[目的/意义] 新形势下推进网络信息资源著作权创新生态系统建设并评估其可靠性,有利于激活著作权活动的创新活力,引导网络版权产业的可持续发展。[方法/过程] 首先,构建网络信息资源著作权创新生态统演化模型,并提出一种基于Markov过程的系统可靠性评估方法并给出相应的解析方程式。然后,利用贝叶斯网络模型计算运行状态转移率的基础数据,求解系统的瞬态可用度,同时通过研究稳态可用度探索影响网络信息资源版权业态发展的关键因素。最后,以网络视频资源著作权创新生态系统为例验证模型的适用性。[结果/结论] 网络视频资源著作权创新生态系统运行过程中,在254h达到演化的稳定值0.758 0。其中,创新投入风险对网络信息资源版权业态发展潜在危害最为严重,创新环境风险、著作权侵权风险次之,技术风险、融合创新风险、网络信息资源风险再次之,中止调整率和定期调整率对恢复系统的稳定性具有明显的修正作用。基于此仿真结果,提出促进网络视频版权产业发展的几点策略。
[Purpose/significance] In the new situation, advancing the construction of an innovation ecosystem for network information resource copyright and evaluating its reliability is conducive to promoting the creative vitality of copyright activities and guiding the sustainable development of network copyright industry. [Method/process] Firstly, this paper constructed the evolution model of network information resource copyright innovation ecosystem, proposed a system reliability evaluation method based on Markov process, and gave the corresponding analytical equations. After that, the Bayesian network model was used to calculate the basic data of the transition rate of each operating state, and the transient availability of the system was solved. Meanwhile, studying the steady availability was to explore the key factors of affecting the development of the copyright industry of network information resources. Finally, the applicability of the model was verified by an example which was the network video resource copyright innovation ecosystem. [Result/conclusion] During the operational process of the network video resource copyright innovation ecosystem, it reaches a stable value of 0.7580 at 254 hours. Among them, the innovation investment risk has the most serious potential harm to the development of network information resource copyright industry, followed by innovation environment risk and copyright infringement risk, followed by technology risk, integration innovation risk and network information resource risk, and the suspension adjustment rate and the periodic adjustment rate have obvious corrective effects on the stability of restoring system. Based on the simulation result, several strategies to promote the development of network video copyright industry are put forward.
[1] 全国人民代表大会常务委员会关于修改《中华人民共和国著作权法》的决定[EB/OL].[2021-02-02]. http://www.xinhuanet.com/2020-11/11/c_1126727505.htm.
[2] 新华网.习近平在中央政治局第二十五次集体学习时强调全面加强知识产权保护工作激发创新活力推动构建新发展格局[EB/OL].[2021-02-02]. http://www.xinhuanet.com/politics/leaders/2020-12/01/c_1126808128.htm.
[3] 宋慧献. 版权生态与版权创新初论[J]. 知识产权, 2006, 16(6):27-32.
[4] AGUILAR-PAREDES C, PEREZ-MONTORO M, SANCHEZ-GOMEZ L. The ecosystem for accessing tv series and films in Spain:an outline of the situation following the intellectual property act 2015[J]. Profesional de la informacion, 2016, 25(6):870-881.
[5] 吉宇宽. 区块链技术构建数字著作权生态系统对图书馆的实践价值[J]. 图书情报工作, 2020, 64(19):24-30.
[6] 娄创. 我国数字音乐产业版权生态链的构建与对策研究[D]. 重庆:重庆理工大学, 2020.
[7] 阎韶宁. 视频网站付费生态构建研究[D]. 济南:山东大学, 2017.
[8] 娄策群, 曾丽, 庞靓. 网络信息生态链演进过程研究[J]. 情报理论与实践, 2015, 13(3):10-13.
[9] 顾桐, 许国良, 李万林,等. 基于集成LightGBM和贝叶斯优化策略的房价智能评估模型[J]. 计算机应用, 2020, 40(9):2762-2767.
[10] 郭茜, 蒲云, 郑斌. 基于故障贝叶斯网的冷链物流系统可靠性分析[J]. 控制与决策, 2015, 30(5):911-916.
[11] 李俊霞, 温小霓. 科技创新关键阶段投资与风险管理研究[J]. 中国软科学, 2018(9):175-183.
[12] 疏学明. 基于Bayes网络的建筑火灾风险评估模型[J]. 清华大学学报(自然科学版), 2020, 60(4):321-327.
[13] 李婵, 张文德, 蓝以信. 网络信息资源著作权侵权风险传导研究[J]. 情报学报, 2014, 33(10):1046-1056.
[14] 张露江, 张利, 杨要伟,等. 基于改进贝叶斯网络的风机齿轮箱自动诊断策略研究[J]. 电力系统保护与控制, 2019, 47(19):145-151.
[15] 郄朝辉, 李威, 崔晓丹,等. 基于分层马尔可夫的可修复稳定控制系统可靠性分析[J]. 中国电力, 2020, 53(3):101-109.
[16] 吴文青, 唐应辉, 张元元. 两水平修理策略的k/n(G)表决系统可靠性分析[J]. 系统工程学报, 2018, 33(6):854-864.
[17] 戴希兵. 安徽省知识产权产业生态系统研究[D]. 合肥:中国科学技术大学, 2018.
[18] 潘苏楠, 李北伟. 基于知识管理的地方智库创新生态系统构建及运行机制研究[J]. 情报资料工作, 2020, 41(2):106-112.
[19] 杨波, 陆嘉琦. 面向企业技术创新风险的竞争情报预警动力学建模与仿真[J]. 情报科学, 2017, 35(4):61-67.
[20] 周大铭. 企业技术创新生态系统运行风险评价研究[J]. 科技管理研究, 2014, 34(8):48-51.
[21] 李婵,陶丽,张文德. 视频类知识付费内容著作权侵权风险评价指标体系构建[J/OL]. 情报理论与实践:1-15[2021-02-10].http://kns.cnki.net/kcms/detail/11.1762.G3.20200917.1300.006.html.
[22] 王怀祖, 黄光辉. 产学研合作创新的知识产权风险研究[J]. 科技管理研究, 2015, 35(3):130-158.
[23] 李小群. 企业技术创新生态系统风险评价研究[D]. 重庆:重庆师范大学, 2011.
[24] ABHARI K, DAVIDSON E J, XIAO B. A risk worth taking? The effects of risk and prior experience on co-innovation participation[J]. Internet research, 2018, 28(3):804-828.
[25] 周园, 袁颖慧. 基于SD模型的合作创新全过程知识产权风险控制研究[J]. 科技管理研究, 2012, 32(20):175-178.
[26] 刘超, 刘健, 朱元坤, 等. 基于Markov过程的水下采油树系统可靠性分析[J]. 西安石油大学学报(自然科学版), 2019, 34(5):91-96,115.
[27] YAGHMAIE P, VANHAVERBEKE W. Identifying and describing constituents of innovation ecosystems:a systematic review of the literature[J]. EuroMed journal of business, 2019, 15(3):283-314.
[28] 曲朝阳, 杨琴, 杨杰明,等. 基于贝叶斯网络的智能变电站风险关联模型[J]. 电力系统自动化, 2016, 40(2):95-99.
[29] ZHANG H, INNAL F, DUFOUR F. Piecewise deterministic markov processes based approach applied to an offshore oil production system[J]. Reliability engineering and system safety, 2014(126):126-134