A data resource health maintenance method, device and equipment

Abstract

The application discloses a data resource health maintenance method, device and equipment, and the data resource includes data computing resource and data storage resource. A health evaluation index for evaluating the health of the data resource is determined, and the index includes resource effective use degree and resource risk level. The resource effective use degree is used to represent the degree of effective use of the data resource, and the resource risk level is used to represent the degree of negative impact of the use of the data resource on the business. The resource effective use degree is negatively correlated with the increase rate of the resource risk level. The weights corresponding to the two indexes are determined, and the health of the data resource is evaluated based on the two indexes and the weights corresponding to the two indexes. In this way, the health of the data resource is evaluated based on the degree of effective use of the resource and the degree of negative impact of the use of the data resource on the business, which can more accurately reflect the health of the resource. In addition, the data resource can be managed to improve the health of the data resource.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method, apparatus, and equipment for maintaining the health of data resources. Background Technology

[0002] In big data scenarios, data-related businesses are rapidly developing, and various business scenarios rely on cluster resources, such as cluster hardware resources. Hardware can be used to build services, undertake computing tasks, and meet the needs of various business scenarios. To ensure the healthy use of cluster hardware resources, health maintenance is required during their use.

[0003] Currently, the health maintenance of cluster hardware resources can be performed manually, but the labor cost is relatively high. Summary of the Invention

[0004] In view of this, this application provides a data resource health maintenance method, apparatus and equipment for accurately assessing the health status of cluster hardware resources, reducing labor costs and enabling data resource governance.

[0005] To solve the above problems, the technical solution provided in this application is as follows:

[0006] In a first aspect, this application provides a method for maintaining the health of data resources, wherein the data resources include data computing resources and data storage resources, and the method includes:

[0007] Obtain health assessment indicators for evaluating the health of data resources; the health assessment indicators include the effective utilization rate of data resources during use and the resource risk level; the effective utilization rate is used to indicate the degree to which the data resources are used effectively, and the resource risk level is used to indicate the degree of negative impact of the use of data resources on business; the effective utilization rate of resources is negatively correlated with the rate of increase of the resource risk level;

[0008] Determine the weight corresponding to the effective utilization of the resource and the weight corresponding to the risk level of the resource;

[0009] Based on the resource utilization efficiency, the resource risk level, the weight corresponding to the resource utilization efficiency, and the weight corresponding to the resource risk level, obtain the health assessment result of the data resource;

[0010] Obtain data resource usage events related to the health assessment results, and perform data resource governance based on the data resource usage events.

[0011] Secondly, this application provides a data resource health maintenance device, wherein the data resources include data computing resources and data storage resources, and the device includes:

[0012] The first acquisition unit is used to acquire health assessment indicators for evaluating the health of data resources; the health assessment indicators include the effective utilization rate of data resources during use and the resource risk level; the effective utilization rate is used to indicate the degree to which the data resources are used effectively, and the resource risk level is used to indicate the degree of negative impact of the use of data resources on business; the effective utilization rate of resources is negatively correlated with the rate of increase of the resource risk level;

[0013] A determining unit is used to determine the weight corresponding to the effective utilization of the resource and the weight corresponding to the risk level of the resource.

[0014] The second acquisition unit is used to acquire the health assessment result of the data resource based on the resource effective utilization, the resource risk level, the weight corresponding to the resource effective utilization, and the weight corresponding to the resource risk level.

[0015] The third acquisition unit is used to acquire data resource usage events related to the health assessment results and to perform data resource governance based on the data resource usage events.

[0016] Thirdly, this application provides an electronic device, comprising:

[0017] One or more processors;

[0018] Storage device, on which one or more programs are stored,

[0019] When the one or more programs are executed by the one or more processors, the one or more processors implement the data resource health maintenance method as described in the first aspect.

[0020] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data resource health maintenance method as described in the first aspect.

[0021] Therefore, this application has the following beneficial effects:

[0022] This application provides a method, apparatus, and equipment for maintaining the health of data resources. Data resources are a quantification of cluster hardware resources, including data computing resources and data storage resources. Health assessment indicators are obtained to evaluate the health of data resources. These indicators include the effective utilization rate of data resources during use and the resource risk level. The effective utilization rate indicates the degree to which data resources are used effectively, while the resource risk level indicates the degree of negative impact of data resource use on business operations. Lower effective utilization rate indicates less effective use of data resources, and thus a lower level of health in data resource usage. Higher resource risk level indicates a greater negative impact of data resource use on business operations, and thus a lower level of health in data resource usage. Furthermore, the effective utilization rate affects the change in resource risk level; lower effective utilization rate indicates more ineffective or inefficient use of data resources, which is more likely to impact business operations and causes the resource risk level to rise faster. Subsequently, the weights corresponding to effective utilization rate and resource risk level are determined. These weights represent the proportion of influence on the health assessment results of data resources. Based on this, a health assessment result for data resources is obtained based on resource effective utilization, resource risk level, the weight corresponding to resource effective utilization, and the weight corresponding to resource risk level. Then, data resource usage events related to the health assessment result are obtained, and data resource governance is performed based on these events. Thus, the maintenance method provided in this application uses resource effective utilization and resource risk level during data resource usage as health assessment indicators, evaluating the health of data resources during use by assessing the degree of effective resource utilization and the negative impact of resource usage on business. It is evident that the provided health assessment indicators are not singular and can accurately reflect the health of data resources, resulting in high accuracy in assessing data resource health. Furthermore, this method can be automated to a certain extent, reducing manual costs. In addition, data resource governance is also performed to improve the health assessment results. Attached Figure Description

[0023] Figure 1 A schematic diagram illustrating an exemplary application scenario provided in this application embodiment;

[0024] Figure 2 A flowchart illustrating a data resource health maintenance method provided in this application embodiment;

[0025] Figure 3 An example diagram illustrating the assessment direction for a data resource health assessment provided in this application embodiment;

[0026] Figure 4 A schematic diagram of a data resource health management system provided in an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of the structure of a data resource health maintenance device provided in an embodiment of this application;

[0028] Figure 6 This is a schematic diagram of the basic structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] To facilitate understanding of the data resource health maintenance method provided in the embodiments of this application, the following is combined with... Figure 1 The example scenario is shown below. See also... Figure 1 As shown in the figure, this figure is a schematic diagram of an exemplary application scenario provided in the embodiments of this application.

[0031] like Figure 1 As shown, the cluster may include Figure 1 The diagram shows devices such as Terminal 1, Terminal 2, and servers. Terminals can be smartphones, tablets, laptops, desktop computers, etc., and are not limited here. Furthermore, the number of Terminal 1, Terminal 2, and servers in the cluster is not limited and can be determined based on actual needs. Cluster hardware resources specifically refer to the resources within Terminal 1, Terminal 2, and servers within the cluster. These resources include memory, CPU, disks, hard drives, and other resources used to build services and undertake computing tasks. Specifically, the implementation of computing tasks in the cluster relies on the computation of processing hardware devices such as CPUs, and the data produced by these tasks, or other data, relies on the storage of storage hardware devices such as memory and disks. Both the computing tasks and the stored data are business-related; for example, computing tasks are computing tasks built to implement business functions, and data is business-related data.

[0032] The data resource health maintenance method provided in this application can be executed by an electronic device, which is not limited to electronic devices but can be a terminal device or a server, such as a terminal device or server in a cluster. Since the cluster hardware resources are used for data processing (such as data computation) and data storage, the data resources can be used as a quantitative result of the cluster hardware resources to represent them. The data resources are provided to users, and users must assess the health of the data resources during use to minimize business problems caused by unhealthy data resources.

[0033] Specifically, first, determine the health assessment indicators used to evaluate the health of data resources. These indicators include the effective utilization rate of data resources during their use and the resource risk level. The effective utilization rate indicates the degree to which data resources are used effectively, while the resource risk level indicates the degree of negative impact of data resource usage on business operations.

[0034] Lower resource utilization efficiency indicates less effective use of data resources, resulting in lower data resource health. Conversely, higher resource risk levels indicate a greater negative impact of data resource usage on business operations, also leading to lower data resource health. Furthermore, resource utilization efficiency influences resource risk levels; lower efficiency means more ineffective or inefficient use of data resources, increasing the likelihood of business disruption and accelerating the rate of increase in resource risk. Therefore, the health of data resources can be assessed by considering both the degree of effective data resource utilization and the extent of its negative impact on business operations.

[0035] Furthermore, the weights corresponding to resource utilization efficiency and resource risk level are determined. These weights represent the proportion of influence on the health assessment results of the data resources. Based on this, and using resource utilization efficiency, resource risk level, the weights corresponding to resource utilization efficiency, and the weights corresponding to resource risk level, the health assessment results of the data resources are obtained.

[0036] Those skilled in the art will understand that Figure 1 The schematic diagram shown is merely one example in which embodiments of this application can be implemented. The scope of application of the embodiments of this application is not limited by any aspect of this framework.

[0037] To facilitate understanding of this application, the following description, in conjunction with the accompanying drawings, illustrates a data resource health maintenance method provided by an embodiment of this application.

[0038] See Figure 2 As shown, this figure is a flowchart of a data resource health maintenance method provided in an embodiment of this application. Figure 2 As shown, the method may include S201-S204:

[0039] S201: Obtain health assessment indicators for evaluating the health of data resources; health assessment indicators include the effective utilization of data resources during use and the resource risk level; the effective utilization of data resources is used to indicate the degree to which data resources are used effectively, and the resource risk level is used to indicate the degree of negative impact of the use of data resources on business; the rate of increase of the effective utilization of data resources is negatively correlated with the rate of increase of the resource risk level.

[0040] The data resources in this embodiment represent cluster hardware resources, which can be determined based on the resource user. The resource user is the cluster hardware resource user; for example, the resource user can be an individual, a department, etc. Therefore, when the resource user is a department, the aforementioned cluster hardware resources are the hardware resources within that department. Thus, the health status of the cluster hardware resources for each department can be assessed.

[0041] The health assessment indicators are those set forth in this application's embodiments to evaluate the health status of data resources during use. See also... Figure 3 , Figure 3 This diagram illustrates an example of the assessment direction for a data resource health assessment, provided as an embodiment of this application. Figure 3 As shown in the relevant section, data resource health assessment can include an assessment of the effective use of data resources and an assessment of the risks associated with their use. For example, the assessment of the effective use of data resources can be represented by the resource utilization efficiency, while the assessment of the risks associated with their use can be represented by the resource risk level. A lower resource utilization efficiency indicates a less effective use of the data resources, resulting in lower health in data resource usage. Conversely, a higher resource risk level indicates a greater negative impact of data resource usage on business operations, also resulting in lower health in data resource usage. Furthermore, resource utilization efficiency influences changes in resource risk level. It is evident that lower resource utilization efficiency indicates more ineffective or inefficient use of data resources, which more easily impacts business operations and causes the resource risk level to rise more rapidly.

[0042] This application does not limit the validity threshold for indicating the effective use of data resources, nor does it limit the risk level threshold for indicating whether data resources are used at low or no risk; both can be set and modified according to actual circumstances. Furthermore, this application does not limit the representation of resource validity; for example, it can be represented by a numerical value within 100 or as a percentage. This application also does not limit the representation of resource risk levels; for example, it can be represented by numbers 0, 1, 2 (the larger the number, the higher the risk level), or by letters such as L0, L1, L2, etc.

[0043] In this application embodiment, data resource usage that conforms to the data resource usage standards refers to effective data resource usage and non-high-risk data resource usage. The data resource usage standards are used to guide the healthy use of data resources. These standards are pre-defined and include data resource usage events related to healthy data resource usage. Data resource usage events refer to events related to the use of data resources in electronic devices. Data resources include data computing resources and data storage resources. Data computing resources are resources that provide computing functions, such as central processing units (CPUs), and can be quantified by the length of computing time; this is not limited here. Data storage resources are resources such as memory, disks, and hard disks that provide storage space for storing data; this can be quantified by the size of the data storage space; this is not limited here either. Therefore, data resource usage events include data resource usage events for data computing resources and data storage resources. For example, after a computing task is constructed, the task needs to be executed. During execution, the computing power of a CPU, etc., is required, consuming data computing resources. In this process, data access frequency below a certain frequency and task runtime below a certain duration are both data resource usage events, specifically data resource usage events for data computing resources. For example, regarding data storage, data access frequency of data stored in a data table is less than xx frequency, and the used data storage space on the disk is less than xx GB. These are all data resource usage events, specifically data resource usage events of data storage resources.

[0044] Based on the above, the data resource usage events for healthy data resource use in the data resource usage standard can include events indicating that data resources are being used effectively. Effective data resource use signifies healthy data resource usage. For example, an event indicating effective data resource usage might be a high access frequency of data produced by a task (indicating that the data produced by the task is valid and can be used effectively; correspondingly, the consumed data computing resources are consumed effectively, and the data resource is being used effectively), or a high access frequency of a data table (indicating that the data storage is effective, the consumed data storage resources are effective, and the data resource is being used effectively). Additionally, data resource usage events for healthy data resource use in the resource usage standard can also include events indicating that resources are risk-free or have low risk. A low risk level indicates that the data resource is also being used healthily. For example, an event indicating risk-free or low-risk data resources might be a task runtime of less than 4 hours (indicating that the task runtime is appropriate, the consumption of data computing resources is normal, there is no risk, and there is no impact on business), or less than 40% of the disk's used data storage space (indicating that storage resources are being used normally, there is no risk, and there is no impact on business). It is understood that the data resource usage events that indicate the effective use of data resources in the data resource usage standard are not limited here, and can be set, added or deleted according to the actual situation.

[0045] In this embodiment, data resources should be used in accordance with the data resource usage events for healthy use as much as possible, as outlined in the data resource usage standards. However, during the use of data resources, a health assessment is still necessary to minimize the possibility of business problems arising from their use.

[0046] Therefore, as an optional example, resource utilization effectiveness is determined by the data resource usage events corresponding to the data resource and the resource utilization effectiveness labeling relationship. The resource utilization effectiveness labeling relationship includes a correspondence between at least one data resource usage event and at least one resource utilization effectiveness.

[0047] The resource effective use calibration relationship lists as many possible data resource usage events as possible and their corresponding resource effective use degrees. That is, the data resource usage events in the resource effective use calibration relationship may be effectively used events, or they may be inefficiently or ineffectively used events. Through the resource effective use calibration relationship, the resource effective use degree corresponding to any data resource usage event can be found. It is known that the resource effective use calibration relationship is obtained through pre-calibration; the calibration method is not limited here and can be determined according to the actual situation.

[0048] For example, a data resource is the computing resource consumed by a task. The corresponding data resource usage event is the access frequency of the data produced by the task, which is 50 times per week. The resource utilization efficiency corresponding to this data resource usage event is found in the resource utilization efficiency indexing relationship. As another example, a data resource is the data storage resource consumed by data storage. The corresponding data resource usage event is the access frequency of the data table, which is 10 times per day. The resource utilization efficiency corresponding to this data resource usage event is found in the resource utilization efficiency indexing relationship.

[0049] As an optional example, the resource risk level is determined by data resource usage events and resource usage risk labeling relationships. These resource usage risk labeling relationships include a correspondence between at least one data resource usage event and at least one resource risk level.

[0050] The resource usage risk labeling relationship lists as many possible data resource usage events as possible, along with their corresponding resource risk levels. That is, the data resource usage events in the resource usage risk labeling relationship may be high-risk or low-risk events. Through the resource usage risk labeling relationship, the resource risk level corresponding to any data resource usage event can be found. It is evident that the resource usage risk labeling relationship is pre-labeled; the labeling method is not limited here and can be determined based on the actual situation.

[0051] For example, data resources refer to the computing resources consumed by a task, and the corresponding data resource usage event is that the task runtime exceeds 8 hours. The resource utilization rate corresponding to this data resource usage event is found in the resource utilization effectiveness index. As another example, data resources refer to the data storage resources consumed by disk storage, and the corresponding data resource usage event is that the disk's used data storage space is less than 30%. The resource utilization rate corresponding to this data resource usage event is found in the resource utilization effectiveness index.

[0052] Based on the above, this application provides an implementation method for determining the effective utilization degree and resource risk level of data resources by using data resource usage events and corresponding resource effective utilization calibration relationships and resource usage risk calibration relationships. The resource effective utilization calibration relationship and resource usage risk calibration relationship are pre-calibrated, which can more accurately reflect the degree of effective use of data resources and the degree of use risk at present, so that the obtained data resource effective utilization degree and resource risk level are also more accurate.

[0053] However, the embodiments of this application are not limited to the above-described implementation methods for obtaining resource effective utilization and resource risk level. Other existing or future methods capable of obtaining resource effective utilization and resource risk level may also be used for implementation.

[0054] Understandably, there are usually multiple data resource usage events. When the resource user is a department, the resource utilization effectiveness and risk level corresponding to each data resource usage event within the department can be obtained. These are used to assess the health of the department's data resources.

[0055] S202: Determine the weights corresponding to the effective utilization of resources and the weights corresponding to the risk level of resources.

[0056] Both resource utilization effectiveness and resource risk level are used to assess the health of data resources during use. After determining resource utilization effectiveness and resource risk level, the weights corresponding to resource utilization effectiveness and resource risk level can be determined. The weights represent the degree of influence of the risk assessment indicators on the health assessment results of the data resources; the larger the weight, the greater the influence of the risk assessment indicator on the health assessment results. For example, the sum of the weights corresponding to resource utilization effectiveness and resource risk level can be 1.

[0057] In one possible implementation, this application provides a specific method for determining the weight corresponding to the effective utilization of resources and the weight corresponding to the resource risk level, including:

[0058] When the total amount of data resources exceeds the target amount of resources, the weight corresponding to the effective utilization of resources is greater than the weight corresponding to the risk level of resources.

[0059] or,

[0060] When the ratio of the amount of data resources used to the total amount of data resources is greater than the target ratio, the weight corresponding to the effective utilization of resources is less than the weight corresponding to the risk level of resources.

[0061] In the first scenario, when the total amount of data resources exceeds the target amount, it indicates that the data resources are sufficient. For example, the total amount of data resources allocated to department A is sufficient. However, insufficient or excessive data resource usage can significantly impact business operations, leading to higher risks and potentially disrupting business processes. Conversely, when data resources are sufficient, the focus of data resource health assessment can be on their effective use, emphasizing whether they are being used efficiently. In this case, the weight corresponding to effective resource utilization can be set higher than the weight corresponding to the resource risk level.

[0062] In the second scenario, when the ratio of data resource usage to total data resources exceeds the target ratio, it indicates insufficient data resources, potentially unsuitable for business needs. This has a higher impact on business operations and carries a greater risk in data resource usage. Therefore, the weight corresponding to effective resource utilization can be set lower than the weight corresponding to resource risk level.

[0063] Understandably, during data resource usage, the effective utilization rate of data resources may be high, indicating that the data resources are being used effectively. From the perspective of assessing the health of data resources based on effective utilization, the data resource usage is healthy. However, the resource risk level of this data resource may also be high, meaning that from the perspective of assessing the health of data resources based on resource risk level, the data resource usage is unhealthy. For example, a disk has 100GB of data storage space, and 80GB of that space is being used effectively, but 20GB remains. Since the remaining 20GB of data storage space is considered a data resource usage event, its corresponding resource risk level is determined to be high-risk using resource usage risk labeling relationships. In this case, it's advisable to give a greater weight to the resource risk level than the effective utilization rate, increasing the proportion of data resource health assessment based on resource risk level, thus making users pay more attention to this situation.

[0064] Based on the above, this application provides methods for determining the weights corresponding to resource utilization effectiveness and resource risk levels in two scenarios, ensuring a more reasonable weight allocation and thus more accurate subsequent resource health assessment results. It is understood that this application does not limit the target resource quantity and target ratio, which can be determined according to actual circumstances. Furthermore, the weights corresponding to resource utilization effectiveness and resource risk levels can be periodically adjusted based on actual resource usage to ensure more accurate weight allocation.

[0065] S203: Based on resource effective utilization, resource risk level, the weight corresponding to resource effective utilization, and the weight corresponding to resource risk level, obtain the health assessment results of data resources.

[0066] For example, the health assessment results of data resources can be represented by a health score, with a higher health score indicating healthier use of the data resources.

[0067] In one possible implementation, this application provides a specific implementation method for obtaining the health assessment results of data resources based on resource effective utilization, resource risk level, the weight corresponding to resource effective utilization, and the weight corresponding to resource risk level, including A1-A3:

[0068] A1: Based on the resource effective utilization rate and the first calibration table, determine the first score corresponding to the resource effective utilization rate; the first calibration table includes at least one resource effective utilization rate, at least one score, and the correspondence between the resource effective utilization rate and the score.

[0069] The first calibration table is pre-calibrated, and the first score corresponding to the effective utilization of resources can be determined based on the first calibration table.

[0070] A2: Based on the resource risk level and the second calibration table, determine the second score corresponding to the resource risk level; the second calibration table includes at least one resource risk level, at least one score, and the correspondence between the resource risk level and the score.

[0071] The second calibration table is pre-calibrated, and the second score corresponding to the obtained resource risk level can be determined based on the second calibration table.

[0072] A3: The weighted sum of the first score, the weight corresponding to the effective utilization of resources, the second score, and the weight corresponding to the risk level of resources is used as the health assessment result of the data resources.

[0073] Therefore, the health score of data resources = (first score × weight corresponding to the effective use of resources + second score × weight corresponding to the risk level of resources).

[0074] As can be seen from A1-A3, in this specific implementation, the effective utilization of resources is transformed into a score, so as to intuitively reflect the health of data resources during use through the score.

[0075] In another possible implementation, this application provides a specific implementation method for obtaining the health assessment results of data resources based on resource utilization efficiency, resource risk level, the weight corresponding to resource utilization efficiency, and the weight corresponding to resource risk level, including:

[0076] Input the resource effective utilization rate, resource risk level, the weight corresponding to the resource effective utilization rate, and the weight corresponding to the resource risk level into the data resource health assessment model to obtain the data resource health assessment results output by the health assessment model.

[0077] In practical applications, a data resource health assessment model can be pre-trained. The inputs to the data resource health assessment model are the resource effective utilization rate, the resource risk level, the weight corresponding to the resource effective utilization rate, and the weight corresponding to the resource risk level. The output of the data resource health assessment model is the health assessment result of the data resource, which can also be represented by a health score.

[0078] For example, the data resource health assessment model is trained based on sample data and the corresponding label values. The sample data includes the historical effective utilization rate of the data resource, the historical risk level of the resource, the weight corresponding to the historical effective utilization rate, and the weight corresponding to the historical risk level. The label values ​​of the sample data are the actual health assessment results of the data resource.

[0079] As an alternative example, the data resource health assessment model can be a model composed of convolutional neural networks, etc. A model with good prediction performance can be used for training and application to make the data resource health assessment results more accurate. The internal structure of the data resource health assessment model is not limited here.

[0080] Understandably, when the resource user is a department, the resource utilization effectiveness and risk level corresponding to each data resource usage event within the department can be obtained, and therefore the resource assessment results for the data resources used in each data resource usage event can also be obtained. When using the entire department as the dimension for data resource health assessment, the health assessment results of each data resource within the department can be statistically analyzed and a comprehensive assessment can be performed; the method of comprehensive assessment is not limited here. For example, when the health assessment results are represented by health scores, the health scores can be accumulated.

[0081] S204: Obtain data resource usage events related to health assessment results and conduct data resource governance based on these events.

[0082] Data resource usage events are those that lead to the health assessment results in S203. Furthermore, data resource usage events can be analyzed and data resource governance can be implemented to improve the health assessment results. See below for details.

[0083] Based on the relevant content of S201-S204 above, this application provides a data resource health maintenance method, which determines health assessment indicators for evaluating the health of data resources. These indicators include the effective utilization rate of data resources and the resource risk level during data resource usage. The effective utilization rate represents the degree to which data resources are effectively used, while the resource risk level represents the degree of negative impact of data resource usage on business operations. A lower effective utilization rate indicates a smaller degree of effective data resource utilization, resulting in a lower level of data resource health. Conversely, a higher resource risk level indicates a greater negative impact of data resource usage on business operations, also resulting in a lower level of data resource health. Furthermore, the effective utilization rate influences changes in the resource risk level; a lower effective utilization rate indicates more ineffective or inefficiently used data resources, making it easier to impact business operations and causing the resource risk level to rise faster. Therefore, the weights corresponding to the effective utilization rate and the resource risk level are determined. These weights represent the proportion of influence on the data resource health assessment result. Based on this, the health assessment result of the data resource is obtained based on the effective utilization rate, resource risk level, the weights corresponding to the effective utilization rate, and the weights corresponding to the resource risk level. Furthermore, data resource usage events related to the health assessment results are obtained, and data resource governance is performed based on these events. Thus, the maintenance method provided in this application uses the effective utilization and risk level of resources during data resource usage as health assessment indicators. It assesses the health of data resources during use by evaluating the degree of effective resource utilization and the negative impact of resource usage on business. It is evident that the provided health assessment indicators are not singular and can accurately reflect the health of data resources, resulting in high accuracy in data resource health assessment. Moreover, this method can be automated to a certain extent, reducing manual costs. In addition, data resource governance is performed to improve the health assessment results.

[0084] Again Figure 3 As shown, as an optional example, the health assessment indicator also includes the data resource utilization value, which can be represented by a value within 100; this is not limited here. The data resource utilization value is used to represent the relationship between the resource output and resource input of the data resource. When the data resource utilization value is high, it indicates that the quality of the resource output is high, thus determining a higher level of data resource health. It can be seen that the data resource utilization value, as another health assessment indicator, enriches the dimensions of data resource health assessment, making the data resource health assessment process more realistic and the assessment results more accurate.

[0085] In one possible implementation, this application provides a specific implementation method for obtaining the health assessment result of data resources in step S203 based on resource effective utilization, resource risk level, the weight corresponding to resource effective utilization, and the weight corresponding to resource risk level, including B1-B2:

[0086] B1: Obtain the weight corresponding to the usage value.

[0087] B2: Based on resource effective utilization, resource risk level, use value, the weights corresponding to resource effective utilization, resource risk level, and use value, obtain the health assessment results of data resources.

[0088] Understandably, with the addition of the "use value" health assessment indicator, the health assessment results of data resources can still be obtained using methods A1-A3. This only requires adding a third calibration table, which is used to determine the third score corresponding to the use value. The third calibration table includes at least one use value, at least one score, and the correspondence between use value and score. Finally, the weighted sum of the first score, the weight corresponding to the resource's effective utilization, the second score, the weight corresponding to the resource risk level, the third score, and the weight corresponding to the use value is taken as the health assessment result of the data resource. Alternatively, with the addition of the use value health assessment indicator, the health assessment results of resources can also be obtained using a health assessment model. This simply requires adding the use value and its corresponding weights as input during the training of the health assessment model.

[0089] In practical applications, data resources are used to build data warehouse models in data warehouses, and data warehouse models are used to store data. Data storage requires resources, such as storage resources.

[0090] In one possible implementation, this application provides a specific method for obtaining the use value of data resources, including C1-C3:

[0091] C1: The quotient of the number of high-value data warehouse models to the total number of data warehouse models is determined as the asset concentration of data resources; asset concentration is used to represent the resource output of data resources.

[0092] A data warehouse model can be understood as a set of data tables. In a data warehouse, these tables can be referred to as the data warehouse model. For example, a data warehouse model that meets one or more of the following conditions is considered a high-value data warehouse model:

[0093] The number of downstream users of the data warehouse model exceeds the target number; the metrics used by the data warehouse model are the target metrics; and the data from the data warehouse model is visualized.

[0094] Understandably, if a data warehouse model (i.e., data tables) is used by a large number of downstream users after its construction, its value is considered high. Furthermore, since the data warehouse model (i.e., data tables) is used for business implementation and is related to the business, its value is considered high when the metrics it acquires are target metrics. Here, the metrics are those involved in the business, and the target metrics are core metrics within the business. Additionally, the visualization of the data warehouse model (i.e., data tables) also indicates its high value.

[0095] The total number of data warehouse models refers to the total number of data tables in the data warehouse. The number of high-value data warehouse models in the data warehouse is counted, and the quotient of this number to the total number of data warehouse models is determined as the asset concentration of the data resources. Data warehouse models can be used to represent the resource output of data resources; that is, data warehouse models are generated by consuming data resources. Therefore, in this step, asset concentration can also be used to represent the resource output of data resources. The higher the asset concentration, the higher the value of the data resource output.

[0096] C2: The quotient of the total data storage volume of the data warehouse and the data storage volume of the operational data storage layer in the data warehouse is determined as the cost investment coefficient of data resources; the operational data storage layer is used to store source data of business systems, and the cost investment coefficient is used to represent the resource investment of data resources.

[0097] A data warehouse typically comprises five layers: Operational Data Storage (ODS), Data Middleware (DWM), Data Detail (DWD), Data Service (DWS), and Dimension Tables (DIM). The ODS layer needs to interface with multiple different types of business system databases. The ODS layer can be viewed as a simple backup of the business system's data source; that is, it stores the source data from the business systems. The remaining three layers of the data warehouse store data obtained after processing the source data from the business systems (e.g., data in the DWD tables). This data processing can be referred to as the upper-level construction of the data layer.

[0098] The total data storage capacity of a data warehouse is the total data storage capacity occupied by the four layers of storage. The quotient of the total data storage capacity of the data warehouse and the data storage capacity of the operational data storage layer in the data warehouse is determined as the cost investment coefficient of data resources. It can be seen that the cost investment coefficient is a coefficient greater than 1, and it is used to represent the resource investment in data resources. The larger the cost investment coefficient, the richer the upper-layer construction and the greater the resource investment in data resources.

[0099] C3: The quotient of asset concentration and cost input coefficient is determined as the utilization value of data resources.

[0100] The quotient of asset concentration and cost input coefficient is calculated and used as the utilization value of data resources. The utilization value of data resources is the ratio of resource output to resource input. When the asset concentration is higher and the cost input coefficient is lower, it indicates that less data resource input is required, but the value of the data warehouse model output is higher, thus indicating a higher utilization value of the data resources.

[0101] Based on the relevant content in C1-C3, this application provides a specific implementation method for determining the utilization value of data resources. The method determines the value of data resources by the proportion of high-value output in resource input. This approach accurately reflects whether data resources are used efficiently, aligns with actual conditions, and yields a high degree of accuracy in determining the utilization value of data resources.

[0102] Furthermore, the embodiments of this application are not limited to the implementation methods for determining the use value of data resources in C1-C3, and can also be implemented using any existing or future method capable of determining the use value of data resources.

[0103] Based on the above, we can understand how to accurately assess the health of data resources so as to promptly alert resource users. See also Figure 4 , Figure 4 This is a schematic diagram of a data resource health management system provided in an embodiment of this application. Figure 4 As shown in the embodiments of this application, a data resource health management system is provided. This system includes not only data resource health assessment, but also data resource health diagnosis, data resource governance, and standard updates, making the data resource health management system relatively complete and forming a comprehensive mechanism. The following sections will describe the data resource health diagnosis, data resource health governance, and standard updates; please see below for details.

[0104] In one possible implementation, this application embodiment provides a specific implementation method for obtaining data resource usage events related to health assessment results in S204, including the following steps:

[0105] Identify the first data resource usage event where the effective utilization rate is lower than the target effective utilization rate, and the second data resource usage event where the resource risk level is higher than the target risk level.

[0106] Based on this, the data resource health assessment method provided in this application embodiment further includes the following steps:

[0107] The first and second data resource usage events are analyzed, and the data resource usage standards are updated based on the analysis results. The data resource usage standards include data resource usage events related to the healthy use of data resources.

[0108] When the effective utilization of resources is lower than the target effective utilization, it is determined that the resource effective utilization is low, affecting the health of data resources, and data resource governance is required. When the resource risk level is higher than the target risk level, it is determined that the use of data resources poses a risk, affecting the health of data resources, and data resource governance is required. The target effective utilization and target risk level are not limited here and can be determined based on the actual situation.

[0109] At this point, a first data resource usage event that causes the effective utilization of data resources to be lower than the target effective utilization, and a second data resource usage event that causes the resource risk level to be higher than the target risk level are identified. The first data resource usage event is the cause of the lower effective utilization of data resources, and the second data resource usage event is the cause of the risk to data resources. This achieves a health diagnosis, the purpose of which is to identify the reasons for the lower health level of data resources. Both the first and second data resource usage events belong to the data resource usage events related to the health assessment results in S204.

[0110] After identifying the first and second data resource usage events, they are analyzed to determine the reasons for the low health of the data resources, and the data resource usage standards are updated based on the analysis results. For example, the second data resource usage event is that the lifespan of the data table is 5 days. This short lifespan makes it easy for the data in the table to be released quickly, affecting business operations. The data storage resources used by the data table are at risk, leading to a low health of the data resources. Through analysis, it is determined that the lifespan of the data table needs to be greater than or equal to 20 days. This reduces the risk of data storage resource usage and improves the health of data resource usage. Therefore, the data resource usage event of "data table lifespan greater than or equal to 20 days" can be added to the data resource usage standards as a healthy data resource usage event, thus updating the data resource usage standards.

[0111] Based on the above, after a health diagnosis, the analysis and summarization of the diagnostic results can be used to update data resource usage standards and establish a mechanism. For example... Figure 4 As shown, this data resource usage standard can be used to guide the subsequent use of data resources, and can help avoid the recurrence of the same problem during health assessments and diagnoses, thus forming a closed loop in data resource management.

[0112] exist Figure 4 In the data resource health management system, after health diagnosis, there is also data resource governance, which is used to improve the health of data resources.

[0113] In one possible implementation, this application embodiment provides a specific implementation method for data resource governance based on data resource usage events in S204, including the following steps:

[0114] When the first data resource usage event is a data storage resource usage event, data resource governance is carried out according to the data storage resource governance method in the data storage resource governance identification relationship in order to improve resource utilization.

[0115] When the first data resource usage event is a data computing resource usage event, data resource governance is carried out according to the data computing resource governance method in the data computing resource governance labeling relationship in order to improve resource utilization.

[0116] The data storage resource governance identification relationship includes the correspondence between at least one data storage resource usage event and at least one data storage resource governance method, while the data computing resource governance identification relationship includes the correspondence between at least one data computing resource usage event and at least one data computing resource governance method.

[0117] Data storage resource governance calibration relationships and data computing resource governance calibration relationships are obtained in advance through calibration methods. There is no specific limitation on the calibration method here, and it can be set according to the actual situation.

[0118] It is understandable that different data resource usage events correspond to different data resource governance methods, which can be determined by searching and identifying the data resource governance identification relationship. When the first data resource usage event is a data storage resource usage event, it is related to the use of data storage resources. Therefore, the corresponding data storage resource governance method is searched according to the data storage resource governance identification relationship, and data storage resource governance is performed based on the found data storage resource governance method. When the first data resource usage event is a data computing resource usage event, the processing method is similar and will not be elaborated here. Thus, the first data resource usage event is divided into two directions: storage and computing. Correspondingly, data resource governance is also divided into two directions: storage and computing. Based on detailed data resource usage events, corresponding detailed governance methods are provided, and this process can be solidified into data resource management processes or related data resource management products. Through automated governance, the governance process is automatically implemented to complete data resource governance.

[0119] In one possible implementation, the data resource health maintenance method provided in this application embodiment further includes the following steps:

[0120] When the first data resource usage event is the target data storage resource usage event, a release assessment is performed on the target data resource or a portion of the target data resource used by the first data resource usage event, and the release assessment result is obtained; the target data storage resource usage event is an event in which the effective utilization of the resource is lower than the target effective utilization because the access frequency of the data using the resource is lower than the target frequency.

[0121] When the release assessment result is positive, the target data resource or a portion of the target data resource is released.

[0122] It is known that when a target data storage resource usage event occurs where the access frequency of the data using the resource is lower than the target frequency, resulting in a lower effective utilization of the resource than the target effective utilization, the target data resource or a portion of the target data resource corresponding to the target data storage resource usage event can be released. Before release, a release assessment is performed to obtain the assessment result, and based on the assessment result, it is determined whether the target data resource or a portion of the target data resource can be released. When the release assessment result is positive (e.g., "yes"), it indicates that the data resource can be released, and thus the target data resource or a portion of the target data resource is released. The release assessment method can be determined based on the specific target data storage resource usage event; this is not limited here. Alternatively, the correspondence between target data storage resource usage events and release assessment methods can be pre-defined to determine the release assessment method based on this mapping.

[0123] For example, if 300 data tables have not been accessed for the past 30 days, after a release assessment, if it is determined that these 300 data tables will not be used by any business within the next month, then the 300 data tables will be deleted (indicating a release assessment result of "yes"), thus releasing data storage resources. Furthermore, the specific release method can be customized; for example, by selecting the 300 data tables, one-click deletion can be achieved, releasing data storage resources.

[0124] For example, if partition data is accessed infrequently, and a release assessment determines that the partition data's usage frequency will be low within a month (indicating a release assessment result of "yes"), then the data can be handled gently, i.e., the number of partition data backups can be reduced, freeing up data storage resources.

[0125] It is evident that the governance methods corresponding to the target data storage resource usage events are all aimed at releasing data storage resources and / or data computing resources, thereby reducing the ineffective and / or inefficient use of data resources and improving their health. Releasing data resources can also improve the computing and storage efficiency of hardware resources.

[0126] In addition, the second data resource usage event can also be divided into data storage resource usage event and data computing resource usage event, and corresponding labeling relationships can be set to find the corresponding governance methods through the labeling relationships. Data resource governance can be carried out through specific governance methods to reduce the risk level of resources and improve the health of data resources.

[0127] Based on the above, it can be seen that in the embodiments of this application, specific governance methods can be provided to users when the health level of data resources is low, so as to respond to the health assessment of data resources in a timely manner and avoid business accidents.

[0128] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0129] Based on the data resource health maintenance method provided in the above-described embodiments, this application also provides a data resource health maintenance device, which will be described below with reference to the accompanying drawings. Since the principle by which the device in this disclosure solves the problem is similar to the data resource health maintenance method described above in this application, the implementation of the device can refer to the implementation of the method, and repeated details will not be elaborated further.

[0130] See Figure 5 As shown, this figure is a schematic diagram of the structure of a data resource health maintenance device provided in an embodiment of this application. Data resources include data computing resources and data storage resources, such as... Figure 5 As shown, the data resource health maintenance device 500 includes:

[0131] The first acquisition unit 501 is used to acquire health assessment indicators for evaluating the health of data resources; the health assessment indicators include the effective utilization rate of data resources during use and the resource risk level; the effective utilization rate of resources is used to indicate the degree to which the data resources are used effectively, and the resource risk level is used to indicate the degree of negative impact of the use of data resources on business; the effective utilization rate of resources is negatively correlated with the rate of increase of the resource risk level;

[0132] The determining unit 502 is used to determine the weight corresponding to the effective utilization of the resource and the weight corresponding to the risk level of the resource.

[0133] The second acquisition unit 503 is used to acquire the health assessment result of the data resource based on the resource effective utilization, the resource risk level, the weight corresponding to the resource effective utilization, and the weight corresponding to the resource risk level.

[0134] The third acquisition unit is used to acquire data resource usage events related to the health assessment results and to perform data resource governance based on the data resource usage events.

[0135] In one possible implementation, the health assessment indicators further include the utilization value of the data resources; the utilization value of the data resources is used to represent the relationship between the resource output and resource input of the data resources.

[0136] The second acquisition unit 503 is specifically used for: acquiring the weight corresponding to the usage value; and acquiring the health assessment result of the data resource based on the resource effective utilization, the resource risk level, the usage value, the weight corresponding to the resource effective utilization, the weight corresponding to the resource risk level, and the weight corresponding to the usage value.

[0137] In one possible implementation, the data resources are used to build data warehouse models in a data warehouse, and the data warehouse models are used to store data. The process of obtaining the use value of the data resources includes: determining the asset concentration of the data resources by dividing the number of high-value data warehouse models by the total number of data warehouse models; the asset concentration is used to represent the resource output of the data resources; determining the cost input coefficient of the data resources by dividing the total data storage volume of the data warehouse by the data storage volume of the operational data storage layer in the data warehouse; the operational data storage layer is used to store source data of the business system, and the cost input coefficient is used to represent the resource input of the data resources; the quotient of the asset concentration and the cost input coefficient is determined as the use value of the data resources.

[0138] Among them, a data warehouse model that meets one or more of the following conditions is a high-value data warehouse model:

[0139] The number of downstream users of the data warehouse model exceeds the target number; the metrics used by the data warehouse model to obtain are the target metrics; and the data of the data warehouse model is visualized.

[0140] In one possible implementation, the determining unit 502 includes:

[0141] The first determining subunit is used to determine that when the total amount of data resources is greater than the target amount of resources, the weight corresponding to the effective utilization of resources is greater than the weight corresponding to the risk level of resources.

[0142] or,

[0143] The second determining subunit is used to determine that when the ratio of the resource usage of the data resource to the total resource amount of the data resource is greater than the target ratio, the weight corresponding to the effective utilization of the resource is less than the weight corresponding to the resource risk level.

[0144] In one possible implementation, the second acquisition unit 503 includes:

[0145] The third determining subunit is used to determine a first score corresponding to the resource effective utilization based on the resource effective utilization and the first calibration table; the first calibration table includes at least one resource effective utilization, at least one score, and the correspondence between the resource effective utilization and the score;

[0146] The fourth determining subunit is used to determine a second score corresponding to the resource risk level based on the resource risk level and the second calibration table; the second calibration table includes at least one resource risk level, at least one score, and the correspondence between the resource risk level and the score;

[0147] The fifth determining subunit is used to take the weighted sum of the first score, the weight corresponding to the effective utilization of the resource, the second score, and the weight corresponding to the risk level of the resource as the health assessment result of the data resource.

[0148] In one possible implementation, the second acquisition unit 503 includes:

[0149] The acquisition subunit is used to input the resource effective utilization, the resource risk level, the weight corresponding to the resource effective utilization and the weight corresponding to the resource risk level into the data resource health assessment model, and obtain the health assessment result of the data resource output by the health assessment model;

[0150] The data resource health assessment model is trained based on sample data and the corresponding label values ​​of the sample data; the sample data includes historical resource utilization efficiency, historical resource risk level, the weight corresponding to the historical resource utilization efficiency, and the weight corresponding to the historical resource risk level; the label values ​​of the sample data are the actual health assessment results.

[0151] In one possible implementation, the effective utilization of the resource is determined by the data resource usage events corresponding to the data resource and the effective utilization of the resource as a labeling relationship, and the resource risk level is determined by the data resource usage events and the resource use risk labeling relationship; the effective utilization of the resource as a labeling relationship includes a correspondence between at least one data resource usage event and at least one effective utilization of the resource, and the resource use risk labeling relationship includes a correspondence between at least one data resource usage event and at least one resource risk level;

[0152] The third acquisition unit includes:

[0153] The sixth determining subunit is used to determine a first data resource usage event in which the effective utilization of resources is lower than the target effective utilization and a second data resource usage event in which the resource risk level is higher than the target risk level.

[0154] The device further includes:

[0155] An update unit is used to analyze the first data resource usage event and the second data resource usage event, and update the data resource usage standard based on the analysis results; the data resource usage standard includes data resource usage events related to healthy data resource usage.

[0156] In one possible implementation, the third acquisition unit includes:

[0157] The first governance subunit is used to perform data resource governance according to the data storage resource governance method in the data storage resource governance labeling relationship when the first data resource usage event is a data storage resource usage event, so as to improve the resource utilization.

[0158] The second governance subunit is used to perform data resource governance according to the data computing resource governance method in the data computing resource governance calibration relationship when the first data resource usage event is a data computing resource usage event, so as to improve the resource utilization.

[0159] The data storage resource governance identification relationship includes a correspondence between at least one data storage resource usage event and at least one data storage resource governance method, and the data computing resource governance identification relationship includes a correspondence between at least one data computing resource usage event and at least one data computing resource governance method.

[0160] In one possible implementation, the device further includes:

[0161] An evaluation unit is configured to, when the first data resource usage event is a target data storage resource usage event, perform a release evaluation on the target data resource or a portion of the data resources used by the first data resource usage event, and obtain a release evaluation result; the target data storage resource usage event is an event in which the effective utilization of the resource is lower than the target effective utilization because the access frequency of the data using the resource is lower than the target frequency.

[0162] The release unit is used to release the target data resource or a portion of the data resources in the target data resource when the release evaluation result is positive.

[0163] Based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods.

[0164] It should be noted that the specific implementation of each unit in this embodiment can be found in the relevant descriptions in the above method embodiments. The division of units in this application embodiment is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. The functional units in this application embodiment can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. For example, in the above embodiments, the processing unit and the sending unit can be the same unit or different units. The integrated unit can be implemented in hardware or as a software functional unit.

[0165] Based on the data resource health maintenance method provided in the above method embodiments, this application also provides an electronic device, including: one or more processors; a storage device storing one or more programs thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data resource health maintenance method described in any of the above embodiments.

[0166] The following is for reference. Figure 6 This document illustrates a structural schematic diagram of an electronic device 600 suitable for implementing embodiments of this application. The terminal devices in these embodiments may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Android Devices), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs (televisions), desktop computers, etc. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0167] like Figure 6 As shown, electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of electronic device 600. Processing device 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0168] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0169] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined in the methods of the embodiments of this application.

[0170] The electronic device provided in this application embodiment and the data resource health maintenance method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0171] Based on the data resource health maintenance method provided in the above method embodiments, this application provides a computer-readable medium storing a computer program thereon, wherein the program, when executed by a processor, implements the data resource health maintenance method as described in any of the above embodiments.

[0172] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0173] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0174] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0175] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the aforementioned data resource health maintenance method.

[0176] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0178] The units described in the embodiments of this application can be implemented in software or in hardware. The names of the units / modules do not necessarily limit the unit itself; for example, a voice data acquisition module can also be described as a "data acquisition module".

[0179] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0180] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0181] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0182] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0183] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0184] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0185] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for maintaining health of data resources, the method comprising: The data resource includes a data computing resource and a data storage resource, and the method includes: ​ obtaining a health evaluation index for evaluating the health of the data resource; the health evaluation index includes a resource effective use degree of the data resource in use and a resource risk level; the resource effective use degree is used to represent the degree of effective use of the data resource, and the resource risk level is used to represent the degree of negative impact of the use of the data resource on the business; the resource effective use degree is negatively correlated with the increase rate of the resource risk level; determining the weight corresponding to the resource effective use degree and the weight corresponding to the resource risk level; based on the resource effective use degree, the resource risk level, the weight corresponding to the resource effective use degree and the weight corresponding to the resource risk level, obtaining the health evaluation result of the data resource; obtaining a data resource use event related to the health evaluation result, and performing data resource management according to the data resource use event.

2. The method of claim 1, wherein, The health evaluation index also includes a use value degree of the data resource; the use value degree of the data resource is used to represent the relationship between resource output and resource input of the data resource; The health evaluation result of the data resource is obtained based on the resource effective use degree, the resource risk level, the weight corresponding to the resource effective use degree and the weight corresponding to the resource risk level, including: obtaining the weight corresponding to the use value degree; based on the resource effective use degree, the resource risk level, the use value degree, the weight corresponding to the resource effective use degree, the weight corresponding to the resource risk level and the weight corresponding to the use value degree, obtaining the health evaluation result of the data resource.

3. The method of claim 2, wherein, The data resource is used to construct a data warehouse model in a data warehouse, and the data warehouse model is used to store data. The process of obtaining the use value degree of the data resource includes: determining the quotient value of the number of high-value data warehouse models and the total number of data warehouse models as the asset concentration of the data resource; the asset concentration is used to represent the resource output of the data resource; determining the quotient value of the total data storage capacity of the data warehouse and the data storage capacity of the operating data storage layer in the data warehouse as the cost input coefficient of the data resource; the operating data storage layer is used to store business system source data, and the cost input coefficient is used to represent the resource input of the data resource; determining the quotient value of the asset concentration and the cost input coefficient as the use value degree of the data resource; wherein one or more of the following conditions is met: the use downstream number of the data warehouse model exceeds the target number, the data warehouse model is used to obtain the target index, and the data of the data warehouse model is visually displayed.

4. The method of claim 1, wherein, ​ ​ ​ When a ratio of a resource usage amount of the data resource to a total resource amount of the data resource is greater than a target ratio, it is determined that a weight corresponding to the resource effective usage degree is less than a weight corresponding to the resource risk level.

5. The method of claim 1, wherein, The health assessment result of the data resource is obtained based on the resource effective usage degree, the resource risk level, the weight corresponding to the resource effective usage degree, and the weight corresponding to the resource risk level. A first score corresponding to the resource effective usage degree is determined based on the resource effective usage degree and a first calibration table. The first calibration table includes at least one resource effective usage degree, at least one score, and a corresponding relationship between the resource effective usage degree and the score. A second score corresponding to the resource risk level is determined based on the resource risk level and a second calibration table. The second calibration table includes at least one resource risk level, at least one score, and a corresponding relationship between the resource risk level and the score. The first score, the weight corresponding to the resource effective usage degree, the second score, and the weight corresponding to the resource risk level are weighted and summed to obtain a score as the health assessment result of the data resource.

6. The method of claim 1, wherein, The health assessment result of the data resource is obtained based on the resource effective usage degree, the resource risk level, the weight corresponding to the resource effective usage degree, and the weight corresponding to the resource risk level. The resource effective usage degree, the resource risk level, the weight corresponding to the resource effective usage degree, and the weight corresponding to the resource risk level are input into a data resource health assessment model to obtain a health assessment result of the data resource output by the health assessment model. The data resource health assessment model is trained based on sample data and label values corresponding to the sample data. The sample data includes historical resource effective usage degrees, historical resource risk levels, weights corresponding to the historical resource effective usage degrees, and weights corresponding to the historical resource risk levels. The label values of the sample data are actual health assessment results.

7. The method of claim 1, wherein, The resource effective usage degree is determined based on data resource usage events corresponding to the data resource and a resource effective usage calibration relationship. The resource risk level is determined based on the data resource usage events and a resource usage risk calibration relationship. The resource effective usage calibration relationship includes a corresponding relationship between at least one data resource usage event and at least one resource effective usage degree. The resource usage risk calibration relationship includes a corresponding relationship between at least one data resource usage event and at least one resource risk level. The data resource usage events related to the health assessment result are obtained, including: First data resource usage events in which the resource effective usage degree is lower than a target effective usage degree and second data resource usage events in which the resource risk level is higher than a target risk level are determined. The method further includes: analyzing the first data resource usage event and the second data resource usage event, updating a data resource usage standard according to an analysis result; the data resource usage standard comprises a data resource usage event of healthy data resource usage.

8. The method of claim 7, wherein, The data resource governance according to the data resource usage event comprises: when the first data resource usage event is a data storage resource usage event, performing data resource governance according to a data storage resource governance mode in a data storage resource governance calibration relationship, so as to improve the resource usage degree; when the first data resource usage event is a data computing resource usage event, performing data resource governance according to a data computing resource governance mode in a data computing resource governance calibration relationship, so as to improve the resource usage degree; The data storage resource governance calibration relationship comprises a corresponding relationship between at least one data storage resource usage event and at least one data storage resource governance mode, and the data computing resource governance calibration relationship comprises a corresponding relationship between at least one data computing resource usage event and at least one data computing resource governance mode.

9. The method of claim 7, wherein, The method further comprises: when the first data resource usage event is a target data storage resource usage event, performing release evaluation on a target data resource used by the first data resource usage event or part of the target data resource, and obtaining a release evaluation result; the target data storage resource usage event is an event that the effective usage degree of the resource is lower than the target effective usage degree due to the access frequency of the data used by the resource being lower than a target frequency; when the release evaluation result is a positive result, releasing the target data resource or part of the target data resource.

10. A data resource health maintenance apparatus, characterized by, The data resource comprises a data computing resource and a data storage resource, and the apparatus comprises: a first obtaining unit configured to obtain a health evaluation index for evaluating healthy data resources; the health evaluation index comprises a resource effective usage degree and a resource risk level of the data resource in a usage process; the resource effective usage degree is used to indicate the degree of effective use of the data resource, and the resource risk level is used to indicate the degree of negative impact of the use of the data resource on a business; the resource effective usage degree and the resource risk level are negatively correlated with an increase rate; a determining unit configured to determine a weight corresponding to the resource effective usage degree and a weight corresponding to the resource risk level; a second obtaining unit configured to obtain a health evaluation result of the data resource based on the resource effective usage degree, the resource risk level, the weight corresponding to the resource effective usage degree, and the weight corresponding to the resource risk level; a third obtaining unit configured to obtain a data resource usage event related to the health evaluation result, and perform data resource governance according to the data resource usage event.

11. An electronic device, comprising: comprise: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the data resource health maintenance method as claimed in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, A computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the data resource health maintenance method as claimed in any one of claims 1-9.

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