Enterprise Innovation Cloud Assessment and Resource Matching System and Methodology

By acquiring and processing data related to enterprise innovation, and combining sliding window and benchmark curve to detect anomalies, an innovation event chain is generated, which solves the problem of insufficient data integration in existing technologies and achieves accurate dynamic evaluation of enterprise innovation and resource allocation.

CN120765110BActive Publication Date: 2026-01-30XIAN INT UNIV
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Patent Information

Application Number
CN202510925449.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-05
Publication Date
2026-01-30
Estimated Expiration
2045-07-05

AI Technical Summary

Technical Problem

Existing technologies cannot effectively integrate industry lifecycle, enterprise innovation indicators, and external data sources, resulting in insufficient comprehensiveness and dynamism in innovation capability assessment. This makes it difficult to identify anomalies and breakout points, leading to lagging or excessive allocation of innovation resources and an inability to accurately support the improvement of enterprise innovation capabilities.

Method used

By periodically acquiring industry lifecycle, innovation characteristics, and external data sources, weighted average calculations and sliding window processing are performed. Deviation detection is conducted in conjunction with a preset benchmark curve to generate an innovation event chain. Finally, a knowledge reasoning and resource matching model is invoked to output an innovation resource intervention plan.

Benefits of technology

It enables dynamic assessment of enterprise innovation capabilities, accurately identifies anomalies and outbreak points, improves the pertinence and timeliness of resource intervention plans, and avoids the lag and bias in resource allocation.

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Abstract

This invention discloses a cloud-based enterprise innovation capability assessment and resource adaptation system and method, relating to the field of enterprise innovation management technology. The system includes the following steps: periodically acquiring industry lifecycle data, innovation characteristic data, and external data source data; performing a weighted average calculation on the industry lifecycle data, innovation characteristic data, and external data source data to obtain integrated innovation characteristic data; and applying sliding window processing to the integrated innovation characteristic data within the same period to output a trend curve of the integrated innovation characteristic data. This solution effectively eliminates interference from industry cycle fluctuations through dynamic alignment of the baseline curve and joint judgment using continuous windows. Simultaneously, by combining the trend curve to locate the start time of anomalies, it provides a precise time anchor point for subsequent causal analysis, accurately identifying persistent abnormal states in the integrated innovation characteristic data and avoiding misjudgments caused by single fluctuations.
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