Port operation risk early warning method and system based on multi-source data association feedback

Abstract

This invention discloses a port operation risk early warning method and system based on multi-source data correlation feedback, relating to the port transportation field. It solves the problem of poor early warning effect in existing port operation risk early warning technologies. The method includes the following steps: Step S1: Obtain the risk assessment priority coefficient corresponding to the port operation area to obtain historical port operation analysis data; Step S2: Obtain the priority assessment operation area based on the historical port operation analysis data, perform real-time operation indicator analysis on the priority assessment operation area, and obtain real-time operation indicator analysis data based on the analysis results; Step S3: Perform operation indicator visualization processing on the priority assessment operation area based on the real-time operation indicator analysis data to obtain a port operation risk assessment graph; Step S4: Perform distributed operation risk early warning on the pre-selected freight ports based on the historical port operation analysis data and the port operation risk assessment graph. This invention can effectively enhance the pertinence and efficiency of port risk prevention and control.

Description

Technical Field

[0001] This invention belongs to the field of port transportation and involves correlation analysis technology, specifically a port operation risk early warning method and system based on multi-source data correlation feedback. Background Technology

[0002] Existing port operation risk early warning technologies have the following shortcomings when conducting port operation risk early warning: 1. Port operation risk early warning technology mostly adopts a "one-size-fits-all" early warning mode, without differentiating and prioritizing according to the functional characteristics, risk exposure level and historical accident frequency of different port operation areas. Current early warning systems often trigger alarms simultaneously for all areas, leading to an imbalance in resource allocation. 2. Existing early warning systems often focus on single-dimensional analysis indicators, neglecting the complex nature of port operation risks. They have not established a cross-dimensional correlation analysis framework, making it impossible to trace the root cause when a single indicator is abnormal. This makes it difficult for the early warning system to capture early signals of complex risks, reducing the accuracy and foresight of risk prediction.

[0003] To address this, we propose a port operation risk early warning method and system based on multi-source data correlation feedback. Summary of the Invention

[0004] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a port operation risk early warning method and system based on multi-source data correlation feedback. This invention aims to improve the pertinence and comprehensiveness of port operation risk early warning.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a port operation risk early warning method based on multi-source data correlation feedback, the specific steps of which are as follows: Step S1: Collect plan view of the pre-selected cargo port, divide the pre-selected cargo port into multiple port operation areas based on the plan view of the pre-selected cargo port, collect and analyze historical early warning data for each port operation area, obtain the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and obtain historical analysis data of port operations. Step S2: Obtain priority assessment operation areas based on historical port operation analysis data, and conduct real-time operation indicator analysis on priority assessment operation areas, and obtain real-time operation indicator analysis data based on the analysis results; Step S3: Visualize the operational indicators of the priority assessment area based on the real-time operational indicator analysis data, and obtain the port operation risk assessment graph corresponding to the priority assessment area based on the processing results. Step S4: Conduct distributed operational risk warnings for the pre-selected freight ports based on historical port operation analysis data and port operation risk assessment graphics.

[0006] Furthermore, in step S1, the specific steps are as follows: Step S11: Obtain the cargo ports that need to be subject to port operation risk warning, and arbitrarily select one pre-selected cargo port from the obtained cargo ports, and collect a planar image of the pre-selected cargo port to obtain a planar image of the pre-selected cargo port. Step S12: During the analysis of the port historical operations of the pre-selected cargo ports, the time value corresponding to the current moment is used as the end time of the cycle to create a port operation historical monitoring cycle with a fixed cycle length. Step S13: Divide the pre-selected cargo port into several port operation areas based on the pre-selected cargo port plan image, conduct historical early warning analysis on the port operation areas that are in the port operation historical monitoring cycle, obtain the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and obtain port operation historical analysis data.

[0007] Furthermore, in step S2, the specific steps are as follows: Step S21: Obtain historical analysis data of port operations, obtain the risk assessment priority coefficient corresponding to each port operation area based on the historical analysis data of port operations, and compare the values ​​of multiple risk assessment priority coefficients obtained. The port operation area corresponding to the highest risk assessment priority coefficient is marked as the priority assessment operation area. Step S22: Acquire the waterway area corresponding to the priority assessment operation area to obtain the target operation waterway, and acquire a planar image of the target operation waterway to obtain a planar image of the target waterway; Step S23: Mark the two sides of the channel in the target channel planar image as the first channel edge and the second channel edge, respectively. Fit the first channel edge and the second channel edge to edge curves to obtain the first channel edge curve and the second channel edge curve. Obtain the channel center axis between the first channel edge curve and the second channel edge curve to obtain the target channel center axis. Step S24: Analyze the width of the narrowest point in the priority assessment area, and obtain the real-time normalized value of the narrowest point width and the weight of the cargo ship passage limit analysis based on the analysis results; Step S25: Perform channel curvature analysis on the priority assessment operation area and obtain the channel limit curvature normalization value based on the analysis results; Step S26: Perform real-time cargo ship density analysis on the target operating channel and obtain the normalized value of cargo ship density based on the analysis results; Step S27: Perform real-time wave difference analysis on the target operating channel to obtain real-time regional wave difference monitoring values, and normalize the obtained real-time regional wave difference monitoring values ​​to obtain normalized regional wave difference values. Step S28: Define the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority evaluation operation area as real-time operation index analysis data.

[0008] Furthermore, in step S24, the specific steps are as follows: Step S241: Discretize the edge curve of the first channel into several edge pixels. Draw a straight line perpendicular to the central axis of the target channel through each edge pixel. Obtain the points where the drawn straight line intersects the edge curve of the second channel to obtain multiple intersection points on the other side. Collect the straight-line distance between each edge pixel and the corresponding intersection point on the other side to obtain multiple real-time channel width values. Compare the values ​​of the multiple real-time channel width values ​​and set the real-time channel width value with the smallest value as the real-time narrow point width. Step S242: Perform narrow point width analysis on the target channel plan image, and obtain the cargo ship passage limit analysis weight corresponding to the priority evaluation operation area based on the analysis results; Step S243: Calculate the product of the real-time narrow point width and the cargo ship passage limit analysis weight to obtain the real-time narrow point width analysis value, and normalize the real-time narrow point width analysis value to obtain the real-time narrow point width normalized value. In step S242, the specific steps are as follows: The edge pixels corresponding to the real-time narrow point width value are obtained to get the narrow and wide edge pixels. The intersection points of the narrow and wide edge pixels with the corresponding other edge are connected to obtain the narrow and wide edge pixel connection line. The port cargo ships that need to pass through the target operation channel during the current period are acquired, and a sample port cargo ship is randomly selected from the acquired multiple port cargo ships. The top-view plane figure of the sample port cargo ship is extracted to obtain the plane fitting figure of the sample cargo ship. The center point of the narrow and wide edge pixel connection is collected to obtain the first comparison feature point. The geometric center point of the sample cargo ship plane fitting graphic is collected to obtain the second comparison feature point. The sample cargo ship plane fitting graphic is controlled to move along the target operation channel and the first comparison feature point and the second comparison feature point are made to coincide. Furthermore, in step S242, the specific steps are as follows: The bow edge of the sample cargo ship's planar fitting image is extracted and fitted into a straight line to obtain the first cargo ship edge fitting line. The stern edge of the sample cargo ship's planar fitting image is extracted and fitted into a straight line to obtain the second cargo ship edge fitting line. The first cargo ship edge fitting line is extended until it intersects with the first channel edge curve and the second channel edge curve to obtain the first cargo ship edge extension line. The second cargo ship edge fitting line is extended until it intersects with the first channel edge curve and the second channel edge curve to obtain the second cargo ship edge extension line. Area values ​​are collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the first channel, and the central axis of the target channel to obtain the first closed coverage area value. Area values ​​are also collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the second channel, and the central axis of the target channel to obtain the second closed coverage area value. The average value of the first closed coverage area and the second closed coverage area is calculated to obtain the average closed coverage area value. The difference between the first closed coverage area value and the second closed coverage area value is calculated, and the absolute value of the difference is taken to obtain the closed coverage area deviation. The ratio of the closed coverage area deviation to the average closed coverage area value is calculated to obtain the narrow passage area occupancy deviation ratio corresponding to the cargo ships in the sample port. Obtain the narrow passage area occupancy deviation ratio corresponding to each port cargo ship, and compare the values ​​of multiple narrow passage area occupancy deviation ratios. Set the narrow passage area occupancy deviation ratio with the largest value as the cargo ship passage limit analysis weight.

[0009] Furthermore, step S25 also includes the following steps: In the target channel planar image, a plane rectangular coordinate system is created with the center point of the target channel center axis as the origin, thus obtaining the axis rectangular coordinate system; The target channel centerline is divided into several micro-segments of equal length, and a sample micro-segment is randomly selected from the multiple micro-segments obtained. The edge points at both ends of the sample axis micro-segment are extracted to obtain the first axis micro-segment point and the second axis micro-segment point. A straight line parallel to the x-axis of the axis rectangular coordinate system is drawn through the first axis micro-segment point to obtain the first micro-segment comparison feature line. The angle between the sample axis micro-segment and the first micro-segment comparison feature line at the first axis micro-segment point is numerically obtained. If the obtained angle value is less than or equal to a preset angle value, the obtained angle value will be directly set as the axis micro-segment intercept angle. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the axis micro-segment intercept angle. Draw a straight line parallel to the x-axis of the rectangular coordinate system through the micro-segment points of the second axis to obtain the second micro-segment comparison feature line. Draw straight lines perpendicular to the first micro-segment comparison feature line and the second micro-segment comparison feature line through the micro-segment points of the first axis and the second axis respectively to obtain the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line. Obtain the intersection points of the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line with the first channel edge curve to obtain the first edge micro-segment intersection point and the second edge micro-segment intersection point. Draw a straight line parallel to the x-axis of the rectangular coordinate system through the second edge micro-segment intersection point to obtain the first edge micro-segment comparison line. Obtain the angle value between the line connecting the first edge micro-segment intersection point and the second edge micro-segment intersection point and the first edge micro-segment comparison line at the second edge micro-segment intersection point. If the obtained angle value is less than or equal to a preset angle value, the obtained angle value will be directly set as the first edge micro-segment intercept angle. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the first edge micro-segment intercept angle.

[0010] Furthermore, step S25 also includes the following steps: Obtain the intersection points of the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line with the second channel edge curve, and obtain the intersection points of the third and fourth edge micro-segments. Draw a line parallel to the x-axis of the rectangular coordinate system through the intersection point of the third edge micro-segment to obtain the second edge micro-segment comparison line. Obtain the angle value between the line connecting the intersection points of the third and fourth edge micro-segments and the second edge micro-segment comparison line at the intersection point of the second edge micro-segment. If the obtained angle value is less than or equal to the preset angle value, the obtained angle value will be directly set as the intercept angle of the second edge micro-segment. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the intercept angle of the second edge micro-segment. The angle of curvature of the axis micro-segment corresponding to the sample axis micro-segment is obtained by calculating the intercept angle of the axis micro-segment, the intercept angle of the first edge micro-segment, and the intercept angle of the second edge micro-segment. Obtain the curvature angle analysis value of each axis micro-segment, compare the values ​​of multiple obtained values, set the axis micro-segment curvature angle analysis value with the largest value as the regional peak curvature angle analysis value, calculate the product of the regional peak curvature angle analysis value and the cargo ship passage limit analysis weight to obtain the channel limit curvature analysis value, and normalize the channel limit curvature analysis value to obtain the channel limit curvature normalized value.

[0011] Furthermore, in step S3, the specific steps are as follows: Obtain real-time operation index analysis data, and based on the real-time operation index analysis data, obtain the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority operation assessment area. A Cartesian coordinate system was constructed using existing charting tools. The normalized values ​​of the real-time narrow point width, channel limit curvature, cargo ship density, and regional wave difference corresponding to the priority operation assessment area were set as the index parameters corresponding to the positive half-axis of the x-axis, the positive half-axis of the y-axis, the negative half-axis of the x-axis, and the negative half-axis of the y-axis in this coordinate system, respectively, thus obtaining the port operation analysis coordinate system. In the port operation coordinate system, the coordinate point with the normalized value of the real-time narrow point width on the horizontal axis and the normalized value of the channel limit curvature on the vertical axis is marked as the first operation analysis coordinate point; the coordinate point with the normalized value of the cargo ship density on the horizontal axis and the normalized value of the channel limit curvature on the vertical axis is marked as the second operation analysis coordinate point; the coordinate point with the normalized value of the cargo ship density on the horizontal axis and the normalized value of the regional wave difference on the vertical axis is marked as the third operation analysis coordinate point; and the coordinate point with the normalized value of the real-time narrow point width on the horizontal axis and the normalized value of the regional wave difference on the vertical axis is marked as the fourth operation analysis coordinate point. Connect the first, second, third, and fourth operational analysis coordinate points in a counter-clockwise direction to obtain the port operation risk assessment graph.

[0012] Furthermore, in step S4, the specific steps are as follows: The port operation risk assessment graphics corresponding to the priority assessment operation area are obtained. The port operation risk assessment graphics are divided into four graphic regions using the x-axis and y-axis. The graphic regions are named the first graphic analysis region to the fourth graphic analysis region according to the coordinate quadrant in which the graphic region is located. The boundaries of the first to the fourth graphic analysis regions are marked respectively. The side lengths of the first to the fourth graphic analysis regions are collected according to the marked boundaries to obtain the side lengths of the first to the fourth graphic regions. The side lengths of the first graphic region to the fourth graphic region are compared numerically. The side length of the graphic region with the largest value is marked as the peak side length of the graphic region, and the side length of the graphic region with the smallest value is marked as the valley side length of the graphic region. The side lengths of the first graphic region to the fourth graphic region are normalized using the peak side length and the valley side length of the graphic region to obtain the first risk assessment weight to the fourth risk assessment weight. Areas are collected from the first to the fourth graphic analysis areas respectively to obtain the graphic area values ​​of the first to the fourth areas. The sum of the products of the graphic area value of each area and the corresponding risk assessment weight is calculated to obtain the comprehensive business risk assessment value. Obtain the preset range for comprehensive operational risk assessment. If the comprehensive operational risk assessment value is within the preset range, an operational risk warning will be issued for the priority assessment area. If the comprehensive operational risk assessment value is not within the preset range, there is no need to issue an operational risk warning for the priority assessment area. Once the risk assessment of the priority assessment area is completed, the risk assessment priority coefficients for each port operation area other than the priority assessment area are obtained based on the historical analysis data of port operations. The port operation area corresponding to the highest risk assessment priority coefficient is replaced with the priority assessment area, and the operational risk assessment and early warning for the priority assessment area are repeated. This process is repeated until the operational risk early warning for each port operation area is completed.

[0013] A port operation risk early warning system based on multi-source data correlation and feedback includes: Data acquisition module: Collects planar images of pre-selected cargo ports, divides the pre-selected cargo ports into multiple port operation areas based on the planar images, collects and analyzes historical early warning data for each port operation area, obtains the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and obtains historical analysis data of port operations. Data Analysis Module: Based on historical port operation data, priority assessment operation areas are identified, and real-time operation indicator analysis is performed on these priority assessment operation areas. Based on the analysis results, real-time operation indicator analysis data is obtained. Visualization module: Based on real-time operational indicator analysis data, the module visualizes the operational indicators of the priority assessment operational areas and obtains the port operation risk assessment graph corresponding to the priority assessment operational areas based on the processing results. Risk warning module: Based on historical port operation analysis data and port operation risk assessment graphics, distributed operation risk warnings are provided for pre-selected freight ports.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention collects planar images of pre-selected cargo ports, divides the pre-selected cargo ports into multiple port operation areas based on the planar images, collects and analyzes historical early warning data for each port operation area, obtains the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and sorts the port operation areas according to the early warning analysis based on the risk assessment priority coefficient. This enables refined and targeted risk classification and judgment of port operation areas, allowing port early warning analysis to focus on high-priority risk operation areas, and significantly improving the accuracy, efficiency and targeted prevention and control of port risk early warning. 2. This invention identifies priority assessment operation areas based on historical port operation data and performs real-time operation indicator analysis on these areas. Based on the analysis results, it obtains real-time normalized values ​​for narrow point width, channel limit curvature, cargo ship density, and regional wave difference. Visual graphics are then created to provide targeted operational risk warnings for these priority assessment operation areas. This allows for precise targeting of high-priority risk operation areas within the port, enhancing the real-time nature, accuracy, and intuitiveness of port operational risk warnings through quantitative indicators and visualization, effectively improving the targeting and efficiency of port risk prevention and control. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a diagram illustrating the implementation steps of the present invention; Figure 2 This is an overall system block diagram of the present invention; Figure 3 This is a schematic diagram of a micro-segment of the sample axis of the present invention; Figure 4 This is a graphical diagram illustrating the port operation risk assessment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 Please see Figure 1 This invention provides a technical solution: a port operation risk early warning method and system based on multi-source data correlation feedback, comprising the following steps: Step S1: Collect planar images of the pre-selected cargo ports, divide the pre-selected cargo ports into multiple port operation areas, and conduct historical early warning analysis on the port operation areas. Based on the analysis results, obtain the risk assessment priority coefficient corresponding to each port operation area and obtain historical analysis data of port operations. The specific steps in step S1 are as follows: Step S11: Obtain the cargo ports that need to be subject to port operation risk warning, and arbitrarily select one pre-selected cargo port from the obtained cargo ports, and collect a planar image of the pre-selected cargo port to obtain a planar image of the pre-selected cargo port. Step S12: During the analysis of the port historical operations of the pre-selected cargo ports, the time value corresponding to the current moment is used as the end time of the cycle to create a port operation historical monitoring cycle with a fixed cycle length. Step S13: Divide the pre-selected cargo port into several port operation areas based on the pre-selected cargo port plan image, conduct historical early warning analysis on the port operation areas that are in the port operation historical monitoring period, obtain the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and obtain port operation historical analysis data. Step S2: Obtain priority assessment operation areas based on historical port operation analysis data, and conduct real-time operation indicator analysis on priority assessment operation areas, and obtain real-time operation indicator analysis data based on the analysis results; In step S2, the specific steps are as follows: Step S21: Obtain historical analysis data of port operations, obtain the risk assessment priority coefficient corresponding to each port operation area based on the historical analysis data of port operations, and compare the values ​​of multiple risk assessment priority coefficients obtained. The port operation area corresponding to the highest risk assessment priority coefficient is marked as the priority assessment operation area. Step S22: Acquire the waterway area corresponding to the priority assessment operation area to obtain the target operation waterway, and acquire a planar image of the target operation waterway to obtain a planar image of the target waterway; Step S23: Mark the two sides of the channel in the target channel planar image as the first channel edge and the second channel edge, respectively. Fit the first channel edge and the second channel edge to edge curves to obtain the first channel edge curve and the second channel edge curve. Obtain the channel center axis between the first channel edge curve and the second channel edge curve to obtain the target channel center axis. Step S24: Analyze the width of the narrowest point in the priority assessment area, and obtain the real-time normalized value of the narrowest point width and the weight of the cargo ship passage limit analysis based on the analysis results; The specific steps in step S24 are as follows: Step S241: Discretize the edge curve of the first channel into several edge pixels. Draw a straight line perpendicular to the central axis of the target channel through each edge pixel. Obtain the points where the drawn straight line intersects the edge curve of the second channel to obtain multiple intersection points on the other side. Collect the straight-line distance between each edge pixel and the corresponding intersection point on the other side to obtain multiple real-time channel width values. Compare the values ​​of the multiple real-time channel width values ​​and set the real-time channel width value with the smallest value as the real-time narrow point width. Step S242: Perform narrow point width analysis on the target channel plan image, and obtain the cargo ship passage limit analysis weight corresponding to the priority evaluation operation area based on the analysis results; In step S242, the specific steps are as follows: The edge pixels corresponding to the real-time narrow point width value are obtained to get the narrow and wide edge pixels. The intersection points of the narrow and wide edge pixels with the corresponding other edge are connected to obtain the narrow and wide edge pixel connection line. The port cargo ships that need to pass through the target operation channel during the current period are acquired, and a sample port cargo ship is randomly selected from the acquired multiple port cargo ships. The top-view plane figure of the sample port cargo ship is extracted to obtain the plane fitting figure of the sample cargo ship. The center point of the narrow and wide edge pixel connection is collected to obtain the first comparison feature point. The geometric center point of the sample cargo ship plane fitting graphic is collected to obtain the second comparison feature point. The sample cargo ship plane fitting graphic is controlled to move along the target operation channel and the first comparison feature point and the second comparison feature point are made to coincide. The bow edge of the sample cargo ship's planar fitting image is extracted and fitted into a straight line to obtain the first cargo ship edge fitting line. The stern edge of the sample cargo ship's planar fitting image is extracted and fitted into a straight line to obtain the second cargo ship edge fitting line. The first cargo ship edge fitting line is extended until it intersects with the first channel edge curve and the second channel edge curve to obtain the first cargo ship edge extension line. The second cargo ship edge fitting line is extended until it intersects with the first channel edge curve and the second channel edge curve to obtain the second cargo ship edge extension line. Area values ​​are collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the first channel, and the central axis of the target channel to obtain the first closed coverage area value. Area values ​​are also collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the second channel, and the central axis of the target channel to obtain the second closed coverage area value. The average value of the first closed coverage area and the second closed coverage area is calculated to obtain the average closed coverage area value. The difference between the first closed coverage area value and the second closed coverage area value is calculated, and the absolute value of the difference is taken to obtain the closed coverage area deviation. The ratio of the closed coverage area deviation to the average closed coverage area value is calculated to obtain the narrow passage area occupancy deviation ratio corresponding to the cargo ships in the sample port. Obtain the narrow passage area occupancy deviation ratio corresponding to each port cargo ship, and compare the values ​​of multiple narrow passage area occupancy deviation ratios. Set the narrow passage area occupancy deviation ratio with the largest value as the cargo ship passage limit analysis weight. Step S243: Calculate the product of the real-time narrow point width and the cargo ship passage limit analysis weight to obtain the real-time narrow point width analysis value, and normalize the real-time narrow point width analysis value to obtain the real-time narrow point width normalized value. Step S25: Perform channel curvature analysis on the priority assessment operation area and obtain the channel limit curvature normalization value based on the analysis results; Step S25 further includes the following steps: In the target channel planar image, a plane rectangular coordinate system is created with the center point of the target channel center axis as the origin, thus obtaining the axis rectangular coordinate system; The target channel centerline is divided into several micro-segments of equal length, and a sample micro-segment is randomly selected from the multiple micro-segments obtained. The edge points at both ends of the sample axis micro-segment are extracted to obtain the first axis micro-segment point and the second axis micro-segment point. A straight line parallel to the x-axis of the axis rectangular coordinate system is drawn through the first axis micro-segment point to obtain the first micro-segment comparison feature line. The angle between the sample axis micro-segment and the first micro-segment comparison feature line at the first axis micro-segment point is numerically obtained. If the obtained angle value is less than or equal to a preset angle value, the obtained angle value will be directly set as the axis micro-segment intercept angle. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the axis micro-segment intercept angle. Draw a straight line parallel to the x-axis of the rectangular coordinate system through the micro-segment points of the second axis to obtain the second micro-segment comparison feature line. Draw straight lines perpendicular to the first micro-segment comparison feature line and the second micro-segment comparison feature line through the micro-segment points of the first axis and the second axis respectively to obtain the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line. Obtain the intersection points of the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line with the first channel edge curve to obtain the first edge micro-segment intersection point and the second edge micro-segment intersection point. Draw a straight line parallel to the x-axis of the rectangular coordinate system through the second edge micro-segment intersection point to obtain the first edge micro-segment comparison line. Obtain the angle value between the line connecting the first edge micro-segment intersection point and the second edge micro-segment intersection point and the first edge micro-segment comparison line at the second edge micro-segment intersection point. If the obtained angle value is less than or equal to the preset angle value, the obtained angle value will be directly set as the first edge micro-segment intercept angle. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the first edge micro-segment intercept angle. Step S25 further includes the following steps: Obtain the intersection points of the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line with the second channel edge curve, and obtain the intersection points of the third and fourth edge micro-segments. Draw a line parallel to the x-axis of the rectangular coordinate system through the intersection point of the third edge micro-segment to obtain the second edge micro-segment comparison line. Obtain the angle value between the line connecting the intersection points of the third and fourth edge micro-segments and the second edge micro-segment comparison line at the intersection point of the second edge micro-segment. If the obtained angle value is less than or equal to the preset angle value, the obtained angle value will be directly set as the intercept angle of the second edge micro-segment. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the intercept angle of the second edge micro-segment. The angle of curvature of the axis micro-segment corresponding to the sample axis micro-segment is obtained by calculating the intercept angle of the axis micro-segment, the intercept angle of the first edge micro-segment, and the intercept angle of the second edge micro-segment. Obtain the curvature analysis value of each axis micro-segment, compare the values ​​of multiple obtained values, set the axis micro-segment curvature analysis value with the largest value as the regional peak curvature analysis value, calculate the product of the regional peak curvature analysis value and the cargo ship passage limit analysis weight to obtain the channel limit curvature analysis value, and normalize the channel limit curvature analysis value to obtain the channel limit curvature normalized value. Step S26: Perform real-time cargo ship density analysis on the target operating channel and obtain the normalized value of cargo ship density based on the analysis results; Step S27: Perform real-time wave difference analysis on the target operating channel to obtain real-time regional wave difference monitoring values, and normalize the obtained real-time regional wave difference monitoring values ​​to obtain normalized regional wave difference values. Step S28: Define the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority evaluation operation area as real-time operation index analysis data; Step S3: Visualize the operational indicators of the priority assessment area based on the real-time operational indicator analysis data, and obtain the port operation risk assessment graph corresponding to the priority assessment area based on the processing results. The specific steps in step S3 are as follows: Obtain real-time operation index analysis data, and based on the real-time operation index analysis data, obtain the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority operation assessment area. A Cartesian coordinate system was constructed using existing charting tools. The normalized values ​​of the real-time narrow point width, channel limit curvature, cargo ship density, and regional wave difference corresponding to the priority operation assessment area were set as the index parameters corresponding to the positive half-axis of the x-axis, the positive half-axis of the y-axis, the negative half-axis of the x-axis, and the negative half-axis of the y-axis in this coordinate system, respectively, thus obtaining the port operation analysis coordinate system. In the port operation coordinate system, the coordinate point with the normalized value of the real-time narrow point width on the horizontal axis and the normalized value of the channel limit curvature on the vertical axis is marked as the first operation analysis coordinate point; the coordinate point with the normalized value of the cargo ship density on the horizontal axis and the normalized value of the channel limit curvature on the vertical axis is marked as the second operation analysis coordinate point; the coordinate point with the normalized value of the cargo ship density on the horizontal axis and the normalized value of the regional wave difference on the vertical axis is marked as the third operation analysis coordinate point; and the coordinate point with the normalized value of the real-time narrow point width on the horizontal axis and the normalized value of the regional wave difference on the vertical axis is marked as the fourth operation analysis coordinate point. Connect the first, second, third, and fourth operational analysis coordinates in a counter-clockwise direction to obtain the port operation risk assessment graph. Step S4: Conduct distributed operational risk warnings for the pre-selected cargo ports based on historical port operation analysis data and port operation risk assessment graphics; In step S4, the specific steps are as follows: The port operation risk assessment graphics corresponding to the priority assessment operation area are obtained. The port operation risk assessment graphics are divided into four graphic regions using the x-axis and y-axis. The graphic regions are named the first graphic analysis region to the fourth graphic analysis region according to the coordinate quadrant in which the graphic region is located. The boundaries of the first to the fourth graphic analysis regions are marked respectively. The side lengths of the first to the fourth graphic analysis regions are collected according to the marked boundaries to obtain the side lengths of the first to the fourth graphic regions. The side lengths of the first graphic region to the fourth graphic region are compared numerically. The side length of the graphic region with the largest value is marked as the peak side length of the graphic region, and the side length of the graphic region with the smallest value is marked as the valley side length of the graphic region. The side lengths of the first graphic region to the fourth graphic region are normalized using the peak side length and the valley side length of the graphic region to obtain the first risk assessment weight to the fourth risk assessment weight. Areas are collected from the first to the fourth graphic analysis areas respectively to obtain the graphic area values ​​of the first to the fourth areas. The sum of the products of the graphic area value of each area and the corresponding risk assessment weight is calculated to obtain the comprehensive business risk assessment value. Obtain the preset range for comprehensive operational risk assessment. If the comprehensive operational risk assessment value is within the preset range, an operational risk warning will be issued for the priority assessment area. If the comprehensive operational risk assessment value is not within the preset range, there is no need to issue an operational risk warning for the priority assessment area. Once the risk assessment of the priority assessment area is completed, the risk assessment priority coefficients for each port operation area other than the priority assessment area are obtained based on the historical analysis data of port operations. The port operation area corresponding to the highest risk assessment priority coefficient is replaced with the priority assessment area, and the operational risk assessment and early warning for the priority assessment area are repeated. This process is repeated until the operational risk early warning for each port operation area is completed.

[0019] Example 2 Please see Figure 2 Based on another concept of the same invention, a port operation risk early warning system based on multi-source data correlation feedback is proposed. The working process of each module is as follows: The data acquisition module will collect planar images of the pre-selected cargo ports, divide the pre-selected cargo ports into multiple port operation areas based on the planar images, collect and analyze historical early warning data for each port operation area, obtain the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and obtain historical analysis data of port operations. Specifically as follows: The cargo ports that require port operation risk warning are acquired, and one pre-selected cargo port is randomly selected from the acquired cargo ports. The pre-selected cargo port is then captured as a planar image to obtain a planar image of the pre-selected cargo port. It should be noted here that: In this application, all cargo ports referred to herein are those equipped with port operation risk early warning systems.

[0020] In the process of analyzing the historical port operations of the pre-selected cargo ports, the current time value is used as the end time of the cycle to create a port operation historical monitoring cycle with a fixed duration. It should be noted here that: In this application, the duration of the historical monitoring period for port operations is consistent with the data coverage period of the port operation risk early warning system; In this application, as the time value corresponding to the current moment changes, and the duration of the port operation historical monitoring cycle remains fixed, the start time value and end time value of the port operation historical monitoring cycle also change accordingly, thereby realizing the dynamic update of the port operation historical monitoring cycle.

[0021] Based on the pre-selected cargo port plan image, the pre-selected cargo port is divided into several port operation areas. Then, a sample port operation area is randomly selected from the obtained port operation areas. Historical early warning analysis is carried out on the sample port operation area that is in the historical monitoring period of port operation. Based on the analysis results, the risk assessment priority coefficient corresponding to the sample operation area is obtained. It should be noted here that: In this application, the port operation area referred to herein is specifically a specialized operating area within a port, divided according to cargo ship type, operational function, and vessel characteristics, possessing independent waterways, land facilities, and dedicated operating systems.

[0022] Specifically as follows: Historical port warnings that occurred in the sample port operation area during the historical port operation monitoring period were collected, and one sample port warning was randomly selected from the multiple historical port warnings obtained. It should be noted here that: In this application, the historical port early warnings referred to herein are specifically port operation risk early warnings issued by the port operation risk early warning system.

[0023] Historical port warnings that are adjacent to the sample port warnings and whose warning time is earlier than the sample port warning time are collected to obtain the first adjacent historical warnings; historical port warnings that are adjacent to the sample port warnings and whose warning time is later than the sample port warning time are collected to obtain the second adjacent historical warnings. Calculate the time difference between the time of the sample port warning and the time of the first adjacent historical warning to obtain the second adjacent warning time difference. Calculate the time difference between the time of the sample port warning and the time of the second adjacent historical warning to obtain the second adjacent warning time difference. Calculate the average of the first adjacent warning time difference and the second adjacent warning time difference to obtain the continuous warning time difference corresponding to the sample port warning. The processing time for the early warnings corresponding to the sample ports was obtained to obtain the sample early warning processing time; It should be noted here that: In this application, the warning response time referred to herein is specifically the time interval between the warning issuance time and the warning cancellation time.

[0024] The warning handling time corresponding to each historical port warning is obtained, resulting in multiple historical warning handling times. The values ​​of the multiple historical warning handling times are compared, and the historical warning handling time with the largest value is marked as the first marked warning handling time, and the warning handling time with the smallest value is marked as the second marked warning handling time. Calculate the difference between the sample early warning response time and the second marked early warning response time to obtain the first early warning response time difference; calculate the difference between the first marked early warning response time and the second marked early warning response time to obtain the second early warning response time difference; calculate the ratio of the first early warning response time difference to the second early warning response time difference to obtain the normalized value of the early warning response time. It should be noted here that: In this application, the historical early warning processing time mentioned herein includes the sample early warning processing time.

[0025] The continuous warning time difference corresponding to the sample port warning is calculated by multiplying the normalized value of the warning handling time by the continuous warning analysis value corresponding to the sample port warning. Obtain the continuous early warning analysis value corresponding to each port operation area, obtain multiple historical continuous early warning analysis values, and compare the values ​​of the multiple historical continuous early warning analysis values. Mark the historical continuous early warning analysis value with the largest value as the first marked continuous early warning analysis value, and mark the continuous early warning analysis value with the smallest value as the second marked continuous early warning analysis value. It should be noted here that: In this application, the historical continuous early warning analysis values ​​referred to herein include sample port early warning analysis values.

[0026] The difference between the continuous early warning analysis value of the sample and the continuous early warning analysis value of the second marker is calculated to obtain the first early warning analysis time difference. The difference between the continuous early warning analysis value of the first marker and the continuous early warning analysis value of the second marker is calculated to obtain the second early warning analysis time difference. The ratio of the first early warning analysis time difference to the second early warning analysis time difference is calculated to obtain the risk assessment priority coefficient corresponding to the sample work area. Repeat the process of obtaining the risk assessment priority coefficient corresponding to the sample operation area, and obtain the risk assessment priority coefficient corresponding to each port operation area to obtain the historical analysis data of port operations. The data analysis module identifies priority assessment areas based on historical port operation data and performs real-time operation indicator analysis on these areas, obtaining real-time operation indicator analysis data based on the analysis results. Specifically as follows: Obtain historical analysis data of port operations, obtain the risk assessment priority coefficient corresponding to each port operation area based on the historical analysis data of port operations, and compare the values ​​of multiple risk assessment priority coefficients. The port operation area corresponding to the highest risk assessment priority coefficient is marked as the priority assessment operation area. The waterway area corresponding to the priority assessment operation area is acquired to obtain the target operation waterway, and a planar image of the target operation waterway is acquired to obtain the planar image of the target waterway; It should be noted here that: In this application, the target operating channel referred to herein is a one-way channel.

[0027] The two sides of the channel in the target channel planar image are marked as the first channel edge and the second channel edge, respectively. The first channel edge and the second channel edge are fitted into edge curves to obtain the first channel edge curve and the second channel edge curve. The channel center axis between the first channel edge curve and the second channel edge curve is obtained to obtain the target channel center axis. Analyze the width of narrow points in the priority assessment operation area and obtain the real-time normalized value of the narrow point width based on the analysis results. Specifically as follows: The edge curve of the first channel is discretized into several edge pixels. A straight line perpendicular to the central axis of the target channel is drawn through each edge pixel. The points where the drawn line intersects with the edge curve of the second channel are obtained, resulting in multiple intersection points on the other side. The straight-line distance between each edge pixel and the corresponding intersection point on the other side is collected to obtain multiple real-time channel width values. The values ​​of the multiple real-time channel widths are compared, and the real-time channel width value with the smallest value is set as the real-time narrow point width. Perform narrowing width analysis on the target channel plan image, and obtain the cargo ship passage limit analysis weight corresponding to the priority evaluation operation area based on the analysis results; Specifically as follows: The edge pixels corresponding to the real-time narrow point width value are obtained to get the narrow and wide edge pixels. The intersection points of the narrow and wide edge pixels with the corresponding other edge are connected to obtain the narrow and wide edge pixel connection line. The port cargo ships that need to pass through the target operation channel during the current period are acquired, and a sample port cargo ship is randomly selected from the acquired multiple port cargo ships. The top-view plane figure of the sample port cargo ship is extracted to obtain the plane fitting figure of the sample cargo ship. It should be noted here that: In this application, the planar fitting graphic referred to herein refers to the regular and continuous planar geometric graphic of the cargo ship's outer contour obtained by extracting the original outer contour of the cargo ship loaded on its deck from the vertical top view of the sample port cargo ship, and then denoising and correcting irregular deviations through a geometric fitting algorithm. This graphic can be directly used for port geometric analysis and waterway passage risk assessment.

[0028] The center point of the narrow and wide edge pixel connection is collected to obtain the first comparison feature point. The geometric center point of the sample cargo ship plane fitting graphic is collected to obtain the second comparison feature point. The sample cargo ship plane fitting graphic is controlled to move along the target operation channel and the first comparison feature point and the second comparison feature point are made to coincide. The bow edge of the sample cargo ship's planar fitting image is extracted and fitted into a straight line to obtain the first cargo ship edge fitting line. The stern edge of the sample cargo ship's planar fitting image is extracted and fitted into a straight line to obtain the second cargo ship edge fitting line. The first cargo ship edge fitting line is extended until it intersects with the first channel edge curve and the second channel edge curve to obtain the first cargo ship edge extension line. The second cargo ship edge fitting line is extended until it intersects with the first channel edge curve and the second channel edge curve to obtain the second cargo ship edge extension line. Area values ​​are collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the first channel, and the central axis of the target channel to obtain the first closed coverage area value. Area values ​​are also collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the second channel, and the central axis of the target channel to obtain the second closed coverage area value. The average value of the first closed coverage area and the second closed coverage area is calculated to obtain the average closed coverage area value. The difference between the first closed coverage area value and the second closed coverage area value is calculated, and the absolute value of the difference is taken to obtain the closed coverage area deviation. The ratio of the closed coverage area deviation to the average closed coverage area value is calculated to obtain the narrow passage area occupancy deviation ratio corresponding to the cargo ships in the sample port. Repeat the process of obtaining the narrow passage area occupancy deviation ratio, obtain the narrow passage area occupancy deviation ratio corresponding to each port cargo ship, and compare the values ​​of the obtained narrow passage area occupancy deviation ratios. Set the narrow passage area occupancy deviation ratio with the largest value as the cargo ship passage limit analysis weight. Calculate the product of the real-time narrow point width and the cargo ship passage limit analysis weight to obtain the real-time narrow point width analysis value, and then normalize the real-time narrow point width analysis value to obtain the real-time narrow point width normalized value. Specifically as follows: Narrow point width analysis values ​​were collected for target operation channels at different historical moments to obtain multiple historical narrow point width analysis values. The obtained historical narrow point width analysis values ​​were compared numerically, and the historical narrow point width analysis value with the largest value was marked as the historical peak narrow point width analysis value, and the historical narrow point width analysis value with the smallest value was marked as the historical valley narrow point width analysis value. The difference between the real-time narrow point width analysis value and the historical valley narrow point width analysis value is calculated to obtain the first narrow point width analysis deviation. The difference between the historical peak narrow point width analysis value and the historical valley narrow point width analysis value is calculated to obtain the second narrow point width analysis deviation. The ratio of the first narrow point width analysis deviation to the second narrow point width analysis deviation is calculated to obtain the real-time narrow point width normalized value. Perform channel curvature analysis on priority assessment operation areas and obtain the channel limit curvature normalization value based on the analysis results; Specifically as follows: In the target channel planar image, a plane rectangular coordinate system is created with the center point of the target channel center axis as the origin, thus obtaining the axis rectangular coordinate system; Mark multiple axis feature points on the center axis of the target channel, connect any two axis feature points that are in an adjacent state to obtain multiple axis micro segments, and arbitrarily select a sample axis micro segment from the multiple obtained axis micro segments; Please see Figure 3 The edge points at both ends of the sample axis micro-segment are extracted to obtain the first axis micro-segment point and the second axis micro-segment point. A straight line parallel to the x-axis of the axis rectangular coordinate system is drawn through the first axis micro-segment point to obtain the first micro-segment comparison feature line. The angle between the sample axis micro-segment and the first micro-segment comparison feature line at the first axis micro-segment point is numerically obtained. If the obtained angle value is less than or equal to the preset angle value, the obtained angle value will be directly set as the axis micro-segment intercept angle. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the axis micro-segment intercept angle. It should be noted here that: In this application, the preset angle value is 90 degrees.

[0029] Draw a straight line parallel to the x-axis of the rectangular coordinate system of the axis through the micro-segment point of the second axis to obtain the second micro-segment comparison feature line. Obtain the intersection point of the first micro-segment comparison feature line and the second micro-segment comparison feature line with the edge curve of the first channel to obtain the intersection point of the first edge micro-segment and the intersection point of the second edge micro-segment. Draw a straight line parallel to the x-axis of the rectangular coordinate system of the axis through the intersection point of the second edge micro-segment to obtain the first edge micro-segment comparison line. Obtain the angle value between the line connecting the intersection point of the first edge micro-segment and the intersection point of the second edge micro-segment and the first edge micro-segment comparison line at the intersection point of the second edge micro-segment. If the obtained angle value is less than or equal to the preset angle value, the obtained angle value will be directly set as the intercept angle of the first edge micro-segment. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the intercept angle of the first edge micro-segment. It should be noted here that: In this application, the preset angle value is 90 degrees.

[0030] Obtain the intersection points of the first micro-segment comparison feature line and the second micro-segment comparison feature line with the second channel edge curve, and obtain the intersection points of the third and fourth edge micro-segments. Draw a straight line parallel to the x-axis of the rectangular coordinate system through the intersection point of the third edge micro-segment to obtain the second edge micro-segment comparison line. Obtain the angle value between the line connecting the intersection points of the third and fourth edge micro-segments and the second edge micro-segment comparison line at the intersection point of the second edge micro-segment. If the obtained angle value is less than or equal to the preset angle value, the obtained angle value will be directly set as the intercept angle of the second edge micro-segment. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the intercept angle of the second edge micro-segment. It should be noted here that: In this application, the preset angle value is 90 degrees.

[0031] The angle of curvature of the axis micro-segment corresponding to the sample axis micro-segment is obtained by calculating the intercept angle of the axis micro-segment, the intercept angle of the first edge micro-segment, and the intercept angle of the second edge micro-segment. The curvature angle analysis value of the axis micro-segment corresponding to the sample axis micro-segment is calculated using the following formula: ; Where Qjf is the curve angle analysis value of the axis micro-segment corresponding to the sample axis micro-segment, Byj1 is the intercept angle of the first edge micro-segment, Byj2 is the intercept angle of the second edge micro-segment, and Jzj is the intercept angle of the axis micro-segment. It should be noted here that: In this application, the following test data exists during the analysis of the micro-segment curvature angle of the axis:

[0032] Repeat the curve angle analysis values ​​of the axis micro-segments corresponding to the sample axis micro-segments, obtain the curve angle analysis values ​​of the axis micro-segments corresponding to each axis micro-segment, and compare the values ​​of the multiple obtained values. Set the axis micro-segment curve angle analysis value with the largest value as the regional peak curve angle analysis value. Calculate the product of the regional peak curve angle analysis value and the cargo ship passage limit analysis weight to obtain the channel limit curvature analysis value. Then, normalize the channel limit curvature analysis value to obtain the channel limit curvature normalized value. It should be noted here that: In this application, the normalization process for the channel limit curvature analysis value is the same as the normalization process for the real-time narrow point width analysis value. Real-time cargo ship density analysis is performed on the target operating channel, and the normalized value of cargo ship density is obtained based on the analysis results. Specifically as follows: The target operation channel in the target channel planar image is marked to obtain the first image feature region. The space covered by port cargo ships in the first image feature region is marked to obtain the second image feature region. The ratio of the area value of the second image feature region to the area value of the first image feature region is calculated to obtain the real-time cargo ship coverage area ratio. The maximum cargo ship coverage area percentage when the priority assessment operation area is in normal operation is obtained to obtain the historical peak cargo ship coverage area percentage. The minimum cargo ship coverage area percentage when the priority assessment operation area is in normal operation is obtained to obtain the historical valley cargo ship coverage area percentage. The real-time cargo ship coverage area percentage is normalized using the historical peak cargo ship coverage area percentage and the historical valley cargo ship coverage area percentage to obtain the cargo ship density normalized value. It should be noted here that: In this application, the normalization process for the real-time cargo ship coverage area ratio is the same as the normalization process for the real-time narrow point width analysis value. Real-time wave difference analysis of the target operating channel is performed using hydrological monitoring devices to obtain real-time regional wave difference monitoring values. The obtained real-time regional wave difference monitoring values ​​are then normalized to obtain normalized regional wave difference values. The real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority evaluation operation area are defined as real-time operation index analysis data. The visual processing module performs visual processing of the operational indicators of the priority assessment area based on real-time operational indicator analysis data, and obtains the port operation risk assessment graphic corresponding to the priority assessment area based on the processing results. Specifically as follows: Obtain real-time operation index analysis data, and based on the real-time operation index analysis data, obtain the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority operation assessment area. A Cartesian coordinate system was constructed using existing charting tools. The normalized values ​​of the real-time narrow point width, channel limit curvature, cargo ship density, and regional wave difference corresponding to the priority operation assessment area were set as the index parameters corresponding to the positive half-axis of the x-axis, the positive half-axis of the y-axis, the negative half-axis of the x-axis, and the negative half-axis of the y-axis in this coordinate system, respectively, thus obtaining the port operation analysis coordinate system. In the port operation coordinate system, the coordinate point with the normalized value of the real-time narrow point width on the horizontal axis and the normalized value of the channel limit curvature on the vertical axis is marked as the first operation analysis coordinate point; the coordinate point with the normalized value of the cargo ship density on the horizontal axis and the normalized value of the channel limit curvature on the vertical axis is marked as the second operation analysis coordinate point; the coordinate point with the normalized value of the cargo ship density on the horizontal axis and the normalized value of the regional wave difference on the vertical axis is marked as the third operation analysis coordinate point; and the coordinate point with the normalized value of the real-time narrow point width on the horizontal axis and the normalized value of the regional wave difference on the vertical axis is marked as the fourth operation analysis coordinate point. Please see Figure 4 Connect the first, second, third, and fourth operational analysis coordinate points in a counter-clockwise direction to obtain the port operation risk assessment graph. The risk warning module provides distributed operational risk warnings for pre-selected cargo ports based on historical port operation analysis data and port operation risk assessment graphics. Specifically as follows: The port operation risk assessment graphics corresponding to the priority assessment operation area are obtained. The port operation risk assessment graphics are divided into four graphic regions using the x-axis and y-axis. The graphic regions are named the first graphic analysis region to the fourth graphic analysis region according to the coordinate quadrant in which the graphic region is located. The boundaries of the first to the fourth graphic analysis regions are marked respectively. The side lengths of the first to the fourth graphic analysis regions are collected according to the marked boundaries to obtain the side lengths of the first to the fourth graphic regions. It should be noted here that: In this application, the side length of the region involved includes the x-axis and y-axis of the divided area.

[0033] The side lengths of the first graphic region to the fourth graphic region are compared numerically. The side length of the graphic region with the largest value is marked as the peak side length of the graphic region, and the side length of the graphic region with the smallest value is marked as the valley side length of the graphic region. The side lengths of the first graphic region to the fourth graphic region are normalized using the peak side length and the valley side length of the graphic region to obtain the first risk assessment weight to the fourth risk assessment weight. Areas are collected from the first to the fourth graphic analysis areas respectively to obtain the graphic area values ​​of the first to the fourth areas. The sum of the products of the graphic area value of each area and the corresponding risk assessment weight is calculated to obtain the comprehensive business risk assessment value. Obtain the preset range for comprehensive operational risk assessment. If the comprehensive operational risk assessment value is within the preset range, an operational risk warning will be issued for the priority assessment area. If the comprehensive operational risk assessment value is not within the preset range, there is no need to issue an operational risk warning for the priority assessment area. It should be noted here that: Historical operational risk comprehensive assessment values ​​were collected for priority assessment work areas that had not issued operational risk warnings. Multiple historical operational risk assessment values ​​were obtained, and the numerical range composed of these historical operational risk assessment values ​​was set as the preset range for comprehensive operational risk assessment.

[0034] Once the risk assessment of the priority assessment area is completed, the risk assessment priority coefficients for each port operation area other than the priority assessment area are obtained based on the historical analysis data of port operations. The port operation area corresponding to the highest risk assessment priority coefficient is replaced with the priority assessment area, and the operational risk assessment and early warning for the priority assessment area are repeated. This process is repeated until the operational risk early warning for each port operation area is completed.

[0035] Compared to the problems described in the background technology, the present invention collects planar images of pre-selected cargo ports, divides the pre-selected cargo ports into multiple port operation areas based on the planar images, collects and analyzes historical early warning data for each port operation area, obtains the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and sorts the port operation areas according to the early warning analysis based on the risk assessment priority coefficient. This enables refined and targeted risk classification and judgment of port operation areas, allowing port early warning analysis to focus on high-priority risk operation areas, and significantly improving the accuracy, efficiency and targeted prevention and control of port risk early warning. Furthermore, this invention obtains priority assessment operation areas based on historical port operation analysis data, and performs real-time operation index analysis on these priority assessment operation areas. Based on the analysis results, it obtains real-time normalized values ​​for narrow point width, channel limit curvature, cargo ship density, and regional wave difference, and creates visualization graphics to provide targeted operational risk warnings for priority assessment operation areas. This allows for precise focus on high-priority risk operation areas in the port, improving the real-time nature, accuracy, and intuitiveness of port operational risk warnings through quantitative indicators and visualization, and effectively enhancing the pertinence and efficiency of port risk prevention and control.

[0036] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A port operation risk early warning method based on multi-source data correlation feedback, characterized in that, include: Step S1: Collect planar images of the pre-selected cargo ports, divide the pre-selected cargo ports into multiple port operation areas, and conduct historical early warning analysis on the port operation areas. Based on the analysis results, obtain the risk assessment priority coefficient corresponding to each port operation area and obtain historical analysis data of port operations. Step S11: Acquire cargo ports that require port operation risk warning, obtain pre-selected cargo ports, collect planar images of the pre-selected cargo ports, and obtain planar images of the pre-selected cargo ports. Step S12: During the analysis of the port historical operations of the pre-selected cargo ports, the time value corresponding to the current moment is used as the end time of the cycle to create a port operation historical monitoring cycle with a fixed cycle length. Step S13: Divide the pre-selected cargo port into several port operation areas based on the pre-selected cargo port plan image, perform historical early warning analysis on the port operation areas that are in the port operation historical monitoring period, obtain the risk assessment priority coefficient corresponding to each port operation area, and obtain port operation historical analysis data. Step S2: Select priority assessment operation areas by analyzing historical port operation data, conduct real-time operation indicator analysis on priority assessment operation areas, and obtain real-time operation indicator analysis data based on the analysis results; Step S21: Obtain historical analysis data of port operations, obtain the risk assessment priority coefficient corresponding to each port operation area based on the historical analysis data of port operations, and mark the port operation area corresponding to the highest risk assessment priority coefficient as the priority assessment operation area. Step S22: Acquire the waterway area corresponding to the priority assessment operation area to obtain the target operation waterway, and acquire a planar image of the target operation waterway to obtain a planar image of the target waterway; Step S23: Fit edge curves to both sides of the channel in the target channel planar image to obtain the first channel edge curve and the second channel edge curve. Obtain the channel center axis between the first channel edge curve and the second channel edge curve to obtain the target channel center axis. Step S24: Analyze the width of the narrowest point in the priority assessment area, and obtain the real-time normalized value of the narrowest point width and the weight of the cargo ship passage limit analysis based on the analysis results; Step S241: Discretize the first channel edge curve into several edge pixels, obtain the real-time channel width value corresponding to each edge pixel, and set the real-time channel width value with the smallest value as the real-time narrow point width. Step S242: Perform narrowing width analysis on port cargo ships in the target channel plan image, and obtain the cargo ship passage limit analysis weight corresponding to the priority evaluation operation area based on the analysis results; Step S243: Calculate the product of the real-time narrow point width and the cargo ship passage limit analysis weight to obtain the real-time narrow point width analysis value, and normalize the real-time narrow point width analysis value to obtain the real-time narrow point width normalized value. Step S242 further includes: The edge pixels corresponding to the real-time narrow point width value are obtained to get the narrow and wide edge pixels. The intersection points of the narrow and wide edge pixels with the corresponding other edge are connected to obtain the narrow and wide edge pixel connection line. Select sample port cargo ships from the port cargo ships that need to pass through the target operation channel in the current period, and extract the top view plane figure of the sample port cargo ships to obtain the plane fitting figure of the sample cargo ships. The center point of the narrow and wide edge pixel connection is collected to obtain the first comparison feature point. The geometric center point of the sample cargo ship plane fitting graphic is collected to obtain the second comparison feature point. The sample cargo ship plane fitting graphic is controlled to move along the target operation channel and the first comparison feature point and the second comparison feature point are made to coincide. The first and last side edges of the sample cargo ship's planar fitting image are extracted and fitted into edge straight lines to obtain the first cargo ship edge fitting line and the second cargo ship edge fitting line. The first cargo ship edge fitting line and the second cargo ship edge fitting line are extended respectively to obtain the first cargo ship edge extension line and the second cargo ship edge extension line. Area values ​​are collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the first channel, and the central axis of the target channel to obtain the first closed coverage area value. Area values ​​are also collected for the closed area enclosed by the extended edge lines of the first cargo ship, the extended edge lines of the second cargo ship, the edge curve of the second channel, and the central axis of the target channel to obtain the second closed coverage area value. The average value of the first closed coverage area and the second closed coverage area are calculated to obtain the average closed coverage area value. The difference between the first closed coverage area value and the second closed coverage area value is calculated, and the absolute value of the difference is taken to obtain the closed coverage area deviation. The ratio of the closed coverage area deviation to the average closed coverage area value is calculated to obtain the narrow passage area occupancy deviation ratio. Obtain the narrow passage area occupancy deviation ratio for each port cargo ship, and set the maximum narrow passage area occupancy deviation ratio as the cargo ship passage limit analysis weight. Step S25: Perform channel curvature analysis on the priority assessment operation area and obtain the channel limit curvature normalization value based on the analysis results; Step S26: Perform real-time cargo ship density analysis on the target operating channel and obtain the normalized value of cargo ship density based on the analysis results; Step S27: Perform real-time wave difference analysis on the target operating channel to obtain the normalized value of the regional wave difference; Step S28: Define the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority evaluation operation area as real-time operation index analysis data; Step S3: Visualize the operational indicators of the priority assessment area based on the real-time operational indicator analysis data, and obtain the port operation risk assessment graph based on the processing results; Step S4: Conduct distributed operational risk warnings for the pre-selected freight ports based on historical port operation analysis data and port operation risk assessment graphics.

2. The port operation risk early warning method based on multi-source data correlation feedback according to claim 1, characterized in that, Step S25 further includes the following steps: In the target channel planar image, a plane rectangular coordinate system is created with the center point of the target channel center axis as the origin, thus obtaining the axis rectangular coordinate system; The target channel centerline is divided into several axis segments of equal length, and a sample axis segment is randomly selected from the multiple axis segments. The edge points at both ends of the sample axis micro-segment are extracted to obtain the first axis micro-segment point and the second axis micro-segment point. A straight line parallel to the x-axis of the axis rectangular coordinate system is drawn through the first axis micro-segment point to obtain the first micro-segment comparison feature line. The angle between the sample axis micro-segment and the first micro-segment comparison feature line at the first axis micro-segment point is numerically obtained. If the obtained angle value is less than or equal to a preset angle value, the obtained angle value will be directly set as the axis micro-segment intercept angle. If the obtained angle value is greater than the preset angle value, the difference between the obtained angle value and the preset angle value will be calculated to obtain the axis micro-segment intercept angle. Draw a straight line parallel to the x-axis of the rectangular coordinate system through the micro-segment points of the second axis to obtain the second micro-segment comparison feature line. Draw straight lines perpendicular to the first micro-segment comparison feature line and the second micro-segment comparison feature line through the micro-segment points of the first axis and the second axis respectively to obtain the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line. Obtain the intersection points of the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line with the first channel edge curve to obtain the first edge micro-segment intersection point and the second edge micro-segment intersection point. Collect the intercept angle values ​​of the line connecting the first edge micro-segment intersection point and the second edge micro-segment intersection point to obtain the intercept angle of the first edge micro-segment. Obtain the intersection points of the first micro-segment longitudinal comparison line and the second micro-segment longitudinal comparison line with the second channel edge curve, and obtain the intersection points of the third and fourth edge micro-segments. Collect the intercept angle values ​​of the line connecting the intersection points of the third and fourth edge micro-segments to obtain the intercept angle of the second edge micro-segment. The angle of curvature of the axis micro-segment corresponding to the sample axis micro-segment is obtained by calculating the intercept angle of the axis micro-segment, the intercept angle of the first edge micro-segment, and the intercept angle of the second edge micro-segment. Obtain the curvature analysis value of each axis micro-segment, set the axis micro-segment curvature analysis value with the largest value as the regional peak curvature analysis value, calculate the product of the regional peak curvature analysis value and the cargo ship passage limit analysis weight to obtain the channel limit curvature analysis value, and normalize the channel limit curvature analysis value to obtain the channel limit curvature normalized value.

3. The port operation risk early warning method based on multi-source data correlation feedback according to claim 1, characterized in that, In step S3, the specific steps are as follows: Obtain real-time operation index analysis data, and based on the real-time operation index analysis data, obtain the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority operation assessment area. A Cartesian coordinate system is constructed, and the normalized values ​​of the real-time narrow point width, the channel limit curvature, the cargo ship density, and the regional wave difference corresponding to the priority operation assessment area are set as the index parameters corresponding to the positive half-axis of the x-axis, the positive half-axis of the y-axis, the negative half-axis of the x-axis, and the negative half-axis of the y-axis in this coordinate system, respectively, to obtain the port operation analysis coordinate system. In the port operation coordinate system, coordinate points are created based on the real-time narrow point width normalization value, channel limit curvature normalization value, cargo ship density normalization value, and regional wave difference normalization value corresponding to the priority operation assessment area, to obtain the first to fourth operation analysis coordinate points. Connect the first and fourth operational analysis coordinates in a counter-clockwise direction to obtain the port operation risk assessment graph.

4. The port operation risk early warning method based on multi-source data correlation feedback according to claim 1, characterized in that, In step S4, the specific steps are as follows: The port operation risk assessment graphics corresponding to the priority assessment operation area are obtained, and the port operation risk assessment graphics are divided into the first graphic analysis area to the fourth graphic analysis area using the x-axis and y-axis. The side lengths of the first graphic analysis region to the fourth graphic analysis region are collected to obtain the side lengths of the first graphic region to the fourth graphic region. Normalize the side lengths of the first graphic region to the fourth graphic region to obtain the first risk assessment weight to the fourth risk assessment weight. Areas are collected from the first to the fourth graphic analysis areas to obtain the graphic area values ​​of the first to the fourth areas. The sum of the products of the graphic area value of each area and the corresponding risk assessment weight is calculated to obtain the comprehensive business risk assessment value. Obtain the preset range for comprehensive operational risk assessment. If the comprehensive operational risk assessment value is within the preset range, then issue an operational risk warning for the priority assessment area. If it is not within the preset range, then there is no need to issue an operational risk warning for the priority assessment area. Obtain the risk assessment priority coefficients for the remaining port operation areas, replace the priority assessment operation areas with the port operation areas corresponding to the highest risk assessment priority coefficients, and repeat the operational risk assessment and early warning for the priority assessment operation areas, thus providing an operational risk early warning for each port operation area.

5. A port operation risk early warning system based on multi-source data correlation feedback, applicable to the port operation risk early warning method based on multi-source data correlation feedback as described in any one of claims 1-4, characterized in that, The port operation risk early warning system includes: Data acquisition module: Collects planar images of pre-selected cargo ports, divides the pre-selected cargo ports into multiple port operation areas based on the planar images, performs historical early warning analysis on the port operation areas, obtains the risk assessment priority coefficient corresponding to each port operation area based on the analysis results, and obtains historical analysis data of port operations. Data Analysis Module: By analyzing historical port operation data, priority assessment operation areas are selected, real-time operation indicator analysis is performed on the priority assessment operation areas, and real-time operation indicator analysis data is obtained based on the analysis results; Visualization module: Based on real-time operational indicator analysis data, visualize the operational indicators of priority assessment areas and obtain port operation risk assessment graphics based on the processing results; Risk warning module: Based on historical port operation analysis data and port operation risk assessment graphics, distributed operation risk warnings are provided for pre-selected freight ports.

Images