487results about How to "Increased sensitivity" patented technology

The invention relates to a bioelectrochemical sensor used for detecting hydrogen peroxide and a manufacturing method of the bioelectrochemical sensor. The novel bioelectrochemical sensor is a sensor with a three-electrode system, wherein the counter electrode is a platinum electrode, the reference electrode is saturated calomel electrode and the working electrode is gold electrode. The sensor is based on orientation combination of a polypeptide horse radish peroxidase with characteristic sequence, the promotion effect of the polypeptide horse radish peroxidase on the activity of the enzyme and the characteristic of high sensitivity of electrochemical detection methods. The sensitivity of hydrogen peroxide sensors is effectively improved and satisfactory results are obtained.

The invention relates to the technical field of image recognition, in particular to a red tide algae recognition method based on a hyperspectral image, and the method comprises the following steps: calculating a gradient value of each pixel in a spatial dimension and a spectral dimension according to an original hyperspectral data cube, setting a diffusion coefficient for diffusion, and obtaining a three-dimensional fidelity hyperspectral data cube. According to the method, the gradient change trends of the spatial dimension and the spectral dimension are extracted at the same time pixel by pixel, and the regional differentiation diffusion operation is matched, so that the expression ability of the alga plaque boundary and the consistency of the internal spectral characteristics are enhanced, and the fidelity of the hyperspectral data in fine-grained region identification is improved; upper convex hull calculation of a spectral reflectivity curve and construction of a continuous spectrum background baseline are introduced, and characterization of pigment absorption morphological characteristics is enhanced; and in combination with the characteristic absorption wave bands of chlorophyll and phycobiliary, the synergistic weighted response to the distribution of the algae pigment is realized, and the sensitivity and selectivity of pigment recognition are improved.

The invention discloses a transformer area distribution transformer reverse heavy overload prediction method and system, and the method comprises the steps: obtaining data which comprises the historical electrical quantity characteristics of a photovoltaic user, the numerical weather forecast characteristics of the geographic position of a transformer area, and a prior graph structure formed according to the topological graph of the transformer area; preprocessing the data to construct a data set; the method comprises the steps of establishing a space-time diagram prediction model, inputting historical electrical quantity characteristics and numerical weather forecast characteristics of N photovoltaic users and a prior diagram structure into the model to obtain a future K-step area distribution transformer load prediction result, performing heavy overload judgment based on a prediction value, and finally obtaining a future K-step area distribution transformer reverse heavy overload prediction result. According to the method, the topological structure of the power distribution network, the electrical characteristic data and the numerical weather forecast characteristics are comprehensively utilized, high-precision prediction of the multi-time-step power of the photovoltaic grid-connected system is achieved through the space-time diagram neural network model, and the method is suitable for application scenes such as operation optimization and early warning management of the power distribution network with distributed photovoltaic access.

This invention relates to the field of medical testing, specifically to a set of biomarkers, reagent kits, and their applications for diagnosing hypertension in children. This invention involves collecting tongue / intestinal samples from obese children with hypertension, obese children, and healthy individuals, performing metagenomic sequencing, and statistically analyzing the sequencing data using bioinformatics to identify disease-related tongue / intestinal flora. By integrating tongue / intestinal flora with disease information, a combination of flora biomarkers is obtained. A binary classification prediction model constructed using this combination can maximally detect hypertension in obese children.

The invention relates to a multi-factor dynamic correction investment benefit intelligent evaluation system, in particular to the field of investment evaluation, and aims to capture macroscopic event and project risk index impact in real time and dynamically generate factor impact coefficients through an event-driven factor correction module, so that the response speed and sensitivity of a model to project sudden change are remarkably improved, and the evaluation efficiency is improved. The self-adaptive weight optimization module utilizes a deep reinforcement learning framework to continuously optimize factor weight distribution based on revenue after risk adjustment in a rolling time window so as to realize self-adaptive adjustment of an investment strategy and optimization of a revenue-risk ratio, and the abnormal conduction early warning module constructs a dynamic directed graph and combines a graph neural network technology to realize early warning of the abnormal conduction. According to the method, abnormal factor conduction paths can be perspectively recognized, systematic engineering risks can be effectively warned, a strategy execution module fuses weight optimization results and risk warning signals, rebalance is executed under the condition that investment cost and risk control constraints are strictly met, and finally a project configuration scheme giving consideration to revenue, robustness and real-time performance is generated.

The invention discloses a large-range landslide mass displacement early warning method based on GNSS and radar data fusion, and the method comprises the steps: obtaining GNSS monitoring sequence data in a landslide region based on the position information of a reference station and a GNSS monitoring station in a GNSS monitoring system, and further obtaining the displacement related information of the landslide region; collecting data through a satellite InSAR, and generating deformation field data of a landslide area by comparing radar images at different time points; performing space-time alignment on the obtained data, and performing two types of data fusion S by adopting an adaptive Kalman filtering method after alignment; decomposing time series data of the fused landslide mass displacement by using wavelet transform, and extracting multi-scale features of a time domain and a frequency domain; the proposed features are combined with historical data of landslide mass displacement, a landslide mass displacement trend prediction model using an LSTM method added with a memory decline factor is input, and future trend prediction of landslide displacement is carried out; setting a multi-level threshold value, judging the change trend of the landslide mass displacement according to the displacement rate, the acceleration and the prediction result, and generating a multi-level early warning signal.

The invention discloses a method for predicting the residual fatigue life of a titanium alloy component based on frequency mixing nonlinear ultrasound. The method comprises the following steps: firstly, building a detection system; the detection system is controlled to generate low-frequency f1 and high-frequency f2 sinusoidal signals to carry out frequency mixing excitation on the titanium alloy component, and ultrasonic response signals are collected; performing frequency domain analysis on the ultrasonic response signal, extracting a characteristic amplitude and calculating a nonlinear modulation parameter beta m; then, a Nazarov-Sutin equation and an Elber equation are combined, and a theoretical prediction model of the nonlinear modulation parameter beta m changing along with the fatigue life percentage f is constructed; finally, actual measurement parameters are substituted into the model to reversely derive the consumed fatigue cycle number N, and the residual fatigue life Nr is predicted by combining the critical failure total cycle number Nt of the component. According to the method, the quantitative mapping relation between the nonlinear acoustic parameters and fatigue damage accumulation is revealed by establishing the physical model, and accurate quantitative prediction of the residual fatigue life of the titanium alloy component is achieved.

The present application belongs to the technical field of data processing, and discloses a gait visual feedback anti-falling safety warning method for a rehabilitation treadmill; including, converting clinical characteristic data of a patient into a network structure gating instruction set; collecting multi-modal original data in a patient's movement process in real time; inputting the multi-modal original data into a mechanism constraint engine to extract biomechanical mechanism characteristic data, neural mechanism characteristic data and cognitive mechanism characteristic data; based on the network structure gating instruction set, dynamically reconstructing internal node connection paths and data flow weights of a preset basic fusion network to form an individualized dynamic fusion model; outputting three-dimensional risk attribution data; according to the proportion distribution of each dimension in the three-dimensional risk attribution data, driving a visual feedback interface of the rehabilitation treadmill to perform differentiated anti-falling warning display of the corresponding dimension; and realizing gait visual feedback anti-falling safety warning for the rehabilitation treadmill.

The application relates to the technical field of yaw slip ring state monitoring, and specifically discloses a multi-dimensional state operation monitoring method and system based on a yaw slip ring, which comprises the following steps: obtaining an original operation data set of the yaw slip ring, wherein the original operation data set contains operation parameters in the electrical, mechanical, attitude and environmental dimensions which are collected via sensors and subjected to redundancy checking; constructing a noise suppression matrix based on the physical mechanism of the yaw slip ring, filtering isolated fluctuations and cross-modal abnormal values in the original operation data set by using the noise suppression matrix, and inputting the noise suppression matrix into a convolutional neural network model to obtain a fusion feature vector. By introducing the noise suppression matrix constructed based on the physical mechanism and the channel interaction mechanism guided by the physical topology, the technical problem that a lightweight model is difficult to extract cross-modal strong coupling features at an edge end is effectively solved, and the sensitivity and accuracy for early composite faults are significantly improved without increasing the computing power burden.

This invention relates to an IMU-assisted online GNSS anomaly detection and reporting method and apparatus. The method involves acquiring GNSS observation data and IMU measurement data and aligning them in time; generating a predicted motion state of the receiver at the current observation epoch based on the IMU measurement data; calculating the predicted GNSS observation value of at least one satellite at the current observation epoch based on the predicted motion state and satellite ephemeris information; constructing a residual sequence based on the predicted GNSS observation value and the corresponding GNSS observation value in the GNSS observation data, performing sliding time window statistics, and determining anomalies based on the statistical results; when an anomaly is determined, generating a time index and a spatial index corresponding to the anomaly, forming a spatial-level interference event, and reporting it. This invention effectively reduces the false alarm rate and missed alarm rate in scenarios involving obstruction, multipath interference, and other interference, exhibits higher sensitivity to observation anomalies caused by moving interference, effectively lowers the implementation threshold at the receiving end, and possesses high engineering application value.

The invention discloses a nonmetal composite hydrogen conveying hose with a multi-form hydrogen blocking layer, and belongs to the technical field of hydrogen conveying hoses, the nonmetal composite hydrogen conveying hose comprises an external pipe, the bottom end of the external pipe is connected with a butt-joint pipe, the bottom end of the butt-joint pipe is connected with a composite hose, the bottom end of the butt-joint pipe is connected with a first sleeve in a penetrating mode, and the top end of the butt-joint pipe is connected with a second sleeve in a penetrating mode; the first sleeve is sleeved on the outer wall of the composite hose, the second sleeve is sleeved on the outer wall of the external pipe, a cavity is arranged in the butt-joint pipe, and a sealing measuring assembly is slidably connected in the cavity of the butt-joint pipe. A complete hydrogen conveying structure is integrally formed by a composite hose, a butt joint pipe and an external pipe, the butt joint pipe is fixedly connected with the composite hose and the external pipe in a sleeving mode through a first sleeve and a second sleeve, an inner cavity provides an installation and sliding space for a test sealing assembly, and the composite hose is of a multi-layer composite structure of a hydrogen conveying pipe, a first hydrogen blocking layer and a second hydrogen blocking layer. And a multi-form hydrogen blocking system with double hydrogen blocking layers and a hydrogen transporting base layer is formed.

This invention discloses a knowledge- and data-driven time series forecasting method and system, belonging to the field of industrial time series forecasting technology. To address the technical problems of existing technologies when processing strongly non-stationary data, such as key signal attention dilution, systematic underestimation of peak amplitude, incomplete capture of multi-scale dynamic patterns, and difficulty in embedding physical domain knowledge, this invention constructs standardized multi-dimensional time series data, performs heterogeneous feature grouping and parallel encoding to obtain multiple sets of feature representations, then extracts and integrates features from multiple sources to obtain intermediate state features, and finally performs multi-branch prediction and adaptive weighted fusion based on the intermediate state features to obtain the final time series forecast result. This invention can achieve active focusing on abrupt events in industrial sequences and accurate characterization of extreme value amplitudes, significantly improving prediction accuracy, physical consistency, and robustness to extreme conditions.

This invention discloses a distribution network line selection method based on a principal component sample matrix of faulted lines, comprising: collecting transient information of faulted lines under different fault conditions; performing principal component analysis on the transient information to construct a principal component sample matrix; calculating the zero-sequence voltage change gradient sum based on the collected zero-sequence voltage; determining whether to activate the line selection device based on the zero-sequence voltage change gradient sum, and activating the line selection device when the gradient sum is greater than a preset threshold; extracting the transient information after each line fault, performing correlation analysis between each line and the principal component sample matrix to form a line selection correlation matrix; calculating a comprehensive line selection matrix based on the line selection correlation matrix; determining a faulted line discrimination matrix based on the comprehensive line selection matrix; and determining the faulted line based on the faulted line discrimination matrix. This invention's distribution network line selection method based on a principal component sample matrix of faulted lines changes the activation criterion of the line selection device, utilizes rich fault transient information to select faulted lines, and improves the reliability of the line selection results.

The present application belongs to the technical field of medicine, and particularly relates to a traditional Chinese medicine composition and application thereof in preparation of an oral care product. The traditional Chinese medicine composition is prepared from 12-18 parts of Qingdai, 7-12 parts of raw gypsum, 7-12 parts of Huangqi, 9-15 parts of Gushugu, 3-7 parts of Honghuajiao, 3-7 parts of Baizhi, 6-10 parts of Fangfeng, 1-5 parts of Bajie, 6-10 parts of Huanylencao, 1-5 parts of Wuji, and 4-8 parts of Baiji as raw medicinal materials. The components of the composition are reasonably matched, and can realize multiple effects of analgesia, antibiosis, anti-inflammation, repair, and tooth fixation and health. The present application is suitable for all kinds of toothache patients, has a wide application range, a safe use mode, and a wide market application prospect.

The present research develops a kind of solid beverage based on multi-target synergistic hypoglycemic compound herbal and cereal protein peptides and its preparation process according to the multi-factor pathogenic characteristics of type Ⅱ diabetes. The solid beverage is prepared by screening homologous herbs and compound cereal powders such as yam, mulberry leaf, gordon euryale seed, lily, coix seed, hawthorn, black pepper, low GI cereals such as wheat, black rice and oat powder, low molecular weight (<1000 Da) wheat oligopeptides prepared by enzymatic hydrolysis and compound fruit and vegetable powder (broccoli, cabbage, spinach, cranberry, blueberry and pine mushroom) treated by freeze-drying process. The solid beverage of compound cereal protein peptides has hypoglycemic effect, which is verified by zebra fish model, and the principle is as follows: the morphology and basic cell structure of zebra fish pancreas are similar to those of mammals, the appetite regulation and insulin regulation function of zebra fish are similar to those of human beings, and the development and function of other organs and systems of zebra fish related to glucose homeostasis (including brain, liver, fat cell volume and skeletal muscle) are similar to those of mammals.

The application discloses a method for judging the extension of the oil reservoir swept range of a microbial polymer system, and the steps are as follows: (1) taking the oil well samples in the oil reservoir before and after the microbial polymer flooding to perform the whole hydrocarbon gas chromatographic analysis of crude oil, and determining the small molecular weight light hydrocarbon marker component reflecting the change rule of the crude oil composition; (2) comparing the chromatographic peak area of the small molecular weight light hydrocarbon marker component with the chromatographic peak area of the high molecular weight carbon number, and obtaining the parameter for characterizing the effective action of the microbial polymer; (3) comparing and analyzing the crude oil samples in the oil well before and after the parameter for characterizing the effective action of the microbial polymer, and determining the swept degree of the oil reservoir by the microbial polymer. The application discloses the application of the above method in the microbial oil production for judging the swept volume of the microbial polymer in the oil reservoir.

The invention discloses a face key point detection network training method, a face key point detection method and a medium, and belongs to the technical field of face key point detection, and the method comprises the steps: obtaining a face image data set; in the face image data set, each sample is a face image marked with key point coordinates; for each sample in the face image data set, key points in the sample are divided into boundary endpoints and boundary internal points, and coordinates of the key points are converted to a heat map coordinate system; under the heat map coordinate system, generating a true value heat map for each boundary endpoint by adopting isotropic Gaussian distribution, generating a true value heat map for each boundary internal point by adopting anisotropic Gaussian distribution, and forming a training data set by the face image and the true value heat map of each key point in the face image; and training a face key point detection network used for predicting a heat map of each key point in the face image by using the training data set to obtain a trained face key point detection network. According to the invention, the detection precision of the face key points can be effectively improved.

This invention proposes a chaotic speech encryption method based on a time-frequency dual-track system, belonging to the field of speech encryption technology. The method includes: preprocessing the original speech data to obtain a mono-channel filtered speech signal; extracting parameter features and calculating normalized speech energy as input to the chaotic system; generating a chaotic sequence and performing multi-step enhancement to obtain an enhanced key stream; generating a scrambling index after Fourier transforming the speech signal; scrambling and reshaping the real and imaginary components in the frequency domain and performing XOR diffusion; recombining the encrypted frequency domain matrix and inversely transforming it back to the time domain; and obtaining the final ciphertext through index permutation and fusion of speech feature parameters. This invention uses a frequency-domain dual-track scrambling-diffusion mechanism combined with an attractorless six-dimensional conservative hyperchaotic system to generate an enhanced key stream, thus avoiding speech distortion in time-domain encryption, triggering a global avalanche effect, and simultaneously improving the algorithm's resistance to various attacks, ensuring both the security of speech encryption and the fidelity of speech restoration.

The invention discloses a composite probiotic preparation capable of improving sensitivity of low-dose nicotine and reducing usage amount of nicotine, and belongs to the crossing field of microbial technology and biological medicine. The compound probiotic preparation only improves the sensitivity of mice to low-concentration nicotine, does not improve the preference of the mice to the existing preference concentration, prevents aggravated smoking addiction, enables individuals to reach the satisfaction of smoking at ordinary times under the condition of low dosage of nicotine, and has the advantages of being simple in preparation process, low in cost and the like. The formulations may be used in combination with products in nicotine replacement therapy or alone. The compound probiotic preparation disclosed by the invention can be used for preparing functional foods, health-care products and medicines capable of improving the sensitivity of the low-concentration nicotine, and has a very wide application prospect.

The application provides an intelligent substation anomaly detection method based on meta-learning technology, first, a multi-working condition task library is constructed for meta-training. Secondly, a double meta-knowledge learning framework is designed to learn task-level model initialization parameters suitable for rapid adaptation. Subsequently, online reconstruction error driven anomaly detection is deployed, and the adapted model is used to calculate data reconstruction error in real time, and combined with the adaptive threshold to accurately distinguish and alarm grading. Finally, the system supports a dynamic updating mechanism, which continuously collects verified samples and periodically triggers meta-updating. The application significantly reduces the data dependence and deployment cost in new scenarios, improves the model's generalization ability to multiple working conditions and sensitivity to rare anomalies through meta-knowledge sharing, and ensures the long-term effectiveness of the detection system through online learning mechanism, providing an efficient, adaptive and evolutionary security protection solution for intelligent substations.

The present application relates to the technical field of photoresist, in particular to a photoresist composition, a preparation method and application thereof.A photoresist composition comprises the following components by mass percentage: phenolic resin 5-40%, photosensitive compound 1-20%, adhesion enhancer 0.01-2%, surface leveling agent 0.01-1% and organic solvent 55-90%; the photosensitive compound is selected from at least two of a first G-line photosensitive compound, a second G-line photosensitive compound and an I-line photosensitive compound; the adhesion enhancer is selected from melamine resin and / or silane coupling agent; and the surface leveling agent is selected from silicon compound and / or fluorocarbon compound.The photoresist composition has high development adhesion, good pattern angle and high sensitivity.

The embodiment of the application provides a combination marker, a kit and application thereof for detecting lung cancer, wherein the combination marker comprises proteins: CDON, CLEC4A, DGKZ, FLT3LG, IL6, KRT19, MLN, MVK, PSIP1, REG4, SIT1 and TGFA; the combination marker has high sensitivity and specificity and high accuracy; and the combination marker is suitable for the technical field of lung cancer markers.

The utility model discloses a flour conveying adjusting device which comprises a machine frame, a control unit, an auger conveying device and a material pipe, a sealing plate is arranged at the connecting position of the auger conveying device and the material pipe, and the sealing plate is connected with the end of the corresponding auger conveying device through an elastic reset assembly. A material level sensor is arranged in the end, corresponding to the material pipe, of the auger conveying device, and a displacement sensor is arranged on the outer side wall of the auger conveying device. And the accurate recognition capability of the blockage and the sensitivity of monitoring and early warning are improved.

The invention discloses a photoelectric sensor system conversion efficiency calibration system and method based on a reverberation chamber, and belongs to the technical field of photoelectric sensor conversion efficiency calibration, the system comprises an electric wave reverberation chamber, a transmitting antenna, a reference antenna and a photoelectric field sensor system; the photoelectric field sensor system is composed of a photoelectric sensor, a laser source, a photoelectric detector, a polarization maintaining optical fiber and a single-mode optical fiber. In the calibration process, the reference antenna and the photoelectric sensor system are used as receiving ends respectively, receiving power at different stirring positions in the reverberation chamber is sampled to obtain two groups of transfer functions, and the total efficiency of the photoelectric sensor system is determined by comparing the two groups of transfer functions under the condition that the radiation efficiency of the reference antenna is known. The conversion efficiency of the photoelectric sensor system is obtained by combining the relation among the power, the field intensity and the total efficiency in the free space; and the effective averaging of the incident direction and the polarization state is realized by using the uniform and isotropic electromagnetic field characteristics of the reverberation chamber.

The invention provides a fan blade crack detection method, which comprises the following steps of: for a fan blade image acquired by an unmanned aerial vehicle, performing deblurring processing on the image through a pre-processing algorithm designed by the invention, and overcoming the problem of motion blurring when the unmanned aerial vehicle acquires the fan blade image; and carrying out crack detection on the acquired image by adopting a crack detection neural network model improved based on YOLOv8. According to the crack detection model, self texture interference of the blade can be avoided, the crack detection precision is improved, crack characteristics of different scales are captured through a Stem module of a multi-branch structure, a CAM module is added into a C2f-1 module, crack attention is integrated, and crack related characteristics are enhanced; a residual connection mode of a residual block is designed in a C2f-2 module to avoid the problem of deep network gradient disappearance, gradient flow and effective transmission of crack characteristics are guaranteed, response of crack-related channels is enhanced through channel re-calibration, and irrelevant channels such as blade textures are inhibited.

Through extensive and deep research, a new path Parkin / Park / Mul1 / Mfn2 for regulating and controlling the drug resistance of a tumor chemotherapy temozolomide (TMZ) drug by the TRPC5 is found, and the new path has a close relationship with the generation of the drug resistance of the tumor chemotherapy temozolomide (TMZ) drug. Compared with primary cells U87 and U251, the expression level of the TRPC5 in temozolomide tolerant cells U87R and U251R is remarkably improved, and a mitochondrial autophagy pathway is activated. By reducing the expression of the TRPC5 gene, the sensitivity of a cell strain to a temozolomide drug can be enhanced, and the proliferation capacity of tumor cells can be inhibited. According to the invention, not only is new data provided for a malignant glioma drug resistance mechanism, but also a new target spot is provided for the design of anti-tumor drug resistance drugs.

The invention relates to the technical field of ultrasonic image analysis, and discloses an image detection method and system for prolonging the second birth process of double-fetal vaginal delivery and SPPH risk based on ultrasonic Speckle-tracing, and the method comprises the steps: obtaining an ultrasonic two-dimensional gray-scale image sequence; marking a conjoint analysis area between the cervical wall and the front and rear walls of the vagina; constructing a joint area displacement field; constructing a joint region strain trajectory; extracting parturition process characteristic indexes; and executing risk assessment through a fuzzy rule. The technical problem that in the prior art, risk judgment depends on clinical experience, and especially under the condition that continuous stretching occurs on the cervix uteri and vagina combined wall in the second birth process of double-fetal vaginal delivery, early recognition of the evolution process of the tissue from reversible stretching to irreversible instability is difficult to achieve is solved. Due to the fact that joint area displacement field modeling and logarithmic nonlinear strain analysis are introduced, quantitative characterization and continuous risk description of the mechanical behavior of the tissue in the labor process are achieved.

The present application relates to a kind of two-dimensional atomic level flat type platinum disulfide material, preparation method and use.Platinum disulfide material has two-dimensional plane spreading structure, and the surface of its plane spreading has atomic level flatness;The thickness of the two-dimensional atomic level flat type platinum disulfide material is 2-31nm.Two-dimensional atomic level flat type platinum disulfide material thickness is 2-31nm, with the spreading structure of atomic level flatness on two-dimensional plane, which will reduce the thickness of device, improve the continuity of film layer, reduce the scattering of electronic, optoelectronic of thin film material, reduce thin film resistance, enhance the photoelectric response efficiency and sensitivity of device, while further reducing device channel size.

The application discloses a self-supervised device running abnormal sound detection method, characterized in that single-channel running normal sound signals of multiple different types of devices in a running state are collected, and frequency spectrum graphs and time spectrum graphs are obtained by conversion; the frequency spectrum graphs and the time spectrum graphs are spliced to obtain double-channel time-frequency feature graphs, the double-channel time-frequency feature graphs after feature enhancement are input into a dynamic feature extraction module, so that final one-dimensional audio feature vectors of different types of devices are extracted, and a probability density function of the final one-dimensional audio feature vectors is fitted; single-channel running sound signals of a device to be detected in a running state are collected, and the above steps are repeated to obtain the probability density of one-dimensional audio feature vectors of the device to be detected; finally, the probability density is input into a classifier to complete the discrimination of normal and abnormal sounds; the method can learn effective features of audio of different types of devices through time-frequency fusion and dynamic feature extraction, and the accuracy and reliability of abnormal sound detection are improved.

The application discloses a cross-cultural communication non-verbal media emotion recognition interaction system, which comprises a non-verbal media data acquisition module, a non-verbal emotion feature extraction module, a non-verbal emotion recognition model construction module, a cross-cultural feature weight parameter self-adaptive acquisition module and an emotion intelligent interaction module. The application relates to the technical field of data processing, in particular to a cross-cultural communication non-verbal media emotion recognition interaction system, which proposes a hierarchical collaborative design of a general non-verbal emotion recognition model and a cross-cultural feature weight parameter self-adaptive acquisition module, can significantly improve the accuracy of non-verbal emotion recognition in a cross-cultural scene, improves the recognition accuracy of the model by designing a three-dimensional structured model input tensor, a customized emotion feature reinforcement filter and an emotion fluctuation extraction pooling operation, and improves the accuracy of cross-cultural non-verbal emotion recognition by adopting a stage-by-stage position updating strategy in a predation stage and combining a dynamic dimension exchange mechanism to improve and optimize the algorithm.