38results about How to "Easy data management" patented technology

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

The invention discloses an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention belongs to the technical field of spectral analysis, and discloses a spectral analysis and image recognition combined intelligent stoma evaluation system and method.A three-dimensional scanning module captures the three-dimensional form of a stoma and the three-dimensional form of surrounding tissue of the stoma through the high-precision laser or structured light scanning technology; the spectral analysis module is used for accurately analyzing the color change of the stoma; the tiny color difference of the stoma area is detected to help to identify potential tissue abnormality; the image recognition and processing module is used for analyzing the collected stoma image by using a deep learning algorithm and automatically recognizing morphological characteristics of the stoma; the data analysis and feedback system is used for automatically generating a stoma health assessment report according to the collected multi-dimensional data; through a set standard reference value, an actual measurement value is compared, and a corresponding suggestion is given, for example, whether a nursing scheme needs to be adjusted or further medical intervention needs to be carried out. According to the method, the real-time state of the stoma is accurately evaluated through non-contact scanning and spectrum detection.

The invention discloses a low-code platform development method and a low-code platform system, and belongs to the technical field of software development, the overall architecture design of a low-code platform is carried out based on Vue2 and TypeScript, and the method comprises the combination of modularization, data management and visual editing function modules; the system comprises a component management module which comprises a component library and user-defined component management; the visual editor is used for realizing real-time layout adjustment and attribute configuration; the user previews the modification effect in real time by dragging and adjusting the position, the size and the hierarchical relationship of the component; the data management module is used for realizing data binding, API integration and dynamic data management; and the performance optimization module comprises a virtual DOM (Document Object Model) optimization and on-demand loading mechanism. The invention provides a development environment which is high in expandability and easy to use. A complex coding process is simplified into visual operation, and cooperation between developers and non-technical users is promoted.

The invention discloses an ultrathin electronic glass roller way speed measurement support and a speed measurement method, and the support comprises a support which is connected to a mounting disc for fixing a supporting roller through a bolt, the support is fixedly provided with an encoder, a detection shaft of the encoder is connected with a connecting disc through a coupler, the connecting disc is clamped at the end part of the supporting roller, and a signal of the coupler is connected with a speedometer. And a driving control system and an alarm of the supporting roller are connected through a linear speed meter. The effect of improving the roller way rotating speed detection precision and frequency is achieved.

The invention provides a printing method and device of an Excel-based printing template system. The method comprises the following steps: making an Excel template according to a receipt printing template requirement of a unit; carrying out the binding of the Excel template; the Excel template obtains data from a database, data filling is carried out through the Apache POI technology, and a complete Excel file filled with the data is generated; converting the Excel file into an HTML (Hypertext Markup Language) code; matching all bound functions in the HTML code through a regular expression, running corresponding function code blocks, and replacing the bound functions with function running results; according to the printing size configured by the template, a script is embedded in an HTML code, and the scaling is adjusted; and the browser executes the printing operation to complete printing, so that the user can quickly print, and the operation difficulty is reduced.

The invention provides a data persistence storage method and device for a vehicle-mounted system and a storage medium. The method comprises the steps that vehicle configuration information is written into a persistent storage area in a key value pair mode; determining a target storage area according to the received key value pair information; vehicle configuration information is written or updated in the target storage area, the vehicle configuration information is stored in a preset format, and data of different partitions are isolated and managed according to identities or permissions of callers; when the vehicle configuration information is read, a reading request is sent to the persistent service module through the persistent service interface by utilizing the upper-layer application, so that the persistent service module determines a corresponding storage area according to the key value pair information in the reading request, and the corresponding vehicle configuration information is acquired from the storage area. According to the invention, the data consistency can be ensured, the data management is simplified, and the scheme can be efficiently integrated in different system environments.

The embodiment of the invention discloses a multichannel annular access method and system for meteorological observation data, and belongs to the technical field of meteorological data processing. According to the embodiment of the invention, the annular buffer structure is adopted to manage meteorological observation data, and compared with a traditional linear stack, the annular buffer has obvious advantages. The linear stack is low in efficiency when the data volume is large, and the annular buffer supports multiple independent data streams to share a storage space, so that continuous writing and cyclic coverage are realized, for example, different sensor data streams can be stored orderly. Read-write pointers circularly move to ensure continuous access of data, the parallel access capability is improved in speed and efficiency, and the multi-task concurrence requirement of meteorological services is met. And an overflow and underflow detection mechanism and a capacity management strategy guarantee data integrity and system stability, and prevent problems of data loss or system crash and the like. Attribute knowledge mining and related mapping are beneficial to accurate management of data, management is carried out according to data characteristics, decision accuracy and efficiency are improved, and data management and operation of a meteorological service system are optimized.

The invention discloses a steel plate drilling automatic online detection system based on machine vision, and relates to the technical field of steel plate drilling machining systems. The system comprises a control system module, the input end of the control system module is electrically connected with an image processing module, the input end of the image processing module is electrically connected with an image acquisition module, and the output end and the input end of the control system module are electrically connected with a deep learning module. The output end and the input end of the control system module are electrically connected with a data processing module. According to the invention, the deep learning module uses a deep learning algorithm to train a large number of drilling images, automatically learns the characteristics and modes of drilling, improves the accuracy and robustness of detection, can continuously optimize the performance of itself, adapts to different types of steel plates and drilling requirements, and improves the accuracy and robustness of detection. And the intelligent diagnosis module is combined with an expert system and data analysis to perform intelligent diagnosis on a detection result.

The invention provides a low-code system for data analysis and data cleaning based on Internet of Vehicles data. The low-code system comprises a multi-protocol adapter, a user-defined analysis rule writing interface, a dragging type rule configuration interface, a data processing module, a forwarding mechanism unit, a monitoring and log tracking unit and a message labeling and fingerprint generation unit. Wherein the multi-protocol adapter is used for supporting log data acquisition of various network protocols; the user-defined analysis rule writing interface allows a user to write specific analysis logic according to a data format and a service requirement; meanwhile, for compatibility of various data codes and formats, rule templates and examples are provided to guide a user to compile analysis rules, and the user is helped to quickly position and correct problems in the analysis rules by integrating corresponding error detection and debugging tools; and a user carries out graphical operation combination input, output and analysis on rule nodes through the drag-and-drop rule configuration interface.

The invention discloses a wool fineness determination method and system based on deep learning, and belongs to the field of wool fineness determination, and the method comprises the following steps: S1, collecting a wool fiber image, and obtaining a data set; s3, obtaining fineness characteristics of wool; s4, constructing a convolutional neural network model, inputting the training set into the convolutional neural network model for training, inputting the verification set into the trained convolutional neural network model, and verifying and evaluating the convolutional neural network model by using cross validation; s5, inputting the test set into the verified convolutional neural network model, predicting the fineness of the wool fibers by using the convolutional neural network model, and outputting a fineness evaluation report; and S6, displaying and storing a result. By adopting the wool fineness determination method and system based on deep learning, wool fineness determination can be efficiently and accurately completed, the advantages of simple operation, non-contact measurement, reliable result and the like are achieved, and the automation level of wool detection is greatly improved.

This invention relates to a tire including an electronic device with a data structure for tire management. The invention provides a tire including an electronic device with a data structure for tire management, the tire being configured to have the electronic device, the electronic device comprising: a substrate portion having circuitry formed thereon; an antenna portion disposed on the substrate; and an EPC memory portion disposed on the substrate; the EPC memory portion being configured to record and read tire IDs of 96 bits or more, and the EPC memory portion having a plurality of memories configured to divide and store product serial number portions.

The invention provides a blast furnace condition diagnosis method based on a multi-time granularity dynamic reasoning mechanism, which comprises the following steps: S1, setting a plurality of time granularities, and establishing a furnace condition diagnosis rule base according to a preset rule; s2, collecting data in real time, storing the data in a dynamic database, extracting the data of the dynamic database every first time granularity, performing furnace condition diagnosis of the first time granularity according to rules of a furnace condition diagnosis rule base, and storing a diagnosis result in a process database; s3, calling data of the process database and the dynamic database every Nth time granularity, performing furnace condition diagnosis of the Nth time granularity according to rules of the furnace condition diagnosis rule base, and storing a diagnosis result in the process database; and S4, calling diagnosis results of different time granularities. According to the blast furnace condition diagnosis method with multiple time granularities, a dynamic reasoning mechanism can reduce a large amount of database read-write burden, and the calculation performance of a furnace condition diagnosis model is improved.

The present application discloses a subject name classification and model training method thereof, a computer device and a storage medium, the method comprising: sampling multiple categories of original subject name data to obtain a subject name sample data set; performing data cleaning on the subject name sample data set to obtain a subject name standard data set; performing word segmentation processing on the subject name standard data set to obtain an initial subject name vocabulary list; performing cross-keyword elimination processing on the initial subject name vocabulary list to obtain a target subject name vocabulary list; extracting the embedded features of each target subject name vocabulary in the target subject name vocabulary list to obtain an embedded feature set; using a preset integrated model framework, training based on the embedded feature set to obtain a subject name classification model, which can effectively improve the accuracy of the subject name classification model, thereby facilitating data management and analysis in risk management, compliance management, business development and customer acquisition, data governance and other aspects.

The invention relates to an industrial data processing method, system and device based on an LAMBDA architecture. The method comprises the following steps: extracting periodic features of periodic historical data, constructing a periodic feature library, and setting corresponding periodic repair rules according to data with different features in the periodic feature library to obtain a periodic repair rule set; performing periodic feature recognition on the real-time data according to the periodic feature library to obtain periodic real-time data and non-periodic real-time data, defining a business rule according to the non-periodic historical data, and performing data repair on the non-periodic real-time data according to the business rule by adopting a rule engine to obtain a repair result. Performing data restoration on the periodic real-time data by utilizing the periodic restoration rule set; and performing data analysis according to the repaired periodic real-time data and the aperiodic real-time data. The method can adapt to periodic change and business requirements of data, and the flexibility and the intelligent level of the system are improved.

The invention discloses an LU-SGS implicit propulsion calculation method under an adaptive Cartesian grid, which relates to the field of numerical simulation, and comprises the following steps: step 1: generating an equidistant and uniform initial Cartesian grid based on a size parameter, a motion parameter and aerodynamic parameter of an aircraft model, and allocating a multidimensional array number to each grid according to a dimension of space; step 2: performing a fork tree adaptive encryption on the grid within a preset range of an object surface boundary based on a geometric shape of the aircraft model, and generating an adaptive Cartesian grid required for calculation; step 3: sorting the generated adaptive Cartesian grid to obtain a sorting result; step 4: performing an LU-SGS implicit propulsion calculation based on the sorting result to obtain a numerical simulation calculation result of the aircraft model; the method can realize the LU-SGS implicit propulsion calculation under an adaptive Cartesian grid, thereby accelerating convergence to improve calculation efficiency.

The utility model discloses a low-energy-consumption high-quality rice bran steaming system. An outlet of a rice bran articulated chute is connected with an inlet of a rice bran steaming machine through an iron remover, a rice bran cleaning sieve, a scraper conveyor and an elevator; the upper three layers of the rice bran steaming machine are steam steaming layers, the lower parts are a hot air drying layer and an air cooling layer, and the tops of a steam interlayer, a hot air interlayer and a cold air interlayer below each layer are respectively provided with a punching sieve plate; a drainage port of the steam interlayer is connected with a medium-temperature condensate water tank through a water seal, an outlet of the medium-temperature condensate water tank is connected with a hot side inlet of an air-water heat exchanger through a medium-temperature condensate water pump, and a hot side outlet of the air-water heat exchanger is connected with a low-temperature condensate water tank. An outlet of the low-temperature condensate water tank is connected with a top water spraying opening of the rice bran steaming machine through a low-temperature condensate water pump; a cold side inlet of the air-water heat exchanger is communicated with atmosphere, and a cold side outlet of the air-water heat exchanger is connected with an air inlet of the hot air interlayer; an inlet of the cold air interlayer communicates with the atmosphere. The system is low in steam consumption, capable of achieving continuous production and environmentally friendly.

The application discloses a distributed adaptive framing output method based on large-range remote sensing images, and relates to the technical field of remote sensing image processing. The preprocessed large-range remote sensing images are stored in a COG format, the framing size and quantity are dynamically adjusted in combination with the target area range and resolution, the tasks are distributed by a data locality perception scheduler after Z-order curve sorting, the computing nodes load the framing data with a buffer area, the framing data is interpreted by a random forest, and the buffer area is cropped, finally, the virtual data set is aggregated, efficient distributed parallel processing of the large-range images is realized, the I / O bottleneck and the computing resource limitation are effectively relieved, the context missing problem on the framing boundary is solved, the intelligent interpretation efficiency and precision are improved, and the multi-resolution on-demand rendering and elastic expansion are supported to adapt to the data scale growth.

The invention discloses a block chain-based scientific research data storage and sharing system and method. The system comprises a data encryption storage module, a block chain network construction module, an intelligent contract application module and a data sharing module. The data encryption storage module is used for encrypting the scientific research data and storing the encrypted data; the block chain network construction module is used for constructing a distributed block chain network based on a scientific research institution, and adding the encrypted data into the block chain network according to nodes of the scientific research institution; the smart contract application module defines an access sharing rule based on the smart contract; and the data sharing module extracts the encrypted data from the data encryption storage module and decrypts the encrypted data by using a sharing rule to complete data sharing. According to the invention, high security and privacy protection of data are realized through AES encryption and DNA data storage technologies; by constructing the distributed block chain network, the data monopoly of centralized storage is broken, and the efficiency and reliability of data sharing are improved.

The invention relates to a method and equipment for generating an audio data mark. According to the method, the deep convolutional neural network pre-trained on a large data set is adopted for feature extraction, robust and high-level abstract features in the audio can be automatically learned and extracted, and the marking accuracy and the generalization ability to complex scenes are remarkably improved. Unreliable prediction results are filtered out by introducing a confidence coefficient screening mechanism; and the context metadata information of the audio is creatively fused, and the preliminary marking result is optimized and corrected, so that the finally generated mark is more reliable and conforms to the actual application scene, and the robustness of the system is effectively improved. Full-process automation of audio marking is realized, manual participation is reduced, marking cost is reduced, marking efficiency is improved, and the method is suitable for construction of a large-scale audio data set. According to the method and the equipment, modular design is adopted, the formed mark database is complete in structure, and subsequent model training, data management and retrieval application are facilitated.

The invention discloses a huge satellite base communication performance degradation analysis method for coverage multiple number sensitivity, and relates to the field of orbit analysis. The problems that an effective evaluation mode for the influence of the coverage multiple number and other performance is lacked and the sensitivity of the coverage multiple number and other performance changes caused by the constellation to the satellite number change is measured are solved. The method comprises the following steps of: 1, counting the satellite coverage condition of a specified place under the condition of step-by-step calculation and the satellite coverage condition of the specified place under the condition of geometric solution calculation; step 2, calling a satellite set which covers the place D in the time period T as S, arranging different coverage time periods corresponding to members in the satellite set S according to an ascending order of coverage ending time, and arranging members corresponding to the same coverage ending time according to an ascending order of coverage starting time; and step 3, determining the splicing time period number according to the average coverage multiple number and the target average coverage multiple number, and completing the sensitivity test. The method is applied to the field of spacecraft system simulation.

The invention discloses an abnormal sound monitoring device for a running gear of a rail train. The abnormal sound monitoring device comprises abnormal sound acquisition equipment, an intelligent terminal and data sharing equipment, the abnormal sound collection device collects abnormal sound signals generated in the train running process and comprises a support, a protection shell and an abnormal sound collection module. Wherein the abnormal sound acquisition module comprises a microphone array unit, an abnormal sound processing unit and a communication unit which are fixedly connected through connecting blocks; the intelligent terminal is in communication connection with the communication unit and is used for receiving and processing the abnormal sound signal; the data sharing device is in communication connection with the intelligent terminal and supports data storage and remote sharing. According to the monitoring device, through high-precision abnormal sound collection, efficient internal communication, stable external connection and comprehensive data management and remote sharing, the monitoring accuracy and reliability of the train running gear are remarkably improved, and a powerful guarantee is provided for safe operation of railway transportation.

The application discloses a kind of multi-density source three-dimensional density inversion method, electronic equipment and storage medium, belong to geophysical exploration and artificial intelligence cross technical field, aim at solving the problem that existing gravity inversion method is difficult to effectively separate and simultaneously invert underground multiple density sources.The method generates training samples through spatial partition strategy;Construct the UNet++ deep learning network of multiple channel output, so that its output channel corresponds to different density sources;Independent normalization strategy is used to process training label to ensure network training stability;Finally, through the trained model, efficient, synchronous inversion of multiple density sources is realized.The present application avoids the error accumulation in the conventional method, greatly improves the inversion accuracy and calculation efficiency, and provides effective spatial constraints for multi-density source separation.

The utility model provides a fruit and vegetable quality inspection table which integrates a workbench main body, a fixed panel, a cleaning unit, a weighing unit, a monitoring unit, an intelligent terminal and a power supply unit which are tightly matched to form an efficient quality inspection system. The working table main body bears each functional unit and provides an operation platform with reasonable layout; the clamping part of the fixed panel enables the functional hanging piece to be flexibly hung, so that the tool management is convenient; the cleaning unit and the weighing unit are arranged adjacently, and the operation process is optimized; the monitoring unit is linked with the intelligent terminal to realize visual management and data recording; the power supply unit provides stable power support and adapts to different scenes, the problems that a traditional quality inspection workbench is low in integration level and needs to be operated back and forth, manual recording is prone to errors and the like are solved, the quality inspection efficiency and accuracy are greatly improved, the data management and tracing capacity is enhanced, operation is more convenient and normative, diversified quality inspection requirements of fruit and vegetable delivery warehouses are met, and the product quality inspection efficiency is improved. The food safety and the sales quality are effectively guaranteed.

A cluster scheduling system and method for inspection robots on a belt conveyor track is disclosed. The system includes a master control inspection robot and slave inspection robots deployed on the other side of the belt conveyor. The master control inspection robot includes a central control inspection robot. The system comprises five steps: Step 1: The central control platform issues fixed-point inspection tasks; Step 2: The fixed-point inspection tasks are sent to the central control inspection robot; Step 3: The central control inspection robot receives the inspection tasks; Step 4: After receiving the inspection instructions, the master control inspection robot transmits the signals to the slave inspection robots; Step 5: Fault information is uploaded to the central control inspection robot through the master control inspection robot. The central control inspection robot's independent processing capability allows for flexible scheduling of the master control inspection robot and slave inspection robots, enabling fixed-point active inspection, automatic inspection, and same-side task substitution inspection modes. Tasks are decomposed layer by layer, allowing the inspection robots to share some of the information processing capacity, thus improving the timeliness and accuracy of fault identification.

The invention discloses an IOS system traceless burying point user behavior data processing method and device, the method is applied to an IOS system, and the method comprises the following steps: after determining that a preset burying point execution condition is met at present, according to a set event interception condition, intercepting a target event corresponding to the IOS system to obtain corresponding event interception data; according to the event interception data, an element identifier of the target event is constructed, and the element identifier is uniquely bound with the target event and is used for identifying the target event; the element identifier is also used for determining event association information between different target events; according to a preset data uploading mechanism, performing event judgment on the element identifier, and when a corresponding event judgment result shows that the element identifier meets an instant uploading condition, performing data uploading on the target event and the corresponding event interception data. Therefore, the data acquisition amount can be reduced, the data acquisition precision can be improved, the data utilization degree can be improved, and the data application scene and applicability can be expanded.