89results about How to "Rich sources of data" patented technology

The invention discloses a method and a system for diagnosing and responding and supporting a nuclear power plant severe accident. The method comprises steps of obtaining accurate information needed by accident management by combining an instrument usability database through accident-correlated-instrument monitoring and correlated important parameter evolvement, realizing simulation of server accident development and an emergency state through the combination of a sever accident simulation module, predicting the development tendency of the server accident and predicting expected effects after various possible intervention measurements, integrating and electronizing the server accident management guidance (SAMG) and giving advices on the following measurements. The invention can start up the corresponding measurement and the emergency plan on the short notice, maximally reduces the accident risk, provides the guarantee and further enhances the safety of the nuclear power plant.

An embodiment of the present invention discloses a video recommendation method. The method comprises: firstly, acquiring label information of a target user; distributing the user into a target interest group according to the label information of the target user; then acquiring behavior data of the target user and target behavior data of an online user in the target interest group; and according to the behavior data of the target user and the target behavior data of the online user, performing online recommendation of a video. Therefore, when online video recommendation is performed, not only online behavior data of the target user is considered, but also the behavior data of the online user in the target interest group where the target user belongs to is combined, so that the data source of online video recommendation is enriched, the type of recommendation results is extended, and the result of video recommendation more satisfies the actual needs of the target user, and user experience is enhanced.

The invention discloses a construction method of an urban geographic semantic knowledge graph. According to the invention, public transport relations between implicit city geographic semantics extracted from social text data and places are integrated, so that a user can form comprehensive cognition of each place in a city, and reference information is provided for decisions such as travel or cityplanning. According to the invention, the conceptual relation is replaced by the public traffic relation in reality, so that the relation network depicted by the knowledge graph is closer to daily life scenes such as place recommendation and place traffic association query; data sources are more diversified, basic information in longitude and latitude, urban areas and other traditional geographicinformation systems is included, and implicit geographic semantics extracted from social text data through the natural language processing technology such as regional function significance and the good reputation rate are further included.

The invention provides an intelligent motor fault recording and early warning system, which belongs to the technical field of motor fault diagnosis. The system comprises an operation parameter acquisition module, a data processing and analysis module, a calendar module, a storage module and a communication module, wherein the operation parameter acquisition module acquires motor operation data in real time; in combination of time information, the data processing and analysis module adopts a first-in first-out storage principle and stores recording data according to a time sequence; after operation stop of the motor is detected, storage updating of the data is stopped, and storage is continued when starting is recovered; and the data processing and analysis module automatically judges and analyzes the motor operation condition according to the real-time recording data of the motor and motor fault early warning is timely carried out. Fault recording on multiple operation parameters of the motor and judgment on a fault reason afterwards are realized, hidden fault can be timely found out and timely processed through motor fault early warning, and the intelligent motor fault recording and early warning system can be widely applied to motor fault monitoring and diagnosis, and has the advantages of low cost, high intelligent degree, strong generality and the like.

The invention discloses a system for carrying out dynamic monitoring on the complete information of a firefighting vehicle, relating to a system for carrying out acquisition and transmission on chassis information, boom information, vehicle-borne equipment monitoring information and audio / video field disaster information and the like of firefighting vehicles in firefighting and rescue fields. Thesystem comprises a vehicle-borne information acquisition and transmission device, a field operation terminal and a monitoring center system, wherein the vehicle-borne information acquisition and transmission device is connected with the field operation terminal and the monitoring center system by a wired / wireless network. By using the system disclosed by the invention, the real-time acquisition and transmission of firefighting vehicle centered complete information of firefighting and rescue fields can be realized, and a comprehensive monitoring management system integrating the functions of complete information acquisition, transmission, processing, storage, positioning tracking, commanding and scheduling is established, therefore, the system is an important complement to an integrated framework for integral firefighting information systems, enriches the sources of basic data. The application of the system greatly improves the informatization application and management level of firefighting equipment.

The invention provides an enterprise risk assessment method, system and medium. The method includes: adopting a network crawler to obtain original data related to an enterprise to be evaluated from different network data sources; extracting the keywords from the original data; constructing standard database; according to the preset semantic similarity threshold, constructing the text vector, and calculating the semantic similarity between the keywords in the original data and the standard keywords; when the semantic similarity exceeds the preset maximum similarity, replacing the keywords in the original data with the standard keywords to obtain the text after the semantic similarity concept is replaced; performing the risk assessment of the enterprise according to the replaced text of thesemantic similarity concept. In this way, the relevant data of the enterprises to be evaluated can be obtained from different network data sources, and the keywords can be extracted from these data tocarry out risk assessment for the enterprises, so that the data sources are more comprehensive, the amount of data to be analyzed is increased, and the accuracy of the assessment is improved.

The invention discloses a geosynchronous satellite wide area power grid wildfire remote sensing signal receiving device comprising a pedestal, a stand column and a receiving pot. A feed source is installed in front of the spherical reflection surface of the receiving pot via a support. The stand column is arranged on the pedestal via a rotating mechanism and can rotate around the vertical axis. The receiving port is hinged on the stand column round the horizontal axis. The receiving device also comprises a rotating driving assembly which is used for driving the stand column to rotate and a pitching driving assembly which is used for driving the receiving pot to perform pitching movement. The geosynchronous satellite wide area power grid wildfire remote sensing signal receiving device has advantages that a power grid wildfire geosynchronous satellite wide area weak signal can be accurately received, monitoring range is wide, and detection real-time performance and reliability are high.

The invention provides a power transmission line icing growth forecasting method based on an atmospheric numerical model. The power transmission line icing growth forecasting method comprises the steps of: utilizing WRF to obtain data including near-surface wind speed Vi and near-surface temperature Ti of a forecasting region, rainfall Ri within a forecasting time period, terrain height H and wind direction alpha of a power transmission line at each moment; and calculating to obtain icing increment delta Li of the line section at the corresponding moment according to a formula. By adopting the power transmission line icing growth forecasting method, the line icing growth trend can be forecasted accurately; the operability is high and data sources are rich; the forecasting accuracy is high, refined icing numerical forecasting can be made for different microtopographic regions, and resolution can be adjusted according to microtopographic size and microtopographic scope; and the power transmission line icing growth forecasting method can be used for instructing power transmission line operating personnel to perform power grid ice melting and removing operations accurately, ensuring safe and stable operation of the power grid, effectively increasing ice removing efficiency, optimizing resource allocation, and reducing the loss of the power transmission line caused by ice disaster of the power grid.

The invention discloses a method for identifying a single intersection signal timing imbalance state. The problems that in the prior art, the data source is single, the judgment accuracy is difficultto guarantee, and timing optimization cannot be guided are solved. The method comprises the following steps of S1, acquiring static information of an intersection and dynamic data of the intersection,S2, sequentially calculating lane saturation, flow direction saturation, flow direction queuing length and flow direction queuing intensity according to the obtained static information and dynamic data, S3, judging a flow direction state according to a calculation result, S4, judging an inlet state according to the flow direction state, and S5, judging whether the intersection state is unbalancedor not through an intersection judgment rule according to the inlet state. According to the scheme, the data volume source is rich, and the judgment accuracy is ensured; a distinguishing system for different dimensions of lanes, flow directions, entrances and intersections is established, specific reasons for problems can be positioned, so that signal optimization is facilitated, and a decision basis is provided for formulation of a timing optimization scheme.

The invention relates to the technical field of information, and discloses a rail transit platform door remote fault diagnosis method and system. The method comprises the steps that the platform doordata are collected, the platform door data are from at least two platforms, and the platform door data include the state parameters of each device; when a hardware fault report is received, the faultcause of the hardware is diagnosed according to the platform door data; the feature parameters, the purchasing information and the logistics information of the hardware are extracted, and the diagnosis report is acquired through combination of the fault cause of the hardware; and the diagnosis report is transmitted to a remote terminal. The diagnostic basis of the remote fault diagnosis technologyis abundant and the feedback is rapid so that the common maintenance personnel can remotely determine the fault and eliminate the fault through the remote terminal with no requirement for the manufacturer to send professional research and development technicians for field maintenance when the fault of the platform door occurs, and thus the fault maintenance speed can be enhanced and the labor cost can be reduced.

The invention discloses a question and answer reading understanding model acquisition method and device, electronic equipment and a storage medium, and relates to the field of deep learning. The method can comprise: for N models with different structures, N being a positive integer greater than 1, carrying out pre-training by using unsupervised training data to obtain N pre-training models, wherein different models correspond to different pre-training tasks respectively; taking a question and answer reading understanding task as a main task, taking other predetermined natural language processing tasks as auxiliary tasks, and performing fine adjustment on each pre-training model by utilizing supervised training data to obtain N fine adjustment models; and determining a finally required question-answer reading understanding model according to the N fine adjustment models. By applying the scheme of the invention, the generalization ability and the like of the model can be improved.

The invention discloses an interdisciplinary recommending method and system. The method comprises the steps of determining behavior preference data of a user in multiple fields, and then utilizing a recommendation algorithm and the behavior preference data to generate a recommendation result for the user. This shows that the behavior preference data is obtained from multiple fields instead of being obtained from a single field, in this way, data sources in an information recommending process can be obviously drastically enriched, and the data acquisition difficulty is thus lowered; meanwhile, the behavior preference data of the user in multiple fields can more comprehensively embody the individual interest of the user, and thus the follow-up recommendation result can be sharply improved.

The invention belongs to the field of power plant safety control systems, and particularly relates to an operation guidance system based on a typical and accident case base of a thermal power plant. The operation guidance system comprises a data collection system (DCS) and is characteriazed in that a memory internally saves a mentoring function module, and stores an accident case base and typicaloperations; a processor encodes and classifies fault messages of different components of the thermal power plant; based on an expert industry knowledge base and analysis on historical fault data, normal threshold ranges of operating indicators of a thermal power unit are defined, a change rule in an operating process of the indicators is mined, data sample sets in different states are constructed,and a fault trigger model is established using a machine learning algorithm. When triggering conditions are reached, a display visually shows the location of a fault and a data distribution rule, andinformation about the cause of the fault and treatment suggestions is displayed on the display; fault early warning is accurate and the operation guidance is accurate and in place.

The invention provides a sensor fault diagnosis method and system of a first-order follow-up system based on a dynamic trend, and relates to a sensor fault diagnosis method of a first-order follow-upcontrol system. The method includes steps: realizing switching between a multiplicative fault module, a non-fault module and an additive fault module through simulation of a mechanical switch, and acquiring experiment data; establishing fault monitoring, fault estimation and fault separation static models based on analysis of normal and fault system dynamic characteristics, and proposing an onlineapplication diagnosis model calibration method and a diagnosis process; and verifying the validity of the fault diagnosis method and high precision of the diagnosis according to online simulation ofa "complex process system innovation experiment platform". The method can be widely applied to sensor online fault diagnosis of the first-order follow-up control system.

The invention discloses a lung cancer-oriented intelligent target region and organ-at-risk segmentation method. Dual-path segmentation is performed by adopting a common segmentation network framework of a lung cancer-oriented target region and an organ at risk; the common segmentation network framework comprises a teacher model and a student model, and the two models both adopt nnU-Net as trunks; and for the organ at risk and the target region, pre-defined auxiliary condition information is respectively given to be embedded in an up-sampling process and is connected to a corresponding decoder in a decoding process, and predicted masks are fused to obtain a final segmentation result. According to the method, the target region and the organ at risk can be accurately and efficiently segmented.

The invention discloses a big data analysis method and system based on a consumption record, so as to solve the problem of incomplete user credit assessment caused by the lack of full access to the user original credit consumption records in the prior art. The method comprises steps: a node acquires user credit consumption information; the legality of the user credit consumption information is judged, and the legal user credit consumption information is analyzed to generate a credit consumption record with a unified format; the credit consumption record with a unified format is written to a block chain, and a user credit score is updated according to the credit consumption record with the unified format; and when the node initiates an access request, the node acquires the user credit score. The method and the system are applied to related fields of big data analysis.

The embodiment of the invention provides an anti-fraud risk control method and device. The method comprises the steps of collecting user and enterprise information data, including obtaining user information and enterprise information input by a user, identifying information in a certificate image uploaded by the user in an OCR mode, obtaining the enterprise information data from a business platform of an enterprise, and obtaining the enterprise information data on an alliance chain through a preset interface; verifying user information in the user and enterprise information data; after the user information passes the verification, verifying the enterprise information in the user and enterprise information data; and after the enterprise information passes the verification, based on the userand enterprise information data, performing real-control person anti-fraud risk assessment, legal person anti-fraud risk assessment and enterprise anti-fraud risk assessment in a trusted execution environment established by the SGX multi-party security computing server. According to the embodiment of the invention, the data source is perfected, the authenticity and security of the data can be ensured, and the accuracy of anti-fraud risk control is improved.

The invention relates to a continuous identity authentication method fusing a sliding track and dynamic characteristics on a smart phone, and belongs to the field of privacy protection and mode recognition. Aims to solve the problems of relatively low availability caused by over-high requirements on equipment hardware, relatively low robustness and accuracy caused by single data source and over-simple characteristics and the like in an existing continuous authentication method based on touch screen sliding behavior characteristics, the present invention provides a continuous identity authentication method fusing a sliding track and dynamic characteristics on a smart phone. According to the method, a touch screen and motion sensors (an acceleration sensor, an angular velocity sensor and a gravity sensor) widely applied to a smart phone are used as data sources, and different motion states of a user when the user uses the smart phone are considered, so that the availability, robustness and authentication accuracy of the authentication system are greatly improved. The method is suitable for continuous system identity authentication of the smart phone.

The invention relates to the technical field of geographic space observation, in particular to a geographic space sensor network body construction method and device. The method comprises the following steps of: constructing a geographic space sensor network observation resource according to hardware physical information of a sensor, attribute information of an observation object, attribute information of an observation process, attribute information of observation data generated by the sensor in the observation process and attribute information of an observation product; configuring semantic description element metadata for the geographic space sensor network observation resource; expanding an inner type and an attribute of a to-be-expanded geographic space sensor network body according to the semantic description element metadata; and under an appointed application circumstance, obtaining the observation data and metadata of the observation data according to the expanded geographic space sensor network body, and carrying out living example type construction, interior associated construction and exterior associated construction on the expanded geographic space sensor network body. The method and device provided by the invention are rich in data sources and are capable of satisfying the requirements, for multi-source observation data, of the users.

The invention provides a crowd-sourcing cooperative spectrum sensing method based on data cleaning. The crowd-sourcing cooperative spectrum sensing method comprises the following steps: modelling crowd-sourcing cooperative spectrum sensing data; performing matriculated representation of the crowd-sourcing cooperative spectrum sensing data; cleaning the crowd-sourcing cooperative spectrum sensing data; fusing the crowd-sourcing cooperative spectrum sensing data; and evaluating a crowd-sourcing cooperative spectrum sensing performance. According to the invention, cooperative spectrum sensing can be carried out according to the crowd-sourcing cooperative spectrum sensing data obtained through portable and popular spectrum sensor equipment; and influence of data error and data fraud on the cooperative spectrum sensing performance in crowd-sourcing cooperative spectrum sensing can be eliminated.

The invention relates to a forest fire prediction method for a power transmission line, and the method comprises the steps: employing WRF to obtain temperature T, wind speed V, precipitation R and humidity Q of a near surface of a prediction region at any moment of a prediction time period; determining industrial and agricultural data factor alpha, historical fire point data beta, forest vegetation delta(i)i( / i) and ground cover data eta(i)i( / i) of the prediction region in the prediction time period; calculating a forest fire index F according to the following formula: F = 0 if R is greater than zero; F = alpha*beta(sigma delta(i)i( / i)*eta(i)i( / i))*T*V*Q if R is equal to zero; and predicting the condition of forest fiber of the power transmission line in the region according to the value of F. The method employs the prediction results of an atmosphere numerical model to achieve the forest fiber of the power transmission line, thereby guaranteeing the instantaneity and convenience of meteorological data of a place adjacent to the power transmission line. The data sources are rich, and the prediction precision is high.

The invention provides a method for predicting a tight oil fracturing range based on a DL model of physical constraints, and belongs to the technical field of tight oil fracturing. The method comprises the following steps: firstly, providing a reasonable physical hypothesis for a studied problem and setting related parameters, then establishing a two-dimensional axisymmetric numerical calculation model, dividing a network and setting time steps, establishing a deep learning model, setting a structure and other parameters of a deep neural network model, and obtaining a prediction result by using a test set; and evaluating a prediction result by using an L2 norm and a decision coefficient R2 as evaluation indexes, comparing the prediction precision of the neural network under the condition of considering the physical constraint and the prediction precision of the neural network under the condition of not considering the physical constraint, and finally changing the training data size on the basis of the neural network added with the physical constraint and measuring the prediction capability of the neural network. The method can be applied to rapid prediction of flow field distribution of two-drive model partitions, is high in prediction accuracy and adaptability, is high in calculation speed, and can well solve the problem of unknown tight oil flow field distribution and the problem of small sample prediction.

The invention discloses a method for identifying plants by using a DNA characteristic sequence. The method is realized by detecting whether a genome of a plant contains DSS of a target species or a reverse complementary sequence thereof; the DSS of the target species is a nucleic acid sequence fragment which has a length of 20-100bp, exists in a nucleic acid sequence of the target species and does not exist in a nucleic acid sequence of a non-target species. For example, bighead atractylodes rhizome DNA can be identified from other atractylis plants with extremely close genetic relationship, and the defect that only rhizoma atractylodis and bighead atractylodes rhizome can be distinguished in the prior art is overcome. The DSS is not limited to a specific region of the genome, the data source is rich, and the specificity is good. The method has important application value in identifying medicinal plants.

The invention discloses a multi-dimensional data fusion investigation system and method, and the system comprises a plurality of information collection modules which are used for carrying out the information collection of a dynamic target in a preset region, obtaining the target information data, enabling the dynamic target to comprise pedestrians and vehicles, and enabling the target informationdata to comprise MAC, license plates, human faces and IMSI feature data; an information processing module which is used for carrying out address identification grouping on the information acquisitionmodules with similar address space positions and carrying out time segmentation processing on the target information data obtained by the information acquisition modules in each group to obtain targetinformation data in different time periods; and an information analysis module which is used for analyzing the target information in different time periods in the same address identifier. The methodhas the advantages that the pain points and difficulties of a traditional investigation means are solved, seamless fusion of biological characteristics and electronic characteristics is creatively achieved, and associated application of structured data and unstructured data is creatively achieved.

The invention relates to a first-order fixed-value control system regulating valve fault diagnosis method and system. The method comprises the following steps: setting different types and different intensities of faults for a first-order fixed-value liquid level control system online, and analyzing fault characteristic values according to discrete data points; acquiring sensor detection data and regulating valve opening data of a normal stable system online to obtain a fault detection threshold value, and determining a fault detection algorithm; determining a fault estimation algorithm; establishing a fault qualitative model; and carrying out example verification of fault diagnosis methods and systems. The method and system can be widely applied to online fault diagnosis of the regulatingvalve of the first-order constant value control system.

According to the government affair reservation service recommendation method based on machine learning disclosed by the invention, recommendation is carried out in various scenes, the scene I carries out data linkage, data sources are enriched, and items needing to be handled can be recommended according to personal records of all government affair departments, and the scene II constructs a user portrait and constructs user features in combination with basic information and behavior data of a user, so that the user experience is improved. According to information such as properties of services and appointment items, article features are constructed, article scores are calculated based on an article similarity model and user portraits, users are divided by adopting a clustering mode, and personalized customization and possible affair handling items can be recommended for both new and old users; in the third scene, the geographic position coordinates of the user and the geographic position coordinates of all the to-do sites are utilized, the coordinate positions are classified through a classification algorithm and then weighted calculation is carried out to give a suggested recent service site, item recommendation for different types of users in different scenes is achieved, the user can use the appointment service module more conveniently, and the user experience is improved. And the working efficiency is improved.

The invention provides a three-dimensional simulation software system and method for a machining digitization process, and belongs to the technical field of machining, and the method specifically comprises the steps that a parameter obtaining module obtains the feature resume and original blank parameters of a to-be-machined part, cutter assembly parameters, clamp three-dimensional model parameters, machine tool three-dimensional model parameters and the tree relation among the four parameters; wherein the modeling module is used for respectively establishing an original blank parameter model, a cutter assembly model, a clamp assembly model and a machine tool model; the process method collection module acquires a to-be-verified process method; the dynamic simulation module generates a processing flow, and outputs the processing flow passing the interference verification after dynamic interference verification; the NC program generation module generates a tool machining path and an NC program; the CMM program generation module generates a CMM program; and the virtual debugging module executes the NC program and the CMM program to perform virtual simulation and verification to obtain process method modeling. Through the processing scheme of the invention, an intelligent decision can be provided for a machining digital process.

The invention discloses a clinical observation table filling method and system to overcome the defect of the prior art that data used for clinical research can hardly meet the requirement for authenticity and integrity. The method comprises the steps of acquiring the hospital data of a patient from an information management system of a hospital, filling the hospital data in a database established in advance for patients, acquiring the out-of-hospital data of the patient according to a follow-up visit questionnaire filled by the patient and a medical history picture uploaded by the patient, filling the out-of-hospital data in the database, and extracting a clinical observation table for the patient from the database. The hospital data is acquired according to the management system inside the hospital, the out-of-hospital data is acquired according to the follow-up visit questionnaire fed back by the patient and the medical history picture uploaded by the patient, and then the authenticity and integrity of data used for clinical research are ensured.