22results about How to "Simple data structure" patented technology

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention relates to the technical field of charging load prediction, in particular to an electric vehicle charging load prediction method and system combining sequence decomposition and feature extraction and a storage medium. Comprising the steps of performing variational mode decomposition on time sequence load data to obtain a plurality of intrinsic mode functions; performing zero padding expansion on each intrinsic mode function, and then converting the intrinsic mode function to a frequency domain; enhancing the frequency domain data through a multi-layer perceptron based on complex domain calculation; constructing a prediction model based on a complex field attention module and a residual module, inputting the enhanced spectrum data into the prediction model, extracting a data characteristic frequency combination through the complex field attention module and the residual module, and carrying out weighted summation on all intrinsic mode functions as the output of the prediction model; and calculating a difference value between real data and prediction data according to a mean square error of a time sequence and an average absolute value error of a frequency spectrum to obtain time domain and frequency domain mixed loss, and updating prediction model parameters. According to the invention, the problem of large prediction error under the influence of random load fluctuation is solved.

The invention relates to the technical field of cable safety, in particular to a cable repairing method based on hierarchical path planning and related equipment, and the method comprises the steps: obtaining an original point cloud of a target cable, carrying out the preprocessing of the original point cloud, and obtaining a damaged region point cloud; according to the method, the damaged area point cloud is subjected to voxelization processing, the damaged area point cloud subjected to voxelization processing is subjected to internal voxel filling, the zigzag path of the target cable is generated according to the voxelization damaged point cloud, the target cable is repaired based on the zigzag path, the method can adapt to the complex form of the damaged area, and the repairing integrity and the repairing quality are improved.

The invention relates to a business data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining a fission sharing path and initial user information of service data, wherein the fission sharing path comprises multiple pieces of fission user information and a data sharing mode between users; constructing a graph network model of the service data according to the information of the multiple fission users and the data sharing mode; screening fission user nodes of the graph network model according to the initial user information to obtain an initial node; constructing a directed propagation path of service data according to the out-degree of the initial node and a node connection relationship between fission user nodes; and if the number of the nodes in the directed propagation path is not less than a preset fission threshold, generating fission sharing information of the service data according to the directed propagation path. By adopting the method, the data structure can be simplified, and the data volume can be compressed, so that the fission sharing information generation efficiency is improved.

The invention discloses a method for predicting the gelation activity of alkali-activated tailings and the strength of concrete prepared from the alkali-activated tailings based on machine learning, and the method comprises the following steps: collecting heat flow data of typical alkali-activated tailings, and converting the heat flow data by using Fourier transform and discrete cosine transform functions; and establishing a prediction model of the gelling activity of the alkali-activated tailings and the strength of the tailings-based concrete based on a machine learning model. Compared with a traditional concrete strength prediction model, the method has the advantages that the hydration dynamic data of the alkali-activated tailings are converted, nonlinear features can be remarkably reduced, a data structure is simplified, and a machine learning model can traverse all data, so that the prediction model has higher prediction precision and reliability.

The invention belongs to the field of software simulation, and particularly discloses a CAE software construction method, system and equipment, and the method comprises the steps: determining a simulation scene which needs to be supported by the current version of CAE software, so as to determine a function which needs to be supported by the current version of CAE software; determining a plurality of sub-function modules needing to be added in the CAE software according to the functions needing to be supported; constructing at least one sub-function module in the plurality of sub-function modules by adopting a data-oriented software design architecture; after the plurality of sub-function modules are constructed, completing iteration of the CAE software of the current version to obtain CAE software of an updated version; the CAE software of the updated version supports the simulation scene. Through the method and the device, the problems of software complexity and performance caused by coupling of a plurality of functional modules in a CAE software architecture are solved, so that the development efficiency of CAE software is improved, the software quality and the execution performance are improved, and the simulation experience of a user is improved.

The embodiment of the invention provides a table data processing method and device, equipment, a medium and a product. The method comprises the steps of displaying at least part of records of a target table in a target interface of a client in response to a data access request which is input by the client and aims at the target table; the record refers to a row of data in the target table; receiving a sub-record editing operation for the target table through the target interface, determining hierarchical description data corresponding to the edited sub-record according to the sub-record editing operation, and updating display data of the target table in the target interface according to the hierarchical description data; the hierarchical description data is used for describing child records in the target table and father-child relation data corresponding to father records containing the child records. According to the technical scheme provided by the embodiment of the invention, the records of the table can be loaded as required, the changed records can be quickly and effectively updated in a targeted manner, and the processing efficiency and the use experience of the table are improved.

The invention discloses a CityMaker-based Revit model batch integration method and device, a storage medium and equipment. The CityMaker-based Revit model batch integration method comprises the following steps of S1, creating a target model; s2, combining element classes of single source models; s3, establishing a configuration file for batch integration of the models; and S4, integrating the source model into a target model. According to the method, data structure simplification from the Revit model to the FDB model, directional automatic replacement and integration from the source FDB model to the target FDB model, optimization of the integrated FDB model, batch conversion from the attributes of the Revit model to the attributes of the FDB model, and batch automatic calculation of the spatial index and the spatial range of the FDB model can be realized. According to the method, idle computer resources can be fully utilized, the model integration time is greatly shortened, and the model integration efficiency is effectively improved.

The invention discloses a webpage content structured processing method, device and system and a storage medium, and relates to the technical field of webpage content processing. The method comprises the following steps: acquiring a template URL, and requesting a URL address to obtain a first HTML tag; the method comprises the following steps of: analyzing and converting a Document object into a Document object, and extracting an Elements object set; removing non-analytic labels to obtain a filtered Elements object set, performing circulation, and setting a CurrentId value; a Tag object is defined, and Listlt is assembled; taggt is carried out; gathering: constructing a tree structure object; through an optimization method of reducing node depth and integrating nodes, the tree structure is further simplified, and the target of extracting the structured data from the webpage and storing the structured data into the database is achieved. According to the embodiment of the invention, the complexity of data extraction is simplified, the efficiency of data extraction is improved, the occupation of a storage space is reduced, and the learning cost and the use threshold of a user are reduced.

The present invention provides a method, device, system and storage medium for processing unstructured data, the method comprising: reading unstructured data to be processed and paragraph configuration information; segmenting the unstructured data to obtain paragraph data; calling corresponding verification rules to verify the paragraph data according to the paragraph configuration information to obtain parsed target data; storing the target data in a database. Thus, the unstructured data can be segmented, simplifying the parsing process of the unstructured data to form a simple and unified data structure, facilitating the editing of the unstructured data, thereby improving the reusability and extensibility of the code.

This invention discloses a lightweight remote data query method for low-bandwidth networks, belonging to the field of computer networks and data communication. Specifically, for scenarios where a client requests a contact list from a server or queries contacts based on keywords, the client obtains a metadata index table supported by the server. Then, the user inputs query conditions, generating a JSON-formatted query data structure. The client defines a minimal query instruction set, and through encoding mapping, converts the query data structure into short bytecode for simplification. The simplified instruction set data is sent to the server, which reverse-parses it back to the query data structure and uses the restored query conditions to query actual contacts. The server converts the query results for actual contacts into a plain data stream and returns it to the client. The user parses the plain data stream using the metadata index table to obtain the contact person and their phone number. This invention reduces the amount of data communicated between the client and server, thereby improving query efficiency.

The invention discloses an interoperability grade evaluation method applied to a radar seeker. The method comprises the following steps: defining a radar seeker interoperability grade model; comprising the following steps: carrying out interoperability grade division, and marking key features of each grade based on related attributes; wherein the related attributes comprise a regulation attribute P, an application attribute A, an infrastructure attribute I and a data attribute D; constructing an index tree for related attributes in the radar seeker interoperability grade model; and based on the index tree, performing interoperability grade evaluation by using a principal component analysis algorithm to generate an interoperability grade evaluation result. According to the method, a comprehensive and systematic evaluation framework is provided for the interoperability of the radar seeker by constructing a detailed interoperability grade model and an attribute index tree. And further combining with a principal component analysis algorithm, effective dimensionality reduction and fusion of multi-dimensional data are realized, and the accuracy of an evaluation result and the calculation efficiency are improved.

The invention provides a mine data anomaly identification method and system based on artificial intelligence, and relates to the technical field of mine data anomaly identification. Historical mine data to be subjected to anomaly identification and data related to the data to be subjected to anomaly identification are acquired, and the mine data to be subjected to anomaly identification are calculated, so that the anomaly of the mine data to be subjected to anomaly identification is identified; the method comprises the steps of selecting a big data type of a correlation coefficient to form a main analysis data set, performing Laplace transformation on data in the main analysis data set, establishing a mine data anomaly detection model, and training the model to obtain a mine data anomaly detection result. Taking the frequency domain features in the main analysis data set at the current time point as the input of the trained mine data anomaly detection model, outputting the frequency domain features of the mine data to be subjected to anomaly recognition at the next time point, comparing the frequency domain features with the actual frequency domain features at the next time point, calculating a feature difference value, and comparing the feature difference value with a set threshold value to obtain the anomaly recognition result of the mine data. And judging whether the data is abnormal or not.

This invention relates to a method and system for identifying and repairing holes in geometric models for fluid simulation, belonging to the field of fluid simulation technology. It solves the problems of unfriendly operation and low repair efficiency in existing technologies. The method includes: acquiring the geometric model of the fluid simulation and mapping the geometric model to obtain model data; receiving a hole identification command, identifying holes based on the model data, acquiring a set of hole data, and displaying the number of holes, a positioning button, and a repair button in the hole repair attribute panel; receiving a command for any positioning button, displaying the hole in the geometric model in the view area based on the hole corresponding to the positioning button; receiving a command for any repair button, repairing the corresponding hole based on the repair button, and updating the model data. This achieves rapid identification and repair of holes in the geometric model.

The embodiment of the invention discloses a user mobility clustering analysis method, which comprises the following steps: acquiring communication signaling data of a user and operator MR data, the communication signaling data of the user comprising a user mobile phone number, a base station cell code and access time of a signaling event; the service provider MR data comprises a service cell, an adjacent cell, a time slice and received signal strength of the user; determining flow position data of the user in a preset area according to the communication signaling data of the user and the MR data of an operator; rasterizing the flow position data to obtain rasterized flow position data; and carrying out clustering processing on the rasterized flow position data to obtain a high-density flow area.

The invention relates to the related technical field of scene generation, in particular to a new energy output prediction scene generation optimization method based on an SOM clustering algorithm. The method comprises the following steps: acquiring new energy data; optimizing the new energy data through a sample optimization strategy based on Kalman gain; based on an SOM clustering algorithm, performing source load data matching on the new energy data, and clustering the source load data; generating a source load day sequence according to a Markov random process to obtain a source load data sequence of a future day; and through a GAN network, generating a new energy output scene based on the source load data sequence of the future day and the cluster of the source load data.

The invention provides a contour light bar extraction method for a line laser light bar image of a thin-wall complex curved surface component in order to solve the problem that an existing point cloud processing method and a method for adjusting a measurement visual angle are difficult to remove abnormal areas in measurement point cloud of the thin-wall complex curved surface component in a universal, rapid and low-cost mode. According to the strategy that contour light bar search is guided according to contour line continuity of a thin-wall complex curved surface component measurement contour, a first key column position, a second key column position and a search starting point of a contour light bar are firstly positioned, and then a search direction and a search area are established; in the process of carrying out contour light bar search region by region along the search direction, a sliding search window of which the size and the position meet specific requirements is arranged in the image column direction to avoid searching abnormal light bars, so that the segmentation of the contour light bars and the abnormal light bars is realized, and the accurate extraction of the contour light bars is ensured. Compared with point cloud data processing, the method is simpler and more efficient, and has lower requirements on computer performance.

The invention provides a method for constructing a general numerical simulation calculation grid data format based on a CESE algorithm, and belongs to the technical field of numerical simulation. Firstly, the general grid data format is defined and used for storing geometric information of a grid, so that the geometric information is irrelevant to the dimension and the unit type of the grid; respectively defining the number of real grid units, the total number of all units, a grid unit data structure, a grid sub-unit data structure and a grid unit surface data structure; secondly, traditional grid data is converted into a new general format, a numerical simulation program suitable for the new data format is written, it is ensured that the new program can correctly process grids of different dimensions and types, and the accuracy of the new program is verified; the method does not depend on the specific shape and dimensionality of the grid, dimensionality and unit type information of the grid does not need to be stored, and the method can be suitable for computational grids of any dimensionality and unit type only by writing codes once.

The present invention discloses a configuration file determination method and device, storage medium and electronic device. The method includes: obtaining configuration item parameters of a target application, and querying in a target database according to the configuration item parameters to obtain an application template file of the target application; parsing the application template file to obtain multiple sub-templates and hierarchical information of each sub-template included in the application template file, wherein the hierarchical information of each sub-template is used to indicate the level of each sub-template in the application template file; deploying each of the multiple sub-templates layer by layer according to the hierarchical information of each sub-template, and loading the corresponding resource file for each deployed sub-template; determining the configuration file for operating and maintaining the target application according to the loading information of the resource file in each sub-template, solving the technical problem that the prior art cannot perform resource arrangement for complex applications and orderly deploy application resources.

In order to solve the problem that the existing point cloud processing methods and methods for adjusting the measurement viewing angle are difficult to eliminate abnormal areas in the measurement point cloud of thin-walled complex curved surface components in a universal, fast and low-cost manner, the present invention proposes a method for extracting contour light stripes from the line laser light strip image of thin-walled complex curved surface components. The present invention guides the search for contour light stripes based on the continuity of the contour line of the measured contour of the thin-walled complex curved surface component, formulates a strategy for first locating the first key column position, the second key column position and the search starting point of the contour light strip, and then establishes the search direction and search area. In the process of searching for contour light strips area by area along the search direction, a sliding search window with a size and position that meets specific requirements is set in the image column direction to avoid searching for abnormal light strips, thereby realizing the segmentation of contour light strips and abnormal light strips, and ensuring the accurate extraction of contour light strips. Compared with the processing of point cloud data, the present invention is simpler and more efficient, and has lower requirements on computer performance.

The invention relates to a deep fusion method based on multi-source data of a transformer substation, and the method comprises the steps: taking a direction with the maximum variance as a main feature according to a PCA algorithm, discretely marking data on a coordinate axis in each orthogonal direction, carrying out the dimension reduction of the data, carrying out the normalization processing of the data, obtaining a group of normalized data, and forming a normalized data set; carrying out dimension reduction on the normalized data to obtain data after dimension reduction; sVM multi-classification is carried out on the data after dimension reduction, then feature fusion is carried out, and screened data with prominent features are obtained. According to the method, the deep fusion speed based on the multi-source data of the transformer substation is increased, meanwhile, interference of redundant data on sample analysis can be reduced, a PCA data dimension reduction method is innovatively used, process noise and measurement noise are both considered in a data processing calculation scheme, meanwhile, an SVM multi-classification method is used for rapidly distinguishing the data, and the accuracy of the data processing calculation scheme is improved. And establishing an analysis chart and providing a strategy for sample learning.