19results about How to "Improve discovery efficiency" patented technology

The present application belongs to the field of geothermal energy development and utilization, bridge engineering, and deicing and snow melting technology, and proposes a heat exchange pipe maintenance and intelligent operation and maintenance control method for an energy pile active bridge deck deicing and snow melting system, which comprises an energy pile active bridge deck deicing and snow melting federated learning operation and maintenance control model and a heat exchange pipe maintenance device based on electromagnetic induction principle; the energy pile active bridge deck deicing and snow melting federated learning operation and maintenance control model preliminarily judges and makes alarm positioning, and the heat exchange pipe maintenance device based on electromagnetic induction principle carries out fine repair work; the damaged point is repaired by automatic repair agent or the inner layer pipe is unscrewed. The heat exchange pipe maintenance device based on electromagnetic induction principle comprises a double-layer intelligent heat exchange pipe structure, a micro magneto-electric sensor, a cable storage tray, a data automatic acquisition instrument and a control end. The present application realizes fine control management and early warning of the energy pile active bridge deck deicing and snow melting system, accurately positions and repairs the damaged point, globally and accurately controls the snow melting system, and improves the deicing and snow melting efficiency.

The application provides a kind of monitoring area abnormal behavior early warning system, including camera, physiological monitoring intelligent wristband, sensor module, data analysis module and abnormal early warning module;Among them, when the camera collects the behavior data of the prisoner in the monitoring area, it can use local fuzzy method to blur the bathroom area and process the blurred shooting, so as to effectively balance the privacy protection and safety supervision demand;Physiological monitoring intelligent wristband and sensor are used to collect physiological data of prisoners and environmental data in the monitoring area respectively, to provide auxiliary basis for abnormal analysis of behavior data by data analysis module, so that it can timely and accurately identify abnormal events, such as environmental abnormal events, personal injury events, psychological abnormal events and bathroom abnormal events, etc.;Abnormal early warning module can evaluate the risk level of the abnormal event that has occurred, and trigger the corresponding early warning notification, so that the on-duty personnel can timely intervene the abnormal behavior of the prisoner.

This invention discloses a Chlorella heptapeptide TGPLGSR with multi-target lipid-lowering activity, its preparation method, and its applications. The amino acid sequence of this heptapeptide is Thr-Gly-Pro-Leu-Gly-Ser-Arg. The preparation method includes: performing ultraviolet and electroporation combined mutagenesis on wild-type Chlorella proteoglycans to obtain a high-protein-content mutant algal strain; obtaining algal protein through fermentation and synergistic extraction and purification with alcohols; directionally digesting the protein with alkaline protease, followed by enriching the active peptide components through ultrafiltration and gel chromatography; and finally, screening the heptapeptide using liquid chromatography-mass spectrometry (LC-MS), computer-aided prediction, and molecular docking techniques. This heptapeptide exhibits potent inhibitory activity against pancreatic lipase and can dose-dependently reduce the accumulation of triglycerides and cholesterol in 3T3-L1 cells. Its mechanism involves multiple target pathways, including inhibition of MMP-9, activation of AMPK phosphorylation, and downregulation of PNLIP, and it can be used to develop lipid-lowering functional foods or health products.

The application relates to the technical field of data storage and processing, and discloses a stock right penetration method based on bidirectional search, a terminal and a storage medium. The method constructs a stock right graph structure: based on original stock holding data, a directed graph is constructed; a vertex set represents an institution entity, and an edge set represents a stock holding relationship between institutions; the weight of an edge in the stock right graph represents a stock holding proportion between institutions, and a time stamp information is additionally attached to the edge to record the timeliness of the stock holding relationship; graph structure cleaning: deleting an unweighted edge in the stock right graph, detecting and eliminating a circular stock holding; executing bidirectional penetration search: for any given starting point and ending point in the stock right graph, initiating a forward search from the starting point to obtain a forward path, initiating a reverse search from the ending point to obtain a reverse path, and when the forward search and the reverse search meet at a vertex, the reverse path of the forward path and the reverse path are combined, so that a complete stock holding link is obtained. The application can effectively realize stock right penetration between institutions.

The invention provides an epoxy resin formula reverse design system based on iterative optimization and interpretable prediction, and the system comprises a prediction oracle module which is used for receiving at least one candidate polymer formula, and calculating and outputting at least one predicted mechanical performance; the generation engine module is used for receiving a reference formula of epoxy resin and at least one optimization target, generating and outputting at least one candidate polymer formula, and generating an explanation text of the successful candidate polymer formula; the feedback controller module is used for sending a reference formula, an optimization target and an explanatory prompt of a successful candidate polymer formula to the generation engine module, sending the candidate polymer formula to the prediction oracle module, and comparing the predicted mechanical performance with the optimization target. And controlling the generation engine module and the predictive oracle module to execute an iterative closed-loop optimization process. According to the invention, an independent, explainable and improved epoxy resin formula design process is realized, and the discovery efficiency of the epoxy resin formula is accelerated.

The application discloses an anti-glyphosate EPSPS protein and application thereof. The amino acid sequence of the EPSPS protein coded by the gene is shown as SEQ ID NO. 2, the nucleotide sequence optimized for tobacco codon is shown as SEQ ID NO. 3, and the EPSPS protein is fused with a chloroplast signal peptide. The gene is introduced into Nicotiana benthamiana by an agrobacterium-mediated method, and the obtained transgenic plant shows extremely high glyphosate resistance and can tolerate a concentration of up to 36000 mg / L, which is significantly better than that of a traditional CP4-EPSPS transgenic plant. The gene is screened from microorganisms by using artificial intelligence assisted evolution analysis, structure prediction and semantic clustering, and has the characteristics of high-efficiency expression and strong resistance, and can be widely applied to herbicide-resistant breeding of various crops, thereby reducing production cost and environmental risk.

This application discloses a method for designing high thermal conductivity aluminum-silicon alloys based on machine learning models, relating to the field of metallurgical materials technology. The method includes: constructing an original dataset of aluminum-silicon alloys containing alloy composition, process parameters, and thermal conductivity properties; preprocessing and feature filtering the dataset to obtain key features affecting thermal conductivity; training several machine learning models based on these key features and using the coefficient of determination as a preliminary screening indicator; further screening using model performance indicators; constructing a final thermal conductivity prediction model through model fusion technology; finally, predicting the thermal conductivity of the virtual alloy composition, selecting high thermal conductivity alloy formulations based on the prediction results, and conducting experimental verification. This application solves the problems of traditional alloy design relying on trial and error, long development cycles, and insufficient prediction accuracy, achieving efficient and accurate prediction of the thermal conductivity of aluminum-silicon alloys, and providing an effective means for the intelligent design and development of high-performance thermally conductive materials.

The invention discloses an elevator water immersion internet-of-things wireless monitoring system, and belongs to the technical field of elevator internet-of-things monitoring. The elevator water immersion internet-of-things wireless monitoring system comprises an elevator shaft, an elevator runs in the elevator shaft, far infrared cameras are arranged on the outer walls of the periphery of the elevator, and photos on the wall of the elevator shaft are shot in real time; the temperature of a water immersion point is lower than the temperature of the periphery of the water immersion point, the water immersion point on the shaft wall of the elevator shaft can be found in time, and the position of the water immersion point on the shaft wall of the elevator shaft can be sent outwards through the wireless internet of things. Sundries covering the upper portion of the elevator shaft bottom water outlet can be cleaned in time, the discovery efficiency and convenience of water immersion points of the elevator shaft wall are improved, and the water drainage effect of the elevator shaft bottom is improved.

The present application belongs to the field of red imported fire ant prevention and control technology, and particularly relates to a red imported fire ant food bait, a preparation method and application thereof. The present application provides a red imported fire ant food bait, which is composed of energy main material and auxiliary material; the energy main material is composed of the following components with the weight content: insect protein powder 50-70%, corn flour 5-20%, peanut oil 9-11%, fish meal 9-11% and sucrose 9-11%. The present application also simultaneously provides an attractant matched with the above-mentioned red imported fire ant food bait. The present application can increase the discovery efficiency of foraging workers of red imported fire ants on the bait, further increase the recruitment behavior of the workers, attract more workers, and has good comprehensive trapping effect.

The invention relates to a commodity shopping guide method and device, equipment and medium in the technical field of e-commerce, and the method comprises the steps: responding to a shopping guide conversation event between a user and an independent station shop customer service, and obtaining an initial text describing the current purchase intention of the user; when it is detected that the specific degree of the current purchase intention described by the initial text does not reach the standard, determining a preference feature data set matched with the initial text based on an interaction behavior sequence of a user; recalling a plurality of shopping guide recommended commodities matched with the initial text, and performing preference priority sorting on the shopping guide recommended commodities based on the preference feature data set to obtain a commodity recommendation list; and pushing the commodity recommendation list to the user so as to display the shopping guide recommended commodities in the list to the user. According to the invention, the customer service of commodity shopping guide is provided for the user, the shopping experience of the user can be greatly improved, and the achievement of commodity transaction is promoted.

The application relates to a network space knowledge extraction method and device based on rule-enhanced prompt learning, which comprises the following steps: acquiring text data to be extracted in a network space security field and a prompt input; splitting the prompt input into a subject prompt, a relationship prompt and an object prompt; connecting sub-prompts of a conditional function related to an ontology rule by using a logical rule with an and normal form to obtain a task-specific prompt for the text to be extracted; performing network space knowledge extraction on the text data to be extracted by using the task-specific prompt and a pre-trained language model trained to output entities and relationships of a current task in the network space security field; and using the entities and relationships to determine threat intelligence data in the current network space security field. The application realizes efficient extraction of entities and relationships in the network space security field by using a prompt tuning technology, and improves the efficiency of threat intelligence data discovery and acquisition in the network space security field.

The present application relates to the technical field of small molecule peptides, in particular to a yak bone anti-aging peptide TGPPGPSGI targeting MMP-1 and application thereof.The amino acid sequence is shown as SEQ ID NO:1.The anti-aging peptide TGPPGPSGI obtained in the present application can be applied in the preparation of health products, cosmetics and food additives.The present application successfully screens and verifies the yak bone source bioactive peptide TGPPGPSGI with good MMP-1 inhibitory activity, which provides a new natural raw material and theoretical basis for the development of anti-skin aging related products.

The application discloses a kind of software development cooperation method and device based on multi-agent system, it is related to software development technical field.The application is converted into structured semantic representation by demand analysis agent using deep learning and field dictionary joint identification technology, and the semantic gap of artificial analysis is eliminated;Through rule verification agent and component optimization agent collaborative work, based on dependency graph construction, rule engine verification and iterative optimization algorithm, automatically identify and correct the logical conflict between form, process, report, solve the maintenance problem of multi-component collaborative consistency;Through seven agent chain cooperation covers the whole process from demand analysis to deployment preparation, the development task that originally needs artificial serial execution is converted into the automatic flow of inter-agent, realizes the whole process automatic intelligent collaborative development from natural language demand to deployable application model, significantly improves software development efficiency and quality.

The application discloses a multi-data-source automatic acquisition and comparison system, relates to the technical field of data processing and automatic test, and comprises the following steps: a scheduling center generates a task context containing a task identifier and a time range; an intelligent sampler determines a sample set based on a strategy-driven random algorithm and a hash modulo operation; a data extraction agent acquires multi-source heterogeneous data in parallel through a unified interface and encapsulates the multi-source heterogeneous data into a virtual data context; a rule execution engine loads a declarative rule, converts data into standard semantic values, constructs a rule execution topology graph, traverses nodes according to a dependency relationship to execute logical assertions or blockages; and a report generation module outputs a diagnostic report by summarizing checking results and original data snapshots. Through full-link data tracking and topological rule checking, the application realizes accurate automatic comparison of multi-source heterogeneous data consistency in a distributed environment.

This invention discloses a method and system for generating key scenarios for simulation testing of autonomous driving systems. The method comprises: 1) constructing a corresponding abstract scenario based on each influential behavioral pattern and defining the behaviors of the participants; the master vehicle is an autonomous driving vehicle; 2) generating multiple different trajectories for each participant in the abstract scenario according to the defined behavioral constraints, combining the trajectories of each participant to obtain multiple abstract scenario instances and instantiating them as test scenarios; 3) for each test scenario, calculating the minimum expected collision time (METTC) of the master vehicle based on the expected driving route of the master vehicle and the trajectories of each participant; if the METTC is greater than a set threshold, adding new participants and their behavioral trajectories to the specific test scenario to generate a new specific test scenario, making its METTC less than or equal to the set threshold; and using the specific test scenarios with a METTC less than or equal to the set threshold as key scenarios.

The application discloses a deep learning-based Internet of Things device firmware vulnerability automatic mining method, comprising the following steps: S1, constructing an interface keyword set; S2, optimizing the interface keyword weight to generate a dynamic weight interface keyword library; S3, analyzing firmware binary files to locate an interface function set; S4, performing static slicing analysis to generate a slicing path set; S5, performing symbolic execution on the slicing path to generate path constraint conditions and solve effective input data; S6, combining historical vulnerability features to determine the vulnerability type and generate a vulnerability type identifier; S7, generating context prompt words according to vulnerability metadata information, inputting a large language model, and generating a vulnerability exploit code PoC; and S8, performing PoC verification to complete the vulnerability automatic mining. The application realizes the improvement of the firmware vulnerability automatic detection efficiency, enhances the vulnerability exploit generation capability, and is suitable for multiple types of Internet of Things device security detection scenes.

The application relates to the field of servers, in particular to a cabinet server monitoring operation and maintenance system, a cabinet server monitoring operation and maintenance method and a cabinet. The system is connected to a switch operating system, is automatically started when the system is started and starts a DHCP service. The system comprises an asset module which is used for monitoring the port state of switches in a cabinet to determine whether a server node is connected, obtaining server configuration information when the server node is found and sending a node addition message; a control module which is used for performing network configuration on the connected server node after receiving the node addition message; a monitoring module which is used for monitoring the running state of the connected server node to generate a standardized alarm object and sending the standardized alarm object by using the configured network; and an alarm module which is used for receiving the standardized alarm object and performing alarm generation, upgrading, downgrading, merging and clearing operations on the standardized alarm object to generate alarm messages of each server node. The scheme of the application realizes automatic discovery and monitoring operation and maintenance of cabinet server nodes.

An annotation-based controller layer interface real-time monitoring method and device, comprising: weaving monitoring probes into each controller to monitor the calling event information of each interface in the controller; storing the corresponding calling event information into a user-side locally deployed lock-free ring buffer when the interface is called; and in the lock-free ring buffer, aggregating the interface calling parameters of each interface in the current time window according to different monitoring dimensions to obtain multiple aggregation results and uploading the aggregation results to a server. The scheme locally deploys a lock-free ring buffer on the user side to store the interface calling event information, and predefines the time window size and window step to perform sliding window processing on the calling event information, thereby realizing sub-second level real-time index statistics and greatly improving the discovery and early warning efficiency of interface exceptions.

This disclosure provides a method for optimizing the substituent structure in a tetrahydrofuran-based NaV1.8 inhibitor based on reinforcement learning, and the inhibitor itself. The method includes: obtaining a receptor spatial model of the NaV1.8 inhibitor and the chemical groups to be updated in the initial substituent structure; performing topological connections, group deletions, or group substitutions on the chemical groups to obtain the updated substituent structure; under the spatial constraints of the receptor spatial model, obtaining multiple conformations by changing the spatial orientation of the rotatable single bonds in the updated substituent structure, and obtaining target evaluation scores based on the conformational fit of each conformation to the receptor spatial model, the metabolic stability of each conformation, and the hydrophilicity characteristics corresponding to the updated substituent structure; updating the strategy parameters of the generation model based on the target evaluation scores until the target evaluation score is greater than or equal to a preset threshold, and then outputting the target substituent structure. This disclosure enables synergistic optimization of substituent fit, metabolic stability, and hydrophilicity / hydrophobicity balance.