of fixed asset management but also effectively reduces the risk of asset failure and increases the efficiency of fixed asset utilization. By optimizing the maintenance plan's trigger mechanism, it makes maintenance cycles more reasonable, thus avoiding over-maintenance or under-maintenance. It can intelligently allocate resources based on the dynamic changes in monitoring data, improving the intelligence level of fixed asset maintenance and management, increasing resource utilization, and reducing management costs.

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

[0163] S41. Conduct on-site inventory of fixed assets using mobile terminal devices and collect current mobile inventory data. scanThis includes the location of fixed assets, fixed asset number, current status of fixed assets, and usage of fixed assets;

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

[0165] D diff =D scan -D record

[0166] Among them, D diff For deviations in moving inventory count data;

[0167] If the moving inventory data deviation is D diff =0, then the status of the moving inventory data is considered normal;

[0168] If the moving inventory data deviation is D diff If the value is not equal to 0, the movement inventory data status is determined to be abnormal.

[0169] S43. For fixed asset status data with deviations in moving inventory counts, calculate the error correction factor δ. asset :

[0170]

[0171] If the error correction factor δ for the status of the moving inventory data asset >δ th If the status deviation of the moving inventory data exceeds the threshold δ, then... th This triggers an exception flag;

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

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

[0174] Among them, D corrected For the adjusted moving inventory data, ω7 and ω8 are data weighting coefficients:

[0175]

[0176] Among them, a smaller ω7 weight indicates a larger deviation in the fixed asset status data, requiring a greater degree of correction. δ 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 the data with the data in the fixed asset history database. It can effectively detect the status deviation of fixed assets, and intelligently analyze abnormal data by calculating error correction factors and using a dynamic weight adjustment mechanism to correct the 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 an automatic fixed asset reconciliation dataset;

[0181] S52. The Mutant Gray Wolf Optimization Algorithm is used 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, generate a correction vector to adjust the data differences between fixed asset management data, mobile inventory data and financial accounting data, and dynamically optimize the correction vector in combination with the historical trend of data change, so as to optimize the matching degree between fixed asset management data, mobile inventory data and financial accounting data.

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

[0184] The automatic fixed asset reconciliation method based on the mutated gray wolf optimization algorithm proposed in this embodiment can efficiently identify matching errors between fixed asset management data, physical 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, this method adopts a multi-source data fusion and adaptive weight optimization strategy, which improves the accuracy and automation of reconciliation. By calculating the data matching error vector and dynamically adjusting the weights according to 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 optimization search based on the mutated gray wolf optimization algorithm enables the data correction strategy to dynamically balance between global optimization and local fine-grained optimization, thereby improving the stability and efficiency of fixed asset reconciliation. Ultimately, this method ensures a high degree of consistency between financial accounting, physical inventory, and management records throughout the entire lifecycle of fixed assets, improves the intelligence level of fixed asset lifecycle management, and reduces asset management risks caused by data mismatch.

[0185] In this embodiment, S6 specifically includes:

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

[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 use of fixed assets;

[0189] Fixed asset maintenance quality rating: A comprehensive assessment of the implementation of the maintenance plan;

[0190] Fixed asset inventory consistency: Calculate the consistency between moving inventory data and database record data;

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

[0192] S63. Based on the intelligent evaluation indicators calculated in step S62, a multi-dimensional optimization analysis method is adopted to dynamically adjust the fixed asset management strategy. The impact of each indicator on fixed asset management is evaluated through information entropy enhancement technology, and optimization weights are set so that the system can 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 intelligent decision support information for fixed assets, including:

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

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

[0196] Fixed asset inventory strategy adjustment: Optimize inventory frequency and methods to reduce data errors;

[0197] Fixed asset reconciliation data optimization solution: Improve the automatic reconciliation weight and increase the matching degree between accounts and actual assets.

[0198] S65. Feedback the fixed asset optimization decision information generated in step S64 to the fixed asset history database, enabling the system to optimize the dynamic management, mobile inventory and automatic reconciliation strategies of fixed assets in real time based on the latest intelligent decision-making schemes. Ultimately, this achieves intelligent, automated and efficient management of the entire fixed asset management process, and improves the accuracy and intelligent decision-making level of fixed asset life cycle management.

[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 currently has more than 35,000 fixed assets, covering multiple categories such as production equipment, testing instruments, office facilities, and warehousing equipment. Among them are 850 CNC machine tools, 1,200 testing equipment and 6,500 office terminals. Due to the heavy production tasks and frequent use of equipment, the status, maintenance, inventory and reconciliation of fixed assets have become management problems.

[0201] Previously, the company managed its assets using manual record-keeping and regular Excel spreadsheet updates. However, a recent financial audit revealed a 5.6% discrepancy between the book data on the entire lifecycle of fixed assets and the actual asset list, affecting over 1,960 fixed assets, including:

[0202] The maintenance records for 876 pieces of equipment are missing or outdated, 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 devices had been transferred to other departments, but the database still recorded them in the original departments.

[0204] The inventory data for 472 pieces of equipment contained errors, mainly due to manual input errors, asset loss, 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 conducted 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 lifecycle database. The system first connects to multiple data sources, including the Enterprise Resource Planning System, Manufacturing Execution System, Financial Management System, and Asset Management Database, and then standardizes the asset lifecycle data.

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

[0208] 15.2% of the data was redundant or duplicate, mainly from manual entry and historical data archives. The system automatically filtered and deleted invalid data.

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

[0210] There were 312 abnormal data entries, including 168 entries with abnormal asset status and 144 entries with incorrect financial records. The system automatically triggered correction suggestions and updated the database.

[0211] After data governance was completed, the integrity of the database increased from 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 logs, and maintenance records of CNC machine tools in real time, and dynamically evaluates the health status of different equipment.

[0213] On March 15, the system detected that the real-time load of machine tool 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 its failure rate has increased by 12.5% ​​in the past 120 days. The system recommended maintenance in advance.

[0214] After receiving the maintenance reminder, the management personnel arranged for the equipment engineer to inspect the equipment and found that the spindle bearing had slight wear. If it continued to operate, it would be seriously damaged within the next two weeks. Ultimately, the equipment was shut down for 3 hours on March 16 for preventive maintenance, avoiding the potential risk of damage. 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] In the previous manual inventory model, it took 12 employees and 15 days to complete one inventory, with an average discrepancy of 5.6% between the recorded and actual inventory. To improve inventory efficiency, the company piloted a mobile inventory module for fixed assets from April 1 to April 7.

[0216] On April 2, the system discovered a high-precision measuring instrument with the serial number STO-20240402-015 during a warehouse inventory check. The database recorded its storage location as warehouse 1, but the actual scanning results showed that it was in laboratory 3.

[0217] Further verification revealed that the equipment was transferred to Laboratory No. 3 in January 2024, but the asset management system failed to update the database in a timely manner, causing an anomaly in this inventory count. The system automatically updated the storage location of the equipment to ensure data consistency.

[0218] On April 5th, the system detected that eight servers in the office area were marked as "missing" during inventory. Upon inspection, it was found that the servers had been moved to a new data center due to office location adjustments, but the financial system still recorded them in their original locations, causing a discrepancy between the records and the actual inventory. The system automatically generated an update suggestion, which was approved by the finance department before the database was updated.

[0219] Ultimately, the inventory check was completed in just 3 days (80% shorter than traditional methods), and the accuracy of the matching between accounts and physical inventory increased to 99.2%.

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

[0221] The system first extracts data from the asset management database, financial system, and production system, and compares them, finding a total of 612 assets with discrepancies between the records and the actual assets.

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

[0223] 120 items were not updated due to transfer, and the system automatically corrected their departmental information.

[0224] For 80 items with missing asset disposal information, the system analyzed their usage records and recommended that the finance department confirm their obsolescence status.

[0225] Ultimately, the reconciliation process took only 7 days (65% shorter than the traditional 20 days), and the reconciliation matching rate improved 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 this invention, Company A has significantly improved the level of intelligence in 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] This invention introduces an information entropy-enhanced data preprocessing algorithm into the processing of fixed asset lifecycle data. By calculating the information entropy contribution of fixed asset lifecycle data, high-value data is screened out, and a dynamic information entropy threshold is set to optimize data storage and processing. Through joint information entropy calculation and gradient detection, low-value data is effectively removed, abnormal data is identified and intelligently corrected, ensuring that the data input into the management system has high information content and accuracy, reducing redundant data storage, improving data processing efficiency, and reducing errors in fixed asset status assessment. This makes the system more accurate and reliable in the data collection and processing process.

[0231] This invention employs the Mutant Gray Wolf optimization algorithm, which innovatively improves the asset maintenance and scheduling optimization problem in the dynamic management of fixed assets. By introducing a dual mutation mechanism (adaptive step size mutation and dynamic leadership mutation), the optimization process possesses high search capability and global convergence. Adaptive step size mutation enhances the exploration capability in the early stage of optimization and improves the search accuracy of the global optimal solution, while dynamic leadership mutation can dynamically adjust the optimization objective according to changes in the status of fixed assets and historical data, making the management strategy more flexible and precise.

[0232] This invention addresses the common problem of discrepancies between accounts and actual assets in fixed asset management by proposing an automatic reconciliation mechanism based on a mutated gray wolf optimization algorithm. Through multi-source data matching optimization, it intelligently matches management data, mobile inventory data, and financial accounting data from the fixed asset history database. Based on data matching error analysis, it constructs a correction vector to intelligently correct abnormal data. This mechanism can adaptively optimize the differences in data throughout the entire lifecycle of fixed assets, making the reconciliation process more accurate and efficient.

[0233] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A data-governed, intelligent, dynamic management system for the entire lifecycle of fixed assets, characterized in that: Includes the following modules: The data acquisition module acquires data on the entire lifecycle of fixed assets and stores it in the fixed asset history database; The information entropy enhancement module preprocesses the fixed asset lifecycle data and calculates the information entropy contribution of the fixed asset lifecycle data. Based on the set dynamic information entropy threshold, it filters high-value data and generates fixed asset management data. The dynamic management module uses the mutated gray 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 fixed asset dynamic management model, 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 Mutant 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, based on optimized fixed asset management data in the fixed asset history database, seamlessly connects and collaboratively optimizes the dynamic management, mobile inventory, and automatic reconciliation processes of fixed assets. The management system includes the following steps: S1. Obtain the full life cycle data of fixed assets, store it in the fixed asset history database, and use information entropy enhancement technology to preprocess the full life cycle data of fixed assets stored in the fixed asset history database to form fixed asset management data; S2. Based on fixed asset management data, a dynamic management model for fixed assets is established using the mutant gray wolf optimization algorithm. A dynamic management model for fixed assets with an intelligent optimization scheduling mechanism is constructed, taking the 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 operation status of fixed assets and the output of the fixed asset dynamic management model, automatically adjust the maintenance plan and resource allocation of fixed assets, and update the adjustment results to the fixed asset history database in real time. S4. Perform mobile inventory of fixed assets, collect current mobile inventory data, and compare the collected mobile inventory data with the data in the updated fixed asset history database to achieve real-time verification; S5. The mutated gray wolf optimization algorithm is used to automatically reconcile the fixed asset management data, mobile inventory data and related financial accounting data in the fixed asset history database. The algorithm identifies the differences between the fixed asset status and accounting information through multi-source data matching, and intelligently corrects the fixed asset life cycle data with discrepancies. Finally, the correction results are fed back to update the fixed asset history database. S6. Perform comprehensive analysis on the data in the updated fixed asset history database in steps S3, S4 and S5, and output intelligent decision support information for the whole life cycle management of fixed assets, so as to achieve seamless connection and collaborative optimization of the dynamic management, mobile inventory and automatic reconciliation process of fixed assets. S3 includes the following steps: S31. Based on the dynamic management model of fixed assets A real-time status monitoring system for fixed assets is established using data from the fixed asset history database. This system monitors the operational status, maintenance plans, and resource allocation of fixed assets in real time. The monitoring data includes the current load status of the fixed assets. Current availability of fixed assets and the rate of change of fixed assets Define real-time status data of fixed assets ; ； in, Represents the current load status of fixed assets. Represents the current availability of fixed assets: ； in, For the current available time of fixed assets, The total monitoring period is [time]. Represents the rate of change of fixed assets: ； in, and These represent the current and previous status of fixed assets, respectively. For time intervals; S32. Dynamically evaluate the real-time status data of fixed assets monitored in step S31 and calculate the health status score of fixed assets. ; ； in, The health status of fixed assets is scored; a higher score indicates a more unstable asset status. These are dynamic weighting coefficients; S33. Based on the health status score of fixed assets A dynamic maintenance and adjustment mechanism is adopted to optimize the maintenance time of fixed assets. and maintenance resource allocation ; ； in, This refers to the adjusted maintenance time for fixed assets. This is the originally scheduled maintenance time. For fixed asset maintenance trigger threshold, if If so, maintenance will be triggered immediately; S34. After adjusting the maintenance plan in step S33, further optimize the allocation of fixed asset resources by calculating the resource adjustment vector based on the mutated gray wolf optimization algorithm. : ； in, As a resource adjustment factor, For optimal fixed asset management strategy, The second-best fixed asset management strategy is... This is the current fixed asset management strategy; S35. Store the optimized maintenance plan and resource allocation adjustment data from steps S33 and S34 into the fixed asset history database.

2. A data-governed, intelligent, dynamic management method for the entire lifecycle of fixed assets, applied to the data-governed, intelligent, dynamic management system for the entire lifecycle of fixed assets as described in claim 1, characterized in that... S1 includes the following steps: A complete lifecycle data set of fixed assets is obtained. Each data item in the complete lifecycle data set of fixed assets includes several dimensions of information, corresponding to the basic attributes, status changes, usage period, maintenance plan, historical inventory information and financial accounting information of the fixed assets. After standardization and transformation, 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 them. The completed value is composed of the weights of multiple similar data items on the information entropy dimension. Based on the structured dataset, a probabilistic model is performed on each data item to assess its frequency of occurrence in the entire fixed asset lifecycle dataset. Then, its joint information entropy value is calculated in a probabilistic weighted form. The joint information entropy reflects the total amount of effective information contained in the entire dataset under a multidimensional structure. Based on the obtained 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 intensity of the data item to the whole life cycle information system, that is, the importance of the information to the asset management system. A dynamic information entropy threshold is set. The dynamic information entropy threshold is calculated by adding an adjustment term consisting of the standard deviation multiplied by a regulation factor to the mean of all information entropy contributions. The dynamic information entropy threshold is used as a judgment boundary to distinguish between data items with high information values ​​and low information values. 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 noisy dataset; data items whose information entropy contribution is higher than or equal to the threshold are classified as high-value datasets, so that the fixed asset management system retains only data with significant information value for subsequent modeling and analysis. For each data item in the high-value dataset, the rate of change of its information entropy in the historical sequence is calculated, i.e., the information entropy gradient, which is used 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 in combination with its historical neighboring data. The dataset after excluding mutation outliers is used as the final fixed asset management data set and stored in the fixed asset history database.

3. The method for intelligent dynamic management of fixed assets throughout their entire lifecycle based on data governance as described in claim 2, characterized in that, S2 includes the following steps: S21. Based on fixed asset management data, extract data on fixed asset operating status, maintenance cycle and usage frequency, and set dynamic management optimization objectives for fixed assets, including minimizing asset maintenance costs, maximizing asset utilization efficiency and optimizing resource consumption for 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 on the basis of the standard gray wolf optimization algorithm, so that the fixed asset management optimization process has high search capability and global convergence. The mutated gray wolf optimization algorithm adopts a dual mutation mechanism, including adaptive step size mutation and dynamic leadership mutation. The adaptive step size mutation adaptively adjusts the search step size of the gray wolf individual during each iteration update, enabling the gray wolf individual to have high exploration ability in the early stage and focus on searching in the later stage. The dynamic leadership mutation is introduced into the optimal fixed asset management strategy of the leader individual in the gray wolf optimization algorithm, so that the leader individual can be dynamically adjusted according to the fitness. S23. Based on historical maintenance records and asset operation status data in the fixed asset history database, define the fitness function of the fixed asset management strategy. ; 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, a dynamic management model for fixed assets is generated. .

4. The intelligent dynamic management method for the entire lifecycle of fixed assets based on data governance as described in claim 3, characterized in that, S4 includes the following steps: S41. Conduct on-site inventory of fixed assets using mobile terminal devices and collect current mobile inventory data. This includes the location of the fixed asset, its serial number, current status, and usage details. S42. Combine the mobile inventory data collected in step S41 with the fixed asset status data stored in the fixed asset history database updated in S3. Compare and calculate the deviation of the moving inventory data. ; S43. For fixed asset status data with discrepancies in moving inventory count data, calculate the error correction factor. ; S44. For moving inventory data where the error correction factor exceeds the threshold, a dynamic weight adjustment mechanism is used to correct the moving 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.

5. The intelligent dynamic management method for the entire lifecycle of fixed assets based on data governance as described in claim 4, characterized in that, S5 includes 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 an automatic fixed asset reconciliation dataset; S52. The Mutant Gray Wolf Optimization Algorithm is used 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, generate a correction vector to adjust the data differences between fixed asset management data, mobile inventory data and financial accounting data, and dynamically optimize the correction vector in combination with the historical trend of data change, so as to optimize the matching degree between fixed asset management data, mobile inventory data and financial accounting data. S54. Store the optimized fixed asset lifecycle data from step S53 into the fixed asset history database and update the fixed asset management system.

6. The intelligent dynamic management method for the entire lifecycle of fixed assets based on data governance as described in claim 5, characterized in that, If the moving inventory data deviation is found in step S42 If the moving inventory data is normal, then the moving inventory data is considered normal; if the moving inventory data is deviated... If so, the status of the moving inventory data is determined to be abnormal.

7. The intelligent dynamic management method for the entire lifecycle of fixed assets based on data governance as described in claim 6, characterized in that, The error correction factor for the status of the moving inventory data in S43 is... If the deviation of the moving inventory data status exceeds the threshold, then... This triggers an exception flag.

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