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

The invention discloses an urban renewal effect evaluation method based on real estate big data, which includes: first constructing an IP proxy pool, a collection channel pool, and traffic load balancing according to corresponding construction principles, and through the IP proxy pool, collection channel pool, and traffic load balancing Cleans, merges, updates and collects the data of the same object from different channels; takes housing surveying and mapping information as the core basic information, uses the name method and address method to process the collected data and conduct data mining to obtain the urban renewal performance evaluation model Metadata used; build a real estate-driven urban renewal performance analysis and evaluation model, use the obtained metadata for statistical analysis to obtain an index system suitable for urban renewal performance evaluation and analysis, and display the indicators on mobile terminals through data visualization data. The invention reflects the gradual evolution and effect of urban form and governance from time and space, and can provide decision-making basis for the long-term sustainable development of the city.

The invention belongs to the field of geological prospecting technology, specifically discloses a method of predicting a prospecting model based on machine learning. The method establishes a unified and easy-to-distinguish prospecting concept model database; analyzes and summarizes existing domestic and foreign prospecting models and ore-controlling elements and data materials of a study area based in the prospecting concept model database by machine learning, and constructs a prospecting prediction model; after determines the ore-controlling elements in the prospecting prediction model, improves data foundation of the prospecting concept model according to a data material list provided about the range of the study area, and recommends an algorithm combination suitable for the ore-controlling elements according to algorithms summarized from a cube quantitative predicting system; finally, based on the prospecting concept prediction model, realizes a quantitative, positioned and probabilistic predictive evaluation. The invention can quickly establish the prospecting model of a study area, and the prospecting model is more comprehensive and objective and more in line with the actual situation.

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 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 discloses a hydraulic bulging testing device, which comprises: a base, a liquid filling tank is arranged on the top, a liquid filling port is provided on the bottom to communicate with the liquid filling tank through a channel; the liquid filling tank is provided with a platform for installing a test sample, The test sample cover is set on the notch of the liquid filling tank to form a liquid filling chamber; the flexible curved surface sensor is attached to the upper surface of the test sample to sense the shape of the curved surface of the test sample after swelling and deformation; the pressure ring is installed on the flexible around the curved surface sensor, and cooperate with the platform to fix the tested piece and the flexible curved surface sensor; the upper cover, the cover is arranged above the liquid filling tank, and is offset against the upper surface of the pressure ring, and the upper cover is provided with a through hole communicating with the pressure ring, and The upper cover is provided with a threading hole; the controller is connected with the flexible curved surface sensor and is used to receive the signal collected by the flexible curved surface sensor to form a sample deformation surface map. Performance measurement accuracy.

The invention provides an experimental analysis device for droplet aggregation and microscopic characteristics in a shear flow field, including: a high-speed microscopic camera system, a flat plate pure shear module, a non-uniform shear module, a microchannel liquid flow shear module, and a syringe pump The outlet of the syringe pump is divided into three paths, which are respectively connected with the flat plate pure shear module, the non-uniform shear module and the microchannel liquid flow shear module; the circulating liquid supply system is used for the non-uniform shear module and the The microchannel liquid flow shearing module provides the circulating flow function of the liquid; the high-speed micro-camera system is used for recording and analyzing the movement of droplets in the plate-level pure shearing module, the non-uniform shearing module, and the microchannel liquid flow shearing module. Motion, Deformation, Fracture, Merge features. The invention can accurately capture, process and analyze the microcosmic evolution process of movement, deformation, rupture and coalescence of dispersed droplets.

The invention relates to an electronic file, and an implementation method and system thereof, in particular to an electronic file for machine-readable information cards, and an implementation method and system thereof. The method comprises the following steps: (1) establishing template information according to standard information in visual identification information of a machine-readable information card; (2) acquiring machine-readable information in the machine-readable information card, and analyzing characteristic information data from the machine-readable information; and (3) synthesizing the characteristic information data and the template information into a whole with consistent visual effect with the machine-readable information card real object to form an electronic file, and obtaining the electronic file data. In the invention, whether the machine-readable information card is real or false is judged and identified by using multiple modes, and the electronic file is synthesized, thereby ensuring reliability and accuracy, and illegal transaction caused by human factors can be effectively prevented, the operational normalization is enhanced and people with illegal certificates is prevented from handling related services due to human factors.

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 belongs to the technical field of plant molecular identification, in particular to a method for identification of strains based on a Dunaliella core gene sequence. The method mainly includes: sample collection, purification and culture; whole genome DNA extraction; construction of DNA sequencing library; acquisition of whole genome sequencing data of the algal strain to be tested and Dunaliella quartolecta; screening and de novo assembly of the core genome sequencing fragment of Dunaliella D. quartolecta , perform gene composition, protein function annotation and genome contig collinearity analysis on the assembled core genome sequence; use single nucleotide polymorphism to construct a phylogenetic tree, when the algal strain to be tested and Dunaliella tetraloba are clustered together, And the data support rate of the branch is between 0.99 and 1.00, and the percentage of genetic similarity is greater than or equal to 99%. The algal strain to be tested is D.quartolecta.

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 relates to a multi-source data modeling method applicable to a distribution network containing a D-PMU, which belongs to the technical field of electric power engineering; the D-PMU is split into measurement value modules already existing in the CIM, and the equipment is connected to the measurement module The terminal modules and equipment modules at the end are sorted and classified, and the missing synchronous clock module, timekeeping module, and synchrophasor measurement module are added in the CIM. Correlate between modules, increase the relationship between equipment modules and terminal modules, terminal modules and measurement modules; establish distribution network measurement data acquisition systems D‑PMU, SCADA, fault filtering, metering Data, marketing data, GIS data, and photovoltaic models cover the integrated model of the distribution grid of "substation - 10kV feeder - distribution transformer - low voltage line - power customer". Provide a more complete data foundation for the advanced decision-making application of the smart grid, and realize the full informationization of the distribution network.