Information processing method and apparatus, and computer-readable storage medium

Abstract

The present disclosure relates to an information processing method and apparatus, and a computer-readable storage medium, which relate to the technical field of computers. The information processing method comprises: on the basis of the current configuration information of an application, reading, from a database, evaluation factor data matching the current configuration information, and an evaluation factor type to which the matched evaluation factor data belongs; using the matched evaluation factor data and the evaluation factor type as nodes to establish a decision tree model on the basis of a subordinate relationship between the matched evaluation factor data and the evaluation factor type; and using the decision tree model to evaluate the current configuration information of the application.

Description

Information processing method, apparatus and computer readable storage medium TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to an information processing method, an information processing apparatus, a computer readable storage medium and a computer program product. BACKGROUND

[0002] In the application development process, evaluating the configuration information of an application is an important link to ensure smooth operation and function safety of the application. With the increasing complexity of application functions, the creation, finalization, configuration, testing and online of the configuration information need to be managed throughout the life cycle.

[0003] In the related art, quality assurance personnel need to evaluate the configuration information of an application.

[0004] SUMMARY

[0005] According to some embodiments of the present disclosure, an information processing method is provided, including: reading, according to current configuration information of an application, evaluation factor data matched with the current configuration information from a database, and an evaluation factor type to which the matched evaluation factor data belongs; taking the matched evaluation factor data and the evaluation factor type as nodes, and establishing a decision tree model according to a dependency relationship between the matched evaluation factor data and the evaluation factor type; and evaluating the current configuration information of the application by using the decision tree model.

[0006] In some embodiments, establishing the decision tree model includes: judging whether to use a machine learning model to determine the weight of the matched evaluation factor data in the decision tree model according to the evaluation factor type. The prediction accuracy of the evaluation factor data belonging to different evaluation factor types is different by using historical data.

[0007] In some embodiments, judging whether to use the machine learning model to determine the weight of the matched evaluation factor data in the decision tree model according to the evaluation factor type includes: in response to the evaluation factor type being a first evaluation factor type, determining the weight of the matched evaluation factor data and the first evaluation factor type by using the machine learning model, and the prediction accuracy of the evaluation factor data belonging to the first evaluation factor type is higher than a threshold by using historical data; and in response to the evaluation factor type being a second evaluation factor type, determining the weight of the matched evaluation factor data and the second evaluation factor type according to expert knowledge data, and the prediction accuracy of the evaluation factor data belonging to the second evaluation factor type is lower than the threshold by using historical data.

[0008] In some embodiments, the first evaluation factor type includes at least one of region information to which the corresponding service of the current configuration information belongs, resource type of resource exchange involved by the corresponding service, and exchange party information of the resource exchange involved; and the second evaluation factor type includes at least one of importance of the corresponding service, user satisfaction of the corresponding service, and test manpower resource required by the current configuration information.

[0009] In some embodiments, the matched evaluation factor data includes the first evaluation factor data and the second evaluation factor data, the first evaluation factor data is subordinate to the second evaluation factor data, and a decision tree model is established according to the matched evaluation factor data and the evaluation factor type as nodes and the subordinate relationship between the matched evaluation factor data and the evaluation factor type.

[0010] In some embodiments, the evaluation of the current configuration information of the application by using the decision tree model includes: calculating a weighted mean of evaluation parameter values of the matched evaluation factor data according to a weight of the matched evaluation factor data in the decision tree model and a weight of the evaluation factor type in the decision tree model; and evaluating the current configuration information of the application according to the weighted mean.

[0011] In some embodiments, the evaluation of the current configuration information of the application according to the weighted mean includes: determining a risk level of the current configuration information of the application according to a numerical interval to which the weighted mean belongs.

[0012] In some embodiments, the determination of the risk level of the current configuration information of the application according to the numerical interval to which the weighted mean belongs includes: processing a distribution of the weighted mean according to historical data of the weighted mean, so that the distribution of the weighted mean conforms to a normal distribution; and determining a risk level corresponding to the numerical interval to which the weighted mean belongs according to the distribution of the weighted mean.

[0013] According to some other embodiments of the present disclosure, an information processing apparatus is provided, which includes: a reading unit configured to read, according to current configuration information of an application, evaluation factor data matched with the current configuration information and an evaluation factor type to which the matched evaluation factor data is subordinate, from a database; an establishing unit configured to establish a decision tree model according to a subordinate relationship between the matched evaluation factor data and the evaluation factor type, with the matched evaluation factor data and the evaluation factor type as nodes; and an evaluation unit configured to evaluate the current configuration information of the application by using the decision tree model.

[0014] In some embodiments, the establishing unit determines whether to use a machine learning model to determine a weight of the matched evaluation factor data in the decision tree model according to the evaluation factor type, wherein a prediction accuracy of evaluation factor data subordinate to different evaluation factor types is different by using historical data.

[0015] In some embodiments, the establishing unit, in response to the evaluation factor type being a first evaluation factor type, determines the weight of the matched evaluation factor data and the first evaluation factor type by using a machine learning model, wherein the prediction accuracy of the evaluation factor data belonging to the first evaluation factor type by using historical data is higher than a threshold, in response to the evaluation factor type being a second evaluation factor type, determines the weight of the matched evaluation factor data and the second evaluation factor type according to expert knowledge data, wherein the prediction accuracy of the evaluation factor data belonging to the second evaluation factor type by using historical data is lower than the threshold.

[0016] In some embodiments, the first evaluation factor type includes at least one of regional information to which a corresponding service of the current configuration information belongs, a resource type of a resource exchange involved by the corresponding service, and exchange party information of the resource exchange involved, and the second evaluation factor type includes at least one of an importance of the corresponding service, a user satisfaction degree of the corresponding service, and a test manpower resource required by the current configuration information.

[0017] In some embodiments, the matched evaluation factor data includes first evaluation factor data and second evaluation factor data, the first evaluation factor data belongs to the second evaluation factor data, and the establishing unit establishes a decision tree model according to the dependency relationship between the first evaluation factor data and the second evaluation factor data.

[0018] In some embodiments, the evaluation unit calculates a weighted mean of the evaluation parameter value of the matched evaluation factor data according to the weight of the matched evaluation factor data in the decision tree model and the weight of the evaluation factor type in the decision tree model, and evaluates the current configuration information of the application according to the weighted mean.

[0019] In some embodiments, the evaluation unit determines a risk level of the current configuration information of the application according to the numerical interval to which the weighted mean belongs.

[0020] In some embodiments, the evaluation unit processes the distribution of the weighted mean according to historical data of the weighted mean, so that the distribution of the weighted mean conforms to a normal distribution, and determines the risk level corresponding to the numerical interval to which the weighted mean belongs according to the distribution of the weighted mean.

[0021] According to still other embodiments of the present disclosure, there is provided an information processing apparatus, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute the information processing method in any of the above embodiments based on instructions stored in the memory.

[0022] According to still other embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program, the program being executed by a processor to implement the information processing method in any of the above embodiments.

[0023] According to still some embodiments of the present disclosure, there is also provided a computer program product comprising instructions which, when executed by a processor, cause the processor to perform the information processing method according to any one of the above embodiments.

[0024] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings, which are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this application, illustrate exemplary embodiments of the present disclosure and together with the description serve to explain the present disclosure. In the drawings:

[0026] FIG. 1 shows a flowchart of an information processing method according to some embodiments of the present disclosure;

[0027] FIG. 2 shows a schematic diagram of a decision tree model establishment method according to some embodiments of the present disclosure;

[0028] FIG. 3 shows a schematic diagram of a weight determination method in a decision tree model according to some embodiments of the present disclosure;

[0029] FIGS. 4a-4d show schematic diagrams of an information processing method according to some embodiments of the present disclosure;

[0030] FIG. 5 shows a block diagram of an information processing apparatus according to some embodiments of the present disclosure;

[0031] FIG. 6 shows a block diagram of another embodiment of an information processing apparatus of the present disclosure;

[0032] FIG. 7 shows a block diagram of yet another embodiment of an information processing apparatus of the present disclosure. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, but not all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present disclosure.

[0034] The foregoing and other embodiments of the present disclosure are presented by way of example only and are not intended to limit the scope of the present disclosure. Similarly, the various embodiments set forth in the drawings merely represent idealized exemplary embodiments and are not intended to limit the scope of the present disclosure. Unless otherwise specifically explained herein, the construction herein is not intended to limit the scope of the present disclosure. The relative dimensions of the various parts shown in the drawings are not necessarily to scale, and the same reference numerals are used throughout the drawings to indicate like parts. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art can not be discussed in detail but are intended to be understood as a part of the specification when appropriate. In all examples shown and discussed herein, any specific values are to be interpreted as merely exemplary and not as a limitation. Thus, other example embodiments of the exemplary embodiments can have different values. It is to be noted that like reference numerals and letters refer to like items in the drawings, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.

[0035] The inventors of the present disclosure found that the above-mentioned related art has the following problem: heavy reliance on human, resulting in high evaluation cost and low accuracy of the configuration information of the application. In view of this, the present disclosure proposes an information processing technical solution, which can reduce the evaluation cost of the configuration information of the application and improve the evaluation accuracy.

[0036] As described above, by building a full-link configuration platform in the application background, the full life cycle management from the creation, finalization, configuration, testing, and online of the configuration information can be realized; and the operation quality of the application can also be guaranteed through the capabilities of approval, verification, gray test, and monitoring. However, due to the large demand for the configuration information of the application, the large number of configuration items involved, and the high operation risk, a large amount of human resources needs to be invested to guarantee the quality of the application. This makes the evaluation method relying on the quality guarantee personnel heavily dependent on human, resulting in high evaluation cost and low accuracy of the configuration information of the application.

[0037] To solve the above technical problem, the technical solution of the present disclosure pre-stores evaluation factor data matched with each configuration information, and establishes a decision tree model by reading the evaluation factor data matched with the current configuration information, to realize automatic evaluation of the current configuration information. In this way, the evaluation cost of the configuration information of the application can be reduced, and the evaluation accuracy can be improved.

[0038] For example, the technical solution of the present disclosure can be realized through the following embodiments.

[0039] FIG. 1 shows a flowchart of an information processing method according to some embodiments of the present disclosure.

[0040] As shown in FIG. 1, in step 110, according to the current configuration information of the application, evaluation factor data matched with the current configuration information is read from the database, and the evaluation factor type to which the matched evaluation factor data belongs.

[0041] In some embodiments, the application to be evaluated can be an application (e.g., set on a user device such as a mobile phone or a computer) for implementing functions of a resource exchange type, an instant messaging type, an information publishing type, etc. Under the application framework to be evaluated, various configuration items can be configured by configuration information to implement corresponding functions.

[0042] For example, the application to be evaluated is used to implement functions of a resource exchange type, which includes a selection page of resource exchange modes. The selection interface includes multiple configuration items (e.g., resource providing mode 1 and resource providing mode 2) for configuring multiple resource exchange modes. The multiple configuration items can be configured by current configuration information (e.g., configuring resource providing mode 1 as institution 1 and configuring resource providing mode 2 as institution 2) to provide multiple resource exchange modes in the selection interface.

[0043] In some embodiments, matching evaluation factor data can be pre-stored in the database for various configuration information. Each configuration information can have a set of matching evaluation factor data.

[0044] For example, the application to be evaluated is used to implement functions of a resource exchange type, and the configuration information is to configure a resource providing mode as a specified institution. Matching evaluation factor data for the configuration information can be pre-stored, including information related to a business execution subject of resource exchange (e.g., identity information and region information of the business execution subject), information of both parties of resource exchange (e.g., identity information and resource type of an initiator, identity information and resource type of a target party), information of human resources required for quality assurance (e.g., number of people required for manual evaluation), business importance of resource exchange, satisfaction degree of a business subject of resource exchange, etc.

[0045] In step 120, the matching evaluation factor data and the evaluation factor type are taken as nodes, and a decision tree model is established according to the dependency relationship between the matching evaluation factor data and the evaluation factor type.

[0046] In some embodiments, each evaluation factor data can belong to different evaluation factor types. For example, different evaluation factor types can be divided according to the difficulty of predicting evaluation factor data using historical data, i.e., the prediction accuracy of evaluation factor data belonging to different evaluation factor types is different using historical data.

[0047] For example, the evaluation factor types include a first evaluation factor type and a second evaluation factor type; evaluation factor data belonging to the first evaluation factor type is less affected by subjectivity and has a relatively fixed change trend, i.e., a prediction accuracy of the evaluation factor data belonging to the first evaluation factor type using historical data is higher than (or equal to) a threshold; evaluation factor data belonging to the second evaluation factor type is more affected by subjectivity and has an unstable change trend, i.e., a prediction accuracy of the evaluation factor data belonging to the second evaluation factor type using historical data is lower than the threshold.

[0048] For example, the relevant information of the business execution subject, the information of the two parties of the resource exchange, and the like belong to the first evaluation factor type, and the information of the human resources required for quality assurance, the importance of the business of the resource exchange, and the satisfaction degree of the business subject of the resource exchange belong to the second evaluation factor type.

[0049] In step 130, the current configuration information of the application is evaluated using the decision tree model.

[0050] In some embodiments, the decision tree model can be used to evaluate the risk and performance of the application using the current configuration information. For example, the application to be evaluated is used to implement a resource exchange type function, and the risk of the application using the current configuration information can be evaluated to implement the resource exchange type.

[0051] In the above embodiments, the evaluation factor data matched with each configuration information is pre-stored, the decision tree model is established by reading the evaluation factor data matched with the current configuration information, and the automatic evaluation of the current configuration information is implemented. In this way, the evaluation cost of the configuration information of the application can be reduced, and the evaluation accuracy can be improved.

[0052] The establishment method of the decision tree model in step 120 is exemplarily described below through some embodiments.

[0053] In some embodiments, the matched evaluation factor data includes first evaluation factor data and second evaluation factor data, and the first evaluation factor data belongs to the second evaluation factor data. According to the dependency relationship between the first evaluation factor data and the second evaluation factor data, the decision tree model is established.

[0054] In some embodiments, the first evaluation factor type includes at least one of regional information of a corresponding business to which the current configuration information belongs, a resource type of a resource exchange involved in the corresponding business, and exchange party information of the resource exchange involved in the corresponding business; and the second evaluation factor type includes at least one of an importance of the corresponding business, a user satisfaction degree of the corresponding business, and test human resources required for the current configuration information.

[0055] For example, the second evaluation factor data belonging to the first evaluation factor type includes the relevant information of the business execution subject, the first evaluation factor data includes the identity information and the belonging area information of the business execution subject; the identity information and the belonging area information of the business execution subject belong to the relevant information of the business execution subject; the decision tree model can be established according to the dependency relationship “the identity information of the business execution subject belongs to the relevant information of the business execution subject, and the relevant information of the business execution subject belongs to the first evaluation factor type”, and “the belonging area information of the business execution subject belongs to the relevant information of the business execution subject, and the relevant information of the business execution subject belongs to the first evaluation factor type”.

[0056] For example, the decision tree model can be established by the embodiment in FIG. 2.

[0057] FIG. 2 shows a schematic diagram of a decision tree model establishment method according to some embodiments of the present disclosure.

[0058] As shown in FIG. 2, the application based on the current configuration information can realize the resource exchange function, and the evaluation factor data matching the current configuration information can be read from the database. Each matched evaluation factor data has a dependency relationship with each evaluation factor type, and each matched evaluation factor data also has a dependency relationship with each other. Taking each evaluation factor type and each matched evaluation factor data as a node, the decision tree model in FIG. 2 is established according to the above dependency relationship, which is used to evaluate the application based on the current configuration information and realize the risk of resource exchange.

[0059] For example, the dependency relationship between the matched evaluation factor data and the evaluation factor type includes: the relevant information of the business execution subject belonging to the first evaluation factor type, the information of the two parties conducting resource exchange, and other relevant information of resource exchange (such as the number of resources involved in resource exchange, the number of configuration information that can cause resource loss, the channel of resource exchange, etc.); the human resource information required for quality assurance, the business importance of resource exchange, and the satisfaction degree of the business subject of resource exchange belonging to the second evaluation factor type.

[0060] For example, the dependency relationship between the matched evaluation factor data includes: the identity information and the belonging area information of the business execution subject belonging to the relevant information of the business execution subject; the user information and the resource exchange routing information belonging to the information of the two parties conducting resource exchange; the resource type of the initiator and the identity information of the initiator belonging to the user information; the identity information of the target party and the resource type of the target party belonging to the resource exchange routing information.

[0061] In the above embodiment, the decision tree model is established according to the dependency relationship between each matched evaluation factor data and each evaluation factor type, and the dependency relationship between each matched evaluation factor data, automatic evaluation of the current configuration information is realized, the evaluation cost of the configuration information of the application is reduced, and the evaluation accuracy is improved.

[0062] The numbers on the connection lines of the nodes of the decision tree model shown in FIG. 2 are the weights of the corresponding nodes, which are used to evaluate the configuration information of the application. The determination method of the weights of each node is exemplarily illustrated by some embodiments as follows.

[0063] In some embodiments, whether to use a machine learning model to determine the weight of the matched evaluation factor data in the decision tree model is determined according to the evaluation factor type. The prediction accuracy of the evaluation factor data subordinate to different evaluation factor types is different by using historical data.

[0064] For example, in response to the evaluation factor type being a first evaluation factor type, the weight of the matched evaluation factor data and the first evaluation factor type is determined by using a machine learning model, and the prediction accuracy of the evaluation factor data subordinate to the first evaluation factor type is higher than a threshold by using historical data. For example, the evaluation factor data subordinate to the first evaluation factor type in FIG. 2 includes relevant information of a business execution subject, information of both parties for resource exchange, other relevant information of resource exchange type, identity information of the business execution subject, belonging area information, user information, resource exchange routing information, resource type of the initiator, identity information of the initiator, identity information of the target party, resource type of the target party, and the like.

[0065] For example, in response to the evaluation factor type being a second evaluation factor type, the weight of the matched evaluation factor data and the second evaluation factor type is determined according to expert knowledge data, and the prediction accuracy of the evaluation factor data subordinate to the second evaluation factor type is lower than a threshold by using historical data. For example, the evaluation factor data subordinate to the second evaluation factor type in FIG. 2 includes human resource information required for quality assurance, business importance of resource exchange, satisfaction degree of the business subject of resource exchange, and the like.

[0066] In the above embodiment, whether to use an artificial intelligence method to predict the weight of the evaluation factor data in the evaluation process is determined according to the difficulty of predicting the evaluation factor data by using historical data. In this way, the appropriate prediction method can be selected according to different change trends of the evaluation factor data, so that the evaluation accuracy is improved.

[0067] For example, the weight can be determined by the embodiment in FIG. 3.

[0068] FIG. 3 shows a schematic diagram of a weight determination method in a decision tree model according to some embodiments of the present disclosure.

[0069] As shown in FIG. 3, the application based on the current configuration information can realize the resource exchange function, and the matching evaluation factor data can be read from the database. The matching evaluation factor data includes: the relevant information of the business execution subject belonging to the first evaluation factor type obj.f1 (including the identity information of the business execution subject obj.f2, the area information to which the business execution subject belongs obj.f3); the human resource information required by the quality guarantee sub.f1, the business importance degree of the resource exchange sub.f2, and the satisfaction degree of the business subject of the resource exchange sub.f3 belonging to the second evaluation factor type.

[0070] For example, the evaluation factor data belonging to the first evaluation factor type and the first evaluation factor type weight (obj.w1~obj.w3) can be determined by using the machine learning model; and the evaluation factor data matching the second evaluation factor type and the weight of the second evaluation factor type (sub.w1~sub.w3) can be determined according to the expert knowledge data.

[0071] The method of evaluating the current configuration information of the application in step 130 will be exemplarily described by some embodiments.

[0072] In some embodiments, the weighted mean of the evaluation parameter value of the matching evaluation factor data is calculated according to the weight of the matching evaluation factor data in the decision tree model and the weight of the evaluation factor type in the decision tree model; and the current configuration information of the application is evaluated according to the weighted mean.

[0073] In some embodiments, the risk level of the current configuration information of the application is determined according to the numerical interval to which the weighted mean belongs. For example, the distribution of the weighted mean is processed according to the historical data of the weighted mean, so that the distribution of the weighted mean conforms to the normal distribution; and the risk level corresponding to the numerical interval to which the weighted mean belongs is determined according to the distribution of the weighted mean.

[0074] For example, the probability of belonging to the first risk level is the highest in the normal distribution, and the probability of belonging to the second risk level and the third risk level is the lowest. The first risk level is higher than the second risk level, and the first risk level is lower than the third risk level. In this way, the probability of falling into the medium risk level can be maximized, so that the guarantee resources can be used in the case of high risk level, and the reliability of the application can be improved.

[0075] For example, the current configuration information of the application can be evaluated by the embodiments in FIGS. 4a-4d.

[0076] FIGS. 4a-4d show schematic diagrams of the information processing method according to some embodiments of the present disclosure.

[0077] As shown in FIG. 4a, in step 410, based on the input configuration information, the evaluation factor data matching the configuration information is read from the database. In this way, the risk evaluation process is started after the configuration information is input, and the problems of the configuration information can be found early, thereby improving the reliability of the application.

[0078] In step 420, it is determined whether the matched evaluation factor data belongs to the first evaluation factor type.

[0079] In step 430, the weight of the evaluation factor data belonging to the first evaluation factor type is determined by using the machine learning model.

[0080] In step 440, the weight of the evaluation factor data belonging to the second evaluation factor type is determined by using the expert knowledge data.

[0081] In step 450, a decision tree model is established based on the matched evaluation factor data and the evaluation factor type as nodes and the dependency relationship between the matched evaluation factor data and the evaluation factor type.

[0082] In step 460, the weighted average of the evaluation parameter value of the matched evaluation factor data is calculated based on the weight of the matched evaluation factor data in the decision tree model and the weight of the evaluation factor type in the decision tree model.

[0083] In step 470, the weighted average is subjected to linear standardization processing and z-score standardization processing.

[0084] In some embodiments, the weighted average calculated in step 460 can be subjected to linear standardization processing so that the processed weighted average is less than a preset value (such as 100, 1, etc.). The processed weighted average can be used as the first evaluation result. For example, based on the linear standardization processing, the weighted average calculated in step 460 is subjected to linear transformation (such as by a transformation function x-min / (max-min)), and the weighted average is mapped to 0-1 as the first evaluation result.

[0085] In this way, the absolute gap between different evaluation results can be reduced, thereby improving the accuracy of the evaluation.

[0086] In some embodiments, the distribution of the first evaluation result can be processed based on historical data of the first evaluation result, so that the distribution of the first evaluation result conforms to a normal distribution; a numerical interval to which the first evaluation result belongs is determined in the normal distribution, which is used to determine the risk level as the second evaluation result. For example, based on z-score standardization processing, the mean and the standard deviation of the first evaluation result are standardized (such as through a conversion function (X-mean) / (standard deviation)), so that the first evaluation result conforms to a standard normal distribution (i.e., the mean is 0 and the standard deviation is 1).

[0087] In this way, the distribution of the risk level can conform to the normal distribution (such as the probability of falling into the medium risk level is the largest, and the probability of falling into the high risk level is the smallest, etc.), so that the guarantee resources can be used in the case of the high risk level, and the reliability of the application is improved.

[0088] In step 480, the risk level of the current configuration information of the application is determined according to the numerical interval to which the processing result of the weighted mean belongs.

[0089] For example, in the normal distribution shown in FIG. 4b, the first evaluation result is in the high risk level in the range [1, +∞), in the medium risk level in the range [-1, 1), and in the low risk level in the range (-∞, -1). The percentage value in FIG. 4b is the probability corresponding to the numerical interval. In this way, the probability of falling into the medium risk level can be the largest, so that the guarantee resources can be used in the case of the high risk level, and the reliability of the application is improved.

[0090] In some embodiments, after the evaluation result (such as the risk level) is determined, the determined evaluation result can also be verified according to the risk level of the application after going online, so as to perform asynchronous training on the machine learning model. For example, the risk level of the application after going online using the configuration information can be determined according to expert knowledge, and the machine learning model can be trained according to the difference between the risk level after going online and the evaluated risk level.

[0091] In this way, the performance of the machine learning model can be improved, so as to improve the accuracy of the evaluation.

[0092] In some embodiments, different quality guarantee processing methods can be adopted according to different risk levels. For example, the quality guarantee processing method can be determined through the embodiments in FIGS. 4c-4d.

[0093] As shown in FIG. 4c, after determining the risk level, different quality assurance processing modes can be configured for different risk levels by using the processing mode control module. Each quality assurance processing mode includes multiple processing links. The higher the risk level, the more processing links, the longer the test time, and the more human resources are needed. Each processing link can be implemented by OPS (Operations Support), QA (Quality Assurance), SRE (Site Reliability Engineering) (such as operation and maintenance personnel) and other related personnel.

[0094] For example, the high risk level corresponds to the first processing mode, the medium risk level corresponds to the second processing mode, and the low risk level corresponds to the third processing mode. The first processing mode includes the most processing links, the longest test time, and the most human resources. The third processing mode includes the least processing links, the shortest test time, and the least human resources.

[0095] As shown in FIG. 4d, a quality setting (config_quality) service module can be set in the system service to provide technical support for the information processing method in any of the above embodiments. For example, the config_quality service module is used to configure risk assessment, inspection center (such as correlation verification, reasoning verification, etc.), change monitoring or alarm, configuration item check, etc.

[0096] For example, the configuration information risk assessment module in FIG. 4 performs the information processing method in any of the above embodiments by using modules such as evaluation model, evaluation result calculation, risk level determination, etc. to determine the risk level of the current configuration information. The configuration information quality control module performs quality assurance processing by using processing links such as verification (which can be performed before entering the configuration information), testing (which can include multiple scene testing methods), monitoring (which can monitor the entire change process of the configuration information), etc. according to the risk level to determine the problem type of the configuration information, including online problems, online accidents, offline defects, rollback rates, etc. The processing method of the configuration information can be used in different scenarios such as old business, online or offline functions, new business, etc.

[0097] In the above embodiments, considering that different configuration information affects different business parties, configuration item modification risks, etc., a combined, dynamic and differentiated evaluation processing flow is implemented for different configuration information, and a risk level determination function and a corresponding differentiated quality assurance processing flow are provided. In this way, the human resource input can be saved under the premise of guaranteeing the functional quality of the application, thereby improving the efficiency and accuracy of information processing and improving the configuration problem rate (such as online accidents, online problems, offline defects, etc.) of QA free testing.

[0098] FIG. 5 shows a block diagram of an information processing apparatus according to some embodiments of the present disclosure.

[0099] As shown in FIG. 5, the information processing apparatus 5 includes a reading unit 51 configured to read, according to current configuration information of an application, evaluation factor data matching the current configuration information and an evaluation factor type to which the matched evaluation factor data belongs; a building unit 52 configured to build a decision tree model according to a dependency relationship between the matched evaluation factor data and the evaluation factor type, with the matched evaluation factor data and the evaluation factor type as nodes; and an evaluation unit 53 configured to evaluate the current configuration information of the application by using the decision tree model.

[0100] In some embodiments, the building unit 51 determines whether to use a machine learning model to determine the weight of the matched evaluation factor data in the decision tree model according to the evaluation factor type, wherein the prediction accuracy of the evaluation factor data belonging to different evaluation factor types is different by using historical data.

[0101] In some embodiments, the building unit 51 determines the weight of the matched evaluation factor data and the first evaluation factor type by using a machine learning model in response to the evaluation factor type being a first evaluation factor type, wherein the prediction accuracy of the evaluation factor data belonging to the first evaluation factor type is higher than a threshold by using historical data, and determines the weight of the matched evaluation factor data and a second evaluation factor type according to expert knowledge data in response to the evaluation factor type being the second evaluation factor type, wherein the prediction accuracy of the evaluation factor data belonging to the second evaluation factor type is lower than the threshold by using historical data.

[0102] In some embodiments, the first evaluation factor type includes at least one of regional information of a corresponding business to which the current configuration information belongs, a resource type of a resource exchange involved by the corresponding business, and exchange party information of the resource exchange; and the second evaluation factor type includes at least one of an importance of the corresponding business, a user satisfaction of the corresponding business, and testing human resources required by the current configuration information.

[0103] In some embodiments, the matched evaluation factor data includes first evaluation factor data and second evaluation factor data, the first evaluation factor data is subordinate to the second evaluation factor data, and the establishing unit 51 establishes the decision tree model according to the subordinate relationship between the first evaluation factor data and the second evaluation factor data.

[0104] In some embodiments, the evaluation unit 53 calculates a weighted mean of the evaluation parameter values of the matched evaluation factor data according to the weights of the matched evaluation factor data in the decision tree model and the weights of the evaluation factor types in the decision tree model, and evaluates the current configuration information of the application according to the weighted mean.

[0105] In some embodiments, the evaluation unit 53 determines the risk level of the current configuration information of the application according to the numerical interval to which the weighted mean belongs.

[0106] In some embodiments, the evaluation unit 53 processes the distribution of the weighted mean according to the historical data of the weighted mean, so that the distribution of the weighted mean conforms to a normal distribution, and determines the risk level corresponding to the numerical interval to which the weighted mean belongs according to the distribution of the weighted mean.

[0107] FIG. 6 shows a block diagram of another embodiment of the information processing apparatus of the present disclosure.

[0108] As shown in FIG. 6, the information processing apparatus 6 of this embodiment includes a memory 61 and a processor 62 coupled to the memory 61, the processor 62 being configured to execute the information processing method in any one of the embodiments of the present disclosure based on instructions stored in the memory 61.

[0109] The memory 61 may, for example, include a system memory, a fixed nonvolatile storage medium, etc. The system memory may, for example, store an operating system, an application program, a Boot Loader, a database, and other programs, etc.

[0110] FIG. 7 shows a block diagram of still another embodiment of the information processing apparatus of the present disclosure.

[0111] As shown in FIG. 7, the information processing apparatus 7 of this embodiment includes a memory 710 and a processor 720 coupled to the memory 710, the processor 720 being configured to execute the information processing method in any one of the preceding embodiments based on instructions stored in the memory 710.

[0112] The memory 710 may, for example, include a system memory, a fixed nonvolatile storage medium, etc. The system memory may, for example, store an operating system, an application program, a Boot Loader, and other programs, etc.

[0113] The information processing apparatus 7 can further include an input / output interface 730, a network interface 740, a storage interface 750, and the like. These interfaces 730, 740, 750, and the memory 710 and the processor 720 can be connected, for example, through a bus 760. Among them, the input / output interface 730 provides a connection interface for display, mouse, keyboard, touch screen, microphone, speaker, and the like input / output devices. The network interface 740 provides a connection interface for various networking devices. The storage interface 750 provides a connection interface for an external storage device such as an SD card and a U disk.

[0114] Those skilled in the art will appreciate that embodiments of the present disclosure can be provided as methods, systems, or computer program products. Accordingly, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure can take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, and the like) containing computer usable program code.

[0115] So far, the information processing method, the information processing apparatus, the computer readable storage medium, and the computer program product according to the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein according to the above description.

[0116] The methods and systems of the present disclosure can be implemented in a number of ways. For example, the methods and systems of the present disclosure can be implemented using software, hardware, firmware, or any combination of software, hardware, and firmware. The above described order of steps for the methods is merely for illustration, and the steps of the methods of the present disclosure are not limited to the above specifically described order, unless otherwise specifically stated. Furthermore, in some embodiments, the present disclosure can also be implemented as programs recorded in recording media, which include machine readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers recording media storing programs for executing the methods according to the present disclosure.

[0117] Although some specific embodiments of the present disclosure have been described in detail by examples, those skilled in the art should understand that the above examples are merely for illustration, and are not intended to limit the scope of the present disclosure. Those skilled in the art should understand that the above embodiments can be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. An information processing method comprising: reading, from a database, evaluation factor data matching current configuration information of an application and an evaluation factor type to which the matching evaluation factor data belongs, according to the current configuration information of the application; establishing a decision tree model according to a dependency relationship between the matching evaluation factor data and the evaluation factor type, with the matching evaluation factor data and the evaluation factor type as nodes; evaluating the current configuration information of the application by using the decision tree model.

2. The information processing method according to claim 1, wherein The establishing of the decision tree model comprises: determining whether to use a machine learning model to determine a weight of the matching evaluation factor data in the decision tree model according to the evaluation factor type, wherein a prediction accuracy of evaluation factor data belonging to different evaluation factor types is different by using historical data.

3. The information processing method according to claim 2, wherein The determining whether to use the machine learning model to determine the weight of the matching evaluation factor data in the decision tree model according to the evaluation factor type comprises: in response to the evaluation factor type being a first evaluation factor type, determining the weight of the matching evaluation factor data and the first evaluation factor type by using the machine learning model, wherein the prediction accuracy of the evaluation factor data belonging to the first evaluation factor type is higher than a threshold by using historical data; in response to the evaluation factor type being a second evaluation factor type, determining the weight of the matching evaluation factor data and the second evaluation factor type according to expert knowledge data, wherein the prediction accuracy of the evaluation factor data belonging to the second evaluation factor type is lower than a threshold by using historical data.

4. The information processing method of claim 3, wherein: the first evaluation factor type comprises at least one of region information to which a corresponding service of the current configuration information belongs, a resource type of a resource exchange involved by the corresponding service, and exchange party information of the involved resource exchange; the second evaluation factor type comprises at least one of an importance of the corresponding service, a user satisfaction of the corresponding service, and a test human resource required by the current configuration information.

5. The information processing method according to any one of claims 1 to 4, wherein the matching evaluation factor data comprises first evaluation factor data and second evaluation factor data, the first evaluation factor data belonging to the second evaluation factor data, The establishing of the decision tree model according to the dependency relationship between the matching evaluation factor data and the evaluation factor type, with the matching evaluation factor data and the evaluation factor type as nodes, comprises: establishing the decision tree model according to a dependency relationship between the first evaluation factor data and the second evaluation factor data.

6. The information processing method according to any one of claims 1 to 4, wherein The evaluating of the current configuration information of the application by using the decision tree model comprises: calculating a weighted mean of an evaluation parameter value of the matching evaluation factor data according to the weight of the matching evaluation factor data in the decision tree model and the weight of the evaluation factor type in the decision tree model; evaluating the current configuration information of the application according to the weighted mean.

7. The information processing method according to claim 6, wherein The evaluating of the current configuration information of the application according to the weighted mean comprises: determining a risk level of the current configuration information of the application according to a numerical interval to which the weighted mean belongs.

8. The information processing method according to claim 7, wherein The determining a risk level of the current configuration information of the application according to a numerical interval to which the weighted mean belongs comprises: processing a distribution of the weighted mean according to historical data of the weighted mean, so that the distribution of the weighted mean conforms to a normal distribution; determining a risk level corresponding to the numerical interval to which the weighted mean belongs according to the distribution of the weighted mean. 9.An information processing apparatus, comprising: a reading unit configured to read, according to current configuration information of an application, evaluation factor data matching the current configuration information from a database, and an evaluation factor type to which the matching evaluation factor data belongs; a building unit configured to build a decision tree model according to the matching evaluation factor data and the evaluation factor type as nodes, and a subordinative relationship between the matching evaluation factor data and the evaluation factor type; an evaluation unit configured to evaluate the current configuration information of the application by using the decision tree model. 10.An information processing apparatus, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform the information processing method according to any one of claims 1-8 based on instructions stored in the memory apparatus. 11.A computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the information processing method according to any one of claims 1-8. 12.A computer program product comprising instructions which, when executed by a processor, cause the processor to carry out the information processing method according to any one of claims 1-8. ​

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