Network resource evaluation method, device, equipment, medium and product

Abstract

The invention relates to a network resource assessment method and device, equipment and a medium. The method comprises the following steps: acquiring a first to-be-assessed data set corresponding to a to-be-assessed network resource; screening the first to-be-evaluated data of which various types of data meet corresponding preset constraint conditions from the first to-be-evaluated data set as second to-be-evaluated data; according to the second to-be-assessed data, determining the data comprehensive quality of the second to-be-assessed data, and screening third to-be-assessed data of which the data comprehensive quality meets a preset quality requirement from the second to-be-assessed data to obtain a third to-be-assessed data set; and calculating an evaluation value of each type of data in each piece of third to-be-evaluated data in the third to-be-evaluated data set, and determining a resource evaluation result corresponding to the to-be-evaluated network resource according to the evaluation value of each type of data in each piece of third to-be-evaluated data in the third to-be-evaluated data set. According to the invention, the network resource evaluation accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for evaluating network resources. Background Technology

[0002] With the deepening of digital transformation, the construction of new power systems and digital grids has placed higher demands on network resource measurement and fault location. Network resources not only include traditional network equipment, but also encompass multi-layered and multi-type resources such as operating systems, databases, middleware, and business applications. In cloud-native environments, network resources exhibit characteristics of complexity, dynamism, and heterogeneity, posing significant challenges to comprehensive resource measurement, accurate assessment, and efficient troubleshooting. However, existing network resource assessment technologies often suffer from low accuracy when dealing with the complex resource structures in cloud-native environments. Summary of the Invention

[0003] Therefore, it is necessary to provide a network resource assessment method, apparatus, equipment, and medium that can improve the accuracy of network resource assessment in response to the above-mentioned technical problems.

[0004] Firstly, this application provides a method for evaluating network resources, including:

[0005] Obtain the first dataset to be evaluated corresponding to the network resource to be evaluated. The first dataset to be evaluated includes at least one piece of first data to be evaluated. The first data to be evaluated includes at least one type of data.

[0006] Select the first set of data to be evaluated from the first dataset to be evaluated, which contains data of each type that meets the corresponding preset constraints, as the second set of data to be evaluated.

[0007] Based on the second set of data to be evaluated, determine the overall data quality of the second set of data to be evaluated, and select the third set of data to be evaluated from each set of second set of data to be evaluated whose overall data quality meets the preset quality requirements, thus obtaining the third set of data to be evaluated.

[0008] Calculate the evaluation value of each type of data in each piece of data in the third dataset to be evaluated, and determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each piece of data in the third dataset to be evaluated.

[0009] In one embodiment, selecting first data to be evaluated from a first dataset to be evaluated, where all types of data meet the corresponding preset constraints, as second data to be evaluated includes: performing a first step of the first count, the first step including: randomly selecting a first number of first data to be evaluated from the first dataset to be evaluated; selecting second data to be evaluated from the first number of first data to be evaluated, where all types of data meet the corresponding preset constraints; correspondingly, selecting third data to be evaluated from each second dataset to be evaluated, where the overall data quality meets the preset quality requirements, to obtain a third dataset to be evaluated includes: selecting the third data to be evaluated with the highest overall data quality from each second dataset obtained by performing the first step, and generating a third dataset to be evaluated that includes the third data to be evaluated obtained from each selection.

[0010] In one embodiment, selecting the third data to be evaluated with the highest overall quality from each of the second data to be evaluated obtained from each execution of the first step includes: for each piece of second data to be evaluated obtained from the execution of the first step, calculating the quality factor of each type of data in the second data to be evaluated, and calculating the overall quality factor of the second data to be evaluated based on the quality factors of each type of data in the second data to be evaluated; wherein the overall quality factor is negatively correlated with the overall data quality; and selecting the third data to be evaluated with the lowest overall quality factor from each of the second data to be evaluated obtained from each execution of the first step.

[0011] In one embodiment, each type of data includes at least one indicator data; selecting first data to be evaluated from the first dataset to be evaluated, where each type of data meets the corresponding preset constraints, as second data to be evaluated includes: selecting second data to be evaluated from the first dataset to be evaluated, where each indicator data in each type of data falls within the corresponding data range, wherein the preset constraints corresponding to each type include the data range corresponding to each indicator data in each type of data.

[0012] In one embodiment, obtaining a first dataset to be evaluated corresponding to a network resource to be evaluated includes: for each type, determining a dynamic evaluation period corresponding to the type, and extracting data of the type corresponding to the network resource to be evaluated according to the dynamic evaluation period; forming an original dataset to be evaluated including at least one original dataset to be evaluated based on the data of each type corresponding to the network resource to be evaluated; calculating the deviation between the data of each type in each original dataset to be evaluated and the mean of the data corresponding to the type; and removing any original dataset to be evaluated whose deviation is greater than a preset deviation, thereby obtaining the first dataset to be evaluated.

[0013] In one embodiment, determining the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each third data set to be evaluated in the third data set to be evaluated includes: for each type, summing the evaluation values ​​of the data of that type in each third data set to be evaluated in the third data set to be evaluated to obtain the total evaluation value of the data of that type; forming a total evaluation value array from the total evaluation values ​​of the data of each type; and using the total evaluation value array as the resource evaluation result corresponding to the network resource to be evaluated.

[0014] Secondly, this application provides a network resource assessment device, comprising:

[0015] The data acquisition module is used to acquire the first dataset to be evaluated corresponding to the network resource to be evaluated. The first dataset to be evaluated includes at least one piece of first data to be evaluated. The first data to be evaluated includes at least one type of data.

[0016] The first screening module is used to screen the first data to be evaluated from the first dataset to be evaluated, which all types of data meet the corresponding preset constraints, as the second data to be evaluated.

[0017] The second screening module is used to determine the overall quality of the second data to be evaluated based on the second data to be evaluated, and to screen the third data to be evaluated from each of the second data to be evaluated whose overall quality meets the preset quality requirements, so as to obtain the third dataset to be evaluated.

[0018] The evaluation and summary module is used to calculate the evaluation value of each type of data in each piece of data in the third dataset to be evaluated, and to determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each piece of data in the third dataset to be evaluated.

[0019] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the various steps in the multi-terminal application generation method based on dynamic templates provided in the first aspect.

[0020] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of the multi-terminal application generation method based on dynamic templates provided in the first aspect.

[0021] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the various steps of the multi-terminal application generation method based on dynamic templates provided in the first aspect.

[0022] The aforementioned network resource assessment method, apparatus, equipment, and medium acquire a first dataset corresponding to the network resource to be assessed. Based on preset constraints for each type, a second dataset is selected from the first dataset, ensuring the rationality of each data type within the second dataset. Then, a third dataset is selected from the second dataset, ensuring its overall quality meets preset quality requirements, thus guaranteeing high data quality. Therefore, by employing two selection processes, a reasonable and high-quality third dataset is obtained, improving the accuracy of network resource assessment. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a network resource evaluation method in one embodiment;

[0025] Figure 2 This is a flowchart illustrating the screening step of the third data to be evaluated in one embodiment;

[0026] Figure 3 This is a flowchart illustrating the steps for obtaining the first dataset to be evaluated in one embodiment;

[0027] Figure 4 This is a flowchart illustrating the steps for determining the resource assessment result in one embodiment;

[0028] Figure 5 This is a structural block diagram of a network resource evaluation device in one embodiment;

[0029] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] In one exemplary embodiment, a method for evaluating network resources is provided. See also Figure 1 The network resource assessment method provided in this embodiment includes:

[0032] S110, Obtain the first dataset to be evaluated corresponding to the network resource to be evaluated. The first dataset to be evaluated includes at least one piece of first data to be evaluated. The first data to be evaluated includes at least one type of data.

[0033] The network resources to be evaluated can also be referred to as network assets to be evaluated. These resources can be operating systems, databases, middleware, or business applications. Of course, other types of network resources can also be evaluated, but this is not a limitation here.

[0034] The data may include at least one of performance, topology, and business data. That is, the data may include at least one of performance-related data, topology-related data, and business-related data. Other types may also be included, but are not limited here.

[0035] In one alternative implementation, each network resource to be evaluated can have a corresponding cloud-native data warehouse, so in S110, the corresponding first dataset to be evaluated can be obtained from the cloud-native data warehouse corresponding to the network resource to be evaluated.

[0036] Understandably, the first set of data to be evaluated includes at least one type of data, meaning that the data in each set of the first set of data to be evaluated is classified. This approach allows subsequent evaluations to be conducted in a targeted manner across different dimensions, which helps to delve deeper into the performance of network resources in various dimensions and improve the accuracy of the evaluation.

[0037] S120, select the first data to be evaluated from the first dataset to be evaluated, where all types of data meet the corresponding preset constraints, as the second data to be evaluated.

[0038] Understandably, if at least one type of data in the first set of data to be evaluated does not meet the corresponding preset constraints, it indicates that there is unreasonable data in the first set of data to be evaluated. If all types of data in the first set of data to be evaluated meet the corresponding preset constraints, it indicates that the first set of data to be evaluated is reasonable data to be evaluated.

[0039] It is evident that setting different preset constraints for different types helps to select data of all types that are reasonable as the second data to be evaluated, thus facilitating subsequent further screening steps.

[0040] S130, Based on the second data to be evaluated, determine the overall data quality of the second data to be evaluated, and select the third data to be evaluated from each of the second data to be evaluated whose overall data quality meets the preset quality requirements, so as to obtain the third dataset to be evaluated.

[0041] Understandably, for each second piece of data to be evaluated, the corresponding overall data quality is determined; based on the overall data quality of each second piece of data to be evaluated, second pieces of data to be evaluated whose overall data quality meets the preset quality requirements are selected as third pieces of data to be evaluated, thereby generating a third dataset to be evaluated that includes each piece of third data to be evaluated.

[0042] As can be seen, the S130 screening process yields a high-quality third dataset for evaluation, which is beneficial for obtaining accurate resource evaluation results.

[0043] S140, calculate the evaluation value of each type of data in each piece of data in the third dataset to be evaluated, and determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each piece of data in the third dataset to be evaluated.

[0044] For example, the types include performance, topology, and services. The third dataset to be evaluated contains 10 data points. For each data point, evaluation values ​​are calculated for performance-related data, topology-related data, and service-related data; that is, three evaluation values ​​are obtained for each data point, resulting in a total of 30 evaluation values ​​for the 10 data points. Based on these 30 evaluation values, the resource evaluation result for the network resource to be evaluated is determined.

[0045] As can be seen, in this embodiment, second data to be evaluated is selected from the first dataset to be evaluated based on the preset constraints corresponding to each type, ensuring the rationality of each type of data in the second dataset to be evaluated; furthermore, third data to be evaluated is selected from the second dataset to be evaluated whose overall data quality meets the preset quality requirements, ensuring that the third dataset to be evaluated has high data quality. Through these two selection processes, a third dataset to be evaluated that is both reasonable and of high quality can be obtained, thereby improving the accuracy of network resource evaluation.

[0046] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment, in which the steps of obtaining the second data to be evaluated and obtaining the third dataset to be evaluated in S120 are refined.

[0047] The steps to obtain the second set of data to be evaluated include:

[0048] S121, execute the first step of the first count, the first step includes: randomly selecting a first number of first data to be evaluated from the first dataset to be evaluated; filtering the first number of first data to be evaluated to select second data to be evaluated from each type of data that meets the corresponding preset constraints.

[0049] The first quantity can be set as needed, for example, set to 20.

[0050] The initial number can be set as needed, for example, to 30.

[0051] Understandably, the traditional method of traversing the entire first dataset to be evaluated to filter the second dataset is time-consuming and lacks flexibility. In contrast, in this embodiment, since the first number and the first quantity can be set as needed, the filtering rate of the second dataset can be controlled by flexibly adjusting these parameters.

[0052] It is evident that in evaluation scenarios with high real-time requirements, the filtering rate of the second set of data to be evaluated can be increased by reducing the number of first-time data and the first quantity, thereby reducing the filtering time and achieving rapid filtering.

[0053] Accordingly, the steps to obtain the third dataset to be evaluated include:

[0054] S122, from each of the second data to be evaluated obtained by executing the first step, select the third data to be evaluated with the highest overall data quality, and generate a third dataset to be evaluated that includes the third data to be evaluated obtained from each selection.

[0055] Understandably, each execution of the first step yields at least one second data point to be evaluated. From this second data point, the one with the highest overall quality is selected as the third data point to be evaluated. In other words, each execution of the first step is followed by a third data point selection process. After executing the first step of the first dataset, the third data point selection process is also executed, resulting in a corresponding number of third data points to be evaluated, thus forming the third dataset to be evaluated.

[0056] As can be seen, since the third data screening step is performed once for each first step, and the screening rate of the second data to be evaluated is adjustable, the screening rate of the third data to be evaluated is also adjustable. Therefore, the rapid screening of the third data to be evaluated can be achieved even with a reduction in the number of first steps.

[0057] Based on the technical solution of the previous embodiment, this application also provides an optional embodiment, in which the screening step of the third data to be evaluated in S122 is further refined. See Figure 2 The detailed screening steps for the third set of data to be evaluated include:

[0058] S210, for each piece of second data to be evaluated obtained from the first step, calculate the quality factor of each type of data in the second data to be evaluated, and calculate the comprehensive quality factor of the second data to be evaluated based on the quality factors of each type of data in the second data to be evaluated; wherein, the comprehensive quality factor is negatively correlated with the comprehensive quality of the data.

[0059] S220: From each of the second evaluation data obtained from the first step, select the third evaluation data with the lowest comprehensive quality factor.

[0060] Among them, the quality factor of performance-related data is used to characterize the volatility of performance-related data, and therefore can be called the performance volatility factor. The calculation formula for the performance volatility factor of each second data point to be evaluated is as follows:

[0061]

[0062] In the formula, This represents the performance fluctuation factor, where n is the number of performance-related data points in the second set of data to be evaluated. This is the i-th performance-related data in the second set of data to be evaluated. This represents the mean of the performance-related data in the second set of data to be evaluated.

[0063] The quality factor of the topology data is used to characterize the connectivity of the topology link data, and is therefore called the topology connectivity factor. The formula for calculating the topology connectivity factor of each second piece of data to be evaluated is as follows:

[0064]

[0065] In the formula, This represents the topology connectivity factor, where m is the number of links corresponding to the second data point to be evaluated. Let be the weight of the i-th link. Give the connectivity score for the i-th link.

[0066] Among them, the quality factor of business-related data is used to characterize the service quality of business-related data, and therefore can be called the business quality factor. The calculation formula for the business quality factor of each second piece of data to be evaluated is as follows:

[0067]

[0068] In the formula, The rating represents the business quality factor, where r is the request response time score, s is the transaction success rate score, u is the concurrent user score, α is the weighting coefficient of the request response time score, β is the weighting coefficient of the transaction success rate score, and γ is the weighting coefficient of the concurrent user score.

[0069] In one alternative implementation, based on the aforementioned performance fluctuation factor, topology connectivity factor, and service quality factor, the comprehensive quality factor can be calculated using the following formula:

[0070]

[0071] In the formula, Indicates the overall quality factor. The weighting coefficients for the performance volatility factor. These are the weighting coefficients of the topological connectivity factor. These are the weighting coefficients for the business quality factor.

[0072] Understandably, the overall quality factor is negatively correlated with the overall data quality; that is, the lower the overall quality factor, the higher the overall data quality. Therefore, in each second set of data to be evaluated obtained from the first step, the third set of data to be evaluated with the lowest overall quality factor can be selected to obtain the third set of data to be evaluated with the highest overall data quality.

[0073] As can be seen, the quality factor corresponding to each type of data is the quality assessment result of that type of data. By determining the comprehensive quality factor of the second data to be assessed through the quality factors corresponding to each type of data in the second data to be assessed, the comprehensive quality factor can reflect the comprehensive quality assessment result of the second data to be assessed. Then, based on the comprehensive quality assessment result, the third data to be assessed can be screened to obtain the third data to be assessed with the highest comprehensive quality, which is conducive to improving the accuracy of subsequent resource assessment.

[0074] Based on the technical solution of the previous embodiment, this application also provides an optional embodiment. In this optional embodiment, each type of data is refined to include at least one indicator data, and the step of screening the second data to be evaluated in S120 above is further refined. The step of screening the second data to be evaluated includes:

[0075] 1. Select second data to be evaluated from the first dataset to be evaluated, where all indicator data of each type of data falls within the corresponding data range. The preset constraints for each type include the data range corresponding to each indicator data in each type of data.

[0076] For example, performance-related metrics may include at least one of CPU (Central Processing Unit) utilization, memory utilization, and disk I / O (Input / Output). Topology-related metrics may include at least one of node connections, link latency, and bandwidth utilization. Business-related metrics may include at least one of request response time, transaction success rate, and concurrent users. Each metric has a corresponding data range. The data range corresponding to each metric for each data type is used as a preset constraint for that type. If the metric falls within the corresponding data range, it is considered reasonable and meets the preset constraint. If the metric does not fall within the corresponding data range, it is considered unreasonable and does not meet the preset constraint. If all metric data in each data type falls within the corresponding data range, the data for that type is considered reasonable; otherwise, the data for that type is unreasonable.

[0077] It is evident that by refining each type of data into at least one indicator, using the data range corresponding to the indicator as a preset constraint, and using the preset constraint to filter the second data to be evaluated, it can be ensured that the indicator data in each type of the filtered second data to be evaluated are reasonable, that is, in line with the actual evaluation needs and logic, thus contributing to the accuracy of subsequent evaluations.

[0078] Based on the technical solution of the previous embodiment, this application also provides an optional embodiment, in which the steps for obtaining the first dataset to be evaluated are further refined. See also Figure 3 The steps for obtaining the first dataset to be evaluated include:

[0079] S310: For each type, determine the corresponding dynamic evaluation period, and extract the data of the type corresponding to the network resource to be evaluated based on the dynamic evaluation period.

[0080] The dynamic evaluation period for each data type is the period during which data of that type is acquired. This period can be adjusted according to actual needs, hence the name dynamic evaluation period. The dynamic evaluation period allows us to capture the changes and trend evolution of the corresponding data at different points in time.

[0081] S320, Based on the various types of data corresponding to the network resources to be evaluated, form an original dataset to be evaluated that includes at least one original data to be evaluated.

[0082] For example, the first dynamic evaluation period for performance-related data is 5 minutes, the second dynamic evaluation period for topology-related data is 10 minutes, and the third dynamic evaluation period for business-related data is 15 minutes. The performance-related data from the most recent 5 minutes, the topology-related data from the most recent 10 minutes, and the business-related data from the most recent 15 minutes are used to form the original dataset to be evaluated. Each minute of performance-related data, each two minutes of topology-related data, and each three minutes of business-related data forms one original dataset to be evaluated, resulting in five original datasets to be evaluated.

[0083] S330, calculate the degree of deviation between each type of data in each original data point in the original dataset to be evaluated and the mean of the data corresponding to that type.

[0084] Each type has a corresponding average data value, which is the average value of all data of the same type throughout the entire dynamic evaluation period.

[0085] S340, remove any data of any type in the original dataset to be evaluated that has a deviation greater than a preset deviation level, and obtain the first dataset to be evaluated.

[0086] Understandably, if any type of data in a set of original data to be evaluated has a deviation greater than the preset deviation level, it indicates that the data of that type is abnormal and should not be used as the basis for evaluating network resources. Therefore, the original data to be evaluated containing that type of data is removed to ensure the quality of the first dataset to be evaluated.

[0087] As can be seen, setting different dynamic evaluation cycles for different types of data aligns with the varying rates of change in different data types, resulting in a high-quality original dataset for evaluation. This provides rich and structured data support for subsequent assessments, facilitating a more comprehensive and dynamic understanding of network resources and ensuring that the resource evaluation results accurately reflect the actual condition of the assets. Next, outlier data is removed from the original dataset for evaluation, resulting in a higher-quality first dataset for evaluation.

[0088] Based on the technical solution of the previous embodiment, this application also provides an optional embodiment, in which the calculation steps of the evaluation value are further refined. The calculation steps of the evaluation value include:

[0089] 1. Sort the data in the third dataset according to the overall quality factor from low to high, and select the second-highest number of the third datasets as the fourth datasets to be evaluated.

[0090] The second quantity can be set as needed and is not limited here.

[0091] 2. Calculate the evaluation value of each type of data in each fourth data point to be evaluated.

[0092] In one optional implementation, the evaluation value of the performance-related data in each fourth data point to be evaluated is calculated using the following formula:

[0093]

[0094] in, This represents the evaluation value of performance-related data, where k is the number of performance-related data points in each of the fourth data points to be evaluated. This represents the weight of the j-th performance-related data point in the fourth set of data to be evaluated. This represents the performance score of the j-th performance-related data in the fourth set of data to be evaluated. This performance score is the comprehensive score of all indicator data in the j-th performance-related data.

[0095] Similarly, the evaluation values ​​of topology-related data and business-related data can be calculated using the formulas for the evaluation values ​​of performance-related data mentioned above.

[0096] Accordingly, in the subsequent steps of determining the resource assessment results, the resource assessment results corresponding to the network resources to be assessed are determined based on the assessment values ​​of each type of data in each of the fourth data to be assessed in the fourth dataset to be assessed.

[0097] It is evident that further screening based on the comprehensive quality factor and the third dataset to be evaluated can further improve the accuracy of subsequent resource assessments.

[0098] Based on the technical solution of the previous embodiment, this application also provides an optional embodiment in which the steps for determining the resource assessment results are further refined. See also Figure 4 The steps for determining the results of resource assessments include:

[0099] S410, for each type, sum the evaluation values ​​of the data of each type in the third dataset to be evaluated to obtain the total evaluation value of the data of that type.

[0100] S420: The total evaluation values ​​of each type of data are combined into a total evaluation value array, and the total evaluation value array is used as the resource evaluation result corresponding to the network resource to be evaluated.

[0101] For example, the third dataset to be evaluated contains 20 pieces of third data to be evaluated. Each piece of third data to be evaluated includes performance-related data, topology-related data, and business-related data. Therefore, for each piece of third data to be evaluated, the evaluation values ​​of the 20 performance-related data are added together to obtain the total evaluation value of the performance-related data. The evaluation values ​​of the 20 topology-related data are added together to obtain the total evaluation value of the topology-related data. The evaluation values ​​of the 20 business-related data are added together to obtain the total evaluation value of the business-related data. These three total evaluation values ​​are combined into an array, namely the total evaluation value array, which is the resource evaluation result corresponding to the network resource to be evaluated.

[0102] As can be seen, the total evaluation value array includes the total evaluation value of each type of data. The total evaluation value array represents the resource evaluation result, which can reflect the evaluation results of each type of data. That is, it realizes a comprehensive evaluation of the network resources to be evaluated from multiple dimensions from the perspective of each type. This multi-dimensional evaluation method can more comprehensively reflect the specific situation of network resources, improve the reliability of evaluation, and provide high reference value for optimizing the allocation and management of network resources.

[0103] In a real-world scenario, upon receiving a network resource evaluation instruction, the region to be evaluated is determined according to the instruction, and a set of network resources to be evaluated for that region is obtained. The set of network resources to be evaluated includes at least one network resource to be evaluated. Then, S110~S140 are executed for each type of network resource to be evaluated to obtain the corresponding resource evaluation result.

[0104] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0105] Based on the same inventive concept, this application also provides a network resource assessment apparatus for implementing the network resource assessment method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more network resource assessment apparatus embodiments provided below can be found in the limitations of the network resource assessment method described above, and will not be repeated here.

[0106] In one exemplary embodiment, such as Figure 5 As shown, a network resource assessment device is provided, including a data acquisition module 510, a first filtering module 520, a second filtering module 530, and an assessment and summary module 540; wherein:

[0107] The data acquisition module 510 is used to acquire a first dataset to be evaluated corresponding to the network resource to be evaluated. The first dataset to be evaluated includes at least one piece of first data to be evaluated. The first data to be evaluated includes at least one type of data.

[0108] The first screening module 520 is used to screen the first data to be evaluated from the first dataset to be evaluated, which all types of data meet the corresponding preset constraints, as the second data to be evaluated.

[0109] The second screening module 530 is used to determine the overall data quality of the second data to be evaluated based on the second data to be evaluated, and to screen the third data to be evaluated from each of the second data to be evaluated whose overall data quality meets the preset quality requirements, so as to obtain the third dataset to be evaluated.

[0110] The evaluation summary module 540 is used to calculate the evaluation value of each type of data in each piece of data in the third dataset to be evaluated, and to determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation value of each type of data in each piece of data in the third dataset to be evaluated.

[0111] In one embodiment, the first screening module performs the following steps to select first data to be evaluated from the first dataset to be evaluated, where all types of data meet the corresponding preset constraints, as second data to be evaluated: performing a first step, which includes: randomly selecting a first number of first data to be evaluated from the first dataset to be evaluated; and selecting second data to be evaluated from the first number of first data to be evaluated, where all types of data meet the corresponding preset constraints. Correspondingly, the second screening module performs the following steps to select third data to be evaluated from each of the second data to be evaluated, where the overall data quality meets the preset quality requirements, to obtain a third dataset to be evaluated: selecting the third data to be evaluated with the highest overall data quality from each of the second data to be evaluated obtained by the first screening module in each execution of the first step, and generating a third dataset to be evaluated that includes the third data to be evaluated obtained from each screening.

[0112] In one embodiment, the second screening module performs the following steps: selecting the third data to be evaluated with the highest overall quality from each set of second data to be evaluated obtained from the execution of the first step. This includes: calculating the quality factor for each type of data in the second data to be evaluated for each set of second data to be evaluated obtained from the execution of the first step; and calculating the overall quality factor of the second data to be evaluated based on the quality factors of each type of data in the second data to be evaluated; wherein the overall quality factor is negatively correlated with the overall data quality; and selecting the third data to be evaluated with the lowest overall quality factor from each set of second data to be evaluated obtained from the execution of the first step.

[0113] In one embodiment, each type of data includes at least one indicator data; the first filtering module is specifically used to: filter second data to be evaluated from the first dataset to be evaluated, where each indicator data of each type of data falls within the corresponding data range, and the preset constraints corresponding to each type include the data range corresponding to each indicator data of each type of data.

[0114] In one embodiment, the data acquisition module is specifically used to: determine the dynamic evaluation period corresponding to each type, and extract the data of the type corresponding to the network resource to be evaluated according to the dynamic evaluation period; form an original dataset to be evaluated including at least one original dataset to be evaluated based on the data of each type corresponding to the network resource to be evaluated; calculate the deviation between the data of each type in each original dataset to be evaluated and the mean of the data corresponding to the type; and remove the original dataset to be evaluated whose deviation is greater than a preset deviation for any type of data in the original dataset to be evaluated, thereby obtaining the first dataset to be evaluated.

[0115] In one embodiment, the evaluation and summarization module performs the following steps to determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each third data set to be evaluated in the third data set to be evaluated: for each type, summing the evaluation values ​​of the data of that type in each third data set to be evaluated in the third data set to be evaluated to obtain the total evaluation value of the data of that type; forming a total evaluation value array from the total evaluation values ​​of the data of each type; and using the total evaluation value array as the resource evaluation result corresponding to the network resource to be evaluated.

[0116] Each module in the aforementioned network resource assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0117] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores a dynamic template library. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned multi-terminal application generation method based on dynamic templates.

[0118] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the network resource assessment method described above.

[0120] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described network resource assessment method.

[0121] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the network resource assessment method described above.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for evaluating network resources, characterized in that, include: Obtain the first dataset to be evaluated corresponding to the network resource to be evaluated, wherein the first dataset to be evaluated includes at least one piece of first data to be evaluated; The first set of data to be evaluated includes at least one type of data; Select the first set of data to be evaluated from the first dataset to be evaluated, which contains data of each type that meets the corresponding preset constraints, as the second set of data to be evaluated. Based on the second data to be evaluated, determine the overall data quality of the second data to be evaluated, and select the third data to be evaluated from each of the second data to be evaluated whose overall data quality meets the preset quality requirements, thus obtaining the third dataset to be evaluated. Calculate the evaluation value of each type of data in each piece of data in the third dataset to be evaluated, and determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each piece of data in the third dataset to be evaluated.

2. The method according to claim 1, characterized in that, The process of obtaining the first dataset to be evaluated corresponding to the network resource to be evaluated includes: For each type, a dynamic evaluation period corresponding to the type is determined, and data corresponding to the type of the network resource to be evaluated is extracted according to the dynamic evaluation period; Based on the various types of data corresponding to the network resources to be evaluated, an original dataset to be evaluated is formed, which includes at least one original data to be evaluated. Calculate the degree of deviation between each type of data in each original data point in the original dataset to be evaluated and the mean of the data corresponding to that type; The first dataset to be evaluated is obtained by removing any data of any type in the original dataset that has a deviation greater than a preset deviation level.

3. The method according to claim 1, characterized in that, The step of selecting first data to be evaluated from the first dataset to be evaluated, where all types of data meet the corresponding preset constraints, as the second data to be evaluated includes: The first step of the first count is performed, which includes: randomly selecting a first number of first data to be evaluated from the first dataset to be evaluated; and filtering second data to be evaluated from the first number of first data to be evaluated, where each type of data meets the corresponding preset constraints. Accordingly, the step of selecting third data to be evaluated from each of the second data to be evaluated that meets the preset quality requirements to obtain the third dataset to be evaluated includes: From each of the second data to be evaluated obtained by executing the first step, the third data to be evaluated with the highest overall data quality is selected, and a third dataset to be evaluated including the third data to be evaluated obtained from each selection is generated.

4. The method according to claim 3, characterized in that, The step of selecting the third data to be evaluated with the highest overall quality from each of the second data to be evaluated obtained from each execution of the first step includes: For each piece of second data to be evaluated obtained by performing the first step, calculate the quality factor of each type of data in the second data to be evaluated, and calculate the comprehensive quality factor of the second data to be evaluated based on the quality factors of each type of data in the second data to be evaluated; wherein, the comprehensive quality factor is negatively correlated with the comprehensive quality of the data; From each of the second data to be evaluated obtained by performing the first step, select the third data to be evaluated with the lowest comprehensive quality factor.

5. The method according to claim 1, characterized in that, Each type of data includes at least one indicator data; the step of selecting the first data to be evaluated from the first dataset to be evaluated, where all data of each type meets the corresponding preset constraints, as the second data to be evaluated includes: The second set of data to be evaluated is selected from the first dataset to be evaluated, in which all the indicator data of each type of data falls within the corresponding data range; the preset constraints for each type include the data range corresponding to each indicator data in each type of data.

6. The method according to claim 1, characterized in that, The step of determining the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each piece of data to be evaluated in the third dataset to be evaluated includes: For each type, the evaluation values ​​of the data of that type in each piece of the third dataset to be evaluated are summed to obtain the total evaluation value of the data of that type; The total evaluation values ​​of each type of data are used to form a total evaluation value array, and the total evaluation value array is used as the resource evaluation result corresponding to the network resource to be evaluated.

7. A network resource assessment device, characterized in that, include: The data acquisition module is used to acquire the first dataset to be evaluated corresponding to the network resource to be evaluated, wherein the first dataset to be evaluated includes at least one piece of first data to be evaluated; The first set of data to be evaluated includes at least one type of data; The first screening module is used to screen the first data to be evaluated from the first dataset to be evaluated, where all types of data meet the corresponding preset constraints, as the second data to be evaluated. The second screening module is used to determine the overall data quality of the second data to be evaluated based on the second data to be evaluated, and to screen the third data to be evaluated from each of the second data to be evaluated whose overall data quality meets the preset quality requirements, so as to obtain the third dataset to be evaluated. The evaluation and summary module is used to calculate the evaluation value of each type of data in each piece of third data to be evaluated in the third dataset to be evaluated, and to determine the resource evaluation result corresponding to the network resource to be evaluated based on the evaluation values ​​of each type of data in each piece of third data to be evaluated in the third dataset to be evaluated.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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