Boundary flow control method and system based on busy water area macroscopic fundamental diagram

Abstract

The invention provides a boundary flow control method and system based on a busy water area macroscopic basic diagram, electronic equipment and a storage medium, and the method comprises the steps: building a water area macroscopic basic diagram which can accurately reflect the macroscopic law of mixed traffic flows of different ship types through collecting and processing the data of a ship automatic recognition system; determining an optimal accumulated ship number target when the overall navigation efficiency of the system is highest; delimiting an easily-congested key area in the target water area as a core water area, and establishing a ship traffic flow dynamic balance model taking the optimal target as a balance point for the core water area as a control object; a fuzzy self-adaptive PID controller is designed and applied, the PID controller can sense the deviation between the actual number of ships in a core water area and a target value and the variation trend of the deviation in real time, control parameters are intelligently adjusted on line through a built-in fuzzy inference engine, the optimal boundary flow control rate is dynamically calculated and output, and therefore the flow of the ships entering the core water area is accurately adjusted. The number of the ships in the water area is actively and stably maintained in the optimal interval.

Description

Technical Field

[0001] This invention relates to the field of intelligent water traffic management and control technology, and in particular to a boundary flow control method and system based on a macroscopic basic map of busy waterways. Background Technology

[0002] Currently, vessel traffic congestion in busy waterways (such as ports and narrow channels) is becoming increasingly severe. Existing maritime traffic management measures, such as speed limits, one-way traffic, and temporary navigation restrictions, mostly focus on micro-management of local sections or individual vessels, lacking the ability to optimize and proactively intervene at the macro-level of the entire waterway system. This "reactive" management model is ill-suited to cope with dynamic fluctuations in traffic demand, resulting in unstable control effects and limited efficiency improvements, failing to fundamentally prevent large-scale congestion.

[0003] The Macroscopic Fundamental Diagram (MFD), as an effective tool for describing the relationship between regional traffic volume and departure flow, has formed a mature theoretical system and application scheme in the field of road traffic, providing a scientific basis for macro-level traffic control. However, research on this theory in the field of ship traffic flow is still lacking. Furthermore, the ship size method commonly used in traditional ship traffic volume conversion only calculates the standard ship equivalent number based on the correlation between ship length and ship type. This method cannot reflect the characteristics of limited ship speed and large traffic flow changes in narrow waterways such as inland waterways, and it is difficult to reflect the mutual influence between ships. As a result, the conversion results have limitations and cannot provide accurate support for the judgment of macro-level traffic conditions in waterways.

[0004] To address congestion, waterway management authorities have implemented temporary navigation bans and cross-river restrictions. However, these measures are largely based on past experience and subjective intuition, lacking solid scientific theoretical justification and dynamic adaptability. When vessel traffic surges, existing measures struggle to respond quickly to changes in traffic conditions, failing to achieve preventative congestion control, resulting in unstable and unsatisfactory control effects. Summary of the Invention

[0005] This invention provides a boundary flow control method, system, electronic device, and storage medium based on a macroscopic basic map of busy waterways, to address the technical problems in the prior art, such as the lack of research on macroscopic basic maps (MFD) in the field of ship traffic flow, the inability of traditional ship size methods to calculate the equivalent number of standard ships to reflect the mutual influence of ships in inland waterways and the existence of limitations, and the fact that existing ship traffic control measures are mostly based on experience, lack scientific demonstration, are difficult to achieve proactive preventive control, and have unstable effects.

[0006] In a first aspect, embodiments of the present invention provide a boundary flow control method based on a macroscopic basic map of busy waterways, comprising: S1. Acquire historical and real-time Automatic Identification System (AIS) data for the target waterway, including vessel position, length, and time sequence information; S2. Based on the AIS data, calculate the cumulative number of vessels and corresponding boundary outflow in the target waterway under different proportions of large vessels. Construct a unified macroscopic fundamental map (WMFD) for the waterway based on the standard vessel equivalent conversion method. Determine the optimal cumulative number of vessels to maximize the boundary outflow based on the WMFD; S3. Define the area within the target waterway requiring flow control as the core waterway. Based on the principle of vessel flow conservation, establish a dynamic equilibrium model of vessel traffic flow within the core waterway, using the optimal cumulative number of vessels as the equilibrium point. This model serves as the controlled entity for boundary flow control. S4. Real-time acquisition of the actual cumulative number of vessels in the core waterway, and calculation of the deviation and rate of change between the actual cumulative number of vessels and the optimal cumulative number of vessels; S5. Inputting the deviation and the rate of change into a fuzzy PID controller, the fuzzy PID controller being input to the deviation and rate of change between the actual cumulative number of vessels in the core waterway at the current moment and the optimal cumulative number of vessels, and outputting a boundary flow control rate; adjusting the PID parameters online through the fuzzy PID controller and generating the boundary flow control rate at the current moment; S6. Adjusting the inflow of vessels allowed into the core waterway according to the boundary flow control rate, so that the actual cumulative number of vessels in the core waterway remains within a preset range corresponding to the optimal cumulative number of vessels.

[0007] Secondly, embodiments of the present invention provide a boundary flow control system based on a macroscopic basic map of busy waterways, comprising: The data acquisition module is used to acquire historical and real-time Automatic Identification System (AIS) data of vessels in the target waters. The AIS data includes vessel position, length, and time sequence information. The water area macro basic map construction module is used to calculate the cumulative number of ships and the corresponding boundary outflow in the target water area under different proportions of large ships based on the AIS data, construct a unified water area macro basic map WMFD based on the standard ship equivalent number conversion method, and determine the optimal cumulative number of ships that maximizes the boundary outflow based on the WMFD. The dynamic equilibrium modeling module is used to define the area in the target water area where flow control needs to be implemented as the core water area. Based on the principle of ship flow conservation, it establishes a dynamic equilibrium model of ship traffic flow in the core water area with the optimal cumulative number of ships as the equilibrium point, which serves as the controlled object of boundary flow control. The vessel status monitoring module is used to acquire the actual cumulative number of vessels in the core waterway in real time, and to calculate the deviation and rate of change between the actual cumulative number of vessels and the optimal cumulative number of vessels. The fuzzy PID control module is used to input the deviation and the rate of change to the fuzzy PID controller. The input of the fuzzy PID controller is the deviation and rate of change between the actual cumulative number of ships in the core water area at the current moment and the optimal cumulative number of ships, and the output is the boundary flow control rate. The fuzzy PID controller adjusts the PID parameters online and generates the boundary flow control rate at the current moment. The flow regulation execution module is used to adjust the inflow of ships allowed to enter the core water area according to the boundary flow control rate, so as to keep the actual cumulative number of ships in the core water area within a preset range corresponding to the optimal cumulative number of ships.

[0008] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the boundary flow control method based on a macroscopic basic map of busy waterways as described in the first aspect of the present invention.

[0009] Fourthly, embodiments of the present invention provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the boundary flow control method based on a macroscopic basic map of busy waterways as described in the first aspect of the present invention.

[0010] This invention provides a boundary flow control method, system, electronic device, and storage medium based on a macroscopic basic map of busy waterways. First, by collecting and processing Automatic Identification System (AIS) data, a macroscopic basic map of the waterway (WMFD) is constructed to accurately reflect the macroscopic patterns of mixed traffic flow for different ship types. Based on this map, the optimal cumulative number of ships is determined when the overall navigation efficiency of the system is maximized. Next, key areas prone to congestion within the target waterway are designated as core waterways, and a dynamic balance model of ship traffic flow is established for these core waterways, using this optimal target as the equilibrium point, as the control object. Finally, a fuzzy adaptive PID controller is designed and applied. The PID controller can perceive the deviation and trend of the actual number of ships in the core waterway from the target value in real time. Through a built-in fuzzy inference engine, it intelligently adjusts control parameters online, dynamically calculates and outputs the optimal boundary flow control rate, thereby precisely regulating the flow of ships entering the core waterway and achieving the control objective of actively and stably maintaining the number of ships in the waterway near the optimal range. It has achieved a fundamental shift from experience-based, localized, and passive management to data-driven, system-level, proactive, and preventative control. By combining macro-situational awareness with intelligent feedback control, it can effectively prevent and alleviate congestion, significantly improve the overall navigation efficiency and traffic flow stability of busy waterways, and provide key technical support for the scientific and intelligent management of water traffic. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a boundary flow control method based on a macroscopic basic map of busy waterways according to an embodiment of the present invention; Figure 2 This is a data processing framework diagram according to an embodiment of the present invention; Figure 3 These are schematic diagrams of WMFD under different large ship proportions according to embodiments of the present invention; Figure 4 This is a schematic diagram of waterway vessel traffic flow according to an embodiment of the present invention; Figure 5 This is a fuzzy PID control structure diagram according to an embodiment of the present invention; Figure 6 This is a block diagram of a PID control system according to an embodiment of the present invention; Figure 7 This is a block diagram of a fuzzy control system according to an embodiment of the present invention; Figure 8 According to an embodiment of the present invention A schematic diagram of the control surface, wherein (a) is (b) is (c) is ; Figure 9 This is based on the boundary flow control principle of MFD according to an embodiment of the present invention; Figure 10 This is a block diagram of the boundary flow control system based on the macroscopic basic map of busy waterways; Figure 11 This is a schematic diagram of the physical structure according to an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] This invention provides a boundary flow control method based on a macroscopic basic map of busy waterways, such as... Figure 1 , Figure 2 As shown, the method includes: S1. Obtain historical and real-time Automatic Identification System (AIS) data for the target waters, including vessel position, length, and time sequence information.

[0015] Among them, the Automatic Identification System (AIS) data is a collection of dynamic and static data broadcast in real time by ships through AIS devices during navigation. AIS data can record the position, length (used to distinguish between large and small ships, such as ships with a length ≥120m being large ships and those ≤120m being small ships) and time sequence information (recorded at 15-minute intervals, covering peak shipping periods such as 0:00-4:00 and 11:00-15:00 daily). The ship's position is the core basis for determining whether a ship is in the target waters, the ship's length is a key indicator for classifying ship types, and the time sequence information ensures that the data can reflect the dynamic changes in traffic flow.

[0016] S2. Based on the AIS data, the cumulative number of vessels and the corresponding boundary outflow in the target water area under different proportions of large vessels are statistically analyzed. Based on the standard vessel equivalent conversion method, a unified macroscopic basic map (WMFD) of the water area is constructed. Based on the WMFD, the optimal cumulative number of vessels that maximizes the boundary outflow is determined.

[0017] The proportion of large vessels is the ratio of the number of large vessels (e.g., vessels with a length ≥ 120m in the Taicang Port scenario) to the total number of vessels in the target waterway. This needs to be categorized and statistically analyzed (e.g., 0.07 < proportion < 0.15 is classified as 0.1, 0.15 < proportion < 0.21 is classified as 0.2). The cumulative vessel volume is the total number of vessels remaining in the target waterway per unit time. The boundary outflow volume is the number of vessels departing from the boundary of the target waterway per unit time. The standard vessel equivalent conversion method is a quantitative method for converting vessels of different sizes and types into a unified standard vessel, solving the problem of statistical bias in mixed vessel traffic flow. The Waterway Macroscopic Fundamental Diagram (WMFD) is a curve describing the relationship between the vessel outflow volume at the waterway boundary and the total cumulative vessel volume within the waterway per unit time. It is trapezoidal (rising segment, steady segment, falling segment), corresponding to three states: low-density free flow, maximum flow, and high-density congestion. The optimal cumulative vessel number is the cumulative vessel volume corresponding to the steady segment of the WMFD, where the boundary outflow volume is the largest and the waterway operation efficiency is the highest.

[0018] S3. Define the area in the target water area where flow control needs to be implemented as the core water area. Based on the principle of ship flow conservation, establish a dynamic balance model of ship traffic flow in the core water area with the optimal cumulative number of ships as the balance point, and use it as the controlled object of boundary flow control.

[0019] The core waterway is a key area within the target waterway with high traffic load and prone to congestion. For example, in the Taicang Port scenario, this includes the main channels on the north and south sides bounded by buoys 1 to 15 and all anchorages. These areas are the main gathering and dispersal points for river-sea transshipment vessels, and are prone to congestion due to concentrated vessels. The principle of vessel flow conservation states that the change in the cumulative number of vessels in the core waterway is equal to the inflow minus the outflow (i.e., inflow = outflow + change in cumulative volume). This principle must be followed in the control of busy waterways to ensure flow balance. The dynamic balance model of vessel traffic flow is a mathematical model describing the change in the cumulative number of vessels in the core waterway over time. It uses the optimal number of cumulative vessels as the balance point and covers the inflow from the outer waterways to the core waterway, the outflow from the core waterway to the outer waterways (such as the flow of vessels from the main channel to the coast or inland waterways), and the internal transfer flow (such as the flow of vessels between anchorages and channels). The controlled object is the target system that needs to be regulated in boundary flow control, which in this case is the vessel traffic flow in the core waterway.

[0020] S4. Obtain the actual cumulative number of vessels in the core waterway in real time, and calculate the deviation and rate of change between the actual cumulative number of vessels and the optimal cumulative number of vessels.

[0021] Among them, the actual cumulative number of vessels is the total number of vessels in the core waters at a certain moment, which is statistically analyzed through real-time AIS data. It is necessary to accurately match the boundary range of the core waters and exclude vessels in the outer waters. The deviation is the difference between the actual cumulative number of vessels in the core waters and the optimal cumulative number of vessels. A positive deviation indicates that there are too many vessels and there may be congestion, while a negative deviation indicates that there are not enough vessels and resources are idle. The rate of change is the amount of change in the deviation between two adjacent statistical periods (such as 15 minutes), which reflects the development trend of the deviation (expansion or contraction).

[0022] S5. Input the deviation and the rate of change to the fuzzy PID controller. The input of the fuzzy PID controller is the deviation and rate of change between the actual cumulative number of ships in the core water area at the current moment and the optimal cumulative number of ships. The output is the boundary flow control rate. The PID parameters are adjusted online through the fuzzy PID controller, and the boundary flow control rate at the current moment is generated.

[0023] Among them, the fuzzy PID controller is an intelligent controller that combines fuzzy inference with traditional PID control. In busy waterway scenarios, it can dynamically adjust the PID parameters (proportional coefficient) through fuzzy rules. K p Integral coefficient K i Differential coefficients K d This avoids the problem of traditional PID parameters being fixed and difficult to adapt to the nonlinear time-varying characteristics of ship traffic (such as sudden increases and decreases in ship traffic during peak periods); online PID parameter adjustment refers to the controller correcting itself in real time based on real-time deviations and rates of change through fuzzy inference.K p , K i , K d (Increase if the deviation is large) K p With rapid response, the smaller the deviation, the greater the effect. K i To eliminate steady-state error); the boundary flow control rate is the proportion of the inflow into the core water area output by the controller (e.g., 0.75 means that 75% of the vessels in the outer water area are allowed to enter the core water area), and the value range is (0,1]. The smaller the control rate, the stricter the flow restriction.

[0024] S6. Adjust the inflow of vessels allowed to enter the core water area according to the boundary flow control rate, so that the actual cumulative number of vessels in the core water area is maintained within a preset range corresponding to the optimal cumulative number of vessels.

[0025] Among them, the vessel inflow is the number of vessels in the outer waters attempting to enter the core waters per unit time; the preset range is a reasonable range of vessel numbers in the core waters determined around the optimal cumulative vessel number. This range corresponds to the stable segment of WMFD and can ensure the maximum boundary outflow and the highest operating efficiency.

[0026] This embodiment adjusts the allowable inflow of vessels into the core waterway based on the boundary flow control rate output by the fuzzy PID controller, overcoming the shortcomings of existing technologies that rely on experience-based control and have unstable effects. It can stably maintain the actual cumulative number of vessels in the core waterway within the preset range corresponding to the optimal cumulative number of vessels, ensuring that the core waterway is always in the stable phase of WMFD, with the maximum boundary outflow and the highest navigation efficiency. This avoids both congestion caused by too many vessels (decreased outflow) and idle waterway resources caused by insufficient vessels, achieving proactive prevention and efficient and stable operation of traffic in busy waterways.

[0027] Based on the above embodiments, as a preferred implementation, step S1 further includes: The AIS data is cleaned to filter out erroneous and invalid data; the original AIS data is filtered and removed if it does not conform to actual navigation patterns or cannot reflect the dynamics of vessels in the target waters; erroneous data includes changes in vessel position caused by AIS equipment failure (such as a vessel suddenly jumping from the main channel of the core waters to a non-navigable waters tens of kilometers away), abnormal vessel length data (such as a vessel labeled as 200m in length but actually a small cargo ship); invalid data refers to data of vessels that have not entered the target waters, and this type of data is irrelevant to the traffic flow in the target waters.

[0028] The ship coordinates in the AIS data are uniformly converted to a preset geographic coordinate system. A geographic coordinate system is a mathematical framework used to determine the position of objects on the Earth's surface. Common coordinate systems in ship AIS data include the WGS84 geodetic coordinate system and the Beijing 54 coordinate system. The preset geographic coordinate system specifically refers to the WGS84 coordinate system in this embodiment. This coordinate system can accurately match the cross-regional navigation needs of Taicang Port as a river-sea transshipment hub (connecting AIS data of ships on different routes along the coast and inland waterways). Ship coordinate conversion refers to the conversion of non-WGS84 coordinates output by different ship AIS devices by default (such as the local coordinate system used by some inland waterway ships) into latitude and longitude in the WGS84 coordinate system through a coordinate conversion algorithm.

[0029] According to the preset statistical time window, the converted AIS data is statistically analyzed to calculate the cumulative number of ships and the outflow from the boundary in the target waters within each time window.

[0030] The preset statistical time window refers to a fixed data statistical interval set to capture dynamic changes in traffic flow. In combination with the traffic flow fluctuation characteristics of Taicang Port during peak periods (0:00-4:00 and 11:00-15:00 daily), this embodiment presets it to 15 minutes. This avoids excessive data fluctuations caused by short windows and prevents long windows from failing to reflect sudden increases and decreases in traffic flow in a timely manner. The cumulative number of vessels refers to the total number of vessels in the target water area as determined by AIS coordinates within each time window. The boundary outflow refers to the number of vessels that depart from the boundary of the target water area (such as the core channel exit or the connecting section from the anchorage to the outer channel) within each time window.

[0031] Based on the above embodiments, as a preferred implementation, in step S2, a unified macroscopic basic map (WMFD) for waterways is constructed based on the standard ship equivalent number conversion method, including: The vessel type with the largest proportion of vessels or the smallest vessel length in the target water area is selected as the standard vessel, and the traffic volume conversion factor of the standard vessel is defined as 1. At least two different proportions of large vessels are obtained, corresponding to MFD piecewise function expressions, where the proportion of large vessels is determined based on whether the vessel length exceeds a preset threshold. Based on the preset traffic volume conversion factor, the MFD piecewise function expressions under the at least two different proportions of large vessels are uniformly converted into WMFD piecewise function expressions that represent only the traffic flow characteristics of the standard vessel, where the preset traffic volume conversion factor is taken according to different stages of the traffic flow state corresponding to the WMFD piecewise function expression.

[0032] The macroscopic basic graph exhibits distinct rising, stable, and falling phases, with a trapezoidal shape, and infers the existence of a network aggregation density range that reaches maximum flow. At this point, the macroscopic basic graph corresponds to three stages of traffic flow change: low-density free flow, maximum flow, and high-density congestion, as shown below. Figure 3 As shown. Its mathematical expression is shown in formula (1): (1) In the formula, This refers to the outflow from the water boundary. This represents the maximum outflow from the water boundary in the system. To study the cumulative number of ships in the waterway; The preceding inflection point, i.e., the cumulative number of ships corresponding to the inflection point from the rising phase to the stable phase of MFD; This refers to the inflection point after the MFD (Mean Difference) stabilizes, which corresponds to the cumulative number of ships at the inflection point between the stable and declining phases. This refers to the cumulative number of vessels during periods of severe waterway congestion. , These are the slopes of the rising and falling segments, respectively. , , These are the constant terms of the fitting functions for the rising segment, the stationary segment, and the falling segment, respectively.

[0033] According to ship traffic flow theory, basic graphical models generally include flow-density models, flow-velocity models, and velocity-density models. These models typically focus on a single waterway and select velocity, flow rate, and density as measurement indicators. (Macroscopic basic waterway map) The Waters Macroscopic Fundamental Diagram (WMFD) describes the relationship between the number of vessels leaving a water boundary per unit time and the total number of vessels within the water boundary. The vertical axis represents the outflow from the water boundary. The horizontal axis represents the cumulative number of vessels in the waterway. .

[0034] Furthermore, WMFD uses the number of vessels as the basic unit without converting vessel types, resulting in scatter plot data with a large distribution range and unstable trends. Directly using this data for flow control leads to significant errors. To overcome the differences between homogeneous and mixed traffic in vessel navigation, calculating the standard vessel equivalent number is a problem that WMFD research aims to solve. The concept of "standard vessel" differs from the standard vessel type in inland waterway regulations, which sets a series of standard requirements regarding vessel type and engine type from a shipbuilding perspective. The standard vessel equivalent number refers to the number of "standard vessels" replaced by larger or other vessels, used to measure the traffic flow operation status of different types of vessels under the same waterway traffic conditions. The "standard vessel" selected is a type of vessel with a smaller size or a larger proportion among all vessel types, with a conversion factor of 1. Since small vessels dominate the Yangtze River waterway, this invention uses small vessels as the "standard vessel," and the proposed "standard vessel" WMFD refers to the WMFD curve when the proportion of large vessels is 0.

[0035] In this embodiment, the following is defined: a. According to the captain Classification, considering only small boats and large ships run.

[0036] b. Assume that only small vessels operate in the study waters, using the WMFD at this point as the benchmark, i.e., the proportion of large vessels is 0.

[0037] c. Definition The proportion of large ships is The cumulative number of vessels in the research waters is The fitting curve function of MFD corresponding to the time.

[0038] d. Definition This is a conversion factor for vessel traffic volume between large and small vessels. Because the competition for spatiotemporal resources generated by vessel operations differs under different traffic conditions, the corresponding... They are also different. Therefore, let the conversion factors for ship traffic volume in the ascending, steady, and descending phases be respectively... .

[0039] When the proportion of large ships is At that time, the piecewise function expression of MFD See equation (2): (2) To plot the MFD curve for a "standard ship", it is necessary to obtain data at least at two different scales of large ships. The following MFD piecewise expression, with Figure 3 Taking the object of analysis as an example, and based on the basic idea of ​​conversion, the analytical expression of the "standard ship" MFD is solved piecewise. .

[0040] Based on the above embodiments, as a preferred implementation, the MFD piecewise function expressions for at least two different large ship proportions are uniformly converted into WMFD piecewise function expressions that characterize only the traffic flow characteristics of the standard ship, including: For the rising, steady, and falling segments of the MFD piecewise function expression, conversion equations based on the preset traffic volume conversion coefficient are constructed respectively. Based on the conversion equations corresponding to the proportions of the at least two large ships, the characteristic parameters of the MFD piecewise function expression of the standard ship in the rising, steady, and falling segments are determined by solving the equations simultaneously. Based on the characteristic parameters, the WMFD piecewise function expression of the standard ship is generated.

[0041] like Figure 3 As shown in the figure, the number of ships in the study waters during the ascending phase is relatively small and the waterway traffic is stable. At this time, the expression for the ascending phase is as shown in equation (3).

[0042] (3) Substitute the ship traffic volume conversion factor Based on the fact that the two curves coincide after conversion, we can obtain equation (4).

[0043] (4) In the formula Since all are unknowns, we take... Equation (5) can be obtained.

[0044] (5) By solving the simultaneous equations, the parameter values ​​can be obtained.

[0045] The steady segment is a constant function segment. After conversion, the outflow from the water boundary under different ship ratios is related to... When the outflow from the water boundary is equal, equation (6) is established.

[0046] (6) Similarly, in the formula Since all are unknowns, we take... By solving the system of equations simultaneously, the parameter values ​​can be obtained.

[0047] During the descent phase, the large number of vessels accumulates, reducing the efficiency of waterway passage and causing traffic congestion.

[0048] At this point, we propose the hypothesis: the x-coordinates of the inflection point and the paralysis point are related to... The relationship is linear. Therefore, the proportion of large ships is defined as follows: , The expression for the descent segment is shown in equation (7).

[0049] (7) In the formula The proportion of large ships is The slope and intercept of the MFD descending segment are given by equation (8) based on the assumptions.

[0050] (8) The coordinates of the inflection point can be determined from the results of the stable segment. Coordinates of the paralysis point Find the slope of two points. As shown in equation (9), the analytical expression of the descending segment can be obtained.

[0051] (9) Furthermore, such as Figure 4 As shown, key shipping lanes or port areas prone to vessel congestion are designated as core waters 1, while the remaining waters are considered peripheral waters 2. When boundary control is implemented in core waters 1, the total number of vessels entering and leaving is conserved. At any given time, the cumulative total number of vessels within core waters 1 It consists of two parts: one part is the number of vessels whose destination is within the waterway. The other part is the number of ships whose destinations are outside the waters. . That is to say Number of ships within the waterway at any given time.

[0052] Therefore, the cumulative number of vessels whose destination is within the waterway can be expressed as: (10) In the formula, for Traffic flow that never entered the core waters 1 from the uncontrolled boundary at any time; for The boundary control rate of traffic flow from outer waterway 2 to waterway 1 at any given time, and satisfying the following conditions. ; for Traffic flow constantly moving from the outer waters 2 into the core waters 1; for Traffic flow within the core waterway 1 at all times.

[0053] The cumulative number of vessels from waterway 1 to waterway 2 can be expressed as: (11) In the formula, for Traffic flow that was never controlled and flowed out of the core waterway 1 at any time; for Traffic flow from waterway 1 to waterway 2 at any given time.

[0054] Known This refers to the outflow from the water boundary, i.e., when the cumulative number of vessels within the water area is... At that time, the traffic flow shifting from waterway 1 to waterway 2 is as follows: (12) Therefore, according to the conservation law, the number of ships in zone 1 can be expressed as follows: (13) When the core water area is in its optimal state, equation (14) can be obtained.

[0055] (14) make , This can be simplified to: (15) in, ; ; ; .

[0056] The change values ​​of each parameter are all The difference between the value at time step and the value of the optimal state. Since control is performed at regular intervals, discretization is performed using the Taylor formula: (16) Simplify using Euler's formula: (17) Finally, the cumulative change model of vessels in the waterway is obtained as shown in equation (19), which is the controlled object of the boundary flow control system. Since the transfer flow within the waterway is difficult to obtain statistically, therefore... and Set as a disturbance, that is... Set as a constant disturbance quantity to simulate disturbances during simulation.

[0057] (18) (19) In the formula, , .

[0058] Based on the above embodiments, as a preferred implementation, in step S5, adjusting the PID parameters online via the PID controller includes: The deviation and the rate of change are input to the fuzzy inference module. Based on the pre-set fuzzy control rules, the adjustment amounts of the proportional, integral, and derivative parameters are calculated in real time, and the parameters of the fuzzy PID controller are self-tuned online. The fuzzy control rules include: Based on the absolute value of the deviation, the proportional parameter and the integral parameter are adjusted; wherein, when the absolute value of the deviation is greater than a first threshold, the proportional parameter is increased and the integral parameter is decreased accordingly. When the absolute value of the deviation is less than or equal to the first threshold and greater than the second threshold, the proportional parameter and the integral parameter are set to preset medium values; when the absolute value of the deviation is less than or equal to the second threshold, the integral parameter is increased and the proportional parameter is decreased accordingly; and, based on the absolute value of the rate of change, the derivative parameter is dynamically adjusted.

[0059] A PID controller is a common feedback controller that achieves precise control of the system output by continuously adjusting the feedback signal of the target system. PID controllers are widely used in stability regulation, speed control, temperature control, motor control, and robot control, demonstrating broad applicability and practicality. Therefore, this invention selects a PID controller as the basic control strategy for boundary flow control. A crucial aspect of PID control lies in determining... Three parameters are required, but once determined, they cannot be changed based on changes in the state of the controlled object. This makes it difficult for PID control alone to meet the requirements of time-varying nonlinear systems. Therefore, this invention introduces fuzzy reasoning into PID control to solve this problem. Figure 3 This is a structural diagram of the fuzzy PID control method.

[0060] The boundary flow control system consists of a control model and a controlled object. The control model is a fuzzy adaptive PID controller, and the controlled object is a model of the cumulative changes in the number of vessels in the waterway. The entire boundary flow control strategy is a closed-loop feedback control strategy, such as... Figure 5 As shown.

[0061] (1) For the boundary flow control model, the input is the expected cumulative number of vessels within the waterway. The output is the actual cumulative number of ships inside the waterway at the next moment. ; (2) For the PID module, the input is the cumulative error of the number of ships in the waterway. , and error change rate , The output is the boundary control parameters. ; (3) For the fuzzy inference module, the input is also the cumulative error of the number of ships in the water. and error change rate The output is change .

[0062] The main idea of ​​a PID controller is to determine the error based on the current error (proportional controller gain). ), accumulated past error (integral control term, And estimates of future errors (differential controller gain, The information guides the system state to a given desired equilibrium.

[0063] As shown in 6, the difference between the control target and the actual output value is... The proportional, integral, and derivative of the error are linearly combined to form the control quantity, and the expression for the control is shown in equation (20): (20) In the formula, The output signal of the controller; The integral time constant; The differential time constant; This is the proportionality coefficient; The integral coefficient; is the differential coefficient.

[0064] PID control through The adjustments are made to achieve the goals of improving the stability, response speed, and accuracy of the control system. The three parameters serve the following functions: (1) Proportional parameters This parameter is mainly used to calculate the ratio between the current error and the control quantity that should be output. Specifically, it determines the magnitude and direction of the output signal based on the magnitude and direction of the deviation, and its adjustment is mainly for controlling the response speed and stability.

[0065] (2) Integral parameters This parameter is mainly used for integrating the error, so as to give the control system the ability to remember previous historical errors, thereby enabling the controller to better eliminate the steady-state error of the system and improve the accuracy of the control system.

[0066] (3) Differential parameters This parameter is primarily used to predict the derivative of the control quantity, thereby helping the control system better control future trends, reduce oscillations, and suppress noise. Specifically, it can reduce the rate of change of the deviation, making the system response more stable and smooth.

[0067] Computers can only perform calculations on discrete data, so formula (20) is discretized to obtain formula (21): (twenty one) Incremental PID controllers and positional PID controllers are two commonly used PID control algorithms, which differ in their input signal processing methods. The input of a positional PID controller is the current error value, and the output is the control quantity. The input of an incremental PID controller is the change in the error value, and the output is the change in the control quantity. This invention selects incremental PID, and from equations (20) and (21), the expression of incremental PID control is obtained as shown in equation (22): (twenty two) Perform z-transformation on equations (17) and (22): (twenty three) (twenty four) Fuzzy control achieves its control objectives by matching input and output values ​​with a series of fuzzy sets and rules, modeling and analyzing nonlinear and fuzzy phenomena during the control process. It consists of three parts: fuzzification, fuzzy inference, and defuzzification. Figure 7 As shown.

[0068] The steps of fuzzy control are as follows: (1) Blurring In fuzzy control, the input and output correspond to precise actual values, and the range of these values ​​is called the fundamental universe of discourse. However, fuzzy control requires fuzzy values, and the range of these values ​​is called the fuzzy universe of discourse. The process of converting precise values ​​into fuzzy values ​​is called "fuzzification." This conversion can be done using linear or nonlinear methods, and can be represented by shapes such as triangles, trapezoids, and Gaussian distributions to describe the uncertainty of the input variables. For example, a single input error... The fundamental domain is Error change rate ( The fundamental domain of discourse is Control variables The fundamental domain is . Assumption The fuzzy domains are as follows:

[0069] To convert the fundamental universe of discourse into a fuzzy universe of discourse, a proportional conversion coefficient needs to be introduced, as shown in equation (25), to obtain the corresponding... proportionality coefficient proportionality coefficient and Proportionality coefficient.

[0070] (25) The fuzzy universe of discourse and the fuzzy linguistic value are assigned using a triangular membership function: (26) Based on the above theory, there are two input parameters: the cumulative error of the number of ships in the waterway. and error change rate There are 3 output parameters, which are According to the analytical formula of the macroscopic basic diagram in Chapter 3, for the core water area, the stable segment range is [429, 596], and the midpoint is taken. The steady segment is divided into steady flow and unstable flow; therefore, the optimal control range is approximately... Therefore, the fundamental domain of discourse for the deviation and rate of change of the expected cumulative number of ships from the actual cumulative number of ships is: Through experimental analysis, three output variables were selected. The fundamental domains are respectively , , Their corresponding fuzzy domains are According to formula (27), the proportional coefficients of each variable can be obtained: (27) (2) Fuzzy reasoning Fuzzy inference refers to establishing a database containing a set of rules that describe the relationships between input variables and the desired output variable values. Then, based on the rules in the rule base and the fuzzy sets of the input variables, the fuzzy set of the output variables is derived. This step is typically performed using fuzzy logic operations. The design of fuzzy control rules is usually based on experience and expertise or on intelligent algorithms such as neural networks and genetic algorithms. This embodiment will... and The corresponding fuzzy language set is set as follows: Therefore, the fuzzy rule table contains a total of 49 rules. Based on general experience in water traffic flow control, this invention conducts a qualitative analysis to determine the relationship between input and output as follows: a. When the deviation When the value is large, the system needs to respond quickly, so a larger value is selected. This enables rapid tracking; at the same time, in order to prevent Increasing the value too quickly can cause differential overflow; choose a smaller value. Maintain stability; b. When deviation When the value is small, it is necessary to reduce the steady-state error to maintain stability, so a larger value is chosen. and smaller Meanwhile, to prevent system oscillations, When the value is large, choose the smaller value. , When the value is smaller, choose the larger value. ; c. When deviation When the size is medium, it is necessary to prevent excessive overshoot while ensuring the system's response speed; therefore, a smaller value is selected. moderate and .

[0071] The final control rules for the three PID control parameters are shown in Table 1.

[0072] Table 1 Fuzzy rule table

[0073] In the table, NB, NM, NS, ZO, PS, PM, and PB represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, respectively.

[0074] Using Matlab, fuzzy logic operations are performed, i.e., if e is NB and Δe is NB, then Ki is PB, ultimately obtaining the control surface as shown. Figure 8 As shown.

[0075] (3) Defuzzification The fuzzy output is converted into a real value. This typically involves determining the value of the output variable, i.e., the actual error output as a specific control quantity. This invention employs a weighted average method: (28) In the formula, The centroid of the membership function, This represents the value of the membership function.

[0076] After weighted calculation, the fuzzy values ​​of the variables are obtained: , , The fuzzy value needs to be multiplied by the proportional coefficient of the output variable to finally obtain the actual change value of the PID control parameter, as shown in equation (29).

[0077] (29) The change is thus obtained. Compare it with the initial value By adding them together, we can obtain the control parameters of the PID controller, as shown in the formula below.

[0078] (30) Based on the above embodiments, as a preferred implementation, a control trigger determination step is further included before step S4: Based on the WMFD, multiple vessel accumulation thresholds are determined for the corresponding free flow, stable flow, unstable flow, and congested flow states. If the actual accumulated number of vessels in the core waterway reaches or exceeds the second half threshold corresponding to the stable flow state, closed-loop control steps S4 to S6 are triggered. The second half threshold is the vessel accumulation threshold corresponding to the inflection point after the stable flow turns into an unstable flow in the WMFD.

[0079] It should be noted that boundary flow control is generally applied in scenarios of surging ship demand. In such cases, the number of ships in the controlled waterway increases rapidly, approaching saturation. If the inflow of ships is not restricted, congestion will occur. Therefore, traffic congestion is closely related to the traffic demand of a waterway. When the ship operation status in the controlled waterway reaches a certain threshold, the input flow should be reasonably controlled to improve the overall operational efficiency of the waterway. Since the macroscopic basic diagram can clearly depict the macroscopic state characteristics of the traffic system, it can be used to assess and predict the navigation capacity of waterways. Based on this, control strategies to improve the performance of the navigation system can be proposed, including boundary control, upstream and downstream coordinated control, hierarchical control, robust control, and feedback gating. The ultimate goal of these control strategies is to maximize the number of trips by system traffic entities to their destinations, thereby improving the stability of the shipping traffic system.

[0080] Boundary flow control is an effective way to optimize the operational efficiency of a transportation system. It typically involves dividing the waterways into two parts: a core waterway, where the waterways often experience high traffic loads and are prone to congestion, and an outer waterway, where other waterways have lower traffic loads. Boundary control strategies primarily focus on entrance control to the core waterway, controlling the volume of traffic entering from outside the core waterway in real time to ensure that vessel traffic within the core waterway remains at an optimal critical level. Nearby fluctuations are managed to ensure the highest operational efficiency across the entire core waterway.

[0081] Depend on Figure 9 It can be seen that when the cumulative number of vessels in the core waters is at This allows the water body to have the maximum outflow. Traffic flow changes are a dynamic process, therefore there exists... ,when At this time, the waterway traffic flow is in the free flow and steady flow stages, during which ships have a certain degree of freedom of navigation and there is no congestion in the waterway; when Continue to increase until hour, Water traffic flow is in an unstable flow stage, with the outflow at the water boundary approaching the maximum flow; when hour, along with As the flow of waterways increases, the flow of waterways decreases, and the waterway traffic flow is in a forced flow stage, which greatly reduces the efficiency of waterway vessel operation.

[0082] This invention adjusts the cumulative number of vessels in the core waterway. Maintain traffic flow at a critically stable level to avoid excessively long queues or waiting times for vessels. After implementing boundary flow control in core waterway 1, the cumulative number of vessels in the waterway... Should be in nearby.

[0083] This invention determines the current waterway traffic status by analyzing the different intervals in which the vessel's cumulative volume falls, and then adopts different control strategies accordingly. (1) Phase A: The rising phase corresponds to the free flow state of traffic flow. There is no need to take boundary control measures for the core water area. The navigation of ships in the water area is relatively free. The space and time resources of the waterway are sufficient and no control measures are needed.

[0084] (2) Phase B: The first half of the stable phase. At this time, the traffic flow is stable and the number of ships operating in the key sections of the waterway is constantly increasing. When the cumulative number of ships in the waterway reaches near the critical value, boundary control should be implemented until the waterway state returns to the free flow phase.

[0085] In the latter half of the stable phase, the traffic flow becomes unstable. Within a certain period, some sections of the core waterway experience vessel congestion or obstructed passage, necessitating boundary control to maintain the accumulated vessel count. This is to prevent traffic flow from turning into congestion.

[0086] (3) Phase C: The second half of the unstable flow and the congested flow, it is urgent to take boundary control measures to regulate the cumulative number of ships in the core waters until the waterway traffic condition returns to a stable state, at which point control will be stopped.

[0087] Based on the above embodiments, as a preferred implementation, it further includes: Based on historical AIS data, a time series prediction model for the inflow rate of ships arriving in the core waterway is constructed. A simulation system is built that includes the dynamic equilibrium model and the fuzzy PID controller; the simulated peak period ship inflow sequence generated by the prediction model is input into the simulation system; the simulation system is run to verify the effectiveness of the boundary flow control method in maintaining the actual cumulative number of ships within the preset range.

[0088] Secondly, embodiments of the present invention provide a boundary flow control system based on a macroscopic basic map of busy waterways, such as... Figure 10 As shown, it includes: The data acquisition module 1010 is used to acquire historical and real-time Automatic Identification System (AIS) data of vessels in the target waters, including vessel position, length and time information. The water area macro basic map construction module 1020 is used to calculate the cumulative number of ships and the corresponding boundary outflow in the target water area under different proportions of large ships based on the AIS data, construct a unified water area macro basic map WMFD based on the standard ship equivalent number conversion method, and determine the optimal cumulative number of ships that maximizes the boundary outflow based on the WMFD. The dynamic equilibrium modeling module 1030 is used to define the area in the target water area where flow control needs to be implemented as the core water area, and based on the principle of ship flow conservation, to establish a dynamic equilibrium model of ship traffic flow in the core water area with the optimal cumulative number of ships as the equilibrium point, which serves as the controlled object of boundary flow control. The vessel status monitoring module 1040 is used to acquire the actual cumulative number of vessels in the core waterway in real time, and to calculate the deviation and rate of change between the actual cumulative number of vessels and the optimal cumulative number of vessels. The fuzzy PID control module 1050 is used to input the deviation and the rate of change to the fuzzy PID controller. The input of the fuzzy PID controller is the deviation and rate of change between the actual cumulative number of ships in the core water area at the current moment and the optimal cumulative number of ships, and the output is the boundary flow control rate. The fuzzy PID controller adjusts the PID parameters online and generates the boundary flow control rate at the current moment. The flow regulation execution module 1060 is used to adjust the inflow of ships allowed to enter the core water area according to the boundary flow control rate, so as to keep the actual cumulative number of ships in the core water area within a preset range corresponding to the optimal cumulative number of ships.

[0089] Based on the same concept, this invention also provides a schematic diagram of a physical structure, such as... Figure 11 As shown, the server may include a processor 1110, a communications interface 1120, a memory 1130, and a communication bus 1140. The processor 1110, communications interface 1120, and memory 1130 communicate with each other via the communication bus 1140. The processor 1110 can call logical instructions in the memory 1130 to execute the steps of the boundary flow control method based on a macroscopic basic map of busy waterways as described in the above embodiments.

[0090] Furthermore, the logical instructions in the aforementioned memory 1130 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0091] Based on the same concept, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program containing at least one piece of code that can be executed by a master control device to control the master control device to implement the steps of the boundary flow control method based on the macroscopic basic map of busy water areas as described in the above embodiments.

[0092] Based on the same technical concept, this application also provides a computer program, which, when executed by a main control device, is used to implement the above-described method embodiments.

[0093] The program may be stored, in whole or in part, on a storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.

[0094] Based on the same technical concept, this application also provides a processor for implementing the above-described method embodiments. The processor can be a chip.

[0095] The various embodiments of the present invention can be combined arbitrarily to achieve different technical effects.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for boundary flow control based on macroscopic fundamental diagram of congested waterways, characterized in that, The method comprises: S1, obtaining historical and real-time ship automatic identification system (AIS) data of a target water area, the AIS data comprising ship position, ship length and time sequence information; S2, according to the AIS data, counting the cumulative number of ships and the corresponding boundary outflow of the target water area under different large ship proportions, constructing a unified water area macroscopic fundamental diagram (WMFD) based on a standard ship equivalent number conversion method, and determining an optimal cumulative ship number that maximizes the boundary outflow according to the WMFD; S3, defining an area in which flow control needs to be implemented in the target water area as a core water area, and establishing a dynamic balance model of ship traffic flow in the core water area with the optimal cumulative ship number as a balance point based on the principle of ship flow conservation, as a controlled object of boundary flow control; S4, obtaining the actual cumulative ship number of the core water area in real time, and calculating the deviation and change rate of the actual cumulative ship number and the optimal cumulative ship number; S5, inputting the deviation and the change rate into a fuzzy PID controller, the input of the fuzzy PID controller being the deviation and the change rate of the actual cumulative ship number in the core water area at the current time and the optimal cumulative ship number, and the output being a boundary flow control rate; adjusting the PID parameters online through the fuzzy PID controller, and generating the boundary flow control rate at the current time; S6, adjusting the inflow of ships allowed to enter the core water area according to the boundary flow control rate, so that the actual cumulative ship number in the core water area is maintained within a preset range corresponding to the optimal cumulative ship number.

2. The method of claim 1, wherein, The S1 further comprises: cleaning the AIS data to filter out error data and invalid data therein; unifying the ship coordinates in the AIS data to a preset same geographic coordinate system; statistically processing the converted AIS data according to a preset statistical time window, and calculating the cumulative number of ships and the boundary outflow of the target water area in each time window.

3. The method of claim 1, wherein, In the S2, the unified water area macroscopic fundamental diagram (WMFD) is constructed based on the standard ship equivalent number conversion method, comprising: selecting a ship type with the largest proportion of ship number or the smallest ship length in the target water area as a standard ship, and defining the traffic volume conversion coefficient of the standard ship as 1; obtaining at least two MFD segmented function expressions respectively corresponding to different large ship proportions, wherein the large ship proportion is determined according to whether the ship length exceeds a preset threshold; based on a preset traffic volume conversion coefficient, uniformly converting the MFD segmented function expressions under the at least two different large ship proportions into WMFD segmented function expressions representing only the traffic flow characteristics of the standard ship, wherein the preset traffic volume conversion coefficient is respectively valued according to different stages of the traffic flow state corresponding to the WMFD segmented function expression.

4. The method for boundary flow control based on macroscopic fundamental diagram of congested waterways according to claim 3, wherein, The MFD segmented function expressions under the at least two different large ship proportions are uniformly converted into WMFD segmented function expressions representing only the traffic flow characteristics of the standard ship, comprising: The conversion equation based on the preset traffic conversion coefficient is constructed for the rising section, the stable section and the falling section of the MFD segmented function expression; The characteristic parameters of the MFD segmented function expression of the standard ship in the rising section, the stable section and the falling section are determined by solving the conversion equations corresponding to the proportions of the at least two large ships; The WMFD segmented function expression of the standard ship is generated according to the characteristic parameters.

5. The method for boundary flow control based on macroscopic fundamental diagram of congested waterways according to claim 1, wherein, In S5, the PID parameters are adjusted online by the PID controller, including: The deviation and the change rate are input into a fuzzy reasoning module, and the adjustment amounts of the proportional, integral and differential parameters are calculated in real time according to the pre-set fuzzy control rules, and the parameters of the fuzzy PID controller are online self-tuned; The fuzzy control rules include: The proportional parameter and the integral parameter are adjusted based on the absolute value of the deviation; When the absolute value of the deviation is greater than a first threshold, the proportional parameter is increased and the integral parameter is decreased; When the absolute value of the deviation is less than or equal to the first threshold and greater than a second threshold, the proportional parameter and the integral parameter are set to pre-set medium values; When the absolute value of the deviation is less than or equal to the second threshold, the integral parameter is increased and the proportional parameter is decreased; And the differential parameter is dynamically adjusted based on the absolute value of the change rate.

6. The busy waterways macroscopic fundamental diagram based boundary flow control method of claim 1, wherein, Before step S4, a control trigger judgment step is further included: Based on the WMFD, a plurality of ship cumulative quantity thresholds corresponding to free flow, stable flow, unstable flow and congestion flow states are determined; If the actual cumulative ship number of the core water area reaches or exceeds the latter half threshold corresponding to the stable flow state, the closed-loop control of steps S4 to S6 is triggered to be executed, wherein the latter half threshold corresponds to the ship cumulative quantity threshold of the latter inflection point corresponding to the stable flow turning into the unstable flow in the WMFD.

7. The busy waterways macroscopic fundamental diagram based boundary flow control method of claim 1, wherein, Further including: Based on historical AIS data, a flow-in rate time series prediction model of ships arriving at the core water area is constructed; A simulation system including the dynamic balance model and the fuzzy PID controller is built; The simulation peak ship flow-in sequence generated by the prediction model is input into the simulation system; The simulation system is run to verify the effectiveness of the boundary flow control method in maintaining the actual cumulative ship number within the pre-set range.

8. A boundary flow control system based on a macroscopic fundamental diagram of a congested water area, characterized in that, Including: A data acquisition module is configured to acquire historical and real-time Automatic Identification System (AIS) data of ships in a target water area, wherein the AIS data includes ship position, ship length and time sequence information; A water area macroscopic basic graph construction module is configured to statistically determine ship cumulative quantity and corresponding boundary outflow under different proportions of large ships in the target water area according to the AIS data, construct a unified water area macroscopic basic graph (WMFD) based on a standard ship equivalent number conversion method, and determine an optimal cumulative ship number that maximizes the boundary outflow according to the WMFD; A dynamic balance modeling module is configured to define an area requiring flow control in the target water area as a core water area, and to establish a dynamic balance model of ship traffic flow in the core water area based on a ship flow conservation principle, with the optimal cumulative number of ships as a balance point, as a controlled object of boundary flow control. A ship state monitoring module is configured to acquire an actual cumulative number of ships in the core water area in real time, and to calculate a deviation and a change rate of the actual cumulative number of ships from the optimal cumulative number of ships. A fuzzy PID control module is configured to input the deviation and the change rate into a fuzzy PID controller, with the deviation and the change rate of the actual cumulative number of ships in the core water area from the optimal cumulative number of ships at a current time as inputs of the fuzzy PID controller, and a boundary flow control rate as an output of the fuzzy PID controller, to adjust PID parameters on line through the fuzzy PID controller, and to generate the boundary flow control rate at the current time. A flow regulation execution module is configured to regulate a ship inflow allowed to enter the core water area according to the boundary flow control rate, so as to maintain the actual cumulative number of ships in the core water area within a preset range corresponding to the optimal cumulative number of ships.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the boundary flow control method based on the macroscopic basic graph of the busy water area according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the boundary flow control method based on the macroscopic basic graph of the busy water area according to any one of claims 1 to 7.

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