Ecological maturity evaluation method and device for RISC-V basic software
By dividing the RISC-V ecosystem into multiple logical layers and conducting multi-dimensional evaluations, the problem of the inability to quantify the maturity of the ecosystem in existing technologies is solved, enabling a systematic evaluation and resource optimization of the RISC-V ecosystem.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INST OF SOFTWARE - CHINESE ACAD OF SCI
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies cannot fully quantify the software maturity of the RISC-V ecosystem. They lack dynamic weights, hierarchical component structure modeling, and heterogeneous computing support, resulting in a vague direction for ecosystem development and inefficient resource investment.
The RISC-V basic software ecosystem is divided into multiple logical layers, including the system firmware layer, operating system and basic library layer, etc. A multi-dimensional evaluation model is used to score the layers and aggregate them vertically to generate an overall ecosystem maturity score, supporting dynamic dimension and hierarchical structure expansion.
It provides a systematic assessment of the RISC-V ecosystem, covering a wide range of software components, exhibiting good versatility and scalability, and offering quantitative data to support investment decisions and resource prioritization.
Smart Images
Figure CN122019336A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of software ecosystem maturity analysis technology, and in particular to a method and apparatus for assessing the ecosystem maturity of RISC-V basic software. Background Technology
[0002] Instruction Set Architecture (ISA) is the core interface specification between computer hardware and software. It defines the set of instructions that a processor can execute, register organization, memory access mechanisms, and exception handling methods. Instruction Set Architecture plays a crucial role in processor manufacturing.
[0003] Currently, the mainstream ISA architecture represented by x86 has formed a mature ecosystem and is widely used in key scenarios such as cloud computing and artificial intelligence. In recent years, the fifth-generation Reduced Instruction Set Computer-V (RISC-V), as an open-source ISA architecture based on RISC principles, has received widespread attention and is gradually being applied to various scenarios such as embedded systems, industrial control, and data centers. With its open-source and modular characteristics, RISC-V is accelerating its penetration into high-value scenarios such as the Internet of Things (IoT) and data centers, becoming a cost-effective alternative to proprietary architectures such as x86 and ARM.
[0004] The maturity of the RISC-V software ecosystem is considered a key factor influencing its industrialization process. Scientific evaluation of RISC-V ecosystem maturity will promote its development, accurately identify its shortcomings, pinpoint key pathways for improvement, fill the gap in quantitative evaluation standards, shorten the data center software adaptation cycle to RISC-V, and accelerate the development and application of RISC-V technologies. Ecosystem maturity includes multiple aspects such as the functional completeness of basic components, the activity of the open-source community, and software and hardware compatibility. However, existing methods for evaluating ecosystems have significant shortcomings: 1. Existing benchmarking tools (such as the SPEC CPU benchmark suite) only focus on hardware performance indicators and cannot cover key software ecosystem elements such as heterogeneous computing support, open source community activity, and functional completeness, making it difficult to comprehensively quantify the maturity of the software ecosystem. 2. Some open-source projects (such as the Community HealthAnalytics Open Source Software (CHAOSS)) provide open-source community health metrics, but lack consideration for software performance and functional completeness for RISC-V. 3. Existing maturity assessment models lack mechanisms such as dynamic weights, hierarchical component structure modeling, and heterogeneous computing support.
[0005] These technical shortcomings will lead to a blurred direction in ecosystem development, making it difficult for developers to identify high-priority optimization areas. Furthermore, they can result in inefficient resource allocation, with companies investing hundreds of millions only to discover that the ecosystem for their target scenarios is not mature enough, severely hindering the rapid development and industrial adoption of the RISC-V ecosystem. Therefore, how to cover the entire software stack and support dynamic weight calibration through quantitative evaluation, thereby addressing the blind spots in ecosystem development and the lag in industry decision-making, has become an urgent problem to be solved, providing guidance and reference for the development of RISC-V in data center infrastructure software. Summary of the Invention
[0006] This invention provides a method and apparatus for assessing the maturity of the RISC-V basic software ecosystem, which addresses the shortcomings of existing technologies that cannot dynamically quantify key elements such as the functional completeness, heterogeneous acceleration, and compilation quality of components at each layer of the RISC-V basic software ecosystem. It enables support for dynamic dimensional and hierarchical expansion and systematic assessment of the ecosystem based on quantifiable multidimensional indicators.
[0007] This invention provides a method for assessing the ecological maturity of RISC-V basic software, comprising: The RISC-V basic software ecosystem is divided into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. These multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple software components that can be added or removed independently. Based on the RISC-V basic software hierarchical model, hierarchical scoring is performed to obtain the scoring results for each level; The scoring results of each level are aggregated vertically to obtain the overall ecosystem maturity score of the RISC-V basic software. The overall ecosystem maturity score is used to assess the ecosystem maturity of the RISC-V foundational software.
[0008] In one possible implementation, the method further includes: For each software component, scores are assigned based on multiple predefined dimensions; Based on the combined scoring results of the corresponding pre-set weights for each dimension, a weighted score for each software component is obtained; The weighted scores of each software component in each level are averaged to obtain the score result for each level.
[0009] In one possible implementation, the multiple predefined dimensions include performance advancement, compilation quality, memory advancement, functional completeness, open source development activity, and heterogeneous computing support.
[0010] In one possible implementation, the method further includes: Assign each level a hierarchical weight that can be dynamically configured according to the evaluation target; The scores from each level are weighted and summed to obtain the overall ecosystem maturity score of the RISC-V basic software.
[0011] In one possible implementation, the method further includes: The overall ecosystem maturity score of the RISC-V basic software is recalculated at fixed intervals and stored in time series form to generate an ecosystem evolution curve. The ecosystem evolution curve is used to provide a quantitative basis for investment decisions and resource allocation priorities.
[0012] In one possible implementation, the method further includes: The overall ecosystem maturity score is used to assess the operational status of the RISC-V basic software.
[0013] This invention also provides an ecosystem maturity assessment device for RISC-V basic software, comprising the following modules: The modeling module is used to divide the RISC-V basic software ecosystem into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. The multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple software components that can be added or removed independently. The scoring module is used to perform hierarchical scoring based on the RISC-V basic software hierarchical model to obtain the scoring results for each level. The aggregation module is used to vertically aggregate the scoring results of each level to obtain the overall ecosystem maturity score of the RISC-V basic software. The evaluation module is used to evaluate the ecological maturity of the RISC-V basic software based on the overall ecological maturity score.
[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the RISC-V basic software ecosystem maturity assessment method as described above.
[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the RISC-V basic software ecosystem maturity assessment method as described above.
[0016] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the RISC-V basic software ecosystem maturity assessment method as described above.
[0017] The present invention provides a method and apparatus for assessing the ecosystem maturity of RISC-V basic software. This method divides the RISC-V basic software ecosystem into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. These logical layers include a system firmware layer, an operating system and basic library layer, a compiler toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple independently addable and deletable software components. The method performs hierarchical scoring based on the RISC-V basic software hierarchical model to obtain a score for each layer. The scores for each layer are then vertically aggregated to obtain an overall ecosystem maturity score for the RISC-V basic software. Finally, the overall ecosystem maturity score is used to assess the ecosystem maturity of the RISC-V basic software. Compared to existing technologies that cannot dynamically quantify key elements such as the functional completeness, heterogeneous acceleration, and compilation quality of components at each layer of the RISC-V basic software ecosystem, this solution not only covers a wide range of basic software components in the RISC-V ecosystem and introduces a multi-dimensional evaluation model and flexible configuration capabilities, but also effectively overcomes the shortcomings of existing technologies in terms of evaluation content, granularity, and adaptability. It has good versatility, scalability, and implementation value, enabling support for dynamic dimensional and hierarchical expansion, and systematic evaluation of the ecosystem based on quantifiable multi-dimensional indicators. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0019] Figure 1 This is one of the flowcharts illustrating the RISC-V basic software ecosystem maturity assessment method provided by this invention.
[0020] Figure 2This is the second flowchart of the RISC-V basic software ecosystem maturity assessment method provided by this invention.
[0021] Figure 3 This is a flowchart of the longitudinal and transverse weighted time-varying evaluation provided by the present invention.
[0022] Figure 4 This is a hierarchical diagram of the RISC-V basic software ecosystem provided by this invention.
[0023] Figure 5 This is a schematic diagram of the structure of the RISC-V basic software ecological maturity assessment device provided by the present invention.
[0024] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0026] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.
[0027] Figure 1 This is one of the flowcharts illustrating the RISC-V basic software ecosystem maturity assessment method provided by this invention, such as... Figure 1 As shown, the method includes the following: S11. Divide the RISC-V basic software ecosystem into multiple logical layers from bottom to top to obtain the RISC-V basic software hierarchical model.
[0028] In this embodiment of the invention, the RISC-V basic software ecosystem is first modeled as a hierarchical structure containing several layers. Each layer includes several software components, which can be freely added or removed according to the evaluation scenario. RISC-V basic software refers to "low-level / system-level" software packages that must run on the RISC-V instruction set and are crucial to the RISC-V ecosystem.
[0029] Multiple logical layers include the system firmware layer, operating system and basic library layer, compilation toolchain and development toolset layer, language runtime layer, video encoding and decoding and AI computing ecosystem layer, big data processing ecosystem layer, virtualization and containerization layer, and cloud computing / cloud native layer. Each logical layer contains multiple software components that can be added or removed independently.
[0030] S12. Perform hierarchical scoring based on the RISC-V basic software hierarchical model to obtain the scoring results for each level.
[0031] For each software component, scores are assigned based on multiple predefined dimensions, with each dimension having a corresponding weight, forming a weight vector. The component score is the weighted sum of the scores of each dimension and their corresponding weights.
[0032] Furthermore, the component scores of each layer are averaged to obtain the maturity score of that layer.
[0033] S13. The scoring results of each level are vertically aggregated to obtain the overall ecosystem maturity score of the RISC-V basic software.
[0034] Scoring for each level is based on a preset level weight. We perform weighted fusion to calculate the overall ecosystem maturity score.
[0035] S14. Evaluate the ecological maturity of the RISC-V basic software based on the overall ecological maturity score.
[0036] The overall ecosystem maturity score is used to assess the operational status of RISC-V basic software, such as assessing "whether these software have mature RISC-V versions and how well they run on RISC-V".
[0037] Optionally, the overall ecosystem maturity score of the RISC-V basic software can be recalculated at fixed intervals and stored in time series form to generate an ecosystem evolution curve. The ecosystem evolution curve is used to provide a quantitative basis for investment decisions and resource allocation priorities.
[0038] The RISC-V basic software ecosystem maturity assessment method provided by this invention divides the RISC-V basic software ecosystem into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. These multiple logical layers include a system firmware layer, an operating system and basic library layer, a compiler toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple independently addable and deletable software components. Based on the RISC-V basic software hierarchical model, a hierarchical score is performed to obtain a score result for each layer. The score results for each layer are then vertically aggregated to obtain an overall ecosystem maturity score for the RISC-V basic software. The overall ecosystem maturity score is used to assess the ecosystem maturity of the RISC-V basic software. Compared to existing technologies that cannot dynamically quantify key elements such as the functional completeness, heterogeneous acceleration, and compilation quality of components at each layer of the RISC-V basic software ecosystem, this solution not only covers a wide range of basic software components in the RISC-V ecosystem and introduces a multi-dimensional evaluation model and flexible configuration capabilities, but also effectively overcomes the shortcomings of existing technologies in terms of evaluation content, granularity, and adaptability. It has good versatility, scalability, and implementation value, enabling support for dynamic dimensional and hierarchical expansion, and systematic evaluation of the ecosystem based on quantifiable multi-dimensional indicators.
[0039] Figure 2 This is the second flowchart illustrating the RISC-V basic software ecosystem maturity assessment method provided by this invention, as shown below. Figure 2 As shown, the method includes the following: S21. Divide the RISC-V basic software ecosystem into multiple logical layers from bottom to top to obtain the RISC-V basic software hierarchical model.
[0040] like Figure 4 The diagram showing the hierarchical structure of the RISC-V basic software ecosystem models the RISC-V basic software ecosystem as a hierarchical structure containing several layers. Each layer includes several software components, and the number of components in each layer can be expanded or adjusted according to the actual application scenario.
[0041] The hierarchical structure consists of eight layers: system firmware layer, operating system and basic library layer, compilation toolchain and development toolset layer, language runtime layer, video encoding / decoding and AI computing ecosystem layer, big data processing ecosystem layer, virtualization and containerization layer, and cloud computing / cloud native layer.
[0042] The total number of software programs is 49, as follows: The system firmware layer includes two components: UEFI and OpenSBI. The operating system and basic libraries include five components: Linux kernel, OpenSSL, musl libc, OpenSSH, and Glibc. The compilation toolchain and development toolset consist of five components: GCC, LLVM, QEMU, Spike, and GDB. The language runtime consists of two components: OpenJDK and Python; The video encoding / decoding and AI computing ecosystem includes 16 components: PyTorch, MXNet, Caffe, Tensorflow, OpenCV, OpenBLAS, MNN, Ffmpeg, scikit-learn, Apache Spark Mllib, CNTK, Deeplearning4j, Apache Mahout, ONNX, x264, and x265. The big data processing ecosystem includes 10 components: Hadoop, Spark, Flink, MySQL, GaussDB, Redis, MariaDB, Storm, Kafka, and Zookeeper. Virtualization and containerization include four components: KVM, StartoVirt, iSulaD, and Docker. Cloud computing / cloud-native includes 5 components: Grafana, Kubernetes, Helm, Istio, and Prometheus.
[0043] S22. For each software component, score it according to multiple predefined dimensions.
[0044] Each component is scored on k predefined dimensions, including Performance Advancement (PA), Compilation Quality (CQ), Memory Advancement (MP), Functional Integrity (FI), Open Source Development Activity (ODA), and Heterogeneous Computing Support (HCS).
[0045] Right now The meanings of each dimension are as follows: Performance Advancement (PA): Evaluates the degree of computational performance optimization of components on the RISC-V platform. The weight of this dimension is... ; Compilation Quality (CQ): Evaluates the build stability, compilation success rate, and compatibility of components on the RISC-V platform. This dimension has the following weights: ; Memory Advancement (MP): This evaluates the component's memory usage and resource scheduling efficiency on the RISC-V platform. The weight of this dimension is... ; Functional Completeness (FI): This assesses whether the main functional modules and system call support for the RISC-V platform are fully implemented. The weight of this dimension is... ; Open Source Development Activity (ODA): Reflects the health and sustainability of the RISC-V open source ecosystem, based on metrics such as code update frequency, community participation, and number of contributors. This dimension has the following weights: ; Heterogeneous Computing Support (HCS): This dimension assesses the RISC-V underlying software ecosystem's support for hardware acceleration such as GPUs, NPUs, and Vector Extensions (e.g., RVV). The weights for this dimension are as follows: .
[0046] The scoring model supports dynamic expansion and adjustment of the number of dimensions and weights to meet the adaptation needs of different evaluation scenarios.
[0047] S23. Based on the pre-set weights of each dimension, a weighted score for each software component is obtained.
[0048] like Figure 3 The flowchart shown below illustrates the weighted time-varying evaluation process, which assigns weights to each dimension to form a weight vector. The default weights for the six dimensions are (20, 20, 20, 20, 10, 10), and the score range for each dimension is... The weights sum to 100, and the total score for all dimensions is 100. The score for each component is the weighted sum of the k dimensions.
[0049] Specifically, it is assumed by default that all components within the same layer contribute equally to the score of that layer, thereby improving the neutrality and credibility of the evaluation. Each component in each layer is then evaluated... The maturity scores for each dimension are represented by the following dimension weight vector: ,in, Indicates the first The first layer Each component , This indicates that the highest score in this dimension accounts for 20% of the total score across all dimensions. The score for each dimension is expressed as follows: The final score of the component The weighted average of the scores for each dimension and their corresponding weights: ,in, S24. Take the arithmetic mean of the weighted scores of each software component in each level to obtain the score result of each level.
[0050] A horizontal, hierarchical maturity score is obtained by aggregating the components in each layer. At each layer... In the middle, assuming that the layer has If there are 1 component, then the maturity score of that layer is... Arithmetic mean of the scores for all components: S25. Assign each level a level weight that can be dynamically configured according to the evaluation target.
[0051] S26. The scores of each level are weighted and summed to obtain the overall ecosystem maturity score of the RISC-V basic software.
[0052] The overall maturity score uses a weighted aggregation model to demonstrate the vertical dependencies and hierarchical weights within the hierarchical structure of the basic software ecosystem. It demonstrates goal-driven reconfigurability, and the vertical weights can be flexibly set according to user scenarios, industry focus, etc. For example, in data center scenarios, the weights of compilation toolchain and AI computing layer can be increased.
[0053] Each level is assigned a weight. The data is aggregated to form an overall ecological maturity score.
[0054] Specifically, after establishing maturity indicators for each layer through multi-dimensional evaluation, the scores of all layers are weighted and merged to form an overall ecosystem maturity score. Let the RISC-V basic software ecosystem structure have... Layer, layer weight is ,satisfy: The overall maturity score is: S27. The overall ecological maturity score of the RISC-V basic software is recalculated at fixed intervals and stored in time series form to generate an ecological evolution curve.
[0055] The overall ecological maturity score S is recalculated at fixed intervals or triggered by events and stored in time series form to generate ecological evolution curves, providing a quantitative basis for investment decisions and resource allocation priorities.
[0056] Optionally, the operational status of RISC-V foundational software can also be assessed through an overall ecosystem maturity score. For example, assessing "whether these software programs have mature RISC-V versions and how well they run on RISC-V".
[0057] The RISC-V basic software ecosystem maturity assessment method proposed in this invention not only covers a wide range of basic software components in the RISC-V ecosystem, but also introduces a multi-dimensional assessment model and flexible configuration capabilities, effectively overcoming the shortcomings of existing technologies in terms of assessment content, granularity and adaptability, and possessing good universality, scalability and implementation value.
[0058] In practice, data collection and scoring can be achieved by combining CI tools (such as Jenkins), open-source metrics platforms (such as CHAOSS), and self-developed testing platforms. Alternatively, "open-source development activity" can be quantitatively scored based on existing ecosystem survey reports or open-source community activity data. Scoring data can be updated regularly and supports time-series comparisons.
[0059] The following describes the RISC-V basic software ecological maturity assessment device provided by the present invention. The RISC-V basic software ecological maturity assessment device described below and the RISC-V basic software ecological maturity assessment method described above can be referred to in correspondence.
[0060] Figure 5 This is a schematic diagram of the structure of the RISC-V basic software ecological maturity assessment device provided by the present invention, specifically including: Modeling module 501 is used to divide the RISC-V basic software ecosystem into multiple logical layers from bottom to top to obtain a RISC-V basic software hierarchical model. The multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding and decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud native layer. Each logical layer contains multiple software components that can be added or removed independently. The scoring module 502 is used to perform hierarchical scoring based on the RISC-V basic software hierarchical model to obtain the scoring results for each level. The aggregation module 503 is used to vertically aggregate the scoring results of each level to obtain the overall ecosystem maturity score of the RISC-V basic software. Evaluation module 504 is used to evaluate the ecological maturity of the RISC-V basic software based on the overall ecological maturity score.
[0061] In one possible implementation, the modeling module 501 is specifically used to divide the RISC-V basic software ecosystem into multiple logical layers from bottom to top to obtain a RISC-V basic software hierarchical model. The multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple software components that can be added or removed independently.
[0062] In one possible implementation, the scoring module 502 is specifically used to score each software component according to multiple predefined dimensions; to obtain a weighted score for each software component by comprehensively analyzing the scoring results based on the corresponding weights preset for each dimension; and to obtain the scoring result for each level by taking the arithmetic mean of the weighted scores of each software component in each level.
[0063] In one possible implementation, the aggregation module 503 is specifically used to assign a level weight that can be dynamically configured according to the evaluation target to each level; and to sum the scores of each level in a weighted manner to obtain the overall ecosystem maturity score of the RISC-V basic software.
[0064] In one possible implementation, the evaluation module 504 is specifically used to recalculate the overall ecosystem maturity score of the RISC-V basic software at fixed intervals and store it in time series form to generate an ecosystem evolution curve. The ecosystem evolution curve is used to provide a quantitative basis for investment decisions and resource allocation priorities.
[0065] In one possible implementation, the evaluation module 504 is also used to evaluate the operation of the RISC-V base software through the overall ecosystem maturity score.
[0066] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communications bus 640. The processor 610 can call logical instructions in the memory 630 to execute a method for assessing the ecosystem maturity of RISC-V basic software. This method includes: dividing the RISC-V basic software ecosystem into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. These multiple logical layers include a system firmware layer, an operating system and basic library layer, a compiler toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple independently addable and deletable software components. Based on the RISC-V basic software hierarchical model, a hierarchical score is performed to obtain a score result for each layer. The score results for each layer are vertically aggregated to obtain an overall ecosystem maturity score for the RISC-V basic software. The overall ecosystem maturity score is then used to assess the ecosystem maturity of the RISC-V basic software.
[0067] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0068] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the RISC-V basic software ecosystem maturity assessment method provided by the above methods. This method includes: dividing the RISC-V basic software ecosystem into multiple logical layers from bottom to top to obtain a RISC-V basic software hierarchical model, wherein the multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer, each logical layer containing multiple independently addable and deletable software components; performing hierarchical scoring based on the RISC-V basic software hierarchical model to obtain a score result for each level; vertically aggregating the score results of each level to obtain an overall ecosystem maturity score for the RISC-V basic software; and assessing the ecosystem maturity of the RISC-V basic software based on the overall ecosystem maturity score.
[0069] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an ecosystem maturity assessment method for RISC-V basic software provided by the above methods. This method includes: dividing the RISC-V basic software ecosystem into multiple logical layers from bottom to top to obtain a RISC-V basic software hierarchical model, wherein the multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer, each logical layer containing multiple independently addable and deletable software components; performing hierarchical scoring based on the RISC-V basic software hierarchical model to obtain a score result for each layer; vertically aggregating the score results of each layer to obtain an overall ecosystem maturity score for the RISC-V basic software; and assessing the ecosystem maturity of the RISC-V basic software based on the overall ecosystem maturity score.
[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for assessing the ecological maturity of RISC-V basic software, characterized in that, include: The RISC-V basic software ecosystem is divided into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. These multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple software components that can be added or removed independently. Based on the RISC-V basic software hierarchical model, hierarchical scoring is performed to obtain the scoring results for each level; The scoring results of each level are aggregated vertically to obtain the overall ecosystem maturity score of the RISC-V basic software. The overall ecosystem maturity score is used to assess the ecosystem maturity of the RISC-V foundational software.
2. The method according to claim 1, characterized in that, The hierarchical scoring based on the RISC-V basic software hierarchical model yields a score for each level, including: For each software component, scores are assigned based on multiple predefined dimensions; Based on the combined scoring results of the corresponding pre-set weights for each dimension, a weighted score for each software component is obtained; The weighted scores of each software component in each level are averaged to obtain the score result for each level.
3. The method according to claim 2, characterized in that, The predefined dimensions include performance advancement, compilation quality, memory advancement, functional completeness, open-source development activity, and heterogeneous computing support.
4. The method according to claim 2, characterized in that, The process of vertically aggregating the scoring results of each level to obtain the overall ecosystem maturity score of the RISC-V basic software includes: Assign each level a hierarchical weight that can be dynamically configured according to the evaluation target; The scores from each level are weighted and summed to obtain the overall ecosystem maturity score of the RISC-V basic software.
5. The method according to claim 4, characterized in that, The method further includes: The overall ecosystem maturity score of the RISC-V basic software is recalculated at fixed intervals and stored in time series form to generate an ecosystem evolution curve. The ecosystem evolution curve is used to provide a quantitative basis for investment decisions and resource allocation priorities.
6. The method according to claim 4, characterized in that, The assessment of the ecosystem maturity of the RISC-V foundational software based on the overall ecosystem maturity score includes: The overall ecosystem maturity score is used to assess the operational status of the RISC-V basic software.
7. A device for assessing the ecological maturity of RISC-V basic software, characterized in that, include: The modeling module is used to divide the RISC-V basic software ecosystem into multiple logical layers from bottom to top, resulting in a RISC-V basic software hierarchical model. The multiple logical layers include a system firmware layer, an operating system and basic library layer, a compilation toolchain and development toolset layer, a language runtime layer, a video encoding / decoding and AI computing ecosystem layer, a big data processing ecosystem layer, a virtualization and containerization layer, and a cloud computing / cloud-native layer. Each logical layer contains multiple software components that can be added or removed independently. The scoring module is used to perform hierarchical scoring based on the RISC-V basic software hierarchical model to obtain the scoring results for each level. The aggregation module is used to vertically aggregate the scoring results of each level to obtain the overall ecosystem maturity score of the RISC-V basic software. The evaluation module is used to evaluate the ecological maturity of the RISC-V basic software based on the overall ecological maturity score.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the RISC-V basic software ecological maturity assessment method as described in any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the RISC-V basic software ecological maturity assessment method as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the RISC-V basic software ecological maturity assessment method as described in any one of claims 1 to 6.