Software asset management method and device, equipment and storage medium

Abstract

The invention discloses a software asset management method, device and equipment and a storage medium, and relates to the field of software management.The method comprises the steps that an operation sequence of a user in a software asset management system is captured in real time, and the operation sequence is analyzed to construct an operation behavior time sequence characteristic graph; matching the software assets uploaded to the software asset management system by the user with the rules in the rule base to obtain a rule matching result; and generating a software asset compliance report according to the operation behavior time sequence characteristic spectrum and a rule matching result. According to the scheme provided by the invention, the efficiency of software asset compliance management is improved.

Description

Technical Field

[0001] This invention relates to the field of software management, and more particularly to a software asset management method, apparatus, device, and storage medium. Background Technology

[0002] Currently, the management of internal software assets in enterprises mainly relies on manual operations to complete software compliance management. Specifically, internal software assets include open-source software, free software, and purchased software. After these software assets are manually uploaded to a unified management system, relevant forms need to be filled out manually and submitted to managers at different levels for manual review. However, software compliance checks often rely on external tools.

[0003] Current software asset compliance management requires a large amount of manual operation and hierarchical review. The entire process is time-consuming and cannot respond in a timely manner. Manual operation is also prone to omissions and errors, making it difficult to achieve efficient compliance management of software assets. Summary of the Invention

[0004] This invention provides a software asset management method, apparatus, device, and storage medium, which improves the efficiency of compliant management of software assets.

[0005] According to one aspect of the present invention, a software asset management method is provided, comprising:

[0006] Real-time capture of user operation sequences in the software asset management system, and analysis of operation sequences to construct a temporal feature map of operation behavior;

[0007] The software assets uploaded by users to the software asset management system are matched with the rules in the rule base to obtain the rule matching results;

[0008] A software asset compliance report is generated based on the temporal feature map of operational behavior and the rule matching results.

[0009] In one embodiment, real-time capture of a user's operation sequence in a software asset management system includes:

[0010] Deploy behavior-aware probes in the software asset management system;

[0011] The system uses sensing probes to capture the user's operation sequence in the software asset management system in real time.

[0012] In one embodiment, after constructing a temporal feature map of operational behavior through operational sequence analysis, the method further includes:

[0013] Perform time-series analysis on the operation sequence to determine whether each operation is an abnormal behavior.

[0014] In one embodiment, software assets uploaded by users to the software asset management system are matched with rules in the rule base to obtain rule matching results, including:

[0015] The software assets uploaded by users to the software asset management system are matched with the rules in the dynamic rule base to obtain the rule matching results. The dynamic rule base dynamically updates the compliance detection rules by analyzing historical compliance cases and non-compliance patterns.

[0016] In one embodiment, after matching the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result, the method further includes:

[0017] A multidimensional risk quantification assessment is performed on the temporal feature map of operational behavior and the rule matching results to obtain a comprehensive risk profile. The multidimensional risk quantification assessment is carried out by quantifying and calculating based on assessment models of multiple different dimensions.

[0018] In one embodiment, multiple evaluation models with different dimensions are trained using federated learning techniques.

[0019] In one embodiment, the method further includes:

[0020] Analyze the changing trends of time-series characteristic graphs within a preset time range in the software asset management system to predict potential compliance risk events within the preset time range.

[0021] According to another aspect of the present invention, a software asset management apparatus is provided, comprising:

[0022] The operation sequence analysis module is used to capture the user's operation sequence in the software asset management system in real time, and to construct a temporal feature map of operation behavior based on the operation sequence analysis.

[0023] The rule matching module is used to match the software assets uploaded by users to the software asset management system with the rules in the rule base to obtain the rule matching results;

[0024] The report generation module is used to generate software asset compliance reports based on the temporal feature map of operational behavior and the rule matching results.

[0025] According to another aspect of the present invention, an electronic device is provided, comprising: a processor, and a memory communicatively connected to the processor;

[0026] The memory stores instructions that the computer executes;

[0027] The processor executes computer execution instructions stored in memory to implement the aforementioned software asset management method.

[0028] According to another aspect of the present invention, a computer program product is provided, comprising a computer program stored in a computer-readable storage medium, wherein at least one processor can read the computer program from the computer-readable storage medium, and the at least one processor implements the above-described software asset management method when executing the computer program.

[0029] The technical solution of this invention captures the user's operation sequence in a software asset management system in real time, analyzes the operation sequence to construct an operation behavior temporal feature map, matches the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result, and finally generates a software asset compliance report based on the operation behavior temporal feature map and the rule matching result. This provides an automatic compliance check method for software assets, improving the efficiency and accuracy of software asset compliance management.

[0030] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating a software asset management method provided in an embodiment of the present invention;

[0033] Figure 2 A flowchart illustrating another software asset management method provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the structure of a software asset management device provided in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of another software asset management device provided in an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present invention. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0038] Figure 1 This is a flowchart illustrating a software asset management method provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the software asset management method provided in this embodiment includes:

[0039] Step S110: Capture the user's operation sequence in the software asset management system in real time, and analyze the operation sequence to construct a temporal feature map of operation behavior.

[0040] The software asset management method provided in this application is used for unified management of software assets within an enterprise. Since various software assets used within an enterprise originate from multiple sources, it is necessary to standardize the management of these assets to avoid various risks and hidden dangers arising from problems with the software assets. The software asset management method provided in this application can be executed by a computer or server, or it can be executed by a software module installed on a computer or server. This computer or server can be deployed within the enterprise requiring software asset management or it can be located in the cloud.

[0041] Most enterprises currently have software asset management systems. Departments and individuals upload their purchased and self-developed software assets to these systems according to relevant rules, and fill in the required information. However, current software asset management systems generally only store software assets and related information. Verifying the compliance of uploaded software assets requires manual review or external scanning tools. Manual review is inefficient and struggles to identify all issues, while external tools often fail to meet the customized needs of individual enterprises.

[0042] This application provides a software asset management method that, based on any existing software asset management system, further manages whether software assets comply with relevant rules. The software asset management method provided in this application is applicable to any software asset management system and can be a module or function within a software asset management system, or an independent module or function separate from the software asset management system.

[0043] Users need to upload software assets and fill in relevant information in the software asset management system, which requires multiple operations. Therefore, judging whether software assets are compliant can include testing the software assets themselves and testing the operational behavior.

[0044] First, during the process of a user uploading software assets to the software asset management system, the user's operation sequence within the system is captured in real time. This sequence includes, but is not limited to, actions such as uploading software assets and filling out relevant forms. Each operation should be executed according to a specific order and standard. If the operation sequence does not conform to the normal operating pattern, the software asset may have compliance risks. Therefore, after capturing the user's operation sequence in the software asset management system in real time, the sequence is analyzed to construct an operation behavior temporal feature graph. This graph reflects the timing of the user's operations during the software asset upload process, along with related operational information. The operation behavior temporal feature graph also reflects various software asset compliance management information beyond the software asset content itself.

[0045] Specifically, behavior-aware probes can be deployed in the software asset management system. These probes capture the user's operation sequence within the system in real time. By deploying behavior-aware probes at key locations within the system, user actions at these locations trigger the probes, thus recording the user's actions. By sequentially recording the user's actions within the software asset management system based on the trigger times of each probe, the corresponding operation sequence can be obtained.

[0046] In one embodiment, after constructing a temporal feature map of operational behavior through operational sequence analysis, temporal analysis can be performed on the operational sequence to determine whether each operation in the sequence constitutes abnormal behavior. After constructing the temporal feature map of operational behavior, the differences between normal and abnormal operational modes can be identified based on the contextual semantics of each operation, enabling early detection of potential non-compliant behaviors.

[0047] Specifically, abnormal operations can be identified by performing time-series analysis on the operation sequence using the following formula:

[0048] .

[0049] Where Score represents the anomaly score corresponding to the operation, P deviation The deviation is F, which represents the degree of timing deviation between this operation and the normal operation. frequency Frequency anomaly indicates the frequency of this operation, R. risk α, β, and γ represent the risk factors for different operations, respectively. deviation F frequency R risk The weighting coefficients.

[0050] Step S120: Match the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result.

[0051] In addition to analyzing the operation sequences of software assets, it is also necessary to inspect the content of software assets uploaded to the software asset management system. A rule base can be configured in the software asset management system, containing various compliance check rules for software assets. For each software asset uploaded by a user to the system, it is matched against each rule in the rule base to obtain a matching result. If the software asset matches a rule in the rule base, the matching result is considered compliant; otherwise, it is considered non-compliant.

[0052] Step S130: Generate a software asset compliance report based on the temporal feature map of operational behavior and the rule matching results.

[0053] Finally, a software asset compliance report is generated based on the temporal feature map of the operational behavior obtained in step S110 and the rule matching results obtained in step S120. The software asset compliance report includes both operational information related to user uploads of software assets and compliance checks on the content information of the software assets. Therefore, users can use the software asset compliance report to determine whether their uploaded software assets pose any compliance risks. Administrators of the software asset management system can also view the software asset compliance reports generated when each user uploads software assets, or check whether each user's upload behavior and the content of the software assets are compliant.

[0054] The software asset management method provided in this embodiment captures the user's operation sequence in the software asset management system in real time, analyzes the operation sequence to construct an operation behavior temporal feature map, matches the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result, and finally generates a software asset compliance report based on the operation behavior temporal feature map and the rule matching result. This provides an automatic compliance check method for software assets, improving the efficiency and accuracy of software asset compliance management.

[0055] In one embodiment, the rule base in the software asset management system can be a dynamic rule base. It matches software assets uploaded by users to the system with rules in the dynamic rule base to obtain matching results. The dynamic rule base dynamically updates compliance detection rules by analyzing historical compliance cases and non-compliance patterns. Alternatively, the rule base can be static, meaning it's simply stored within the system and updated periodically or irregularly as needed. Or, it can be dynamic, determining the relevant characteristics of compliant / non-compliant software assets based on the analysis of historical compliance cases and non-compliance patterns, thereby generating new compliance detection rules or updating existing rules. The rules in the dynamic rule base are updated in real time; various changes in the software asset management system may lead to updates to the dynamic rule base. By setting up a dynamic rule base, the software asset management system can self-optimize and evolve based on actual detection results, ensuring the timeliness and accuracy of the rules.

[0056] For example, a dynamic rule base uses an incremental learning mechanism to continuously optimize detection accuracy based on feedback. The dynamic rule base can be dynamically updated using the following formula:

[0057] .

[0058] Where θ new For the new model parameters, θ old Here are the old model parameters, η is the learning rate, L(θ) is the loss function, and ▽ is used to calculate the gradient of the loss function L(θ) with respect to the model parameters θ.

[0059] Alternatively, decision trees or rule templates can be used to classify user actions. Although the recognition accuracy is lower compared to dynamic rule bases, it can effectively reduce implementation costs. Updates to dynamic rule bases can also be non-real-time, using periodic batch updates, such as weekly or monthly model updates, to reduce the computational resource consumption required for updates.

[0060] Figure 2 A flowchart illustrating another software asset management method provided in an embodiment of the present invention is shown below. Figure 2 As shown, the software asset management method provided in this embodiment includes:

[0061] Step S210: Capture the user's operation sequence in the software asset management system in real time, and analyze the operation sequence to construct a temporal feature map of operation behavior.

[0062] Step S220: Match the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result.

[0063] Step S230: Generate a software asset compliance report based on the temporal feature map of operational behavior and the rule matching results.

[0064] Step S240: Perform multi-dimensional risk quantification assessment on the temporal feature map of the operation behavior and the rule matching results to obtain a comprehensive risk profile. The multi-dimensional risk quantification assessment is performed according to multiple assessment models of different dimensions.

[0065] After generating a software asset compliance report, further risk assessments can be conducted to evaluate the compliance risks associated with users uploading software assets, providing users with more intuitive compliance risk check results. Specifically, multi-dimensional risk quantification assessments can be performed on the temporal feature maps of operational behaviors and rule matching results to obtain a comprehensive risk profile. Multi-dimensional risk quantification assessment means that quantitative assessment calculations are performed based on multiple assessment models of different dimensions. Software assets uploaded to the software asset management system may have compliance risks from multiple dimensions, such as technical risks, legal risks, business risks, security risks, and operational risks. Each dimension reflects the compliance of the software asset from different perspectives. Each dimension has a corresponding assessment model, an independent assessment engine, and a quantitative indicator system. By inputting the temporal feature maps of operational behaviors and rule matching results into the corresponding assessment models of each dimension, a comprehensive risk profile is generated through a weighted fusion algorithm, thus providing a comprehensive risk view for decision-making. Specifically, the comprehensive risk value can be calculated using the following comprehensive risk calculation formula:

[0066] .

[0067] Where R total For the overall risk value, ω i Let r be the weight of the i-th dimension. i Let be the risk value for the i-th dimension, and n be the number of dimensions. Based on the assessment model for each dimension, the corresponding risk value for each dimension can be obtained. Then, using the formula above, the quantified comprehensive risk value can be derived.

[0068] Alternatively, when conducting risk assessment, only a few key dimensions from a preset set of dimensions can be selected for risk assessment to reduce computational complexity. Alternatively, a weighted scoring method can be used instead of a complex vector fusion algorithm, which can also effectively reduce computational complexity.

[0069] Furthermore, federated learning techniques can be used to train the evaluation models for each dimension to ensure the security of privacy data. The evaluation models for each dimension need to be trained based on historical operational behavior time-series feature maps and rule matching results. A software asset management system may be used to collect software assets from different departments within an enterprise, or even branches of a large enterprise. However, there may be privacy data confidentiality requirements between departments or branches. Therefore, a federated learning mechanism can be used, where each department or branch trains the model locally, ensuring that privacy and sensitive data do not leave the local environment. Only encrypted model parameters are shared, without sharing the original data, thus protecting data privacy while enabling the accumulation and utilization of big data.

[0070] The software asset management methods provided in the above embodiments can identify and judge whether the process of users uploading software assets and the content of software assets are compliant. This application, as an embodiment of the software asset management method, can further predict potential compliance risk events in the software asset management system. Potential compliance risk events within a preset time range can be predicted by analyzing the changing trends of time-series characteristic graphs in the software asset management system. Specifically, based on time series analysis and machine learning prediction models, risk evolution trends can be analyzed to predict compliance risk events that may occur within a preset time range (e.g., within 15-30 days). By establishing a risk transmission model, risk sources and propagation paths can be identified, enabling early warning and proactive prevention of risks.

[0071] Specifically, risk prediction can be performed using the ARIMA model combined with neural networks, for example, using the following formula:

[0072] .

[0073] in, Let f() represent the predicted risk value at time t+h, and let f() be the time series model. t y t-1 ... y t-p For the time series model from time t to tp, g(x) t ) represents the function of external influencing factors.

[0074] For predicting potential compliance risk events, simple moving averages or exponential smoothing methods can also be used. The predicted future time frame may be shortened, but the maintenance cost of the predictive model can be significantly reduced.

[0075] In one embodiment, blockchain technology can also be applied to the software asset management system to establish an audit chain for software assets. This involves using blockchain technology to record all compliance testing and approval processes, ensuring that compliant software assets are traceable and tamper-proof. Alternatively, a centralized log system with numbers preceding the data, combined with regular backups and hash verification, can achieve similar auditing effects to blockchain technology at a lower cost.

[0076] In summary, the software asset management method provided in this application first monitors user operations in real time through a behavior perception layer, inputting the captured behavioral data into an intelligent analysis engine. The analysis engine uses deep learning algorithms to understand the operational intent and identify potential risk points. Simultaneously, an adaptive rule engine dynamically generates and updates detection rules based on the latest compliance requirements and historical experience. A multi-dimensional risk assessment module quantifies and assesses risk levels from different perspectives, while a prediction module predicts future risk trends based on historical data. Finally, an intelligent report containing risk assessments, compliance recommendations, and improvement measures is automatically generated. This design enables the software asset management system not only to passively respond to compliance inspection needs but also to proactively identify potential risks and predict future trends, truly realizing a shift from "post-event remediation" to "pre-event prevention."

[0077] Figure 3 This is a schematic diagram of the structure of a software asset management device provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the software asset management device provided in this embodiment includes:

[0078] The operation sequence analysis module 31 is used to capture the operation sequence of users in the software asset management system in real time and construct the operation behavior time sequence feature map based on the operation sequence analysis; the rule matching module 32 is used to match the software assets uploaded by users to the software asset management system with the rules in the rule base to obtain the rule matching result; the report generation module 33 is used to generate a software asset compliance report based on the operation behavior time sequence feature map and the rule matching result.

[0079] The software asset management device provided in this embodiment is used to implement... Figure 1 The software asset management method provided in the illustrated embodiment has a similar implementation principle and technical effect, and will not be described in detail here.

[0080] In one embodiment, the operation sequence analysis module 31 is specifically used to deploy behavior awareness probes in the software asset management system; and to capture the user's operation sequence in the software asset management system in real time based on the awareness probes.

[0081] In one embodiment, the operation sequence analysis module 31 is further configured to perform time-series analysis on the operation sequence to determine whether each operation is an abnormal behavior.

[0082] In one embodiment, the rule matching module 32 is specifically used to match the software assets uploaded by the user to the software asset management system with the rules in the dynamic rule base to obtain the rule matching result. The dynamic rule base dynamically updates the compliance detection rules by analyzing historical compliance cases and non-compliance patterns.

[0083] Figure 4 This is a schematic diagram of another software asset management device provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the software asset management device provided in this embodiment... Figure 3 In addition to this, it also includes:

[0084] The quantitative assessment module 34 is used to perform multi-dimensional risk quantitative assessment on the temporal feature map of operational behavior and rule matching results to obtain a comprehensive risk profile. The multi-dimensional risk quantitative assessment is performed according to multiple assessment models of different dimensions.

[0085] In one embodiment, multiple evaluation models with different dimensions are trained using federated learning techniques.

[0086] In one embodiment, the software asset management device may further include: a prediction module 35, used to analyze the trend of time-series feature map changes within a preset time range in the software asset management system, and predict potential compliance risk events within the preset time range.

[0087] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device may include: transceiver 51, processor 52, and memory 53.

[0088] Processor 52 executes computer execution instructions stored in memory, causing processor 52 to perform the scheme in the above embodiments. Processor 52 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0089] The memory 53 is connected to the processor 52 via the system bus and completes communication between them. The memory 53 is used to store computer program instructions.

[0090] Transceiver 51 can be used to obtain the task to be run and its configuration information.

[0091] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0092] The electronic device provided in this application embodiment may be a computer or server deployed by the software asset management device in the above embodiments.

[0093] This application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the technical solution of the software asset management method described in the above embodiments.

[0094] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the technical solution of the software asset management method in the above embodiments.

[0095] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0096] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A software asset management method, characterized in that, include: Real-time capture of user operation sequences in the software asset management system, and analysis of the operation sequences to construct a temporal feature map of operation behavior; The software assets uploaded by the user to the software asset management system are matched with the rules in the rule base to obtain the rule matching result; A software asset compliance report is generated based on the temporal feature map of the operational behavior and the rule matching results.

2. The method according to claim 1, characterized in that, The real-time capture of user operation sequences in the software asset management system includes: Deploy behavior-aware probes in the software asset management system; The sensing probe captures the user's operation sequence in the software asset management system in real time.

3. The method according to claim 1, characterized in that, After constructing the temporal feature map of the operation behavior by analyzing the operation sequence, the method further includes: Perform time-series analysis on the operation sequence to determine whether each operation is an abnormal behavior.

4. The method according to any one of claims 1 to 3, characterized in that, The step of matching the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result includes: The software assets uploaded by the user to the software asset management system are matched with the rules in the dynamic rule base to obtain the rule matching result. The dynamic rule base dynamically updates the compliance detection rules by analyzing historical compliance cases and non-compliance patterns.

5. The method according to any one of claims 1 to 3, characterized in that, After matching the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result, the process further includes: A multidimensional risk quantification assessment is performed on the temporal feature map of the operation behavior and the rule matching result to obtain a comprehensive risk profile. The multidimensional risk quantification assessment is performed according to multiple assessment models of different dimensions.

6. The method according to claim 5, characterized in that, The evaluation models with multiple different dimensions are trained using federated learning techniques.

7. The method according to any one of claims 1 to 3, characterized in that, Also includes: Analyze the changing trends of time-series feature maps within a preset time range in the software asset management system to predict potential compliance risk events within the preset time range.

8. A software asset management device, characterized in that, include: The operation sequence analysis module is used to capture the user's operation sequence in the software asset management system in real time, and to construct an operation behavior temporal feature map based on the operation sequence analysis. The rule matching module is used to match the software assets uploaded by the user to the software asset management system with the rules in the rule base to obtain the rule matching result; The report generation module is used to generate a software asset compliance report based on the temporal feature map of the operation behavior and the rule matching result.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the software asset management method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the software asset management method as described in any one of claims 1-7.

11. A computer program product, characterized in that, It includes a computer program stored in a computer-readable storage medium, at least one processor can read the computer program from the computer-readable storage medium, and the at least one processor can implement the software asset management method as described in any one of claims 1 to 7 when executing the computer program.

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