A port-based unmanned container truck safety control method and system

Abstract

The present application relates to the field of traffic control technology and discloses a safety control method and system for unmanned container trucks based in ports. The method includes: obtaining load information through port lifting equipment to establish a load-braking mapping table, calculating the passage difficulty coefficient in combination with the channel width to form a load channel restriction matrix, identifying load conflict scenarios to perform differentiated avoidance responsibility allocation, setting load-graded safety distances, guiding low-load vehicles to perform active avoidance operations, and adjusting vehicle speeds and paths to achieve load-differentiated collaborative avoidance control. The present application solves the technical problem in existing port unmanned container truck safety control technologies that cannot perform differentiated control and precise collaborative avoidance based on load differences.

Description

Technical Field

[0001] The present application relates to the field of traffic control technology, and in particular to a port-based unmanned container truck safety control method and system. Background Art

[0002] Existing safety control technologies for unmanned container trucks in ports primarily employ standardized vehicle control methods. These methods utilize sensors such as GPS, LiDAR, and cameras to acquire vehicle position and environmental information, and then coordinate multi-vehicle control based on pre-set safety distances and avoidance rules. Traditional control systems employ first-come, first-served or randomly assigned avoidance strategies, applying the same braking distance standards and steering parameters to all vehicles. Vehicle routing and traffic coordination are managed uniformly through a central dispatch system. Control algorithms in existing technologies primarily focus on basic parameters such as vehicle position, speed, and direction of travel, making avoidance decisions based on simple distance assessments and time priorities.

[0003] However, existing technologies have significant shortcomings and are unable to effectively handle the actual situation of significant differences in load weight in port container transportation. The unified safety control standard ignores the huge differences in braking distance, turning radius and maneuverability between unloaded and heavily loaded vehicles, resulting in frequent braking difficulties and steering restrictions for heavily loaded vehicles in narrow passages. Existing avoidance strategies lack consideration of load factors, and heavy-loaded vehicles are often forced to brake urgently or unloaded vehicles are often forced to wait in vain, seriously affecting port operation efficiency. Traditional control methods are unable to set differentiated safety distances and assign priorities based on the actual load status of container trucks, and are prone to decision conflicts and coordination failures in multi-vehicle intersection scenarios.

[0004] Given the limitations of the aforementioned existing technologies, the safe control of unmanned container trucks in ports faces deeper technical challenges. Existing technologies lack a precise mapping mechanism between load status and vehicle dynamics, and are unable to establish a quantitative relationship between load differences and channel adaptability. This results in the inability to implement intelligent collaborative control based on load characteristics in the complex traffic environment of ports. Existing multi-vehicle avoidance algorithms lack the ability to dynamically allocate load priorities, making it impossible to reasonably allocate avoidance responsibilities based on load differences. In particular, when loaded and unloaded vehicles pass through narrow areas at the same time, there is a lack of effective load compensation control mechanisms to achieve precise collaborative avoidance operations. Summary of the Invention

[0005] The present application provides a port-based unmanned container truck safety control method and system, which is used to solve the technical problem that the existing port unmanned container truck safety control technology cannot perform differentiated control and precise coordinated avoidance according to load differences.

[0006] In a first aspect, the present application provides a port-based unmanned container truck safety control method, which includes: obtaining real-time load information of container trucks through port lifting equipment load data, establishing a load-braking mapping table based on the influence of load on braking distance and turning radius, and obtaining load-grade braking parameters; matching and analyzing the load-grade braking parameters with the width of narrow channels in the port, calculating the difficulty coefficient of heavy-loaded vehicles in channels of different widths through a load channel adaptation algorithm, and obtaining a load channel restriction matrix; identifying load conflict scenarios when multiple vehicles intersect according to the load channel restriction matrix, and differentially allocating avoidance responsibilities for heavy-loaded vehicles and empty vehicles to obtain a load-differentiated avoidance strategy; converting the load-differentiated avoidance strategy into a safety distance classification standard, setting exclusive safety intervals for vehicles of different load grades through a load-grade distance algorithm, and obtaining a load-grade safety distance; guiding low-load vehicles to perform active avoidance operations according to the load-grade safety distance, adjusting vehicle speed and path through a load compensation control algorithm, and obtaining a collaborative avoidance control strategy based on load differences.

[0007] In a second aspect, the present application provides a port-based unmanned container truck safety control system, the port-based unmanned container truck safety control system comprising:

[0008] Establish a module to obtain real-time load information of container trucks through the load data of port lifting equipment, establish a load-brake mapping table based on the impact of load on braking distance and turning radius, and obtain load-level braking parameters;

[0009] A matching module is used to match and analyze the load-grade braking parameters with the width of the narrow channel of the port, calculate the difficulty coefficient of heavy-loaded vehicles passing through channels of different widths through a load channel adaptation algorithm, and obtain a load channel restriction matrix;

[0010] an allocation module for identifying load conflict scenarios when multiple vehicles converge based on the load channel restriction matrix, differentially allocating avoidance responsibilities between heavily loaded vehicles and unloaded vehicles, and obtaining a load-differentiated avoidance strategy;

[0011] A setting module is used to convert the load-differentiated avoidance strategy into a safety distance classification standard, set exclusive safety intervals for vehicles of different load levels through a load-classified distance algorithm, and obtain a load-classified safety distance;

[0012] The adjustment module is used to guide the low-load vehicle to perform active avoidance maneuvers according to the load-graded safety distance, adjust the vehicle speed and path through the load compensation control algorithm, and obtain a collaborative avoidance control strategy based on load differences.

[0013] In a third aspect, a port-based unmanned container truck safety control device is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the port-based unmanned container truck safety control device executes the above-mentioned port-based unmanned container truck safety control method.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned port-based unmanned container truck safety control method.

[0015] In the technical solution provided by the present application, by establishing a load-braking mapping table and a load-channel restriction matrix, the precise quantitative correlation between the load differences of port container trucks and the vehicle dynamics performance is achieved for the first time, solving the fundamental problem in the existing technology that the unified control standard cannot adapt to load changes. The load channel adaptation algorithm can accurately calculate the difficulty coefficient of vehicles with different load levels in channels of various widths, providing a scientific basis for the safe passage of heavy-loaded vehicles in narrow channels, and avoiding the safety hazards caused by heavy-loaded vehicles due to restricted steering in traditional methods. The load-differentiated avoidance strategy establishes an intelligent coordination mechanism based on load characteristics through a load priority ranking table, so that heavy-loaded vehicles obtain priority right of way and empty vehicles take the responsibility of active avoidance, fundamentally changing the control logic of multi-vehicle coordination in the port. The load-grading distance algorithm sets exclusive safety intervals according to different load levels, realizes dynamic adjustment of the safety distance, significantly reduces the potential collision risk caused by insufficient safety margin, and avoids the loss of traffic efficiency caused by overly conservative safety settings.

[0016] The application of the load-compensation control algorithm in port container transportation demonstrates the key contribution of the algorithm's features to the overall solution. By calculating the load-bearing avoidance distance gap and matching deceleration parameters, the algorithm achieves precise avoidance control for lightly loaded vehicles, making avoidance maneuvers smoother and more predictable. The algorithm considers the actual impact of load differences on braking performance and develops differentiated deceleration strategies for unloaded and lightly loaded vehicles, ensuring both safety requirements and operational smoothness during avoidance maneuvers. The load-bearing channel adaptation algorithm, through risk classification and difficulty coefficient calculation, provides quantitative decision-making support for vehicle navigation in narrow port channels. Algorithmic features enable the system to proactively identify high-risk traffic scenarios and take appropriate preventative measures. The load-graded distance algorithm, combined with speed and environmental correction mechanisms, dynamically adjusts safety distances based on multiple factors. The algorithm's adaptive nature makes safety control more precise and intelligent, providing stable and reliable safety assurance, particularly in the complex and changing port operating environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are some embodiments of the present invention. Those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0018] Figure 1 This is a schematic diagram of an embodiment of a port-based unmanned container truck safety control method according to an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of an embodiment of a port-based unmanned container truck safety control system in an embodiment of the present application;

[0020] Figure 3 It is a schematic block diagram of the structure of a port-based unmanned container truck safety control device in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The embodiments of the present application provide a port-based unmanned container truck safety control method and system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a safety control method for an unmanned container truck at a port includes:

[0023] Step S101: obtaining real-time load information of container trucks through port hoisting equipment load data, establishing a load-braking mapping table based on the influence of load on braking distance and turning radius, and obtaining load-level braking parameters;

[0024] Step S102: Matching and analyzing the load-grade braking parameters with the width of the narrow channel at the port, and calculating the difficulty coefficient of heavy-loaded vehicles passing through channels of different widths through a load channel adaptation algorithm to obtain a load channel restriction matrix;

[0025] Step S103: Identify the load conflict scenario when multiple vehicles intersect based on the load channel restriction matrix, and perform differential avoidance responsibilities on heavy-loaded vehicles and empty vehicles to obtain a load-differentiated avoidance strategy;

[0026] Step S104: Convert the load-differentiated avoidance strategy into a safety distance classification standard, set exclusive safety intervals for vehicles of different load levels using a load classification distance algorithm, and obtain a load classification safety distance;

[0027] Step S105: Instruct the low-load vehicle to perform active avoidance maneuvers based on the load-graded safety distance, adjust the vehicle speed and path through the load compensation control algorithm, and obtain a collaborative avoidance control strategy based on load differences.

[0028] It is understandable that the execution subject of this application can be a port-based unmanned container truck safety control system, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, container weight values are obtained in real time through data interfaces with quay cranes and yard cranes, and load classification data is divided into four intervals: 0-5 tons empty, 5-15 tons light load, 15-25 tons medium load, and 25-35 tons heavy load. The braking distance test is conducted by measuring vehicles of different load levels at an initial speed of 30 kilometers per hour in the port test area. The braking distance for empty vehicles is 12 meters, for light-loaded vehicles is 15 meters, for medium-loaded vehicles is 18 meters, and for heavy-loaded vehicles is 22 meters. These basic braking distance values reflect the direct impact of load on braking performance. The turning radius test is conducted at the same 45-degree steering angle. The turning radius for empty vehicles is 4.5 meters, for light-loaded vehicles is 5.2 meters, for medium-loaded vehicles is 5.8 meters, and for heavy-loaded vehicles is 6.5 meters. The load impact coefficient is calculated by applying a proportional coefficient to the numerical relationship between load, braking distance, and turning radius. Compared to unladen vehicles, the braking distance of a heavily loaded vehicle increases by 83%, and the turning radius increases by 44%. The load-braking impact coefficient and the load-turning impact coefficient are set to 1.83 and 1.44, respectively. The load-braking mapping table associates load grades with braking performance parameters, creating a load-grade braking parameter data structure that includes standard values for braking distance and turning radius.

[0030] The load channel adaptation algorithm classifies channel widths based on port road surveying data, using main roads (over 6 meters), branch roads (4-6 meters), and narrow channels (3-4 meters) as the channel width classification data. Width adaptability is determined by comparing the turning radius of the vehicle's load level with the channel width. A heavy-loaded vehicle with a 6.5-meter turning radius cannot maneuver in a 3-4-meter narrow channel, while a medium-loaded vehicle with a 5.8-meter turning radius is barely passable in a 4-meter wide channel, but poses a risk. The difficulty coefficient is calculated by combining the standard braking distance with channel length data to calculate the safe passage distance. A heavy-loaded vehicle in an 80-meter narrow channel requires a 22-meter braking distance plus a 6.5-meter turning allowance, resulting in an actual safe passage distance requirement of 28.5 meters, representing 35.6% of the channel length. The load channel restriction matrix is constructed as a two-dimensional array with load level as rows and channel width as columns. The difficulty coefficient for heavy-loaded vehicles in narrow channels is 0.9, 0.6 for medium-loaded vehicles, 0.3 for light-loaded vehicles, and 0.1 for unladen vehicles.

[0031] Load conflict identification uses the vehicle position monitoring system to obtain load information for each vehicle in the intersection area and matches the difficulty coefficient in the load channel restriction matrix with the real-time vehicle load level. When a heavy-loaded vehicle and an unladen vehicle intersect in a narrow channel, the heavy-loaded vehicle's difficulty coefficient of 0.9 is much higher than the unladen vehicle's 0.1. Load priority sorting assigns a high priority value of 8 to heavy-loaded vehicles and a low priority value of 2 to unladen vehicles. Load avoidance responsibility allocation assigns high-priority loaded vehicles as the preferred vehicle and low-priority loaded vehicles as the active avoidance vehicle. Differentiated avoidance instructions include slowing down and yielding instructions for the active avoidance vehicle and normal passage instructions for the priority vehicle.

[0032] The load-classified distance algorithm sets baseline safety distances for different load levels based on a load priority table: 15 meters for unladen vehicles, 20 meters for lightly loaded vehicles, 25 meters for medium-loaded vehicles, and 30 meters for heavy-loaded vehicles. The speed correction calculation multiplies the vehicle's current speed by the baseline safety distance by a speed coefficient. At a vehicle speed of 20 kilometers per hour, the speed coefficient is 1.2, resulting in a corrected safety distance of 36 meters for heavy-loaded vehicles. Environmental corrections are implemented to account for the port's unique weather conditions. A 20% safety margin is added in rainy conditions, resulting in a final safety distance of 43.2 meters for heavy-loaded vehicles. A 50% safety margin is added in foggy conditions, resulting in a final safety distance of 54 meters. The load-compensation control algorithm identifies underloaded vehicles requiring active avoidance. Unladen vehicles are marked as such when intersecting with heavy-loaded vehicles. The avoidance distance calculation calculates the distance difference between the current position of an underloaded vehicle and that of a heavy-loaded vehicle. The current distance is 25 meters, while the safety distance required for heavy-loaded vehicles is 43.2 meters, leaving an 18.2-meter gap in the load avoidance distance. The deceleration parameter matching calculates the avoidance distance gap and the vehicle's braking performance. An unloaded vehicle needs to decelerate to 15 kilometers per hour based on the existing speed. The load-compensated deceleration control parameters include reducing the throttle opening by 40% and increasing the brake pressure to 0.3 MPa. Load-differentiated collaborative avoidance control is achieved through on-board actuators.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] The container weight values are collected by the loading and unloading sensors of the quay cranes and yard cranes, and the container weight values are divided into load intervals to obtain load classification data of 0-5 tons empty, 5-15 tons light load, 15-25 tons medium load, and 25-35 tons heavy load;

[0035] Braking distance tests are conducted based on load classification data. Actual braking distance data for vehicles of different load levels at the same initial speed is collected and processed to obtain basic braking distance values for each load level.

[0036] Based on the basic braking distance value, the turning radius test is carried out. The measured data of the turning radius of vehicles with different load levels at the same steering angle is collected and processed to obtain the basic turning radius value of each load level;

[0037] The load influence coefficient is calculated based on the basic values of braking distance and turning radius, and the proportional coefficient is extracted from the numerical relationship between load, braking distance and turning radius to obtain the load-braking influence coefficient and load-turning influence coefficient;

[0038] A load-braking mapping table is constructed based on the load-braking influence coefficient and the load-steering influence coefficient, and the load grade and braking performance parameters are associated and assigned to obtain the load grade braking parameters including the standard value of the braking distance and the standard value of the turning radius.

[0039] Specifically, the sensor obtains the original voltage signal of the container weight through a pressure measuring device, converts the analog signal into a digital signal through an analog-to-digital converter, and then eliminates noise interference through signal amplification and filtering to finally obtain an accurate weight value. The load range classification processing is based on the actual load distribution characteristics of port container transportation. The obtained weight value is classified and judged according to the preset threshold. When the weight value is in the range of 0-5 tons, it is marked as unloaded, the range of 5-15 tons is marked as light load, the range of 15-25 tons is marked as medium load, and the range of 25-35 tons is marked as heavy load. The load classification data is stored in the form of digital codes, with unloaded coded as 1, light load coded as 2, medium load coded as 3, and heavy load coded as 4. The data collection and processing of the braking distance test is carried out by setting up a distance sensor array in a dedicated test area of the port. When the unmanned container truck starts braking from a fixed starting point at a uniform initial speed, the distance sensor records the vehicle position changes in real time. The braking start time is recorded as zero point. The difference between the position where the vehicle completely stops and the zero point at the end of braking is the basic braking distance value. The basic braking distance value of vehicles with different load levels is obtained by taking the average value of multiple repeated tests. The basic braking distance value of unladen vehicles is 12 meters, 15 meters for light loads, 18 meters for medium loads, and 22 meters for heavy loads.

[0040] The turning radius test is based on the acquisition of basic braking distance values. A standardized turning test path is set up in the port test area. Data processing for the basic turning radius value uses a gyroscope and GPS positioning device mounted on the vehicle chassis to collect real-time changes in the vehicle's position coordinates during steering. When the vehicle turns at a fixed steering angle, the GPS coordinate data forms a turning trajectory curve. This trajectory curve is then geometrically fitted to an arc, and the arc radius is the basic turning radius value. The basic turning radius value for an unladen vehicle is 4.5 meters, 5.2 meters for a light load, 5.8 meters for a medium load, and 6.5 meters for a heavy load. The data processing process for calculating the load influence coefficient uses the basic braking distance and turning radius values as input parameters. Through numerical relationship analysis, the influence of load on braking and steering performance is extracted. The load braking influence coefficient is calculated by dividing the braking distance of a heavy-loaded vehicle by the braking distance of an unladen vehicle (i.e., 22 meters divided by 12 meters, equals 1.83). The load steering influence coefficient is calculated by dividing the turning radius of a heavy-loaded vehicle by the turning radius of an unladen vehicle (i.e., 6.5 meters divided by 4.5 meters, equals 1.44).

[0041] The load-braking mapping table is constructed based on the load-braking influence coefficient and the load-steering influence coefficient. A mapping relationship is established between load grades and corresponding braking performance parameters through associated assignment. The mapping table is stored in a two-dimensional array structure, with row indices corresponding to load grade codes and column indices corresponding to braking performance parameter types. The standard braking distance and turning radius values serve as the core data elements of the mapping table. The standard braking distance for unladen vehicles is set to 12 meters, and the standard turning radius is set to 4.5 meters. The standard braking distance for light-loaded vehicles is calculated by multiplying the unladen base value by the load correction coefficient to 15 meters, and the standard turning radius is 5.2 meters. The standard values for medium-loaded and heavy-loaded vehicles are calculated using the same method and stored in the load-braking mapping table. The mapping table data structure supports fast query and update operations. When load-grade braking parameters need to be called, the corresponding standard braking distance and turning radius values are directly accessed using the load grade code as an index. The load-grade braking parameters serve as the basic input data for the subsequent load channel adaptation algorithm and load-grade distance algorithm, solving the technical problem of the existing technology that cannot perform differentiated control based on the load differences of container trucks.

[0042] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0043] The actual width values of different channels are collected through port road surveying data, and the actual width values are processed into channel classification data to obtain channel width classification data for main roads over 6 meters, branch roads 4-6 meters, and narrow channels 3-4 meters;

[0044] Width adaptability is determined based on the standard turning radius value in the load-grade braking parameters and the channel width classification data. The turning radius and channel width of the load-grade vehicle are numerically compared to obtain the channel adaptability determination result of the load-grade vehicle.

[0045] The difficulty coefficient of passage is calculated based on the results of the suitability judgment of the loaded vehicle channel. The standard braking distance value in the load level braking parameter and the channel length data are used to calculate the safe passage distance to obtain the passage difficulty coefficient value of each load level in channels of different widths;

[0046] The values of the passage difficulty coefficient are arranged in a matrix according to the load level and channel width, and a two-dimensional array is constructed for the passage difficulty coefficient of the load level and channel width to obtain a load channel restriction matrix with load level as row and channel width as column.

[0047] Specifically, the port road surveying and mapping data collection process uses laser ranging equipment to scan the port's internal roads section by section. The laser beam transmitter emits laser pulses on both sides of the road, and the receiver records the reflected laser signal. The road boundary distance is calculated by multiplying the laser round-trip time and the speed of light. The difference between the two boundary distances is the actual road width. The channel classification process classifies the collected actual width values according to preset thresholds. When the width value is greater than 6 meters, it is classified as a main road. The width value between 4-6 meters is classified as a branch road. The width value between 3-4 meters is classified as a narrow channel. The channel width classification data is stored in a coded form, with the main road coded as A, the branch coded as B, and the narrow channel coded as C. Each channel segment also has the corresponding specific width value and geographic coordinate information. The width adaptability judgment is based on a numerical comparison between the standard value of the turning radius in the load grade braking parameters and the channel width classification data. The judgment process first extracts the standard value of the turning radius corresponding to the load grade, and then divides it with the channel width value. When the ratio of the standard value of the turning radius divided by the channel width is less than 0.5, it is judged to be well adapted. When the ratio is between 0.5-0.7, it is judged to be generally adapted. When the ratio is greater than 0.7, it is judged to be difficult to adapt. The results of the channel adaptability judgment of loaded vehicles are stored in the adaptability level code, with good adaptation coded as 1, general adaptation coded as 2, and difficult adaptation coded as 3.

[0048] The calculation of the difficulty coefficient is based on the adaptability judgment result of the loaded vehicle channel. The standard braking distance value in the load grade braking parameter and the channel length data are used to calculate the safe passing distance. The calculation process first adds the standard braking distance value and the standard turning radius value to obtain the basic safety distance requirement, and then corrects the basic safety distance requirement according to the adaptability judgment result. In the case of difficult adaptation, the basic safety distance requirement is multiplied by 1.5 times the safety factor. In the case of general adaptation, it is multiplied by 1.2 times the safety factor. In the case of good adaptation, the basic safety distance requirement remains unchanged. The corrected safety distance requirement is divided by the channel length data to obtain the distance occupancy ratio. The distance occupancy ratio value is directly used as the value of the difficulty coefficient. The larger the value, the higher the difficulty of passing. The process of constructing the load channel restriction matrix arranges the values of the difficulty coefficient of passage into a two-dimensional array according to the load level and channel width. The row index of the matrix corresponds to the load level code, the column index corresponds to the channel width classification code, and the matrix element value is the value of the difficulty coefficient of passage for the corresponding load level in a channel of a specific width. The array construction process traverses all combinations of load levels and channel widths through nested loops, and fills the calculated difficulty coefficient values into the corresponding positions of the matrix to form a complete load channel restriction matrix data structure.

[0049] The data processing logic of the load channel adaptation algorithm is reflected in the precise quantitative analysis of load differences. The standard turning radius of a heavy-loaded vehicle is 6.5 meters. When judging adaptability in a narrow channel with a width of 3.5 meters, the ratio of the turning radius to the channel width is 6.5 divided by 3.5, which is equal to 1.86, much larger than the adaptation difficulty threshold of 0.7. The judgment result is adaptation difficulty. When calculating the safety distance requirement, the standard braking distance value of 22 meters for heavy-loaded vehicles is added to the standard turning radius value of 6.5 meters to obtain a basic safety distance requirement of 28.5 meters. Due to the difficulty of adaptation, it is necessary to multiply it by 1.5 times the safety factor. The corrected safety distance requirement is 42.75 meters. When the narrow channel is used, the standard braking distance value of 22 meters for heavy-loaded vehicles is added to the standard turning radius value of 6.5 meters to obtain a basic safety distance requirement of 28.5 meters. Due to the difficulty of adaptation, it is necessary to multiply it by 1.5 times the safety factor. The corrected safety distance requirement is 42.75 meters. When the road length is 80 meters, the distance occupancy ratio is 42.75 divided by 80, which is equal to 0.53. This value is stored in the load channel restriction matrix as the difficulty coefficient for heavy-loaded vehicles in narrow channels. The standard value of the turning radius of an unladen vehicle is 4.5 meters. In the same channel, the ratio is 4.5 divided by 3.5, which is equal to 1.29. It is also difficult to adapt. However, its standard braking distance is only 12 meters, and the basic safety distance requirement is 16.5 meters. After correction, it is 24.75 meters. The difficulty coefficient is 0.31, which is significantly lower than that of heavy-loaded vehicles. This differentiated quantification result of the difficulty of passage solves the technical problem that the existing technology cannot be controlled according to load differences.

[0050] In a specific embodiment, the process of calculating the passage difficulty coefficient according to the result of the heavy vehicle channel adaptability judgment step may specifically include the following steps:

[0051] Based on the results of the heavy vehicle channel adaptability judgment, the risk level is divided and the ratio of turning radius to channel width is graded to obtain three levels of heavy vehicle passage risk: high, medium and low.

[0052] The braking distance safety factor is set according to the load risk level, and the standard braking distance values of different risk levels are multiplied to obtain the load level safety braking distance value;

[0053] Based on the load level safe braking distance value and the channel length, the adequacy verification is carried out, and the braking distance and channel length ratio is evaluated and processed to obtain the load channel length adaptability rating;

[0054] The complexity of the steering operation is calculated based on the load channel length adaptability rating, and the steering difficulty of different ratings is numerically quantified to obtain the load steering operation complexity value;

[0055] Convert the load-carrying risk level into a risk coefficient, assign numerical values to the high, medium and low risk levels, and obtain the load-carrying risk coefficient value;

[0056] A weighted calculation is performed based on the load-carrying risk coefficient value and the load-carrying steering operation complexity value, and the two values are summed up according to the weight ratio to obtain the load-carrying difficulty coefficient value.

[0057] Specifically, the risk level of loaded traffic is divided into grades based on the ratio of turning radius to channel width in the results of the channel adaptability judgment of loaded vehicles. The grading process classifies the ratio data into three levels according to the preset threshold value. When the ratio is greater than 0.7, it is classified as a high-risk level, when the ratio is between 0.5-0.7, it is classified as a medium-risk level, and when the ratio is less than 0.5, it is classified as a low-risk level. The risk level of loaded traffic is stored in a digital code, with high risk coded as 3, medium risk coded as 2, and low risk coded as 1. The logic of the grading process is based on the safety margin of the steering operation of port container trucks in narrow channels. The larger the ratio, the tighter the turning space, and the higher the risk level. The braking distance safety factor is set by multiplying the standard braking distance value according to the load traffic risk level. The safety factor for high-risk level is set to 1.5, the safety factor for medium-risk level is set to 1.2, and the safety factor for low-risk level is set to 1.0. The coefficient doubling process is performed by multiplying the standard braking distance value with the corresponding safety factor to obtain the load level safe braking distance value. The safe braking distance value of heavy-loaded vehicles at high risk level is 22 meters multiplied by 1.5, which is equal to 33 meters. The safe braking distance value of medium-loaded vehicles at medium risk level is 18 meters multiplied by 1.2, which is equal to 21.6 meters. The setting of the safety factor reflects the differentiated requirements for braking safety margin under different risk levels.

[0058] The load channel length adaptability rating is based on the load grade safe braking distance value and the channel length for adequacy verification. The assessment process is quantitatively analyzed by calculating the proportional relationship between the braking distance and the channel length. When the ratio is greater than 0.6, it is rated as severely insufficient, the ratio between 0.4-0.6 is rated as generally insufficient, the ratio between 0.2-0.4 is rated as basically sufficient, and when the ratio is less than 0.2, it is rated as completely sufficient. The load channel length adaptability rating is stored in letter codes, with severely insufficient code D, generally insufficient code C, basically sufficient code B, and completely sufficient code A. The rating results reflect the degree of space adequacy for container trucks to complete safe braking within a specific channel length. The numerical calculation of the complexity of load-carrying steering operations quantifies the steering difficulty based on the load channel length adaptability rating. The steering operation complexity value for the severely insufficient rating is set at 0.8, the generally insufficient rating is set at 0.6, the basically sufficient rating is set at 0.4, and the completely sufficient rating is set at 0.2. The numerical quantification is based on the analysis of the operational difficulty and time cost of steering operations performed by port container trucks in channels of different lengths. The less sufficient the channel length, the more precise and complex the driver needs to perform steering operations, and the complexity value increases accordingly.

[0059] The numerical conversion of the load-carrying passage risk coefficient assigns a numerical value to the load-carrying passage risk level. The high-risk level is assigned a risk coefficient value of 0.7, the medium-risk level is assigned a value of 0.5, and the low-risk level is assigned a value of 0.3. The numerical assignment is based on a weighted analysis of the impact of different risk levels on overall passage safety in the port environment. The higher the risk level, the larger the corresponding risk coefficient value, and it occupies a greater proportion in the subsequent weighted calculation. The weighted calculation of the passage difficulty coefficient value is based on the sum of the load-carrying passage risk coefficient value and the load-carrying steering operation complexity value according to the weight ratio. The weighted calculation adopts a 6 to 4 weight distribution, that is, the risk coefficient value multiplied by 0.6 plus the steering operation complexity value multiplied by 0.4. The weight distribution takes into account the relative impact of the channel adaptation risk and operation complexity on the overall passage difficulty during the passage of container trucks in the port. The channel adaptation risk, as the main factor, has a higher weight.

[0060] The correlation in the data processing process is reflected in the calculation of the difficulty of heavy-loaded vehicles passing through a 3.8-meter-wide channel. The ratio of the standard value of the turning radius of heavy-loaded vehicles of 6.5 meters to the channel width of 3.8 meters is 1.71. It exceeds the 0.7 threshold and is classified as a high-risk level. The corresponding safe braking distance after the safety factor is multiplied by 1.5 is 33 meters. When the channel length is 60 meters, the braking distance ratio is 33 divided by 60, which is equal to 0.55, which is a generally insufficient rating. The steering operation complexity value is 0.6, and the risk coefficient value for high-risk level conversion is 0.7. The weighted calculation result is 0.7 multiplied by 0.6 plus 0.6 multiplied by 0.4, which is equal to 0.66. This value is used as the final difficulty coefficient for heavy-loaded vehicles in this specific width channel. The closer the value is to 1, the greater the difficulty of passing.

[0061] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0062] The vehicle load information of the multi-vehicle intersection area is obtained through the vehicle position monitoring system. The traffic difficulty coefficient in the load channel restriction matrix is matched with the real-time vehicle load level to obtain the intersection vehicle load conflict identification result.

[0063] Based on the load conflict identification results of the intersecting vehicles, the load priority is sorted, high priority values are assigned to heavy-loaded vehicles, and low priority values are assigned to unloaded vehicles, and a load priority sorting table is obtained;

[0064] According to the load priority ranking table, the avoidance responsibility allocation principle is formulated, and high-priority load vehicles are set as the priority passing party, and low-priority load vehicles are set as the active avoidance party to obtain the load avoidance responsibility allocation plan;

[0065] Based on the load avoidance responsibility allocation scheme, differentiated avoidance instructions are generated, slowdown and yield instructions are issued to vehicles that actively avoid, and normal passage instructions are issued to vehicles that have priority, thus obtaining a load-differentiated avoidance strategy.

[0066] Specifically, the vehicle location monitoring system uses the GPS positioning network and lidar sensors deployed within the port to obtain real-time location data for multiple vehicle intersections. This location data includes vehicle coordinates, movement direction, and speed. Simultaneously, the system obtains vehicle load information through a data interface with the port's lifting equipment. This load information is then associated with the vehicle identification code to form comprehensive vehicle status data that includes both location and load. The matching query process associates the traffic difficulty coefficient stored in the traffic lane restriction matrix with the real-time vehicle load level. The query process first extracts the traffic difficulty coefficient code for each vehicle in the intersection area. Then, using the traffic difficulty coefficient code as the row index and the traffic lane width classification code as the column index, the traffic difficulty coefficient value corresponding to the traffic lane restriction matrix is retrieved. The query results generate a traffic conflict identification result for the intersecting vehicles, which includes each vehicle's traffic difficulty coefficient, and a conflict severity indicator. A traffic difficulty coefficient difference of more than 0.3 for multiple vehicles of different load levels is identified as a high conflict; a difference between 0.1 and 0.3 is identified as a medium conflict; and a difference less than 0.1 is identified as a low conflict. The load priority sorting is based on the load level information in the intersection vehicle load conflict identification results and is numerically assigned. Heavy-loaded vehicles are assigned a priority value of 8, medium-loaded vehicles are assigned a priority value of 6, light-loaded vehicles are assigned a priority value of 4, and empty vehicles are assigned a priority value of 2. The larger the priority value, the higher the right of way in the intersection conflict. The load priority sorting table is arranged from high to low according to the priority value to form an orderly vehicle priority sequence.

[0067] The load avoidance responsibility allocation principle divides intersecting vehicles into two categories: priority passing party and active avoidance party according to the load priority ranking table. The allocation process determines the responsibility by comparing the priority values of each vehicle. The vehicle with a higher priority value is set as the priority passing party, and the vehicle with a lower priority value is set as the active avoidance party. When the priority values are the same, the priority is determined according to the time of arrival at the intersection. The load avoidance responsibility allocation plan is stored in the vehicle role code, with the priority passing party coded as P and the active avoidance party coded as A. The allocation plan also includes the specific avoidance action requirements and execution timing arrangements for each vehicle. Differentiated avoidance command generation formulates corresponding control commands for vehicles of different roles based on the load avoidance responsibility allocation scheme. The deceleration and yielding commands of the active avoidance vehicle include the target deceleration amplitude, deceleration starting distance and complete stop position coordinates. The deceleration amplitude is determined according to the vehicle load level and braking performance. The deceleration amplitude of unloaded vehicles is set to 70% of the current speed, and that of lightly loaded vehicles is set to 60%. The normal passage instructions for vehicles with priority include maintaining the current speed, continuing to drive along the established path and accelerating and recovering after passing the intersection. The load-differentiated avoidance strategy arranges all control commands according to the execution sequence to form a complete multi-vehicle collaborative avoidance control sequence.

[0068] The relevance of data processing is reflected in the control process of a heavy-loaded container truck and an empty container truck intersecting in a narrow channel at the port. The heavy-loaded vehicle's load level is coded as 4, and its difficulty coefficient in a narrow channel with a width of 3.5 meters is 0.66. The empty vehicle's load level is coded as 1, and its difficulty coefficient in the same channel is 0.31. The difference in the difficulty coefficients of the two vehicles is 0.35, exceeding the 0.3 threshold and being marked as high conflict. The heavy-loaded vehicle is given a priority value of 8, and the empty vehicle is given a priority value of 2. Based on the priority difference, the heavy-loaded vehicle is set as the party with priority to pass, and the empty vehicle is set as the party that actively avoids. The empty vehicle receives a deceleration and yield instruction, reducing its current speed from 20 kilometers per hour to 14 kilometers per hour, with the deceleration starting distance set at 50 meters from the intersection. The heavy-loaded vehicle receives a normal passage instruction and maintains a speed of 18 kilometers per hour through the intersection. This precise control allocation based on load difference solves the technical problems of low efficiency and decision-making conflicts in multi-vehicle coordinated control in existing technologies, and establishes a direct correlation between load and avoidance responsibility.

[0069] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] The safety distance benchmark is set based on the load priority ranking table in the load-differentiated avoidance strategy. A 15-meter benchmark safety distance is set for unloaded vehicles, a 20-meter benchmark safety distance is set for light-loaded vehicles, a 25-meter benchmark safety distance is set for medium-loaded vehicles, and a 30-meter benchmark safety distance is set for heavy-loaded vehicles. The load-level benchmark safety distance value is obtained.

[0071] The distance correction calculation is performed based on the load level benchmark safety distance value combined with the current vehicle speed. The vehicle speed and the benchmark safety distance are multiplied by the speed coefficient to obtain the load speed correction safety distance value;

[0072] Based on the load speed correction safety distance value and the port environmental conditions, environmental correction is performed, adding a 20% safety margin for rainy conditions and a 50% safety margin for foggy conditions to obtain the load environment correction safety distance value;

[0073] The load environment corrected safety distance value is formulated according to the load grade classification standard, and the corrected safety distance of vehicles with different load grades is standardized to obtain the load grade safety distance.

[0074] Specifically, the load-grading distance algorithm sets a safety distance benchmark based on the load priority ranking table in the load-differentiated avoidance strategy. The benchmark setting process determines different safety distance values according to the degree of influence of the load grade on the vehicle's braking performance. An empty vehicle is set with a 15-meter benchmark safety distance due to its light weight and short braking distance. A light-loaded vehicle is set with a 20-meter benchmark safety distance as the braking distance is extended due to the increase in load. A medium-loaded vehicle is set with a 25-meter benchmark safety distance as the braking performance further deteriorates. A heavy-loaded vehicle is set with a 30-meter benchmark safety distance as the braking distance is the longest and turning is the most difficult. The load-grade benchmark safety distance values are stored in array form, with the array index corresponding to the load grade code and the array element value corresponding to the corresponding benchmark safety distance value. The benchmark safety distance reflects the basic safety interval requirements for container trucks of different load grades in a port environment. The distance correction calculation is dynamically adjusted based on the load grade benchmark safety distance value combined with the vehicle's current speed. The correction calculation quantifies the impact of speed on the safety distance through speed coefficient multiplication. The speed coefficient calculation divides the vehicle's current speed by the standard reference speed of 20 kilometers per hour to obtain the speed ratio, and then multiplies the speed ratio by the benchmark safety distance to obtain the load-speed corrected safety distance value. When a heavy-loaded vehicle travels at 24 kilometers per hour, the speed coefficient is 24 divided by 20, which equals 1.2. The corrected safety distance is 30 meters multiplied by 1.2, which equals 36 meters. The speed correction mechanism takes into account the direct impact of vehicle speed on braking distance. The higher the speed, the greater the safety distance required.

[0075] Environmental correction is further adjusted based on the load-speed-corrected safety distance value and the special environmental conditions of the port. Port environmental conditions include influencing factors such as weather conditions, visibility and road conditions. The environmental correction processing incrementally calculates the corrected safety distance through a preset safety margin percentage. In rainy conditions, the safety distance will be increased by 20% due to the slippery road surface and reduced braking effect. In foggy conditions, the safety distance will be increased by 50% due to reduced visibility and prolonged reaction time. The environmental correction calculation multiplies the load-speed-corrected safety distance value by the corresponding environmental correction coefficient to obtain the load-environment-corrected safety distance value. The environmental-corrected safety distance for heavy-loaded vehicles under rainy conditions is 36 meters multiplied by 1.2, which is equal to 43.2 meters, and under foggy conditions, it is 36 meters multiplied by 1.5, which is equal to 54 meters. The environmental correction takes into account the impact of complex meteorological conditions in the port on the driving safety of container trucks. The classification standard is formulated to classify and organize the load environment corrected safety distance values according to load grade. The standardization process realizes the unified management of the safety distance of vehicles of different load grades by establishing a correspondence table between load grade and corrected safety distance. The load classification safety distance data structure is stored in a two-dimensional matrix. The row index corresponds to the load grade, the column index corresponds to the environmental condition type, and the matrix element value is the standard safety distance value of the corresponding load grade under specific environmental conditions.

[0076] The relevance of data processing is reflected in the process of determining the safe distance of medium-load container trucks in rainy weather conditions at the port. The load grade code of the medium-load vehicle is 3, which corresponds to a 25-meter baseline safety distance. When the vehicle is traveling at a speed of 18 kilometers per hour, the speed coefficient is 18 divided by 20, which is equal to 0.9. The speed-corrected safety distance is 25 meters multiplied by 0.9, which is equal to 22.5 meters. The rainy weather conditions require an additional 20% safety margin, and the environmental correction coefficient is 1.2. The final load-environment-corrected safety distance is 22.5 meters multiplied by 1.2, which is equal to 27 meters. This value is used as the load-environment-corrected safety distance of the medium-load vehicle under rainy conditions. The re-graded safety distances are stored in a safety distance matrix. When multiple vehicles of different load levels in the port need to coordinate avoidance, the corresponding safety distance standard values are directly queried from the load-graded safety distance matrix. Medium-loaded vehicles maintain a safety distance of 27 meters from other vehicles, heavy-loaded vehicles maintain a larger safety distance under the same conditions, and unloaded vehicles maintain a relatively smaller safety distance. This graded safety distance standard based on load differences solves the technical problem in the existing technology that the unified safety distance setting cannot adapt to load differences, and establishes a precise correlation between load, speed, environmental conditions and safety distance.

[0077] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0078] Based on the load-classified safety distance, low-load vehicles that need to be actively avoided are identified, and the low-priority vehicles in the load priority sorting table are screened for avoidance to obtain a list of vehicles that need to be actively avoided;

[0079] The avoidance distance is calculated based on the active avoidance vehicle list and the load classification safety distance. The distance difference between the current position of the low-load vehicle and the position of the high-load vehicle is calculated to obtain the load avoidance distance gap value;

[0080] Formulate a speed adjustment strategy based on the load avoidance distance gap value, calculate the deceleration amplitude for low-load vehicles, match the avoidance distance gap with the vehicle's braking performance to obtain the load-compensated deceleration control parameters;

[0081] The load-compensated deceleration control parameters are converted into vehicle execution instructions, and control signals are generated and processed for the throttle and braking systems of the low-load vehicle to obtain a collaborative avoidance control strategy based on load differences.

[0082] Specifically, the load compensation control algorithm identifies low-load vehicles that need to be actively avoided based on the load classification safety distance. The identification process screens and judges by comparing the load classification safety distance data with the current vehicle distance. When the actual vehicle distance is less than the load classification safety distance standard value, the active avoidance requirement is triggered. The avoidance vehicle screening process extracts vehicles with lower priority values from the load priority sorting table as candidate avoidance objects. The priority value 2 of an empty vehicle and the priority value 4 of a light-loaded vehicle are both lower than the priority value 6 of a medium-loaded vehicle and the priority value 8 of a heavy-loaded vehicle. The screening algorithm marks vehicles with a priority value less than 6 as low-priority vehicles. The active avoidance vehicle list contains basic information such as the identification code, current location coordinates, load level and driving speed of these low-priority vehicles. The list data is stored in the form of a structured array for subsequent processing and call. The avoidance distance calculation is based on an accurate distance difference analysis of the active avoidance vehicle list and the load-graded safety distance. The calculation process first obtains the current position coordinates of the low-load vehicle and the position coordinates of the high-load vehicle, and calculates the actual distance between the two vehicles through the coordinate difference. Then, the standard safety distance corresponding to the load level and environmental conditions is queried from the load-graded safety distance matrix. The load avoidance distance gap value is obtained by subtracting the actual distance from the standard safety distance. When the actual distance between the unloaded vehicle and the heavy-loaded vehicle is 25 meters and the standard safety distance requirement is 43 meters, the avoidance distance gap is 43 minus 25, which is equal to 18 meters. This value indicates the additional safety interval distance required for the unloaded vehicle.

[0083] The speed regulation strategy is formulated to calculate the deceleration amplitude for low-load vehicles based on the load avoidance distance gap value. The deceleration amplitude calculation takes into account the comprehensive relationship between the vehicle's current speed, braking performance and gap distance. The deceleration parameter matching processing compares and analyzes the avoidance distance gap with the vehicle's braking distance standard value. When the gap distance is greater than half of the vehicle's braking distance standard value, a large deceleration is required. When the gap distance is less than a quarter of the braking distance standard value, a small deceleration is required. The standard braking distance for an unloaded vehicle is 12 meters. When the avoidance distance gap is 18 meters, it exceeds half of the braking distance and a large deceleration is required. The deceleration amplitude is set to 40% of the current speed. The load-compensated deceleration control parameters include specific values such as the target deceleration ratio, deceleration start time and deceleration duration. These parameters are set differently according to the braking characteristics of vehicles with different load levels. The control signal generation and processing converts the load-compensated deceleration control parameters into an instruction format recognizable by the on-board execution system. The throttle control signal reduces speed by adjusting the throttle opening percentage. The brake control signal achieves precise braking by setting the brake pressure value. When an unloaded vehicle needs to decelerate by 40%, the throttle opening is reduced from the current 70% to 42%, and the brake pressure is increased from 0 to 0.2 MPa. The control signal also includes execution timing arrangements and safety monitoring parameters to ensure that the deceleration process is smooth and controllable.

[0084] The relevance of data processing is reflected in the complete control process of the coordinated avoidance of light-loaded container trucks and heavy-loaded container trucks. The load level code of the light-loaded vehicle is 2, corresponding to the priority value 4, and the load level code of the heavy-loaded vehicle is 4, corresponding to the priority value 8. When the two vehicles meet in a narrow channel at the port, the light-loaded vehicle is identified as a low-loaded vehicle that needs to be actively avoided and is included in the list of active avoidance vehicles. The current position of the light-loaded vehicle is 28 meters away from the heavy-loaded vehicle. According to the load-graded safety distance matrix query, it is found that the light-loaded vehicle needs to maintain a safety distance of 32 meters from the heavy-loaded vehicle under the current environmental conditions, and the avoidance distance gap The calculation is 32 minus 28, which equals 4 meters. The gap distance is less than one-fourth of the standard braking distance of 15 meters for light-loaded vehicles, and a slight deceleration is required. The deceleration range is set to 15% of the current speed. The light-loaded vehicle decelerates from 20 kilometers per hour to 17 kilometers per hour. The control signal adjusts the throttle opening from 60% to 51%, and the brake pressure is maintained at a light braking state of 0.1 MPa. Through this precise control parameter adjustment based on load differences, light-loaded vehicles actively make enough safety space for heavy-loaded vehicles, solving the technical problem of the lack of consideration of load differences in the coordinated control of multiple vehicles in the existing technology.

[0085] The above describes the safety control method of the unmanned container truck based on the port in the embodiment of the present application. The following describes the safety control system of the unmanned container truck based on the port in the embodiment of the present application. Figure 2In the embodiment of the present application, an embodiment of the port-based unmanned container truck safety control system includes:

[0086] Establish a module to obtain real-time load information of container trucks through the load data of port lifting equipment, establish a load-brake mapping table based on the impact of load on braking distance and turning radius, and obtain load-level braking parameters;

[0087] A matching module is used to match and analyze the load-grade braking parameters with the width of the narrow channel of the port, calculate the difficulty coefficient of heavy-loaded vehicles passing through channels of different widths through a load channel adaptation algorithm, and obtain a load channel restriction matrix;

[0088] an allocation module for identifying load conflict scenarios when multiple vehicles converge based on the load channel restriction matrix, differentially allocating avoidance responsibilities between heavily loaded vehicles and unloaded vehicles, and obtaining a load-differentiated avoidance strategy;

[0089] A setting module is used to convert the load-differentiated avoidance strategy into a safety distance classification standard, set exclusive safety intervals for vehicles of different load levels through a load-classified distance algorithm, and obtain a load-classified safety distance;

[0090] The adjustment module is used to guide the low-load vehicle to perform active avoidance maneuvers according to the load-graded safety distance, adjust the vehicle speed and path through the load compensation control algorithm, and obtain a collaborative avoidance control strategy based on load differences.

[0091] above Figure 2 The port-based unmanned container truck safety control system in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The port-based unmanned container truck safety control device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0092] Reference Figure 3 In an embodiment of the present invention, a safety control device for an unmanned container truck based on a port is also provided. The safety control device for an unmanned container truck based on a port can be a server, and its internal structure can be as follows: Figure 3As shown. The port-based unmanned container truck safety control device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The computer-designed processor is used to provide computing and control capabilities. The memory of the port-based unmanned container truck safety control device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the port-based unmanned container truck safety control device is used to store the corresponding data in this embodiment. The network interface of the port-based unmanned container truck safety control device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0093] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the port-based unmanned container truck safety control device to which the solution of the present invention is applied.

[0094] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the port-based unmanned container truck safety control method.

[0095] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or 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 for causing a port-based unmanned container truck safety control device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A safety control method for unmanned container trucks based on ports, characterized in that: The method comprises: The real-time load information of container trucks is obtained through the load data of port lifting equipment. A load-braking mapping table is established based on the influence of load on braking distance and turning radius to obtain load-level braking parameters. The load-grade braking parameters are matched and analyzed with the width of the narrow channel of the port, and the difficulty coefficient of heavy-loaded vehicles passing through channels of different widths is calculated through the load channel adaptation algorithm to obtain the load channel restriction matrix; Identifying load conflict scenarios when multiple vehicles converge based on the load channel restriction matrix, differentially allocating avoidance responsibilities between heavily loaded vehicles and unloaded vehicles, and obtaining load-differentiated avoidance strategies; Convert the load-differentiated avoidance strategy into a safety distance classification standard, set exclusive safety intervals for vehicles of different load levels through a load-classified distance algorithm, and obtain a load-classified safety distance; The low-load vehicle is guided to perform active avoidance maneuvers according to the load-graded safety distance, and the vehicle speed and path are adjusted through a load compensation control algorithm to obtain a collaborative avoidance control strategy based on load differences.

2. The port-based unmanned container truck safety control method according to claim 1 is characterized in that: The real-time load information of container trucks is obtained through the load data of port hoisting equipment, and a load-braking mapping table is established according to the influence of load on braking distance and turning radius to obtain load-level braking parameters, including: The container weight values are collected by the load sensors of the quay cranes and yard cranes, and the container weight values are divided into load intervals to obtain load classification data of 0-5 tons empty, 5-15 tons light load, 15-25 tons medium load, and 25-35 tons heavy load; Based on the load classification data, a braking distance test is performed, and the measured data of the braking distances of vehicles of different load levels at the same initial speed are collected and processed to obtain a basic value of the braking distance for each load level; Conducting a turning radius test based on the basic braking distance value, collecting and processing measured data on the turning radius of vehicles of different load levels at the same steering angle, and obtaining a basic turning radius value for each load level; The load influence coefficient is calculated based on the basic value of the braking distance and the basic value of the turning radius, and the proportional coefficient is extracted from the numerical relationship between the load, the braking distance, and the turning radius to obtain the load-braking influence coefficient and the load-steering influence coefficient; A load-braking mapping table is constructed based on the load-braking influence coefficient and the load-steering influence coefficient, and the load grade and braking performance parameters are associated and assigned to obtain load grade braking parameters including standard values of braking distance and steering radius.

3. The port-based unmanned container truck safety control method according to claim 1 is characterized in that: The load level braking parameters are matched and analyzed with the width of the narrow channel of the port, and the difficulty coefficient of heavy-loaded vehicles passing through channels of different widths is calculated through the load channel adaptation algorithm to obtain the load channel restriction matrix, including: The actual width values of different channels are collected through port road surveying data, and the actual width values are processed into channel classification data to obtain channel width classification data of main roads over 6 meters, branch roads of 4-6 meters, and narrow channels of 3-4 meters; Based on the standard value of the turning radius in the load-grade braking parameter and the channel width classification data, width adaptability is judged, and the turning radius and channel width of the load-grade vehicle are numerically compared to obtain a channel adaptability judgment result of the load-grade vehicle; The difficulty coefficient of passage is calculated based on the results of the suitability judgment of the load vehicle channel, and the standard value of the braking distance in the load level braking parameter and the channel length data are used to calculate the safe passage distance to obtain the value of the difficulty coefficient of passage of different widths for each load level; The values of the difficulty coefficients are arranged in a matrix according to the load level and channel width, and the difficulty coefficients of the load level and channel width are constructed into a two-dimensional array to obtain a load channel restriction matrix with load levels as rows and channel widths as columns.

4. The port-based unmanned container truck safety control method according to claim 3 is characterized in that: The calculation of the passage difficulty coefficient is performed based on the result of the load vehicle channel adaptability judgment, and the standard value of the braking distance in the load level braking parameter and the channel length data are used to calculate the safe passage distance to obtain the passage difficulty coefficient values of each load level in channels of different widths, including: Based on the results of the heavy vehicle channel adaptability judgment, risk levels are divided, and the ratio of turning radius to channel width is graded to obtain three levels of heavy vehicle passage risk: high, medium and low; The braking distance safety factor is set according to the load passage risk level, and the standard braking distance values of different risk levels are multiplied by the coefficient to obtain the load level safety braking distance value; Based on the load level safe braking distance value and the channel length, the adequacy verification is carried out, and the braking distance to channel length ratio is evaluated and processed to obtain the load channel length adaptability rating; Calculating the complexity of the steering operation according to the load channel length adaptability rating, performing numerical quantification processing on the steering difficulty of different ratings, and obtaining a load steering operation complexity value; Convert the load-carrying risk level into a risk coefficient, and assign numerical values to the high, medium, and low risk levels to obtain a load-carrying risk coefficient value; A weighted calculation is performed based on the load-carrying passage risk coefficient value and the load-carrying steering operation complexity value, and the two values are summed up according to the weight ratio to obtain the passage difficulty coefficient value.

5. The port-based unmanned container truck safety control method according to claim 1 is characterized in that: The method of identifying a load conflict scenario when multiple vehicles intersect based on the load channel restriction matrix, differentially allocating avoidance responsibilities to heavy-loaded vehicles and unloaded vehicles, and obtaining a load-differentiated avoidance strategy includes: The vehicle load information of the multi-vehicle intersection area is obtained through the vehicle position monitoring system, and the traffic difficulty coefficient in the load channel restriction matrix is matched with the real-time vehicle load level to obtain the intersection vehicle load conflict identification result; Based on the load conflict identification results of the intersecting vehicles, load priority is sorted, high priority values are assigned to heavy-loaded vehicles, and low priority values are assigned to unloaded vehicles, so as to obtain a load priority sorting table; Formulate an avoidance responsibility allocation principle based on the load priority ranking table, set high-priority load vehicles as priority passing parties, and set low-priority load vehicles as active avoidance parties to obtain a load avoidance responsibility allocation plan; Based on the load avoidance responsibility allocation scheme, differentiated avoidance instructions are generated, slowdown and yield instructions are issued to vehicles that actively avoid, and normal passage instructions are issued to vehicles that have priority, thereby obtaining a load-differentiated avoidance strategy.

6. The port-based unmanned container truck safety control method according to claim 1 is characterized in that: The load-differentiated avoidance strategy is converted into a safety distance classification standard, and a load-classified distance algorithm is used to set exclusive safety intervals for vehicles of different load classes to obtain a load-classified safety distance, including: The safety distance benchmark is set based on the load priority ranking table in the load-differentiated avoidance strategy, with a 15-meter benchmark safety distance set for an unloaded vehicle, a 20-meter benchmark safety distance set for a light-loaded vehicle, a 25-meter benchmark safety distance set for a medium-loaded vehicle, and a 30-meter benchmark safety distance set for a heavy-loaded vehicle, to obtain a load-level benchmark safety distance value; A distance correction calculation is performed based on the load level reference safety distance value combined with the current vehicle speed, and the vehicle speed and the reference safety distance are multiplied by a speed coefficient to obtain a load speed correction safety distance value; An environmental correction is performed based on the load speed correction safety distance value and the port environmental conditions, with a 20% safety margin added for rainy conditions and a 50% safety margin added for foggy conditions, to obtain a load environment correction safety distance value; The load environment corrected safety distance value is formulated according to the load grade classification standard, and the corrected safety distances of vehicles of different load grades are standardized to obtain the load graded safety distance.

7. The port-based unmanned container truck safety control method according to claim 1 is characterized in that: The method of guiding the low-load vehicle to perform active avoidance maneuvers based on the load-graded safety distance and adjusting the vehicle speed and path through a load compensation control algorithm to obtain a collaborative avoidance control strategy based on load differences includes: Based on the load-classified safety distance, low-load vehicles that need to be actively avoided are identified, and low-priority vehicles in the load priority ranking table are screened to obtain an active avoidance vehicle list; The avoidance distance is calculated based on the active avoidance vehicle list and the load classification safety distance, and the distance difference between the current position of the low-load vehicle and the position of the high-load vehicle is calculated to obtain the load avoidance distance gap value; Formulate a speed adjustment strategy based on the load avoidance distance gap value, calculate the deceleration amplitude for the low-load vehicle, match the avoidance distance gap with the vehicle braking performance to obtain the load compensation deceleration control parameters; The load-compensated deceleration control parameters are converted into vehicle execution instructions, and control signal generation processing is performed on the throttle and brake systems of the low-load vehicle to obtain a collaborative avoidance control strategy based on load differences.

8. A port-based unmanned container truck safety control system, characterized in that: The method for controlling a port-based unmanned container truck safety according to any one of claims 1 to 7 is used to implement the method, wherein the port-based unmanned container truck safety control system comprises: Establish a module to obtain real-time load information of container trucks through the load data of port lifting equipment, establish a load-brake mapping table based on the impact of load on braking distance and turning radius, and obtain load-level braking parameters; A matching module is used to match and analyze the load-grade braking parameters with the width of the narrow channel of the port, calculate the difficulty coefficient of heavy-loaded vehicles passing through channels of different widths through a load channel adaptation algorithm, and obtain a load channel restriction matrix; an allocation module for identifying load conflict scenarios when multiple vehicles converge based on the load channel restriction matrix, differentially allocating avoidance responsibilities between heavily loaded vehicles and unloaded vehicles, and obtaining a load-differentiated avoidance strategy; A setting module is used to convert the load-differentiated avoidance strategy into a safety distance classification standard, set exclusive safety intervals for vehicles of different load levels through a load-classified distance algorithm, and obtain a load-classified safety distance; The adjustment module is used to guide the low-load vehicle to perform active avoidance maneuvers according to the load-graded safety distance, adjust the vehicle speed and path through the load compensation control algorithm, and obtain a collaborative avoidance control strategy based on load differences.

9. A port-based unmanned container truck safety control device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the port-based unmanned container truck safety control method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is enabled to execute the port-based unmanned container truck safety control method according to any one of claims 1 to 7.

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