384 results about "Pattern analysis" patented technology

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention relates to the technical field of data analysis, provides an intelligent factory automatic monitoring method and system based on knowledge base enhancement, and realizes more accurate anomaly analysis and more effective process adjustment of an intelligent factory. The method comprises the steps of performing knowledge enhancement fusion processing on an obtained real-time monitoring data set of an intelligent factory through a pre-constructed process knowledge base and a pre-constructed monitoring rule base, and generating a process knowledge graph; performing abnormal mode recognition processing on the process knowledge graph based on a semantic matching strategy, extracting feature description of an abnormal event and a semantic association path with a historical monitoring text, and generating an abnormal mode analysis result containing abnormal root cause inference; according to the abnormal mode analysis result and the dynamic incidence relation in the process knowledge graph, an automatic monitoring report containing root cause priority ranking and optimization operation guidance is generated, and the automatic monitoring report is fed back to the intelligent factory control terminal to trigger process adjustment operation.

The invention discloses a two-phase immersed liquid cooling liquid level intelligent dynamic regulation and control system and method, and relates to the technical field of liquid cooling intelligent processing. The two-phase immersion type liquid cooling liquid level intelligent dynamic regulation and control method comprises the steps that S1, state observation data, execution control data and log data in the liquid cooling liquid level regulation and control process are collected and preprocessed, and a standardized liquid cooling state data set is constructed; s2, the synchronism of the evaporation strengthening trend is evaluated, and the opening degree of an electronic control valve is adjusted; s3, analyzing a control feasible region under gas-liquid balance constraint, and optimizing the working mode of the two-way pump; and S4, the consistency of the feedback trend and the rolling decision is analyzed, and the circulating flow of the cooling coil and the heat exchange intensity of the condenser are adjusted. The problems that when power consumption of an existing two-phase immersion liquid cooling system changes dramatically, due to lag control of a single sensor and lack of multi-dimensional data prediction, evaporation and liquid supplementing actions are prone to being mismatched, and liquid level decline, pressure abnormity and chip dry burning are caused are solved.

The invention discloses an AI-based digital advertisement material multi-modal adaptive generation method and system, and relates to the technical field of artificial intelligence and digital content, and the method comprises the steps: S1, obtaining historical operation records of a user in a material creation process, carrying out the classified storage of selection actions in each interaction, and obtaining a preliminary data set of the selection tendency of the user; s2, extracting style preference characteristics of the user in different creation scenes by adopting a behavior pattern analysis method according to the preliminary data set, and determining a dynamic change rule of the style preference of the user; s3, aiming at the dynamic change rule, constructing a dynamic capture mechanism, obtaining the latest selection tendency data, and judging the instant adjustment demand of the style preference of the user; according to the AI-based digital advertisement material multi-mode adaptive generation method and system, intelligence and individuation in the material creation process are realized, and a new technical scheme is provided for the field of creative design.

The invention discloses a user security feature recognition method based on behavior pattern analysis, and aims to solve the problems of inaccurate recognition of power utilization security features of power consumers and insufficient robustness in the prior art. The method comprises the following steps: preprocessing and segmenting original power consumption time sequence data; then, a self-supervised learning model based on an expert hybrid architecture is constructed, the architecture integrates five neural networks to construct an expert model, expert weights are dynamically distributed through a gating network, and a power utilization mode deep embedding vector is output through self-supervised training; clustering the embedded vectors by using a clustering algorithm, determining an optimal clustering number in combination with an elbow method and a contour coefficient method, generating a user portrait, and performing visualization and feature analysis; and finally, according to the user portrait data, carrying out transaction behavior pattern recognition on the input to-be-recognized user data, and outputting a security feature recognition result. The method can comprehensively and accurately identify the power utilization safety characteristics of the user, and is suitable for scenes such as intelligent power grid safety monitoring.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention discloses a transformer substation risk detection method and system based on multi-modal decision-level fusion, and relates to the technical field of data fusion, and the method comprises the steps: obtaining a real-time image data stream of a transformer substation dangerous area, synchronously obtaining time sequence operation parameters collected by a power equipment sensor, carrying out the target detection analysis of the image data stream, and obtaining a real-time image data stream; and identifying personnel targets in the dangerous area and generating a first early warning signal, performing abnormal mode analysis on the time sequence operation parameters, identifying equipment operation state abnormity and generating a second early warning signal, performing decision-level fusion on the first early warning signal and the second early warning signal, and outputting a comprehensive risk assessment result through a weighted voting mechanism. According to the invention, by combining the image data and the sensor data, the perception capability of potential risks in the transformer substation is enhanced, the monitoring accuracy and the early warning reliability are improved, and the safety and stability of the whole power grid are further improved.

A modular open system architecture for common intelligence picture generation is disclosed. The system receives intelligence requirements through a multimodal artificial intelligence system and calculates collection feasibility across multiple intelligence sources based on physical and temporal conditions. The system develops integrated collection plans through the containerized analytics workbench using containerized analytics modules and processes intelligence through GPU-accelerated deep learning models for automated target recognition. Satellite collection is orchestrated through satellite data acquisition optimization platform by evaluating weather conditions, orbital parameters, and sensor capabilities, while space domain awareness is maintained through space domain awareness system for real-time collection asset management. Multi-source intelligence data is fused through multi-source intelligence fusion system to populate a common intelligence picture. The system implements automated workflows for intelligence analysis and dissemination while maintaining security controls, with the containerized analytics workbench providing pattern of life analysis and dynamic exploitation through containerized microservices.

The invention relates to the technical field of elevator fault diagnosis, and discloses an elevator fault diagnosis method and system.The elevator fault diagnosis method comprises the steps that elevator operation data are collected, operation stages are divided for the elevator operation process according to the elevator operation data, and the residual error of the elevator operation data is calculated in the operation stages; based on an elevator structure, a causal chain graph reflecting the relation between components is established through fault mode analysis, and a direct upstream node set of downstream nodes in the causal chain graph serves as a parent set of the downstream nodes; in the operation stage, performing block replacement processing for keeping time sequence characteristics on an upstream node residual sequence of each edge in the causal chain graph to generate a contrast residual sequence, and calculating the propagation intensity of the edge according to the contrast residual sequence; effective propagation paths are screened according to the propagation intensity of the edges, the effective propagation paths are sorted to obtain candidate nodes, residual errors of the candidate nodes are set to be zero through virtual pinch-off, forward propagation is carried out through a semi-physical model, and fault nodes are determined in the candidate nodes based on forward propagation results.

The invention relates to the technical field of intelligent robot health monitoring, and discloses a health monitoring management system based on an intelligent robot. The system comprises a data cleaning unit, an attribute extraction unit, a period division unit, a coordinate conversion unit and a mode analysis unit. The data cleaning unit collects multi-source health data of the robot, and generates an integrated health data set through format standardization, data mode unification, structural difference analysis and redundancy elimination according to a time sequence. The attribute extraction unit separates a monitoring timestamp, a health index code and an individual type label; a period division unit divides the integrated data set into time slices according to timestamps to form a period health data set; the coordinate conversion unit maps the health state to a standard health coordinate system by using the periodic data set and the health index code to generate a health distribution map; and the mode analysis unit classifies health change events according to the atlas, the periodic data set and the individual type labels, calculates a target periodic health activity degree and outputs a health change trend index.

The invention relates to the technical field of multi-agent cooperative control, and discloses a multi-agent autonomous decision-making method based on deep reinforcement learning. The method comprises the steps of synchronously detecting an initial collaborative state of a cluster, performing joint situation assessment, and judging a collaborative operation mode according to a quantitative situation. And analyzing the capability of each agent and the real-time task load, and constructing a distributed task knowledge graph. And utilizing the atlas to drive a deep reinforcement learning network, coupling computing resource allocation and a task path, and generating a preliminary behavior strategy of each agent. And performing cluster-level conflict detection and iterative negotiation adjustment, and finally issuing an executable action instruction sequence. According to the method, integrated optimization of resource allocation and action paths is realized, the situation understanding and negotiation mechanism is enhanced through the knowledge graph to guarantee the collaborative consistency, and the collaborative decision-making efficiency and task execution robustness of a multi-agent system in a dynamic environment are improved.

The invention discloses a wind power gear box online fault diagnosis method based on multi-source data fusion, and particularly relates to the field of mechanical fault detection, and the method comprises the steps: S1, collecting high-frequency dynamic, medium-frequency working condition and low-frequency thermal state data, and outputting a standardized data set through time alignment, quality verification and physical constraint verification; s2, performing multi-scale decomposition to retain a fault sensitive frequency band, inverting physical parameters such as gear contact stress and the like, constructing a physical cause and effect graph, and determining fault sensitive characteristics and threshold values; s3, constructing a multi-modal feature tensor, and obtaining a low-dimensional health representation vector through CP decomposition fusion, graph neural network reasoning and variational auto-encoder dimension reduction; s4, calculating a weight by using an entropy weight method, calculating a dynamic health degree in combination with a health benchmark, predicting a trend by using LSTM, and establishing a five-level health system; s5, judging a fault mode through double-layer identification, analyzing a root cause and formulating a hierarchical operation and maintenance suggestion; the method is based on multi-source fusion and data mechanism dual drive, and precise diagnosis and operation and maintenance guidance are achieved.

The invention relates to the field of unmanned aerial vehicle communication, in particular to an unmanned aerial vehicle communication frequency band monitoring method and interference method.The method comprises the following steps that 1, background noise signals of a full frequency band are collected, Fourier transform is conducted on the collected background noise signals, and a signal frequency band with energy exceeding a preset threshold value is extracted; step 2, acquiring each signal frequency band with energy, and performing adaptive IMF component decomposition on each signal frequency band with energy; 3, screening the IMF components according to the impact energy of the IMF components, and performing signal reconstruction on the screened IMF components; and step 4, inputting the reconstructed signal into the neural network model for rule analysis, and generating a risk coefficient of existence of communication in a signal frequency band according to the rule analysis.

The invention belongs to the technical field of unmanned system reliability, and provides an unmanned cluster fault mode analysis and transverse and longitudinal propagation analysis method and system based on a task chain, electronic equipment and a storage medium. The method comprises the steps of model construction, fault mode recognition, severity setting, fault transmission recognition, adjacency matrix construction, network model construction and fault transmission influence analysis. According to the method, the unmanned cluster is subjected to hierarchical division, so that the problem of complex calculation process of a physical model is avoided, and the real-time performance of fault mode analysis is improved; through fault mode identification, severity setting and fault transmission identification, analysis of complex cooperation relation and interaction among individuals is realized, and adaptability of a dynamic change environment is improved. Through adjacency matrix construction, network model construction and fault transmission influence analysis, the analysis of the mutual dependency relationship between different fault modes is improved, and the accuracy of the propagation path is improved.

The invention provides a wrong question book intelligent learning system and method based on OCR and similarity analysis. The method comprises the following steps: S1, carrying out wrong question picture preprocessing and feature extraction; s2, multi-dimensional similarity calculation is associated with knowledge points; s3, knowledge point error rate statistics and error pattern analysis; and S4, generating and pushing personalized exercises. According to the method, intelligence and individuation of error question management are realized through multi-technology fusion, the bottleneck of current error question management is broken through, and the learning efficiency and the knowledge mastering depth are improved.

The invention discloses a signaling road network matching method based on a heterogeneous graph convolutional network and an attention mechanism, and belongs to the crossing field of mobile communication and intelligent traffic technologies. According to the method, a multi-relation graph containing a road segment set and a base station sampling point set is constructed, wherein an edge set covers associated edges of base station sampling points and road segments, topological edges of the road segments and time edges of the base station sampling points; performing feature extraction on the multi-relation graph by adopting a 3-5-layer heterogeneous graph convolutional network to obtain low-dimensional vector representation of nodes; dynamically calculating an observation probability and a transition probability based on an attention mechanism; and performing path search by using an improved Viterbi algorithm in combination with a backtracking mechanism and a pruning strategy, and outputting a path with the highest score as a matching result. The method can effectively solve the problems of poor adaptability to complex road networks, insufficient robustness to high positioning error signaling data, low precision caused by matching probability immobilization and the like in the prior art, significantly improves the accuracy and robustness of signaling road network matching in complex scenes, and can be applied to the fields of traffic flow monitoring, travel mode analysis, urban planning and the like.

The invention relates to the technical field of network security, in particular to a security monitoring method and system for network traffic. The method comprises the following steps: capturing a network flow data flow in real time, and extracting a network entity and a direct communication relationship to construct a basic communication graph; identifying and quantifying a high-order interaction mode between network entities, and taking the high-order interaction mode as an implicit feature enhanced basic communication graph to generate an enhanced security graph; processing the enhanced security map by using a multi-scale time sequence diagram neural network, and capturing a short-term burst mode and a long-term evolution mode at the same time; a dynamic anomaly score is calculated based on the network entity historical behavior baseline and the current network situation, and a security alert is generated when an adaptive threshold is exceeded. The system correspondingly comprises a flow capture module, a feature extraction module, a high-order mode analysis module, a security map construction module, a multi-scale analysis module, a dynamic risk assessment module and an intelligent alarm module, and comprehensive and accurate network threat detection is realized.

The invention discloses an active city cooling method based on a three-dimensional city form and a circuit theory, and belongs to the technical field of city environment regulation and heat island effect governance, and the method comprises the steps: S1, recognizing long-term stable cold and heat source patches through obtaining multi-temporal surface temperature (LST) data and combining mathematical form spatial pattern analysis (MSPA) and a landscape connectivity index; s2, adopting a spatial principal component analysis (SPCA) method to construct a comprehensive resistance surface fused with the three-dimensional urban form factors; s3, connecting a cold and heat source to simulate an airflow conduction path based on a circuit theory, extracting a cooling gallery and identifying key nodes; and S4, finally, through a stable surface-connecting line-key point mode active cooling network, space optimization is carried out. According to the method, accurate identification and efficient communication of cold and heat sources can be realized, the heat island effect relieving efficiency is improved, the long-term adaptability of a cooling network is ensured, and a popularized urban thermal environment governance path is provided.

A new computational approach may provide improved detection of disease conditions and comorbidities, such as PTSD, Parkinson's, Alzheimer's, depression, etc. For example, in an embodiment, a computer-implemented method for detecting a disease condition may comprise receiving a plurality of data streams, each data stream representing a measurement of a brain activity comprising physical and chemical phenomena and performing pattern analysis on the plurality of data streams to detect at least one fundamental code unit of a brain code corresponding to a disease condition based on a combination of the plurality of data streams.

The invention relates to the technical field of network security, in particular to an AI-based abnormal traffic monitoring and defense system, which comprises a data acquisition module, an equipment judgment module, a baseline determination module, a behavior analysis module and a risk defense module, the data acquisition module is used for acquiring current detection data in a current time period in a current acquisition period; the equipment judgment module is used for identifying multiple access equipment; the baseline determination module is used for constructing a historical and current comparison baseline; the behavior analysis module is used for judging regional characteristics, analyzing an access mode and identifying an abnormal access behavior; and the risk defense module is used for carrying out risk grade division and triggering an alarm when a threshold value is reached. According to the system, through regionalized screening, dynamic baseline construction and multi-dimensional behavior analysis, accurate identification and rapid grading protection of abnormal traffic are realized, and the precision and efficiency of airport network security protection are improved.

The invention provides a material quality detection method and device based on quantum sensor detection, and the method comprises the steps: obtaining an original quantum sensing data sequence collected by a quantum sensor in a material detection scene, carrying out the multi-dimensional feature conversion of the original quantum sensing data sequence, generating a quantum feature set reflecting the internal structural features of a material, and carrying out the detection of the internal structural features of the material; and calling a pre-trained defect diagnosis model to perform defect mode analysis processing on the quantum feature set, outputting defect probability distribution information and defect type confidence information in the material, and determining defect positioning information and defect type classification information of the material based on the defect probability distribution information and the defect type confidence information. And according to the defect positioning information and the defect type classification information, generating a quality detection report containing a defect space distribution diagram and a type analysis table. According to the invention, the comprehensiveness, accuracy and processing efficiency of material internal defect detection can be improved.

The invention relates to an accelerated test system and method based on a weak link of an electronic component, and belongs to the technical field of electronic product testing, and the method comprises the steps: carrying out the hierarchy analysis and modeling preparation of a whole machine structure, and carrying out the structural layering of a target whole machine system; weak link identification: identifying a key failure path influencing the reliability in the whole machine, and determining a weak component; acceleration pre-test: carrying out acceleration pre-test on the identified weak component, and obtaining degradation behavior data of the weak component through multi-stage acceleration stress loading; constructing a complete machine acceleration model, and calculating the influence of temperature stress, electric stress and vibration stress on the failure degradation rate of the product; acceleration factor calculation: calculating an acceleration factor based on the activation energy of the weak link part; and predicting the service life of the whole machine through acceleration factors, failure mode analysis and component service life data. The failure of the whole machine is converted into the failure of a weak link; the service degradation process of the whole machine can be reflected more truly, and the accuracy of a prediction result is improved.

The invention discloses a wavy feathering method and system of a digital direct injection printer for flag cloth and electronic equipment, the method abandons a traditional gradual feathering mode, and adopts a wavy distorted feathering pattern, and the method comprises the following steps: firstly, analyzing the permeability characteristic of the cloth; according to the method, a two-way complementary template is finally generated by setting morphological parameters, creating an S-shaped fluctuation track, filling ink dots and leaving white space, so that adjacent PASS patterns can be occluded in an interlocking manner at a seam; and finally controlling the nozzle to spray ink according to the pattern. The system correspondingly comprises a pattern analysis module, a pattern generation module and a printing control module, through irregular ink dot distribution of wavy patterns, smooth gradual change is formed through natural permeation of ink, the contrast ratio of a feathering area and a non-feathering area is effectively reduced, accurate alignment of joints can be ensured through real-time error compensation, accordingly, PASS channels are eliminated, and the printing quality is improved. And the overall quality and the front and back color consistency of a flag cloth printing image are improved.

The invention relates to the technical field of image recognition, and discloses an artificial intelligence-based image recognition method and system, and the method comprises the following steps: constructing a multi-modal fusion network based on a multi-modal data set, carrying out the feature complementation between modals through employing a cross attention mechanism, and outputting the fused three-dimensional dynamic semantic features; performing cross-scene element training by using an MAML framework to generate a lightweight model with a rapid adaptation capability; based on a lightweight model, behavior pattern analysis is carried out on the three-dimensional dynamic semantic features through a hierarchical space-time Transform, and behavior semantic tags containing time sequence action units are extracted; and generating a trained image recognition model based on the behavior semantic tag, and outputting a recognition result based on the image recognition model. According to the invention, the technical bottlenecks that the dynamic scene performance is reduced and the cross-scene adaptation efficiency is low in the prior art are fundamentally solved.

The invention provides a Tibetan chicken intensive breeding health monitoring and early warning method and system, and relates to the technical field of computers, and the method comprises the steps: obtaining the three-dimensional motion and interval time sequence data of a Tibetan chicken flock in a breeding house, and the original chirp audio time sequence data; performing motion mode analysis according to the three-dimensional motion and the interval time sequence data to obtain a vector field sequence; carrying out stress level inference according to the vector field sequence to obtain a group recessive stress index; according to the group implicit stress index and the original chirp audio time sequence data, performing model construction to obtain a chirp-stress association model; acquiring real-time chirp audio data, and inputting the real-time chirp audio data into the chirp-stress correlation model for health state critical point prediction to obtain an instability precursor signal; and performing graded early warning according to the instability precursor signal to obtain a health early warning grade. According to the invention, prediction and early warning of health risks caused by group stress of the Tibetan chickens in an intensive breeding environment are realized.

The invention relates to a turntable data calibration method and device and computer equipment. The method comprises the following steps: acquiring target rotation plane data and initial rotation plane data corresponding to a tracking turntable; under the condition that the initial rotation plane data and the target rotation plane data are different, the calibration mode of the tracking rotary table is analyzed by using the initial rotation plane data, and calibration mode analysis data are obtained; performing operation control analysis on the tracking turntable according to the calibration mode analysis data to obtain turntable calibration control data; controlling the tracking turntable to move according to the turntable calibration control data to obtain turntable calibration data of the tracking turntable; the turntable calibration data is used for eliminating the difference between the target rotation plane data and the initial rotation plane data. By adopting the method, the tracking precision of the tracking turntable during the tracking task can be improved.

The invention relates to the technical field of image recognition, in particular to a personnel abnormal behavior supervision method and system for important places. The method comprises the following steps: acquiring a monitoring video frame image in real time, matching in combination with a preset abnormal foreign object image library, recognizing an abnormal behavior image, and confirming the identity of an abnormal person through face recognition to generate first alarm information; then, through analyzing the action sequence of the abnormal behaviors, identifying a second abnormal behavior image and a third abnormal behavior image, and calculating a situation response deviation degree and a behavior coherence imbalance index, generating second alarm information; finally, based on the two pieces of alarm information, the system performs alarm processing on the abnormal personnel, and abnormal behavior monitoring and timely response are effectively realized. According to the invention, through accurate behavior identification, intelligent mode analysis and time sequence association evaluation, an efficient and accurate abnormal behavior monitoring and alarm function is provided, and the place safety is improved.

A new computational approach may provide improved detection of disease conditions and comorbidities, such as PTSD, Parkinson's, Alzheimer's, depression, etc. For example, in an embodiment, a computer-implemented method for detecting a disease condition may comprise receiving a plurality of data streams, each data stream representing a measurement of a brain activity comprising physical and chemical phenomena and performing pattern analysis on the plurality of data streams to detect at least one fundamental code unit of a brain code corresponding to a disease condition based on a combination of the plurality of data streams.

The embodiment of the invention discloses a handwriting image identification method and system based on artificial intelligence, and the method comprises the steps: firstly collecting a to-be-identified handwriting image and a known sample handwriting image, and generating a target handwriting image set; sequentially performing stroke region segmentation, stroke feature extraction and texture mode analysis on the handwriting images in the set to generate a mixed feature data set containing a traditional handwriting feature set and a deep learning handwriting feature set; inputting the mixed feature data set into a Triplet network model, and generating a first discriminative feature vector of the to-be-identified handwriting image and a second discriminative feature vector of a known sample handwriting image through triple loss function joint optimization training; and finally, generating a handwriting image identification report containing the feature matching score and the author affiliation probability based on the first discriminative feature vector and the second discriminative feature vector. The accuracy and reliability of handwriting identification are improved.

The invention relates to the technical field of intelligent water transportation information, in particular to an intelligent water transportation standard system construction method based on a four-dimensional level model. The method comprises the following steps: learning decision weights of different development value orientations according to idea features and execution features of a case, and determining a target development value of an actual water transportation service at a value dimension level; a spatial semantic tree is constructed by adopting a semantic layering mode, and traversal of the tree is driven by a target development value, so that matching of key element-scene requirements under a spatial dimension is realized. The method comprises the following steps of: partitioning a whole-cycle flow of a service, and performing time-dimension sequence pattern analysis on standard events at each stage to obtain corresponding time sequence construction requirements; and carrying out analysis and decision-making on operation paths among different elements from a logic dimension level to form a target implementation path and a support strategy. According to the method, a full-factor, full-period and full-scene intelligent water transportation standardization system can be constructed, and reliable multi-dimensional standard guidance is effectively provided for water transportation engineering construction.