9 results about "Technology assessment" patented technology

The invention discloses a multi-field review method and system driven by dynamic rules, and belongs to the technical field of intelligent review. Comprising the steps of dynamic rule engine configuration, expert management configuration, automatic grouping matching, expert online review and meeting voting. And full-dimension coverage of cross-project type, cross-organization department and cross-business scene is realized. For talent selection, technical evaluation of scientific research projects and title evaluation, dynamic construction and flexible adjustment of a differentiated evaluation process can be completed based on the same platform, so that the evaluation efficiency and scientificity are effectively improved, and a standardized and intelligent technical solution is provided for multi-subject and multi-scene collaborative evaluation business; the problems of flow stiffness, weak functions, extensive management, lack of transparency and security vulnerability of an existing review system are solved.

A device may generate information to cause the display of a graphical user interface (GUI) element reflecting how a selected enterprise network is performing on a technology assessment metric relative to other enterprise networks, where the generating includes: determining, based on data values reflecting individual performance of the plurality enterprise networks on the technology assessment metric, a part of a normal distribution curve to be shown, a first point to be shown on the part of the normal distribution curve indicating a percentile for the data value reflecting the individual performance of the selected enterprise network, and a second point along the part of the normal distribution curve reflecting a goal for the individual performance of the selected enterprise network. A device may send, to a client device, the information to cause the GUI element to be presented in a dashboard on the graphical user interface of the client device.

The invention belongs to the technical field of artificial intelligence task decomposition, and particularly relates to an achievement transformation large model task decomposition method and system based on LoRA fine tuning. According to the method, a LoRA (Low-RankAdaptation) technology is utilized to carry out efficient parameter fine tuning on a large achievement transformation model, and the method comprises four modules of achievement transformation task data set construction, task step decomposition, relation prediction and joint optimization training. A task data set is constructed by collecting multi-step tasks such as technology evaluation and market analysis and marking dependency relationships among steps. Fine tuning is performed on the result transformation large model based on the LoRA technology, and step decomposition loss and relation prediction loss are calculated respectively to realize task step decomposition and inter-step relation prediction; and through joint optimization of the two types of loss functions, the model can generate a reasonable task execution sequence. Through LoRA fine tuning, the method can quickly adapt to a new task under a small amount of data, reduces training time and resource consumption, improves the accuracy and flexibility of task decomposition, and is suitable for various achievement transformation scenes.

The present application relates to the technical field of hotspot technology evaluation, and particularly relates to a hotspot technology evaluation method, device and equipment based on generative artificial intelligence, which responds to a hotspot technology keyword, queries technical literature corresponding to the hotspot technology keyword and metric data corresponding to the technical literature through a target search engine; analyzes an importance centrality index of the hotspot technology keyword compared to the technical literature through a preset importance evaluation model; calculates hotness data of the technical literature according to the importance centrality index and the metric data; generates a hotspot technology hotness data change curve based on the hotness data; performs hotspot technology prediction evaluation based on the hotspot technology hotness data change curve, and generates a hotspot technology evaluation report, which analyzes a hotspot technology keyword input by a user through a generative artificial intelligence model, and provides efficient hotspot technology analysis results.

The application discloses a kind of dynamic rule driven multi-field review method and system, belong to intelligent review technical field;Including: dynamic rule engine configuration, expert management configuration, automatic grouping matching, expert online review and general meeting voting;Realize cross-project type, cross organization department, cross business scene full-dimension coverage.No matter is talent selection, scientific research project technical evaluation, or title review, can be based on the same platform to complete the dynamic construction and flexible adjustment of differentiating review process, effectively improve the review efficiency and scientificity, provide standardized, intelligent technical solutions for multi-agent, multi-scene collaborative review business;Solve the problems of the current review system in process rigidification, weak function, extensive management, transparency loss and security vulnerability.

The invention relates to the technical field of technology transfer path recommendation, in particular to a technology transfer path recommendation system based on big data analysis. Comprising a heterogeneous data acquisition module, a technology transfer data lake module, a space-time association mining module, a dynamic knowledge graph generation module, a path strategy network module, a personalized path recommendation module, a visual sand table simulation module and an executable scheme generation module. By capturing and fusing multi-dimensional and PB-level massive heterogeneous data of patents, papers, policies, investment and financing, industrial requirements and the like in real time, an exclusive technology transfer data lake is constructed, a comprehensive and timely information basis is provided for subsequent analysis, the limitation that in traditional technology transfer, the information source is single, lagged and one-sided is overcome, and the information transfer efficiency is improved. And the technology evaluation and the market matching are more accurate.

The invention discloses a green technology premium calculation and industrial suitability evaluation method under a dual-carbon target, and relates to the technical field of green technology evaluation and dual-carbon management. The method comprises the following steps: firstly, constructing a multi-dimensional dynamic basic database covering technical attributes, market transactions, dual-carbon policies and industrial characteristics; taking a correction market comparison method as a core, introducing a dual-carbon policy correction coefficient and a carbon cost internalization coefficient to establish a premium calculation model, and combining with an actual carbon emission reduction deviation to realize dynamic correction; then constructing a hierarchical evaluation index system of carbon adaptation, economic adaptation and technical adaptation; and establishing a premium-suitability coupling evaluation mechanism through an entropy weight-TOPSIS method and the coupling coordination model, and outputting an adaptation level, a premium reasonable interval and an optimization suggestion. According to the method, the defects that dual-carbon elements are ignored and calculation and adaptation evaluation are disjointed in a traditional method are overcome, the premium calculation accuracy and the adaptation evaluation scientificity are improved, and accurate decision support can be provided for enterprise technology type selection and government policy making.

This invention relates to the field of data processing systems or methods for the transformation of scientific and technological achievements. Specifically, it provides a service system for the transformation of scientific and technological achievements and its data processing method. Through multi-source data fusion, dynamic risk assessment and intelligent matching, it achieves full-chain coverage, dynamic optimization and visualization of the transformation of scientific and technological achievements. From data collection to commercialization, it seamlessly connects each link and realizes full-process management from technology evaluation to commercialization. Through TRL model, knowledge graph and blockchain technology, it improves the efficiency and success rate of transformation. It is suitable for technology transfer scenarios in universities, research institutions and enterprises.

The application relates to the field of artificial intelligence technology evaluation, and discloses an intelligent evaluation method and system based on multi-modal and reinforcement learning, which comprises the following steps: generating a feature tensor through improved data cleaning and cross-modal alignment processing; performing weight optimization based on a pre-training model and Bayesian inference; generating an optimized model through homomorphic encryption and secure aggregation; generating a weight matrix through reinforcement learning strategy iteration and stability verification; realizing blockchain notarization by combining hash encryption and a smart contract; and finally generating an evaluation report based on fuzzy comprehensive evaluation and visual processing. The application effectively improves the accuracy and robustness of multi-modal data evaluation, guarantees data security and traceability, and enhances the explainability and practicality of the result through dynamic weight optimization and visual output.