84 results about "Compositional data" patented technology

Disclosed in the present invention is a deep learning-based power distribution network load prediction method, comprising: acquiring regional load data and renewable energy power generation data to form a data set, and preprocessing the data set to obtain a first data set; using a convolutional neural network to extract a time series feature in the first data set, and converting the time series feature into a data form of a deep learning model by means of an embedding layer to obtain one-dimensional time series data; performing fast Fourier transform on the one-dimensional time series data to obtain a frequency curve, extracting amplitude values on the frequency curve to calculate corresponding periods, and selecting the corresponding periods to slice the one-dimensional time series data and form same into two-dimensional matrixes; using a two-dimensional convolutional network to perform feature extraction, reshaping the two-dimensional matrixes that have undergone feature extraction into one-dimensional arrays, and performing adaptive fusion on the one-dimensional arrays to obtain a first time series feature; and inputting the first time series feature into a fully connected layer for weight calculation and linear transformation to obtain a power distribution network load prediction result, thereby improving the stability and reliability of electric power supply.

Systems, computer program products, and methods are described herein for material- related determination models. With reference to an example method, the method receives a first dataset including one or more first data entries associated with a material, generates a second dataset including one or more second data entries associated with the material, and outputs the second dataset associated with the material. The one or more first data entries include a material identifier, and generating the second dataset includes providing material identifier data and material composition data to an artificial intelligence (AT) model, simulating the material composition data in a first set of contextual conditions, generating one or more material properties, via the simulation, of the material in the first set of contextual conditions, and generating the second dataset including one or more second data entries associated with the one or more material properties for the material.

The invention relates to the technical field of engineering detection, and discloses a block chain-based engineering detection data tracing method, which comprises the following steps of: presetting a data tracing rule, and determining a triggering condition and a tracing condition type of a data tracing request; collecting engineering detection data in real time, and performing hash operation on the engineering detection data to generate a data hash value; forming a data tuple by the data hash value, the detection timestamp, the detection equipment identifier and the detection personnel identifier; storing the data tuple in a distributed account book of a block chain, and verifying the uplink operation of the data tuple through a smart contract; and when a preset data traceability request triggering condition is met, receiving a data traceability request, and retrieving a matched data tuple in a distributed account book of the block chain according to a traceability condition in the request. According to the invention, data full-process protection is realized through the block chain technology, and integrity, security and efficient traceability are ensured.

The invention discloses a construction waste treatment feeding control method and system, and the method comprises the following steps: carrying out the real-time material characteristic collection of entering construction waste, so as to obtain the material composition data, particle size distribution data and water content data of the construction waste; a construction waste feeding initial characteristic data set is generated based on the material composition data, the particle size distribution data and the water content data and transmitted to the central control unit for comparison, and a material-process matching scheme is generated; the control module is used for sending a control instruction based on a material-process matching scheme, realizing dynamic feeding adjustment parameter control of the construction waste, acquiring operation state data of the processing module in real time, and generating a feeding-operation state data set; and feeding back the feeding-operation state data set to the central control unit for dynamic correction, and adjusting operation parameters of a feeding adjustment execution mechanism to form a corresponding closed-loop feeding control flow. The feeding control efficiency in the construction waste treatment process can be improved.

The invention discloses a method and device for rapidly identifying a degradable material, and the method comprises the steps: carrying out the multi-modal data collection of a multilayer-structure degradable material, and obtaining the physical structure data, chemical composition data and preliminary degradation characteristic data; performing interlayer interface characteristic analysis according to the data to obtain interface characteristic parameters; evaluating the degradation synergistic effect to obtain interlayer interaction quantitative description data; an optimized random forest model is adopted to predict the overall degradation potential; analyzing the dynamic degradation path to obtain a degradation path diagram, a key intermediate product timeline and a potential degradation bottleneck point; and finally calculating the comprehensive degradation index. According to the technical scheme, the overall degradation potential of the multilayer structure degradable material can be rapidly, accurately and nondestructively evaluated, the interaction between layers of the material and the comprehensive influence of the layers on the degradation process are considered, a scientific basis is provided for efficient waste classification and treatment, the resource recovery efficiency is improved, and the environmental risk is reduced.

The invention relates to the technical field of environmental protection, in particular to an environmental analysis rating system based on environmental influence data, which provides a visual and understandable environmental quality evaluation result for a user mainly by collecting, processing, analyzing and rating the environmental influence data. The system is composed of a data collection unit, a data processing unit, an analysis rating unit and a display output unit, the data collection unit can collect various environmental influence data in real time through a built-in high-precision sensor and a collection device, and the data processing unit adopts an advanced data analysis algorithm to carry out deep mining and analysis on the data and displays the data in real time. According to the method, key information and rules in the data can be extracted, and the analysis rating unit is used for rating the environment quality by adopting a preset rating model and algorithm based on the processed data, so that the scientificity and accuracy of rating are ensured. Meanwhile, the self-learning ability can continuously optimize the rating model and algorithm, and the rating reliability is improved.

The invention relates to the technical field of big data processing, in particular to an energy big data secure storage method, a storage medium and a system.The method comprises the steps that energy data of any type of energy is obtained, the energy data comprises multiple data types, and any group of data in any data type is arranged according to a time sequence to form a data sequence; determining an association value and a data regularity of each type of data; the encryption strength coefficient of each type of data is obtained; determining the local feature approximation degree of each type of data based on the mutation moment difference and the distribution range difference corresponding to the subsequences between each type of data and all other types of data; determining the period approximation degree of each type of data; further obtaining a key length coefficient of each type of data; and when the energy data is stored, encrypting various types of data by adopting an AES encryption algorithm based on the key length coefficient. The invention aims to improve the encryption strength while ensuring the encryption efficiency.

The invention discloses a Mars soil simulant performance evaluation method based on spectrum-mechanics combined characterization, which comprises the following steps: acquiring surface soil chemical components and physical and mechanical reference data of a target Mars area, taking basalt, magnetite and hematite as raw materials, solving the optimal mass ratio by adopting an optimization algorithm, and preparing a sample. Then, systematic spectrum characterization is carried out on the sample, and components and spectrum characteristic data of the sample are obtained; and meanwhile, carrying out physical and mechanical property test on the sample to obtain mechanical property data. Further, spectrum and component data serve as independent variables, mechanical data serve as dependent variables, an associated data set is constructed, the influence weight is analyzed, and a component-structure-performance quantitative relation is established. And finally, based on all the data and the internal association thereof, calculating the comprehensive similarity between the simulant and the real Mars soil, and generating a comprehensive performance evaluation report. According to the method, multi-dimensional and standardized quantitative evaluation of chemical, mineral and mechanical properties of the Mars soil simulants is realized.

A computer-implemented method for machine learning based rig-site on-demand drilling mud characterization, property prediction and optimization is described. The method includes predicting rheological properties of drilling mud from compositional data based on a first empirical relationship across the compositional data. The method also includes predicting sensor based rheological performance of drilling mud based on a second relationship between the predicted rheological properties of the drilling mud and sensor responses from one or more sensors. Additionally, the method includes providing feedback to the one or more sensors, wherein the feedback comprises the sensor based rheological performance, sensor responses, and a formulation associated with the drilling mud, wherein the feedback enables updating the formulation associated with the drilling mud.

A system is configured for automatic recognition of data store architecture and tracking dynamic changes and evolution in data store architecture. The system is a complementary system, which can be added onto an existing data store system using the existing interfaces or can be integrated with a data store system. The system comprises three main components that are configured to compose an approximation of the data store architecture. The first of these components is adapted to execute an analysis of the architecture of the data store; the second of the components is adapted to collect and compile statistics from said data store; and the third of the components is adapted to compose an approximation of the architecture of said data store.

The application discloses an iterative classification matching data association method suitable for complex environment, and comprises the following steps: performing primary association; the characteristic group of primary association success constitutes a data set Z t + and F t + ; the characteristic group of primary association failure constitutes a data set Z t ‑ and F t ‑ ; solving a least square matching vector Θ according to the data set Z t + and F t + ; updating the data set F t ‑ by the least square matching vector Θ to obtain an updated data set, combining the data set Z t ‑ and F into new input, and performing iteration until a mean square error detection is satisfied. The application optimizes the data association method, improves the consistency of algorithm estimation, and reduces the algorithm calculation complexity, and makes up for the deficiency of ICNN and JCBB algorithms in large-scale underwater environment.

The invention discloses a short-wave communication equalization method and system based on decision feedback iteration, and the method specifically comprises the steps: enabling a transmitting end to carry out the coding, interweaving and mapping of data information, generating a plurality of unknown data blocks, enabling known data sequences to be inserted between the unknown data blocks at intervals, forming a data segment sequence, and transmitting the data segment sequence to a receiving end through a channel; a receiving end extracts known data sub-segments and sends the known data sub-segments to a channel estimation module to estimate channel impulse response, the known data sub-segments and unknown data sub-segments extracted by the receiving end are sent to a first equalizer at the same time for segment-by-segment equalization, and then LLR calculation, de-interleaving and decoding are carried out; judging whether the decoding result meets an iteration ending condition or not, and if yes, ending iteration; and otherwise, coding, interleaving and mapping a decoding result, dividing a mapping result into mapping sub-segments, inputting the mapping sub-segments as initial values of iteration into a second equalizer for segment-by-segment equalization, and then performing LLR calculation, de-interleaving and decoding processes until iteration is finished. The short-wave communication receiving performance is improved, the operand is low, and the numerical calculation stability is good.

The application relates to a commodity title abstract generation method and device, equipment and medium in the computer technology field, the method comprising: acquiring a title text of a commodity; extracting knowledge entries belonging to product words and belonging to attribute words from the title text; determining information scores of each knowledge entry according to statistical features of the knowledge entries; selecting corresponding product word and attribute word combination texts to construct a first candidate abstract set; calculating the similarity between multiple long texts combined by part word elements in the title text and the title text; selecting long texts with high similarity to construct a second candidate abstract set; inputting data pairs composed of the title text and each candidate abstract in the first candidate abstract set and the second candidate abstract set into a pre-trained text classification model to the convergence, predicting the quality scores corresponding to each candidate abstract, and selecting a candidate abstract with a high quality score as the abstract of the title text. The application can generate high-quality abstracts.

The application discloses a fly ash treatment production line real-time monitoring system and method based on multi-sensor fusion, relates to the technical field of fly ash treatment production line monitoring, and comprises the following steps: based on a fly ash treatment process, stages of a fly ash treatment production line are divided, after the division is completed, input parameters of each stage are obtained, the input parameters are output by an upstream stage, and output parameters of the input parameters are transmitted to a downstream stage; data information of each stage of the fly ash treatment production line is collected through a sensor, and data of each stage is respectively composed into a data set; by comprehensively considering weights, real-time values, target values and upper limits of running deviations of monitored physical parameters in the stage, a safety risk assessment value of the stage under a current running state is calculated; a risk transmission coefficient is calculated, a dynamic adjustment model is established by analyzing a relationship between the calculated risk transmission coefficient and a product failure rate, and fly ash treatment production line real-time monitoring is realized.

The invention relates to a data storage method and device, computer equipment and a storage medium. The method comprises: for different types of composition data in anti-cheating data, according to data request information corresponding to each type of composition data, determining a target database suitable for storing each type of composition data, the target database being a storage database, and different storage databases corresponding to different storage costs, different types of component data in the anti-cheating data are stored in different storage databases under the condition that the storage cost is reduced, so that the storage cost of the anti-cheating data is reduced, and the problem that the database storage cost is high due to the fact that a single database is adopted for storage in an existing anti-cheating process is solved.

Systems and methods for describing a composition of an article of manufacture are disclosed. In one aspect, a method includes receiving article composition data for an article of manufacture that identifies a set of parts of the article, a stated composition for each part of the set of parts, and a physical quantity of the stated composition. The method further includes classifying the stated composition of each part of the set of parts into a normalized composition that includes a set of normalized chemicals. The method further includes outputting an aggregated physical quantity of each normalized chemical for the set of parts of the article. The method can include classifying a normalized composition of each part into a material category within a hierarchical taxonomy based on the set of normalized chemicals of that normalized composition and outputting an aggregated physical quantity of each material category for the parts.

This invention discloses a dynamic error control method for automatic drilling and riveting equipment based on an error mechanism model, comprising: acquiring production data and classifying the production data according to the causes of errors to construct a processing dataset; constructing a mathematical model based on the error generation mechanism, inputting the processing dataset into the mathematical model, and obtaining optimal parameters affecting processing quality based on the principle of minimum parameters; labeling the optimal parameters based on processing quality, and combining the labels and optimal parameters into a dataset; constructing an initial model and training it using the dataset to obtain a predictive model for predicting the processing quality of the automatic drilling and riveting equipment. This invention also provides a dynamic error control device for automatic drilling and riveting equipment. The method provided by this invention can solve the problem of poor product processing quality caused by processing errors.

The invention discloses a data screening method and device, a storage medium and a program product. The method comprises the following steps: a data consumption network element sends a first request to a data screening device, the first request indicates to perform data screening on data in a collected data set, and the first request comprises a method for screening the data in the data set; the method for screening the data in the data set comprises the following steps: data types contained in each piece of data forming the data set in the data set; the data screening device sends a first response to the data consumption network element, the first response comprises first data, and the first data is a data set composed of data meeting the data screening method in the data set. The data consumption network element instructs the data screening device to screen the data in the collected data set, and provides a method for screening the data in the collected data set, so that the data screening device screens the data in the data set according to the method, and effective data meeting the requirements of the data consumption network element is provided.

This invention discloses a two-phase flow identification system and method based on critical elevation maps, comprising the following steps: acquiring relative position data of all equipment, pipes, and valves in the pipeline system to be analyzed, from the starting equipment to the final equipment, as well as operating temperature, operating pressure, and medium composition data of each material flow point; drawing a critical elevation map based on the collected data; and analyzing the impact of pressure and temperature changes at each material flow point on the medium phase state using the critical elevation map, combined with the thermodynamic properties and flow characteristics of the medium, to identify locations prone to gas-liquid two-phase flow. This invention, by establishing critical elevation maps, can identify pipelines prone to two-phase flow in advance, preventing gas-liquid two-phase flow in process equipment pipelines, which can lead to long-term pipeline vibration, resulting in pipeline fatigue, support structure failure, weld cracking, and flange leakage. Furthermore, for pump inlet pipelines, it avoids the presence of two-phase flow that could cause pump cavitation and damage to the pump equipment.

Method for selecting target foam pieces that comprise target foam material that is suitable for a predetermined recycling process, the method (102) comprising the steps of: providing a scrap material obtained by dismantling and / or shredding a vehicle, the scrap material comprising one or more foam pieces, performing a first analysis step (102A) for determining first composition data of a sample foam piece, the first composition data being indicative of a composition of the sample foam piece, performing a second analysis step (102B) for determining second composition data of the sample foam piece, the second composition data being indicative of the composition of the sample foam piece; and selecting (102C) the sample foam piece as a target foam piece upon determining that the first composition data and the second composition data are indicative of the target foam material.

The invention relates to a rendering scene division and storage optimization method and system oriented to a mass model, and the method comprises the steps: collecting geometric data of all components in a scene, obtaining vertex information, calculating the bounding box information of the components and primitives, carrying out the statistics of material information and reuse conditions, and obtaining the preprocessing information of the components; initializing a BVH root node based on the component preprocessing information and setting a global bounding box, and carrying out BVH division on the component based on the length of a diagonal line of a space of the bounding box to obtain a scene division result; according to a scene division result, organizing geometric data files according to BVH node numbers, classifying vertexes, indexes, normal lines and uv data, putting the vertexes, the indexes, the normal lines and the uv data into Blocks, and recording geometric body position information in the blocks; a streaming rendering Node structure directory is constructed based on a BVH tree, and component transformation information, material markers and independent rendering block composition data are recorded in nodes.

The embodiment of the invention provides a method for generating a macro layout data set for machine learning, which belongs to the technical field of data processing, and specifically comprises the following steps of: 1, importing original layout design data of a data set needing to be generated into a layout device; 2, extracting layout information corresponding to the original layout design data in a macro layout stage of an automatic layout and wiring process at the back end of the physical design; step 3, setting all operable macro blocks in the layout information from a fixed state to a movable state; step 4, obtaining a target instruction input by a user; 5, moving operation is circularly executed on all the operable macro blocks according to the target instruction and the constraint conditions, and a macro layout data set containing multiple pieces of layout design data is generated. By means of the scheme, a large amount of layout data of different macro layouts are generated, the generated data form the data set, and the problem that a public data set is lacked is solved.

The application discloses a rib center line extraction method and system based on point cloud, and the method comprises the following steps: step 1: a rib original CT data, an independent rib label and an independent rib center line label are combined to form a data set; step 2: the rib CT data is converted into point cloud data; step 3: the rib point cloud data is down-sampled; step 4: the rib point cloud data is pre-processed; step 5: an independent rib segmentation model based on point cloud is constructed; step 6: the model of step 5 is trained and verified; and step 7: rib CT prediction and rib center line extraction. The application utilizes point cloud to efficiently use spatial convolution, which is beneficial to extracting multi-scale and multi-level local feature information. Meanwhile, the point cloud data operation has the advantage of high efficiency, and can effectively avoid the technical problems of long time consumption and insufficient precision in the previous rib center line extraction method.

The invention discloses a vertical mill air inlet duct obtaining method and evaluation method based on topological optimization and volute guide. At present, there is no standard and quantitative acquisition and evaluation processing mode for the air inlet duct of the vertical mill, and the evaluation method for the air inlet duct of the vertical mill comprises the following steps: after determining an internal fluid domain of the vertical mill in an original model, carrying out two-dimensional topology processing on the internal fluid domain of the vertical mill to form first composition data; after the first composition data is compared with the internal fluid domain of the vertical mill, corresponding data is determined as first composition optimization data at the minimum pressure loss value of a main machine, and the internal fluid domain of the vertical mill is guided by a volute to form second composition data; comparing the second composition data with the internal fluid domain of the vertical mill, determining corresponding data as second composition optimization data when the wind speed of the wind ring outlet and the wind speed of the main machine outlet are at the highest value in the uniform state, and establishing an ultimate model of the whole vertical mill according to the first composition optimization data and the second composition optimization data. And performing a grading evaluation process on the ultimate model by adopting a steady-state solving method.

The invention discloses a soil microbial community composition data batch processing system, method and equipment, and relates to the technical field of microbial community processing, the system comprises: a flora data acquisition module acquires a flora data file, the flora data file comprises original flora measurement data of each soil sample in a plurality of soil samples, and the original flora measurement data is stored in a database; the sorting rule configuration module generates sorting rule data, the flora data sorting module performs sorting processing on the flora data file based on the sorting rule data, determines a parent class of each subclass in each flora original measurement data in the flora data file, obtains a sorted flora data file, and sends the sorted flora data file to the data processing module; and the flora data statistics module calculates the content of each subclass and the content of each parent class in each piece of flora measurement data in the sorted flora data file to obtain a flora data file after statistics. According to the invention, batch processing of the soil microbial community composition data can be automatically completed, and the processing efficiency is improved.

The application relates to the technical field of data prediction, and discloses a marine foundation bearing capacity prediction method and system based on scour pit fractal reconstruction and deep learning, a terminal and a storage medium.The method comprises the following steps: acquiring measured three-dimensional point cloud data, performing interpolation and reconstruction, and generating a three-dimensional scour pit digital geometric model; based on the three-dimensional scour pit digital geometric model, the foundation limit bearing capacity under different scour pit morphologies is calculated, and a scour pit morphology quantitative descriptor is extracted to form a data pair sample library; the deep learning model is trained by taking the data pair sample library as a training set, and a trained deep learning prediction model is obtained; the newly detected scour pit morphology data is input into the deep learning prediction model after being reconstructed and quantitatively described, and the foundation prediction bearing capacity under the current morphology is obtained. The application realizes end-to-end rapid prediction from detection data to bearing capacity evaluation, and provides an efficient and accurate decision support tool for the safe operation and maintenance of marine structures.

The application discloses a disinfection process risk dynamic early warning and tracing method and system based on a multi-source data chain. The method comprises the following steps: collecting multi-source sensor data of a disinfection process according to a first period to generate standardized data records; for each key parameter, a dynamic safety boundary is dynamically determined according to historical data, and when the average value of the parameter exceeds the boundary, the boundary breakthrough is determined and the risk integral is accumulated; the standardized data records and their statistical characteristics are combined into data blocks according to a second period greater than the first period, and the hash chain is used for storage; when the trigger condition is met, the tracing is triggered, the hash chain integrity is verified by reverse scanning from the latest data block, the risk source is located according to a preset risk propagation rule table, and the risk propagation path is output. The application separates real-time early warning from hash storage, realizes dynamic adaptive boundary, risk accumulation attenuation, anti-jitter triggering and credible tracing, can quickly output the risk propagation chain, and effectively improves the safety and fault troubleshooting efficiency of the disinfection process.

The invention discloses a data display method and system based on multi-source data fusion, and relates to the technical field of big data. When the system runs, data feature extraction is carried out on access information of a user through a log extraction module, and a data type log information group is formed; performing type classification marking, quantity marking and data state information marking on the data type log information group through a data type marking module to obtain a data marking information group, and processing through a type level extraction module to form a first data set, a second data set and a third data set; a data cache index module analyzes and obtains a data type processing index Clzs, a data cache index is obtained after matching, an evaluation scheme is established, specific execution is carried out according to the content of the scheme, the user query access rate is dynamically evaluated, the resource utilization rate is improved for data display, and the user experience is improved. And the multi-source data fusion display effect can be displayed more effectively and quickly.

This invention relates to a ground identification method and apparatus, mobile tools, and related products. The method includes: performing ring-direction alignment processing on a single frame of multi-ring point cloud transmitted by a multi-line lidar to generate a multi-ring aligned point cloud; for each ring aligned point cloud, forming data elements from each data point in the ring aligned point cloud and its forward, backward, and left points and storing them in a queue, and sequentially calculating the connectivity relationships of the data points in each data element; generating a connectivity graph of the ring aligned point cloud based on the connectivity relationships of the data points in each data element; determining suspected ground points from the multi-ring aligned point cloud based on the connectivity graph corresponding to the multi-ring aligned point cloud; fitting a ground equation based on the suspected ground points; and determining ground points from the multi-ring aligned point cloud based on the ground equation. This invention reduces the algorithmic complexity of ground identification in point clouds and improves computational efficiency by designing the connectivity graph of each data point in the multi-ring point cloud.