It is a challenge to realize high strength, toughness, and energy storage, as well as excellent capacitive self-recovery, fatigue-resistant, and self-healing performances simultaneously in a single all-in-one supercapacitor aiming for wearable electronics. Herein, based on the self-crosslinking and molecular template, a supramolecular poly­(vinyl alcohol)/poly (N-hydroxyethyl acrylamide) (PVA/PHEA) hydrogel electrolyte (HGE) decorated by polyaniline (PANI) was prepared by in situ rapid polymerization of high-concentration aniline on the PVA/PHEA gel containing H2SO4. The multiple hydrogen bonds, rapid polymerization, and decoration endowed PANI-decorated PVA/PHEA HGE-based all-in-one flexible supercapacitor with the integrated high performances, which include high specific capacitance, good cycling stability, high strength, excellent toughness, rapid self-recovery, excellent fatigue-resistant, and self-healing capabilities, as well as high capacitance retention during or after the large deformations or after the self-healing. Thus, the current work presents a novel and promising strategy to design the integrated high-performance supercapacitors aiming for wearable electronics.

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Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

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