Data asset active management method and system based on DCMM

Abstract

The invention relates to the field of data asset management, and discloses a DCMM-based data asset active management method and system, and the method comprises the following steps: collecting data assets from all business systems, extracting multi-dimensional features, and generating a data attribute matrix; constructing a data value quantification model, and calculating the value of each data asset; based on a multi-objective optimization model, data assets are subjected to classification management, and optimization balance of data value, security, access efficiency and storage cost is realized; dynamically adjusting the weight of an optimization target according to different levels of the DCMM to enable a data management strategy to adapt to the data management maturity requirement of an enterprise; and performing resource allocation and priority control on the data assets according to an optimization result to realize active management. According to the method and the system, the accuracy, the flexibility and the resource utilization rate of data asset management are improved, and optimal configuration and efficient management of data assets in different development stages of enterprises are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset management, and in particular to a DCMM-based data asset active management method and system. Background Art

[0002] In the data management process of modern enterprises, especially diversified and group-type enterprises, the complexity of data management continues to increase due to the diverse and scattered data sources. However, there are still many deficiencies in the current data asset management in practice, which makes it difficult for enterprises to fully tap the commercial value of data and assist in business decision-making.

[0003] Existing data asset management methods usually lack unified standards, lack clear definitions and classifications of data, and fail to effectively clarify the correlation between different data and their impact on the business. In addition, the core of data management is often limited to basic storage and access control, lacks comprehensive multi-objective optimization methods, and cannot take into account multiple requirements such as data value, security, access efficiency, and storage costs. Especially in resource allocation, existing methods are difficult to dynamically adapt to the business development needs of enterprises, resulting in low resource utilization and inability to achieve priority management of high-value data.

[0004] As enterprises become more dependent on data assets, differences in the maturity of data management are gradually emerging. The differences in the levels of different enterprises in the Data Capability Maturity Model (DCMM) require that data management methods be able to provide corresponding optimization strategies based on different maturity levels. However, existing technologies fail to achieve adaptive adjustments to the needs of different levels, resulting in a lack of flexibility and adaptability in data management solutions in practical applications. These deficiencies make it difficult for enterprises to achieve optimal resource allocation in data management, increase operating costs, and weaken the support role of data for business decisions. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method and system for active data asset management based on DCMM, which can realize efficient, flexible and accurate management of data assets at different data management maturity levels, while taking into account the optimization of data value, security, access efficiency and storage cost.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data asset active management method based on DCMM, comprising the following steps: Collect data assets from various business systems, extract multi-dimensional features of data and generate data attribute matrix; According to the attributes of data assets, a data value quantification model is constructed to calculate the value of each data asset; Based on a multi-objective optimization model, data assets are managed in a classified manner to optimize data value, security, access efficiency, and storage costs; Dynamically adjust the weights of optimization objectives according to different levels of DCMM to meet the data management maturity requirements of the enterprise; Allocate resources and control priorities of data assets based on optimization results to achieve proactive management.

[0007] Preferably, the steps of collecting data assets from various business systems, extracting multi-dimensional features of the data and generating a data attribute matrix include: Obtain data asset collection from various business systems , each data asset It is an independent business data item; For each data asset Extract multi-dimensional features, including usage frequency, business impact, sensitivity, access efficiency, and storage cost attributes, and define these feature values ​​as attribute vectors of data assets ; The attribute vectors of all data assets form a data attribute matrix , which is defined as follows:

[0008] in, Representing data assets No. attribute value; Each column in the data attribute matrix is ​​standardized to normalize each attribute value to the interval [0,1].

[0009] Preferably, the step of constructing a data value quantification model based on the attributes of the data assets and calculating the value of each data asset includes: Define the attribute weight vector of the data asset , where each weight Indicates The relative importance of each attribute in the data value assessment is calculated, and all weights are normalized to ensure that the sum of the weights of each attribute is equal to 1; According to the data attribute matrix and the weight vector , calculate each data asset by weighted summation The value of , the specific formula is:

[0010] in, Representing data assets No. attribute values, Indicates The weight of each attribute; The value calculation results of all data assets constitute a data value vector , where each element Representing data assets The value of is used for subsequent multi-objective optimization analysis.

[0011] Preferably, the objectives of the multi-objective optimization model include: Maximizing data value by selecting data assets that meet management conditions , so that the total value of data assets is maximized, the objective function is defined as:

[0012] in, Representing data assets The value of Indicates whether to select data assets To manage; The data security minimization goal is to reduce the risk of data leakage by selecting the security indicators and access frequency of data assets. The objective function is defined as:

[0013] in, Representing data assets sensitivity, Representing data assets The frequency of visits, Indicates whether to select data assets To manage; The access efficiency maximization goal is to maximize the average access efficiency of the system by optimizing the selected data assets. The objective function is defined as:

[0014] in, Representing data assets The access efficiency, Indicates whether to select data assets To manage; The storage cost minimization goal is to reduce the total storage cost of the system by selecting data assets. The objective function is defined as:

[0015] in, Representing data assets The storage cost, Indicates whether to select data assets To manage.

[0016] Preferably, the multi-objective optimization model is solved using a non-dominated sorting genetic algorithm to obtain a set of Pareto optimal solutions, and multiple sets of data management strategies that meet different objective balance requirements are used as optimization results to provide a basis for classification management of data assets and resource allocation.

[0017] Preferably, the step of dynamically adjusting the weight of the optimization target according to different levels of DCMM to adapt to the data management maturity requirements of the enterprise includes: Define DCMM layer weight matrix ,in Represents the number of levels of DCMM. Each row of the matrix represents the weight combination at different DCMM levels. Corresponding to Level The weight of the optimization objective; When the system is initialized, according to the current data management maturity level of the enterprise Choosing the weight vector , the weight vector includes weights for maximizing data value, minimizing security, maximizing access efficiency, and minimizing storage cost; According to the selected weight vector , reconstruct the objective function of the multi-objective optimization model to adapt to the priority requirements of the current DCMM level, and comprehensively optimize the management strategy of data assets so that the priority of each goal at different levels meets the actual needs of the enterprise.

[0018] Preferably, the step of dynamically adjusting the weight of the optimization target according to different levels of DCMM to adapt to the data management maturity requirements of the enterprise also includes: Regularly monitor the data management maturity of the enterprise and evaluate it through the DCMM model. When changes are detected in the data management maturity level of the enterprise, the weight vector is dynamically updated. is the weight combination of the current level , reconfigure the objective weights of the multi-objective optimization model, thereby achieving adaptive adjustment of the optimization objectives; After the weight adjustment, data assets are reclassified and managed, and resources are allocated based on the updated multi-objective optimization model, so that the data management requirements of the enterprise at different maturity stages can be continuously met.

[0019] Preferably, the step of performing resource allocation and priority control on data assets according to the optimization results to achieve active management includes: Generate a priority vector based on the optimization score for each data asset ,in Representing data assets The higher the score, the higher the priority of the asset; Data assets are divided into three priority levels: high, medium, and low, and corresponding system resources are allocated according to the data requirements of each priority level and the weight of the optimization objectives.

[0020] The present invention also provides a data asset active management system based on DCMM, comprising: Data collection module, used to collect data assets from business systems and build data attribute matrix; The value calculation module is used to calculate the value of data assets based on the data value quantification model; Optimization classification module, used to classify and manage data assets based on a multi-objective optimization model; The level adjustment module is used to dynamically adjust the weights of the optimization model according to the DCMM level; Resource management module, used for resource allocation and priority management of data assets.

[0021] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the above method is implemented.

[0022] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0023] The present invention provides a data asset active management method and system based on DCMM, which has the following beneficial effects: 1. The present invention quantitatively evaluates the value of each data asset through a data value quantification model, ensuring that high-value data is given priority in resource allocation, making data management more accurate and scientific, and helping enterprises invest more resources and attention in key data.

[0024] 2. The present invention adopts a multi-objective optimization model, comprehensively considers the value, security, access efficiency and storage cost of data, balances different management needs, makes data asset management more global and systematic, and meets the comprehensive needs of enterprises in all aspects.

[0025] 3. The present invention uses the dynamic weight adjustment of the DCMM hierarchical model to adaptively adjust the weight of the optimization target according to the data management maturity of the enterprise, so that the system can provide the most suitable management strategy at different development stages and improve the adaptability and flexibility of data management.

[0026] 4. The present invention reasonably allocates storage, computing and other resources through priority-based resource allocation and active management strategies to ensure that high-priority data obtains resource guarantees while saving the management cost of low-priority data, thereby reducing overall operating costs.

[0027] 5. The system of the present invention regularly monitors the usage and management effects of data assets, dynamically adjusts the weight combination of resource allocation and optimization goals, so that the data management strategy can respond to changes in enterprise needs at any time, and improve the long-term stability and adaptability of the data management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the system architecture of the present invention; Figure 3 It is a schematic diagram of the computer device structure of the present invention.

[0029] Among them, 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0031] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for active management of data assets based on DCMM, which realizes active management of data assets by collecting data assets, quantifying data value, performing data classification management based on multi-objective optimization, dynamically adjusting DCMM level weights, and resource allocation and priority control. The implementation method of the present invention will be described in detail below in combination with specific steps.

[0032] S1. Data collection and attribute extraction In the embodiment of the present invention, step S1 mainly collects data assets from various business systems of the enterprise and extracts multi-dimensional features to form an attribute matrix that can effectively describe the characteristics of the data assets, providing basic data support for subsequent data management, analysis and optimization.

[0033] In this embodiment, various types of data assets are first collected from multiple business systems within the enterprise. The data asset collection can be defined as , where each data asset Represents an independent business data item. In order to better manage data assets, it is necessary to extract different attribute features from each data asset for subsequent data value calculation and optimization.

[0034] In some embodiments, the characteristics of a data asset may include, but are not limited to, the following attributes: Frequency of use: indicates the frequency of access and use of data in the business process; Business impact: indicates the degree of impact of data on the core business of the enterprise; Data sensitivity: measures the privacy or security needs of the data; Access efficiency: refers to the response speed of accessing the data in the system; Storage cost: represents the storage resource overhead occupied by data in the system.

[0035] In this embodiment, for each data asset , by extracting the above multi-dimensional features, constructing the attribute vector of the data ,in The number of dimensions representing the feature. For specific application scenarios in some embodiments, an enterprise may increase or decrease the feature dimensions according to actual needs to reflect the different values ​​of data assets in a specific business environment.

[0036] Then, the attribute vectors of all data assets are integrated to form a data attribute matrix ,in Represents the total number of data assets, Represents the number of dimensions of the attribute. The data attribute matrix is ​​defined as follows:

[0037] The elements in the matrix Representing data assets In the The value of an attribute.

[0038] In order to ensure the comparability between different attributes and avoid deviations in management strategies due to different attribute dimensions, in this embodiment, the data attribute matrix The attribute columns in are normalized. Specifically, the minimum-maximum normalization method can be used to adjust the value of each attribute to the interval [0,1]:

[0039] in, is the normalized attribute value, Indicates Column (i.e. The set of all values ​​of attributes), and are the minimum and maximum values ​​of the column attributes respectively. Through this standardization process, the data attribute matrix Converted to a standardized matrix , each element of which falls within the interval [0,1], thus ensuring that the relative weights between different features will not be unbalanced due to differences in attribute values.

[0040] In some embodiments, in order to further improve the comparability of data, other data preprocessing methods may be used, such as zero-mean normalization or Z-score standardization. The specific method may be selected based on the actual business needs of the enterprise and the distribution of data characteristics.

[0041] Through this step, a standardized data attribute matrix containing multi-dimensional features is constructed, providing a unified basic data input for subsequent steps such as data value quantification and optimization management.

[0042] S2. Quantification of data value In the embodiment of the present invention, step S2 constructs a data value quantification model based on the attributes of data assets to calculate the value of each data asset. By assigning different weights to the multi-dimensional features of data assets, the value of each data asset is quantified in a weighted summation manner, providing data support for subsequent optimization and classification management.

[0043] In this embodiment, firstly, the attribute weights of data assets are set according to the business needs of the enterprise and the goals of data management. Each element in Indicates The relative importance of each attribute in the data value assessment. These weights are usually set according to the degree of influence of each attribute on the data management goal, and the sum of all weights is 1 to ensure that each attribute can relatively balance the overall value of the data in the process of value quantification.

[0044] In some embodiments, if the attributes of data assets include usage frequency, business impact, sensitivity, etc., the corresponding weights can be set according to the actual needs of the enterprise. For example, if the business impact of data is crucial to enterprise decision-making, a higher weight can be given to the business impact, while for data with lower access frequency, its weight in the data value calculation can be reduced accordingly.

[0045] After completing the weight vector setting, according to the data attribute matrix and the weight vector , calculate the value of each data asset by weighted summation The specific calculation formula for data value is as follows:

[0046] in, Representing data assets The value of Represents data assets in the data attribute matrix No. attribute values, is the weight of the corresponding attribute. This formula implements the weighted summation calculation of data value, thereby obtaining the quantitative value of each data asset.

[0047] For example, in some embodiments, if a data asset The attribute values ​​of usage frequency, business influence, and sensitivity are 0.8, 0.6, and 0.9 respectively, and their corresponding weights are , and are 0.4, 0.4 and 0.2 respectively, then the data assets The value of is calculated as follows:

[0048] Through the above calculation, we can get the data assets The quantitative value is 0.74. The value calculation results of all data assets are organized into a data value vector , where each element Represents the value of the corresponding data asset.

[0049] In some embodiments, the enterprise can adjust the weight vector according to the actual business situation. The proportion of each weight in the data can be adjusted to meet the needs of different management scenarios. By flexibly adjusting the weights, the quantitative model of the present invention can dynamically adapt to the data management needs in different business environments.

[0050] This step enables the value of data assets to be presented in a quantifiable manner, providing an important reference for subsequent multi-objective optimization and resource allocation.

[0051] S3. Construction of multi-objective optimization model and data classification management In the embodiment of the present invention, the purpose of step S3 is to classify and manage data assets based on a multi-objective optimization model to achieve a balanced optimization of data value, security, access efficiency and storage cost. Through multi-objective optimization, the system can find the optimal resource allocation strategy under multiple requirements and classify and manage according to the characteristics of different data.

[0052] In this embodiment, four main optimization goals are first set for the management of data assets, including maximizing data value, minimizing security, maximizing access efficiency, and minimizing storage costs. Each optimization goal corresponds to a management requirement, and through the multi-objective optimization model, the system can find the best balance between these requirements.

[0053] In terms of maximizing data value, the goal is to ensure that high-value data is given priority in resource support. The specific objective function is defined as:

[0054] in, Representing data assets The value of is a binary decision variable used to indicate the data asset whether it has been selected for management; In terms of security minimization, the goal is to ensure data security by reducing the risk of data leakage. The security objective function is:

[0055] in, Representing data assets sensitivity, Representing data assets The frequency of visits, Indicates whether to select data assets This function calculates the risk of data leakage by considering the sensitivity and access frequency of the data, and prioritizes reducing the possibility of leakage of high-risk data.

[0056] In terms of maximizing access efficiency, the goal is to improve the average access efficiency of the managed data. The objective function of access efficiency is defined as:

[0057] in, Representing data assets The access efficiency, Indicates whether to select data assets By maximizing access efficiency, the present invention manages and prioritizes improving access speed of key data under the condition that resources permit.

[0058] In terms of storage cost minimization, the goal is to reduce the storage cost in the data management process. The objective function is:

[0059] in, Representing data assets The storage cost, Indicates whether to select data assets Management and storage cost optimization can help enterprises control the overall cost of data management when allocating resources.

[0060] To achieve the above multi-objective optimization, the present invention uses a non-dominated sorting genetic algorithm (NSGA-II) to solve the optimization model. Through the NSGA-II algorithm, the system can obtain a set of Pareto optimal solutions, representing the optimal data management strategy under different objective balance combinations. Enterprises can select the most suitable classification management strategy from the Pareto optimal solution set according to specific needs to meet actual management needs.

[0061] In this embodiment, the steps of NSGA-II algorithm are as follows: 1. Initialize the population First, randomly initialize the population in the solution space , the population size is , each individual represents a possible resource allocation scheme. The gene encoding method of each individual adopts binary encoding or real number encoding, where each gene corresponds to a data asset Choice decision , that is, whether the data is included in the management scope. 1 indicates that the data asset is selected, and 0 indicates that it is not selected.

[0062] 2. Objective function evaluation For each individual in the population, its fitness is calculated according to the four objective functions in the multi-objective optimization model. Each individual has the calculation results of the above four objective functions, thus forming a multi-objective fitness vector.

[0063] 3. Non-dominated sorting After the fitness calculation is completed, non-dominated sorting is performed to sort the individuals in the population according to their fitness vectors. Specifically, if the individual All target values ​​of and outperforms , then it is called Dominate Through this dominance relationship, individuals are divided into multiple non-dominated levels (Pareto levels), and individuals in each level do not dominate each other.

[0064] All non-dominated levels are arranged according to priority. Individuals with higher non-dominated levels have better fitness. In NSGA-II, individuals with higher non-dominated levels are retained first to ensure that the population can evolve towards the Pareto frontier.

[0065] 4. Crowding calculation In order to ensure the diversity of solutions, crowding is calculated based on non-dominated sorting. Crowding is a distance metric used to measure the sparsity of individuals in the target space. Specifically, for individuals in the same non-dominated level, the crowding distance is calculated based on their relative position on the objective function value. The larger the distance, the sparser the solutions around the individual. Such individuals are preferentially retained to ensure the diversity of solutions.

[0066] In some embodiments, the present invention adopts the following crowding calculation method: for each target, the minimum and maximum values ​​of the target are equally divided into several small intervals, and then the crowding distance in different target spaces is calculated for each individual. Individuals with higher crowding are retained first to ensure the diversity of the population.

[0067] 5. Selection, crossover and mutation After completing the non-dominated sorting and crowding calculation, individuals with higher fitness are preferentially selected to enter the next generation population according to the crowding and non-dominated levels. In this embodiment, a binary tournament selection operation is used to ensure the inheritance of excellent individuals.

[0068] After the selection operation, the next generation population is generated through crossover and mutation operations: Crossover operation: Perform crossover operation on some individuals in the population to generate new individuals. The crossover operation enables the population to approach the Pareto frontier faster by exchanging some genetic information of individuals.

[0069] Mutation operation: Perform gene mutation on some individuals, changing the value of some genes from 0 to 1 or from 1 to 0, thereby introducing new solution space exploration capabilities and improving the algorithm's ability to escape from the local optimal solution.

[0070] In some embodiments, in order to further improve the solution efficiency, the probability of crossover and mutation operations can be dynamically adjusted. For example, the crossover probability is high at the beginning and gradually decreases as the number of generations increases to ensure the stability of the algorithm in the later stage and the convergence of the solution.

[0071] 6. Update and Iteration After the next generation of population is generated, the above steps are repeated, including fitness evaluation, non-dominated sorting, crowding calculation, selection, crossover and mutation, and the iteration is continued until the maximum number of generations or convergence conditions are reached. After multiple iterations, the algorithm will gradually approach the Pareto optimal solution frontier, and the population finally generated will be distributed on the Pareto frontier, forming a set of solutions with multi-objective balance.

[0072] 7. Selection of Pareto Optimal Solution Set After the algorithm terminates, the final population obtained is the Pareto optimal solution set. In the embodiment of the present invention, the multiple solutions contained in the solution set weigh the goals of data value, security, access efficiency and storage cost respectively, and the enterprise can select one or more most suitable solutions as the classification management strategy for data asset management.

[0073] Through the NSGA-II algorithm, the present invention can obtain a set of Pareto optimal solutions under a multi-objective optimization model, and ensure the diversity of solutions through non-dominated sorting and congestion calculation. Different Pareto solutions can balance multiple management needs, so that enterprises can flexibly choose data management solutions according to actual conditions to meet the optimization needs under different stages and conditions. At the same time, because the NSGA-II algorithm has high convergence efficiency and global search capabilities, it can avoid the trouble of local optimal solutions, thereby improving the solution effect of the present invention and the adaptability of the data management strategy.

[0074] In some embodiments, if an enterprise attaches particular importance to a certain goal (such as data security), a higher weight can be given to the goal during the optimization process of NSGA-II to ensure that the optimization result is more inclined to achieve the goal. By flexibly adjusting the target weight, the present invention can adapt to the management requirements of different enterprises or application scenarios, making data asset classification management more targeted and adaptable.

[0075] Through this step, the system can reasonably classify and manage data assets, so that the value, security, access efficiency and storage cost of data can be comprehensively optimized, providing technical support for the efficient management of data assets.

[0076] S4, DCMM level dynamic adjustment In the embodiment of the present invention, the purpose of step S4 is to dynamically adjust the weights of the multi-objective optimization model according to the different levels of DCMM (data capability maturity model), so that the system can adapt to the needs of enterprises at different data management maturity stages. By dynamically adjusting the weights of the optimization objectives, the data management solution can be made more flexible at different DCMM levels, thereby achieving scientific management of data assets and optimal control of resource allocation.

[0077] In this embodiment, the DCMM level weight matrix is ​​first defined ,in is the number of levels of DCMM, each row Indicates The weight combination of each level corresponds to the weights of the four optimization goals of data value, security, access efficiency, and storage cost. Through different weight combinations, different priorities can be given to data management goals at different levels to meet the needs of enterprises with different maturity levels.

[0078] In this embodiment, an appropriate weight combination is selected based on the current data management maturity of the enterprise. For example, at a low maturity level, the enterprise may be more concerned about storage cost control and basic data security requirements, so the storage cost weight can be set at this level. and security weight The weights of data value and access efficiency are relatively low. As the maturity of enterprise data management increases, the system can automatically adjust the weight combination so that data value is and access efficiency The weight of each layer is gradually increased to give priority to supporting fast access to and efficient use of high-value data at higher levels.

[0079] In some embodiments, the weight adjustment can be flexibly configured according to the business development stage and data management strategy changes of the enterprise. For example, if the enterprise hopes to improve the access efficiency of key data within a period of time, it can appropriately increase the access efficiency weight at the current DCMM level. , thereby guiding more resources to data assets that prioritize access efficiency during the multi-objective optimization process, achieving dynamic optimization effects.

[0080] In this embodiment, to ensure the effectiveness of weight adjustment, the system regularly evaluates the enterprise's data management maturity and confirms the current level based on business needs and data management results. Promote to level , then the weight vector of the current optimization model is dynamically updated as ,Right now:

[0081] In this way, the optimization model can automatically adapt to new data management level requirements, thereby meeting the resource allocation needs of the enterprise at different stages.

[0082] In some embodiments, the system can also regularly fine-tune the weight combination according to the actual data management effect to ensure that the optimization objectives at the same DCMM level can achieve the best balance. The fine-tuning process can be adjusted through the feedback of historical data. For example, if the storage cost exceeds the budget within a period of time, the system can be prompted to appropriately increase the storage cost weight to achieve better cost control effect.

[0083] The design of this step ensures the adaptive capability of the optimization model, so that the data management solution can be dynamically adjusted as the maturity of the enterprise changes, thereby achieving the refinement of management strategies and maximization of optimization results at different DCMM levels.

[0084] S5. Resource allocation and priority control In the embodiment of the present invention, the purpose of step S5 is to perform resource allocation and priority control on data assets according to the results of the optimization model to achieve active management of data assets. Through the optimized priority sorting, the present invention can classify and manage different data assets and allocate corresponding system resources according to their priorities, thereby achieving high efficiency and pertinence of data management under limited resources.

[0085] In this embodiment, firstly, according to the multi-objective optimization result in step S3, a priority vector of data assets is generated. ,in Representing data assets The priority vector is determined based on the comprehensive scores of multiple optimization objectives such as data value, security, access efficiency, and storage cost. The higher the score, the higher the priority of the asset.

[0086] In some embodiments, the system divides data assets into three categories: high priority, medium priority, and low priority, and allocates system resources based on the priority of each category of data. For example: For high-priority data assets, the system will allocate more storage space, higher access bandwidth, and more powerful computing resources to ensure efficient access and timely processing of this data.

[0087] For medium-priority data assets, the system will appropriately limit storage resources and access frequency while ensuring daily needs to achieve a balance in resource usage.

[0088] For low-priority data assets, the system adopts a cold storage strategy, storing them on low-speed, low-cost storage media and calling them only under specific business needs to save system resources.

[0089] In this embodiment, in order to ensure the rationality of resource allocation, the system regularly monitors dynamic indicators such as the access frequency and usage of data assets and adjusts the resource allocation strategy. For example, when the access frequency of high-priority data changes significantly, the system will automatically adjust its storage and access resources to meet the needs of the current business and ensure that high-priority data is always given priority.

[0090] In some embodiments, the system may further optimize resources for low-priority data based on historical usage data to avoid wasting resources. For example, low-priority data that has not been accessed for a long time may be migrated to an offline storage medium to reduce the storage load of the system.

[0091] In this embodiment, the system maintains a resource allocation table to record the resource allocation and priority status of each data asset in real time. Through the resource allocation table, the system can achieve comprehensive control of the use of data asset resources, thereby providing technical support for long-term data management.

[0092] Through the above-mentioned resource allocation and priority control mechanism, the present invention can realize active management of data assets, so that system resources are reasonably allocated, thereby saving overall storage and computing resources while ensuring fast access to high-priority data, and realizing efficient management and low-cost operation of data assets.

[0093] In general, the method of the present invention first collects data and extracts multi-dimensional features, calculates the data value through a quantitative model, and classifies and manages the data based on a multi-objective optimization model to take into account the needs of value, security, access efficiency, and storage cost. At the same time, according to the different maturity levels of DCMM, the weights of the optimization goals are dynamically adjusted so that the data management strategy can adapt to the stage of enterprise development. Finally, the data assets are allocated resources in a hierarchical manner through the optimization results to achieve priority control and active management. The present invention improves the scientificity and adaptability of data asset management, and helps enterprises achieve the optimal configuration of data management under limited resources.

[0094] The data asset active management system based on DCMM described below and the data asset active management method based on DCMM described above can be referenced to each other.

[0095] Please refer to the attached Figure 2 The present invention also provides a data asset active management system based on DCMM, including: Data collection module, used to collect data assets from business systems and build data attribute matrix; The value calculation module is used to calculate the value of data assets based on the data value quantification model; Optimization classification module, used to classify and manage data assets based on a multi-objective optimization model; The level adjustment module is used to dynamically adjust the weights of the optimization model according to the DCMM level; Resource management module, used for resource allocation and priority management of data assets.

[0096] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0097] Please refer to the attached Figure 3 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is executed.

[0098] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41, the above method is executed.

[0099] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data asset active management method based on DCMM, characterized in that: The following steps are involved: Collect data assets from various business systems, extract multi-dimensional features of data and generate data attribute matrix; According to the attributes of data assets, a data value quantification model is constructed to calculate the value of each data asset; Based on a multi-objective optimization model, data assets are classified and managed to optimize data value, security, access efficiency and storage cost; Dynamically adjust the weights of optimization objectives according to different levels of DCMM to meet the data management maturity requirements of the enterprise; Allocate resources and control priorities of data assets based on optimization results to achieve proactive management.

2. The method for active data asset management based on DCMM according to claim 1, characterized in that: The steps of collecting data assets from various business systems, extracting multi-dimensional features of the data and generating a data attribute matrix include: Obtain data asset collection from various business systems , each data asset It is an independent business data item; For each data asset Extract multi-dimensional features, including usage frequency, business impact, sensitivity, access efficiency, and storage cost attributes, and define these feature values ​​as attribute vectors of data assets ; The attribute vectors of all data assets form a data attribute matrix , which is defined as follows: ； in, Representing data assets No. attribute value; Each column in the data attribute matrix is ​​standardized to normalize each attribute value to the interval [0,1].

3. The method for active data asset management based on DCMM according to claim 2, characterized in that: The steps of constructing a data value quantification model based on the attributes of data assets and calculating the value of each data asset include: Define the attribute weight vector of the data asset , where each weight Indicates The relative importance of each attribute in the data value assessment is calculated, and all weights are normalized to ensure that the sum of the weights of each attribute is equal to 1; According to the data attribute matrix and the weight vector , calculate each data asset by weighted summation The value of , the specific formula is: ； in, Representing data assets No. attribute values, Indicates The weight of each attribute; The value calculation results of all data assets constitute a data value vector , where each element Representing data assets The value of is used for subsequent multi-objective optimization analysis.

4. The method for active data asset management based on DCMM according to claim 1, characterized in that: The objectives of the multi-objective optimization model include: Maximizing data value by selecting data assets that meet management conditions , so that the total value of data assets is maximized, the objective function is defined as: ； in, Representing data assets The value of Indicates whether to select data assets To manage; The data security minimization goal is to reduce the risk of data leakage by selecting the security indicators and access frequency of data assets. The objective function is defined as: ； in, Representing data assets sensitivity, Representing data assets The frequency of visits, Indicates whether to select data assets To manage; The access efficiency maximization goal is to maximize the average access efficiency of the system by optimizing the selected data assets. The objective function is defined as: ； in, Representing data assets The access efficiency, Indicates whether to select data assets To manage; The storage cost minimization goal is to reduce the total storage cost of the system by selecting data assets. The objective function is defined as: ； in, Representing data assets The storage cost, Indicates whether to select data assets To manage.

5. The method for active data asset management based on DCMM according to claim 4, characterized in that: The multi-objective optimization model is solved by using a non-dominated sorting genetic algorithm to obtain a set of Pareto optimal solutions, and multiple sets of data management strategies that meet different objective balance requirements are used as optimization results to provide a basis for the classification management of data assets and resource allocation.

6. The method for active data asset management based on DCMM according to claim 1, characterized in that: The steps of dynamically adjusting the weights of optimization objectives according to different levels of DCMM to adapt to the data management maturity requirements of the enterprise include: Define DCMM layer weight matrix ,in Represents the number of levels of DCMM. Each row of the matrix represents the weight combination at different DCMM levels. Corresponding to Level The weight of the optimization objective; When the system is initialized, according to the current data management maturity level of the enterprise Choosing the weight vector , the weight vector includes weights for maximizing data value, minimizing security, maximizing access efficiency, and minimizing storage cost; According to the selected weight vector , reconstruct the objective function of the multi-objective optimization model to adapt to the priority requirements of the current DCMM level, and comprehensively optimize the management strategy of data assets so that the priority of each goal at different levels meets the actual needs of the enterprise.

7. The method for active data asset management based on DCMM according to claim 6, characterized in that: The step of dynamically adjusting the weights of optimization objectives according to different levels of DCMM to adapt to the data management maturity requirements of the enterprise also includes: Regularly monitor the data management maturity of the enterprise and evaluate it through the DCMM model. When changes are detected in the data management maturity level of the enterprise, the weight vector is dynamically updated. is the weight combination of the current level , reconfigure the objective weights of the multi-objective optimization model, thereby achieving adaptive adjustment of the optimization objectives; After the weight adjustment, data assets are reclassified and managed, and resources are allocated based on the updated multi-objective optimization model, so that the data management requirements of the enterprise at different maturity stages can be continuously met.

8. The method for active data asset management based on DCMM according to claim 1, characterized in that: The steps of allocating resources and controlling priorities of data assets according to the optimization results to achieve active management include: Generate a priority vector based on the optimization score for each data asset ,in Representing data assets The higher the score, the higher the priority of the asset; Data assets are divided into three priority levels: high, medium, and low, and corresponding system resources are allocated according to the data requirements of each priority level and the weight of the optimization objectives.

9. A data asset active management system based on DCMM, executing the method according to any one of claims 1 to 8, characterized in that: include: Data collection module, used to collect data assets from business systems and build data attribute matrix; The value calculation module is used to calculate the value of data assets based on the data value quantification model; Optimization classification module, used to classify and manage data assets based on a multi-objective optimization model; The level adjustment module is used to dynamically adjust the weights of the optimization model according to the DCMM level; Resource management module, used for resource allocation and priority management of data assets.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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