Method, medium and system for dynamic testing of waterway traffic risks

Abstract

This invention discloses a method, medium, and system for dynamic testing of water traffic risks. The method includes: establishing an internal information set of the water traffic system by extracting information from ship equipment, maritime departments, and captain consultations; this information set includes navigation environment information, human-computer interaction information, and ship-to-shore communication information; extracting and measuring the non-uniform characteristics of information transmission within the water traffic system using information flow theory; and using wavelet transform and spectral analysis to extract and analytically model the dynamic characteristics of water traffic risks; then constructing a dynamic testing model of water traffic risks using a data-driven approach; finally, inputting the abnormal characteristic values ​​of the water traffic system information in the test area into the dynamic risk testing model to obtain the calculated risk value for that area. This invention can quickly achieve dynamic testing of water traffic risks, improving the efficiency and targeting of water traffic safety supervision.

Description

Technical Field

[0001] This invention belongs to the field of water traffic risk assessment technology, and in particular relates to a dynamic testing method, medium and system for water traffic risks. Background Technology

[0002] With the increasing traffic volume on the Yangtze River main line and the increasingly complex and changeable navigation environment, water transportation is facing unprecedented development opportunities and risks and challenges, which puts forward higher requirements for the level of maritime safety supervision.

[0003] Traditional methods for assessing maritime traffic safety risks, such as the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE), are well-suited for maritime applications. However, AHP and FCE have significant limitations in identifying and assessing maritime traffic safety risks in complex waterways. Consequently, relying solely on AHP and FCE results for early warning and control of maritime traffic safety risks during maritime regulatory processes leads to substantial biases and delays. This results in maritime regulatory decision-making departments being unable to effectively identify traffic safety risks in their regulated waters, reducing the efficiency of maritime regulatory departments in early warning of traffic safety risks.

[0004] In recent years, with the widespread application of Automatic Identification Systems (AIS) and the development of computer information technology, some scholars have begun to leverage the vast amounts of ship traffic data accumulated by AIS to conduct research on navigation hazard assessment, ship domains, and traffic flow. Ship AIS technology provides a solid data foundation for maritime safety analysis, enhancing the reliability and practicality of the results. Liu Zhengjiang et al., combining the specific characteristics of AIS data, proposed a spatiotemporal clustering algorithm for shipborne AIS data. Shao Zheping et al. established a dynamic domain model for ships in confined waters based on AIS data. Zheng Zhongyi et al. proposed a method for extracting ship encounter information from AIS data and plotted specific ship encounter distributions based on massive amounts of AIS data. Although the aforementioned research based on AIS data was not specifically focused on traffic risk assessment, its findings have provided important theoretical and technical accumulation for regional traffic risk assessment.

[0005] Domestic and international research indicates that traditional traffic risk assessment models can effectively extract and characterize static risks within the human-ship-environment system, and their relational models can also reveal, to some extent, the macroscopic risk impact patterns of the traffic system. However, limited by their definitions, traditional methods and models cannot directly and effectively describe and evaluate the dynamic characteristics of traffic risks at the regional or temporal levels.

[0006] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0007] (1) Traditional water traffic safety risk assessment methods mostly characterize the static risks of the human-ship-environment system. As a result, there are significant deviations and delays in the early warning and control of water traffic safety risks based on the assessment results during the maritime supervision process. This makes it impossible for maritime regulatory decision-making departments to effectively identify the traffic safety risks in the regulated waters, thus reducing the efficiency of maritime regulatory departments in early warning of traffic safety risks.

[0008] (2) Existing traffic risk assessment methods rely on static historical data for risk assessment analysis and modeling. However, there is no mature method for analyzing and modeling the dynamic characteristics of water traffic risks. The dynamic characteristics of water traffic risks are not yet clear, and there is a lack of methods for analyzing and dynamically assessing water traffic risk situations. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a method, medium, and system for dynamic testing of water traffic risks.

[0010] This invention is implemented as follows: a dynamic testing method for water traffic risks, comprising the following steps:

[0011] Step 1: Establish an internal information set for the water transportation system by exporting information from ship equipment, collecting information from maritime authorities, and extracting information through consultation and research with captains;

[0012] Step two: Extract and measure the structural features of the internal information of the water transportation system;

[0013] Step 3: Using wavelet transform and spectral analysis, the dynamic characteristics of water traffic risks are extracted and analytically modeled.

[0014] Step four: Next, using a data-driven approach, a dynamic testing model for water traffic risks is constructed.

[0015] Step 5: Input the abnormal feature values ​​of the water traffic system information of the test water area into the risk dynamic test model to obtain the risk calculation value of the water area.

[0016] Furthermore, in step one, the information set within the water transportation system includes: navigation environment information, human-machine interaction information, and ship-to-shore communication information.

[0017] Furthermore, in step two, the extraction and measurement of the structural features of the internal information of the water transportation system specifically includes:

[0018] (1) Measuring the unevenness of information interaction in water transportation system based on asymmetric theory: Under the same regional environment, analyze the information interaction and distribution of human-ship-environment space, determine the flow, transformation, obstruction and external triggering conditions of human-ship-environment space information, and use asymmetric theory to study the unevenness of information among four types of relationships: human-human, human-ship, human-environment and ship-environment, and construct a hierarchical evaluation system for information set and unevenness of water transportation system.

[0019] (2) Based on the human-machine conflict, the time delay of the water transportation system is measured: According to the information transmission model, the information operation mechanism between the subject and object of cognition is constructed. Taking into account human reaction time, data delay and information transmission obstruction, a method for calculating the time delay of the regional water transportation system is proposed. The time delay of the water transportation system is characterized and quantified in three dimensions using the system time-varying theory.

[0020] Furthermore, in step (1), the measurement of the non-uniform characteristics of information interaction in the water transport system based on asymmetric theory specifically includes:

[0021] 1) The information interaction relationships within the water transportation system include four types: human-human interaction, human-ship interaction, human-environment interaction, and ship-environment interaction. These interaction relationships are used to analyze and describe the uneven distribution of information in the transportation system.

[0022] 2) Utilize classification methods to statistically analyze different forms of interaction relationships. Combined with shipboard equipment, documents, and consultations and surveys with maritime experts and captains, analyze the impact of navigation environment, navigation rules, crew quality, and management system factors on the internal information interaction of the water transportation system. Determine the generation, transformation, and dissipation process of uneven information distribution, as well as its evolution mechanism and external triggering conditions.

[0023] 3) The problem of measuring the unevenness of information in the transportation system is characterized as the problem of traffic safety information asymmetry. Direct and indirect measurement methods are used to measure the sources of various asymmetries in the water transportation system, and an evaluation system for the unevenness of information in typical water transportation systems is constructed.

[0024] Furthermore, step (2) also includes: based on historical ship accident data, using human-machine conflict theory, constructing human-machine conflict scenarios under different navigation environments and decision-making conditions, and determining the human-machine conflict time-delay safety domain in combination with the dynamic evolution law of risk.

[0025] Furthermore, in step three, the extraction of risk dynamic characteristics based on the internal structure model of the transportation system includes:

[0026] 1) Using wavelet transform and spectral analysis, study the volatility characteristics of systemic risk and determine the volatility threshold of systemic risk;

[0027] 2) Study the spatial distribution differences in different water areas to determine the spatial boundaries of risks;

[0028] 3) Using data distribution theory and combined with the research results of system safety modes, we determine the characteristics of uneven distribution of system information and propose a method for characterizing system risk status.

[0029] Furthermore, in step three, the construction of the regional waterway transportation system dynamic risk analysis model includes:

[0030] 1) Based on the information exchange of the regional waterway transportation system, and combined with the knowledge of information transmission, determine the conditions under which risks arise;

[0031] 2) Analyze the relationship between the volume and speed of regional traffic information transmission and the relationship between the flow velocity and density of regional traffic information. Comprehensively utilize statistical discrimination, empirical judgment, and theoretical derivation methods to observe the discreteness, multiphase, and nonlinear characteristics of data point distribution. Determine a reasonable relationship model type from the macro data level. Based on the external evolution law of risk, deduce the comprehensive risk evolution characteristics of the system from the micro-level evolution of individual risk sources.

[0032] 3) Based on the macro-analysis of the aforementioned relationship model type, using single-structure and multi-structure modeling methods, and taking the maximum fluctuation range of regional traffic risk, imbalance rate, and information anomaly threshold characteristic values ​​as basic parameters, a regional traffic risk analysis model with multiple parameters and multiple dimensions is formed.

[0033] 4) To address the ambiguity commonly found in traffic risk modeling, computer simulation and numerical calculation methods are used to achieve accurate and efficient model parameter calibration, thus overcoming the impact of ambiguity on the analytical results.

[0034] Furthermore, in step four, the construction of the dynamic testing model for water traffic risks includes:

[0035] (1) Based on the risk analysis model of the water transportation system, select the research water area and extract the information elements of the water transportation system;

[0036] (2) The abnormal characteristics of information transmission in the waterway traffic system are analyzed by using the information transmission model. The abnormal thresholds of information delay, error, missing and overload in the waterway traffic system are calculated, and a risk dynamic calculation model based on the risk function is established.

[0037] (3) Based on the aforementioned risk dynamic calculation model, and by combining computer simulation and numerical reasoning methods, the risk measurement standard and semantic expression are determined.

[0038] Another object of the present invention is to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the dynamic testing method for water traffic risks.

[0039] Another object of the present invention is to provide a dynamic testing system for water traffic risks that implements the aforementioned dynamic testing method for water traffic risks, the dynamic testing system for water traffic risks comprising:

[0040] The system structure feature extraction and measurement module is used for extracting and measuring the internal structural features of the water transportation system.

[0041] The risk dynamic characteristics extraction and analysis modeling module is used for the extraction and analysis modeling of dynamic characteristics of water traffic risks.

[0042] The risk dynamic test model construction module is used to construct dynamic test models for water traffic risks.

[0043] The theoretical and technological innovations of this invention are mainly reflected in:

[0044] (1) Based on catastrophe theory and Bayesian networks, this paper takes the perspective of information interaction within the human-ship-environment system and explores the intrinsic relationship between abnormal information interaction in the water transportation system and water transportation risks. It delves into three aspects: internal structural characteristics and measurement of the water transportation system, extraction and analysis modeling of regional water transportation risk dynamic characteristics, and dynamic evaluation of typical water transportation system risks. This forms a systematic theory and method for dynamic testing of water transportation risks, providing theoretical and technical support for early warning and control of traffic safety risks in waterways such as curved waterways, continuous bridge areas, confluence waterways, and reservoir areas.

[0045] (2) This invention conducts original research on the dynamic characteristics of water traffic risks in multidimensional time and space, and systematically forms a regional water traffic risk situation analysis theory and method, providing theoretical and methodological support for the micro-level research and regulatory innovation of water traffic systems in complex waters.

[0046] (3) It is the first to analyze the intrinsic causes of traffic risks from the perspective of abnormal information interaction within the water transportation system, and proposes a method for measuring regional water transportation information inhomogeneity and system time delay in complex water areas, so as to realize a quantitative description of the internal structural characteristics of regional water transportation systems.

[0047] (4) Explore the relationship between abnormal information interaction within the water transportation system and water transportation safety, and propose a dynamic risk characteristic index and model for the water transportation system based on information asymmetry. This model can describe the relationship between regional water transportation risk operation indicators and traffic safety status. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the dynamic testing method for water traffic risks provided in an embodiment of the present invention.

[0050] Figure 2 This is a schematic diagram of the logical relationship of the dynamic testing method for water traffic risks provided in the embodiments of the present invention.

[0051] Figure 3 This is a schematic diagram of anomaly measurement in information transmission of a regional water transportation system provided in an embodiment of the present invention.

[0052] Figure 4 This is the regional water traffic risk analysis and modeling process provided in the embodiments of the present invention.

[0053] Figure 5 This is a schematic diagram illustrating the process of establishing a dynamic risk assessment model for water traffic provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] The present invention aims to study a dynamic testing method for water traffic risks, solve the risk assessment problem in water traffic risk research, assist maritime regulatory authorities in achieving early warning and pre-control of water traffic safety risks, thereby reducing the occurrence of water traffic accidents and improving water traffic safety.

[0056] This invention, through collecting and studying waterway traffic information, establishes a waterway traffic risk analysis model and a dynamic testing model, thereby achieving the evaluation and testing of waterway traffic risks. Figure 1 As shown, the dynamic testing method for water traffic risks provided in this embodiment of the invention includes the following steps:

[0057] S101, by exporting information from ship equipment, collecting information from maritime authorities, and extracting information through consultation and research with captains, an internal information set for the water transportation system is established. The information collected in this embodiment mainly includes:

[0058] Navigation environment information: channel width, channel curvature radius, channel depth, water flow rate, water flow velocity, wind force, wind speed, and visibility.

[0059] Human-machine interaction information: cockpit operation commands, radar collision avoidance information, AIS interaction information, and VHF communication information.

[0060] Ship-to-shore communication information: ship movement information, voyage information, navigation warning information, and safety supervision information.

[0061] S102, combining information flow theory, classifies and defines the uneven characteristics of information transmission within a water transportation system; these include four characteristics: information delay, information error, information loss, and information overload. The specific classification of uneven transmission within the information set is shown in Table 1.

[0062] Table 1. Classification of Uneven Information Transmission within Water Transportation Systems

[0063]

[0064]

[0065] S103 utilizes wavelet transform and spectral analysis to extract and analytically model the dynamic characteristics of water traffic risks; through wavelet transform and spectral analysis, it studies the volatility characteristics of system risks and determines the volatility threshold of system risks.

[0066] The Morlet wavelet is a complex exponential function under a Gaussian envelope, with the real part being:

[0067]

[0068] The Fourier transform can be expressed as:

[0069]

[0070] In the formula: f c Here, 'a' is the center frequency, 'a' is the transformation scale, and 'f' is the transformation scale. b These are parameters that control the shape of the wavelet. The center frequency f c The oscillation frequency and shape parameter f of the wavelet waveform are determined. b It determines the rate at which the waveform oscillation decays.

[0071] Based on the internal structure of the transportation system, an internal interactive information set is established. According to the flow feedback characteristics of the interactive information, the boundary values ​​of risk nodes are calculated. Combining the characteristics of risk temporal variation volatility, spatial distribution differences, and spatiotemporal variation imbalance, waveform parameters of water transportation system risks are obtained.

[0072] S104. Next, using a data-driven approach, a dynamic testing model for water traffic risks is constructed. This model is based on monitoring values ​​of risk indicators changing over time, combined with a risk assessment index, to establish a risk function for dynamic testing of water traffic risks. The calculation method for the water traffic risk function is as follows:

[0073] The monitoring values ​​of risk indicators are presented as a time series:

[0074] X i ={x i (t i1 ),x i (t i2 ),x i (t i3 ),x i (t i4 ),...,x i (t ia )

[0075] Among them, X i Let x be the risk monitoring value sequence of indicator i. i (t ia ) is the index i in t ia The monitoring value at time 'a' represents the total number of times indicator i is monitored.

[0076] The standardized risk indicator monitoring values ​​can be represented as the following time series:

[0077] R i ={R i (t i1 ),R i (t i2 ),R i (t i3 ),R i (t i4 ),...,R i (t ia )

[0078] The method for calculating the risk function, which reflects the dynamic characteristics of water traffic risks, is as follows:

[0079]

[0080] Where m is the number of risk assessment indicators, w i Let be the risk coefficient of the i-th risk indicator. The risk coefficient corresponds to 5 levels: [very low, 0.1; low, 0.3; medium, 0.5; high, 0.7; very high, 0.9].

[0081] S105: Finally, input the abnormal characteristic values ​​of the waterway traffic system information of the test area into the risk dynamic test model to obtain the risk calculation value of the water area.

[0082] The collected time series values ​​of risk monitoring indicators are input into the risk function calculation model to finally obtain the dynamic time series curve of risk for the water area.

[0083] The method for dynamic testing of water traffic risks provided by this invention can also be implemented by those skilled in the art using other steps. Figure 1 The dynamic testing method for water traffic risks provided by this invention is merely a specific embodiment.

[0084] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented, in whole or in part, as a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0085] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic testing method for water traffic risk, characterized in that, The water traffic risk dynamic testing method comprises the following steps: Step 1: Extract information by ship equipment export, maritime department collection and captain consultation research, and establish a water traffic system internal information set; Step 2: Extract and measure the structural characteristics in the water traffic system internal information set in combination with the information flow theory, wherein the structural characteristics in the water traffic system internal information set include unevenness of water traffic system internal information transmission, and the unevenness of water traffic system internal information transmission includes water traffic system internal information delay, water traffic system internal information error, water traffic system internal information loss and water traffic system internal information overload; Step 3: Extract and analyze modeling of water traffic risk dynamic characteristics by using wavelet transform and spectrum analysis, wherein the extraction and analysis modeling of water traffic risk dynamic characteristics by using wavelet transform and spectrum analysis comprises the following steps: establishing an internal interaction information set of the traffic system based on the internal structure of the traffic system, calculating the boundary value of the risk node according to the flow feedback characteristics of the interaction information, and obtaining the waveform parameters of the water traffic system risk in combination with the characteristics of risk time change volatility, spatial distribution difference and time-space change imbalance; Step 4: Then, a water traffic risk dynamic testing model is constructed by using a data-driven method; Step 5: The water traffic system information abnormal characteristic value of the test water area is input into the above water traffic risk dynamic testing model, and the risk calculation value of the water area is obtained.

2. The method of claim 1, wherein the watercraft risk dynamic test is performed by a watercraft risk dynamic test system. In step 1, the water traffic system internal information set comprises: Navigation environment information, human-computer interaction information and ship-shore communication information.

3. The method of claim 1, wherein the watercraft risk dynamic test is performed by a watercraft risk dynamic test system. In step 2, the extraction and measurement of the structural characteristics of the water traffic system internal information set comprises measurement of the unevenness of water traffic system information interaction based on the asymmetric theory: 1) The internal information interaction relationship of the water traffic system includes four kinds of human-human interaction, human-ship interaction, human-environment interaction and ship-environment interaction, and the interaction relationship is used to analyze and describe the unevenness of traffic system information distribution; 2) Different forms of interaction relationship are analyzed by using classification method, and the influence of navigation environment, navigation rules, crew quality and management system factors on water traffic system internal information interaction is analyzed in combination with consultation and research of shipborne equipment, file data and maritime experts and captains, so as to explore the generation, transformation and dissipation process of information uneven distribution, the evolution mechanism and external triggering conditions; 3) Direct and indirect measurement methods are used to measure each asymmetric source of the water traffic system, analyze the physical meaning of unevenness distribution characteristics and index characteristic values, and construct a typical water area traffic system information unevenness evaluation system in combination with field knowledge.

4. The method of claim 1, wherein the watercraft risk dynamic test is performed by a watercraft risk dynamic test system. In step 3, the extraction and analysis modeling of water traffic risk dynamic characteristics specifically comprises: (1)Risk dynamic characteristics extraction based on internal structure model of traffic system: based on the internal structure of traffic system, the internal interaction information set of traffic system is established, the boundary value algorithm of risk node is analyzed according to the flow feedback characteristics of interaction information, the specific quantitative indicators and measurement methods suitable for the dynamic characteristics of water traffic system risk are proposed by combining the risk time change volatility, spatial distribution difference and time-space change imbalance, and the traditional static risk definition is expanded in dimension; (2)Construction of regional water traffic system dynamic risk analysis model: based on the information interaction mode, the risk dynamic characteristics operation index is proposed, the information node dispersion characteristics including discreteness, multiphase and nonlinearity are analyzed, the relationship between traffic system internal information interaction anomaly and traffic safety is explored, the single structure and multi-structure modeling are used to construct the regional traffic system risk multi-parameter model, and the parameter calibration method is proposed.

5. The dynamic test method of water traffic risk according to claim 4, characterized in that, The risk dynamic characteristics extraction based on the internal structure model of the traffic system further comprises: 1) The wave volatility characteristics of the system risk are studied by using wavelet transform and spectrum analysis, and the system risk volatility threshold is determined; 2) The spatial distribution difference characteristics are studied for different water areas, and the risk spatial boundary is determined; 3) The data distribution theory is used to determine the imbalance characteristics of the system information distribution tilt in combination with the system safety modal research results, and the system risk state representation method is proposed.

6. The dynamic test method of water traffic risk according to claim 4, characterized in that, The construction of the regional water traffic system dynamic risk analysis model comprises: 1) Based on the obtained regional water traffic system information interaction set in different space-time regions, the risk generation conditions are determined in combination with the information transmission knowledge; 2) The relationship between regional traffic information transmission quantity and transmission speed and the relationship between regional traffic information flow rate and regional traffic information density are analyzed, and the statistical discrimination, experience judgment and theoretical derivation means are comprehensively used to observe the discrete, multiphase and nonlinear characteristics of data point dispersion, and to explore the reasonable relationship model type from the macro data level; based on the external evolution law of risk, the system comprehensive risk evolution characteristics are derived from the micro individual risk source evolution level, and the construction of the risk analysis model is guided; 3) Based on the macro analysis of the relationship model type, the single structure and multi-structure modeling means are used to form a regional traffic risk analysis model with multiple parameters and multiple dimensions, taking the characteristic values of the maximum fluctuation amplitude, imbalance rate and information anomaly threshold of regional traffic risk as basic parameters; 4) In view of the fuzziness existing in traffic risk modeling, the computer simulation and numerical calculation method is used to realize accurate and efficient model parameter calibration, and the influence of fuzziness on the analysis result is overcome.

7. The method of claim 4, wherein the watercraft risk dynamic test is performed by a watercraft risk dynamic test system. The water traffic risk dynamic test model construction comprises: (1) Based on the water traffic system risk analysis model, the research water area is selected, and the water traffic system information elements are extracted; (2) The information transmission anomaly characteristics of the water traffic system are analyzed by using the information transmission model, the abnormal threshold values of water area traffic system information delay, error, loss and overload are calculated, and the risk dynamic calculation model based on the risk function is established; (3) Based on the risk dynamic calculation model of waterway traffic system, the measurement standard and semantic expression of risk are determined by combining computer simulation and numerical reasoning method. 8.A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the waterway traffic risk dynamic testing method according to any one of claims 1 to 7.

9. A water traffic risk dynamic testing system for implementing the water traffic risk dynamic testing method according to any one of claims 1 to 7, characterized by, The waterway traffic risk dynamic testing system comprises: an information input module for establishing internal information set of waterway traffic system; a characteristic extraction and analysis module for waterway traffic risk dynamic characteristic extraction and analysis modeling; a risk dynamic testing module for waterway traffic risk dynamic testing.

Images