252 results about "Central database" patented technology

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

An automated damage severity assessment system and method is disclosed. The present system scans and assesses roof damage, and generates a damage severity report without the oversight of a roof inspector. The system comprises a central database of reference images and data that is constantly reviewed and updated to ensure accuracy of the reports, and aims to reduce the errors and waste inherent in the current methods.

The invention relates to the technical field of space governance, and discloses a multi-agent space collaborative governance method which comprises the following steps: processing multi-source heterogeneous data such as satellite images and sensor readings, establishing cross-type semantic association through a geographic space data embedding technology, generating a unified structured text after optimizing the satellite images through vLLM, and synchronizing the unified structured text to a central database; an agent role portrait is dynamically generated by the central server large language model based on a preset Prompt template, and generation does not depend on a fixed rule; then, selecting a target node in the edge-center architecture, disassembling a total task into sub-tasks, establishing semantic mapping, calculating a matching probability, and performing optimal distribution by a reward borrowing function; generating a governance scheme in a perception layer-decision layer-execution layer framework, and outputting a coded operation instruction; based on an execution feedback updating strategy, a multi-level mechanism is set, roles are automatically redistributed, and the governance continuity is guaranteed. According to the invention, the overall efficiency and reliability of space governance can be improved in the face of dynamic scenes or emergency situations.

The invention relates to the technical field of circuit board detection, in particular to a circuit board sensor nondestructive testing method based on big data, which comprises the following steps of: acquiring temperature, pressure, current and voltage data of key positions of a circuit board sensor, sending the data to a central database through real-time communication for summarizing, and synchronously performing data preliminary screening; and checking the integrity and availability of the data to form sensor summarized data. According to the method, the difference value between each data point and the adjacent data point is calculated, and the variation amplitude of the continuous data points is monitored, so that tiny abnormal fluctuation can be more sensitively captured, potential fault signals can be marked, an abnormal mode can be compared with a recorded fault case, and the fault mode and the frequent occurrence area of the fault mode can be identified; the fault mode is directly associated with the operation condition, the accuracy of fault prediction is enhanced, the dynamic adaptability of the detection system is ensured through monitoring adjustment of detailed fault analysis, and the efficiency of fault response is remarkably improved.

The invention relates to the technical field of die parameter optimization, and discloses a process optimization method and system for a multi-part adaptive self-adjusting stamping die, and the method comprises the following steps: generating a structured process input parameter set through input parameters, and storing the structured process input parameter set in a central database; searching related cases and rules from the knowledge graph according to the input parameters; and calculating the similarity between the current parameter and a historical case, using a hybrid algorithm, carrying out weighted averaging, explaining the weight of each parameter, processing special constraints, and finally carrying out normalization to obtain a comprehensive similarity index. Real-time monitoring and dynamic adjustment are achieved through an intelligent control system, self-adaptive mold design optimization and centralized material characteristic database management are combined, an integrated quality detection system automatically recognizes and classifies defects through image recognition and machine learning technologies, and the problems that key parameters cannot be adjusted in time according to actual working conditions, and the working efficiency is high are effectively solved. The production efficiency is low; and the product quality is unstable.

The invention relates to the field of data processing, in particular to a data acquisition method based on an electric energy meter, which comprises the following steps: deploying an intelligent electric energy meter in a workshop to acquire power consumption data of equipment, transmitting the data to a central database, and predicting future electric quantity by adopting an improved ARIMA model based on preprocessed data. According to the model, relative fluctuation sensitivity, equipment state correction and an abnormal significance index are introduced on the basis of traditional ARIMA, and dynamic suppression of prediction residual errors is achieved. And after the final predicted value is compared with the actual power consumption data, when the deviation exceeds a preset threshold value, the system automatically triggers early warning, and timely notifies maintenance personnel to check and repair equipment abnormity. According to the invention, the data is acquired through the intelligent electric energy meter, and the real-time prediction and early warning of the abnormal power consumption of the industrial equipment are realized by combining the improved ARIMA model and the dynamic residual suppression.

The invention relates to the technical field of torque sampling inspection, in particular to a torque sampling inspection method and system. Comprising the following steps: tool identity digitization: performing laser etching on an encrypted two-dimensional code in a tool stress concentration area, and associating and storing tool specification parameters, detection standards and use constraint conditions to a central database; intelligent task allocation: the system generates a dynamic detection plan according to a production line real-time state, an equipment service cycle and an operator skill level; multi-source data acquisition: acquiring tool identity information through a scanning device, and synchronously acquiring a physical signal of a torque detection device and a digital image of a tool identification file; grading judgment processing: performing process conformity verification on the measurement data, and generating a process stability evaluation report in combination with historical detection records; according to the closed-loop control strategy, the detection scheme is automatically adjusted according to the quality fluctuation characteristics. According to the scheme, the problem that a traditional method is seriously insufficient in the aspects of detection efficiency, abnormal traceability, process control and the like can be solved.

The invention provides an underground comprehensive pipe gallery intelligent operation and maintenance management system based on BIM, and relates to the technical field of building information, and the system comprises a three-dimensional integration module which is used for constructing a three-dimensional digital integration platform of an underground comprehensive pipe gallery at a cloud end, uploading full life cycle data, ownership unit information and real-time operation parameters of the communication pipeline to a cloud central database, distributing a unique code for each pipeline unit, and establishing a corresponding cloud index relationship; and the space correction module is used for presetting three positioning monitoring nodes at the main inlet of the pipe gallery, the ventilation key node and the pipeline intersection based on the three-dimensional digital integration platform, constructing a stable triangular positioning monitoring architecture, calculating the geometric center and side length relation of the monitoring architecture through the three-dimensional coordinates of the three positioning monitoring nodes, and correcting the three-dimensional coordinates of the three positioning monitoring nodes according to the three-dimensional coordinates of the three positioning monitoring nodes. And generating a space topology correction value. According to the invention, the safety of the operation and maintenance of the underground comprehensive pipe gallery is realized by integrating the whole life cycle data of the pipe gallery, spatial positioning, intelligent risk early warning and multi-department cooperative linkage.

The invention discloses a warehouse management method based on the industrial Internet of Things, and the method comprises the following steps: automatically collecting image data of goods through Internet of Things equipment, taking the image data as goods information, and storing the goods information in a central database for recognition; image data and temperature and humidity data of a warehouse shelf are obtained through Internet of Things equipment to serve as shelf information, and the shelf information is stored in a central database to be recognized; matching is carried out according to the goods information and the goods shelf information, the optimal goods shelf is selected to store goods on the basis of goods types, sizes, storage requirements and storage condition factors, and a unique classification number is distributed to each piece of goods; the goods are stored to the corresponding goods shelf positions according to the distributed classification numbers and the matching results; through automatic and intelligent warehouse management, the operation efficiency of the warehouse and the accuracy of inventory management are improved, and complex storage conditions and dynamically changing inventory requirements can be dealt with, so that the overall efficiency and precision of warehouse management are effectively improved.

The invention belongs to the technical field of trajectory planning, and provides an electric shovel unmanned mining method and system based on digital twinning in order to solve the problem that the design of an electric shovel unmanned mining trajectory is difficult to deal with a complex heap surface, and the method comprises the following steps: obtaining sensing data, a historical mining trajectory and historical heap surface point cloud data; an electric shovel unmanned mining digital twin system is constructed based on the sensing data; uploading the historical mining trajectory and the historical heap surface point cloud data to a central database, and training by using generative artificial intelligence to obtain prediction trajectories suitable for different mine fields; constructing an agent model by using a neural network, calculating to obtain a predicted fullness rate, and performing simulated excavation according to the predicted fullness rate and the predicted trajectory to obtain electric shovel excavation control data; according to the method, deep reinforcement learning is utilized, the electric shovel unmanned excavation track is generated based on the electric shovel excavation control data, the electric shovel is controlled to conduct unmanned excavation in real time, the safety of the excavation process is guaranteed, and the track control precision of the electric shovel is also improved.

The invention discloses an energy equipment health monitoring method and system based on big data, and relates to the technical field of energy equipment health monitoring, and the method comprises the steps: configuring a sensor to collect data, storing the data in a central database through an industrial Internet of Things, and carrying out the data processing; performing data analysis to identify a trend, and calculating a health index; and performing fault prediction, and implementing maintenance optimization. According to the energy equipment health monitoring method based on big data provided by the invention, the trend is analyzed and identified through data, the long-term state of the equipment can be accurately reflected, the accuracy and consistency of the data are ensured by adopting a standardized communication protocol, reliable data are provided for subsequent analysis, the LSTM combined with an Attention mechanism is adopted, and the energy equipment health monitoring method based on big data can be used for monitoring the energy equipment health. According to the method, data of different time steps are subjected to weighted processing, so that the model can pay more attention to a time period related to a current fault in historical data, the prediction accuracy is greatly improved, and better effects are achieved in the aspects of result accuracy and analysis reliability.

The invention discloses a laboratory instrument data automatic acquisition and analysis method and system, and relates to the technical field of laboratory instrument acquisition and analysis, and the method comprises the steps: obtaining sample identification information, automatically sorting and distributing the sample identification information to different analyzers, uploading detection data to a central database in real time, and associating a sample number, a detection timestamp and an instrument number; the method comprises the following steps: constructing a multi-dimensional feature space based on historical detection data, performing sub-cluster division by adopting a density clustering algorithm, establishing a cross-index logic constraint relationship in combination with an expert rule base, mapping real-time detection data to the multi-dimensional feature space, judging a data exception type according to sub-cluster affiliation and the logic constraint relationship, and when judging that the data is exceptional, judging that the data is abnormal. And triggering a secondary detection process, automatically dispatching a mechanical arm to send the sample back to a specified instrument to execute differentiated reinspection operation, and generating a comprehensive report containing detection process tracing. According to the invention, the automation level of detection is improved, the labor cost is reduced, the detection quality and efficiency are improved, and the laboratory detection work efficiency is improved.

The invention relates to the technical field of health management and medical information, and discloses an all-dimensional intelligent cardiopulmonary health management system and method and electronic equipment, and the system comprises a biological sensing module which is used for continuously collecting key physiological indexes of the heart rate, the oxyhemoglobin saturation, the respiratory rate and the motion state of a user; the data processing module is used for storing the collected physiological data and historical exercise records of the user through a central database of a cloud computing platform; the deep learning analysis module is a prediction model built based on a deep learning framework and is used for analyzing the physiological data of the user and generating a personalized exercise plan; the real-time monitoring module is used for detecting the physiological state of the user and automatically adjusting the exercise intensity or type when the physiological state exceeds a preset threshold value. The technical scheme that the multi-mode biosensor is combined with the real-time monitoring module is adopted, and the technical effect of comprehensively monitoring the cardiopulmonary function state of the user in real time is achieved.

The invention provides an intelligent progress management and control method and system for information engineering supervision, and relates to the technical field of information, and the method comprises the steps: pushing early warning information to a first-party system and a second-party system through message middleware, collecting a feedback state to complete closed-loop cooperative processing, and updating a processing result to a central database; based on the central database, combining historical project data, performing pattern recognition analysis to extract a progress deviation rule, and dynamically optimizing a plan node threshold and buffer time of a subsequent stage according to the progress deviation rule to obtain an optimization strategy; and on the basis of the optimization strategy, constructing a configurable progress management and control template library, so as to quickly adapt and generate a corresponding progress management and control node and an early warning threshold value in the new-type project. According to the invention, a full-process closed-loop management system from data integration, intelligent optimization to dynamic adaptation is constructed, and the fundamental transformation of project progress management and control from passive response to active prediction and from experience driving to data driving is realized.

The invention discloses a food quality safety tracking management system based on big data, and relates to the technical field of food safety, the system can collect various quality data in real time at key nodes of dairy product production and storage, and realize automatic association of batch information. The parameter acquisition module acquires dairy product production data in real time through acquisition equipment installed at a production node, and acquires storage data at a storage node at the same time. After the data is transmitted to the central database, the system preprocesses the data, and finally a complete preservative traceability data set is generated. And associating the quality data of each batch by using the unique identifier, and associating the dairy product batch ID with the preservative batch ID to form a dairy product component association table PCA, and establishing a detailed tracing relationship between different batches. The module enhances the traceability and transparency of the product quality, and is convenient for quickly finding out the problem source when the quality problem occurs.

The invention discloses a chronic disease screening return visit method and system, and relates to the technical field of health management, and the method comprises the steps: obtaining the basic information of a to-be-screened person, and constructing a screening file; generating a screening voucher containing an identity label; parallel data acquisition posts are set, personnel hold vouchers to autonomously select posts, and the posts scan identification associated archives and input multi-dimensional data to a central database in real time to form an associated data set; when the data set contains a preset basic data item, calling a risk scoring model to calculate a chronic disease risk score; and generating a differential detection strategy based on a comparison result of the score and a preset threshold value. According to the method, through parallel post setting and data real-time integration, the problems of post congestion and low efficiency of data integration in a traditional screening process are solved, intelligent risk assessment and detection strategy distribution of to-be-screened personnel are realized, the chronic disease screening efficiency and the resource utilization rate are improved, and a systematized data management scheme is provided for early prevention and control of chronic diseases.

The invention discloses an equipment maintenance management system based on RFID. The system comprises an RFID tag, an RFID reader-writer, a tool management terminal, a maintenance personnel management terminal, an equipment management terminal and a central database. According to the system, identification and tracking of equipment, tools and maintenance personnel are achieved through the RFID technology, management and control of the equipment maintenance process are achieved through the tool management terminal, the maintenance personnel management terminal and the equipment management terminal, and storage and analysis of equipment maintenance data are achieved through the central database. The system can improve the efficiency and quality of equipment maintenance, reduce the cost and risk of equipment maintenance, and guarantee the safety and reliability of equipment.

The invention discloses a chain retail industry production and marketing MES integration system based on the Internet of Things technology, and belongs to the technical field of the Internet of Things technology, data are collected in all links of a production workshop, a warehouse, a logistics distribution center, a retail store and the like, the data are transmitted through a wired or wireless communication network, the collected data are transmitted to a central database or a cloud platform, and the central database or the cloud platform is connected with the Internet of Things technology. The data analysis and processing module is used for analyzing and processing data transmitted to the central system, potential information and rules in the data are mined by applying a big data analysis technology, a machine learning algorithm and the like, and logistics distribution management arranges logistics distribution of products according to sales orders and inventory conditions through an integrated system. Logistics distribution management is composed of real-time monitoring and intelligent scheduling, inventory management optimization, distribution center layout optimization, information collaboration and sharing, intelligent packaging and loading and unloading and reverse logistics management, and real-time monitoring and intelligent scheduling are composed of comprehensive real-time monitoring and intelligent path planning and scheduling.

A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.

The invention discloses a concrete material strength detection and analysis system, and relates to the technical field of constructional engineering quality detection. The system comprises a data acquisition module, a data preprocessing module, a fusion and analysis module, a fusion and analysis module and a visual report generation module. Wherein the data acquisition module is used for acquiring multi-source original data of concrete strength; the data preprocessing module is used for performing cleaning, format standardization and invalid value elimination operation on the multi-source original data and storing the multi-source original data in the central database; the fusion and analysis module is used for carrying out strength prediction in future time by adopting a dual-mode comparison prediction method, and sending warning information to detection personnel when a strength prediction value exceeds a prediction threshold value; and the visual report generation module is used for automatically generating a detection report and an analysis chart conforming to a standard format. According to the method, the analysis and evaluation of the concrete strength can be realized by fusing the multi-source detection data and the process data and utilizing a machine learning algorithm.

The invention discloses a multi-region cloud platform data consistency processing method, device, product and system, and relates to the field of cloud computing. In the method, after data change of a central database is detected, the data is converted into messages in a message queue, the messages are transmitted to a message queue of an edge region cloud platform in a cross-domain manner, and the edge region cloud platform updates own cache according to the messages in the message queue. The data consistency of the database of the cloud platform deployed in the central region and the cached data of the cloud platform deployed in the edge region is ensured; and after the data change of the database of the cloud platform deployed in the central region is detected, the data in the cache of the cloud platform deployed in the central region is updated, so that the data consistency of the cache of the cloud platform deployed in the central region and the database of the cloud platform deployed in the central region is ensured. According to the method, the consistency of the data of the database and the cached data of the cloud platform deployed in the central region and the cached data of the cloud platform deployed in the edge region is realized.

The invention discloses an intelligent fire-fighting resource dynamic scheduling method based on the Internet of Things, and relates to the technical field of fire-fighting emergency intelligent scheduling, and the method comprises the steps: collecting fire-fighting global situation awareness data, synchronizing the data to a central database through an encryption protocol, and constructing a fire-fighting real-time commanding combat information system; based on a combat information system of fire-fighting real-time commanding, a combat guarantee scheme is formulated, meanwhile, fire-fighting power information, geographical and meteorological information and field real-time information are collected, and a dynamic dispatching instruction is generated; dynamically mapping the dynamic scheduling instruction to a fire-fighting resource state, generating a resource state mapping table, optimizing resource allocation through a resource priority scoring function and a linear programming model, and generating a resource scheduling scheme; performing Bayesian verification and rating processing on professional pointing information in execution of the resource scheduling scheme to generate a targeted action instruction; according to the method, the continuous evolution capability of the scheduling model is realized through a multi-agent voxel learning parameter adjustment framework of the empirical rule base.

The invention discloses an energetic material 3d printing parameter adjusting system and method based on data fusion, and relates to the technical field of energetic material additive manufacturing. Transmitting sensor data to a central database, wherein the data transmission adopts encryption and error correction technologies; the received sensor data is preprocessed; analyzing the correlation between the operation parameter group and the corresponding performance group; constructing a screening database based on the operation parameter groups of which the correlation is greater than a preset correlation threshold value and the corresponding performance scores; analyzing a corresponding safety threshold value according to the operation parameter group corresponding to the energetic material grain marked as the defective product; and monitoring the operation parameters in real time, performing system early warning on abnormal conditions, and adjusting the operation parameters based on a preset parameter adjustment rule base. The dynamic conversion of the safety threshold is realized; and the abnormity identification capability is improved.

An inventory tracking and management system includes storage devices comprising carts, cabinets, or shelves, sensors and / or monitoring devices associated with the storage devices, a central database connecting the storage devices, sensors, and monitoring devices within a hospital, and a processing server associated with the central database. The processing server including a software system controlling operation of the inventory tracking and management system.

The invention discloses a payment transaction record management method and system, and relates to the technical field of payment transaction management, and the method comprises the steps: collecting payment transaction record data, carrying out the preprocessing, and constructing a data classification model for data classification; carrying out segmented encryption on the classified payment transaction record data and transmitting the payment transaction record data to a central database, and decrypting the encrypted payment transaction record data and storing the encrypted payment transaction record data in the central database; and setting access control authority management storage data, and constructing a visual interface to display a detected data classification result and an access record in real time. According to the method, payment transaction record data is collected and preprocessed, and a data classification model is constructed for data classification; carrying out segmented encryption on the classified payment transaction record data and transmitting the payment transaction record data to a central database, and decrypting the encrypted payment transaction record data and storing the encrypted payment transaction record data in the central database; the security and management efficiency of the data are remarkably improved, and the overall transmission efficiency of the system is improved.

This invention identifies vehicles on arrival, and matches exiting vehicles to their corresponding entry event. The system uses at least one camera to capture not only the vehicle license plate but also the visual features. A “signature” formed out of the optical features of the vehicle is compared against the pre-generated signatures of vehicles in a whitelist. The “signature” for each exiting vehicle is compared against all signatures of vehicles that entered the garage. A plurality of filters reduce the possible matching candidates based on factors like license plate based edit distance, location information of vehicle owner. Synchronizing a database in each local processing unit (LPU) with a central database enables signature from images from sites that have good license plate reads, to assist in authentication in other sites where the reads are lower quality. Spatial diversity in cameras and / or illuminators enable hologram and 3D Printing detection and decoding.

The embodiment of the invention discloses an anomaly detection method, device and equipment, a program product and a storage medium, relates to the field of artificial intelligence and can also be applied to the field of financial science and technology, and the method comprises the following steps: obtaining account data from a central database according to a preset time interval; performing graph structure processing and time analysis on all account data to obtain graph structure data and time sequence features; based on a predetermined graph neural network model, the graph structure data, the time sequence features and a predetermined time sequence analysis model, obtaining predetermined input data of an enhancement module; inputting the input data into an enhancement module, and obtaining a target abnormal score of each account data through the enhancement module; and determining exception prompt information based on the target exception score of each account data, and displaying the exception prompt information to the user. According to the method, the complex customer association relationship can be effectively processed, so that anomaly detection can be more accurately carried out on each account.

A central database system accesses historical employee spreadsheets along with their modified versions, which have undergone data modification operations. The system creates a training dataset from those spreadsheets to train a machine-learned model. The model is configured to analyze a target employee spreadsheet, identify a historical spreadsheet similar to it, and generate executable scripts based on data modifications performed on the identified historical spreadsheet. The set of scripts is applied to the target spreadsheet to produce a target modified spreadsheet, mimicking historical modification. The modified spreadsheet is sent to an employee spreadsheet processing system for further processing.

The invention provides an unmanned workshop automatic control method based on B / S architecture, which is used in the field of new energy battery detection, and comprises the following steps: S1, a laboratory information management system LIMS initiates a test task request; by integrating a laboratory information management system LIMS, a warehouse management system WMS, an AGV scheduling system, an equipment control system and a central database, a unified and collaborative intelligent management and control system is constructed, and full-process automatic and intelligent management from test task initiation, arrangement and distribution test, battery cell placement, AGV carrying, test execution to final ex-warehouse verification is realized. The bottleneck of a traditional system is broken through from multiple dimensions, and the new energy battery detection process is comprehensively promoted to be transformed and upgraded to be intelligent and unmanned. The battery cell information is automatically identified through code scanning, the corresponding process parameters and the test storage location are matched in a linkage mode, the detection task is intelligently generated, a traditional mode of manual instruction issuing and manual resource allocation is replaced, the labor cost is reduced, and the task processing accuracy and execution efficiency are improved.

The invention discloses a PCB production monitoring method based on the industrial internet and a related device. The method comprises the following steps: deploying a two-dimensional code identifier of a PCB product, installing a plurality of cameras and a plurality of acquisition units to acquire a two-dimensional code picture and environment information of PCB production, and installing a 5G gateway of PLC equipment to transmit the running state of the equipment in real time; on the basis of a 5G network and a wireless sensing technology, the two-dimensional code picture, the operation state and the environment information are preprocessed and uploaded to a central database; performing data association processing on the two-dimensional code picture, the operation state and the environment information through a central database; and when detecting that the produced PCB has a quality problem, calling the information of the corresponding two-dimensional code picture, the operation state and the environment information from the central database, and carrying out quality traceability analysis to generate a quality traceability strategy. According to the invention, accurate monitoring and quality tracing of the PCB production process are realized, so that the intelligent level and the product quality of an enterprise are improved.