Application migration suitability evaluation method and device and storage medium

Abstract

The invention provides an application migration suitability evaluation method and device and a storage medium, relates to the technical field of information, and can comprehensively determine an application migration suitability evaluation result. The method comprises the following steps: acquiring multi-source data in an application migration process; the multi-source data comprises a migration scheme for planning an application migration process, migration performance test information for evaluating application migration performance and a migration result for representing an application migration result; determining a score of each evaluation index in the plurality of evaluation indexes based on the multi-source data; based on the score and the weight of each evaluation index in the plurality of evaluation indexes, determining a comprehensive score of the application migration process; and determining the suitability level of the application migration process based on the comprehensive score.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to an application migration adaptability evaluation method, apparatus and storage medium. Background Technology

[0002] With the rapid development of information technology, domestically developed IT applications are widely used in various industries. A key concern is whether these applications can maintain performance levels comparable to their intended business functions in the new environment after migration. Therefore, without a suitable migration compatibility evaluation method to assess the suitability of domestically developed IT applications, issues such as operational interruptions, data loss, and performance degradation may occur due to running incompatible services.

[0003] Currently, the evaluation methods for application migration and adaptation of related technologies mainly involve evaluating the adaptability of each of the multiple applications to be migrated or evaluating the compatibility of the migrated applications through the code of the migrated applications. However, the business scenarios involved in the migration of existing new applications are often quite complex, and as the requirements of the information technology application innovation industry for the systems after the migration of information technology application innovation are becoming increasingly higher, the evaluation methods for application migration and adaptation of related technologies have the problem of one-sided evaluation results. Summary of the Invention

[0004] This application provides an application migration adaptability evaluation method, apparatus, and storage medium, which can comprehensively determine the application migration adaptability evaluation results.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides an application migration adaptability evaluation method, which includes: acquiring multi-source data of the application migration process; the multi-source data includes: migration schemes for planning the application migration process, migration performance test information for evaluating application migration performance, and migration results characterizing the application migration outcome; determining the score of each evaluation indicator among multiple evaluation indicators based on the multi-source data; determining the comprehensive score of the application migration process based on the score and weight of each evaluation indicator among multiple evaluation indicators; and determining the adaptability level of the application migration process based on the comprehensive score.

[0006] The above technical solution brings at least the following beneficial effects: This application obtains multi-source data on the application migration process to determine the scores corresponding to multiple evaluation indicators. The multi-source data obtained by this application is diverse and comprehensive, reflecting multiple aspects of the application migration process and providing comprehensive data for subsequent determination of the application migration level. By scoring the application migration process through multiple evaluation indicators, the adaptability level of the application migration process is determined. These multiple evaluation indicators correspond to multiple evaluation perspectives, and evaluating the application migration adaptability from multiple perspectives allows for a more comprehensive determination of the application migration adaptability evaluation results.

[0007] In one possible implementation, the method further includes: obtaining the business requirements of the application migration process and the historical scores of each evaluation indicator in at least one historical migration process; determining a first weight for each evaluation indicator based on the business requirements of the application migration process; determining a second weight for each evaluation indicator based on the historical scores of each evaluation indicator; and weighted summing of the first weight and the second weight to determine the weight of each evaluation indicator.

[0008] In one possible implementation, based on the business requirements of the application migration process, the first weight of each evaluation indicator is determined, including: determining the relative scale between every two evaluation indicators among multiple evaluation indicators based on the business requirements of the application migration process; the relative scale is used to indicate the importance of one evaluation indicator relative to another; constructing a relative scale matrix by using multiple evaluation indicators as rows and columns of a relative scale matrix, and the relative scale as elements of the relative scale matrix; normalizing the relative scale matrix, and determining the average value of each row element of the normalized relative scale matrix as the first weight of each evaluation indicator.

[0009] In one possible implementation, determining the second weight of each evaluation indicator based on its historical scores includes: constructing a historical score matrix by using at least one historical migration process as a column of the historical score matrix, each evaluation indicator as a row of the historical score matrix, and historical scores as elements of the historical score matrix; normalizing the historical score matrix to determine the normalized historical score matrix; and determining the second weight of each evaluation indicator based on the entropy method and the normalized historical score matrix.

[0010] In one possible implementation, the method further includes: determining the second weight of each evaluation indicator based on the entropy method and the normalized historical rating matrix, including: determining the feature weight of the first element as the ratio of the first element to the sum of the elements in the column containing the first element; the first element is an element in the normalized historical rating matrix; determining the entropy value of the evaluation indicator corresponding to the column containing the first element based on the feature weight of the elements in the column containing the first element; and determining the second weight of the evaluation indicator corresponding to the column containing the first element based on the entropy value of the evaluation indicator corresponding to the column containing the first element.

[0011] In one possible implementation, the normalized historical rating matrix satisfies the following formula:

[0012] in, Let be the element in the i-th row and j-th column of the normalized historical rating matrix. Let be the element in the i-th row and j-th column of the historical rating matrix before normalization. The maximum value of the elements in the j-th column. It is the minimum value of the elements in the j-th column.

[0013] In one possible implementation, the entropy value satisfies the following formula:

[0014] in, Let the entropy value of the evaluation index corresponding to column j be _____. Let represent the feature weight corresponding to the element in the i-th row and j-th column, n be the total number of historical migration processes, and i and j be integers.

[0015] Secondly, this application provides an application migration adaptability evaluation device, which includes: an acquisition unit and a determination unit; the acquisition unit is used to acquire multi-source data of the application migration process; the multi-source data includes: migration schemes for planning the application migration process, migration performance test information for evaluating the application migration performance, and migration results characterizing the application migration results; the determination unit is used to determine the score of each evaluation indicator among multiple evaluation indicators based on the multi-source data; the determination unit is also used to determine the comprehensive score of the application migration process based on the score and weight of each evaluation indicator among multiple evaluation indicators; the determination unit is also used to determine the adaptability level of the application migration process based on the comprehensive score.

[0016] Thirdly, this application provides an application migration adaptability evaluation apparatus, which includes: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to implement the application migration adaptability evaluation method as described in the first aspect and any possible implementation of the first aspect.

[0017] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform the application migration adaptability evaluation method as described in the first aspect and any possible implementation thereof.

[0018] Fifthly, this application provides a computer program product containing instructions that, when the computer program product is run on an application migration adaptability evaluation device, causes the application migration adaptability evaluation device to perform the application migration adaptability evaluation method as described in the first aspect and any possible implementation thereof.

[0019] In a sixth aspect, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement the application migration adaptability evaluation method as described in the first aspect and any possible implementation thereof.

[0020] Specifically, the chip provided in this application also includes a memory for storing computer programs or instructions. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of an application migration adaptability evaluation system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the composition of an application migration adaptability evaluation device provided in an embodiment of this application; Figure 3 A flowchart of an application migration adaptability evaluation method provided in an embodiment of this application; Figure 4 A flowchart illustrating a weight determination method provided in an embodiment of this application; Figure 5 A flowchart of a first weight determination method provided in an embodiment of this application; Figure 6 A flowchart of a second weight determination method provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an application migration adaptability evaluation device provided in an embodiment of this application. Detailed Implementation

[0022] The application migration adaptability evaluation method, apparatus, and storage medium provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0023] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0024] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0025] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0026] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0028] Currently, the information technology application innovation industry (hereinafter referred to as the IT innovation industry) is gradually becoming a key force in promoting technological self-reliance and controllability. IT innovation aims to achieve domestic substitution in the field of information technology, covering multiple levels such as chips, operating systems, databases, middleware, and application software, in order to reduce excessive dependence on foreign technology products and safeguard national information security and strategic autonomy. In the telecommunications industry, operators face multiple challenges in migrating and adapting domestically developed (IT) applications. On the one hand, operators' information systems are massive in scale and complex in architecture, with both centralized and distributed architectures coexisting. They also have numerous legacy systems, some of which suffer from outdated technology, missing documentation, and disbanded development teams, and are deeply coupled with traditional non-IT-developed hardware and software. On the other hand, the IT product ecosystem still needs improvement, with shortcomings in functionality, performance, and compatibility. Furthermore, operators have extremely high requirements for business continuity; any migration interruption will have serious consequences. Coupled with the shortage of IT professionals and insufficient technical skills, the migration and adaptation process requires overcoming numerous compatibility, performance optimization, and functional adjustments, posing a severe test to the stability, service quality, and user experience of the migrated system.

[0029] As described above regarding the evaluation methods for application migration and adaptation of related technologies, these methods primarily evaluate the compatibility of each of the multiple applications to be migrated or assess the compatibility of the migrated applications through their code. However, the migration of existing new applications often involves complex business scenarios, and as the requirements for the migrated systems from these applications become increasingly stringent, these evaluation methods suffer from biased assessment results.

[0030] Therefore, this application obtains multi-source data on the application migration process to determine scores corresponding to multiple indicators. The multi-source data obtained in this application is diverse and comprehensive, reflecting multiple aspects of the application migration process and providing comprehensive data for subsequent determination of the application migration level. By scoring the application migration process using multiple evaluation indicators, the suitability level of the application migration process is determined. These multiple evaluation indicators correspond to multiple evaluation perspectives, and evaluating the application migration suitability from multiple perspectives allows for a more comprehensive determination of the application migration suitability evaluation results.

[0031] The technical solutions provided in this application can be applied to various communication systems, such as New Radio (NR) communication systems using 5G, future evolution systems, or multiple communication convergence systems.

[0032] For example, such as Figure 1 The diagram shown is a structural schematic of an application migration adaptability evaluation system provided in an embodiment of this application. The application migration adaptability evaluation system may include a data acquisition device 101 and an application migration adaptability evaluation device 102. The data acquisition device 101 and the application migration adaptability evaluation device 102 are communicatively connected. Figure 1 Only one data acquisition device 101 and one application migration adaptability evaluation device 102 are shown in the illustration. This application embodiment does not impose any limitation on the number of data acquisition devices 101 and application migration adaptability evaluation devices 102.

[0033] In one possible implementation, the data acquisition device 101 can be a device with wireless transceiver capabilities, and this application does not impose any restrictions on this. For example, the data acquisition device 101 can be a mobile device (such as a mobile phone, tablet computer, or VR glasses).

[0034] In one possible implementation, the data acquisition device 101 is used to collect multi-source data of the application migration process; the multi-source data includes: migration schemes for planning the application migration process, migration performance test information for evaluating the application migration performance, and migration results characterizing the application migration outcome. In one possible implementation, a migration adaptability evaluation device 102 is used to receive multi-source data from a data acquisition device 101, and based on the multi-source data, determine a score for each of multiple evaluation indicators. Based on the scores and weights of each evaluation indicator, a comprehensive score for the application migration process is determined. Based on the comprehensive score, the adaptability level of the application migration process is determined.

[0035] It should be noted that, Figure 1This is just an example framework diagram. Figure 1 The number of nodes included and the names of the devices are unlimited, except for... Figure 1 In addition to the functional nodes shown, the application migration adaptability evaluation system may also include other nodes, such as core network equipment, and this application does not impose any restrictions on this.

[0036] The application scenarios of the embodiments in this application are not limited. The system architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems. For example, the data acquisition device 101 and the application migration adaptability evaluation device 102 can be separate devices or different functional modules on the same device.

[0037] In practical implementation, Figure 1 All the equipment in the middle can be adopted Figure 2 The shown composition structure, or including Figure 2 The components shown. Figure 2 This is a schematic diagram illustrating the composition of an application migration adaptability evaluation device 20 provided in an embodiment of this application. The application migration adaptability evaluation device 20 can be an application migration adaptability evaluation equipment 102 or a chip or system-on-a-chip within the application migration adaptability evaluation equipment 102. For example... Figure 2 As shown, the application migration adaptability evaluation device 20 may include a processor 201, a bus 202, a communication interface 203, and a memory 204.

[0038] The processor 201, memory 204 and communication interface 203 can be connected via bus 202.

[0039] The processor 201 can be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 201 can also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation.

[0040] Bus 202 is used to transmit information between the components included in the application migration adaptability evaluation device 20.

[0041] Communication interface 203 is used to communicate with other devices or other communication networks. These other communication networks can be Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc. Communication interface 203 can be a module, circuit, communication interface, or any device capable of enabling communication.

[0042] Memory 204 is used to store instructions. These instructions can be computer programs.

[0043] The memory 204 can be a read-only memory (ROM) or other type of static storage device that can store static information and / or instructions; it can also be a random access memory (RAM) or other type of dynamic storage device that can store information and / or instructions; it can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

[0044] It should be noted that the memory 204 can exist independently of the processor 201 or can be integrated with the processor 201. The memory 204 can be used to store instructions, program code, or some data. The memory 204 can be located inside or outside the application migration adaptability evaluation device 20, without restriction.

[0045] In one example, processor 201 may include one or more CPUs.

[0046] As an optional implementation, the application migration adaptability evaluation device 20 includes multiple processors.

[0047] As an optional implementation, the application migration adaptability evaluation device 20 may also include output devices and input devices. For example, the input device is a keyboard, mouse, microphone or joystick, and the output device is a display screen, speaker, or other similar device.

[0048] It should be noted that the application migration adaptability evaluation device 20 can be a desktop computer, laptop computer, network server, mobile phone, tablet computer, wireless terminal, embedded device, chip system, or other device. Figure 1 Equipment with a similar structure. Furthermore... Figure 2 The composition shown does not constitute a basis for the interpretation of this invention. Figure 1 as well as Figure 2 The limitations of each device in the process, except Figure 2 In addition to the components shown, Figure 1 as well as Figure 2 The various devices may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0049] In this embodiment of the application, the chip system may be composed of chips or may include chips and other discrete devices.

[0050] Furthermore, the actions, terms, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages exchanged between the various devices in the embodiments of this application are merely examples, and other names may be used in specific implementations without limitation.

[0051] The following is combined Figure 1 The application migration adaptability evaluation system shown describes the application migration adaptability evaluation method provided in the embodiments of this application. The actions, terminology, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages exchanged between various devices in the embodiments of this application are merely examples; other names can be used in specific implementations without limitation. The actions involved in the various embodiments of this application are merely examples; other names can be used in specific implementations, such as replacing "included in" with "carried on" or "carried in," etc.

[0052] In order to solve the problems existing in the above-mentioned prior art, such as Figure 3 The diagram shown is a flowchart of an application migration adaptability evaluation method provided in an embodiment of this application. This application migration adaptability evaluation method can comprehensively determine the application migration adaptability evaluation results. The method includes: S301. Obtain multi-source data during the application migration process.

[0053] The multi-source data includes: migration plans that plan the application migration process, migration performance test information that evaluates the application migration performance, and migration results that characterize the application migration outcome.

[0054] Multi-source data refers to data from multiple different sources related to the application or target operating environment after application migration. These sources may include databases, sensors, text files, images, time series data, etc. Multi-source data can reflect the operational status, results, or performance of the application after migration. Multi-source data can be used to characterize the overall effectiveness and potential problems after application migration, providing solid data support for subsequent evaluation of application migration adaptability.

[0055] After an application is migrated to the target environment, and its adaptability to the target environment needs to be evaluated, testers input an application migration adaptability evaluation command into the data acquisition device, triggering the acquisition of multi-source data. This multi-source data may specifically include: the migration scheme used to migrate the application, the actual migration process, the post-migration application's test report, the post-migration application's performance data, and any problems encountered with the post-migration application.

[0056] In some embodiments, triggering the acquisition of multi-source data does not always require evaluating the application's adaptability to the target environment; it may also be triggered manually or by specific software. Therefore, preset triggering conditions can be used to determine whether to initiate the multi-source data acquisition process.

[0057] In one possible implementation, S301 above can be implemented as: in response to receiving an application migration adaptability evaluation instruction, acquiring multi-source data.

[0058] Migration compatibility evaluation instructions can be issued by testers in various ways, such as by triggering through the physical buttons / touch panel of the data acquisition device, by clicking the acquisition function button in the operation interface of the supporting control software, by inputting a preset shortcut key, by sending instructions through a remote control terminal, or by initiating through voice commands, API calls, etc. This application embodiment does not limit this.

[0059] For example, as one implementation method, after receiving the application migration adaptability evaluation instruction, the application's running status data and error and failure data can be obtained from the application's running logs, or the application can be tested using compatibility testing tools or performance stress testing tools to obtain the application's test report. Alternatively, the actual running data of the application can be obtained from the running results of the application running actual business, and the migration scheme used to migrate the application and the actual process of migrating the application can be obtained from the documents generated after the migration is completed.

[0060] It should be noted that this application embodiment does not limit the method of obtaining multi-source data during the application migration process. In actual application, it can be set according to needs to cover different application scenarios. For example, when the multi-source data is voice data, it can be obtained by acquiring the voice communication between online customer service and application users. When the multi-source data is text data, it can be obtained by acquiring application running log documents or by acquiring staff usage reports.

[0061] S302. Based on multi-source data, determine the score for each evaluation indicator among multiple evaluation indicators.

[0062] Evaluation metrics are a quantitative measure of the performance of an application when it is migrated to a new environment, used to determine the application's adaptability to the new environment after migration.

[0063] For example, multiple evaluation indicators may include indicators at three levels. The first-level evaluation indicators include planning and design capabilities, migration and adaptation capabilities, testing and deployment capabilities, and summary and improvement capabilities. Further refining the first-level indicators yields second-level indicators, and further refining the second-level indicators yields third-level indicators. Table 1 shows an evaluation indicator table provided in an embodiment of this application, including three levels of indicators and their corresponding definitions.

[0064] Table 1 Evaluation Indicators

[0065] After acquiring multi-source data, it is necessary to obtain the score for each of the aforementioned preset evaluation indicators using this data. Specifically, the degree to which the application meets the corresponding evaluation indicators after migration can be determined by analyzing the multi-source data, and the evaluation indicators can then be scored accordingly.

[0066] In one possible implementation, S302 above can be implemented as follows: For each evaluation indicator, based on multi-source data, if it is determined that the application migration can only meet the basic requirements of the corresponding evaluation indicator or achieve basic functions, the score for the corresponding evaluation indicator is determined to be one point. If it is determined that the application migration can reach the industry average level of the corresponding indicator and has good stability, the score for the corresponding evaluation indicator is determined to be two points. If it is determined that the corresponding indicator performs excellently after the application migration and has a demonstration effect, the score for the corresponding evaluation indicator is determined to be three points.

[0067] For example, after migrating an online office application, an adaptability assessment is conducted. Given multi-source data, including post-migration performance data, the performance data is analyzed to determine the migration adaptability score. Specifically, after migration, under normal business load, the application can basically complete functional operations, but the response time is long. For instance, in an online office application, opening a document may take a long time, and even simple editing operations will exhibit noticeable lag. It can only meet the most basic user needs, exhibiting poor performance. Therefore, the migration adaptability score for this application can be determined as follows: point.

[0068] For example, after the application migration, its performance reached the average level of online office applications in the industry. Under normal business scenarios, editing operations were smooth, and it could stably support a certain number of users working online simultaneously without frequent lag or crashes. The migration adaptability of this application can be rated as [percentage missing]. point.

[0069] For example, after the application migration, its performance was excellent, exhibiting extremely fast response times and powerful concurrent processing capabilities. Documents opened instantly, and editing operations responded in real time. Even during peak hours, it easily supported a large number of users working online simultaneously, and the system resource utilization was low, providing an excellent example of performance optimization in the industry. The migration adaptability of this application can be rated as follows: point.

[0070] S303. Based on the score and weight of each evaluation indicator among multiple evaluation indicators, determine the comprehensive score of the application migration process.

[0071] After determining the score for each evaluation metric, a comprehensive score for the application migration process needs to be calculated based on the scores of each metric. In one implementation, the comprehensive score can be determined by averaging the scores of each evaluation metric. However, the importance of these metrics varies depending on the application running different business operations. Therefore, a better approach is to assign different weights to evaluation metrics of varying importance—higher weights for more important metrics and lower weights for less important ones—and then determine the comprehensive score by weighted summation of the scores for each evaluation metric.

[0072] For example, when evaluating the migration of e-commerce software applications, the primary evaluation indicators—planning and design capability, migration adaptation capability, testing and deployment capability, and summary and improvement capability—are assigned weights of 0.35, 0.25, 0.25, and 0.15, respectively. Among these, due to the high concurrency, complexity, and rapid changes inherent in e-commerce operations, sound planning and design are fundamental to the stable operation of the e-commerce system in a new environment, directly impacting user experience and transaction volume. Inadequate planning and design can lead to poor system performance, business interruptions, and other problems, causing significant losses to the enterprise; therefore, it is given a higher weight.

[0073] In one possible implementation, after determining the score and weight of each evaluation indicator, the overall score of the application migration process can be determined by weighted summation of the scores of each evaluation indicator.

[0074] In one possible implementation, the overall score of the application migration process satisfies the following formula 1: Formula 1: F = αA + βB + γC + δD

[0075] Where F is the overall score of the application migration process, A is the score of planning and design capability, α is the weight of planning and design capability, B is the score of migration adaptation capability, β is the weight of migration adaptation capability, C is the score of testing and deployment capability, γ is the weight of testing and deployment capability, D is the score of summary and improvement capability, and δ is the weight of summary and improvement capability.

[0076] S304. Based on the comprehensive score, determine the adaptability level of the application migration process.

[0077] Among them, the adaptability level is used to quantitatively assess the degree of adaptability of the application in the new environment after migration.

[0078] For example, the compatibility levels include: Basic Compatibility Level, Compliance Compatibility Level, High-Efficiency Compatibility Level, and Intelligent Compatibility Level. Basic Compatibility Level indicates that the application's core business functions can operate in a domestically developed IT environment without serious compatibility issues. It indicates that no optimization has been made for the characteristics of domestically developed IT hardware and software, and performance indicators only meet the minimum operating requirements. The migration process lacks systematic planning and relies on piecemeal adaptation operations.

[0079] The compliance-adaptation level indicates that it fully meets the compliance requirements for domestic substitution in the information technology innovation industry, and compatibility issues have been completely resolved. Basic performance optimization strategies have been developed specifically for the information technology innovation environment, and key business indicators have reached the industry average level. Preliminary migration plans are in place to ensure the continuity of core business operations.

[0080] High-efficiency adaptation level indicates deep adaptation to the characteristics of domestically developed software and hardware, achieving performance leaps through code and architecture optimization, with key indicators exceeding the industry average. A complete migration adaptation evaluation system has been established, covering multiple dimensions such as compatibility, performance, and risk. The migration process is standardized, supporting efficient batch application migration.

[0081] Intelligent adaptation level means integrating automated detection, repair and evaluation tools to achieve intelligent identification and handling of compatibility issues; performance optimization is aligned with the evolution of the domestic IT innovation ecosystem, supporting dynamic tuning and elastic expansion; a full lifecycle evaluation mechanism is built to monitor migration and adaptation effects in real time and continuously iterate and optimize.

[0082] In one possible implementation, S304 above can be implemented as follows: after determining the comprehensive score, determine the adaptability level of the application migration process based on the interval in which the comprehensive score is located.

[0083] For example, when the comprehensive score is in the range of [1, 1.5), the adaptability level of the application migration process is determined to be the basic adaptability level; when the comprehensive score is in the range of [1.5, 2), the adaptability level of the application migration process is determined to be the compliant adaptability level; when the comprehensive score is in the range of [2, 2.5), the adaptability level of the application migration process is determined to be the efficient adaptability level; and when the comprehensive score is in the range of [2, 2.5], the adaptability level of the application migration process is determined to be the intelligent adaptability level.

[0084] For example, when the overall score is determined to be 2.3 by weighted summation of the scores of each evaluation indicator, since 2.3 is in the interval [2,2.5), the adaptability level of the application migration process is determined to be the efficient adaptability level.

[0085] It should be noted that the above example of dividing the comprehensive scoring range to determine the adaptability level of the application migration process is only one possible implementation. In actual application, it can be set according to needs to cover different application scenarios. For example, for transaction-related application migration, since transaction business has extremely high requirements for stability, security, and performance, any small failure may lead to serious economic losses and reputational damage. Therefore, it is necessary to expand the range corresponding to the basic adaptability level and compliance adaptability level, and reduce the range corresponding to the high-efficiency adaptability level and intelligent adaptability level.

[0086] To address the problems existing in the prior art, this application obtains multi-source data on the application migration process and determines scores corresponding to multiple indicators. The multi-source data obtained in this application is diverse and comprehensive, reflecting multiple aspects of the application migration process and providing comprehensive data for subsequent determination of the application migration level. By scoring the application migration process using multiple indicators, the suitability level of the application migration process is determined. These multiple indicators correspond to multiple evaluation perspectives, and evaluating the application migration suitability from multiple perspectives allows for a more comprehensive determination of the application migration suitability evaluation results.

[0087] In some embodiments, since setting the weight of each evaluation indicator by human or expert experience has the problem of unreasonable weight allocation due to strong human subjectivity or lack of experience, before S303, a method that can more accurately and objectively determine the weight of each evaluation indicator is needed to ensure the accuracy of the application migration adaptation evaluation results.

[0088] As one implementation of the embodiments of this application, such as Figure 4 As shown, the process of determining the weight of each evaluation indicator can be implemented as follows: S401. Obtain the business requirements for the application migration process and the historical scores of each evaluation metric in at least one historical migration process.

[0089] The business requirements during application migration refer to the operational requirements of the application after it has been migrated to the new environment. These requirements ensure the application runs stably and efficiently in the new environment and continues to generate value for the business. The importance of different evaluation metrics can be analyzed based on these business requirements.

[0090] For example, the business requirements for the application migration process can include business continuity requirements, performance requirements, cost requirements, security and compliance requirements, compatibility requirements, scalability requirements, and data confidentiality requirements.

[0091] When an application needs to be evaluated for its adaptability to the target environment after it has been migrated to a new environment, testers can input an application migration adaptability evaluation command into the data acquisition device. This triggers the acquisition of multi-source data and can also trigger the acquisition of business requirements for the application migration process and historical scores of each evaluation indicator in at least one historical migration process.

[0092] In one possible implementation, S401 above can be implemented as follows: in response to receiving an application migration adaptability evaluation instruction, obtaining the business requirements of the application migration process and the historical scores of each evaluation indicator in at least one historical migration process.

[0093] For example, the business requirements of the application migration process can be obtained by acquiring the operation process documents after the application is migrated to the new environment, by acquiring user feedback information, or by conducting in-depth analysis of the business data after the application migration.

[0094] For example, the application migration adaptability evaluation device may have a built-in data storage module, which can be a database. The data storage module stores historical scores for each evaluation indicator during the historical migration process. Upon receiving an application migration adaptability evaluation instruction, the historical scores for each evaluation indicator during the historical migration process can be retrieved from the data storage module.

[0095] It should be noted that this application embodiment does not limit the business requirements of the application migration process or the method of obtaining historical scores for each evaluation indicator during at least one historical migration process. In actual application, it can be set according to needs to cover different application scenarios. For example, it can be obtained by consulting internal testers.

[0096] S402. Based on the business requirements of the application migration process, determine the first weight of each evaluation indicator.

[0097] The importance of each evaluation metric may vary depending on the different business needs. Therefore, after obtaining the business needs of the application migration process, the importance of each evaluation metric to the application's business needs can be compared. Evaluation metrics that are more important to the application's business needs should be given higher weights, while evaluation metrics that are less important to the application's business needs should be given lower weights.

[0098] In one possible implementation, S402 above can be implemented as follows: based on the business requirements of the application migration process, determine the importance of each evaluation indicator relative to the business requirements of the application migration process, and based on the importance of each evaluation indicator relative to the business requirements of the application migration process, determine the first weight of each evaluation indicator.

[0099] For example, considering a business requirement of conducting online platform transactions through a migrated application, an analysis of this requirement reveals that the online platform needs high concurrency and planning / design capabilities. The importance of the primary metrics, ranked from highest to lowest, is determined as follows: planning / design capability, testing and deployment capability, migration and adaptation capability, and summary and improvement capability. Therefore, the first weights assigned to these primary metrics, from highest to lowest, are: 0.35, 0.3, 0.2, and 0.15, respectively.

[0100] S403. Based on the historical scores of each evaluation indicator, determine the second weight of each evaluation indicator.

[0101] The historical scores of each evaluation indicator during the historical migration process can reflect the patterns of historical data to a certain extent. By analyzing the historical scores of each evaluation indicator, we can explore the importance of each indicator through historical data and avoid subjective errors.

[0102] In one possible implementation, S403 above can be implemented as follows: based on the historical scores of each evaluation indicator, determine the historical importance of each evaluation indicator, and based on the historical importance of each evaluation indicator, determine the second weight of each evaluation indicator.

[0103] For example, during a historical migration process, the historical scores for planning and design capabilities, migration adaptation capabilities, testing and deployment capabilities, and summary and improvement capabilities are 90, 85, 80, and 70, respectively. The ratio of the historical score for each evaluation indicator to the sum of the historical scores for all evaluation indicators is determined as the second weight: 0.277, 0.262, 0.246, and 0.215.

[0104] S404. Calculate the weighted sum of the first and second weights to determine the weight of each evaluation indicator.

[0105] After determining the primary and secondary weights, their importance needs to be compared. If the primary weight, representing the business requirements after application migration, is more important than the secondary weight, which represents historical data patterns, then the primary weight should be assigned a higher weight. Conversely, if the secondary weight, representing historical data patterns, is more important, then the secondary weight should be assigned a higher weight.

[0106] In one possible implementation, S404 above can be implemented as follows: Based on the importance of the first weight and the second weight, determine the weight of the first weight and the weight of the second weight. Then, sum the weighted values ​​of the first weight and the second weight to determine the weight of each evaluation indicator.

[0107] For example, when prioritizing the first weight, which represents the business needs after application migration, over the second weight, which represents the patterns in historical data, the weight of the first weight can be set to 0.6, and the weight of the second weight can be set to 0.4. Table 2 shows a weight table for an evaluation index provided in an embodiment of this application.

[0108] Table 2 Weighting Table of Evaluation Indicators

[0109] The weights of the primary indicators are determined using the method described above. Similarly, the weights of the secondary and tertiary indicators can also be determined using the same method. For example, Table 3 shows a weight table for primary, secondary, and tertiary indicators provided in an embodiment of this application. The percentage in parentheses after each indicator represents the weight of the corresponding indicator.

[0110] Table 3. Weighting of Primary, Secondary, and Tertiary Indicators

[0111] By using the first and second weights to jointly determine the weights of each evaluation indicator, we can comprehensively consider business needs and historical data patterns to determine more accurate weights for the evaluation indicators. This provides accurate data support for subsequently determining the adaptability level of application migration, thereby improving the accuracy of the application migration adaptability evaluation results.

[0112] In one possible implementation, such as Figure 5 The above S402 can be implemented as follows: S501. Based on the business requirements of the application migration process, determine the relative scale between every two evaluation indicators among multiple evaluation indicators.

[0113] The relative scale is used to indicate the importance of one evaluation indicator relative to the other. The larger the relative scale value, the greater the importance of one evaluation indicator relative to the other.

[0114] As exemplarily shown in Table 4, this application provides a relative scaling example table, including the values ​​of the relative scales and the meanings corresponding to the values ​​of the relative scales. Furthermore, if the relative scale of the first evaluation index relative to the second evaluation index is n, then the relative scale of the second evaluation index relative to the first evaluation index is 1 / n.

[0115] Table 4 Examples of Relative Scales

[0116] In one possible implementation, S501 above can be implemented as follows: by comparing the importance of each pair of evaluation metrics to business needs, and based on a relative scaling example table, determine the relative scaling between each pair of evaluation metrics.

[0117] For example, for the business operations performed after a software migration, the summary and improvement capability indicator is significantly more important than the planning and design capability indicator. Therefore, the relative scale between planning and design capability and summary and improvement capability is set to 7. When comparing the same evaluation indicators, since the same evaluation indicators are necessarily of equal importance, the relative scale for the same evaluation is always 1.

[0118] S502. Construct a relative scaling matrix by using multiple evaluation indicators as rows and columns of a relative scaling matrix, and the relative scale as an element of the relative scaling matrix.

[0119] In one possible implementation, S502 above can be implemented as follows: each evaluation index is used as a row of the relative scaling matrix, each evaluation index is used as a column of the relative scaling matrix, and the relative scale of two evaluation indices is used as the element at the intersection of the corresponding evaluation indices in the row and column to construct the relative scaling matrix.

[0120] For example, for the business operations performed after a software migration, the summary improvement capability indicators are significantly more important than the planning and design capability indicators. Therefore, the number of elements corresponding to the planning and design capability indicators in the rows and the summary improvement capability indicators in the columns is determined to be 7. After comparing every two evaluation indicators among multiple evaluation indicators, a relative scaling matrix is ​​generated, as shown in Table 5, which is a relative scaling matrix provided in an embodiment of this application. For ease of description, the following matrix is ​​represented in tabular form.

[0121] Table 5 Relative Scaling Matrix

[0122] S503. Normalize the relative scaling matrix and determine the average value of each row element of the normalized relative scaling matrix as the first weight of each evaluation index.

[0123] In one possible implementation, normalizing the relative scaling matrix can be achieved by dividing each element in the relative scaling matrix by the sum of the columns containing that element, thus normalizing that element.

[0124] For example, for the row corresponding to the planning and design capability index and the column corresponding to the migration and adaptation capability index, Table 5 shows that the element is 3, and the column sum of this element is 3 + 1 + 1 / 3 + 1 / 5, which is approximately 4.533. Dividing this element by the sum of its column, 3 / 4.533, yields approximately 0.662. Normalizing each element in the relative scaling matrix generates a normalized relative scaling matrix, as shown in Table 6, which is a normalized relative scaling matrix provided in this embodiment of the application.

[0125] Table 6. Normalized Relative Scaling Matrix

[0126] For example, the average value of each row of the normalized relative scaling matrix shown in Table 6 is used to determine the first weight of each indicator. Among them, the first weight of planning and design capability is 0.558, the first weight of migration adaptation capability is 0.263, the first weight of testing and deployment capability is 0.122, and the first weight of summary and improvement capability is 0.057.

[0127] Determining the primary weight of each indicator by considering business requirements allows us to identify a primary weight that closely aligns with business objectives, providing data support for subsequently determining the weight of each indicator.

[0128] In one possible implementation, such as Figure 6 As shown, the above S403 can be implemented as follows: S601 constructs a historical scoring matrix by using at least one historical migration process as a column of the historical scoring matrix, each evaluation indicator as a row of the historical scoring matrix, and historical scores as elements of the historical scoring matrix.

[0129] For example, each historical migration process is used as a column of the historical scoring matrix, each evaluation indicator is used as a row of the historical scoring matrix, and the score of each evaluation indicator in the corresponding historical migration process is used as an element of the historical scoring matrix, constructing the historical scoring matrix as shown in Table 7. Here, P1 to P5 represent different historical migration processes.

[0130] Table 7 Historical Rating Matrix

[0131] S602 normalizes the historical rating matrix to determine the normalized historical rating matrix.

[0132] After determining the historical rating matrix, it is necessary to normalize it because the rating criteria may differ in different historical migration processes. Specifically, historical ratings can be mapped to ratings under the same standard through preset rules.

[0133] In one possible implementation, S602 above can be implemented as follows: by substituting the historical rating matrix into a preset formula, the historical rating matrix is ​​normalized, and the normalized historical rating matrix is ​​determined.

[0134] In one possible implementation, the normalized historical rating matrix satisfies the following formula 2: Formula 2.

[0135] in, Let be the element in the i-th row and j-th column of the normalized historical rating matrix. Let be the element in the i-th row and j-th column of the historical rating matrix before normalization. The maximum value of the elements in the j-th column. It is the minimum value of the elements in the j-th column.

[0136] For example, in the historical rating matrix, the row corresponds to P1 and the column corresponds to the element of migration adaptation ability. As shown in Table 7, the element is 85, the minimum value of the element in the column is 80, and the maximum value of the element in the column is 90. Substituting into Formula 2, we get the normalized value of the element as 0.5. Substituting the elements in the historical rating matrix into Formula 2 above, we determine the normalized historical rating matrix as shown in Table 8.

[0137] Table 8 Normalized Historical Rating Matrix

[0138] It should be noted that the above example of normalizing the historical rating matrix is ​​only one possible implementation method. In actual application, specific normalization methods can be set according to requirements to cover different application scenarios.

[0139] S603 determines the second weight of each evaluation indicator based on the entropy method and the normalized historical rating matrix.

[0140] In one possible implementation, determining the second weight of each evaluation indicator based on the entropy method and the normalized historical rating matrix can be achieved as follows: the ratio of the first element to the sum of the elements in the column containing the first element is determined as the feature weight of the first element; the first element is an element in the normalized historical rating matrix. Based on the feature weight of the elements in the column containing the first element, the entropy value of the evaluation indicator corresponding to the column containing the first element is determined. Based on the entropy value of the evaluation indicator corresponding to the column containing the first element, the second weight of the evaluation indicator corresponding to the column containing the first element is determined.

[0141] For example, in the normalized historical rating matrix, this is used Let represent the element in the i-th row and j-th column of the normalized historical score matrix. For the element in the column containing planning and design capabilities, the sum of the elements in that column is 0.5 + 0.0 + 0.7 + 0.3 + 1.0 = 2.5. Then, the element in that column... The characteristic weight is 0.5 / 2.5 = 0.200. The characteristic weight is 0.0 / 2.5 = 0.000. The characteristic weight is 0.7 / 2.5 = 0.280. The characteristic weight is 0.3 / 2.5 = 0.120. The characteristic weight is 1.0 / 2.5 = 0.400.

[0142] In one possible implementation, the feature weights satisfy the following formula 3: Formula 3.

[0143] in, Let represent the element in the i-th row and j-th column of the normalized historical rating matrix, where i and j are integers. The element in the i-th row and j-th column of the normalized historical rating matrix represents the feature weight, and n is the total number of historical migration processes.

[0144] In one possible implementation, the entropy value satisfies the following formula 4: Formula 4.

[0145] in, Let the entropy value of the evaluation index corresponding to column j be _____. Let represent the feature weight corresponding to the element in the i-th row and j-th column, n be the total number of historical migration processes, and i and j be integers.

[0146] In one possible implementation, the entropy value of the evaluation index corresponding to the column containing the first element is determined based on the characteristic weight of the elements in the column containing the first element. This can be achieved by substituting the characteristic weight of the elements in the column containing the first element into the above formula 4 to determine the entropy value of the evaluation index corresponding to the column containing the first element.

[0147] For example, for the elements in the column containing planning and design capabilities, the characteristic weight of the elements in that column is input into Formula 4 to obtain the entropy value of the planning and design capability index. The value is 0.821. Similarly, the entropy value of migration adaptation capability, testing and deployment capability, and summary and improvement capability are determined using the above methods. The entropy value is 0.789, representing the test of online capability. The entropy value is 0.852, summarizing the improvement capability. It is 0.765.

[0148] In one possible implementation, determining the second weight of the evaluation indicator corresponding to the column containing the first element, based on the entropy value of the evaluation indicator, can be achieved as follows: For the entropy value of each evaluation indicator, the information utility value of the corresponding evaluation indicator is determined by subtracting the entropy value from 1. The ratio of the information utility value of the corresponding evaluation indicator to the sum of the information utility values ​​of all evaluation indicators is determined as the second weight of the corresponding evaluation indicator.

[0149] For example, the entropy value of this evaluation indicator for planning and design capabilities. The value is 0.821. The information utility value of this evaluation indicator is 1 - 0.821 = 0.176. Using the above method, the information utility value of each evaluation indicator is calculated. The information utility value of migration adaptation capability is 0.211, the information utility value of testing and deployment capability is 0.148, and the information utility value of summary and improvement capability is 0.235.

[0150] For example, the information utility values ​​of all evaluation indicators are summed to determine the total information utility value of all evaluation indicators as 0.773. The ratio of the information utility value of each evaluation indicator to the total information utility value is calculated, and the second weight of planning and design capability is determined to be 0.231, the second weight of migration and adaptation capability is 0.273, the second weight of testing and deployment capability is 0.1191, and the second weight of summary and improvement capability is 0.304.

[0151] Determining the second weight by using historical scores can reflect historical data patterns to a certain extent. By analyzing the historical scores of each evaluation indicator, the importance of each indicator can be mined from historical data, thus avoiding subjective errors.

[0152] It is understood that the above-described application migration adaptability evaluation method can be implemented by an application migration adaptability evaluation device. To achieve the above functions, the application migration adaptability evaluation device includes hardware structures and / or software modules corresponding to each function. Those skilled in the art should readily recognize that, based on the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, the embodiments disclosed in this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is implemented in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments disclosed in this application.

[0153] The embodiments disclosed in this application can divide functional modules according to the application migration adaptability evaluation device generated by the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in the embodiments disclosed in this application is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0154] Figure 7 This is a schematic diagram of an application migration adaptability evaluation device provided in an embodiment of the present invention. Figure 7 As shown, the application migration adaptability evaluation device 70 can be used to perform... Figure 3The application migration adaptability evaluation method is shown. The application migration adaptability evaluation device 70 includes: an acquisition unit 701 and a determination unit 702; the acquisition unit 701 is used to acquire multi-source data of the application migration process; the multi-source data includes: migration schemes for planning the application migration process, migration performance test information for evaluating application migration performance, and migration results characterizing the application migration outcome; the determination unit 702 is used to determine the score of each evaluation indicator among multiple evaluation indicators based on the multi-source data; the determination unit 702 is also used to determine the comprehensive score of the application migration process based on the score and weight of each evaluation indicator among multiple evaluation indicators; the determination unit 702 is also used to determine the adaptability level of the application migration process based on the comprehensive score.

[0155] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] This disclosure also provides a computer-readable storage medium storing instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the application migration adaptability evaluation method provided in the embodiments of this disclosure described above.

[0157] This disclosure also provides a computer program product containing instructions that, when run on an electronic device, cause the electronic device to execute the application migration adaptability evaluation method provided in the above-described embodiments of this disclosure.

[0158] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires; a portable computer disk drive; a hard disk drive; a random access memory (RAM); a read-only memory (ROM); an erasable programmable read-only memory (EPROM); a register; a hard disk drive; an optical fiber; a portable compact disc read-only memory (CD-ROM); an optical storage device; a magnetic storage device; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0159] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating application transferability, characterized in that, include: Acquire multi-source data during the application migration process; The multi-source data includes: migration schemes for planning application migration processes, migration performance test information for evaluating application migration performance, and migration results characterizing application migration outcomes. Based on the multi-source data, the score for each evaluation indicator among multiple evaluation indicators is determined; A comprehensive score for the application migration process is determined based on the score and weight of each of the multiple evaluation indicators. Based on the comprehensive score, the adaptability level of the application migration process is determined.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the business requirements of the application migration process and the historical scores of each evaluation metric from at least one historical migration process; Based on the business requirements of the application migration process, determine the first weight of each evaluation indicator; Based on the historical scores of each evaluation indicator, a second weight for each evaluation indicator is determined; The weight of each evaluation index is determined by weighted summation of the first weight and the second weight.

3. The method according to claim 2, characterized in that, Based on the business requirements of the application migration process, the first weight of each evaluation indicator is determined, including: Based on the business requirements of the application migration process, a relative scale is determined between every two evaluation indicators among the plurality of evaluation indicators; the relative scale is used to indicate the importance of one evaluation indicator relative to the other evaluation indicator. The relative scaling matrix is ​​constructed by using the multiple evaluation indicators as rows and columns of a relative scaling matrix, and the relative scaling as elements of the relative scaling matrix. The relative scaling matrix is ​​normalized, and the average value of each row element of the normalized relative scaling matrix is ​​determined as the first weight of each evaluation index.

4. The method according to claim 2, characterized in that, The determination of the second weight for each evaluation indicator based on its historical scores includes: The historical scoring matrix is ​​constructed by using the at least one historical migration process as a column of the historical scoring matrix, each evaluation index as a row of the historical scoring matrix, and the historical score as an element of the historical scoring matrix. The historical rating matrix is ​​normalized to determine the normalized historical rating matrix; Based on the entropy method and the normalized historical rating matrix, the second weight of each evaluation indicator is determined.

5. The method according to claim 4, characterized in that, The determination of the second weight for each evaluation indicator based on the entropy method and the normalized historical rating matrix includes: The ratio of the first element to the sum of the elements in the column containing the first element is determined as the feature weight of the first element; the first element is an element in the normalized historical rating matrix. Based on the characteristic weight of the elements in the column where the first element is located, determine the entropy value of the evaluation index corresponding to the column where the first element is located. Based on the entropy value of the evaluation index corresponding to the column where the first element is located, the second weight of the evaluation index corresponding to the column where the first element is located is determined.

6. The method according to claim 3, characterized in that, The normalized historical rating matrix satisfies the following formula: in, Let be the element in the i-th row and j-th column of the normalized historical rating matrix. Let be the element in the i-th row and j-th column of the historical rating matrix before normalization. The maximum value of the elements in the j-th column. It is the minimum value of the elements in the j-th column.

7. The method according to claim 5, characterized in that, The entropy value satisfies the following formula: in, Let the entropy value of the evaluation index corresponding to column j be _____. Let represent the feature weight corresponding to the element in the i-th row and j-th column, n be the total number of historical migration processes, and i and j be integers.

8. An application migration adaptability evaluation device, characterized in that, The application migration adaptability evaluation device includes: an acquisition unit and a determination unit; The acquisition unit is used to acquire multi-source data of the application migration process; the multi-source data includes: migration schemes for planning the application migration process, migration performance test information for evaluating application migration performance, and migration results characterizing the application migration outcome. The determining unit is used to determine the score of each evaluation indicator among multiple evaluation indicators based on multi-source data; The determining unit is also used to determine a comprehensive score for the application migration process based on the score and weight of each of the multiple evaluation indicators. The determining unit is also used to determine the adaptability level of the application migration process based on the comprehensive score.

9. An application migration adaptability evaluation device, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being configured to run computer programs or instructions to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the method described in any one of claims 1-7.

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