16results about How to "Improve data availability" patented technology

The invention provides an erasure code compatible read-write method and system based on a bidirectional data access agency, and relates to the technical field of distributed storage, and the method comprises the steps: employing multi-agent reinforcement learning to construct a heterogeneous resource portrait and optimize task allocation, combining a space-time graph convolutional network and a federated learning framework to construct a data format conversion model, and constructing an erasure code compatible read-write model; data features are analyzed through an encoder based on an attention mechanism, a fragmentation strategy is generated, self-adaptive encoding is achieved through a capsule neural network, data integrity verification and repairing are conducted through a double-layer space attention network and a graph attention network, the data access efficiency of a heterogeneous storage system is improved, data format compatibility is enhanced, and the data access efficiency is improved. And the data integrity is ensured.

The invention discloses a method for automatically generating a dynamic language test case based on a large language model, which comprises the following steps of: firstly, in a preprocessing stage, arranging related information of a to-be-tested program into prompts, analyzing a parameter type of the to-be-tested program by the large language model according to the prompts, and generating code information required by a test through an abstract syntax tree; in the test case generation stage, a dynamic multi-objective optimization algorithm is adopted, test cases are continuously crossed and mutated through branch coverage feedback, in the partial evolution process, test data which cannot be generated by conventional variation are generated by utilizing a large language model, and finally, the screened excellent test cases are organized into a complete test suite. In a program repair phase, the test suite is run to detect potential errors, the errors are analyzed and iteratively repaired using a large language model, and an executable and error-free test suite is generated. According to the method, the test coverage rate of the dynamic language is remarkably improved, and the achievement of a coverage target is accelerated.

The invention discloses a differential privacy federal learning method and system based on double knowledge transfer, and relates to the field of machine learning, and the method comprises the steps: training a teacher model through a data subset; determining a teacher voting result of each sample by using a weight-based voting mechanism; based on a teacher voting result, determining a label of an unmarked teacher data set, and training a student model by using a marked teacher data set; determining a student voting result of each sample in the medical server by using a weight-based voting mechanism; labels of unmarked student data sets are determined based on student voting results, and a global model is trained by using marked student data sets; and performing fine tuning and further self-training on the global model by using an importance sampling mechanism to obtain a final global model. According to the invention, a voting mechanism based on weight is introduced in knowledge transfer, and the model weight is adjusted according to the individual difference of the teacher model, so that the model effectiveness of federated learning is improved, and the communication overhead is effectively reduced at the same time.

This invention belongs to the field of photocrosslinking probe technology, specifically relating to a GUSB photocrosslinking probe, its preparation method, and its application. The GUSB photocrosslinking probe has the general chemical formula C0. 13 H 18 N2O6-R, wherein R is any one of the oxalic group elements or peptide bonds. The R group covalently connects the glucuronic acid recognition core and the photosensitive unit in the GUSB photocrosslinking probe, and the R group contains no more than 4 atoms, resulting in a small size and low steric hindrance. The GUSB photocrosslinking probe provided by this invention uses glucuronic acid as the recognition core, combined with a small-volume diazacyclopropane photosensitive unit and a terminal alkyne group, to achieve µM-level in-situ covalent capture of active GUSB in complex systems such as cells or tissues. The probe of this invention significantly improves positioning accuracy, sensitivity, data availability, quantification, and enrichment, while also possessing advantages such as modular synthesis, mild conditions, and strong system compatibility, effectively addressing the technical shortcomings of existing technologies that struggle to accurately obtain GUSB activity in situ under low-dose conditions.

The invention relates to the technical field of environment monitoring, and discloses a plateau lake basin cultivated land soil pollution multi-index synchronous monitoring method. The method is suitable for plateau lake basin cultivated land, and under the condition that plateau lake basin cultivated land soil measured data is high in acquisition cost and difficult to acquire in quantity, a spatial distribution diagram of cultivated land soil non-point source pollution source load indexes is acquired in a large-scale, low-cost and high-efficiency manner by assisting limited field monitoring data through a satellite remote sensing means. Through long-term soil pollution index content and variable quantity monitoring and risk level evaluation, visualization, quantifiability, traceability and prevention and control of non-point source pollution are realized, the limitation of replacing surface by point in traditional point location measurement is overcome, and the method is the most efficient method for evaluating the potential risk of agricultural non-point source pollution.

The invention discloses a smart park articulated naturality web node data distributed storage system, and relates to the technical field of smart park data storage. Comprising a data acquisition layer, a distributed storage node cluster, a metadata management module, an intelligent scheduling module, a data security guarantee module, a data verification module, a data adjustment module and an application interface layer, and all the modules cooperate to realize efficient storage, quality control and management of articulated naturality web node data. Aiming at a smart park articulated naturality web data storage pain point, efficient storage and management and control are realized through multi-module cooperation, a distributed architecture, mixed storage and intelligent scheduling are adopted, the storage efficiency and the resource utilization rate are improved, multi-copy backup, cross-physical deployment and automatic fault repair are realized, data reliability is guaranteed, and the system is suitable for large-scale popularization and application. The method comprises the steps of full-process encryption, authority control and multi-dimensional verification, building of a data security defense line, data dynamic optimization and standardization of an interface, enhancement of data availability and system compatibility, and adaptation to the dynamic increase demand of park data.

The invention discloses a concrete mixing quality prediction method based on mixing condition self-adaption and an AI model, and belongs to the technical field of concrete mixing quality prediction, and the method comprises the following steps: collecting batch working condition information related to a concrete mixing technology and a multi-source process signal in a mixing process, judging the working condition type of the current batch, and determining the working condition type of the current batch; determining feature normalization parameters of the current batch, performing feature extraction on the collected multi-source process signals according to process stages to obtain normalized feature expression, constructing the normalized feature expression and batch working condition information as feature input of the current batch, and inputting the feature input into an artificial intelligence model to obtain an artificial intelligence model; and obtaining a prediction result including the concrete quality index set. According to the method, the problems that the cross-working-condition generalization ability is insufficient, the dependence on calibration data is relatively strong, and the output stability and the engineering suitability need to be improved in an engineering scene with complex working condition fluctuation, process event disturbance, equipment difference and long-term drift are solved.

The application relates to the technical field of data processing, and discloses a multi-source heterogeneous data edge processing method and device for field monitoring and a medium, which comprises the following steps: based on a data access link, parallelly receiving original data generated by different monitoring devices in a field monitoring site; extracting multi-dimensional features to determine the data source types corresponding to the original data; calling corresponding analysis engines according to the data source types to generate analysis results through differential analysis; determining the time field priority according to the data source types, selecting target time fields from the analysis results, performing unified event time axis mapping, and generating standardized data; writing the standardized data into a classified cache queue, determining the time effectiveness level according to the time difference between the standard event time and the current time, and performing hierarchical sending scheduling based on the time effectiveness level and the network state. The edge access capability of multi-source heterogeneous data in the field ecological monitoring scene, the cross-source time correlation capability and the key data sending capability under the weak network condition are improved.

The invention is applied to the field of logistics management, and discloses an intra-city delivery service provider scheduling method and system, and the method comprises the steps: obtaining real-time heterogeneous service data of each delivery service provider and real-time context data corresponding to an intra-city delivery request in response to the intra-city delivery request; performing protocol conversion on each piece of real-time heterogeneous service data to obtain standardized distribution service data; splicing the standardized distribution service data with the real-time context data to generate distribution feature vectors corresponding to distribution service providers; inputting each delivery feature vector into a pre-trained scheduling decision model, screening a target delivery service provider based on a model output result, and generating a standard scheduling instruction of the target delivery service provider; performing reverse protocol conversion on the standard scheduling instruction to obtain a target execution instruction adaptive to the target distribution service provider; and executing the target execution instruction to complete the city-wide distribution request. According to the method provided by the invention, the service scheduling efficiency is improved.

The invention discloses a molecular structure-activity relationship modeling method based on symbol regression KAN, and belongs to the field of chemoinformatics. The method comprises the following steps: constructing and cleaning molecular property data to form a molecular-chemical descriptor-target property data set; calculating and screening descriptors, and reducing redundancy and colinearity by adopting variance screening and correlation analysis; and training the KAN to obtain a target property prediction function and exporting an explicit expression. By taking the organic amine CO2 absorption cycle period as an example, the upper / lower limit auxiliary screening of the cycle period can be predicted. According to the modeling method, an interpretable basis is provided under a small sample condition, and the dependence on a large amount of experimental data is reduced.

According to the user track protection method and device in crowdsourcing service, the equipment and the storage medium provided by the invention, firstly, the track data generated by the user is acquired, the privacy budget corresponding to the track data is calculated, the target position point in the track data is predicted, and the prediction result corresponding to the target position point is obtained; and then an error between the target position point and a prediction result corresponding to the target position point is calculated to obtain an importance evaluation result corresponding to the target position point, and finally the privacy budget is correspondingly allocated to the target position point according to the importance evaluation result. The privacy of the user can be effectively protected, the maximization of the data value can be ensured, and a safe and credible interaction relationship is established between the user and a service provider. By applying the Kalman filtering technology and comparing a predicted position point with an actual position point, the importance of the position point is evaluated, and a better privacy protection effect and higher data availability are achieved.

The invention discloses a prepaid function remote control method based on a power supply transfer main body and a terminal user, relates to the technical field of prepaid function remote control, and solves the problem that in the prior art, energy consumption data analysis cannot be performed on the terminal user and the corresponding power supply transfer main body when the terminal user performs remote meter reading; the method specifically comprises the following steps: performing energy consumption data analysis on a power supply main body and a terminal user, deducing whether the current power consumption is abnormal or not according to the energy consumption data analysis, and performing remote meter reading when determining that the current power consumption is abnormal; performing real-time energy consumption prediction on the terminal user, and performing prepayment recommendation according to the energy consumption prediction; according to the real-time energy consumption demand prediction analysis of the terminal user, deducing whether the electricity consumption habit and the electricity consumption demand of the current terminal user need to be prepaid or not; and carrying out supply efficiency analysis according to the power supply process of the power supply transfer main body and the terminal user, and formulating an electric loss sharing rule according to an efficiency analysis result.

The invention provides a data privacy protection method and system based on data annotation, and the method comprises the steps: carrying out the data form reconstruction operation of a talent data set, obtaining a semantic data unit set, carrying out the feature cluster division operation of the semantic data unit set, generating a feature cluster set, and carrying out the data privacy protection based on the semantic density value and field association strength value of each feature cluster. Calculating a noise type and a noise parameter corresponding to each feature cluster by combining sensitive attribute tags of the feature clusters to obtain a feature cluster noise parameter comparison table, and mapping the semantic data unit set into the feature cluster noise parameter comparison table according to a feature cluster attribution relation; performing corresponding noise generation and injection on feature cluster data fields of different sensitive attribute tags to generate a disturbance data set, performing correlation degree evaluation on the disturbance data set, and judging whether a preset correlation maintaining threshold value is met or not by calculating a correlation degree change rate between fields in a semantic data unit before and after disturbance; and if yes, outputting a privacy protection data set for labeling the task. According to the method, the privacy protection accuracy and the data availability are improved.

This invention discloses a method for protecting electricity metering data, comprising the steps of: acquiring electricity metering data; acquiring a predefined localized differential privacy function; binarizing the electricity metering data and performing random perturbation using the localized differential privacy function and a Cuckoo filter; sending the data to a server; the server receiving the data, obtaining statistical information on specific categories of data, calculating an unbiased estimate of the proportion of the specific categories of data, thereby obtaining the filtered statistical results of the electricity metering data. Implementing this invention, by employing a localized differential privacy protection method, can improve user privacy regarding electricity metering data.

The present invention relates to a method and system for generating global land surface temperature data based on MODIS data. The method comprises: performing mean processing on global land surface temperature LST data and global sea surface temperature SST data; resampling SST data according to the resolution of LST data; splicing the resampled SST data with LST data; interpolating the spliced ​​data, including interpolation of mid-latitude missing value areas and interpolation of Arctic missing value areas; wherein for missing values ​​in the Arctic region, the temperature value of the region is reassigned; and generating global land surface temperature GST data according to the interpolation result. The method and system for generating global land surface temperature data based on MODIS data provided by the present invention can not only take into account the information of clear sky images, but also avoid the problem of low data accuracy caused by the warming / cooling effect of cloud cover on the surface temperature.

The present invention relates to the field of artificial intelligence technology, and provides a method, system, device, product and medium for generating a multi-sea condition detection data set, including deploying a drone and a camera in a target sea area; the camera tracks a passing ship in the target sea area, and collects and obtains the first ship automatic identification system information of the passing ship, selects a calibration key point set by an approximate grid selection method, and completes the calibration of the camera by the calibration key point set and the first ship automatic identification system information; the camera monitors the target sea area, and a monitoring module monitors and obtains the second ship automatic identification system information, thereby obtaining a multi-modal ship sample; the multi-modal ship sample and the sea condition description text are input into a text image generation model to obtain an initial multi-sea condition ship data set; the initial multi-sea condition ship data set is tested to obtain a target multi-sea condition ship data set. The present invention can effectively and automatically generate a multi-sea condition ship data set.