721 results about "Static data" patented technology

The invention discloses a project cost risk monitoring method and a project cost management platform, and the method comprises the steps: S1, carrying out the real-time collection and standardization processing of multi-source data: collecting dynamic data in real time through an Internet of Things device at a construction site, carrying out the butt joint of a design drawing, a financial system and the like, obtaining static data, and employing a data cleaning, conversion and integration technology; s2, carrying out risk factor identification and correlation analysis based on a knowledge graph; s3, implementing risk quantitative prediction and early warning driven by artificial intelligence; s4, real-time monitoring and automatic verification of contract performance of blockchain enabling are realized; s5, providing dynamic cost adjustment and optimization decision support; through real-time collection and standardization processing of multi-source data, data islands of all parties participating in a project are broken, and accuracy, integrity and timeliness of key data such as construction progress and cost expenditure are ensured. Based on big data analysis and artificial intelligence prediction, more accurate risk assessment and cost prediction are provided for project managers.

The invention discloses a personalized recommendation method driven by user intention recognition, which comprises the following steps of: collecting multi-dimensional information such as browsing records, click behaviors and comment data of a user, and constructing a user behavior data set; preprocessing the collected user behavior data to obtain a session sequence; the current demand of the user is speculated by analyzing the input text and the behavior mode of the user, and the intention of the user is recognized; constructing a user interest preference prediction model according to the identified user intention; and in combination with real-time feedback of the user, the recommendation model is dynamically updated, and the response speed and the individuation degree of the recommendation system are improved. The problem that a traditional recommendation system only depends on static data and does not comprehensively consider real-time feedback of users is solved, self-adaptive ability is injected for personalized recommendation, intention behaviors of different users in multiple scenes can be analyzed and understood, accurate recommendation service is provided, and recommendation accuracy and user satisfaction are improved.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention discloses a static database self-adaptive fuzzification desensitization implementation method for highly sensitive data in the power industry. The method comprises the following steps: a data preprocessing module, a sensitivity analysis module, a fuzzification algorithm module, a self-adaptive mechanism module and an effect evaluation and verification module. Compared with the prior art, the method has the advantages that 1, self-adaptive dynamic adjustment is performed: a desensitization strategy is automatically optimized according to data characteristics and use scenes, and privacy protection indexes (such as k-anonymity values) are improved by 30%-50%; 2, multi-dimensional balance: the information loss rate is reduced by 10%-20%, the query accuracy is improved by 5%-10%, and safety and service requirements are considered; 3, system expansibility: a plug-in architecture supports dynamic loading of new algorithms and rules, and a configuration management module realizes strategy real-time adjustment; and 4, compliance guarantee: through a law and regulation rule base and a compliance check algorithm, the compliance is ensured to meet the standard.

The invention discloses a building low-carbon reconstruction evaluation method based on multi-objective optimization. The method comprises the following steps: acquiring real-time data; obtaining static data; based on the real-time data and the static data, a multi-target evaluation model covering the total carbon emission amount, the transformation cost, the energy-saving income and the investment payback period is constructed, and indoor comfort and project implementation constraint conditions are set for the multi-target evaluation model; an NSGA-II algorithm is adopted to carry out optimization solution on the multi-target evaluation model; and performing automatic screening on the Pareto optimal solution set generated by the NSGA-II algorithm by using an ideal point method to generate a unique recommended transformation scheme, wherein the recommended transformation scheme comprises an optimal low-carbon transformation strategy and various key performance indexes corresponding to the optimal low-carbon transformation strategy. According to the method, the problems of evaluation benchmark distortion, insufficient multi-target collaboration and strong subjectivity of a decision-making process of a current existing building low-carbon reconstruction evaluation method are solved.

The invention provides a storage cluster fault diagnosis method and system based on a spiking neural P calculation model, and the method comprises the steps: collecting operation indexes containing static data and dynamic data from a monitoring system of a storage cluster, defining a neuron for each operation index after preprocessing through the construction and training of the spiking neural P calculation model, and carrying out the fault diagnosis of the storage cluster. Initializing an initial state value of each neuron according to the collected data; and performing state updating and reasoning by using pulse signal propagation and fuzzy reasoning through a trained spiking neural membrane calculation model, judging a fault according to a state value of a neuron, and when the state value of a certain neuron exceeds a set fault threshold value, considering that an index has a fault or a potential fault, and identifying a specific fault type in the storage cluster through state combination of the plurality of neurons. According to the invention, efficient fault diagnosis of the distributed storage system is realized.

The invention discloses an urban low-altitude-oriented grid dynamic evaluation and intelligent route planning system, and belongs to the field of urban low-altitude airspace and routes. And the data processing module is used for inputting static data including the task number, the task time, the unmanned aerial vehicle number, the take-off and landing field number and the total airspace, the continuous airspace is divided into grid units by a project, and a quantitative evaluation model with seven factors including the underlying surface, the communication quality, the navigation quality, the meteorological environment, the electromagnetic environment, the noise and the countering as the core is constructed. The airworthiness score of each grid is calculated through normalization processing and weighted summation, a real-time and scientific decision basis is provided for path planning of the unmanned aerial vehicle, crossing from static planning to dynamic evaluation is achieved, and a set of airspace gridding dynamic evaluation and task planning system is provided and implemented. The system aims to cope with flight risks caused by multiple factors such as complex underlying surfaces, dynamic weather and communication navigation interference in urban low-altitude flight, and the safety and reliability of unmanned aerial vehicle logistics are improved.

Apparatus and methods for identifying a wireless signal-emitting device are disclosed. The apparatus is configured to sense and measure wireless communication signals from signal-emitting devices in a spectrum. The apparatus is operable to automatically detect a signal of interest from the wireless signal-emitting device and create a signal profile of the signal of interest; compare the signal profile with stored device signal profiles for identification of the wireless signal-emitting device; and calculate signal degradation data for the signal of interest based on information associated with the signal of interest in a static database including noise figure parameters of a wireless signal-emitting device outputting the signal of interest. The signal profile of the signal of interest, profile comparison result, and signal degradation data are stored in the apparatus.

The invention discloses a collision detection method and a computer readable storage medium. The collision detection method comprises the following steps: determining all static components belonging to a preset static type and all dynamic components belonging to a preset dynamic type in a three-dimensional building model; static SDF data are constructed for all static components in the three-dimensional building model; wherein the static SDF data comprises a plurality of static SDF values, and the static SDF values are SDF values from a space point in the three-dimensional building model to the surface of the nearest static component; constructing dynamic SDF data for all dynamic components in the three-dimensional building model; wherein the dynamic SDF data comprises a plurality of dynamic SDF values, the dynamic SDF values are SDF values from a space point in a dynamic influence area to the surface of a dynamic component used for generating the dynamic influence area, and the dynamic influence area is obtained after the boundary of a minimum three-dimensional bounding box of the dynamic component is expanded outwards by a first preset distance; and detecting whether a collision point exists in the three-dimensional building model based on the static SDF data and the dynamic SDF data.

The invention provides a plate forming control method and device based on deep learning, and relates to the technical field of stamping control, and the method comprises the steps: training an original CNN model through a sample data set, and obtaining an intermediate CNN model; the sample data set comprises plate static data and springback data; the plate static data comprises initial size parameters and material parameters; the rebound data comprises a first displacement field after the pressure head is evacuated and the plate is rebounded under different punching depths; determining displacement field compensation values corresponding to the different stamping depths based on a first displacement field error corresponding to the experimental data and the simulation data before the rebounding of the plate under the different stamping depths and a second displacement field error after the rebounding of the plate; adjusting the parameters of the intermediate CNN model by using the displacement field compensation value until the prediction error of the intermediate CNN model is less than a preset threshold, and obtaining a target CNN model; and the plate static data of the to-be-processed plate and the target forming effect are input into the target CNN model, and the target stamping depth is obtained.

The invention discloses a coal spontaneous combustion early warning method based on deep learning and physical constraint. The coal spontaneous combustion early warning method comprises the following steps: acquiring coal static data including composition data, physical structure data and temperature rising speed and time sequence gas data including various gas concentrations and the current coal temperature; respectively inputting the processed static data input and time sequence gas data into a deep multi-layer perceptron module and a Transformer encoder module capable of learning a position scaling factor to obtain a first static feature and a first dynamic feature; inputting the features into a gating attention fusion module to obtain fused features, and predicting a critical temperature and a pyrolysis temperature corresponding to spontaneous combustion of the coal; in the training stage of the coal spontaneous combustion early warning model, the total loss comprises physical constraint loss and mean absolute error loss, and the physical constraint loss comprises temperature sequence constraint loss and mass conservation constraint loss; and the temperature range, the early warning grade and the chemical evolution stage are obtained through the critical temperature, the pyrolysis temperature, the coal temperature and a preset threshold value.

The invention discloses an intelligent dynamic prediction method for a coal and gas outburst dangerous area of a working face, and the method comprises the steps: building an integrated early warning information analysis platform, dynamically collecting key parameters in a mine, and constructing a dynamic data set; and calculating a coal seam firmness factor and a gas emission factor by utilizing a data fusion technology and combining historical data and regression analysis. Furthermore, in combination with a static data set, such as geological and ventilation system distribution data, a multi-parameter coupling prediction model is constructed. The model is trained and verified through a machine learning method so as to predict the coal and gas outburst risk of the working face in real time. The system automatically triggers early warning according to a model prediction result and a preset threshold value, a scientific basis is provided for coal mine safety management, and coal and gas outburst accidents are effectively prevented.

The invention discloses an automatic page generation method of a software construction platform based on a large language model, which comprises the following steps of: extracting a scene, a page and constraint description from a natural language demand, and generating JSON (JavaScript Object Notation) content containing a data model, a query condition, a table column and static data through the large language model; according to component type constraints and verification rules, the JSON is mapped to components and combinations of buttons, labels, texts, pictures, pull-down selection, multi-choice, single choice, tables, dates and the like of the code-free construction platform; and automatically filling a component set based on the upper and lower partition templates, and injecting static data and verification logic. Through industry knowledge adaptation, component semantic binding and dynamic constraint, it is ensured that the page conforms to field specifications and interaction logic, the learning cost is reduced, manual intervention is reduced, and the method is suitable for the fields of smart cities, natural resources, urban and rural residence construction, emergency management, enterprise digital transformation and the like.

Systems and automated processes are described to provide collection of wireless network status data, such as a 5G network, and to automatically respond to queries regarding the performance of the network. Systems and automated processes may, in response to a request for network performance information, obtain performance and static data from network data sources, analyze the static data to generate validated site data, apply a KPI formula to the performance data to generate KPI data, and generate a network performance report based on the validated site data and KPI data. In addition, the systems and automated processes may use an appropriate machine learning model from a library of models to determine recommended actions to increase network performance and may automatically implement such actions. A top offender system may be implemented to analyze the status data to determine the network elements having the largest negative impact on network performance.

The invention provides an addressing method for a strategy control network element, an application side network element and a control plane network element, and the method comprises the steps: obtaining a device address of a strategy binding network element retrieval database network element based on the local static data configuration information of the application side network element; retrieving a device address of a database network element according to the policy binding network element, and sending the control plane network element query message to a corresponding target policy binding network element retrieval database network element, so as to query an address of a target control plane network element in the target policy binding network element retrieval database network element according to the control plane network element query information; according to the address of the target control plane network element, sending the policy control network element address query request to the target control plane network element; and receiving a query result corresponding to the policy control network element address query request sent by the target control plane network element. According to the invention, the problem that the application side network element cannot acquire the IP address of the policy control network element according to the user IP address information is avoided.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention relates to a system and method for low-voltage judgment of a power failure area based on mobile optical modem equipment, and the method comprises the steps: collecting the static data and offline event data of each mobile optical modem in real time, and storing the data after the data cleaning operation; based on historical offline event data of each mobile optical modem, an initial weight is distributed for each optical modem offline event, and then the offline weight of each mobile optical modem is dynamically adjusted according to the spatial and temporal distribution characteristics of the real-time offline event data of each mobile optical modem; in combination with the offline weight of each mobile optical modem, determining the power failure judgment weight of the corresponding area through space-time scanning statistics; and comparing the power failure judgment weight of each area with a corresponding power failure threshold value to judge whether a power failure fault occurs in the area, and if the power failure fault occurs, outputting position information and power failure warning information corresponding to the area. Compared with the prior art, the method can timely and accurately identify the regional power failure event and determine the fault region.

The invention discloses a trusted execution environment construction method, system, equipment and medium for electric power intelligent equipment, and relates to the technical field of electric power intelligent equipment monitoring, and the method comprises the following steps: carrying out Hash measurement on a to-be-detected area in an operating system kernel, carrying out comparison based on a preset reference value, and monitoring the consistency of kernel static data and codes; performing list management and integrity verification on the system kernel module, and identifying illegal loading, unloading or tampering behaviors; monitoring a kernel module event, sensing the change of the life cycle of the module and dynamically triggering a verification mechanism; performing Hash verification on an execution file of the specified user space process, and identifying process-level illegal replacement or injection behaviors; and updating the trusted measurement database based on a detection result, and executing log recording, alarming and blocking strategy response. The method does not depend on a chip, a virtualization platform or hardware extension, realizes security monitoring only through a software means, and is suitable for an embedded terminal with limited resources.

The invention relates to a reservoir dispatching management method and system based on digital twinning, and the method comprises the following steps: carrying out the real-time parameter collection of a target reservoir, and obtaining a reservoir characteristic parameter matrix; performing three-dimensional space modeling based on the reservoir characteristic parameter matrix and the reservoir hydrogeological environment in the target reservoir to obtain a reservoir digital twinborn model; performing drainage basin rainfall-runoff dynamic response analysis on the reservoir digital twin model to obtain a drainage basin hydrological response characteristic curve group; performing prospective water balance calculation on the watershed hydrological response characteristic curve group to obtain a multi-period reservoir flow prediction sequence; and formulating an optimal scheduling instruction set of the target reservoir based on the multi-period reservoir inflow prediction sequence, and regulating and controlling a reservoir gate control system in the target reservoir based on the optimal scheduling instruction set. And the method is difficult to adapt to hydro meteorological conditions and multi-target scheduling requirements which change in real time.

The invention relates to the technical field of machine learning intelligent decision, and discloses an AI intelligent decision reasoning method and system based on machine learning, and the method comprises the steps: monitoring a micro event which does not reach an alarm threshold value to open a time sequence window, coding the time sequence causal association between a plurality of sensors into a fingerprint vector which does not contain an original amplitude value in the window, according to the method, the limitation that traditional machine learning depends on static data snapshots is overcome, the hidden dynamic causal chain is directly coded into identifiable fingerprints, and therefore the recognition accuracy of the fingerprints is improved, and the recognition accuracy of the fingerprints is improved. The system can make accurate early warning in the germination period instead of the outbreak period of a systematic risk formed by accumulation of a plurality of tiny events, and the transformation from post-event diagnosis to beforehand foreseeing is realized.

The invention relates to a thermal power plant multi-agent collaborative operation platform construction method, which comprises the following steps: collecting time sequence and static data of a whole plant, and carrying out data preprocessing; sending the data into a digital twinborn body, performing data feature fusion by using a neural network model, generating a high-dimensional feature vector, and sending the high-dimensional feature vector into a digital twinborn model for prediction; a specialized agent is constructed, and training is carried out in the digital twinborn body; a plurality of agents perform multi-step time sequence prediction by using a built-in prediction model to obtain a local optimization decision; constructing a collaborative brain based on a large model, receiving a local optimization decision, and generating a global state code; constructing a decision planner, receiving the global state code and outputting a collaborative strategy instruction; and integrating the digital twinborn body, the specialized agent, the collaborative brain and the domain knowledge base, constructing a unified collaborative operation platform, and carrying out iterative optimization on the digital twinborn prediction model and the collaborative brain. Compared with the prior art, the method has the advantage of high cooperative processing efficiency.

Systems and automated processes are described to provide collection of wireless network status data, such as a 5G network, and to automatically respond to queries regarding the performance of the network. Systems and automated processes may, in response to a request for network performance information, obtain performance and static data from network data sources, analyze the static data to generate validated site data, apply a KPI formula to the performance data to generate KPI data, and generate a network performance report based on the validated site data and KPI data. In addition, the systems and automated processes may use an appropriate machine learning model from a library of models to determine recommended actions to increase network performance and may automatically implement such actions. A top offender system may be implemented to analyze the status data to determine the network elements having the largest negative impact on network performance.

The invention discloses a supplied land idle risk assessment method and system based on spatio-temporal data, and belongs to the technical field of geographic information, and the method comprises the steps: screening out a first land parcel from supplied land parcels according to the static data of each supplied land parcel; calculating an idle risk assessment index corresponding to the first land parcel according to the static data; extracting historical construction time-space sequence data from the space remote sensing data of the first land parcel from the time when the first land parcel is supplied to now; extracting space-time trend change characteristic data through a time window moving average method by adopting a long-short-term memory network model and taking the historical construction space-time sequence data as input, and predicting a development progress corresponding to the first land parcel according to the space-time trend change characteristic data; and when the development progress and the idle risk assessment index do not meet the preset conditions, determining that the first plot has an idle risk. Through the method and the device, the problem of low timeliness and accuracy of supplied land idle risk assessment can be solved.

The invention provides a transaction and scheduling optimization method and system for a virtual power plant participating in a spot market, relates to the technical field of energy systems, and solves the problem of electricity purchase cost minimization of the virtual power plant participating in spot transaction and the problem of how to report and regulate adjustable resources in the virtual power plant containing multiple distributed energy main bodies. The method comprises the following steps: acquiring first data and second data; according to the spot transaction price, the static data, the first data and the second data, constructing an optimization target for minimizing the overall power purchase cost of the virtual power plant, and setting a comprehensive power constraint condition and an energy storage system constraint condition; and solving through a linear programming solver to obtain an optimal spot declaration strategy and a regulation and control strategy of corresponding equipment. The method is used in the virtual power plant transaction and scheduling optimization process, the declaration strategy of the electric power spot market is considered, the electric quantity of the real-time market is adjusted through adjustable resources such as adjustable equipment and energy storage equipment on the user side, and the electricity purchase cost can be reduced and the marketization risk can be controlled.

The invention relates to the technical field of intelligent travel, and particularly discloses an intelligent travel itinerary automatic recommendation system and method. Comprising the steps that a multi-source data fusion module aggregates user behaviors, POI attributes and environment dynamic data in real time; the dynamic user portrait module fuses dynamic and static data through a time sequence neural network and a graph neural network to generate a space-time preference portrait; the travel decision engine generates an initial scheme based on the portrait; the real-time optimization execution module dynamically adjusts node parameters in combination with positioning and event data; and the interactive feedback module realizes closed-loop optimization. The innovation lies in that the dynamic portrait improves the recommendation matching degree by 50% +; 2.1-minute-level re-planning is achieved through an elastic buffering mechanism (+ / -20% staying floating); the space-time network model supports cross-city transfer node insertion, and user fatigue is reduced by 35%; federal learning and differential privacy (epsilon is less than or equal to 0.3) guarantee data security. And the problems of high interruption rate and response delay are solved.

The invention discloses a multi-path library bank landslide susceptibility evaluation method and device based on dynamic-static feature fusion and a storage medium. The method comprises the steps of obtaining multi-source monitoring data including dynamic InSAR deformation time sequence data and static environment influence factors in a target area and a landslide catalog library; decomposing the dynamic InSAR deformation time series data into a dynamic trend term and a periodic term by adopting an empirical mode decomposition method; the multi-source monitoring data and the landslide catalog library are preprocessed, and data sets are divided; constructing a multi-path dynamic-static feature coupling model, and training and outputting a reservoir bank landslide susceptibility evaluation model based on dynamic-static feature coupling; inputting the multi-source monitoring data of the to-be-detected area into the trained landslide susceptibility evaluation model to obtain a susceptibility evaluation index, and determining a prediction result according to the evaluation index; according to the method, the problems of high static data dependence, dynamic deformation feature deficiency, insufficient real-time early warning capability and the like of a traditional landslide susceptibility zoning method are solved.

The invention provides a dynamic optimization and visual decision-making system for port and navigation multimodal transport knowledge graph collaboration, and solves the problems of high transport mode conversion cost, difficult information sharing and the like. Aiming at the defects that a traditional static data warehouse (such as Hadoop) is insufficient in real-time performance, a general data fusion framework (such as Apache NiFi) is poor in field adaptability and the like, the system innovatively constructs a multi-source heterogeneous database, integrates port production, economic association, infrastructure and freight data, and realizes accurate path planning and personalized logistics scheme recommendation through an intelligent algorithm. The system adopts a React + ECharts + ShadcnUI technology stack to realize knowledge graph visualization, practice shows that the operation efficiency can be remarkably improved, the transportation cost is reduced, and technical support is provided for sustainable development of ports.

The invention provides a policy control network element addressing method, an application side network element and a control plane network element, and the method comprises the steps: obtaining the addresses of a plurality of control plane network elements based on the local static data configuration information of the application side network element according to a user terminal address and a mobile communication network access external network identifier; determining a first target control plane network element, and sending a policy control network element address query request to the first target control plane network element; if the session association information of the user terminal is not queried in the first target control plane network element, acquiring an address of a second target control plane network element sent by the first target control plane network element; sending a policy control network element address query request to the second target control plane network element according to the address of the second target control plane network element; and receiving a query result sent by the second target control plane network element. According to the invention, the problem that the application side network element cannot acquire the IP address of the policy control network element according to the user IP address information is avoided.

The invention provides a software supply chain risk component calling identification method, and belongs to the technical field of basic safety. The method comprises the following steps: loading components in a Java Web application, and dynamically capturing a loaded component list; storing the component list locally and sending the component list to an external intelligence system, acquiring risk component information, and generating a risk calling interface signature according to the risk component information; a preset instrumentation rule file is loaded based on the risk calling interface signature, and accurate monitoring of the risk interface is realized through dynamic method matching and byte code enhancement; screening risk interfaces triggered during operation according to cross comparison of static data and dynamic calling data, and marking high-risk components which must be repaired and risk components which can be postponed to be repaired; the risk report generation module generates repair suggestions and priority reports. According to the method, the accuracy of risk assessment is fundamentally improved, meanwhile, the unnecessary repair cost is remarkably reduced, and an efficient and innovative technical solution is provided for supply chain risk management.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.