An asset life cycle management system

Abstract

The present invention belongs to the technical field and discloses an asset life cycle management system, comprising a data acquisition module for collecting asset information data, asset economic data and external environment data; a data processing module for pre-processing the collected asset information data, asset economic data and external environment data and fusing them to obtain a comprehensive feature data set; an asset life cycle prediction module for obtaining an asset life cycle prediction model based on training of the comprehensive feature data set, and obtaining the asset status change rate through prediction of the asset life cycle prediction model; a resource scheduling module for determining whether to generate asset risk warning information based on the asset status change rate; if the asset risk warning information is generated, extracting the status change rate trend through statistical analysis, and dynamically adjusting resource allocation based on the status change rate trend; thus, resources can be more accurately directed to assets that need support, thereby avoiding resource waste.

Description

Technical Field

[0001] The present invention relates to the technical field, and more particularly, to an asset full life cycle management system. Background Art

[0002] Patent publication number CN113888293A discloses an asset lifecycle management system, where an acquisition terminal is used to obtain asset information; a server is used to establish a unique identity for the asset, perform asset valuation, asset change assessment, asset change forecast analysis, financial management, and data query throughout the asset lifecycle; each blockchain node separately records the status and change information of each asset throughout its lifecycle; the management terminal is used to interact with the server and also to obtain asset status and change information from each blockchain node. Asset management throughout the asset lifecycle is achieved through remote interaction; server-based data management reduces manpower input; blockchain nodes are used for data storage to prevent asset data from being tampered with; and the server can proactively perform automated asset change assessments and asset change forecast analysis, improving automation and computing efficiency for large amounts of data while also avoiding the possibility of asset waste.

[0003] The existing asset lifecycle management system has the following major problems:

[0004] Failure to consider the correlation between features and target variables may result in the selected features being unable to accurately reflect changes in the target variable, thereby reducing the generalization ability of the model; failure to consider the redundancy between features may result in the model containing a large number of redundant features, which not only do not provide additional information but may also increase the complexity of the model, thereby increasing the risk of overfitting; failure to set a redundancy tolerance threshold or fail to perform adaptive adjustments based on the redundancy correction function may result in the feature selection process being too rigid and unable to adapt to the feature distribution of different data sets; failure to dynamically adjust the redundancy tolerance threshold based on the relationship between the number of currently selected features and the total number of features may result in the feature selection process being unable to flexibly adjust as the data set changes;

[0005] Without dynamic adjustment of resource allocation, resources may be wasted on assets with stable status or low demand, while assets with rapid deterioration or high demand will not receive sufficient resource support, and limited resources cannot be optimally allocated, reducing overall utilization efficiency. Failure to identify rapidly degrading assets through status change rate trends will result in failure to increase resource input in a timely manner, leading to severe degradation and even irreversible failures. The failure of key assets may trigger a chain reaction, affecting the operation of the entire system. Lack of real-time monitoring and inability to quickly respond to changes in asset status may lead to the accumulation of problems, ultimately requiring higher costs for remediation. Failure to adapt to changes in demand at different stages of the asset life cycle will result in asset scrapping at an earlier age and increased asset replacement costs.

[0006] In view of this, the present invention proposes an asset life cycle management system to solve the above problems. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned objectives, the present invention provides the following technical solutions: an asset life cycle management system, comprising:

[0008] Data collection module, used to collect asset information data, asset economic data and external environment data;

[0009] The data processing module is used to pre-process the collected asset information data, asset economic data and external environment data to obtain an asset information feature data set, an asset economic feature data set and an external environment feature data set; and to fuse the asset information feature data set, the asset economic feature data set and the external environment feature data set to obtain a comprehensive feature data set;

[0010] The asset life cycle prediction module is used to obtain an asset life cycle prediction model based on comprehensive feature data sets, and predict the asset status change rate through the asset life cycle prediction model;

[0011] The resource scheduling module is used to determine whether to generate asset risk warning information based on the asset status change rate. If asset risk warning information is generated, the status change rate trend is extracted through statistical analysis, and resource allocation is dynamically adjusted based on the status change rate trend. The various modules are connected via wired and / or wireless means.

[0012] Furthermore, the asset information data includes the type, specifications, purchase date and usage status of the asset; the asset economic data includes the purchase cost, depreciation and maintenance costs of the asset; and the external environment data includes market interest rates, industry development trends and changes in policies and regulations.

[0013] Furthermore, the method of preprocessing the collected asset information data, asset economic data, and external environment data to obtain the asset information feature data set, the asset economic feature data set, and the external environment feature data set includes:

[0014] Missing values ​​in asset information data, asset economic data, and external environment data are filled using the mean filling method, and outliers in asset information data, asset economic data, and external environment data are detected and eliminated using the box plot method; standard deviation normalization is performed to convert them into standard normal distribution;

[0015] The improved mRMR algorithm is used to analyze the correlation between asset information data, asset economic data, external environmental data and asset status change rate, while minimizing the redundancy between the features in the asset information data, asset economic data and external environmental data, thereby obtaining the asset information feature data set, asset economic feature data set and external environmental feature data set. The specific steps are as follows:

[0016] Define the target variable Y, where Y is the rate of change of the asset status; calculate the mutual information between each feature f in the asset information data, asset economic data, and external environment data and the target variable Y by maximizing the correlation formula;

[0017] The formula for maximizing correlation is: Among them, p(f,Y) is the joint probability distribution of feature f and target variable Y, which indicates the probability that feature f and target variable Y occur simultaneously; p(f) is the marginal probability distribution of feature f, which indicates the probability that feature f occurs independently; p(Y) is the marginal probability distribution of target variable Y, which indicates the probability that target variable Y occurs independently; I(f,Y) is the mutual information between feature f and target variable Y, which is used to measure the correlation between feature f and target variable Y.

[0018] The redundancy between features is calculated by minimizing the redundancy formula, that is, the mutual information between feature f and feature f′; the minimizing redundancy formula is: Where p(f,f′) is the joint probability distribution between feature f and feature f′, which indicates the probability that feature f and feature f′ occur simultaneously; p(f) and p(f′) are the marginal probability distributions of feature f and feature f′, respectively, which indicate the probability that feature f and feature f′ occur independently; I(f,f′) is the mutual information between feature f and feature f′, which is used to quantify the redundancy between feature f and feature f′.

[0019] By improving the optimization objective function, the most relevant and least redundant features are selected to form the feature subset S; the improved optimization objective function is: Where J(S) is the improved optimization objective function; |S| is the number of features in the feature subset S; g(f,f′) is the redundancy correction function, which is dynamically adjusted according to the redundancy tolerance between features;

[0020] Set a redundancy tolerance threshold, dynamically adjust the redundancy tolerance between features according to the redundancy correction function, and adaptively adjust the set redundancy tolerance threshold;

[0021] The redundancy correction function is: Among them, τ is the set redundancy tolerance threshold.

[0022] Furthermore, the method for adaptively adjusting the set redundancy tolerance threshold includes:

[0023] The set redundancy tolerance threshold is adaptively adjusted through the redundancy tolerance threshold adjustment formula. The redundancy tolerance threshold adjustment formula is: Among them, τ′ is the adjusted redundancy tolerance threshold; n cr is the number of features currently selected; n tl is the total number of features; τ max is the maximum redundancy tolerance threshold.

[0024] Furthermore, the method of fusing the asset information feature dataset, the asset economic feature dataset, and the external environment feature dataset to obtain a comprehensive feature dataset includes:

[0025] The normalized asset information feature dataset, asset economic feature dataset, and external environment feature dataset are fused through a weighted model to obtain a comprehensive feature dataset; the asset information feature dataset is denoted as K1, the asset economic feature dataset is denoted as K2, and the external environment feature dataset is denoted as K3;

[0026] The weighted model is: ZC=K1·ω1+K2·ω2+K3·ω3; where ω1 is the weight coefficient of the asset information feature data set; ω2 is the weight coefficient of the asset economic feature data set; and ω3 is the weight coefficient of the external environment feature data set.

[0027] Furthermore, the training method of the asset life cycle prediction model includes:

[0028] The dataset is divided into a training set, a validation set, and a test set; the dataset includes a historical comprehensive feature dataset and the corresponding asset status change rate; a sliding window method is used to generate a sample set, which is a subset of the dataset. Each sample set includes a historical comprehensive feature dataset and the corresponding asset status change rate over a period of time.

[0029] The asset lifecycle prediction model is constructed using an LSTM model, specifically consisting of an input layer, an LSTM layer, a fully connected layer, and an output layer. The input layer is used to input a historical comprehensive feature dataset; the number of neurons in the input layer matches the number of features in the historical comprehensive feature dataset; the LSTM layer is used to receive data from the input layer and capture the temporal characteristics of asset state changes during its lifecycle. The model's learning ability is adjusted by changing the number of LSTM units and layers; the fully connected layer is used to map the output of the LSTM layer to the target prediction value; and the output layer uses a linear activation function to output the rate of change of the asset state.

[0030] The mean squared error (MSE) is used as the loss function to measure the error in the model's predictions. The asset lifecycle prediction model is trained on the training set, and the Adam optimizer is used to minimize the loss function. The model's generalization ability is evaluated on the validation set, and the model's hyperparameters are tuned based on the evaluation results until the preset stopping condition is reached, resulting in the final trained asset lifecycle prediction model.

[0031] The current comprehensive feature dataset is input into the trained asset life cycle prediction model to predict the corresponding asset status change rate.

[0032] Furthermore, the method for determining whether to generate asset risk warning information based on the asset status change rate includes:

[0033] If the predicted asset status change rate is greater than or equal to the preset asset status change rate threshold, the asset is determined to be in an abnormal state and an asset risk warning information is generated;

[0034] If the predicted asset status change rate is less than the preset asset status change rate threshold, the asset is determined to be in a healthy state and no asset risk warning information is generated.

[0035] Furthermore, if asset risk warning information is generated, the method of extracting the state change rate trend through statistical analysis includes performing fluctuation analysis and second-order derivative calculation on the asset state change rate, and then obtaining the change rate fluctuation amplitude, change rate fluctuation cycle and asset degradation acceleration; based on the change rate fluctuation amplitude, change rate fluctuation cycle and asset degradation acceleration, the state change rate trend is reflected.

[0036] Furthermore, the method for dynamically adjusting resource allocation based on the state change rate trend includes constructing a resource allocation adjustment formula according to the state change rate trend to dynamically adjust resource allocation.

[0037] Furthermore, the method of constructing a resource allocation adjustment formula based on a state change rate trend includes:

[0038] The resource allocation adjustment formula is: Where α is the resource allocation adjustment coefficient; ΔR is the fluctuation amplitude of the change rate; T is the fluctuation period of the change rate; A is the acceleration of asset degradation; k1 and k2 are adjustment parameters; e is the base of the exponential function;

[0039] A resource allocation adjustment coefficient threshold β is preset. When β≤α≤1, the resource allocation ratio of the current asset needs to be increased; when 0≤α<β, the resource allocation ratio of the current asset needs to be reduced. The obtained resource allocation adjustment coefficient is normalized and the resource allocation ratio is recalculated to perform resource allocation.

[0040] The technical effects and advantages of the asset life cycle management system of the present invention are as follows:

[0041] By improving the mRMR algorithm, the present invention not only considers the correlation between features and target variables, but also the redundancy between features, thereby selecting the most relevant feature subset with the lowest redundancy. This helps to simplify the model, improve the model's generalization ability, and reduce computational complexity. The selected feature subset is more representative, which helps to better understand the relationship between the asset state change rate and asset information, asset economics, and the external environment. A redundancy tolerance threshold is set and adaptively adjusted according to a redundancy correction function, making the feature selection process more flexible and adaptive. The redundancy tolerance threshold is dynamically adjusted based on the relationship between the number of currently selected features and the total number of features. This means that as the feature selection process proceeds, the system can more flexibly cope with redundancy in the data set.

[0042] By analyzing the trend of status change rate, resource allocation is dynamically adjusted so that resources can flow more accurately to assets that need support; resource waste is avoided by allocating limited resources preferentially to assets with faster status degradation; resource utilization efficiency is improved and redundant allocation of resources to low-demand assets is reduced; using dynamic adjustment formulas and real-time monitoring mechanisms, it can quickly respond to changing trends in asset status and adjust resource allocation ratios; resources are adjusted according to real-time changes in asset status to prevent greater losses due to failure to respond in time to status deterioration; the threshold mechanism ensures that resource allocation adjustments are reasonable and will not change frequently due to short-term fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a schematic diagram of the structure of an asset life cycle management system of the present invention;

[0044] Figure 2 The figure is a flow chart of an asset life cycle management method according to the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Example 1

[0047] See also Figure 1 As shown, the asset lifecycle management system described in this embodiment includes:

[0048] Data collection module, used to collect asset information data, asset economic data and external environment data;

[0049] The data processing module is used to pre-process the collected asset information data, asset economic data and external environment data to obtain an asset information feature data set, an asset economic feature data set and an external environment feature data set; and to fuse the asset information feature data set, the asset economic feature data set and the external environment feature data set to obtain a comprehensive feature data set;

[0050] The asset life cycle prediction module is used to obtain an asset life cycle prediction model based on comprehensive feature data sets, and predict the asset status change rate through the asset life cycle prediction model;

[0051] The resource scheduling module is used to determine whether to generate asset risk warning information based on the asset status change rate. If asset risk warning information is generated, the status change rate trend is extracted through statistical analysis, and resource allocation is dynamically adjusted based on the status change rate trend. The various modules are connected via wired and / or wireless means.

[0052] The asset information data includes the type, specifications, purchase date and usage status of the asset; the asset economic data includes the purchase cost, depreciation and maintenance costs of the asset; and the external environment data includes market interest rates, industry development trends and changes in policies and regulations.

[0053] The method of preprocessing the collected asset information data, asset economic data, and external environment data to obtain the asset information feature data set, the asset economic feature data set, and the external environment feature data set includes:

[0054] Missing values ​​in asset information data, asset economic data, and external environment data are filled using the mean filling method, and outliers in asset information data, asset economic data, and external environment data are detected and eliminated using the box plot method; standard deviation normalization is performed to convert them into standard normal distribution;

[0055] The improved mRMR algorithm is used to analyze the correlation between asset information data, asset economic data, external environmental data and asset status change rate, while minimizing the redundancy between the features in the asset information data, asset economic data and external environmental data, thereby obtaining the asset information feature data set, asset economic feature data set and external environmental feature data set. The specific steps are as follows:

[0056] Define the target variable Y, where Y is the rate of change of the asset status; calculate the mutual information between each feature f in the asset information data, asset economic data, and external environment data and the target variable Y by maximizing the correlation formula;

[0057] The formula for maximizing correlation is: Among them, p(f,Y) is the joint probability distribution of feature f and target variable Y, which indicates the probability that feature f and target variable Y occur simultaneously; p(f) is the marginal probability distribution of feature f, which indicates the probability that feature f occurs independently; p(Y) is the marginal probability distribution of target variable Y, which indicates the probability that target variable Y occurs independently; I(f,Y) is the mutual information between feature f and target variable Y, which is used to measure the correlation between feature f and target variable Y.

[0058] The redundancy between features is calculated by minimizing the redundancy formula, that is, the mutual information between feature f and feature f′; the minimizing redundancy formula is: Where p(f,f′) is the joint probability distribution between feature f and feature f′, which indicates the probability of feature f and feature f′ occurring simultaneously; p(f) and p(f′) are the marginal probability distributions of feature f and feature f′, respectively, indicating the probability of feature f and feature f′ occurring independently; I(f,f′) is the mutual information between feature f and feature f′, which is used to quantify the redundancy between feature f and feature f′. A higher mutual information means that the two features provide similar information, and therefore have higher redundancy.

[0059] By improving the optimization objective function, the most relevant and least redundant features are selected to form the feature subset S; the improved optimization objective function is: Where J(S) is the improved optimization objective function; |S| is the number of features in the feature subset S; g(f,f′) is the redundancy correction function, which is dynamically adjusted according to the redundancy tolerance between features;

[0060] Set a redundancy tolerance threshold, dynamically adjust the redundancy tolerance between features according to the redundancy correction function, and adaptively adjust the set redundancy tolerance threshold;

[0061] The redundancy correction function is: Among them, τ is the set redundancy tolerance threshold;

[0062] For example, the redundancy tolerance threshold is set to 0.6, and the mutual information I(f,f′) between feature f and feature f′ is 0.8. Since 0.8>0.6, according to the redundancy correction function g(f,f′)=1, this means that the redundancy of the two features is high, and one of the features should be reduced.

[0063] If the mutual information I(f,f′) between feature f and feature f′ is 0.4, according to the redundancy correction function This means that the redundancy of these two features is low and their simultaneous existence can be tolerated.

[0064] The method for adaptively adjusting the set redundancy tolerance threshold includes:

[0065] The set redundancy tolerance threshold is adaptively adjusted through the redundancy tolerance threshold adjustment formula. The redundancy tolerance threshold adjustment formula is: Among them, τ′ is the adjusted redundancy tolerance threshold; n cr is the number of features currently selected; n tl is the total number of features; τ max is the maximum redundancy tolerance threshold;

[0066] For example, if the total number of features is 50, the number of currently selected features is 30, and the maximum redundancy tolerance threshold is 0.8, then the adjusted redundancy tolerance threshold is

[0067] The total number of features is 50, the number of currently selected features is 45, and the maximum redundancy tolerance threshold is 0.8. Then the adjusted redundancy tolerance threshold is At this time, the redundancy tolerance threshold is 0.72, which allows more redundant features to exist and adapts to a wider range of feature selection.

[0068] The method of fusing the asset information feature dataset, the asset economic feature dataset, and the external environment feature dataset to obtain a comprehensive feature dataset includes:

[0069] The normalized asset information feature dataset, asset economic feature dataset, and external environment feature dataset are fused through a weighted model to obtain a comprehensive feature dataset; the asset information feature dataset is denoted as K1, the asset economic feature dataset is denoted as K2, and the external environment feature dataset is denoted as K3;

[0070] The weighted model is: ZC=K1·ω1+K2·ω2+K3·ω3; where ω1 is the weight coefficient of the asset information feature data set; ω2 is the weight coefficient of the asset economic feature data set; and ω3 is the weight coefficient of the external environment feature data set.

[0071] The training method of the asset life cycle prediction model includes:

[0072] The dataset is divided into a training set, a validation set, and a test set; the dataset includes a historical comprehensive feature dataset and the corresponding asset status change rate; a sliding window method is used to generate a sample set, which is a subset of the dataset. Each sample set includes a historical comprehensive feature dataset and the corresponding asset status change rate over a period of time.

[0073] The asset lifecycle prediction model is constructed using an LSTM model, specifically consisting of an input layer, an LSTM layer, a fully connected layer, and an output layer. The input layer is used to input a historical comprehensive feature dataset; the number of neurons in the input layer matches the number of features in the historical comprehensive feature dataset; the LSTM layer is used to receive data from the input layer and capture the temporal characteristics of asset state changes during its lifecycle. The model's learning ability is adjusted by changing the number of LSTM units and layers; the fully connected layer is used to map the output of the LSTM layer to the target prediction value; and the output layer uses a linear activation function to output the rate of change of the asset state.

[0074] The mean squared error (MSE) is used as the loss function to measure the error in the model's predictions. The asset lifecycle prediction model is trained on the training set, and the Adam optimizer is used to minimize the loss function. The model's generalization ability is evaluated on the validation set, and the model's hyperparameters are tuned based on the evaluation results until the preset stopping condition is reached, resulting in the final trained asset lifecycle prediction model.

[0075] The current comprehensive feature dataset is input into the trained asset life cycle prediction model to predict the corresponding asset status change rate.

[0076] The method for determining whether to generate asset risk warning information based on the asset status change rate includes:

[0077] If the predicted asset status change rate is greater than or equal to the preset asset status change rate threshold, the asset is determined to be in an abnormal state and an asset risk warning information is generated;

[0078] If the predicted asset status change rate is less than the preset asset status change rate threshold, the asset is determined to be in a healthy state and no asset risk warning information is generated.

[0079] If asset risk warning information is generated, the method for extracting the state change rate trend through statistical analysis includes performing fluctuation analysis and second-order derivative calculation on the asset state change rate, thereby obtaining the change rate fluctuation amplitude, change rate fluctuation cycle and asset degradation acceleration; based on the change rate fluctuation amplitude, change rate fluctuation cycle and asset degradation acceleration, reflecting the state change rate trend;

[0080] By counting the discrete degree of the asset status change rate, the fluctuation amplitude of the change rate is defined as the standard deviation of the asset status change rate, which reflects the volatility of the asset status; the change rate fluctuation period is calculated through Fourier transform to measure the regularity of asset status changes; the asset degradation acceleration is obtained by calculating the second-order derivative of the asset status change rate to reflect the speed of asset status deterioration.

[0081] The method for dynamically adjusting resource allocation based on the state change rate trend includes constructing a resource allocation adjustment formula based on the state change rate trend and dynamically adjusting resource allocation; assets with high volatility require priority resource allocation to cope with uncertainty; short-cycle assets require frequent monitoring to avoid sudden changes; assets with high degradation acceleration require more resources to slow down the degradation trend; resource allocation is readjusted by comprehensively considering the current asset's rate of change fluctuation amplitude, rate of change fluctuation cycle and asset degradation acceleration to ensure accurate resource allocation and improve system efficiency and reliability.

[0082] The method for constructing a resource allocation adjustment formula according to a state change rate trend includes:

[0083] The resource allocation adjustment formula is: Where α is the resource allocation adjustment coefficient; ΔR is the fluctuation amplitude of the change rate; T is the fluctuation period of the change rate; A is the acceleration of asset degradation; k1 and k2 are adjustment parameters; e is the base of the exponential function;

[0084] A resource allocation adjustment coefficient threshold β is preset. When β≤α≤1, it indicates that the asset is in a period of aggravated degradation or unstable state, and the resource allocation ratio of the current asset needs to be increased; when 0≤α<β, it indicates that the asset is in a stable period, and the resource allocation ratio of the current asset needs to be reduced; the obtained resource allocation adjustment coefficient is normalized and the resource allocation ratio is recalculated for resource allocation.

[0085] For example, assume there are three existing assets, D1, D2, and D3, with a total resource of 100 and a resource allocation adjustment coefficient threshold of β = 0.6. The current state change rate trend is as follows:

[0086] assets Rate of change fluctuation Rate of change fluctuation period Acceleration of asset degradation D1 0.8 2.0 0.4 D2 0.6 1.5 0.3 D3 0.4 3.0 0.2

[0087] Adjust parameters k1 = 0.1, k2 = 0.2; according to the resource allocation adjustment formula, we can get:

[0088]

[0089] Therefore, asset D1 will receive a larger resource allocation ratio; assets D2 and D3 will receive a corresponding reduction in resource allocation ratios. Taking asset D1 as an example, the result of the reallocation of resources is: That is, the number of resources obtained by asset D1 is 44.

[0090] This embodiment improves the mRMR algorithm by considering not only the correlation between features and target variables but also the redundancy between features, thereby selecting the most relevant and least redundant feature subset. This helps simplify the model, improves the generalization ability of the model, and reduces computational complexity. The selected feature subset is more representative, which helps better understand the relationship between the asset state change rate and asset information, asset economics, and the external environment. A redundancy tolerance threshold is set and adaptively adjusted based on a redundancy correction function, making the feature selection process more flexible and adaptive. The redundancy tolerance threshold is dynamically adjusted based on the relationship between the number of currently selected features and the total number of features. This means that as the feature selection process proceeds, the system can more flexibly cope with redundancy in the dataset.

[0091] By analyzing the trend of status change rate, resource allocation is dynamically adjusted so that resources can flow more accurately to assets that need support; resource waste is avoided by allocating limited resources preferentially to assets with faster status degradation; resource utilization efficiency is improved and redundant allocation of resources to low-demand assets is reduced; using dynamic adjustment formulas and real-time monitoring mechanisms, it can quickly respond to changing trends in asset status and adjust resource allocation ratios; resources are adjusted according to real-time changes in asset status to prevent greater losses due to failure to respond in time to status deterioration; the threshold mechanism ensures that resource allocation adjustments are reasonable and will not change frequently due to short-term fluctuations.

[0092] Example 2

[0093] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A method for asset life cycle management is provided, including:

[0094] S1. Collect asset information data, asset economic data and external environment data;

[0095] S2. Preprocessing the collected asset information data, asset economic data, and external environment data to obtain an asset information feature dataset, an asset economic feature dataset, and an external environment feature dataset; fusing the asset information feature dataset, the asset economic feature dataset, and the external environment feature dataset to obtain a comprehensive feature dataset;

[0096] S3. Obtain an asset lifecycle prediction model based on the comprehensive feature data set training, and obtain the asset status change rate through the asset lifecycle prediction model;

[0097] S4. Determine whether to generate asset risk warning information based on the asset status change rate; if asset risk warning information is generated, extract the status change rate trend through statistical analysis, and dynamically adjust resource allocation based on the status change rate trend.

[0098] Since the electronic device introduced in this embodiment is an electronic device used to implement an asset life cycle management system in the embodiment of this application, based on the asset life cycle management system introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as technical personnel in this field implement the electronic device used in an asset life cycle management system in the embodiment of this application, it falls within the scope of protection of this application.

[0099] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0100] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An asset life cycle management system, characterized in that: include: Data collection module, used to collect asset information data, asset economic data and external environment data; The asset information data includes the asset type, specifications, acquisition date and usage status; asset economic data includes the asset acquisition cost, depreciation and maintenance costs; external environment data includes market interest rates, industry development trends and changes in policies and regulations; The data processing module is used to pre-process the collected asset information data, asset economic data and external environment data to obtain an asset information feature data set, an asset economic feature data set and an external environment feature data set; The asset information feature dataset, asset economic feature dataset and external environment feature dataset are integrated to obtain a comprehensive feature dataset; The method of preprocessing the collected asset information data, asset economic data, and external environment data to obtain the asset information feature data set, the asset economic feature data set, and the external environment feature data set includes: Missing values ​​in asset information data, asset economic data, and external environment data are filled using the mean filling method, and outliers in asset information data, asset economic data, and external environment data are detected and eliminated using the box plot method; standard deviation normalization is performed to convert them into standard normal distribution; The improved mRMR algorithm is used to analyze the correlation between asset information data, asset economic data, external environmental data and asset status change rate, while minimizing the redundancy between the features in the asset information data, asset economic data and external environmental data, thereby obtaining the asset information feature data set, asset economic feature data set and external environmental feature data set. The specific steps are as follows: Define the target variable Y, where Y is the rate of change of the asset status; calculate the mutual information between each feature f in the asset information data, asset economic data, and external environment data and the target variable Y by maximizing the correlation formula; The formula for maximizing correlation is: Among them, p(f,Y) is the joint probability distribution of feature f and target variable Y, which indicates the probability that feature f and target variable Y occur simultaneously; p(f) is the marginal probability distribution of feature f, which indicates the probability that feature f occurs independently; p(Y) is the marginal probability distribution of target variable Y, which indicates the probability that target variable Y occurs independently; I(f,Y) is the mutual information between feature f and target variable Y, which is used to measure the correlation between feature f and target variable Y. The redundancy between features is calculated by minimizing the redundancy formula, that is, the mutual information between feature f and feature f′; the minimizing redundancy formula is: Where p(f,f′) is the joint probability distribution between feature f and feature f′, which indicates the probability that feature f and feature f′ occur simultaneously; p(f) and p(f′) are the marginal probability distributions of feature f and feature f′, respectively, which indicate the probability that feature f and feature f′ occur independently; I(f,f′) is the mutual information between feature f and feature f′, which is used to quantify the redundancy between feature f and feature f′. By improving the optimization objective function, the most relevant and least redundant features are selected to form the feature subset S; the improved optimization objective function is: Where J(S) is the improved optimization objective function; |S| is the number of features in the feature subset S; g(f,f′) is the redundancy correction function, which is dynamically adjusted according to the redundancy tolerance between features; Set a redundancy tolerance threshold, dynamically adjust the redundancy tolerance between features according to the redundancy correction function, and adaptively adjust the set redundancy tolerance threshold; The redundancy correction function is: Among them, τ is the set redundancy tolerance threshold; The asset life cycle prediction module is used to obtain an asset life cycle prediction model based on comprehensive feature data sets, and predict the asset status change rate through the asset life cycle prediction model; The resource scheduling module is used to determine whether to generate asset risk warning information based on the asset status change rate. If asset risk warning information is generated, the status change rate trend is extracted through statistical analysis and resource allocation is dynamically adjusted based on the status change rate trend. The modules are connected via wired and / or wireless means. The method for dynamically adjusting resource allocation based on the state change rate trend includes constructing a resource allocation adjustment formula according to the state change rate trend to dynamically adjust resource allocation; the resource allocation adjustment formula is: Where α is the resource allocation adjustment coefficient; ΔR is the fluctuation amplitude of the change rate; T is the fluctuation period of the change rate; A is the acceleration of asset degradation; k1 and k2 are adjustment parameters; e is the base of the exponential function; A resource allocation adjustment coefficient threshold β is preset. When β≤α≤1, the resource allocation ratio of the current asset needs to be increased; when 0≤α<β, the resource allocation ratio of the current asset needs to be reduced. The obtained resource allocation adjustment coefficient is normalized and the resource allocation ratio is recalculated to perform resource allocation.

2. An asset life cycle management system according to claim 1, characterized in that: The method for adaptively adjusting the set redundancy tolerance threshold includes: The set redundancy tolerance threshold is adaptively adjusted through the redundancy tolerance threshold adjustment formula. The redundancy tolerance threshold adjustment formula is: Among them, τ′ is the adjusted redundancy tolerance threshold; n cr is the number of features currently selected; n tl is the total number of features; τ max is the maximum redundancy tolerance threshold.

3. An asset life cycle management system according to claim 2, characterized in that: The method of fusing the asset information feature dataset, the asset economic feature dataset, and the external environment feature dataset to obtain a comprehensive feature dataset includes: The normalized asset information feature dataset, asset economic feature dataset, and external environment feature dataset are fused through a weighted model to obtain a comprehensive feature dataset; the asset information feature dataset is denoted as K1, the asset economic feature dataset is denoted as K2, and the external environment feature dataset is denoted as K3; The weighted model is: ZC=K1·ω1+K2·ω2+K3·ω3; where ω1 is the weight coefficient of the asset information feature data set; ω2 is the weight coefficient of the asset economic feature data set; and ω3 is the weight coefficient of the external environment feature data set.

4. An asset life cycle management system according to claim 3, characterized in that: The training method of the asset life cycle prediction model includes: The dataset is divided into a training set, a validation set, and a test set; the dataset includes a historical comprehensive feature dataset and the corresponding asset status change rate; a sliding window method is used to generate a sample set, which is a subset of the dataset. Each sample set includes a historical comprehensive feature dataset and the corresponding asset status change rate over a period of time. The asset lifecycle prediction model is constructed using an LSTM model, specifically consisting of an input layer, an LSTM layer, a fully connected layer, and an output layer. The input layer is used to input a historical comprehensive feature dataset; the number of neurons in the input layer matches the number of features in the historical comprehensive feature dataset; the LSTM layer is used to receive data from the input layer and capture the temporal characteristics of asset state changes during its lifecycle. The model's learning ability is adjusted by changing the number of LSTM units and layers; the fully connected layer is used to map the output of the LSTM layer to the target prediction value; and the output layer uses a linear activation function to output the rate of change of the asset state. The mean squared error (MSE) is used as the loss function to measure the error in the model's predictions. The asset lifecycle prediction model is trained on the training set, and the Adam optimizer is used to minimize the loss function. The model's generalization ability is evaluated on the validation set, and the model's hyperparameters are tuned based on the evaluation results until the preset stopping condition is reached, resulting in the final trained asset lifecycle prediction model. The current comprehensive feature dataset is input into the trained asset life cycle prediction model to predict the corresponding asset status change rate.

5. An asset life cycle management system according to claim 4, characterized in that: The method for determining whether to generate asset risk warning information based on the asset status change rate includes: If the predicted asset status change rate is greater than or equal to the preset asset status change rate threshold, the asset is determined to be in an abnormal state and an asset risk warning information is generated; If the predicted asset status change rate is less than the preset asset status change rate threshold, the asset is determined to be in a healthy state and no asset risk warning information is generated.

6. An asset life cycle management system according to claim 5, characterized in that: If asset risk warning information is generated, the method of extracting the state change rate trend through statistical analysis includes performing fluctuation analysis and second-order derivative calculation on the asset state change rate, thereby obtaining the change rate fluctuation amplitude, change rate fluctuation period and asset degradation acceleration; Reflects the state change rate trend based on the change rate fluctuation amplitude, change rate fluctuation cycle and asset degradation acceleration.

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