Research On Unnormalized Knowledge Processing Technic Of Machinery Manufacture Information Resource

With the development of socioeconomic and advancement of science and technology, manufacturing system becomes more and more intelligentized and flexible, and these characters are based on speediness and availability of information and knowledge acquirement and disposal. Manufacturing knowledge has characters of diversity, complexity, empirical and unnormalized, and has abundance connotation. These characters make it difficult to induce, coordinate, mining and apply, as well as reduce the possibility of re-use and integration, and make it harder for enterprises' information interactive. How to diminish and eliminate the disadvantages of unnormalized information, and achieve the goal of acquire, dispose, spread and apply information rapidly and efficiently, is a key problem should be researched.From the aspect of structure, unnormalized information refers to half-structuring and non-structuring information, and from the aspect of conformation, unnormalized information refers to uncertain, un-precise, imperfection, un-correspond and unstable information. On the one hand, unormalized information is an important part of enterprise information resource and valuable fortune, on the other hand, it makes information environment much more complicated, increases information's use-cost, and decreases information's use-value. The goal of disposal of unnormalized information is to diminish the disadvantages mentioned above, and achieve the target of pass the right information to the right staff at the right time. The main contents of this research are as follows.(1) Demands toward manufacturing information and knowledge resource and deficiencies of existing information resource system are analyzed, and put forward the general goal of the research of information resource and knowledge base oriented intelligent manufacturing, and bring forward the system's skeleton structure and function model. The classify method of multi-model knowledge form within web environment is constructed, and put forward manufacturing knowledge's elementary express model to resolve the problem of unified description.(2) The uncertainty within manufacturing system is analyzed and the source of uncertainty is inducted and expatiated. Defined and analyzed unnormalized information and its source and represent, and based on the work mentioned above, explained the relation and distinguish between unnormalize and uncertainty. Sum up characters of unnormalize manufacturing information. Based on the explain of information granularity, set forth the necessity of information granularity computing, and bring forward the concept of unnormalized information's degree of criterion, and based on theory of rough sets, put forward describe method of relativity for unnormalized manufacturing information.(3) Aimed at peculiarities of unnormalized manufacturing information, constructed describe mechanism of manufacturing knowledge based on knowledge elementary cells. Take design and process elementary cells knowledge, construct corresponding manufacturing elementary cells knowledge system and bring forward knowledge cells' basic format. Based on these work, put forward meta-data expression method for knowledge cells and explain how to transfer and disposal it within enterprises' information environment.(4) The connection and difference of intelligent retrieve and knowledge discovery are discussed, and put forward the retrieve model of unnormalized manufacturing knowledge under web environment as well as its function model, workflow and key technology. Bring forward a intelligent retrieve method include semantics match and semantics reasoning, through the degree of similarity and correlation to achieve intelligent match of retrieve demands and objects, and to get the aim of improve retrieve method's recall ratio and precision ratio.(5) An architecture of individuation manufacturing knowledge initiative recommend service is put forward, and through Web content mining, usage mining and structure mining, combine with initiative and passivity learning method to capture users' interesting and preference toward manufacturing knowledge. Put forward corresponding algorithm such as users' information acquire, Web pages comparability judge and similar Web pages' collection.(6) Applying uncertain language multiple attribute decision making method for process evaluation, and discussed methods to get multi process plan and the multi attribute system of process evaluation. Based on these work, applying uncertain expansion order weighting average operator for process evaluation, and using uncertain language evaluation method for process aided decision-making to make the best of technologists' work experience. (7) Take manual nesting plan as part of genetic algorithm's initial population so as to mix human experience with the algorithm, and take the transformation interval of finity excellence individual as new initial evolution interval and run standard genetic algorithm again, so the evolvement and accelerate inheritance go along at the same time and get the best result step by step. The improvements mentioned above not only has the effect of accelerate the algorithm, but also make use of workers' experience and avoid the problem that the algorithm be divorced from any human intervention, at the same time, the improved genetic algorithm has better computing performance and lend itself to get the balance of better optimize results and higher optimize efficiency.(8) Adopted uniformed local data exchange and plug-in technology to develop knowledge base software prototype system, and the system mainly including manufacturing information and knowledge base, knowledge mining, issue and application models. Among them, the knowledge base including function modules such as dimensional tolerance and form and position tolerance intelligence query and choose module, metal-cutting computing and query module, and the knowledge application model including function modules such as process plan evaluation module based on uncertain language multi-attributes decision making method and cutting stock module based on improved genetic algorithm.