Information system resource information classification method and device, equipment and storage medium
By obtaining application names, development languages and highly available type information in the information system, and using gradient improvement decision tree to train the classification model, the problem of low efficiency and low accuracy of information system resource classification is solved, automated classification is realized, and information update speed and accuracy are improved.
Patent Information
- Application Number
- CN202510535916.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-08-08
AI Technical Summary
In the prior art, the information system resource classification efficiency is low, information update is lagging and the accuracy is low, which affects subsequent decision-making and resource allocation.
By obtaining application name, development language and highly available type information from the information system, using the gradient enhancement decision tree to train the classification model, the application type information is automatically determined and recorded in the service table.
It improves the efficiency of resource information classification, reduces the workload of manual verification by 80%, reduces the error rate to below 5%, and increases the data consistency and accuracy rate to above 95%, providing a more reliable basis for resource planning and fault analysis.
Smart Images
Figure CN120448991A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to an information system resource information classification method, device, equipment and storage medium. Background Art
[0002] In the healthcare and financial sectors, CMDB (Configuration Management Database) serves as an information system for enterprise storage and management of IT infrastructure, providing a centralized view of enterprise resources. In addition to maintaining information synchronized from the cloud or other upstream sources, CMDB also needs to classify resource information. However, existing technologies generally use manual maintenance to classify CMDB resource information, which is inefficient. As system environments become increasingly complex, manual maintenance becomes increasingly cumbersome, especially when resources change frequently. Operations and maintenance personnel must manually check each piece of information, which can easily lead to delayed updates. Furthermore, this method has a low accuracy rate, resulting in incomplete or inaccurate information, impacting subsequent decision-making or resource allocation. Summary of the Invention
[0003] The purpose of the present invention is to provide an information system resource information classification method, device, equipment and storage medium to solve the technical problems of low efficiency, delayed information update and low accuracy in the prior art.
[0004] The technical solution of the present invention is as follows: providing a method for classifying information of information system resources, comprising:
[0005] Obtain application name information, development language information, and high availability type information from the information system;
[0006] Determining corresponding application type information according to the application name information, the development language information, the high availability type information, and a classification model, wherein the classification model is obtained by training the application name information sample, the development language information sample, the high availability type information sample, and a gradient boosting decision tree;
[0007] The application name information, the development language information, the high availability type information and the application type information are recorded in a service table.
[0008] Furthermore, the training step of the classification model includes:
[0009] Obtain application name information samples, development language information samples, and high-availability type information samples to form a training set, use multiple decision trees to construct a gradient boosting decision tree, and gradually optimize the classification performance of the gradient boosting decision tree through the training set to obtain a classification model.
[0010] Furthermore, the classification performance of the gradient boosting decision tree is gradually optimized through the training set to obtain a classification model, including:
[0011] The classification performance of the gradient boosting decision tree is gradually optimized through the training set. In each iteration, the growth of a new tree is guided according to the prediction error of the current model and the negative gradient of the loss function to obtain a classification model.
[0012] Furthermore, the classification performance of the gradient boosting decision tree is gradually optimized through the training set to obtain a classification model, including:
[0013] The classification performance of the gradient boosting decision tree is gradually optimized through the training set. If the classification performance index of the gradient boosting decision tree reaches the corresponding preset value or the training reaches a preset number of iterations, the training is stopped to obtain a classification model.
[0014] Furthermore, the information system resource information classification method also includes, after each iteration of the gradient boosting decision tree, accumulating the prediction results of all decision trees of the gradient boosting decision tree to obtain the total prediction result of the gradient boosting decision tree, and determining the classification performance index of the gradient boosting decision tree based on the total prediction result and the true prediction result.
[0015] Furthermore, the classification performance indicator includes at least one of an accuracy score, a precision rate, a recall rate, and an F1 score.
[0016] Furthermore, the application type information includes web service, batch processing, front-end, third-party system, middleware, NAS component, public component, technical component and logical entity.
[0017] Another technical solution of the present invention is as follows: providing an information system resource information classification device, comprising an information acquisition module, a classification module, and a recording module;
[0018] The information acquisition module is used to obtain application name information, development language information and high availability type information from the information system;
[0019] The classification module is configured to determine corresponding application type information based on the application name information, the development language information, the high-availability type information, and a classification model, wherein the classification model is obtained by training the application name information sample, the development language information sample, the high-availability type information sample, and a gradient boosting decision tree;
[0020] The recording module is used to record the application name information, the development language information, the high availability type information and the application type information into a service table.
[0021] Another technical solution of the present invention is as follows: an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and when the processor executes the computer program, it implements the information system resource information classification method as described in any technical solution.
[0022] Another technical solution of the present invention is as follows: a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the information system resource information classification method as described in any of the above technical solutions is implemented.
[0023] The beneficial effects of the present invention are: obtaining application name information, development language information and high-availability type information from an information system; determining corresponding application type information based on the application name information, the development language information, the high-availability type information and a classification model, wherein the classification model is obtained through application name information samples, development language information samples, high-availability type information samples and gradient boosting decision tree training; recording the application name information, the development language information, the high-availability type information and the application type information into a service table; through the above technical solution, the application type can be determined to realize the classification of resource information, the efficiency of resource information classification can be improved, the speed of information update can be improved to greatly avoid information update delays, and the accuracy of resource information classification can be improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only part of the embodiments of the present application, rather than all the embodiments. For ordinary technicians in this field, without paying any creative work, other drawings obtained based on these drawings are all within the scope of protection of this application.
[0025] Figure 1 A flowchart of a method for classifying information system resource information provided in an embodiment of the present application;
[0026] Figure 2 A schematic diagram of a confusion matrix provided in an embodiment of the present application;
[0027] Figure 3 A schematic diagram of a service table provided in an embodiment of the present application;
[0028] Figure 4 A schematic diagram of the structure of the information system resource information classification device provided in an embodiment of the present application;
[0029] Figure 5A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0031] In the description of this application, words such as "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order. In this specification, the terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.
[0032] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0033] Figure 1 It is a flow chart of the information system resource information classification method according to an embodiment of the present invention. It should be noted that the information system resource information classification method according to the present invention is not limited to the following examples if the results are substantially the same. Figure 1 The process sequence shown is limited. Figure 1 As shown, the information system resource information classification method mainly includes the following steps:
[0034] S101, obtaining application name information, development language information, and high availability type information from the information system;
[0035] In some embodiments, the above application name information may be an abbreviation of the service name, such as SPRINGBOOT-ACCOUNTING, the development language may be JAVA, H5, etc., and the high availability type (HA type) information may be SPRINGBOOT, NGINX, etc.
[0036] S102, determining corresponding application type information based on the application name information, the development language information, the high-availability type information, and a classification model, wherein the classification model is obtained by training the application name information sample, the development language information sample, the high-availability type information sample, and a gradient boosting decision tree;
[0037] In an optional embodiment, the classification model training step includes:
[0038] Obtain application name information samples, development language information samples, and high-availability type information samples to form a training set, use multiple decision trees to construct a gradient boosting decision tree, and gradually optimize the classification performance of the gradient boosting decision tree through the training set to obtain a classification model.
[0039] In some embodiments, historical application name information, development language information, and high-availability type information are used to form application name information samples, development language information samples, and high-availability type information samples, respectively, thereby forming a training set. A gradient boosting decision tree is constructed through a series of weak learners, such as multiple decision trees. After the gradient boosting decision tree is trained, a classification model is obtained. The classification model can be stored, and the application name information, the development language information, and the high-availability type information can be input into the classification model. The classification model performs feature extraction to obtain the corresponding application type information.
[0040] In an optional embodiment, the classification performance of the gradient boosting decision tree is gradually optimized by the training set to obtain a classification model, including:
[0041] The classification performance of the gradient boosting decision tree is gradually optimized through the training set. In each iteration, the growth of a new tree is guided according to the prediction error of the current model and the negative gradient of the loss function to obtain a classification model.
[0042] In some embodiments, at each iteration, the growth of a new tree is guided by the prediction error of the current model and the negative gradient of the loss function, so that the model focuses on correcting previous errors and ultimately obtains a classification model.
[0043] In an optional embodiment, the classification performance of the gradient boosting decision tree is gradually optimized by the training set to obtain a classification model, including:
[0044] The classification performance of the gradient boosting decision tree is gradually optimized through the training set. If the classification performance index of the gradient boosting decision tree reaches the corresponding preset value or the training reaches a preset number of iterations, the training is stopped to obtain a classification model.
[0045] In an optional embodiment, the classification performance indicator includes at least one of an accuracy score, a precision rate, a recall rate, and an F1 score.
[0046] In some embodiments, training is stopped when the classification performance index of the gradient boosting decision tree reaches a corresponding preset value or the training reaches a preset number of iterations. For example, when the classification performance index includes an accuracy score, when the accuracy score is greater than 0.9 or the training reaches a preset number of iterations (for example, 500 times), the training of the gradient boosting decision tree is stopped to take into account both training efficiency and model accuracy.
[0047] In an optional embodiment, the information system resource information classification method further includes, after each iteration of the gradient boosting decision tree, accumulating the prediction results of all decision trees of the gradient boosting decision tree to obtain the total prediction result of the gradient boosting decision tree, and determining the classification performance index of the gradient boosting decision tree based on the total prediction result and the true prediction result.
[0048] In some embodiments, the prediction results of all decision trees of the gradient boosting decision tree are accumulated to obtain the total prediction result of the gradient boosting decision tree, and the classification performance index of the gradient boosting decision tree is determined based on the total prediction result and the true prediction result. The classification performance index may include at least one of the accuracy score, precision rate, recall rate and F1 score. For example, the classification performance index includes the accuracy score, precision rate, recall rate and F1 score, and the accuracy score, precision rate, recall rate and F1 score correspond to 0.9111, 0.8521, 0.9111 and 0.8759 respectively.
[0049] In some embodiments, in addition to evaluating the model through classification performance indicators such as accuracy, a confusion matrix can also be drawn to more intuitively understand the performance of the model on the training set. The schematic diagram of the confusion matrix is as follows: Figure 2 As shown, Figure 2 The numbers 1 to 9 in the first column and the last row represent different application types, namely web services, batch processing, front-end, third-party systems, middleware, NAS (Network Attached Storage) components, common components, technical components, and logical entities. Figure 2 The other numbers in the confusion matrix are the values of the confusion matrix, which represent the number of corresponding results. For example, the value 1 corresponding to the second column (the number of columns corresponding to the number 2 in the last row) and the third row (the number of columns corresponding to the number 3 in the first column) indicates that the prediction result is batch processing and the true result is the number of front-ends.
[0050] S103: Record the application name information, the development language information, the high availability type information, and the application type information into a service table.
[0051] In an optional implementation, the application type information includes web service, batch processing, front-end, third-party system, middleware, NAS component, public component, technical component and logical entity.
[0052] In some embodiments, the application name information, the development language information, the high availability type information and the application type information are recorded in the service table, and an updated service table can be obtained. The schematic diagram of the service table is as follows: Figure 3 As shown, Figure 3 The second column indicates the subsystem to which the application name (service name abbreviation) belongs.
[0053] The information system resource information classification method provided by the embodiment of the present invention obtains application name information, development language information and high-availability type information from the information system; determines the corresponding application type information based on the application name information, the development language information, the high-availability type information and the classification model, and the classification model is obtained by training the application name information sample, the development language information sample, the high-availability type information sample and the gradient boosting decision tree; records the application name information, the development language information, the high-availability type information and the application type information in a service table; the application type can be determined to realize the classification of resource information, the efficiency of resource information classification can be improved, the speed of information update can be increased to greatly avoid information update lag, and the accuracy of resource information classification can be improved.
[0054] The information system resource information classification method provided by the embodiment of the present invention improves the efficiency of operation and maintenance work through automated classification and labeling, reduces the workload originally required for manual verification by about 80%, reduces the error rate to below 5%, and greatly improves data consistency and accuracy. Based on the classification model of historical data, the classification accuracy can reach more than 95%, which can provide a more reliable data basis for subsequent resource planning and fault analysis.
[0055] The information system resource information classification method provided in embodiments of the present invention can be constructed based on artificial intelligence (AI). This technology can be used to acquire and process relevant data, enabling unattended AI-powered information system resource information classification. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.
[0056] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.
[0057] Figure 4 FIG. 1 is a structural diagram of an information system resource information classification device according to an embodiment of the present invention. Figure 4 As shown, the information system resource information classification device 40 includes an information acquisition module 41, a classification module 42 and a recording module 43;
[0058] The information acquisition module 41 is used to obtain application name information, development language information and high availability type information from the information system;
[0059] The classification module 42 is configured to determine corresponding application type information based on the application name information, the development language information, the high-availability type information, and a classification model, wherein the classification model is obtained by training the application name information sample, the development language information sample, the high-availability type information sample, and a gradient boosting decision tree;
[0060] The recording module 43 is configured to record the application name information, the development language information, the high availability type information, and the application type information into a service table.
[0061] In an optional embodiment, the information system resource information classification device 40 also includes a training module, which is used to obtain application name information samples, development language information samples and high-availability type information samples to form a training set, and use multiple decision trees to construct a gradient boosting decision tree, and gradually optimize the classification performance of the gradient boosting decision tree through the training set to obtain a classification model.
[0062] In some embodiments, historical application name information, development language information, and high-availability type information are used to form application name information samples, development language information samples, and high-availability type information samples, respectively, thereby forming a training set. A gradient boosting decision tree is constructed through a series of weak learners, such as multiple decision trees. After the gradient boosting decision tree is trained, a classification model is obtained. The classification model can be stored, and the application name information, the development language information, and the high-availability type information can be input into the classification model. The classification model performs feature extraction to obtain the corresponding application type information.
[0063] In an optional embodiment, the training module gradually optimizes the classification performance of the gradient boosting decision tree through the training set to obtain a classification model, including:
[0064] The classification performance of the gradient boosting decision tree is gradually optimized through the training set. In each iteration, the growth of a new tree is guided according to the prediction error of the current model and the negative gradient of the loss function to obtain a classification model.
[0065] In some embodiments, at each iteration, the growth of a new tree is guided by the prediction error of the current model and the negative gradient of the loss function, so that the model focuses on correcting previous errors and ultimately obtains a classification model.
[0066] In an optional embodiment, the training module gradually optimizes the classification performance of the gradient boosting decision tree through the training set to obtain a classification model, including:
[0067] The classification performance of the gradient boosting decision tree is gradually optimized through the training set. If the classification performance index of the gradient boosting decision tree reaches the corresponding preset value or the training reaches a preset number of iterations, the training is stopped to obtain a classification model.
[0068] In an optional embodiment, the training module is also used to accumulate the prediction results of all decision trees of the gradient boosting decision tree after each iteration of the gradient boosting decision tree to obtain the total prediction result of the gradient boosting decision tree, and determine the classification performance index of the gradient boosting decision tree based on the total prediction result and the true prediction result.
[0069] In some embodiments, training is stopped when the classification performance index of the gradient boosting decision tree reaches a corresponding preset value or the training reaches a preset number of iterations. For example, when the classification performance index includes an accuracy score, when the accuracy score is greater than 0.9 or the training reaches a preset number of iterations (for example, 500 times), the training of the gradient boosting decision tree is stopped to take into account both training efficiency and model accuracy.
[0070] In an optional embodiment, the classification performance indicator includes at least one of an accuracy score, a precision rate, a recall rate, and an F1 score.
[0071] In some embodiments, the prediction results of all decision trees of the gradient boosting decision tree are accumulated to obtain the total prediction result of the gradient boosting decision tree, and the classification performance index of the gradient boosting decision tree is determined based on the total prediction result and the true prediction result. The classification performance index may include at least one of the accuracy score, precision rate, recall rate and F1 score. For example, the classification performance index includes the accuracy score, precision rate, recall rate and F1 score, and the accuracy score, precision rate, recall rate and F1 score correspond to 0.9111, 0.8521, 0.9111 and 0.8759 respectively.
[0072] In an optional implementation, the application type information includes web service, batch processing, front-end, third-party system, middleware, NAS component, public component, technical component and logical entity.
[0073] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 5As shown, the electronic device 50 includes a processor 51 and a memory 52 communicatively connected to the processor 51 .
[0074] The memory 52 stores program instructions for implementing the information system resource information classification method of any of the above embodiments.
[0075] The processor 51 is configured to execute program instructions stored in the memory 52 to classify information system resource information.
[0076] The processor 51 may also be referred to as a CPU (Central Processing Unit). The processor 51 may be an integrated circuit chip having signal processing capabilities. The processor 51 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.
[0077] An embodiment of the present invention provides a storage medium, which stores program instructions that can implement all the above methods. The storage medium can be non-volatile or volatile. The program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.
[0078] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.
[0079] In addition, the functional modules in the various embodiments of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. The above is only an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.
[0080] The above description is only an embodiment of the present invention. It should be pointed out that those skilled in the art can make improvements without departing from the creative concept of the present invention, but these improvements all fall within the scope of protection of the present invention.
Claims
1. A method for classifying information of information system resources, characterized in that: include: Obtain application name information, development language information, and high availability type information from the information system; Determining corresponding application type information according to the application name information, the development language information, the high availability type information, and a classification model, wherein the classification model is obtained by training the application name information sample, the development language information sample, the high availability type information sample, and a gradient boosting decision tree; The application name information, the development language information, the high availability type information and the application type information are recorded in a service table.
2. The information system resource information classification method according to claim 1, characterized in that: The training steps of the classification model include: Obtain application name information samples, development language information samples, and high-availability type information samples to form a training set, use multiple decision trees to construct a gradient boosting decision tree, and gradually optimize the classification performance of the gradient boosting decision tree through the training set to obtain a classification model.
3. The information system resource information classification method according to claim 2, characterized in that: The classification performance of the gradient boosting decision tree is gradually optimized through the training set to obtain a classification model, including: The classification performance of the gradient boosting decision tree is gradually optimized through the training set. In each iteration, the growth of a new tree is guided according to the prediction error of the current model and the negative gradient of the loss function to obtain a classification model.
4. The information system resource information classification method according to claim 2, characterized in that: The classification performance of the gradient boosting decision tree is gradually optimized through the training set to obtain a classification model, including: The classification performance of the gradient boosting decision tree is gradually optimized through the training set. If the classification performance index of the gradient boosting decision tree reaches the corresponding preset value or the training reaches a preset number of iterations, the training is stopped to obtain a classification model.
5. The information system resource information classification method according to claim 4, characterized in that: The information system resource information classification method also includes, after each iteration of the gradient boosting decision tree, accumulating the prediction results of all decision trees of the gradient boosting decision tree to obtain a total prediction result of the gradient boosting decision tree, and determining the classification performance index of the gradient boosting decision tree based on the total prediction result and the true prediction result.
6. The information system resource information classification method according to claim 4, characterized in that: The classification performance indicator includes at least one of an accuracy score, a precision rate, a recall rate, and an F1 score.
7. The information system resource information classification method according to claim 1, characterized in that: The application type information includes web service, batch processing, front end, third-party system, middleware, NAS component, public component, technical component and logical entity.
8. An information system resource information classification device, characterized in that: Including information acquisition module, classification module and recording module; The information acquisition module is used to obtain application name information, development language information and high availability type information from the information system; The classification module is configured to determine corresponding application type information based on the application name information, the development language information, the high-availability type information, and a classification model, wherein the classification model is obtained by training the application name information sample, the development language information sample, the high-availability type information sample, and a gradient boosting decision tree; The recording module is used to record the application name information, the development language information, the high availability type information and the application type information into a service table.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the processor executes the computer program, the information system resource information classification method according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the information system resource information classification method according to any one of claims 1 to 7 is implemented.