197 results about "Analytical strategy" patented technology

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision generation method based on multi-dimensional data verification, which comprises the following steps: receiving an authentication request and completing operation authority granting, obtaining a target data set, and performing multi-dimensional verification on the target data set based on an analysis strategy set, generating a first verification result, performing multi-source consistency verification through an authenticity verification service interface, generating a second verification result, generating a candidate decision scheme set based on data features of the first verification result and the second verification result, and generating a decision file containing an analysis conclusion according to the candidate decision scheme set. And feeding back the generation state of the decision file. According to the method, a scheme generation and feedback mechanism driven by the data verification result is constructed, so that joint verification of compliance and authenticity of the target data set is realized, and the interpretability of the result and the transparency of data processing are guaranteed while the decision-making efficiency is improved.

The invention relates to the technical field of internet risk control management, and discloses an internet risk control optimization method based on multi-dimensional data analysis, which comprises the following steps: acquiring internet user behavior data, transaction data, equipment information and environment data in real time, and carrying out data preprocessing on the data to generate a sample data set; key risk control features are extracted from the sample data set based on a feature importance analysis strategy, and a feature subset is generated; performing parameter optimization on the integrated gradient boosting tree and the deep learning network by using an improved sparrow search algorithm, and obtaining a risk control model after optimization; and inputting the feature subset into a risk control model for analysis, outputting a risk score, and generating a risk control decision in combination with a dynamic threshold adjustment strategy. According to the method, the model iteration period is shortened, the training convergence speed is improved, the overall prediction performance and generalization ability of the risk control model are improved, and more reliable technical guarantee is provided for risk identification.

The invention relates to the technical field of data processing and analysis, and discloses a cost consultation data arrangement and analysis system based on a cloud platform, and the system comprises a data collection module which is used for obtaining and standardizing original cost data; the data clustering module is used for clustering data according to specific dimensions and extracting key feature vectors; the strategy generation module is used for constructing candidate analysis strategies; the strategy optimization module is used for screening an optimization strategy; and the execution engine module processes the data according to the optimization strategy to generate an analysis result. In addition, a data verification module is arranged to verify results, a dynamic adjustment module is arranged to monitor data changes, and a knowledge graph module is arranged to construct an entity relation graph. The system can solve the problems of data source heterogeneity, single analysis strategy and the like in cost data processing, realizes efficient data processing and accurate analysis, ensures that the result is accurate and reliable, and improves the data processing and analysis level of the cost consultation industry.

The invention discloses a method and a device for constructing an 0DAY vulnerability detection model based on an AI large model, relates to the technical field of network security, and solves the problems that different analysis strategies are difficult to adopt aiming at codes of different levels, problem positioning and performance optimization are difficult to carry out in a targeted manner, the advantages of various models are difficult to combine, and the detection efficiency is high. And different analysis parameters are fully analyzed. By adding the adversarial sample into the vulnerability detection data set, the AI large model can learn more diversified vulnerability features in the training process, so that the generalization ability of the model is enhanced, and the model can better adapt to vulnerability detection tasks in different scenes. Through the AST abstract syntax tree replacement technology, the non-system code function in the data set can be quickly positioned and replaced, the dependence on the original code is reduced, and the vulnerability detection efficiency is improved. And different analysis strategies are adopted for different levels of codes, so that more targeted problem positioning is facilitated.

The invention relates to the field of artificial intelligence and data analysis, in particular to a report data automatic analysis report generation system based on an AI agent. Comprising a multi-modal intention understanding and dialogue analysis module; a dynamic domain knowledge graph construction and reasoning module; the self-adaptive narrative generation and visualization module is used for dynamically generating a report structure according to an analysis result and providing an interpretable insight and a personalized report style; and the agent arrangement and self-learning closed-loop module is used for decomposing the user intention into sub-tasks, scheduling specialized micro-agents to cooperatively complete the sub-tasks, and optimizing an analysis strategy and updating a knowledge graph based on user feedback. Through multi-modal interaction and conversational analysis, a user does not need to learn a complex query language, and can naturally explore data like a conversation with human experts, so that the analysis process is more intuitive and more efficient.

The invention discloses a lake water surface area regulation and control scheme generation method and device. According to the method, lake basic data of a region to be regulated and controlled are collected, and a high-precision digital elevation model (DEM) and a lake database are constructed, so that the landform and water body attributes of a research region can be accurately reflected; an initial simulation model is constructed by combining an environmental fluid dynamic model, a water body boundary condition, a DEM model and a lake database, and the coupling influence of a hydrodynamic process and topographic features on lake water level and water surface distribution can be fully considered; after real-time monitoring data is obtained, an initial model is optimized based on parameter prior distribution and a sample sampling strategy, and the precision and the response speed of a simulation model are remarkably improved; the target simulation model is used for simulating various water level scenes to obtain response indexes under the scenes, and a regulation and control scheme is generated based on the multi-target constraint curve and the analysis strategy, so that the regulation and control accuracy of the lake water surface area is improved.

The invention provides a quantitative intelligent stock selection method and system based on multi-factor fusion, and belongs to the technical field of data processing. Determining an association degree evaluation analysis strategy of the association subject portraits in different target users in combination with composition data of the association subject portraits which are subjected to association degree evaluation analysis processing by adopting a preset strategy in different target users; determining an updating processing strategy of the associated theme portraits of the target user based on the coincident data of the associated theme portraits of different target users, the number of the associated theme portraits of the target user and an evaluation analysis strategy of the association degree of the different associated theme portraits, and updating the associated theme portraits of the target user based on the updated associated theme portraits. And in combination with the retrieval data of the target user, quantitative recommendation processing of the stock is carried out based on a multi-factor fusion mode, so that the recommendation accuracy of the stock selection processing result is improved.

The invention discloses a scenarized hierarchical management and control method for an education application in a data space, and the method comprises the steps: obtaining a multi-modal input content in a corresponding preset period after the starting of the current time in response to a starting instruction of the education application in a target data space; calling a corresponding analysis strategy according to the content type of the multi-modal input content; extracting target features from the multi-modal input content based on an analysis strategy; according to an education scene corresponding to the target data space, obtaining a matching result of the target feature and a target database; determining a corresponding data space risk level according to a matching result; and calling and executing an application management and control strategy corresponding to the data space risk level. According to the method, education application hierarchical management and control adaptive to different scenes can be provided, the management and control precision is high, meanwhile, automatic analysis and management and control processes are achieved, and the method is suitable for scenes such as classroom teaching management, after-class and extracurricular training, remote examination and family interactive learning.

The invention discloses a data leakage early warning method, device and equipment based on buried point collection and a storage medium, and relates to the technical field of system exception monitoring, and the method comprises the steps: obtaining exception log information and historical data information sent by a buried point collection service node; performing aggregation counting on the abnormal log information according to preset time granularity to obtain abnormal quantity statistical data of each time granularity; performing transverse and longitudinal analysis on the abnormal quantity statistical data and the historical data information according to a preset transverse and longitudinal analysis strategy to obtain a comparison deviation value; and performing data leakage early warning according to the comparison deviation value. According to the method, time-sharing processing and multi-dimensional analysis are carried out on the abnormal log data of the system, so that real-time early warning and service subdivision monitoring of buried point data leakage are realized, and the reliability and fault positioning efficiency of the system are improved.

The invention discloses a matrix decomposition and reconstruction method for a large model reasoning scene, and the method comprises the following steps: 1, carrying out the error statistics of conventional nodes of a network layer in a large model, and screening weight nodes capable of carrying out low-rank decomposition through a sensitivity analysis strategy; step 2, gradually performing low-rank setting, decomposing the weight matrixes in the weight nodes which can be subjected to low-rank decomposition and are obtained by screening in the step 1, and obtaining and recording respective optimal low-rank values of the decomposable weight matrixes; and step 3, performing network reconstruction on the original model based on the decomposable weight matrix and the corresponding low-rank value. The invention further discloses a system for implementing the method and a corresponding application, and the system and the application have a wide application scene.

The invention discloses a ship scheduling system and method based on shipping dynamic algorithm evaluation, and relates to the technical field of shipping scheduling. Obtaining ship operation data and importing the ship operation data into a ship operation analysis strategy for ship operation analysis; acquiring weather data and importing the weather data into a weather analysis strategy for weather analysis; acquiring port data and importing the port data into a port analysis strategy for port analysis; evaluating a comprehensive shipping abnormal condition based on ship operation analysis, weather analysis and port analysis; judging whether the ship needs to be intercepted when entering the target port based on the comprehensive shipping abnormal condition; according to the method, the influence of ship operation, weather conditions and port conditions on shipping is comprehensively considered, potential shipping abnormity can be found by monitoring the ship navigation state, weather changes and port states, the ship can be dispatched in time, accidents are avoided, and the shipping safety is improved.

The invention discloses a fault information acquisition method and system for transformer substation relay protection equipment. The method comprises the following steps: acquiring a multi-source fault data sequence of relay protection equipment; generating a fusion fault feature sequence through a multi-source data fusion strategy; identifying a key fault period based on a dynamic sudden change threshold, and intercepting target and non-target data subsequences; the morphological difference degree of adjacent target subsequences is calculated, if the morphological difference degree exceeds a threshold value, the adjacent target subsequences are marked as a doubt sequence, and fault feature analysis is carried out by adopting a hierarchical analysis strategy; and further carrying out fault wave and range judgment on adjacent non-target subsequences. Through the multi-source information fusion and dynamic threshold technology, the limitation of a single data source and a fixed threshold is overcome, early faults and hidden defects can be accurately recognized, normal operation and real faults can be effectively distinguished, the accuracy, reliability and early warning capability of fault recognition are remarkably improved, and the method is suitable for popularization and application. The method is suitable for state monitoring and fault diagnosis of intelligent substation relay protection equipment.

The invention belongs to the field of computer vision, and particularly relates to an unsupervised visible light infrared pedestrian re-identification method. According to the method, a ResNet50 neural network is used as a feature extraction backbone network, a dual-module collaborative framework composed of a boundary pseudo-tag correction module and a distribution perception soft alignment module is constructed, the boundary pseudo-tag correction module is used as a basic component, and a core sample and a boundary sample are explicitly distinguished and clustered through a contour coefficient analysis strategy, so that the feature extraction accuracy is improved. And a dynamic evaluation mechanism in a Jaccard distance space is adopted to process samples with different confidence coefficients, so that the problem of disordered feature space structure caused by wrong distribution of boundary samples is effectively relieved.

The invention relates to the technical field of vegetation remote sensing observation, in particular to a chlorophyll fluorescence observation system based on laser radar, which comprises a target evaluation module used for responding to an observation evaluation condition to determine an observation analysis strategy of each observation target; the observation analysis module is used for determining the dominant direction of the mirror surface of the observation target and determining an inversion execution strategy of the observation target in response to the inversion execution condition; the dominant inversion module is used for determining a pre-estimation execution wave band set and determining observation pre-estimation data based on the polarization reflection parameters of the pre-estimation execution wave bands; the multi-parameter inversion module is used for determining an inversion weight coefficient of each observation wave band and determining whether to adjust the inversion weight index of each observation wave band in response to a weight analysis condition; the auxiliary analysis module is used for responding to the auxiliary analysis condition to determine the auxiliary analysis mode of the observation target, and the reliability of the fluorescence observation result of the observation target is improved.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent traffic jam real-time optimization method based on artificial intelligence. The method comprises the following steps: firstly, acquiring system operation state parameters and dynamic regulation and control parameters, defining action environment elements, and formulating core optimization parameters in combination with application scene features and existing bottleneck information; scheduling model training data and algorithm design data, determining a traffic flow regulation and control topology, and evaluating a collaborative adaptation effect to determine a parameter priority sequence; then querying a related algorithm to simulate and construct an optimization prototype, analyzing strategy regulation and control characteristics, calculating an efficiency prediction equivalent value to evaluate performance stability, determining regulation and control node requirements and operation condition constraints, and analyzing adaptation compatibility; and an optimal optimization algorithm is screened in combination with factors in multiple aspects, and an optimization implementation scheme is generated. The method can improve the accuracy and real-time performance of traffic congestion optimization, is suitable for different traffic scenes, and effectively alleviates congestion.

The invention innovatively provides a laser cladding processing device control system which comprises the following steps: a target extraction step: collecting to-be-processed workpiece image data acquired by a visual sensor to obtain a total data set, and performing similarity analysis on the total data set in a rough classification strategy to obtain a target detection data set; an image analysis step: training the target detection data set to obtain a plurality of to-be-processed workpiece target detection models, obtaining a dimension reduction plane through point projection according to the to-be-processed workpiece target detection models, and constructing the dimension reduction plane into a plurality of grid matrixes; a defect extraction step: according to the grid matrix, a change grid is obtained through change ratio analysis in a fine analysis strategy, and the change grid reflects a change point of the workpiece to be machined; and a defect judgment step: obtaining the change grid and judging whether the workpiece to be machined has defects or not through a state analysis strategy.

An audiovisual system uses a spatial position detection module to process audio signals and determine the location and orientation of a speaking participant. Based on this data, the system dynamically controls sensors to optimize audio and video capture. Behavioral and contextual information may also be used to train intelligence models for improved system performance. Further, sensor arrays may be utilized to identify gaze vectors of participants to select a camera sensor of the sensor array. Meeting content collected with the sensor array may be organized into meeting metrics in accordance with an analytics strategy before training an intelligence module with the meeting metrics. Meeting content may also be configured into digital tiles in accordance with a tile strategy. At least one of the digital tiles may be altered in response to a meeting condition detected by the sensor array.

The invention provides a data recommendation method and device based on artificial intelligence, computer equipment and a storage medium. The method comprises the following steps: acquiring key business data of an initial crowd; analyzing the key business data based on an index analysis strategy to generate analysis reasons and optimization suggestions; performing data integration on the key business data, the analysis reasons, the optimization suggestions and the user behavior logs to obtain target data; based on the target data, performing crowd optimization processing on the initial crowd by using a preset large model, a diffusion model and a hybrid model to determine an optimized target crowd; performing effect prediction on the target crowd, and judging whether effect prediction data accords with expectation or not; if yes, binding the target crowd with a marketing strategy, and verifying the target crowd based on an AB experiment; and if the experiment result of the AB experiment is a forward result, performing product recommendation processing on the target crowd based on a total delivery strategy. According to the method, the marketing recommendation efficiency and accuracy are effectively improved.

The invention belongs to the technical field of computer software, is applied to the field of medical health and the field of financial science and technology, and discloses a useless dependency analysis method, device and equipment and a storage medium. Analyzing a service side of the to-be-analyzed project; for the service side, matching a useless dependency analysis strategy corresponding to the service side; when the service side is a client, constructing an abstract syntax tree corresponding to the client according to a program file of the client; and according to the abstract syntax tree, performing analysis to obtain useless dependency of the to-be-analyzed project on the client. The technical problem that useless dependencies of items of various scenes cannot be identified in the prior art is solved.

The invention belongs to the crossing field of artificial intelligence and an electric power system, and discloses an electric power customer service oriented large model prompt thinking chain construction method and related device.Firstly, a target topic type is determined according to an electric power customer service problem, and then comprehensive representation of all fused thinking chains of the target topic type is obtained; and selecting a fusion thinking chain corresponding to the comprehensive representation with the highest similarity based on the similarity, and taking the fusion thinking chain as a large model prompt thinking chain of the electric power customer service problem for the electric power customer service. According to the method, the selection strategy based on the topic type is adopted, the complexity of the selection strategy is effectively reduced, and the selection efficiency and the cue word quality are improved. Meanwhile, the comprehensive representation of each fusion thinking chain of the target topic type adopts a multi-thinking chain example fusion analysis strategy based on a graph attention network, and the reasoning process information of multiple thinking chain examples is integrated, so that the reasoning stability and accuracy of the finally selected fusion thinking chain are improved; the problem of instability caused by reasoning based on a single thinking chain example is effectively solved.

The invention relates to the technical field of ecological environment monitoring, in particular to an environment assessment method and device based on carbon footprint and regional ecological data. The method comprises the following steps: acquiring carbon footprint data and regional ecological data of a target region; performing dimension alignment on the carbon footprint data and the regional ecological data to obtain an associated data set associated with a preset grid of the target region; and based on the associated data set, through at least one of a time sequence similarity analysis strategy, a spatial distribution overlapping degree analysis strategy and a causal relationship influence analysis strategy, determining an environment assessment result of the target area. Compared with the prior art, a manual evaluation mode is replaced, and the analysis efficiency is improved. And on the other hand, the carbon footprint data and the regional ecological data are aligned according to the preset grids of the region, so that the problem of non-uniform comparison dimensions and indexes is solved. And meanwhile, three analysis strategies are provided for reflecting the internal relation of the two types of data, so that the data analysis precision is improved.

The invention relates to the technical field of water conservancy facility construction monitoring, in particular to a risk early warning method and system based on construction site multi-modal data driving. Structure stress monitoring data and water flow field change data of the water conservancy facility in the construction process are imported into a stress vibration danger analysis strategy for stress vibration danger analysis; the operation state risk of the construction equipment, the stress vibration dangerous state of the water conservancy facility under the change of the water flow field and the construction position data of the construction equipment are imported into a construction dangerous position analysis strategy for construction dangerous position analysis in the next construction stage; based on the dangerous position identification of the water conservancy facility construction site in the next construction stage, a dangerous position reinforcing scheme is obtained. Therefore, safe and stable operation of equipment is ensured; the accident risk caused by structural instability and water flow impact is reduced, and the integrity of the facility structure is guaranteed.

According to the automatic early warning method and system for remote sensing monitoring of the ground disaster, when the important analysis strategy of the target ground disaster monitoring attribute is determined, the geological disaster monitoring information of the target ground disaster monitoring attribute in the first geological disaster risk stage can be converted into the important analysis strategy of the target ground disaster monitoring attribute; the method comprises the following steps: acquiring geological disaster monitoring information, performing matching operation in important analysis strategies triggered by a first geological disaster type partition, determining a result of the important analysis strategy of a target geological disaster monitoring attribute, and comparing matching measurement values of acquired geological disaster monitoring information and type constraint conditions with key early warning contents; and the important analysis strategy of the target ground disaster monitoring attribute is determined, and the technical effect of improving the precision and reliability of geological disaster risk early warning can also be achieved.

The invention relates to the field of data analysis, in particular to a wearable device monitoring method based on data analysis, which comprises the following steps: periodically acquiring various target monitoring data of a target monitoring person, determining key analysis data, and obtaining a key monitoring video corresponding to each key analysis data; determining an analysis strategy of the corresponding key analysis data according to the abnormal coincidence coefficient; if the abnormal coincidence coefficient is greater than a preset abnormal coincidence coefficient, detecting a data change period of each type of regional data; if the abnormal coincidence coefficient is smaller than or equal to a preset abnormal coincidence coefficient, performing associated personnel search for the key monitoring video, and determining a video information tracing mode according to an interaction duration ratio of target monitoring personnel in the key monitoring video and the number of associated personnel; under the condition that information analysis is completed, early warning information of all key analysis data is sent to the user, and the effectiveness of the early warning information is improved.

The invention provides an information processing method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: identifying a target knowledge field to which to-be-processed request information belongs; based on the request information, querying from a first database to obtain a plurality of analysis strategies of the target knowledge field; determining the matching degree between each analysis strategy and the request information; determining at least one target analysis strategy based on the matching degree between each analysis strategy and the request information and the plurality of analysis strategies; and generating reply information of the request information based on the request information and the target analysis strategy. Through the method and the device, the efficiency and the accuracy of replying the request information can be improved.

The invention provides an auxiliary identification method and system for a flap transplantation state. The method comprises the following steps: acquiring physical detection information of a flap area and monitoring results of various physiological monitoring types detected by equipment such as a laser speckle instrument; identifying a first state probability of the flap area in each flap state and monitoring change information of each physiological monitoring type; identifying a state change degree corresponding to each piece of monitoring change information and a second state probability of each flap state through a monitoring analysis strategy of each physiological monitoring type; based on the first state probability of the flap region in each flap state and the second state probability of the flap region in each flap state, identifying the target state probability of the flap region in each flap state, and identifying the target state probability of the flap region in each flap state as the second state probability of the flap region in each flap state; and taking the information as flap transplantation state auxiliary identification information of the flap area. By adopting the scheme, the recognition accuracy of a worker on the flap transplantation state can be improved.

The embodiment of the invention provides a cue word screening method and device, equipment and a medium. The method comprises the following steps: providing a group of sample images and a group of cue words for a trained multi-modal model, wherein the group of cue words is used for indicating an analysis strategy of the trained multi-modal model for analyzing the group of sample images; obtaining a group of prediction labels generated by the multi-modal model based on the group of cue words, wherein the group of prediction labels indicate a prediction analysis result of the group of sample images; determining first evaluation information for the set of cues based on a comparison of the set of prediction tags and the set of reference tags for the set of sample images; and at least based on the first evaluation information, determining the use of a group of cue words in the training process of another multi-modal model or in the reasoning process of another trained multi-modal model. Therefore, prompt words with higher quality can be selected to be input into other multi-modal models with higher quality and efficiency for model reasoning or training.

The invention relates to the technical field of data processing, and discloses a deployable pipeline generation method, device and equipment and a storage medium, the method comprises the following steps: using a directed acyclic graph modeling tool to create a plurality of workflow nodes, and configuring the plurality of workflow nodes to obtain a workflow, the plurality of workflow nodes comprise operation types, data types and input and output parameters, applying a model evaluation analysis strategy and a model construction algorithm to the training data set to obtain a plurality of model results, selecting a deployment model by comparing the plurality of model results, obtaining a dependency relationship of the deployment model from the workflow, and establishing a deployment model; and generating a deployable pipeline according to the dependency relationship. The workflow is created by using the directed acyclic graph modeling tool, the deployment model is selected by comparing the multiple model results, and the deployable pipeline is generated according to the dependency relationship, so that the accuracy and the high efficiency of the deployable pipeline generation process are ensured, and the complexity and the error rate of the deployable pipeline generation process are reduced.

The embodiment of the invention provides a method for predicting factory production electricity consumption, a control device and a storage medium, and belongs to the technical field of factory energy management. The method comprises the following steps: acquiring historical power consumption data of each power consumption unit; determining the correlation between the historical electricity consumption and the electricity consumption influence factors; based on the determined correlation, determining a power utilization quantitative analysis strategy of each power utilization unit through the historical power utilization amount and the value of the power utilization influence factor; and predicting the production power consumption of each power consumption unit and the production power consumption of the factory through the power consumption quantitative analysis strategy of each power consumption unit. According to the embodiment of the invention, the corresponding power consumption quantitative analysis strategy is determined through the correlation between the power consumption and the power consumption influence factors, so that the factory production power consumption in days, months, years and the like can be accurately predicted. And the power utilization energy cost of the production line can be effectively managed and controlled, energy key points and waste points can be identified, and energy conservation and consumption reduction actions can be carried out in a targeted manner.

The invention provides a contract content extraction and integration method and system and a storage medium, and the method comprises the steps: obtaining a contract file uploaded by a user through an OA system, recognizing the context features of the contract file, and dynamically triggering an analysis strategy adaptive to the contract file based on the context features; performing content extraction on the contract file by using an analysis strategy to generate a preliminary analysis result containing core content and risk reminding; obtaining the complexity of the contract file and the confidence coefficient of the initial analysis result, and determining an auditing path of the initial analysis result based on the complexity and the confidence coefficient; and auditing the preliminary analysis result by using the auditing path to generate auditing feedback information, and correcting the preliminary analysis result in real time based on the auditing feedback information to generate an optimized analysis result. According to the method and the device, intelligent and dynamic analysis and auditing guidance of the contract file can be realized in an OA system environment, the efficiency and the accuracy of contract auditing are effectively improved, and adaptive adjustment of an analysis strategy and an auditing path is realized.