13results about How to "Improve risk assessment efficiency" patented technology

The invention belongs to the field of artificial intelligence and financial science and technology, and relates to a risk assessment method based on user description, which comprises the following steps: extracting a user image sequence and user voice data from video data of a target user; inputting the user image sequence and the user voice data into the trained risk prediction model, and sequentially performing element segmentation, multi-modal feature extraction and prediction classification to obtain corresponding multi-dimensional risk tags; and performing comprehensive risk assessment based on the multi-dimensional risk tag to obtain a risk result of the target user. The invention further provides a risk assessment device and equipment based on user description and a storage medium. In addition, the invention also relates to a block chain technology, and the video data of the target user can be stored in a block chain. The risk assessment efficiency and accuracy can be improved.

The invention relates to a rainfall mass occurrence landslide risk assessment method and device and computer equipment. The method comprises the following steps: acquiring historical rainfall group occurrence landslide data of a historical heavy rainfall event, and constructing landslide sample data; based on the landslide attribute parameters and the rainfall mass-texting landslide data distribution information of the landslide sample data, analyzing to obtain landslide development characteristics; according to landslide geographic features and landslide geometric features, analyzing a landslide generation background rule by adopting landslide sample data and a plurality of candidate landslide influence indexes, and establishing a landslide risk evaluation index system; and performing correlation analysis on the plurality of candidate landslide influence indexes to determine a target landslide influence index, and combining the target landslide influence index and the landslide risk evaluation index system to obtain a landslide risk evaluation model constructed based on an automatic machine learning framework. By adopting the method, the risk assessment efficiency of the rainfall group landslide can be improved, and the risk assessment precision of the rainfall group landslide is effectively improved.

The invention belongs to the field of artificial intelligence and the field of digital medical treatment, and relates to an artificial intelligence-based risk assessment device, computer equipment and a storage medium, and the device comprises the steps: obtaining target voice data of a target user through the device; performing audio conversion on the target voice data to obtain a spectrogram; converting the spectrogram to obtain an embedded vector; screening out a target risk assessment model from all pre-trained risk assessment models; and evaluating the embedded vector based on the target risk evaluation model to generate a risk evaluation result of the target user. The method can be applied to risk assessment scenes in the field of digital medical treatment. In addition, the risk assessment result can be stored in the block chain. According to the method and the device, risk assessment is performed on the voice data based on the target risk assessment model, so that the risk assessment efficiency of judging whether the user has the risk of suffering from the Alzheimer's disease or not is effectively improved, and the accuracy of the risk assessment result is improved.

The invention provides a bidding file risk early warning method based on machine learning, and relates to the technical field of risk management, and the method comprises the steps: obtaining real-time bidding files, carrying out the file classification, obtaining a first classification file set, processing each bidding file in the first classification file set, and obtaining a second classification file set; determining a risk keyword and a corresponding risk rule based on the bidding type of the bidding file to obtain a first risk type; screening a matched machine learning algorithm based on the first risk type to obtain a first learning algorithm, and performing risk assessment on the bidding document; and analyzing the first risk type in combination with the risk assessment result to obtain a risk early warning scheme of the corresponding bidding file, thereby performing risk early warning. By classifying the bidding files, the risk type of each bidding type is determined, and the corresponding machine learning algorithm is matched to carry out risk assessment, so that the risk assessment efficiency can be improved, and risk early warning can be carried out more quickly and accurately.

The invention discloses a drug production quality risk management and control method, device and system and a storage medium, and belongs to the technical field of drug production risk control. The drug production quality risk management and control method comprises the following steps: monitoring a production environment area in real time and obtaining monitoring data of a monitoring point; determining risk points according to the monitoring data; determining corresponding questionnaire generation prompt information based on the risk points; obtaining an investigation test result corresponding to the investigation test; and obtaining a risk management and control result corresponding to the production environment area according to the survey test result and the monitoring data. Through real-time monitoring, risk point accurate identification, personalized investigation prompting, comprehensive analysis and risk management and control result generation, effective management and control of quality risks in the drug production process are realized, the production efficiency and the product quality are improved, and the safety and reliability of drugs are ensured.

The invention discloses an information evaluation method and device, electronic equipment and a storage medium, and relates to the field of financial credit evaluation, and the specific implementation scheme is as follows: obtaining financial business data of a user, and determining feature data of the financial business data; determining enhanced feature data based on the integrated learning model and the feature data; processing the enhanced feature data according to an attention model to obtain attention feature data; and determining risk assessment information of the user according to the financial risk assessment model and the attention feature data. In the embodiment of the invention, the generation of the feature data is enhanced, so that the sensitivity of the financial risk assessment model to data fluctuation is reduced, and the accuracy of risk identification is enhanced; by introducing the attention model to focus on the key information in the enhanced feature data, the attention feature data is extracted, and the extracted attention feature data is input into the financial risk assessment model, so that the risk assessment precision and the assessment efficiency are improved.

The invention discloses a code risk determination method and device, equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: storing modification information corresponding to a first modification code in a code set, wherein the first modification code comprises a code modified for solving a code defect; obtaining a second modification code corresponding to the code set, wherein the second modification code comprises a code modified by updating the version of the code set; and determining a risk value of the second modified code according to the modification information, wherein the risk value is used for reflecting the possibility that the second modified code has code defects. The first modified code comprises the code modified for solving the code defect, so that the risk value of the second modified code is determined by referring to the modification information corresponding to the first modified code, the risk of the second modified code can be assessed by referring to the code defect appearing in the past, and the process does not need to depend on artificial experience analysis; therefore, the efficiency of carrying out risk assessment on the modified code can be improved.

The invention discloses a risk assessment method and device for a power system and electronic equipment. The method comprises the steps that electric power parameters of an electric power system are acquired, a first risk report is determined according to the electric power parameters, the electric power parameters comprise at least one of current, voltage and temperature of the electric power system during operation, and the first risk report represents a safety analysis result of the electric power parameters; a source code and a running program of industrial control software in the power system are obtained, a second risk report is determined according to the source code and the running program, the source code is a programming language used when the industrial control software is written, the running program is an execution form after the source code is compiled, and the second risk report represents a security analysis result of the source code and the running program; and determining a risk assessment result of the power system according to the first risk report and the second risk report. According to the method and the device, the technical problem of relatively low risk assessment efficiency of the power system caused by vulnerabilities and limitations of a risk assessment method in related technologies is solved.

The present invention discloses a method for generating risk list data. The method for generating risk list data comprises: upon receiving a risk list data generation request, obtaining new data and existing risk list detail data according to the risk list data generation request; analyzing and processing the new data to obtain incremental risk list detail data, and clearing the existing risk list detail data to clear expired data; merging the cleared existing risk list detail data and incremental risk list detail data to obtain risk list summary data. The present invention also discloses a risk list data generation device, equipment and computer storage medium. The present invention can realize intelligent generation of risk list summary data, thereby facilitating banks and other financial institutions to directly query the credit risk data of enterprises or individuals based on the risk list summary data, thereby improving risk assessment efficiency. In addition, the present invention can also be combined with blockchain technology.

The embodiment of the invention provides a risk assessment method and device based on resource transfer, equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the steps of determining candidate evaluation feature data based on a sample resource transfer category and initial evaluation feature data; inputting the candidate evaluation feature data into the initial risk evaluation model, and extracting numerical value type feature data and label type feature data from the candidate evaluation feature data; vectorizing the label type feature data to obtain a label type feature vector, and splicing the label type feature vector with the numerical value type feature data to obtain sample spliced data; performing risk assessment on the sample splicing data based on the risk assessment sub-model, and determining a model loss value based on the obtained prediction object risk category and the sample object risk category so as to construct a target risk assessment model; and performing risk assessment on the target assessment feature data of the target object based on the target risk assessment model. According to the embodiment of the invention, the efficiency of risk assessment based on resource transfer can be improved.

The invention discloses a safety operation risk assessment method and related device for an electric power system in extreme weather, and the method comprises the steps: obtaining the weather data of an area where a to-be-assessed electric power system is located, and carrying out the analysis according to the weather data, and obtaining a weather condition which may affect the electric power system; acquiring comprehensive operation data of each transformer in the to-be-evaluated power system, wherein the comprehensive operation data comprises load data and operation data; predicting the load condition of the step-down transformer according to the load data, and evaluating the operation state of the step-down transformer according to the operation data; and performing risk assessment on the safe operation of the to-be-assessed power system by combining the load condition and the operation state of the step-down transformer and the weather condition. Therefore, the problems of low efficiency and poor accuracy in the prior art are solved.

This disclosure relates to a risk assessment method, apparatus, electronic device, and computer-readable storage medium. The method includes: upon triggering a risk assessment operation for target software, collecting multivariate data for the target software, including business characteristic data and domain knowledge data of the target software; extracting feature data corresponding to each risk factor from the multivariate data, and constructing a target prompt based on the feature data corresponding to each risk factor and a prompt template; inputting the target prompt into a large-scale risk assessment model, and outputting a risk assessment result for the target software. This method can improve the accuracy of risk assessment results.

The present disclosure relates to a vehicle risk assessment method, storage medium and device to improve the efficiency of vehicle risk assessment. The method comprises: obtaining a target assessment index of a target vehicle; determining the safety level corresponding to the target assessment index according to the correspondence between the preset assessment index and the safety level; determining the target risk type information of the target vehicle according to the safety level corresponding to the target assessment index and a preset risk assessment model, wherein the risk assessment model is a PSO-SVR model.