Server resource evaluation method and system, computer equipment, readable storage medium and program product

Abstract

The invention relates to a server resource evaluation method and system, computer equipment, a readable storage medium and a program product. The method comprises the steps of obtaining basic information of a target product; inputting the basic information into a necessary agent set to obtain first evaluation data; under the condition that the first evaluation data does not trigger the boundary limitation condition, generating an evaluation report according to the first evaluation data and information of a preset standard server; under the condition that the first evaluation data triggers the boundary limitation condition, inputting the first evaluation data into the unnecessary agent set to obtain second evaluation data; generating an evaluation report according to the first evaluation data and the second evaluation data; the evaluation report comprises at least one of hardware information, software information, performance information and restrictive information. According to the evaluation limitation, an evaluation report adapting to various requirements can be generated, and the adaptability of server resource configuration is improved while the evaluation efficiency is considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource evaluation, in particular to a server resource evaluation method and system, computer device, computer readable storage medium and computer program product. BACKGROUND

[0002] With the development of the Internet field, the resources of the server present diversified needs, and the evaluation of the server resources will directly affect the stability of the service and the operating cost, therefore, it is necessary to quickly and accurately complete the evaluation of the server resources.

[0003] The traditional server resource evaluation method includes manual method and template method. The manual method is that the operation and maintenance personnel evaluates based on the product and user characteristics, this method has high labor cost, low efficiency and uncontrollable data accuracy, and is also easily affected by subjective experience. The template method is a static evaluation based on fixed rules, which is difficult to meet the personalized needs of users, has low flexibility and poor adaptability. SUMMARY

[0004] Therefore, it is necessary to provide a server resource evaluation method, system, computer device, computer readable storage medium and computer program product which can improve efficiency and adaptability.

[0005] In a first aspect, the present application provides a server resource evaluation method, comprising:

[0006] obtaining the basic information of the target product;

[0007] inputting the basic information into a necessary agent set to obtain first evaluation data;

[0008] if the first evaluation data does not trigger a boundary limit condition, generating an evaluation report according to the first evaluation data and the information of a preset standard server;

[0009] if the first evaluation data triggers a boundary limit condition, inputting the first evaluation data into a non-necessary agent set to obtain second evaluation data;

[0010] generating an evaluation report according to the first evaluation data and the second evaluation data; the evaluation report includes at least one of hardware information, software information, performance information and limiting information.

[0011] In a second aspect, the present application further provides a server resource evaluation system, comprising:

[0012] a data acquisition module for acquiring the basic information of the target product;

[0013] A necessary intelligent agent module is used to obtain first evaluation data based on the basic information;

[0014] The non-essential agent module is used to obtain the second evaluation data based on the first evaluation data when the first evaluation data triggers the boundary constraint condition.

[0015] An evaluation report generation module is used to generate an evaluation report based on the first evaluation data and information from a preset standard server when the first evaluation data does not trigger boundary limiting conditions; or to generate an evaluation report based on the first evaluation data and the second evaluation data. The evaluation report includes at least one of hardware information, software information, performance information, and limiting information.

[0016] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0017] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0018] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0019] The aforementioned server resource evaluation methods, systems, computer equipment, computer-readable storage media, and computer program products can achieve rapid evaluation under normal requirements by generating an evaluation report using the first evaluation data and preset standard server information when the first evaluation data does not trigger boundary constraints. By generating second evaluation data based on the first evaluation data after the first evaluation data triggers boundary constraints, and then generating an evaluation report based on both the first and second evaluation data, that is, after the first evaluation output triggers boundary constraints, a further in-depth evaluation of the basic information is performed based on the first evaluation data to obtain second evaluation data that is more closely matched to the preset standard server information, thus achieving in-depth evaluation for complex requirements. By generating evaluation reports according to different scenarios, unnecessary overhead can be avoided by calling unnecessary intelligent entities under normal requirements. It can also avoid evaluation results that do not meet requirements due to the failure to call unnecessary intelligent entities in complex scenarios. The aforementioned evaluation constraints can generate evaluation reports that adapt to various requirements, improving the adaptability of server resource configuration while maintaining evaluation efficiency. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for evaluating server resources in one embodiment;

[0022] Figure 2 This is a flowchart illustrating a method for evaluating server resources in another embodiment;

[0023] Figure 3 This is a schematic diagram of an evaluation report in one embodiment;

[0024] Figure 4 This is a block diagram of a server resource evaluation system in one embodiment;

[0025] Figure 5 This is a block diagram of a server resource evaluation system in another embodiment;

[0026] Figure 6 This is a block diagram of a server resource evaluation system in another embodiment;

[0027] Figure 7 This is a block diagram of a server resource evaluation system in another embodiment;

[0028] Figure 8 This is a block diagram of a server resource evaluation system in another embodiment;

[0029] Figure 9 This is an internal structural diagram of a computer device in one embodiment.

[0030] Figure reference numerals: Data acquisition module: 402; Necessary agent module: 404; Software relationship agent: 4041; Performance data agent: 4042; Non-essential agent module: 406; Hardware data agent: 4061; Constrained configuration agent: 4062; Evaluation report generation module: 408; Data verification module: 410. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0033] In one embodiment, such as Figure 1 As shown, this application embodiment provides a method for evaluating server resources, including steps 102 to 108. Wherein:

[0034] Step 102: Obtain basic information about the target product.

[0035] The target product can be enterprise-oriented software products that users have already purchased or have not yet purchased. Enterprise-oriented software products are usually based on platforms such as Software as a Service (SaaS) and Platform as a Service (PaaS) to provide software products to enterprises. For example, enterprise-oriented software products can be Kingdee Cloud Starry Sky, Kingdee Cloud Starry Sky, Kingdee Cloud Starry Sky, etc.

[0036] Basic information about a target product typically includes the software product's module information and the number of users. If the software product has already been purchased, the basic information may include the specific modules included in the purchased target product and the number of licensed users. If the software product has not been purchased, the basic information may include the modules to be purchased and the expected number of users.

[0037] Step 104: Input the basic information into the set of necessary intelligent agents to obtain the first evaluation data.

[0038] The necessary intelligent agent set includes multiple necessary intelligent agents (Agents). These multiple necessary intelligent agents are different. An intelligent agent refers to a proxy that can perceive the environment and take actions to achieve a specific goal. In the embodiments of this application, the intelligent agent obtains the set target data through preset rules and built-in algorithm models. For example, the necessary intelligent agent is used to obtain the first evaluation data based on basic information.

[0039] Step 106: If the first evaluation data does not trigger boundary constraint conditions, generate an evaluation report based on the first evaluation data and the information of the preset standard server.

[0040] Information about the default standard server can be retrieved from storage, and typically includes the configured hardware information, the operating system adapted to the hardware, and the corresponding performance thresholds.

[0041] Understandably, the fact that the first assessment data did not trigger boundary constraints indicates that the purchased software products and the pre-installed standard servers meet the user's basic information requirements, and no additional software or hardware is needed to improve performance.

[0042] Step 108: If the first evaluation data triggers the boundary constraint condition, input the first evaluation data into the set of unnecessary agents to obtain the second evaluation data, and generate an evaluation report based on the first evaluation data and the second evaluation data.

[0043] Steps 106 and 108 are parallel steps. If step 106 is executed, step 108 is not executed, and if step 108 is executed, step 106 is not executed.

[0044] The evaluation report includes at least one of the following: hardware information, software information, performance information, and limiting information.

[0045] Hardware information includes processor model, number of cores, memory size, hard drive type, etc.

[0046] Software information includes module information of the purchased software product or information on modules that need to be purchased.

[0047] Performance information includes response time, throughput, concurrency, etc.

[0048] Limiting information can be the corresponding boundaries or constraints of performance information. For example, the concurrency threshold of module A is 20, which means that module A can handle a maximum of 20 user sessions or task requests at the same time.

[0049] The set of non-essential agents includes at least one non-essential agent. The non-essential agent is invoked when the first evaluation data triggers the boundary constraint condition. If the first evaluation data does not trigger the boundary constraint condition, the non-essential agent is not invoked.

[0050] Understandably, the first evaluation data triggers boundary constraints, indicating that the resources of the standard servers preset within the necessary intelligent agents are insufficient to meet the requirements corresponding to the basic information, or that the purchased software products are insufficient to meet the requirements corresponding to the basic information, requiring the support of other hardware or software. By inputting the first evaluation data into the non-essential intelligent agent set for processing, the server resource information that meets the requirements corresponding to the basic information can be determined. In other words, the second evaluation data can be understood as the server resource information that meets the requirements corresponding to the basic information, output by the non-essential intelligent agents based on the first evaluation data.

[0051] The aforementioned server resource evaluation method, by generating an evaluation report using the first evaluation data and preset standard server information when the first evaluation data does not trigger boundary constraints, can achieve rapid evaluation under normal requirements. By generating second evaluation data based on the first evaluation data after the first evaluation data triggers boundary constraints, and then generating an evaluation report based on both the first and second evaluation data, that is, after the first evaluation output triggers boundary constraints, a further in-depth evaluation of the basic information is performed based on the first evaluation data to obtain second evaluation data that is more closely matched to the preset standard server information, enabling in-depth evaluation of complex requirements. By generating evaluation reports according to different scenarios, unnecessary overhead can be avoided by calling unnecessary intelligent entities under normal requirements. It can also avoid evaluation results that do not meet requirements due to the failure to call unnecessary intelligent entities in complex scenarios. The aforementioned evaluation constraints can generate evaluation reports that adapt to various requirements, improving the adaptability of server resource configuration while maintaining evaluation efficiency.

[0052] In one embodiment, obtaining basic information about the target product includes:

[0053] Parse the target product's license to obtain basic information about the target product.

[0054] If a user has already purchased a software product, basic information about the target product can be obtained by parsing the license of the purchased software product.

[0055] The parser can obtain a key by calling the user-authorized cloud interface through an encrypted channel, and then use the key to parse the license. It can obtain the product code (SKU) of the purchased software product and the number of authorized users. Based on the product code, it can determine the specific module information included in the purchased software product. For example, the purchased financial product includes general ledger management module, fixed asset management module, cost management module, tax management module, and fund management module, etc.

[0056] By analyzing the license, accurate basic information about the target product can be obtained.

[0057] In another embodiment, obtaining basic information about the target product includes:

[0058] Parse user language to obtain basic information about the target product.

[0059] When a user has not yet purchased the software product but has only clearly identified the modules they need to use, basic information can be obtained by parsing the user's language. Parsing the user's language can be achieved through a Large Language Model (LLM).

[0060] The large language model incorporates a mapping relationship between standard module names and common aliases, functional descriptions, and other language elements. By decomposing the user language, if the standard module name is extracted, the corresponding module information is directly obtained. If common aliases and functional descriptions are extracted, the standard module name is obtained through the mapping relationship. The large language model also includes a language associated with user volume; when user descriptions and related language are used, the number of users can be parsed to obtain the total number of users.

[0061] For example, the standard name of a module built into a large language model includes a user management module, with the built-in function described as managing users. The user description language is "approximately ten thousand users need to be managed". The large language model can then determine that the user needs the user management module and the number of users is ten thousand.

[0062] If the user's description of the language is insufficient to obtain module information and the number of users, multiple rounds of questioning can be used to guide the user to describe their needs.

[0063] The large language model can also embed standard names of hardware, standard names of software, and other data, as well as corresponding associated descriptions. The parsing method is the same as that of the aforementioned modules, and will not be elaborated here.

[0064] By parsing user language, we can not only obtain basic information such as module information and number of users, but also obtain users' personalized needs for hardware and software.

[0065] In another embodiment, obtaining basic information about the target product includes:

[0066] Parse the target product's license and parse the user's language to obtain basic information about the target product.

[0067] The methods for parsing licenses and user languages ​​have been described in detail in the foregoing embodiments and will not be repeated here.

[0068] If there is a conflict or contradiction between the basic information obtained from parsing the license and the basic information obtained from parsing the user's language, the basic information obtained from parsing the license shall prevail.

[0069] By parsing the license and user language, we can not only accurately obtain the module information and number of users of the target product, but also obtain the personalized needs of users.

[0070] In one embodiment, the necessary set of intelligent agents includes a performance data intelligent agent and a software relationship intelligent agent; the basic information includes a first set of modules in the target product.

[0071] The first module set includes the modules contained in the purchased software product or the modules required for the software product to be purchased. The first module set can be obtained by parsing the license or parsing the user language.

[0072] Input the basic information into the necessary intelligent agent set to obtain the first evaluation data, see reference. Figure 2 This includes steps 202 to 204, wherein:

[0073] Step 202: Input the first module set into the software relational agent to obtain the second module set that has association relationships with each module in the first module set.

[0074] The software relational agent can use a word to vector (Word2Vec) model to obtain the association distance between modules, and filter out modules with association distance less than the association distance threshold by setting an association distance threshold.

[0075] Word embedding model is an algorithm that converts words into numerical vectors. In this embodiment, the word embedding model maps the name of each module in the first module set to a high-dimensional space to obtain the vector of the module. The cosine similarity between the vectors is calculated as the association distance, and the corresponding modules with a cosine similarity greater than a set similarity threshold are selected as associated modules. All associated modules form a second module set. The second module set may include only all modules in the first module set, or it may include other modules besides the first module set.

[0076] It is understood that related modules may not be modules within the software product purchased by the user; they can be other software or middleware. Software can be an operating system or a module within another software product, and middleware can be a gateway, message queue middleware, etc., without limitation. For example, the general ledger management module is related to the IBM AIX operating system; correspondingly, the second module set includes the IBM AIX operating system.

[0077] Step 204: Input the basic information and the second module set into the performance data agent to obtain the performance data of the modules in the second module set.

[0078] Performance data can include response time, latency, throughput, concurrency, etc., and there are no restrictions on this.

[0079] The performance data agent can use the Term Frequency-Inverse Document Frequency (TF-IDF) model to obtain the performance data of the modules in the second module set.

[0080] For example, the term frequency-inverse document frequency (TF-IDF) model identifies performance data of different modules in different scenarios from historical data. Scenarios can be determined using basic information. Specifically, it retrieves all performance data for each module in the same scenario from historical data, constructs a performance document based on all performance data in the same scenario, and constructs a total performance document for that module based on all performance data in all scenarios. A document library is then built based on the total performance documents of all modules. This document library is transformed into a TF-IDF weight matrix, where the rows of the weight matrix represent modules, the columns represent performance data, and the values ​​of the weight matrix indicate the importance of performance data to the module. Performance data with weights greater than a set weight are output as the performance data for that module.

[0081] In this embodiment, the scenario can be the number of users or the base concurrency. For example, if the number of users is 2000, the base concurrency can be the product of the number of users and the average activity coefficient of users in the industry. This base concurrency is then input into the term frequency-inverse document frequency model to obtain the concurrency of each module. It is understood that the concurrency is also related to time, module functionality, etc., and the weight of the concurrency allocated to each module can be adjusted based on different application times or application domains. The first evaluation data includes the second module set and the performance data of the modules within the second module set.

[0082] The first evaluation data includes the modules of the target product required by the user, as well as the performance data of the corresponding modules. It may also include modules that are related to the modules of the target product, as well as the performance data of the corresponding related modules.

[0083] Understandably, performance data is the foundation for evaluating server resources. Obtaining performance data allows for precise matching with resources, thereby improving resource utilization.

[0084] In one embodiment, the set of non-essential agents includes at least one non-essential agent. The first evaluation data triggering boundary constraint includes: the second module set contains modules not belonging to the first module set; in this case, the non-essential agent can be a hardware data agent.

[0085] As mentioned earlier, the second module set may include modules that do not belong to the first module set. It is understood that the addition of software may cause the original hardware to be insufficient to support the operation of the software, such as touching the impact resource boundary of the hardware. Therefore, it is necessary to input the first evaluation data into the non-essential intelligent agent to further evaluate the first data, so as to ensure that the obtained second evaluation data can support the operation of the modules in the second module set.

[0086] By limiting the activation of unnecessary agents only when boundary conditions are met, data processing efficiency can be improved and unnecessary resource waste can be reduced.

[0087] The first evaluation data is input into the set of non-essential agents to obtain the second evaluation data, including:

[0088] The performance data of the second module is input into the hardware data agent to obtain the type of server hardware required by the target product and the quantity of the corresponding type of hardware. The second evaluation data includes the type of server hardware and the quantity of the corresponding type of hardware.

[0089] The hardware data agent may include a Long Short-Term Memory (LSTM) network model. This LSTM model may include a classification branch and a regression branch. The classification branch outputs the hardware type, and the regression branch outputs the number of hardware units. In this embodiment, the hardware type can be refined to different version numbers of the same hardware. For example, the LSTM network model can use the hardware version number and performance data as input to the classification branch. When the performance data exceeds the performance threshold corresponding to that version number, the hardware version number is upgraded until a version number that meets the performance data requirements is obtained, and the corresponding hardware version number is output. The LSTM network model can use the module type and hardware version number as input to the regression branch. After obtaining the selected hardware version number, the regression branch can obtain the business processing rules based on the module type, and then determine the number of hardware units based on the business processing rules.

[0090] The first evaluation data includes the modules in the second module set and the corresponding performance data of the modules. The type of the module determines the characteristics of the performance data, and the performance data of the corresponding module quantifies the module's hardware requirements. In this embodiment, the Long Short-Term Memory Network Model can be used to match the type and quantity of hardware that meet the performance requirements.

[0091] When boundary constraints are triggered, the hardware data agent can match the hardware type that matches the module performance data based on the first evaluation data, improving the compatibility between the hardware and the target product. Furthermore, the first evaluation data, processed by other agents, is more closely aligned with the actual application scenario of the target product. Obtaining the hardware type and quantity through the first evaluation data offers higher compatibility and data processing efficiency compared to directly obtaining the hardware type and quantity based on the target product's basic information.

[0092] It is understandable that when the user directly specifies the hardware type, the data output by the hardware data agent includes the hardware type specified by the user.

[0093] In one embodiment, the first evaluation data trigger boundary constraint condition may further include: the performance data of the modules in the second module set exceeds a preset performance boundary. In this case, the set of non-essential agents includes constraint configuration agents and hardware data agents.

[0094] In this embodiment, the boundary constraint is a preset performance boundary, which can be determined by a preset standard server. Specifically, it can be determined by the hardware performance of the preset standard server, which will not be elaborated here.

[0095] Understandably, if the performance data of the modules in the second module set does not exceed the preset performance boundary, it indicates that the preset standard server is sufficient to meet the performance requirements of the modules in the second module set during operation, and the preset standard server can be used directly for processing. If the performance data of the modules in the second module set exceeds the preset performance boundary, it indicates that the preset standard server is insufficient to meet the performance requirements of the modules in the second module set during operation, and other hardware or additional hardware is required to meet the requirements. Therefore, the first evaluation data is input into the non-essential intelligent agent set to obtain the second evaluation data, which includes hardware information sufficient to support the operation of the modules in the second module set.

[0096] The first evaluation data is input into the set of non-essential agents to obtain the second evaluation data, which also includes:

[0097] The performance data of the second module is input into the restricted configuration agent, and the performance data of the module is compared with the performance indicators of the preset standard server to obtain the overflow data of the module's performance data.

[0098] The restricted configuration agent can use a Belief-Desire Intention (BDI) model to obtain overflow data of module performance data. Overflow data can be either the overflow amount or the overflow rate. For example, the Belief-Desire Intention model uses the performance data of modules in the second module set, along with the performance metrics of a preset standard server, as beliefs. When the belief identifies that the performance data of one module exceeds the performance metrics that the standard server can provide, it calculates the overflow data of that performance data as a desire, and further intends to execute the desire, i.e., intends to calculate the overflow data of that performance data. For example, the preset standard server's video rendering throughput is 15 frames per second, while the module's corresponding rendering throughput is 8 frames per second; the overflow data could be 7 frames per second.

[0099] The overflow data and modules in the second module set are input into the hardware data agent to obtain the type of server hardware required by the target product and the quantity of corresponding types of hardware. The second evaluation data includes the overflow data of the performance data of the modules in the second module set, as well as the type of server hardware and the quantity of corresponding types of hardware.

[0100] It is understandable that the long short-term memory network model in the hardware data intelligent agent can also learn the overflow data of the performance data of different hardware types compared with the performance data of the preset standard server. The corresponding hardware type can be obtained through the overflow data of the module performance data. The method of obtaining the number of hardware has been described in detail in the previous embodiments and will not be repeated here.

[0101] By setting restrictions on the configuration of intelligent agents and hardware data intelligent agents, the corresponding hardware type can be quickly matched when the preset standard server does not meet the requirements, thereby improving adaptability and processing efficiency.

[0102] In another embodiment, there may be situations where the second module set contains modules that do not belong to the first module set, and the performance data of the modules in the second module set exceeds a preset performance boundary. In this case, the non-essential intelligent agent set includes restricted configuration intelligent agents and hardware data intelligent agents. The method for obtaining the second evaluation data is the same as in the previous embodiment, except that in the previous embodiment, the modules in the second module set are the same as the modules in the first module set, while in this embodiment, modules that do not belong to the first module set are included. The method for obtaining the second evaluation data will not be described in detail here.

[0103] In one embodiment, the method further includes, before generating the evaluation report:

[0104] A confidence test is performed on the performance data of the modules in the second module set and the modules in the second module set. If the confidence test passes, the first evaluation data and the information of the preset standard server are used to generate an evaluation report.

[0105] For example, when the software relational agent includes a word embedding model, the average cosine similarity between module A in the first module set and the K most similar module vectors in the historical data can be calculated. At the same time, it is observed whether module B, which is most similar to module A in the second module set, belongs to one of the K modules. If the cosine similarity between module A and module B is greater than the average cosine similarity and module B belongs to one of the K modules, module B is determined to pass the confidence test. All modules in the second module set other than those in the first module set should be subjected to the confidence test.

[0106] For example, historical performance data for a corresponding scenario is obtained, where the number of users is the same. Based on this historical data, normally distributed performance data is obtained. The performance data is standardized based on both the module's performance data and the normally distributed data. A confidence level is determined based on the standardized data. If the confidence level is greater than a preset performance confidence level, the confidence level test is considered passed. The preset performance confidence level can be within the range of 85% to 95%, and can be any value within the range of 85%, 90%, 95%, or 85% to 95%, without restriction.

[0107] In another embodiment, the method further includes, prior to generating the evaluation report:

[0108] A confidence test is performed on the modules in the second module set, as well as the performance data and overflow data of the modules in the second module set. If the confidence test passes, the first evaluation data and the second evaluation data are used to generate an evaluation report.

[0109] The confidence tests for modules and performance data have been described in detail in the foregoing embodiments, and will not be repeated here. The confidence test method for overflow data in this embodiment can be similar to the confidence test method for performance data.

[0110] For example, historical data on performance overflow in the corresponding scenario is obtained. Based on the historical data, normally distributed data of the overflow data is obtained. Based on the performance overflow data and the normally distributed data, the overflow data is standardized. A confidence level is determined based on the standardized data. If the confidence level is greater than a preset overflow confidence level, the confidence level test is considered passed. The preset overflow confidence level can be in the range of 85% to 95%, or it can be any value within the range of 85%, 90%, 95%, or 85% to 95%, without any restrictions.

[0111] If any confidence level check fails, the corresponding agent is used to re-analyze the corresponding data. For example, if the confidence level check required by the module fails, the performance data agent re-analyzes the module's performance data.

[0112] In another embodiment, the method further includes, prior to generating the evaluation report:

[0113] Confidence checks are performed on the modules in the second module set, as well as on the performance data and overflow data of the modules in the second module set, and conflict checks are performed on the hardware types. If both the confidence checks and conflict checks pass, the first evaluation data and the second evaluation data are used to generate an evaluation report.

[0114] The confidence tests for modules, performance data, and performance overflow data have been described in detail in the foregoing embodiments, and will not be repeated here. Conflict testing is mainly used to check whether the hardware models, interface protocols, and architecture specifications are compatible. In the case of incompatibility, the hardware data agent re-matches the hardware until the output hardware types are compatible.

[0115] Confidence testing improves data reliability and enhances the feasibility of subsequent evaluation reports. Conflict testing optimizes hardware configuration, avoids resource conflicts, and improves stability.

[0116] In one embodiment, generating an evaluation report based on the first evaluation data and the second evaluation data includes:

[0117] The server's hardware information is generated based on the type of server hardware and the quantity of hardware of that type.

[0118] For example, hardware information could include processor model, memory capacity, disk type, disk capacity, interface type, network card type, etc.

[0119] The server's software information is generated based on the modules in the second module set of the server.

[0120] For example, software information may include modules, operating systems, and middleware of the target product.

[0121] Generate server performance information based on server performance data.

[0122] For example, performance information could include processor utilization, memory usage, throughput, parallelism, network bandwidth, etc.

[0123] Generate server restriction information based on the server's overflow data.

[0124] For example, the limiting information includes system parameters, such as thresholds for various functional points in the module, temporary table size thresholds, memory call count thresholds, etc., which are not limited here. For example, the threshold for the export function is 1 million.

[0125] In one embodiment, the evaluation report includes at least one of a resource configuration report, a resource deployment architecture diagram report, and a personalized configuration report.

[0126] Understandably, the first and second evaluation data are substantial and complex, making direct presentation difficult. A classification algorithm can be used to categorize the data into different types, yielding hardware, software, performance, and limiting information. This classification algorithm can learn judgment rules from historical data. By inputting either the first or second evaluation data into the algorithm, it makes decisions based on the learned rules, ultimately outputting information of different categories.

[0127] In this embodiment, future server requirements can also be predicted using an Autoregressive Integrated Moving Average (ARIMA) model. For example, time-series hardware information can be input into the ARIMA model to predict hardware information for a future period; alternatively, time-series performance information can be input to predict performance information for a future period. Based on the predicted hardware or performance information, a linear programming algorithm is used for long-term server planning to ensure the server can continuously meet demand.

[0128] The resource configuration report includes at least one of the following: hardware information, software information, performance information, and limiting information. For example, the report may include the type, quantity, specifications, and model of the hardware; and the type, model, and version of the software. For instance, hardware may include physical hosts, CPUs, hard drives, etc. The number of physical hosts is 3, with specifications of ThinkSystem SR650 V2 and model number 2U2. The number of CPUs is 6, with specifications of Intel Xeon Gold, 24C, 85W, 2.4GHz processors and model number 6336Y. The number of hard drives is 14, with specifications of 3.84TB, read-intensive, SAS 24Gb, hot-swappable, 2.5-inch SSDs. Software may include databases, operating systems, caching middleware, etc. The specific models and version numbers of the databases, operating systems, and caching middleware are set according to the actual situation and are not limited here.

[0129] Resource allocation reports can also include future plans, for example, 0 to 2 years, with an increase of 800GB in hard disk space and 64GB in memory space, and 2 to 5 years, with an increase of 4 CPU cores, 2TB in hard disk space, and 256GB in memory space, etc.

[0130] The resource allocation report can also show the resource allocation for each piece of hardware. For example, the application server occupies two physical machines, the database occupies one physical machine, and the resource allocation of the main business application and middleware in the application server is divided in a 3:1 ratio. The CPU virtualization level follows the principle of not exceeding a 1:2 ratio, etc.

[0131] The foregoing examples are not intended to limit the embodiments of this application.

[0132] The resource deployment architecture report includes a visual architecture diagram of at least one of the hardware and software information. The resource deployment architecture diagram can be a visual topology diagram composed of specific modules (software) and hardware; see [reference needed]. Figure 3 The resource deployment architecture diagram can also include a module heatmap, which is used to mark the modules with high usage rates. The module heatmap can be generated based on performance data.

[0133] A personalized configuration report includes at least one of the following: hardware information, software information, performance information, and limiting information corresponding to the user's customized needs. It is understood that the personalized configuration report is not limited in its display format; it needs to show the server resource information corresponding to the user's personalized needs, and may also include some general server resource information. For example, a personalized configuration report may include the operating system, CPU, etc., specified by the user.

[0134] By providing different report modes, users can be given multi-perspective server resource reports.

[0135] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0136] Based on the same inventive concept, this application also provides a server resource evaluation system for implementing the server resource evaluation method described above. The solution provided by this system is similar to the implementation described in the above method; therefore, the specific limitations of one or more server resource evaluation system embodiments provided below can be found in the limitations of the server resource evaluation method described above, and will not be repeated here.

[0137] In one exemplary embodiment, such as Figure 4As shown, a server resource evaluation system is provided, including: a data acquisition module 402, a necessary agent module 404, a non-necessary agent module 406, and an evaluation report generation module 408, wherein:

[0138] The data acquisition module 402 is used to acquire basic information about the target product.

[0139] Basic information about a target product typically includes the software product's module information and the number of users. If the software product has already been purchased, the basic information may include the specific modules included in the purchased target product and the number of licensed users. If the software product has not been purchased, the basic information may include the modules to be purchased and the expected number of users.

[0140] The necessary intelligent agent module 404 is used to obtain the first evaluation data based on the basic information.

[0141] The necessary agent module 404 includes multiple necessary agents. These agents are different. An agent is a proxy that can perceive the environment and take actions to achieve a specific goal. In this embodiment, the agent obtains the set target data through preset rules and built-in algorithm models. For example, the necessary agent is used to obtain the first evaluation data based on basic information.

[0142] The non-essential agent module 406 is used to obtain second evaluation data based on the first evaluation data when the first evaluation data triggers boundary constraint conditions.

[0143] The non-essential agent module 406 includes at least one non-essential agent, which is invoked when the first evaluation data triggers the boundary constraint condition; if the first evaluation data does not trigger the boundary constraint condition, the non-essential agent is not invoked.

[0144] Understandably, the first evaluation data triggers boundary constraint conditions, indicating that the resources of the standard server preset in the necessary intelligent agent are insufficient to meet the requirements corresponding to the basic information, or the purchased software products are insufficient to meet the requirements corresponding to the basic information, and other hardware or software support is required. The first evaluation data is input into the non-necessary intelligent agent module 406 for processing, and the server resource information that meets the requirements corresponding to the basic information can be determined. That is, the second evaluation data can be understood as the server resource information that meets the requirements corresponding to the basic information output by the non-necessary intelligent agent based on the first evaluation data.

[0145] The evaluation report generation module 408 is used to generate an evaluation report based on the first evaluation data and information from a preset standard server, provided that the first evaluation data does not trigger boundary limiting conditions. Alternatively, it can generate an evaluation report based on the first evaluation data and the second evaluation data, provided that the first evaluation data triggers boundary limiting conditions. The evaluation report includes at least one of the following: hardware information, software information, performance information, and limiting information.

[0146] Hardware information includes processor model, number of cores, memory size, hard drive type, etc.

[0147] Software information includes module information of the purchased software product or information on modules that need to be purchased.

[0148] Performance information includes response time, throughput, concurrency, etc.

[0149] Limiting information can be the corresponding boundaries or constraints of performance information. For example, the concurrency threshold of module A is 20, which means that module A can handle a maximum of 20 user sessions or task requests at the same time.

[0150] Information about the default standard server can be retrieved from storage, and typically includes the configured hardware information, the operating system adapted to the hardware, and the corresponding performance thresholds.

[0151] Understandably, the fact that the first assessment data did not trigger boundary constraints indicates that the purchased software products and the pre-installed standard servers meet the user's basic information requirements, and no additional software or hardware is needed to improve performance.

[0152] The aforementioned server resource evaluation system can achieve rapid evaluation for routine needs by generating an evaluation report using the first evaluation data and preset standard server information when the first evaluation data does not trigger boundary constraints. However, after the first evaluation data triggers boundary constraints, it generates second evaluation data based on the first evaluation data, and then generates an evaluation report based on both the first and second evaluation data. This means that after the first evaluation output triggers boundary constraints, it performs a further in-depth evaluation of the basic information based on the first evaluation data to obtain second evaluation data that better matches the preset standard server information, enabling in-depth evaluation for complex needs. By generating evaluation reports based on different scenarios, it avoids calling unnecessary intelligent entities under routine needs, thus avoiding unnecessary overhead. It also avoids evaluation results that do not meet requirements in complex scenarios due to the failure to call unnecessary intelligent entities. The aforementioned evaluation constraints can generate evaluation reports adaptable to various needs, improving the adaptability of server resource configuration while maintaining evaluation efficiency.

[0153] In one embodiment, the data acquisition module 402 can obtain basic information about the target product by parsing its license, by parsing user language, and by parsing both the license and user language. If there is a conflict or contradiction between the basic information obtained from parsing the license and the basic information obtained from parsing the user language, the basic information obtained from parsing the license shall prevail. The specific parsing process is described in the foregoing method embodiments and will not be repeated here.

[0154] By parsing the license and user language, we can not only accurately obtain the module information and number of users of the target product, but also obtain the personalized needs of users.

[0155] In one embodiment, see [reference] Figure 5 The necessary intelligent agent module 404 includes a software relationship intelligent agent 4041 and a performance data intelligent agent 4042; the basic information includes a first module set in the target product. The first module set includes the modules contained in the purchased software product or the modules required by the software product to be purchased, and the first module set can be obtained by parsing the license or parsing the user language.

[0156] The software relational agent 4041 is used to obtain a second set of modules that are associated with each module in the first set of modules. The software relational agent 4041 can use a word embedding model (Word to Vector, Word2Vec) to obtain the association distance between modules, and filter out modules with association distances less than the association distance threshold by setting an association distance threshold.

[0157] The performance data agent 4042 is used to obtain the performance data of the modules in the second module set based on the basic information and the second module set.

[0158] Performance data can include response time, latency, throughput, concurrency, etc., and there are no restrictions on this.

[0159] The performance data agent 4042 can use the Term Frequency-Inverse Document Frequency (TF-IDF) model to obtain the performance data of the modules in the second module set.

[0160] The process by which software relational agent 4041 obtains the second module set and performance data, and agent 4042 obtains the performance data of the modules in the second module set, is the same as described in the aforementioned method embodiment and will not be repeated here.

[0161] Performance data is the foundation for evaluating server resources. Obtaining performance data enables precise matching with resources, thereby improving resource utilization.

[0162] In one embodiment, see [reference] Figure 6 The software relational agent 4041 is used to determine the trigger boundary restriction condition of the second module set when the second module set contains modules that do not belong to the first module set, and input the first evaluation data into the non-essential agent module 406. The non-essential agent module 406 includes the hardware data agent 4061.

[0163] As mentioned earlier, the second module set may include modules that do not belong to the first module set. It is understood that the addition of software may cause the original hardware to be insufficient to support the operation of the software, such as touching the impact resource boundary of the hardware. Therefore, it is necessary to input the first evaluation data into the non-essential intelligent agent to further evaluate the first data, so as to ensure that the obtained second evaluation data can support the operation of the modules in the second module set.

[0164] By limiting the activation of unnecessary agents only when boundary conditions are met, data processing efficiency can be improved and unnecessary resource waste can be reduced.

[0165] The hardware data intelligent agent 4061 is used to obtain the type of server hardware required for the target product and the quantity of corresponding type hardware based on the performance data of the second module. The second evaluation data includes the type of server hardware and the quantity of corresponding type hardware.

[0166] The hardware data agent 4061 may include a Long Short-Term Memory (LSTM) network model. The LSTM network model may include a classification branch and a regression branch. The classification branch is used to output the type of hardware, and the regression branch is used to output the quantity of data hardware. In this embodiment, the hardware type can be refined to different version numbers of the same hardware. The method for obtaining the hardware type and quantity has been described in detail in the foregoing embodiments and will not be repeated here.

[0167] When boundary constraints are triggered, the hardware data agent 4061 can match the hardware type that matches the module performance data based on the first evaluation data, improving the compatibility between the hardware and the target product. Furthermore, the first evaluation data is processed by other agents, making it more relevant to the actual application scenario of the target product. Obtaining the hardware type and quantity through the first evaluation data offers higher compatibility and data processing efficiency compared to directly obtaining the hardware type and quantity based on the target product's basic information.

[0168] Understandably, when the user directly specifies the hardware type, the data output by the hardware data agent 4061 includes the hardware type specified by the user.

[0169] In one embodiment, see [reference] Figure 7 The performance data agent 4042 is used to determine that the first evaluation data triggers the boundary restriction condition when the performance data of the second module exceeds the preset performance boundary, and inputs the first evaluation data into the non-essential agent module 406. The non-essential agent module 406 includes a hardware data agent 4061 and a restriction configuration agent 4062.

[0170] The restricted configuration agent 4062 is used to compare the performance data of the modules in the second module set with the performance indicators of the preset standard server to obtain the overflow data of the module's performance data.

[0171] The restricted configuration agent 4062 can use the Belief-Desire Intention (BDI) model to obtain overflow data of module performance data. The overflow data can be the overflow amount or the overflow rate.

[0172] The hardware data agent 4061 is used to obtain the type of server hardware required by the target product and the quantity of corresponding types of hardware based on overflow data and modules in the second module set. In this embodiment, the second evaluation data includes overflow data of performance data from modules in the second module set, as well as the type of server hardware and the quantity of corresponding types of hardware.

[0173] It is understandable that the long short-term memory network model in the hardware data intelligent agent 4061 can also learn the overflow data of the performance data of different hardware types compared with the performance data of the preset standard server. The corresponding hardware type can be obtained through the overflow data of the module performance data. The method of obtaining the number of hardware has been described in detail in the previous embodiments and will not be repeated here.

[0174] By setting restrictions on the configuration of agent 4062 and hardware data agent 4061, the corresponding hardware type can be quickly matched when the preset standard server does not meet the requirements, thereby improving compatibility and processing efficiency.

[0175] In another embodiment, there may be situations where the second module set contains modules that do not belong to the first module set, and the performance data of the modules in the second module set exceeds a preset performance boundary. In this case, the non-essential intelligent agent set includes restricted configuration intelligent agents and hardware data intelligent agents. The method for obtaining the second evaluation data is the same as in the previous embodiment, except that in the previous embodiment, the modules in the second module set are the same as the modules in the first module set, while in this embodiment, modules that do not belong to the first module set are included. The method for obtaining the second evaluation data will not be described in detail here.

[0176] In one embodiment, see [reference] Figure 8 The system also includes a data verification module 410.

[0177] The data verification module 410 is used to perform confidence verification on the performance data of the modules in the second module set and the modules in the second module set. If the confidence verification is passed, the first evaluation data and the information of the preset standard server are output to the evaluation report generation module 408.

[0178] In another embodiment, the data verification module 410 performs confidence checks on the modules in the second module set, their performance data, and overflow data. If the confidence check passes, the first evaluation data and the second evaluation data are output to the evaluation report generation module 408.

[0179] In another embodiment, the data verification module 410 is used to perform confidence checks on the modules in the second module set, the performance data and overflow data of the modules in the second module set, and to perform conflict checks on the hardware type. If both the confidence check and the conflict check pass, the first evaluation data and the second evaluation data are output to the evaluation report generation module 408.

[0180] If any confidence check fails, the corresponding agent re-analyzes the corresponding data. For example, if the confidence check required for a module fails, performance data agent 4042 re-analyzes the module's performance data. If the conflict check fails, hardware data agent 4061 re-matches the hardware. Confidence checks and conflict checks have been described in detail in the foregoing embodiments and will not be repeated here.

[0181] Confidence testing improves data reliability and enhances the feasibility of subsequent evaluation reports. Conflict testing optimizes hardware configuration, avoids resource conflicts, and improves stability.

[0182] In one embodiment, the evaluation report generation module 408 is used to generate server hardware information based on the type of server hardware and the quantity of hardware of the corresponding type, generate server software information based on the modules in the second module set of the server, generate server performance information based on server performance data, and generate server limitation information based on server overflow data.

[0183] In one embodiment, the evaluation report generated by the evaluation report generation module 408 includes at least one of a resource configuration report, a resource deployment architecture diagram report, and a personalized configuration report.

[0184] Understandably, the first and second evaluation data are substantial and complex, making direct presentation difficult. A classification algorithm can be used to categorize the data into different types, yielding hardware, software, performance, and limiting information. This classification algorithm can learn judgment rules from historical data. By inputting either the first or second evaluation data into the algorithm, it makes decisions based on the learned rules, ultimately outputting information of different categories.

[0185] In this embodiment, the future demand for the server can also be predicted using an Autoregressive Integrated Moving Average (ARIMA) model. Based on the predicted hardware or performance information, a linear programming algorithm is used to perform long-term planning for the server to ensure that it can continuously meet the demand.

[0186] The resource allocation report includes at least one of hardware information, software information, performance information, and limiting information. The resource allocation report may also include future resource planning. More specifically, the resource allocation report may also include resource allocation for each piece of hardware, as detailed in the foregoing method embodiments.

[0187] The resource deployment architecture report includes a visual architecture diagram of at least one of the hardware and software information. The resource deployment architecture diagram can be a visual topology diagram composed of specific modules (software) and hardware; see [reference needed]. Figure 4 The resource deployment architecture diagram can also include a module heatmap, which is used to mark the modules with high usage rates. The module heatmap can be generated based on performance data.

[0188] A personalized configuration report should include at least one of the following: hardware information, software information, performance information, and limiting information, corresponding to the user's customized needs. It is understood that the personalized configuration report is not limited in its presentation format; it needs to display server resource information corresponding to the user's personalized needs, and may also include some general server resource information.

[0189] By providing different report modes, users can be given multi-perspective server resource reports.

[0190] Each module in the aforementioned server resource evaluation system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can invoke and execute the corresponding operations of each module.

[0191] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for evaluating server resources. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0192] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0193] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the method embodiments described above.

[0194] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0195] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the method embodiments described above.

[0196] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0197] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0198] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0199] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for evaluating server resources, characterized in that, The method includes: Obtain basic information about the target product; The basic information is input into the necessary intelligent agent set to obtain the first evaluation data; If the first evaluation data does not trigger boundary constraint conditions, an evaluation report is generated based on the first evaluation data and the information of a preset standard server. If the first evaluation data triggers boundary constraint conditions, the first evaluation data is input into a set of unnecessary agents to obtain the second evaluation data; an evaluation report is generated based on the first evaluation data and the second evaluation data. The evaluation report includes at least one of the following: hardware information, software information, performance information, and limiting information.

2. The method according to claim 1, characterized in that, The necessary set of intelligent agents includes performance data intelligent agents and software relationship intelligent agents; the basic information includes the first module set in the target product; The step of inputting the basic information into the necessary intelligent agent set to obtain the first evaluation data includes: The first module set is input into the software relational agent to obtain a second module set that is associated with each module in the first module set. The basic information and the second module set are input into the performance data agent to obtain the performance data of the modules in the second module set; The first evaluation data includes the second module set, and the performance data of the modules in the second module set.

3. The method according to claim 2, characterized in that, The first evaluation data triggers boundary constraint conditions including: the second module set contains modules that do not belong to the first module set, and the non-essential intelligent agent set includes hardware data intelligent agents; The step of inputting the first evaluation data into the set of unnecessary agents to obtain the second evaluation data includes: The modules in the second module set and the performance data of the modules are input into the hardware data agent to obtain the type of hardware required for the target product and the quantity of the corresponding type of hardware. The second evaluation data includes the type of hardware and the quantity of the corresponding type of hardware.

4. The method according to claim 2, characterized in that, The first evaluation data trigger boundary restriction conditions include: the performance data of the modules in the second module set exceeds the preset performance boundary, and the set of non-essential intelligent agents includes restricted configuration intelligent agents and hardware data intelligent agents; The step of inputting the first evaluation data into the set of unnecessary agents to obtain the second evaluation data includes: The performance data of the second module is input into the restricted configuration agent, and the performance data of the module is compared with the performance indicators of the preset standard server to obtain the overflow data of the module's performance data. The overflow data and the modules in the second module set are input into the hardware data agent to obtain the type of hardware required by the target product and the quantity of the corresponding type of hardware; the second evaluation data includes the overflow data of the performance data of the modules in the second module set, as well as the type of hardware and the quantity of the corresponding type of hardware.

5. The method according to claim 4, characterized in that, Before generating the assessment report, the following are also included: A confidence test is performed on the modules in the second module set, the performance data of the modules in the second module set, and the overflow data, and a conflict test is performed on the type of the hardware. If both the confidence test and the conflict test pass, the first evaluation data and the second evaluation data are used to generate an evaluation report.

6. The method according to claim 4, characterized in that, The step of generating an evaluation report based on the first evaluation data and the second evaluation data includes: The server's hardware information is generated based on the type of the server's hardware and the quantity of the corresponding type of hardware. The software information of the server is generated according to the modules in the second module set of the server; Generate the server's performance information based on the server's performance data; The server's restriction information is generated based on the server's overflow data.

7. The method according to claim 1, characterized in that, The acquisition of basic information about the target product includes: Parse the license of the target product to obtain basic information about the target product, and / or Parse the user's language to obtain basic information about the target product.

8. A server resource evaluation system, characterized in that, The system includes: The data acquisition module is used to acquire basic information about the target product; A necessary intelligent agent module is used to obtain first evaluation data based on the basic information; The non-essential agent module is used to obtain the second evaluation data based on the first evaluation data when the first evaluation data triggers the boundary constraint condition. An evaluation report generation module is used to generate an evaluation report based on the first evaluation data and information from a preset standard server, or based on the first evaluation data and the second evaluation data, when the first evaluation data does not trigger boundary limiting conditions. The evaluation report includes at least one of hardware information, software information, performance information, and limiting information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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