31results about How to "Rich data base" patented technology

The invention discloses an abnormal data detection and modification method based on a numerical value relevance model. The method includes the following steps that first, abnormal data judgment conditions are defined by a display module and stored into a source indicator database; second, a data access layer traverses service indicator data in the source indicator database, judges whether the service indicator data meet the abnormal data judgment conditions or not and displays the service indicator data meeting the abnormal data judgment conditions in the display module, and meanwhile the third step is executed; third, the service indicator data meeting the abnormal data judgment conditions are modified by a service logic module, and the modified data are stored into the source indicator database according to needs. The abnormal data detection and modification method based on the numerical value relevance model greatly improves accuracy of service data of a power grid and accuracy for calculating statistical data groups, makes the statistical analysis result more reliable and provides favorable data support for enterprises in the respects of investment evaluation, benefit analysis and the like.

The invention relates to a method for constructing a global financial risk knowledge graph based on an artificial intelligence technology. The method specifically comprises the following steps: constructing a relational network among all existing credit subjects of a financial institution; extracting corresponding association risk and group fraud risk portrait features based on the updated relationship network; processing the structural data of the credit main body with the same user ID into risk image data of an individual dimension; processing the unstructured data of the credit main body with the same user ID into individual dimension risk portrait data; integrating the association and gang risk portrait features based on the relational network and the risk portrait data of the individual dimension to jointly form a global financial risk knowledge graph. According to the method, the financial risk knowledge graph construction method based on the social relation network, natural language processing and deep learning is used for improving the financial risk control capability and reducing the credit risk.

The embodiment of the invention provides a knowledge data processing method and a knowledge data processing device. The knowledge data processing method comprises the following steps of: acquiring entity data comprising identifier, attributes and attribute values thereof of a first entity object; extracting information of a second entity object from a preset entity information base, wherein the information is respectively matched with the attribute values in the entity data; associating the second entity object with the first entity object, thereby updating knowledge graph related to the first entity object. The knowledge data processing method and the knowledge data processing device, provided by the embodiment of the invention, can automatically and quickly associate the entity object described in the entity data with the entity object in the entity information base so as to update and complete the knowledge graph related to the entity object.

The invention discloses a deep excavation risk evaluation method based on network reasoning, in particular to a deep excavation engineering risk evaluation method based on a risk tree network for probabilistic reasoning, which comprises the following steps: establishing a deep excavation engineering risk evaluation and computation model according to risk identification and analysis; taking preliminary statistic deep excavation engineering quality safety accident data as basis for probabilistic computation and loss assessment, and adopting the risk tree network to conduct the linear reasoning of risk event occurrence probability and the dynamic update of the probability; adopting expert investigation and historical experience to conduct the assessment of losses caused by risks; and combining the established risk evaluation model to evaluate deep excavation risks to obtain the overall risk level of the deep excavation engineering and the average risk level of each risk event. The invention has the advantages that the method can be used for conducting deep excavation engineering construction risk evaluation and dynamic risk evaluation, the risk evaluation is enabled to more accurate and objective, the dynamic risks in the whole construction process can be reflected, the relevant prevention, control and reduction of the construction risks are facilitated and the safety is guaranteed.

The invention discloses a cloud service platform kitchen work environment intelligent alarm method. The method concretely comprises the steps that state parameters in the kitchen work environment are acquired by an induction module; the state parameters are uploaded to a cloud service platform by the communication module in an alarm system; the historical data of the acquired state parameters in the kitchen work environment are enabled to act as training sample data by the cloud service platform so as to establish a user kitchen work environment law model; the abnormal level is set according to the matching degree of the state parameters and the kitchen work environment law model, and an alarm instruction corresponding to the abnormal level is issued; and an alarm control module responds to the alarm instruction and performs emergency processing measures. The acquired state parameters are used, the unique use law of every family is intelligently analyzed through the cloud service platform, and different levels of emergency processing is performed on the gas stove use situations which do not meet the law so that the method and the system which actively discover hidden dangers and actively alarm before the accident can be provided.

The invention discloses a fault handling method for a distribution network including a distributed power source. The fault handling method is characterized by including the steps of firstly, performing fault location, namely monitoring all remote signal displacement information in the distribution network, selecting fault signals existing in the distribution network according to starting conditions, starting fault analysis if faults are acquired, and performing fault location according to a location analysis method of directional signals; secondly, performing fault isolation, namely performing fault isolation analysis according to the principle of least power loss of load, and providing an isolation strategy; and thirdly, performing fault recovery, integrating the distributed power source into a fault recovery decision under the premise of complying with related rules and standards, and increasing power reliability of the distribution network while fully utilizing the distributed power source. By the fault handling method for the distribution network including the distributed power source, accurate location and quick isolation are provided for faults of the distribution network including the distributed power source, and accordingly fault recovery of the distribution network and adjustment of operation manner after recovery are achieved.

The invention belongs to the technical field of plant molecular identification, and particularly relates to a strain identification method based on a dunaliella core gene sequence. The method mainly comprises the following steps: collecting, purifying and culturing a sample; extracting the DNA of the whole genome; constructing a DNA sequencing library; obtaining whole genome sequencing data of thealgae strain to be detected and the Dunaliella Quartolecta; screening and de novo assembly of sequencing fragments of the D. Quartolecta core genome are carried out, and genome segmentation, proteinfunction annotation and genome contig colinearity analysis are carried out on the assembled core genome sequence; constructing a phylogenetic tree by utilizing single nucleotide polymorphism, and whenan algae strain to be detected and Dunaliella Quartolecta are gathered into a cluster, the data support rate of branches is 0.99-1.00, the genetic similarity percentage is greater than or equal to 99%, and the algae strain to be detected is D.quartolecta.

The present invention relates to a D-PMU-including power distribution network multi-source data modeling method, belonging to the technical field of electric power engineering. The D-PMU is divided into a measurement value module, a terminal module connecting the equipment to a measurement end and equipment module existing in CIM, and a synchronous clock module, a punctuality module and a synchronous phasor measurement module missing in the CIM are added after sorting and classification. The modules are associated, and the association between the equipment module and the terminal module and the association between the terminal module and the measurement module are added. A power distribution network measurement data acquisition system D-PMU, SCADA, fault filtering, measurement data, marketing data, GIS data and photovoltaic model is built, including a "substation-10kV feeder-distribution transformer-low voltage line-customer" power distribution grid integration model. The method provides a more perfect data foundation for the advanced decision-making application of a smart grid and realizes full informationization of the power distribution network.

The invention aims to provide a method and device for connecting wireless access points. Compared with the prior art, a wireless access point list is acquired, and access information of the wireless access points in the wireless access point list is inquired in a local database; and further, a mapping relationship is established between the access information of a first wireless access point and the first wireless access point, wherein the first wireless access point is the wireless access point that the access information is inquired. The method can provide great convenience for a user to connect the first wireless access point, and connection can be realized without inputting the access information of the first wireless access point by the user, so that user experience is improved.

The invention provides small program sub-service identification method and device, equipment and a storage medium. The original feature data of the applet is obtained, and the operation feature data generated in the applet use process is collected by using the computer network technology, so that the features of the applet are expanded, and the operation efficiency of the applet is improved. Content understanding is carried out on the use process of the applet by collecting external operation characteristic data, so that the characteristics can better represent the function of the applet. Meanwhile, the form of the features is not limited, rich data bases can be provided for multi-modal features such as texts, pictures and statistical data, the specific functions of the applet can be represented more accurately, the accuracy of applet sub-service recognition is improved, and application and popularization of the applet are further facilitated.

The invention discloses a city updating effect evaluation method based on real estate big data, and the method comprises the steps: firstly respectively constructing an IP agent pool, a collection channel pool and a flow load balance according to corresponding construction principles; conducting cleaning, merging, updating and data collection on the same object data from different channels throughthe IP agent pool, the collection channel pool and the flow load balance; using house surveying and mapping information as core basic information, using a name method and an address method for processing the collected data, conducting data mining, and obtaining metadata used by a city updating performance evaluation model; and by the constructed city updating performance analysis and evaluation model taking real estate as a driving force, conducting statistical analysis by utilizing the obtained metadata to obtain an index system suitable for city updating performance evaluation and analysis,and displaying index data on a mobile terminal through data visualization. According to the method, the urban form and the gradual evolution and effect of governance are reflected spatio-temporally,and a decision basis can be provided for long-term sustainable development of cities.

The invention provides a log file automatic generation method based on model driving, and belongs to the field of combination of software system structure modeling and log file automatic generation. The method comprises: based on a software system structure modeling idea, coding functional components of all layers of an application program, and using an automatic mapping mode to replace log information containing internal definitions of an application system; and then intercepting user information corresponding to corresponding operation behaviors, generating log contents together with other necessary system description information, and storing the log contents in a log file or database file form. Meanwhile, collection work of the log information can be started or stopped at will by modifying the configuration file, and existing log information can be expanded or deleted according to real-time needs. Physical separation and logic independence of the log system and the application system are achieved, comprehensive, detailed and extensible log content is guaranteed, the application system can be maintained, and the follow-up data analysis work efficiency is improved.

The invention relates to a skin type identification method and system based on a multi-wavelength photoacoustic spectrum. The method comprises the steps of obtaining a photoacoustic time domain signal of skin, carrying out the power spectrum analysis and integration processing, obtaining photoacoustic quantization spectrum parameters under different wavelengths, training a support vector machine model based on the photoacoustic quantization spectrum parameters under different wavelengths, and inputting the photoacoustic quantization spectrum parameters of the to-be-identified skin, which represent the content of the characteristic molecules in the skin tissues, into the support vector machine model to obtain the skin type of the to-be-identified skin. Compared with the prior art, the method has the advantages that the recognition time can be saved through machine learning, and the skin tissue type can be recognized more objectively, quantitatively and accurately through the photoacoustic spectrum characteristic parameters under multiple wavelengths.

The invention relates to a building column grid size obtaining method based on room area requirements. The building column grid size obtaining method comprises the following steps: S1, building column grid size initial information is obtained; S2, calculating an achievable area number corresponding to each column grid size; and S3, searching and outputting the column grid size corresponding to the maximum achievable area number. Compared with the prior art, the uniform column grid size obtaining method provided by the invention has the advantages that the efficiency and the accuracy are improved, the error of a non-technical mode that a designer selects according to experience in the past is avoided, and the economic efficiency of room design and commissioning is effectively improved.

The invention provides a drop coalescence micro characteristic experiment analysis device in a shear flow field, which comprises a high-speed microscope camera system, a flat-plate pure shear module,a non-uniform shear module, a micro-channel liquid flow shear module and an injection pump, wherein the outlet of the injection pump is divided into three channels, and the three channels are connected with the flat-plate pure shear module, the non-uniform shear module and the micro-channel liquid flow shear module respectively; a circulating liquid supply system is used to provide the circulatingflow function of liquid for the non-uniform shear module and the micro-channel liquid flow shear module; and the high-speed microscope camera system is used to record and analyze the movement, deformation, fracture and coalescence characteristics of drops in the flat-plate pure shear module, the non-uniform shear module and the micro-channel liquid flow shear module. The micro evolution processessuch as movement, deformation, fracture and coalescence characteristics of dispersed drops can be accurately captured, processed and analyzed.