135results about How to "Promote collaboration" patented technology

The invention relates to the technical field of artificial intelligence and health management, in particular to a health management method and system based on multi-dimensional data analysis, and the method comprises the steps: collecting the physiological, behavior, environment and psychological data of a user, and carrying out the modeling through a recurrent neural network, and generating health state data; extracting a key causal relationship of the health risk based on a causal analysis model of a Bayesian network; comprehensively analyzing health changes through the short-term time sequence model and the long-term trend prediction model, and generating health risk data; a personalized health intervention scheme is designed by using a conditional generative adversarial network, and safe sharing and dynamic management of the scheme are realized through a block chain technology. According to the method, the health risk can be accurately identified, scientific intervention measures can be designed, the health management process is dynamically optimized, powerful technical support is provided for an intelligent old-age nursing scene, and the health management efficiency and effect are improved.

The invention relates to the technical field of APT killing chain reconstruction and prediction methods, and provides an APT killing chain reconstruction and prediction method and system based on causal reasoning. The method aims at solving the problems that in an existing APT detection and defense technology, multi-source heterogeneous data integration is difficult, space-time dynamic feature modeling is insufficient, killer chain reconstruction is inaccurate, the predictive capacity is limited, interpretability is poor, and real-time performance and accuracy are difficult to consider at the same time. The method comprises the steps of obtaining multi-source heterogeneous data, constructing a causal graph, performing multi-modal feature extraction based on the causal graph, and obtaining node initial feature representation; inputting the node initial feature representation into a time sequence causal graph convolution learning algorithm to obtain an optimized node representation; executing killer chain reconstruction based on the optimized node representation to obtain a possible killer chain sequence; predicting a future attack path according to the possible killing chain sequence to obtain a potential attack path; and generating an interpretability analysis report based on the potential attack path.

The invention provides an inventory management method for e-commerce, which integrates links of multi-source data analysis and prediction, intelligent replenishment, dynamic inventory tracking and return management, constructs a demand prediction model by using historical sales records, user behaviors and market dynamic information, sets a safe inventory level based on a prediction result, and provides an inventory management method for e-commerce. An automatic purchase request and a quick response replenishment mechanism are implemented, real-time inventory synchronization is achieved through an API, inventory changes are monitored through the Internet of Things technology, a commodity processing decision is guided for a returned commodity development prediction model, unsalable commodities are regularly examined, products close to the expiration date are reasonably handled, and a KPI is set and comprises the inventory turnover rate and the order accuracy rate. And closed-loop control of inventory management is realized.

The invention discloses an electric power engineering defect diagnosis method based on AI image recognition and a block chain technology, and relates to the technical field of electric power engineering defect diagnosis, and the method comprises the following steps: setting the initial flight rate of an unmanned plane according to the monitoring demands and field conditions of an electric power line, it is ensured that the unmanned aerial vehicle can cover all key areas of the power line in the inspection process, and meanwhile the inspection efficiency is kept. According to the invention, by intelligently adjusting the flight rate of the unmanned aerial vehicle, the unmanned aerial vehicle can stay in a potential abnormal area for a longer time, and detailed acquisition of temperature data is ensured, so that tiny temperature changes, especially tiny temperature anomalies caused by electric arcs and electrical overload, can be captured more accurately. According to the intelligent adjusting mechanism, in the inspection process, the inspection efficiency of the whole process can be guaranteed, accurate monitoring of the key area can be guaranteed, and the risk of detail loss or temperature anomaly missing detection caused by the too high flight speed is reduced.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention relates to the field of robot technology, machine vision, deep learning, computer graphics and digital twinning, in particular to a mechanical arm action recognition and control method based on the machine vision and the digital twinning technology. The motion state and environment information of the mechanical arm are captured in real time through the machine vision technology, high-precision recognition of the motion of the mechanical arm can be achieved in combination with an image processing algorithm, and a control strategy is adjusted in real time according to the recognition result; based on the digital twin model, the state change of the mechanical arm can be monitored in real time, and the mechanical arm and the virtual model are synchronously driven, so that the action of the mechanical arm is ensured to be consistent with the expected action; through intelligent identification and automatic adjustment, manual operation errors are reduced, the working efficiency and the production precision are improved, and the complexity of system maintenance is reduced; real-time optimization of the operation process of the mechanical arm can be achieved through combination of digital twinning and machine vision, potential faults are avoided through simulation and prediction, and stable operation of a production line is ensured.

The invention discloses a visual database system supporting real-time collaboration and computer equipment, and the system comprises a space management module which is used for managing a database space and dividing a database into a plurality of spaces, and the use permissions of different users are restrained to be in the corresponding spaces; the user system module is used for managing user attributes and user permissions of the users in each space based on configuration operation of an administrator on the visual page; the database management module is used for performing data storage in each space based on preset database configuration data, and querying the data stored in the corresponding space based on a data query instruction input by a user on the visual page; the operation auditing module is used for querying and auditing the data modification information of all the spaces based on the user permissions of different users and recording the data modification information; and the API management module is used for accessing a RESTful interface to query and modify the data in the database management module.

The invention discloses a lake and reservoir inflow and outflow scheduling scheme optimization method and system based on a three-dimensional hydrodynamic force-water quality-water ecological model, and the method comprises the steps: firstly constructing a three-dimensional hydrodynamic force-water quality-water ecological model of a target lake and reservoir, setting a scheduling constraint, a scoring constraint, a scoring single item, and a comprehensive scoring weight by a decision maker, stratified sampling and one-dimensional uniform random sampling are carried out on each flow range of a water replenishing port, a flow outlet and a water suction pump station which can be regulated and controlled manually according to scheduling constraints set by a decision maker, and a plurality of inflow and outflow scheduling schemes are generated; and performing big data mining on the model calculation results of all the access flow scheduling schemes, performing comprehensive scoring according to the scoring single item and the comprehensive scoring weight, and selecting the access flow scheduling scheme with the highest comprehensive score as an optimal scheduling scheme for recommendation. The method has the advantages of high simulation precision, flexible decision support, multi-target comprehensive balance and the like, and a scientific and efficient technical solution can be provided for lake and reservoir inflow and outflow scheduling.

The invention relates to an unsupervised region representation learning method and system for learning urban region embedding, and belongs to the technical field of urban calculation and intelligent systems. The method specifically comprises the following steps: S1, constructing a regional mixed graph network, and capturing pairing and grouping relationships among regions by using multi-modal data; s2, graph and hypergraph comparative learning is executed, and discriminative representation of region nodes and a high-order relation between regions are learned from graph and hypergraph structures through a parallel comparative learning module; s3, cross-module comparative learning is carried out, information exchange between graph and hypergraph node representation is promoted through a controller assembly, and the learning ability of the model is enhanced; and S4, optimizing model parameters, and generating more effective region embedding through joint optimization of graph loss, hypergraph loss and cross-module loss so as to support various downstream tasks. According to the technical scheme provided by the invention, the accuracy and efficiency of urban area analysis are improved, and the capturing capability of the model on complex interaction between the areas is enhanced.

The invention relates to the technical field of data processing, and discloses a data management method and system based on a block chain and a meta universe, and the method comprises the steps: collecting initial data in a smart city through employing an intelligent device, carrying out the preprocessing of the initial data, determining a corresponding relation between data under the chain and a block record on the chain, and carrying out the collection of the data. Constructing a meta-universe virtual digital space of the smart city by using a three-dimensional modeling technology based on the second data in the block chain; constructing an intelligent prediction model through a GAN adversarial network, an LSTM network and an improved dung beetle optimization algorithm, and performing data prediction on the second data by using the intelligent prediction model to obtain a prediction result; and the user accesses the data of the smart city through the virtual reality device, and personalized recommendation is performed by the smart city. The data management level of the smart city can be improved, powerful support is provided for city planning and resource allocation, and the smart city can be promoted to develop towards a more intelligent direction.

The invention relates to the technical field of intelligent manufacturing, in particular to a distributed flexible job shop scheduling method and system based on double deep reinforcement learning and a multi-layer agent. A global complex problem is decomposed into a plurality of local problems by introducing a multi-level agent scheduling framework and through cooperative work and hierarchical decision making of a top layer agent, a middle layer agent and a bottom layer agent; besides, different scheduling tasks of the distributed flexible job shop are executed dispersedly through the intelligent agent of each level, each decision does not need to be managed by a central coordinator, and when the environment is locally changed, each level can quickly adapt and make an optimal decision according to the specific context of the environment, so that the scheduling efficiency of the distributed flexible job shop is improved. And meanwhile, the overall goal of the whole system is still served. The objective of the invention is to solve the problem of how to improve the scheduling precision of the distributed flexible job shop.

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 relates to the field of equipment testing, and discloses an EMC testing method and system for an electronic product, and the method comprises the steps: obtaining product information, obtaining an EMC testing strategy of the product in a cloud data server, and enabling the EMC testing strategy to comprise an EMI testing strategy and an EMS testing strategy; the method comprises the following steps of: obtaining products to be tested from produced products according to a set proportion, dividing the products to be tested into an EMI test group and an electromagnetic sensitivity test group, carrying out electromagnetic emission test on the electromagnetic emission test group, carrying out electromagnetic sensitivity test on the electromagnetic sensitivity test group, and according to an electromagnetic sensitivity qualification rate and an electromagnetic emission test qualification rate, carrying out electromagnetic sensitivity test on the products to be tested. And obtaining the qualified rate of the to-be-tested product according to the product classification and the qualified rate, obtaining the EMC data of the to-be-tested product, and completing the EMC test of the electronic product. The EMC performance qualification rate of the product is obtained by comprehensively evaluating the results of the electromagnetic emission test and the electromagnetic sensitivity test, and powerful support is provided for quality control of the product.

The invention belongs to the technical field of optical fiber sensing, and discloses a high-speed Fabry-Perot signal demodulation method and system based on a sparse spectrum. The method comprises the following steps: selecting a cavity length range of a Fabry-Perot cavity, and generating a cavity length sequence; repeating simulation to obtain reflection spectrums of the Fabry-Perot cavity under all cavity lengths in the cavity length sequence; adding noise to the reflection spectrum to obtain an analog acquisition spectrum; selecting a channel according to the output wavelength of the wavelength division multiplexer, obtaining a sparse spectrum, and constructing a data set; training the neural network model through the data of the data set; after training is completed, a reflection signal of the Fabry-Perot cavity is obtained through a wavelength division multiplexer and a photoelectric detector array and converted into a sparse spectrum; and inputting the obtained sparse spectrum into the neural network model to obtain the cavity length of the optical fiber Fabry-Perot sensor and realize Fabry-Perot signal demodulation. The demodulation rate and the monitoring efficiency of the optical fiber Fabry-Perot sensor are improved, and an efficient and reliable remote monitoring solution is provided for multiple fields.

The invention discloses an AI-based customer behavior analysis and prediction system and method, and the system comprises a data collection module which is used for automatically collecting customer behavior data from a plurality of data sources; the data processing module is used for cleaning and standardizing the collected data so as to facilitate analysis; the AI model training module is used for training a machine learning model by utilizing the processed data; the prediction analysis module is used for predicting future behaviors of the customer by using the trained AI model; the user interface module is used for displaying a prediction result and allowing a user to adjust prediction parameters; the report generation module is used for automatically generating a report containing prediction information according to the selection of the user; the customer behavior data can be automatically collected from multiple data sources, the customer behavior mode can be learned and recognized through advanced data processing and the AI model training module, the prediction accuracy can be improved, the user experience can be improved, and enterprises can better adapt to market changes and customer requirements.

The invention discloses an intelligent building engineering management system based on a BIM platform, and relates to the technical field of building engineering management, and the system comprises a BIM model database, a participant interface module, a real-time synchronization module, an automatic notification module, an authority management module and a block chain recording module. According to the intelligent building engineering management system based on the BIM platform, computing resources and storage capacity are flexibly adjusted according to project requirements, and the requirements of projects of different scales are met; the block chain recording module supports a multi-chain synchronization function, and different participants can record on independent chains; therefore, the security of the data is ensured, the expandability of the building engineering management system is improved, and the individual requirements of different participants are met; the automatic notification module adopts a plurality of channel notification modes, and supports a user-defined reminding mode and priority setting of a participant; therefore, information transmission is more flexible and diversified, and working habits and requirements of different participants are met.

The invention relates to the technical field of tunnel construction, in particular to a tunnel work efficiency management system. Real-time data communication is carried out between construction site sensing equipment and a software platform, real-time position information of personnel and machinery, working condition information of a construction area and personnel access information are transmitted to a data processing server in real time, and the current construction process is judged through a system algorithm, so that process time consumption, interval time and cycle operation time of tunnel construction are calculated. According to the method, the reasonability and stability of the construction progress can be comprehensively evaluated, the situation that the progress is lagged or advanced can be found in time, the development trend of the follow-up construction progress is predicted, a scientific basis is provided for adjustment of a construction plan and reasonable allocation of resources, and it is ensured that tunnel construction can be smoothly carried out according to preset time nodes and quality requirements. The method is mainly applied to a tunnel work efficiency management system.

The invention discloses a file data circulation security system based on a block chain technology, and the system deploys block chain nodes based on the block chain technology, and constructs a block chain network system. An upper-layer application is established on the basis of digital identity authentication, a security policy smart contract, process management and control and uplink log auditing, and the upper-layer application comprises a block chain background management module, a file storage cloud service module and a component core capability module; in a business application level, a data owning party and a data using party can realize safe circulation of data through encryption authorization and authorization decryption services provided by the platform. According to the method, data privacy can be protected, data traceability and non-tampering performance are achieved, and meanwhile efficient data circulation and cross-platform interoperability can be achieved.

A transparent visual power grid operation and maintenance system comprises a data acquisition and preprocessing module which is composed of a sensor network and SCADA system docking unit and a data preprocessing unit; the data processing and analysis module is composed of a fault prediction and diagnosis unit, an operation and maintenance strategy optimization unit and a three-dimensional modeling and dynamic simulation unit; the visual display and user interaction module is composed of an AR / VR interface development unit and a user-defined interface and report generation unit; and the system integration and communication module is composed of a standardized API interface and cloud platform unit and a remote access and cooperation unit, and the scheme of the invention solves the problem that the complexity of power grid operation and maintenance system data causes that detection information is difficult to provide visually, thereby affecting the operation and maintenance work quality and efficiency.

The invention discloses a territorial space planning surveying and mapping three-dimensional analogue simulation system, device and terminal, and the system comprises a data collection and processing module, a three-dimensional modeling module, a planning scheme importing and editing module, an analogue simulation and analysis module, a result output and sharing module and an intelligent decision support module which have corresponding functions and performance indexes. The method has the advantages that data acquisition precision, model precision, simulation accuracy and the like are achieved, various innovative functions and technologies such as model dynamic updating, multi-person collaborative editing, scene simulation and the like are achieved, and scientificity and efficiency of territorial space planning are improved; the precision and efficiency of territorial space planning are improved, accurate data acquisition, three-dimensional fine modeling, comprehensive simulation analysis, intelligent decision support and diversified result output are realized through a multi-module cooperation and innovation technology, scientific and reasonable planning is promoted, and industry development is promoted.

The invention belongs to the field of data processing, and particularly relates to an intelligent service analysis optimization method and system based on a multi-dimensional data warehouse, and the method comprises the steps: obtaining the data attribute information of a to-be-recognized object for the to-be-processed object loaded in the data warehouse; inputting the data attribute information into a dynamic data analysis model, and performing data identity recognition on the to-be-recognized object to obtain target identity information of the to-be-recognized object; based on the target identity information, performing identity tracing on the to-be-identified object by adopting a dynamic tracing model to obtain target tracing information of the to-be-identified object; and based on the target identity information and the target traceability information, triggering a target data service for the to-be-identified object to complete intelligent service analysis of the to-be-processed object. According to the method, the data identity recognition and traceability capability can be improved, powerful support is provided for intelligent service analysis, and the method is dedicated to realizing data-driven decision making.

The invention provides a micro-unit code branch management method based on GitLab and DevOps, and belongs to the field of research and development test management.According to the method, GitLab is adopted for branch management, Dev serves as a trunk for development, function development is conducted through a derived branch MyDev, and after testing, the MyDev is merged to a Release branch, and Tag issuing is conducted. And aiming at an online problem, repairing from a Release branch and printing a new Tag. Meanwhile, in combination with DevOps practice, automatic testing, code review, safety review and automatic packaging deployment are carried out, each large process and link are divided into micro-units, and smooth and efficient automatic implementation of the version control and release process is ensured.

The invention discloses an enterprise information sharing management method, system and device and a storage medium, which are convenient for managing enterprise data, can promote cooperation and communication among departments in an enterprise, and can improve the utilization efficiency and value of data. The method comprises the following steps: acquiring department data uploaded by each department, and judging whether the format of the department data uploaded by each department accords with an uploading format or not through a regular expression; when it is determined that the format of the department data accords with an uploading format, a unique identification identifier is automatically generated according to the department data, and the identification identifier comprises a timestamp and department information corresponding to the department data; classifying the department data according to department information to obtain classified data; uploading the classified data to an information management system; and when application information of target data is received, judging whether the target data has the access permission or not according to the application information.

The invention provides a method and a system for extracting a central axis and a shape of a pipe. The system comprises a file import module which receives and processes a workpiece drawing in a 3D format uploaded by a user; the data analysis module analyzes the imported workpiece drawing in the 3D format; the central axis segment extraction module identifies and extracts a central axis segment related to the pipe; the central axis construction module processes the extracted central axis segment; the shape data extraction module defines a data structure of a pipe shape and converts the constructed central axis into the data structure for representation; the graph display module is used for displaying an original 3D workpiece drawing and a processed graph only containing a pipe part; and the output module outputs the processed central axis shape data in a format suitable for subsequent processing. According to the method, direct importing of the 3D format file is supported, a new algorithm and technology are introduced, so that the pipe forming system can directly read and process the 3D CAD file, and the step of converting a 3D model into a 2D drawing is omitted.

The invention provides an ideological and political education achievement authentication system based on a block chain technology, and aims to improve transparency, authenticity and traceability of education achievements. According to the system, a comprehensive, safe and efficient authentication platform is constructed by integrating user authentication and authority management, activity participation and achievement recording, multi-dimensional evaluation and authentication, intelligent contract execution and authentication recording and a cross-mechanism authentication and mutual recognition mechanism. The innovation point is that a multi-factor authentication technology is introduced to enhance the security; a multi-dimensional evaluation mechanism is adopted, and comprehensiveness and fairness of evaluation are achieved; the efficiency of the authentication process is improved through automatic execution of the smart contract; the cross-mechanism authentication and mutual recognition mechanism promotes the universality and convenience of the authentication result. The system not only optimizes the authentication process of ideological and political education achievements, but also provides possibility for international mutual recognition of the education achievements, and has important social value and practical significance.

The invention relates to the technical field of data analysis, and discloses an early myocardial injury monitoring system based on medical internet of things. The system comprises: a quality control module, which treats a peripheral blood sample based on immunochromatography and analyzes a detection result; the extraction module completes data structuring and integrates the data with biomarker data; the encryption module realizes secure data transmission; the classification module performs labeling classification and standardization processing; the evaluation module executes risk evaluation and space-time analysis; and the grading module grades the early warning data and feeds back and stores the data. Early accurate monitoring of myocardial injury is achieved, disease change is found in time, risk early warning is carried out, and therefore the out-of-hospital medical treatment delay time is shortened.

The invention provides a group multi-scene cognitive training method and device based on MR and a multi-modal brain-computer interface, and relates to the technical field of cognitive training. The method comprises the following steps: performing cognitive competence evaluation based on a cognitive competence evaluation scale to obtain a cognitive competence evaluation result; based on a cognitive ability database, a preset group number of people and a candidate group database, performing cognitive ability matching according to a cognitive ability evaluation result; based on a cognitive task library, performing task matching according to the group cognitive ability vector; under the MR scene, based on the cooperative training group, the competition training group and the matched cognition task, group cognition training is carried out, signal collection is carried out, and multi-modal physiological signals are obtained; and performing cognitive competence evaluation by using the cognitive competence evaluation model to obtain a trained cognitive competence evaluation result. The group cognition training method based on the BCI multiple scenes is high in training efficiency, large in generalization degree and high in applicability.

The invention relates to the field of commodity information management of supply chains, in particular to a commodity information management method based on a supply chain, and the method comprises the steps: generating a bar code for the information of each produced commodity through commodity production management, carrying out the traceability of the commodity through the bar code, carrying out the inventory information analysis of a user, obtaining the information of each inventory in real time, and analyzing the inventory turnover rate; the sales information feedback is used for analyzing the collected sales parameters of the commodities and feeding back analysis results; according to the received signals, early warning processing is carried out on the received signals; according to commodity safety monitoring, a high-definition camera is used for monitoring commodities in real time in the transportation process, the safety state of the commodities is judged, smooth operation of a supply chain is effectively ensured, and the customer satisfaction degree is effectively improved.

The invention provides an operation and maintenance work order management method and device based on budget management integration, and the method comprises the steps: obtaining work orders, and carrying out the classification according to the work order information, and the work order information comprises a work order type and a priority level; the method comprises the following steps: performing keyword extraction in combination with a TF-IDF statistical method, performing evaluation in combination with priorities, sorting work orders with high priorities according to overdue time, reasonably distributing the work orders to corresponding customer service personnel by using a time scheduling algorithm and keywords, and performing marking processing on the overdue work orders for work order positioning; secondary packaging and workflow micro-service based on an activiti framework are introduced into a micro-service architecture, and the workflow micro-service is used for managing the process of a work order and generating a to-be-processed list, so that process processing and real-time state tracking are realized; the work order needs to complete all circulation of the work order according to the process until the processing is completed, so that the operation and maintenance efficiency is improved, and the maintenance cost is reduced.

The invention relates to the technical field of reinforcement learning, in particular to a deep reinforcement learning method and system based on multiple agents. The method comprises the following steps of performing operation log extraction on multiple agents and constructing a multi-agent interaction graph to obtain the multi-agent interaction graph; carrying out resource release limited interval calculation among different load nodes on the multi-agent interaction map to obtain a node resource release limited interval; according to the node resource release limited interval, making a multi-agent resource competition strategy and carrying out multi-agent hierarchical convergence behavior coupling to obtain hierarchical convergence behavior coupling data; and constructing a multi-agent resource competition balance model according to the layered convergence behavior coupling data to obtain the multi-agent resource competition balance model, and sending the multi-agent resource competition balance model to a cloud platform to execute multi-agent deep reinforcement learning. According to the method, multi-agent collaboration is more perfect through optimization processing of multi-agent reinforcement learning.