Unmanned container truck safety control method and system based on port

Abstract

The invention relates to the technical field of traffic control, and discloses an unmanned container truck safety control method and system based on a port. The method comprises the steps that load information is obtained through port hoisting equipment, a load braking mapping table is established, a passage difficulty coefficient is calculated in combination with channel width, a load channel limiting matrix is formed, load conflict scenes are recognized, differential avoidance responsibility distribution is carried out, a load grading safety distance is set, and a low-load vehicle is guided to execute active avoidance operation. And the vehicle speed and path are adjusted to realize load differentiation collaborative avoidance control. The technical problem that differential control and accurate cooperative avoidance cannot be performed according to the load difference in the existing port unmanned container truck safety control technology is solved.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, and particularly to a safety control method and system for driverless container trucks based on ports. Background Art

[0002] Existing safety control technologies for driverless container trucks in ports mainly adopt a unified standard vehicle control method. Vehicle position and environmental information are obtained through sensors such as GPS positioning, lidar, and cameras, and multi-vehicle coordinated control is carried out by combining preset safety distances and avoidance rules. The traditional control system is based on an avoidance strategy of first-come-first-served or random allocation, using the same braking distance standard and steering parameter settings for all vehicles, and centrally managing vehicle path planning and traffic coordination through a central dispatching system. The control algorithms in the existing technologies mainly focus on basic parameters such as vehicle position, speed, and driving direction, and make avoidance decisions through simple distance judgment and time priority.

[0003] However, the existing technologies have significant deficiencies and cannot effectively handle the actual situation with significant load differences in port container transportation. The unified safety control standard ignores the huge differences in braking distance, turning radius, and maneuverability between empty and fully loaded vehicles, resulting in frequent braking difficulties and steering limitations for fully loaded vehicles in narrow channels. The existing avoidance strategies lack consideration of the load factor, and situations often occur where fully loaded vehicles are forced to brake urgently or empty vehicles wait in vain, seriously affecting the port operation efficiency. The traditional control method cannot set different safety distances and allocate priorities according to the actual load status of container trucks, and is prone to decision-making conflicts and coordination failures in multi-vehicle intersection scenarios.

[0004] Due to the limitations of the above existing technologies, the safety control of driverless container trucks in ports faces deeper technical challenges. The existing technologies lack an accurate mapping mechanism between the load status and vehicle dynamics performance, and cannot establish a quantitative relationship between the load difference and channel adaptability, resulting in the inability to achieve intelligent collaborative control based on load characteristics in the complex port traffic environment. The existing multi-vehicle avoidance algorithms lack the ability to dynamically allocate load priorities and cannot reasonably allocate avoidance responsibilities according to load differences. Especially when fully loaded vehicles and empty vehicles pass through a narrow area simultaneously, there is a lack of an effective load compensation control mechanism to achieve precise collaborative avoidance operations. Summary of the Invention

[0005] This application provides a safety control method and system for driverless container trucks based on ports, which is used to solve the technical problem that the existing safety control technologies for driverless container trucks in ports cannot perform differential control and precise collaborative avoidance according to load differences.

[0006] In a first aspect, the present application provides a safety control method for driverless container trucks based on a port. The safety control method for driverless container trucks based on a port includes: obtaining real-time load information of the container truck through the load data of the port hoisting equipment, establishing a load-braking mapping table based on the influence relationship between the load and the braking distance and steering radius, and obtaining load-level braking parameters; performing a matching analysis on the load-level braking parameters and the width of the narrow channels in the port, calculating the passing difficulty coefficients of heavy-duty vehicles in channels with different widths through a load-channel adaptation algorithm, and obtaining a load-channel restriction matrix; identifying load conflict scenarios during multi-vehicle intersections according to the load-channel restriction matrix, and differentially allocating the avoidance responsibilities for heavy-duty vehicles and empty vehicles to obtain a load-differentiated avoidance strategy; converting the load-differentiated avoidance strategy into a safety distance grading standard, setting exclusive safety intervals for vehicles with different load levels through a load-grading distance algorithm, and obtaining a load-grading safety distance; guiding low-load vehicles to perform active avoidance operations according to the load-grading 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.

[0007] In a second aspect, the present application provides a safety control system for driverless container trucks based on a port. The safety control system for driverless container trucks based on a port includes: a building module for obtaining real-time load information of the container truck through the load data of the port hoisting equipment, establishing a load-braking mapping table based on the influence relationship between the load and the braking distance and steering radius, and obtaining load-level braking parameters; a matching module for performing a matching analysis on the load-level braking parameters and the width of the narrow channels in the port, calculating the passing difficulty coefficients of heavy-duty vehicles in channels with different widths through a load-channel adaptation algorithm, and obtaining a load-channel restriction matrix; an allocation module for identifying load conflict scenarios during multi-vehicle intersections according to the load-channel restriction matrix, and differentially allocating the avoidance responsibilities for heavy-duty vehicles and empty vehicles to obtain a load-differentiated avoidance strategy; a setting module for converting the load-differentiated avoidance strategy into a safety distance grading standard, setting exclusive safety intervals for vehicles with different load levels through a load-grading distance algorithm, and obtaining a load-grading safety distance; an adjustment module for guiding low-load vehicles to perform active avoidance operations according to the load-grading 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.

[0008] In a third aspect, a safety control device for driverless container trucks based on a port is provided, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to enable the safety control device for driverless container trucks based on the port to execute the above-mentioned safety control method for driverless container trucks based on the port.

[0009] In a fourth aspect, a computer-readable storage medium is provided, in which instructions are stored, and when it runs on a computer, it enables the computer to execute the above-mentioned safety control method for driverless container trucks based on the port.

[0010] In the technical solution provided by this application, by establishing a load braking mapping table and a load channel restriction matrix, the accurate quantitative correlation between the load difference of port container trucks and vehicle dynamics performance is realized for the first time, solving the fundamental problem that the unified control standard in the prior art cannot adapt to the load change. The load channel adaptation algorithm can accurately calculate the passing difficulty coefficient of vehicles with different load levels in various width channels, providing a scientific basis for the safe passing of heavy-load vehicles in narrow channels and avoiding the safety hazards caused by limited steering of heavy-load vehicles in the traditional method. The load-differentiated avoidance strategy establishes an intelligent coordination mechanism based on load characteristics through a load priority ranking table, enabling heavy-load vehicles to obtain the right of way first and empty-load vehicles to take the initiative to avoid, fundamentally changing the control logic of multi-vehicle coordination in the port. The load-level distance algorithm sets exclusive safety intervals according to different load levels, realizing the dynamic adjustment of safety distances, significantly reducing the potential collision risks caused by insufficient safety margins, and at the same time avoiding the loss of passing efficiency caused by overly conservative safety settings.

[0011] The application of the load compensation control algorithm in the field of port container transportation fully reflects the key contribution of algorithm characteristics to the overall solution. Through the calculation of the load avoidance distance gap and the matching process of deceleration parameters, the algorithm realizes the accurate avoidance control of low-load vehicles, making the avoidance operation smoother and more predictable. The algorithm takes into account the actual impact of load differences on braking performance and formulates differentiated deceleration strategies for empty-load and light-load vehicles to ensure both safety requirements and operational fluency during the avoidance process. The load channel adaptation algorithm provides quantitative decision support for vehicle passing in narrow port channels through risk level division and passing difficulty coefficient calculation. The algorithm characteristics enable the system to identify high-risk passing scenarios in advance and take corresponding preventive measures. The load-level distance algorithm combines speed correction and environment correction mechanisms to realize the dynamic adjustment of safety distances considering multiple factors. The adaptive characteristics of the algorithm make the safety control more accurate and intelligent, especially providing stable and reliable safety protection in the complex and changeable operating environment of the port. Description of the Drawings

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings; Figure 1 It is a schematic diagram of an embodiment of the safety control method for driverless container trucks based on ports in the embodiments of the present application; Figure 2 It is a schematic diagram of an embodiment of the safety control system for driverless container trucks based on ports in the embodiments of the present application; Figure 3 It is a structural schematic block diagram of the safety control device for driverless container trucks based on ports in the embodiments of the present invention. Specific embodiments

[0013] The embodiments of the present application provide a safety control method and system for driverless container trucks based on ports. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0014] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the safety control method for driverless container trucks based on ports in the embodiments of the present application includes: Step S101: Obtain the real-time load information of the container truck through the load data of the port lifting equipment, establish a load-braking mapping table according to the influence relationship between the load and the braking distance and steering radius, and obtain the load-level braking parameters; Step S102: Match and analyze the load-level braking parameters with the width of the narrow channels in the port, and calculate the passing difficulty coefficient of heavy-duty vehicles in channels of different widths through the load-channel adaptation algorithm to obtain the load-channel restriction matrix; Step S103: Identify the load conflict scenarios during multi-vehicle intersection according to the load channel restriction matrix, and differentially allocate the avoidance responsibilities for heavy-load vehicles and empty-load vehicles to obtain a load-differentiated avoidance strategy; Step S104: Convert the load-differentiated avoidance strategy into a safety distance grading standard, and set exclusive safety intervals for vehicles with different load levels through the load grading distance algorithm to obtain the load-graded safety distance; Step S105: Guide the low-load vehicles to perform active avoidance operations according to the load-graded safety distance, and adjust the vehicle speed and path through the load compensation control algorithm to obtain a collaborative avoidance control strategy based on load differences.

[0015] It can be understood that the execution entity of this application can be a port-based unmanned container truck safety control system, or a terminal or a server. Specifically, it is not limited here. This application embodiment is described by taking the server as the execution entity as an example.

[0016] Specifically, the container weight value is obtained in real time through the data interfaces with the quay crane and the yard crane. The load classification data is divided and processed into four intervals: 0-5 tons empty load, 5-15 tons light load, 15-25 tons medium load, and 25-35 tons heavy load. The braking distance test is conducted by actually measuring different load-level vehicles at an initial speed of 30 kilometers per hour in the port test area. The braking distance of the empty-load vehicle is 12 meters, the light-load vehicle is 15 meters, the medium-load vehicle is 18 meters, and the heavy-load vehicle 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 turning angle. The turning radius of the empty-load vehicle is 4.5 meters, the light-load vehicle is 5.2 meters, the medium-load vehicle is 5.8 meters, and the heavy-load vehicle is 6.5 meters. The load influence coefficient is calculated by extracting the proportionality coefficient from the numerical relationship between the load weight and the braking distance and the turning radius. The braking distance of the heavy-load vehicle increases by 83% compared with the empty-load vehicle, and the turning radius increases by 44%. The load braking influence coefficient and the load turning influence coefficient are set to 1.83 and 1.44 respectively. The load braking mapping table associates and assigns the load level with the braking performance parameters to form a load-level braking parameter data structure including the standard values of the braking distance and the turning radius.

[0017] The load-carrying channel adaptation algorithm classifies the channel width based on the port road survey data, taking the main roads over 6 meters, branch roads of 4-6 meters, and narrow channels of 3-4 meters as the channel width classification data. The width adaptability judgment numerically compares the turning radius of the load-carrying grade vehicle with the channel width. The turning radius of 6.5 meters for heavy-duty vehicles cannot complete the turning operation in a narrow channel of 3-4 meters, and the turning radius of 5.8 meters for medium-duty vehicles can barely pass through a 4-meter-wide channel but there is a risk. The calculation of the passage difficulty coefficient calculates the safe passage distance by comparing the standard value of the braking distance with the channel length data. For heavy-duty vehicles in a narrow channel with a length of 80 meters, a braking distance of 22 meters plus a turning reserved space of 6.5 meters is required, and the actual safe passage distance requirement is 28.5 meters, accounting for 35.6% of the channel length. The load-carrying channel restriction matrix is constructed as a two-dimensional array with the load-carrying grade as the row and the channel width as the column. The passage difficulty coefficient of heavy-duty vehicles in narrow channels is 0.9, that of medium-duty vehicles is 0.6, that of light-duty vehicles is 0.3, and that of empty vehicles is 0.1.

[0018] The load conflict identification obtains the load information of each vehicle in the intersection area through the vehicle position monitoring system, and matches and queries the passage difficulty coefficient in the load-carrying channel restriction matrix with the real-time vehicle load-carrying grade. When a heavy-duty vehicle and an empty vehicle meet in a narrow channel, the passage difficulty coefficient of the heavy-duty vehicle is 0.9, which is much higher than that of the empty vehicle at 0.1. The load priority sorting assigns a high priority value of 8 to the heavy-duty vehicle and a low priority value of 2 to the empty vehicle. The load avoidance responsibility assignment sets the high-priority load-carrying vehicle as the priority passing party and the low-priority load-carrying vehicle as the active avoidance party. The differential avoidance instruction formulates a deceleration and yielding instruction for the active avoidance party and a normal passage instruction for the priority passing party.

[0019] The load classification distance algorithm sets the benchmark safety distances for different load levels based on the load priority sorting table, which are 15 meters for unloaded vehicles, 20 meters for lightly loaded vehicles, 25 meters for medium-loaded vehicles, and 30 meters for heavily loaded vehicles. The speed correction calculation performs a speed coefficient multiplication operation on the current driving speed of the vehicle and the benchmark safety distance. When the vehicle speed is 20 kilometers per hour, the speed coefficient is 1.2, and the corrected safety distance for heavily loaded vehicles is 36 meters. The environmental correction is adjusted for special weather conditions in the port. Under rainy conditions, a 20% safety margin is added, and the final safety distance for heavily loaded vehicles is 43.2 meters. Under foggy conditions, a 50% safety margin is added, and the final safety distance is 54 meters. The load compensation control algorithm identifies low-load vehicles that need to actively avoid. Unloaded vehicles are marked as actively avoiding vehicles when intersecting with heavily loaded vehicles. The avoidance distance calculation calculates the distance difference between the current position of the low-load vehicle and the position of the high-load vehicle. The current spacing is 25 meters, and the safety distance requirement for heavily loaded vehicles is 43.2 meters, so the load avoidance distance gap is 18.2 meters. The deceleration parameter matching calculates the avoidance distance gap and the braking performance of the vehicle. Unloaded vehicles need to decelerate to 15 kilometers per hour based on the existing speed. The load compensation deceleration control parameters include a 40% reduction in the throttle opening and an increase in the braking pressure to 0.3 megapascals, and the load-differentiated cooperative avoidance control is achieved through in-vehicle actuators.

[0020] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Collect the container weight values through the load sensors of the quay crane and the yard crane, perform load interval division processing on the container weight values, and obtain load classification data of 0-5 tons unloaded, 5-15 tons lightly loaded, 15-25 tons medium-loaded, and 25-35 tons heavily loaded; Based on the load classification data, conduct braking distance tests, collect and process the measured data of the braking distances of vehicles with different load levels at the same initial speed, and obtain the basic braking distance values for each load level; According to the basic braking distance values, conduct turning radius tests, collect and process the measured data of the turning radii of vehicles with different load levels at the same turning angle, and obtain the basic turning radius values for each load level; Calculate the load influence coefficients for the basic braking distance values and the basic turning radius values, extract the proportionality coefficients for the numerical relationships between the load weight and the braking distance and the turning radius, and obtain the load braking influence coefficient and the load turning influence coefficient; Based on the load braking influence coefficient and the load turning influence coefficient, construct a load braking mapping table, perform correlation assignment processing on the load level and the braking performance parameters, and obtain the load level braking parameters including the standard braking distance value and the standard turning radius value.

[0021] 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 processing to finally obtain an accurate weight value. The load range division process is based on the actual load distribution characteristics of port container transportation. The obtained weight values are classified and judged according to a preset threshold. When the weight value is in the range of 0 - 5 tons, it is marked as unloaded; 5 - 15 tons is marked as lightly loaded; 15 - 25 tons is marked as moderately loaded; 25 - 35 tons is marked as heavily loaded. The load classification data is stored in the form of digital codes. The unloaded code is 1, the lightly loaded code is 2, the moderately loaded code is 3, and the heavily loaded code is 4. The data acquisition and processing of the braking distance test is carried out by setting up a distance sensor array in a dedicated port test area. When the driverless container truck starts braking from a fixed starting point at a uniform initial speed, the distance sensors record the vehicle position changes in real time. The braking start moment is recorded as zero point, and the distance difference between the position where the vehicle completely stops at the end of braking and the zero point is the basic braking distance value. The basic braking distance values of vehicles with different load levels are obtained by taking the average of multiple repeated tests. The basic braking distance value of an unloaded vehicle is 12 meters, 15 meters for lightly loaded, 18 meters for moderately loaded, and 22 meters for heavily loaded.

[0022] The turning radius test is based on the acquisition of the basic braking distance value. A standardized turning test path is set up in the port test area. The data processing of the basic turning radius value is to collect the position coordinate changes during the vehicle turning process in real time through a gyroscope and GPS positioning device installed on the vehicle chassis. When the vehicle turns at a fixed turning angle, the GPS coordinate data forms a turning trajectory curve. The trajectory curve is fitted into an arc through geometric calculation methods, and the arc radius is the basic turning radius value. The basic turning radius value of an unloaded vehicle is 4.5 meters, 5.2 meters for lightly loaded, 5.8 meters for moderately loaded, and 6.5 meters for heavily loaded. The data processing process of calculating the load influence coefficient takes the basic braking distance value and the basic turning radius value as input parameters, and extracts the influence degree of load on braking performance and turning performance through numerical relationship analysis. The load braking influence coefficient is calculated by dividing the braking distance of a heavily loaded vehicle by the braking distance of an unloaded vehicle, that is, 22 meters divided by 12 meters equals 1.83. The load turning influence coefficient is calculated by dividing the turning radius of a heavily loaded vehicle by the turning radius of an unloaded vehicle, that is, 6.5 meters divided by 4.5 meters equals 1.44.

[0023] The construction process of the load braking mapping table is based on the load braking influence coefficient and the load steering influence coefficient. By means of associated assignment processing, a mapping relationship is established between the load level and the corresponding braking performance parameters. The mapping table is stored in a two-dimensional array structure. The row index corresponds to the load level code, and the column index corresponds to the type of braking performance parameters. The standard values of braking distance and turning radius are used as the core data elements of the mapping table. The standard value of the braking distance of an empty vehicle is set to 12 meters, and the standard value of the turning radius is set to 4.5 meters. The standard value of the braking distance of a lightly loaded vehicle is calculated by multiplying the basic value of the empty vehicle by the load correction coefficient to be 15 meters, and the standard value of the turning radius is 5.2 meters. The standard values of medium-loaded and heavy-loaded vehicles are calculated and stored in the load braking mapping table in the same way. The data structure of the mapping table supports fast query and update operations. When the braking parameters of the load level need to be called, the standard values of the braking distance and the turning radius corresponding to the load level code are directly accessed as the index. The braking parameters of the load level are used as the basic input data for the subsequent load channel adaptation algorithm and the load classification distance algorithm, solving the technical problem in the prior art that differential control cannot be performed according to the load difference of container trucks.

[0024] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Collect the actual width values of different channels through port road mapping data, perform channel classification processing on the actual width values, and obtain the 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 braking parameters of the load level and the channel width classification data, perform width adaptability judgment, compare the turning radius of the load level vehicle with the channel width, and obtain the judgment result of the load vehicle channel adaptability. Calculate the passing difficulty coefficient according to the judgment result of the load vehicle channel adaptability, and perform safety passing distance calculation processing on the standard value of the braking distance in the braking parameters of the load level and the channel length data, and obtain the passing difficulty coefficient values of each load level in channels with different widths. Arrange the passing difficulty coefficient values in a matrix according to the load level and the channel width, and perform two-dimensional array construction processing on the passing difficulty coefficient of the load level and the channel width to obtain a load channel restriction matrix with the load level as the row and the channel width as the column.

[0025] Specifically, in the process of collecting port road surveying and mapping data, the internal roads of the port are scanned section by section through a laser ranging device. The laser beam emitter emits laser pulses to both sides of the road, and the receiver records the reflected laser signals. The distance to the road boundary is obtained by calculating the product of the round-trip time of the laser and the speed of light. The difference between the two boundary distances is the actual width value of the road. The channel classification process classifies and judges the collected actual width values according to a preset threshold. When the width value is greater than 6 meters, it is classified as a main road; when the width value is in the range of 4 - 6 meters, it is classified as a branch road; when the width value is in the range of 3 - 4 meters, it is classified as a narrow channel. The channel width classification data is stored in encoded form. The encoding for the main road is A, the encoding for the branch road is B, and the encoding for the narrow channel is C. Each channel section is also accompanied by the corresponding specific width value and geographical coordinate information. The width adaptability judgment is based on the comparison of the standard value of the turning radius in the braking parameters of the load level with the channel width classification data. In the judgment process, the standard value of the turning radius corresponding to the load level is first extracted, and then a division operation is performed 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 determined that the adaptability is good; when the ratio is between 0.5 - 0.7, it is determined that the adaptability is average; when the ratio is greater than 0.7, it is determined that the adaptability is difficult. The judgment result of the load vehicle channel adaptability is stored in encoded form. The encoding for good adaptability is 1, the encoding for average adaptability is 2, and the encoding for difficult adaptability is 3.

[0026] The calculation of the traffic difficulty coefficient is based on the judgment result of the load vehicle channel adaptability. The standard value of the braking distance in the braking parameters of the load level is used to calculate the safe traffic distance with the channel length data. In the calculation process, the standard value of the braking distance and the standard value of the turning radius are first added to obtain the basic safe distance requirement, and then the basic safe distance requirement is corrected according to the adaptability judgment result. In the case of difficult adaptability, the basic safe distance requirement is multiplied by a safety factor of 1.5; in the case of average adaptability, it is multiplied by a safety factor of 1.2; in the case of good adaptability, the basic safe distance requirement remains unchanged. The corrected safe distance requirement and the channel length data are used for a division operation to obtain the distance occupancy ratio. The value of the distance occupancy ratio is directly used as the value of the traffic difficulty coefficient. The larger the value, the higher the traffic difficulty. In the process of constructing the load channel restriction matrix, the values of the traffic difficulty coefficient are arranged in a two-dimensional array according to the load level and the channel width. The row index of the matrix corresponds to the load level encoding, and the column index corresponds to the channel width classification encoding. The matrix element value is the value of the traffic difficulty coefficient for the corresponding load level in a channel with a specific width. The array construction process traverses all combinations of load levels and channel widths through nested loops, and fills the calculated values of the traffic difficulty coefficient into the corresponding positions in the matrix to form a complete data structure of the load channel restriction matrix.

[0027] The data processing logic of the load-carrying channel adaptation algorithm is reflected in the precise quantitative analysis of the load difference. When making an adaptability judgment for a heavy-duty vehicle with a standard turning radius of 6.5 meters 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, far greater than the adaptation difficulty threshold of 0.7. The judgment result is difficult adaptation. When calculating the safety distance requirement, the standard braking distance of 22 meters of the heavy-duty vehicle is added to the standard turning radius of 6.5 meters to obtain a basic safety distance requirement of 28.5 meters. Since the adaptation is difficult, it needs to be multiplied by a safety factor of 1.5, and the corrected safety distance requirement is 42.75 meters. When the length of the narrow channel 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-carrying channel limit matrix as the passage difficulty coefficient of the heavy-duty vehicle in the narrow channel. For an empty vehicle with a standard turning radius of 4.5 meters, the ratio in the same channel is 4.5 divided by 3.5, which is equal to 1.29, also belonging to difficult adaptation. However, its standard braking distance is only 12 meters, the basic safety distance requirement is 16.5 meters, and after correction, it is 24.75 meters, and the passage difficulty coefficient is 0.31, significantly lower than that of the heavy-duty vehicle. This differential quantification result of the passage difficulty solves the technical problem in the prior art that cannot be controlled according to the load difference.

[0028] In a specific embodiment, the process of performing the passage difficulty coefficient calculation according to the load vehicle channel adaptability judgment result may specifically include the following steps: Based on the load vehicle channel adaptability judgment result, perform risk level division, perform grading processing on the ratio of the turning radius to the channel width, and obtain high, medium, and low load passage risk levels; Set a braking distance safety factor according to the load passage risk level, perform coefficient multiplication processing on the standard braking distance values of different risk levels, and obtain the load level safety braking distance value; Based on the load level safety braking distance value and the channel length, perform sufficiency verification, evaluate the ratio of the braking distance to the channel length, and obtain the load channel length adaptability rating; Calculate the steering operation complexity according to the load channel length adaptability rating, perform numerical quantification processing on the steering difficulty of different ratings, and obtain the load steering operation complexity value; Convert the load passage risk level into a risk coefficient, perform numerical assignment processing on the high, medium, and low risk levels, and obtain the load passage risk coefficient value; Based on the load passage risk coefficient value and the load steering operation complexity value, perform weighted calculation, perform summation processing on the two values according to the weight ratio, and obtain the passage difficulty coefficient value.

[0029] Specifically, the load-carrying passage risk level classification is based on the ratio of the turning radius to the passage width in the judgment result of the load-carrying vehicle passage adaptability. The classification process classifies the ratio data into three levels according to the preset threshold. When the ratio is greater than 0.7, it is classified as a high-risk level; when the ratio is between 0.5 and 0.7, it is classified as a medium-risk level; when the ratio is less than 0.5, it is classified as a low-risk level. The load-carrying passage risk level is stored in digital coding. The high-risk code is 3, the medium-risk code is 2, and the low-risk code is 1. The logic of the classification process is based on the consideration of the safety margin of the turning operation of port container trucks in narrow passages. The larger the ratio, the more tense the turning space, and the corresponding risk level increases. The safety factor of the braking distance is set by multiplying the standard value of the braking distance by the corresponding safety factor according to the load-carrying passage risk level. The safety factor for the high-risk level is set to 1.5, the safety factor for the medium-risk level is set to 1.2, and the safety factor for the low-risk level is set to 1.0. The load-level safety braking distance value is calculated by multiplying the standard value of the braking distance by the corresponding safety factor. The safety braking distance value of a heavy-duty vehicle at the high-risk level is 22 meters multiplied by 1.5, which is equal to 33 meters. The safety braking distance value of a medium-duty vehicle at the 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 different requirements for the braking safety margin at different risk levels.

[0030] The adequacy verification of the load-carrying passage length adaptability is based on the load-level safety braking distance value and the passage length. The evaluation process conducts quantitative analysis by calculating the proportional relationship between the braking distance and the passage length. When the ratio is greater than 0.6, the rating is severely insufficient; when the ratio is between 0.4 and 0.6, the rating is generally insufficient; when the ratio is between 0.2 and 0.4, the rating is basically sufficient; when the ratio is less than 0.2, the rating is completely sufficient. The load-carrying passage length adaptability rating is stored in letter coding. The severely insufficient code is D, the generally insufficient code is C, the basically sufficient code is B, and the completely sufficient code is A. The rating result reflects the adequacy of the space for a container truck to complete a safe braking within a specific passage length. The numerical calculation of the load-carrying turning operation complexity quantifies the turning difficulty according to the load-carrying passage length adaptability rating. The turning operation complexity value for the severely insufficient rating is set to 0.8, the generally insufficient rating is set to 0.6, the basically sufficient rating is set to 0.4, and the completely sufficient rating is set to 0.2. The numerical quantification process is based on the analysis of the operation difficulty and time cost of port container trucks when turning in passages of different lengths. The less sufficient the passage length, the more precise and complex the turning operation the driver needs to perform, and the corresponding complexity value increases.

[0031] The numerical conversion of the load passage risk coefficient assigns numerical values to the load passage risk levels. The risk coefficient value for the high-risk level is assigned as 0.7, the medium-risk level is assigned as 0.5, and the low-risk level is assigned as 0.3. The numerical assignment is based on the analysis of the influence weights of different risk levels on the overall passage safety in the port environment. The higher the risk level, the greater the corresponding risk coefficient value, which occupies a larger proportion in the subsequent weighted calculation. The weighted calculation of the passage difficulty coefficient value is processed by summing according to the weight ratio of the load passage risk coefficient value and the load steering operation complexity value. The weighted calculation adopts a weight distribution of 6 to 4, that is, the risk coefficient value is multiplied by 0.6 plus the steering operation complexity value is multiplied by 0.4. The weight distribution considers the relative influence degrees of the channel adaptation risk and operation complexity on the overall passage difficulty during the passage of port container trucks. The channel adaptation risk, as the main factor, occupies a higher weight.

[0032] The relevance in the data processing process is reflected in the calculation of the passage difficulty of heavy-duty vehicles in a 3.8-meter-wide channel. The ratio of the standard turning radius value of 6.5 meters of heavy-duty vehicles to the channel width of 3.8 meters is 1.71, which exceeds the 0.7 threshold and is classified as the high-risk level. The safety braking distance after a 1.5-fold increase in the safety factor is 33 meters. When the channel length is 60 meters, the braking distance ratio is 33 divided by 60, which equals 0.55, belonging to the general insufficient rating. The steering operation complexity value is 0.6, and the risk coefficient value converted from the high-risk level is 0.7. The weighted calculation result is 0.7 multiplied by 0.6 plus 0.6 multiplied by 0.4, which equals 0.66. This value is used as the final passage difficulty coefficient of heavy-duty vehicles in this specific width channel. The closer the value is to 1, the greater the passage difficulty.

[0033] In a specific embodiment, the process of executing step S103 may specifically include the following steps: Obtain the vehicle load information in the multi-vehicle intersection area through the vehicle position monitoring system, and perform a matching query process on the passage difficulty coefficient in the load passage restriction matrix and the real-time vehicle load level to obtain the intersection vehicle load conflict recognition result; Based on the intersection vehicle load conflict recognition result, conduct a load priority ranking, assign a high-priority value to heavy-duty vehicles, and assign a low-priority value to empty-load vehicles to obtain a load priority ranking table; According to the load priority ranking table, formulate an avoidance responsibility allocation principle, set the high-priority load vehicles as the priority passage parties, and set the low-priority load vehicles as the active avoidance parties to obtain a load avoidance responsibility allocation plan; Generate a differential avoidance instruction based on the load avoidance responsibility allocation plan, formulate a deceleration and yielding instruction for the active avoidance party vehicles, and formulate a normal passage instruction for the priority passage party vehicles to obtain a load differential avoidance strategy.

[0034] Specifically, the vehicle position monitoring system obtains real-time position data of the multi-vehicle intersection area through the GPS positioning network and lidar sensors deployed within the port. The position data includes vehicle coordinates, moving direction, and speed information. At the same time, it obtains the load information of the corresponding vehicle through the data interface with the port lifting equipment. The load information is associated and bound with the vehicle identification code to form comprehensive vehicle status data including position and load. The matching query process correlates the passage difficulty coefficient stored in the load channel restriction matrix with the real-time vehicle load level. In the query process, first, the load level codes of each vehicle in the intersection area are extracted. Then, using the load level code as the row index and the intersection area channel width classification code as the column index, the corresponding passage difficulty coefficient value is retrieved in the load channel restriction matrix. The query result generates an identification result of load conflicts among the intersecting vehicles, which includes the load level, passage difficulty coefficient, and conflict severity identification of each vehicle. When the difference in the passage difficulty coefficients of multiple vehicles with different load levels exceeds 0.3, it is identified as a high conflict; when the difference is between 0.1 - 0.3, it is identified as a medium conflict; and when the difference is less than 0.1, it is identified as a low conflict. The load priority sorting performs numerical assignment processing based on the load level information in the identification result of load conflicts among the intersecting vehicles. Heavy-load vehicles are assigned a priority value of 8, medium-load vehicles are assigned a priority value of 6, light-load vehicles are assigned a priority value of 4, and empty-load vehicles are assigned a priority value of 2. The larger the priority value, the higher the passage priority in the intersection conflict. The load priority sorting table is arranged in descending order of the priority value to form an ordered vehicle priority sequence.

[0035] The load avoidance responsibility assignment principle divides the intersecting vehicles into two categories: the priority passage party and the active avoidance party according to the load priority sorting table. The assignment process determines the responsibility attribution by comparing the priority values of each vehicle. The vehicle with a higher priority value is set as the priority passage 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 sequence of arriving at the intersection point. The load avoidance responsibility assignment plan is stored with vehicle role codes. The code for the priority passage party is P, and the code for the active avoidance party is A. The assignment plan also includes the specific avoidance action requirements and execution timing arrangements for each vehicle. The differential avoidance instruction generation formulates corresponding control instructions for vehicles with different roles based on the load avoidance responsibility assignment plan. The deceleration and yielding instruction for the active avoidance party vehicle includes the target deceleration amplitude, deceleration start distance, and full stop position coordinates. The deceleration amplitude is determined according to the vehicle load level and braking performance. The deceleration amplitude for the empty-load vehicle is set to 70% of the current speed, and for the light-load vehicle is set to 60%. The normal passage instruction for the priority passage party vehicle includes maintaining the current speed, continuing to drive along the established path, and the acceleration recovery instruction after passing through the intersection point. The load differential avoidance strategy arranges all control instructions according to the execution timing to form a complete multi-vehicle cooperative avoidance control sequence.

[0036] The relevance of data processing is reflected in the control process of the intersection of fully-loaded container trucks and empty container trucks in the narrow channels of the port. The load level code of the fully-loaded vehicle is 4, and the traffic difficulty coefficient in a narrow channel with a width of 3.5 meters is 0.66. The load level code of the empty vehicle is 1, and the traffic difficulty coefficient in the same channel is 0.31. The difference in traffic difficulty coefficients between the two vehicles is 0.35, which exceeds the 0.3 threshold and is identified as a high conflict. The fully-loaded vehicle obtains a priority value of 8, and the empty vehicle obtains a priority value of 2. According to the priority difference, the fully-loaded vehicle is set as the priority passing party, and the empty vehicle is set as the active avoidance party. The empty vehicle receives a deceleration and yielding instruction to reduce the current speed from 20 kilometers per hour to 14 kilometers per hour. The deceleration start distance is set at 50 meters from the intersection point. The fully-loaded vehicle receives a normal passing instruction and passes through the intersection point at a speed of 18 kilometers per hour. This precise control allocation based on load differences solves the technical problems of low efficiency in multi-vehicle coordinated control and decision-making conflicts in the prior art, and establishes a direct correlation between load and avoidance responsibility.

[0037] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Based on the load priority ranking table in the load-differentiated avoidance strategy, set the safety distance benchmark. Set a 15-meter benchmark safety distance for empty vehicles, a 20-meter benchmark safety distance for lightly-loaded vehicles, a 25-meter benchmark safety distance for medium-loaded vehicles, and a 30-meter benchmark safety distance for fully-loaded vehicles, and process to obtain the load level benchmark safety distance value; According to the load level benchmark safety distance value, combine with the current driving speed of the vehicle to perform distance correction calculation, and perform a speed coefficient multiplication operation on the vehicle speed and the benchmark safety distance to obtain the load speed correction safety distance value; Based on the load speed correction safety distance value and the port environmental conditions, perform environmental correction. Increase a 20% safety margin for rainy conditions and a 50% safety margin for foggy conditions, and process to obtain the load environmental correction safety distance value; Formulate a grading standard for the load environmental correction safety distance value according to the load level, and perform a standardized value determination process on the corrected safety distances of vehicles with different load levels to obtain the load-graded safety distance.

[0038] Specifically, the load classification distance algorithm sets the safety distance benchmark based on the load priority ranking table in the load differential avoidance strategy. In the benchmark setting process, different safety distance values are determined according to the influence degree of the load level on the vehicle braking performance. For an empty vehicle, due to its light weight and short braking distance, a benchmark safety distance of 15 meters is set. For a lightly loaded vehicle, the increased load leads to an extended braking distance, so a benchmark safety distance of 20 meters is set. For a medium-loaded vehicle, the braking performance further deteriorates, and a benchmark safety distance of 25 meters is set. For a heavily loaded vehicle, the braking distance is the longest and the steering is the most difficult, so a benchmark safety distance of 30 meters is set. The benchmark safety distance values for load levels are stored in the form of an array. The array index corresponds to the load level code, and the array element value is the corresponding benchmark safety distance value. The benchmark safety distance reflects the basic safety interval requirements of container trucks with different load levels in the port environment. The distance correction calculation is dynamically adjusted based on the benchmark safety distance value for the load level in combination with the current driving speed of the vehicle. The correction calculation realizes the quantification of the influence of speed on the safety distance through the multiplication operation of the speed coefficient. The speed coefficient calculation divides the current speed of the vehicle 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 heavily loaded vehicle is traveling at a speed of 24 kilometers per hour, the speed coefficient is 24 divided by 20, which is equal to 1.2, and the corrected safety distance is 30 meters multiplied by 1.2, which is equal to 36 meters. The speed correction mechanism takes into account the direct influence of the vehicle speed on the braking distance. The higher the speed, the greater the required safety distance.

[0039] The environmental correction is further adjusted based on the load-speed corrected safety distance value and the special environmental conditions of the port. The port environmental conditions include influencing factors such as weather conditions, visibility, and road surface conditions. The environmental correction process performs an incremental calculation on the corrected safety distance through a preset safety margin percentage. Under rainy conditions, due to the slippery road surface and reduced braking effect, the safety distance is increased by 20%. Under foggy conditions, due to reduced visibility and extended reaction time, the safety distance is increased by 50%. 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 correction safety distance for a heavily loaded vehicle 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 formulation classifies and organizes the load-environment corrected safety distance values according to the load level. The standardization process realizes the unified management of the safety distances of vehicles with different load levels by establishing a correspondence table between the load level and the corrected safety distance. The load classification safety distance data structure is stored in a two-dimensional matrix. The row index corresponds to the load level, the column index corresponds to the type of environmental condition, and the matrix element value is the standard safety distance value for the corresponding load level under specific environmental conditions.

[0040] The relevance of data processing is reflected in the process of determining the safety distance of medium-loaded container trucks in rainy weather at the port. The load level code of medium-loaded vehicles is 3, corresponding to a benchmark safety distance of 25 meters. When the vehicle is traveling at a speed of 18 kilometers per hour, the speed coefficient is 18 divided by 20, which is 0.9. After speed correction, the safety distance is 25 meters multiplied by 0.9, which is 22.5 meters. The rainy weather conditions require an additional 20% safety margin, and the environmental correction coefficient is 1.2. Finally, the load and environment corrected safety distance is 22.5 meters multiplied by 1.2, which is 27 meters. This value is stored in the safety distance matrix as the load classification safety distance of medium-loaded vehicles in rainy conditions. When multiple vehicles with different load levels in the port need to cooperate in avoidance, the corresponding safety distance standard value is directly queried from the load classification safety distance matrix. Medium-loaded vehicles maintain a 27-meter safety interval from other vehicles, heavy-loaded vehicles maintain a larger safety interval under the same conditions, and empty-loaded vehicles maintain a relatively smaller safety interval. This classification safety distance standard based on load differences solves the technical problem that the unified safety distance setting in the prior art cannot adapt to load differences, and establishes an accurate correlation relationship between load, speed, environmental conditions, and safety distance.

[0041] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Based on the load classification safety distance, identify low-load vehicles that need to actively avoid, and perform screening processing on the low-priority vehicles in the load priority ranking table to obtain a list of actively avoiding vehicles; According to the list of actively avoiding vehicles and the load classification safety distance, calculate the avoidance distance. Calculate the distance difference between the current position of the low-load vehicle and the position of the high-load vehicle to obtain the load avoidance distance gap value; Based on the load avoidance distance gap value, formulate a speed adjustment strategy, calculate the deceleration amplitude of the low-load vehicle, and match the avoidance distance gap with the vehicle braking performance to obtain the load compensation deceleration control parameter; Convert the load compensation deceleration control parameter into a vehicle execution instruction, and generate a control signal for the throttle and braking systems of the low-load vehicle to obtain a cooperative avoidance control strategy based on load differences.

[0042] 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 spacing. When the actual vehicle spacing is less than the standard value of the load classification safety distance, an active avoidance requirement is triggered. The avoidance vehicle screening process extracts vehicles with lower priority values from the load priority ranking table as candidate avoidance objects. The priority value of the empty-load vehicle is 2 and the priority value of the light-load vehicle is 4, both of which are lower than the priority value of the medium-load vehicle (6) and the priority value of the heavy-load vehicle (8). The screening algorithm marks vehicles with priority values less than 6 as low-priority vehicles. The active avoidance vehicle list contains basic information such as the identification codes, current position coordinates, load levels, and driving speeds of these low-priority vehicles. The list data is stored in the form of a structured array for convenient subsequent processing and calling. The avoidance distance calculation performs precise distance difference analysis based on the active avoidance vehicle list and the load classification 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, calculates the actual distance between the two vehicles through coordinate difference calculation, and then queries the standard safety distance corresponding to the load level and environmental conditions from the load classification 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 empty-load vehicle and the heavy-load vehicle is 25 meters and the standard safety distance requirement is 43 meters, the avoidance distance gap is 43 minus 25, which equals 18 meters. This value represents the additional safety interval distance that the empty-load vehicle needs to increase.

[0043] The speed adjustment strategy formulation calculates the deceleration amplitude for the low-load vehicle based on the load avoidance distance gap value. The deceleration amplitude calculation considers the comprehensive relationship among the vehicle's current speed, braking performance, and gap distance. The deceleration parameter matching process compares and analyzes the avoidance distance gap with the standard value of the vehicle's braking distance. When the gap distance is greater than half of the standard value of the vehicle's braking distance, significant deceleration is required. When the gap distance is less than one-fourth of the standard value of the braking distance, minor deceleration is performed. The standard value of the braking distance for the empty-load vehicle is 12 meters. When the avoidance distance gap is 18 meters, which exceeds half of the braking distance, significant deceleration is required, and the deceleration amplitude is set to 40% of the current speed. The load compensation deceleration control parameters include specific values such as the target deceleration ratio, deceleration start time, and deceleration duration. These parameters are differentially set according to the braking characteristics of vehicles with different load levels. The control signal generation process converts the load compensation deceleration control parameters into an instruction format recognizable by the vehicle-mounted execution system. The throttle control signal reduces the speed by adjusting the throttle opening percentage, and the brake control signal achieves precise braking by setting the brake pressure value. When the empty-load vehicle needs to decelerate by 40%, the throttle opening decreases from the current 70% to 42%, and at the same time, the brake pressure increases from 0 to 0.2 MPa. The control signal also includes the execution timing arrangement and safety monitoring parameters to ensure a smooth and controllable deceleration process.

[0044] The relevance of data processing is reflected in the complete control process of the coordinated avoidance of light-load container trucks and heavy-load container trucks. The load level code of the light-load vehicle is 2, corresponding to a priority value of 4, and the load level code of the heavy-load vehicle is 4, corresponding to a priority value of 8. When the two vehicles meet in a narrow channel of the port, the light-load vehicle is identified as a low-load vehicle that needs to actively avoid and is included in the list of actively avoiding vehicles. The current position of the light-load vehicle is 28 meters away from the heavy-load vehicle. According to the load classification safety distance matrix, it is found that the light-load vehicle needs to maintain a safety distance of 32 meters from the heavy-load vehicle under the current environmental conditions. The avoidance distance gap is calculated as 32 minus 28, which is equal to 4 meters. Since this gap distance is less than one-fourth of the braking distance standard value of 15 meters for the light-load vehicle, a small deceleration is required. The deceleration amplitude is set to 15% of the current speed. The light-load vehicle decelerates from 20 kilometers per hour to 17 kilometers per hour, and the control signal adjusts the throttle opening from 60% to 51%. The braking pressure is maintained at 0.1 MPa in a slight braking state. Through this precise control parameter adjustment based on load differences, the light-load vehicle actively creates enough safety space for the heavy-load vehicle, solving the technical problem in the prior art that the coordinated control of multiple vehicles lacks consideration of load differences.

[0045] The above describes the method for safe control of driverless container trucks based on ports in the embodiments of the present application. Next, the safe control system of driverless container trucks based on ports in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the safe control system of driverless container trucks based on ports in the embodiments of the present application includes: A establishing module, configured to obtain the real-time load information of the container truck through the load data of the port hoisting equipment, and establish a load braking mapping table according to the influence relationship between the load and the braking distance and turning radius, so as to obtain the load level braking parameters; A matching module, configured to perform matching analysis on the load level braking parameters and the width of the narrow channel in the port, and calculate the passing difficulty coefficient of the heavy-load vehicle in channels with different widths through a load-channel adaptation algorithm, so as to obtain a load-channel restriction matrix; An allocation module, configured to identify the load conflict scenarios when multiple vehicles meet according to the load-channel restriction matrix, and perform differential allocation of the avoidance responsibilities for the heavy-load vehicle and the empty-load vehicle, so as to obtain a load-differentiated avoidance strategy; A setting module, configured to convert the load-differentiated avoidance strategy into a safety distance classification standard, and set exclusive safety intervals for vehicles with different load levels through a load classification distance algorithm, so as to obtain a load classification safety distance; An adjustment module, configured to guide the low-load vehicle to perform an active avoidance operation according to the load classification safety distance, and adjust the vehicle speed and path through a load compensation control algorithm, so as to obtain a coordinated avoidance control strategy based on load differences.

[0046] The aboveFigure 2 The safety control system of the driverless container truck based on the port in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the safety control device of the driverless container truck based on the port in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0047] Referring to Figure 3 , in the embodiment of the present invention, a safety control device for a driverless container truck based on a port is further provided. The safety control device for the driverless container truck based on the port may be a server, and its internal structure may be as Figure 3 shown. The safety control device for the driverless container truck based on the port includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the safety control device for the driverless container truck based on the port 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 the computer program in the non-volatile storage medium. The database of the safety control device for the driverless container truck based on the port is used to store the corresponding data in this embodiment. The network interface of the safety control device for the driverless container truck based on the port is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0048] Those skilled in the art can understand that Figure 3 the structure shown in

[0049] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the safety control device for the driverless container truck based on the port to which the solution of the present invention is applied.

[0050] Those skilled in the art can 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 foregoing method embodiments, and will not be repeated here.

[0051] When the integrated unit is implemented in the form of 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, in essence, or the part that contributes to the prior art, or all or part of this 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 may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0052] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A safety control method for driverless container trucks based on ports, characterized in that, The method includes: Obtaining the real-time load information of container trucks through the load data of port lifting equipment, establishing a load-braking mapping table based on the influence relationship between the load weight and the braking distance and turning radius, and obtaining the braking parameters for different load levels; Performing a matching analysis on the braking parameters for different load levels and the width of narrow channels in the port, calculating the passing difficulty coefficients of heavy-duty vehicles in channels with different widths through a load-channel adaptation algorithm, and obtaining a load-channel restriction matrix; Identifying the load conflict scenarios during multi-vehicle intersections based on the load-channel restriction matrix, and differentially allocating the avoidance responsibilities for heavy-duty vehicles and empty-load vehicles to obtain a load-differentiated avoidance strategy; Converting the load-differentiated avoidance strategy into a safety distance classification standard, and setting exclusive safety intervals for vehicles with different load levels through a load-classified distance algorithm to obtain a load-classified safety distance; Guiding low-load vehicles to perform active avoidance operations according to the load-classified 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.

2. The safety control method for driverless container trucks based on ports according to claim 1, wherein, The step of obtaining the real-time load information of container trucks through the load data of port lifting equipment, establishing a load-braking mapping table based on the influence relationship between the load weight and the braking distance and turning radius, and obtaining the braking parameters for different load levels includes: Collecting the container weight values through the load sensors of quay cranes and yard cranes, performing load interval division processing on the container weight values, and obtaining load classification data of 0-5 tons for empty load, 5-15 tons for light load, 15-25 tons for medium load, and 25-35 tons for heavy load; Based on the load classification data, performing braking distance tests, collecting and processing the measured data of the braking distances of vehicles with different load levels at the same initial speed, and obtaining the basic braking distance values for each load level; According to the basic braking distance values, performing turning radius tests, collecting and processing the measured data of the turning radii of vehicles with different load levels at the same turning angle, and obtaining the basic turning radius values for each load level; Calculating the load influence coefficients for the basic braking distance values and the basic turning radius values, and extracting the proportionality coefficients for the numerical relationships between the load weight and the braking distance and the turning radius to obtain the load-braking influence coefficient and the load-turning influence coefficient; Constructing a load-braking mapping table based on the load-braking influence coefficient and the load-turning influence coefficient, and performing an associated assignment process on the load level and the braking performance parameters to obtain the braking parameters for different load levels including the standard braking distance value and the standard turning radius value.

3. The safety control method for driverless container trucks based on ports according to claim 1, wherein, The step of performing a matching analysis on the braking parameters for different load levels and the width of narrow channels in the port, calculating the passing difficulty coefficients of heavy-duty vehicles in channels with different widths through a load-channel adaptation algorithm, and obtaining a load-channel restriction matrix includes: Collecting the actual width values of different channels through port road surveying and mapping data, performing channel classification processing on the actual width values, and obtaining channel width classification data of main roads with a width of more than 6 meters, branch roads with a width of 4-6 meters, and narrow channels with a width of 3-4 meters; Based on the standard value of the turning radius and the channel width classification data in the load rating braking parameters, a width adaptability judgment is made, and a numerical comparison process is carried out on the turning radius and the channel width of the load rating vehicle to obtain the judgment result of the load vehicle channel adaptability; According to the judgment result of the load vehicle channel adaptability, a passing difficulty coefficient is calculated. The standard value of the braking distance in the load rating braking parameters and the channel length data are processed for calculating the safe passing distance to obtain the passing difficulty coefficient values of each load rating in channels with different widths; The passing difficulty coefficient values are arranged in a matrix according to the load rating and the channel width, and a two-dimensional array construction process is carried out on the passing difficulty coefficients of the load rating and the channel width to obtain a load channel limit matrix with the load rating as the row and the channel width as the column.

4. The safety control method for driverless container trucks based on ports according to claim 3, characterized in that, The calculation of the passing difficulty coefficient according to the judgment result of the load vehicle channel adaptability, and the processing of calculating the safe passing distance by using the standard value of the braking distance in the load rating braking parameters and the channel length data to obtain the passing difficulty coefficient values of each load rating in channels with different widths includes: Based on the judgment result of the load vehicle channel adaptability, a risk level is divided, and a grading process is carried out on the ratio of the turning radius to the channel width to obtain high, medium, and low load passing risk levels; According to the load passing risk level, a braking distance safety factor is set, and a coefficient multiplication process is carried out on the standard values of the braking distances of different risk levels to obtain the load rating safety braking distance values; Based on the verification of the sufficiency of the load rating safety braking distance value and the channel length, an evaluation process is carried out on the ratio of the braking distance to the channel length to obtain the load channel length adaptability rating; According to the load channel length adaptability rating, the turning operation complexity is calculated, and a numerical quantification process is carried out on the turning difficulty of different ratings to obtain the load turning operation complexity value; The load passing risk level is converted into a risk coefficient, and a numerical assignment process is carried out on the high, medium, and low risk levels to obtain the load passing risk coefficient value; Based on the load passing risk coefficient value and the load turning operation complexity value, a weighted calculation is carried out, and the two values are summed according to the weight ratio to obtain the passing difficulty coefficient value.

5. The safety control method for driverless container trucks based on ports according to claim 1, wherein, The identification of the load conflict scenario during multi-vehicle intersection according to the load channel limit matrix, and the differential assignment of the avoidance responsibilities for heavy-load vehicles and empty-load vehicles to obtain the load differential avoidance strategy includes: The vehicle load information in the multi-vehicle intersection area is obtained through the vehicle position monitoring system, and the passing difficulty coefficient in the load channel limit matrix is matched and queried with the real-time vehicle load rating to obtain the identification result of the load conflict of the intersecting vehicles; Based on the identification result of the load conflict of the intersecting vehicles, a load priority ranking is carried out. A high priority value is assigned to the heavy-load vehicle, and a low priority value is assigned to the empty-load vehicle to obtain a load priority ranking table; According to the load priority ranking table, an avoidance responsibility assignment principle is formulated. The high-priority load vehicle is set as the priority passing party, and the low-priority load vehicle is set as the active avoidance party to obtain a load avoidance responsibility assignment plan; Generate differential avoidance instructions based on the load-bearing avoidance responsibility allocation scheme, formulate a deceleration and yielding instruction for the vehicle of the active avoidance party, and formulate a normal passing instruction for the vehicle of the priority passing party, so as to obtain a load-bearing differential avoidance strategy.

6. The safety control method for driverless container trucks based on ports according to claim 1, wherein Convert the load-bearing differential avoidance strategy into a safety distance grading standard, and set exclusive safety intervals for vehicles with different load-bearing levels through the load-bearing grading distance algorithm to obtain the load-bearing graded safety distance, including: Set the safety distance benchmark based on the load-bearing priority ranking table in the load-bearing differential avoidance strategy. Set a benchmark safety distance of 15 meters for empty-load vehicles, a benchmark safety distance of 20 meters for light-load vehicles, a benchmark safety distance of 25 meters for medium-load vehicles, and a benchmark safety distance of 30 meters for heavy-load vehicles, so as to obtain the numerical value of the load-bearing level benchmark safety distance; Perform distance correction calculation according to the numerical value of the load-bearing level benchmark safety distance combined with the current driving speed of the vehicle, and perform speed coefficient multiplication operation on the vehicle speed and the benchmark safety distance to obtain the numerical value of the load-bearing speed correction safety distance; Perform environmental correction based on the numerical value of the load-bearing speed correction safety distance and the port environmental conditions. Increase the safety margin by 20% for rainy days and increase the safety margin by 50% for foggy days, so as to obtain the numerical value of the load-bearing environmental correction safety distance; Formulate a grading standard for the numerical value of the load-bearing environmental correction safety distance according to the load-bearing level, and perform standardization numerical determination on the corrected safety distance of vehicles with different load-bearing levels to obtain the load-bearing graded safety distance.

7. The safety control method for driverless container trucks based on ports according to claim 1, characterized in that, According to the load-bearing graded safety distance, guide low-load vehicles to perform active avoidance operations, and adjust the vehicle speed and path through the load-bearing compensation control algorithm to obtain a collaborative avoidance control strategy based on load-bearing differences, including: Identify low-load vehicles that need to actively avoid based on the load-bearing graded safety distance, and perform screening of avoidance vehicles on the low-priority vehicles in the load-bearing priority ranking table to obtain a list of active avoidance vehicles; Calculate the avoidance distance according to the list of active avoidance vehicles and the load-bearing graded safety distance, and calculate the distance difference between the current position of the low-load vehicle and the position of the high-load vehicle to obtain the numerical value of the load-bearing avoidance distance gap; Formulate a speed adjustment strategy based on the numerical value of the load-bearing avoidance distance gap, calculate the deceleration amplitude of the low-load vehicle, and perform deceleration parameter matching on the avoidance distance gap and the vehicle braking performance to obtain the load-bearing compensation deceleration control parameter; Convert the load-bearing compensation deceleration control parameter into a vehicle execution instruction, and generate control signals for the throttle and braking systems of the low-load vehicle to obtain a collaborative avoidance control strategy based on load-bearing differences.

8. A safety control system for driverless container trucks based on a port, characterized in that, For implementing the safety control method of an unmanned container truck based on a port as described in any one of claims 1-7, the safety control system of the unmanned container truck based on a port includes: A building module for obtaining the real-time load information of the container truck through the load data of the port hoisting equipment, and establishing a load-bearing braking mapping table according to the influence relationship between the load-bearing capacity and the braking distance and steering radius, so as to obtain the load-bearing level braking parameters; A matching module, configured to match and analyze the load level braking parameters with the width of the narrow channel in the port, calculate the passing difficulty coefficients of heavy-duty vehicles in channels with different widths through a load channel adaptation algorithm, and obtain a load channel restriction matrix; An allocation module, configured to identify load conflict scenarios during multi-vehicle intersection according to the load channel restriction matrix, and perform differential allocation of avoidance responsibilities for heavy-duty vehicles and empty-load vehicles to obtain a load differential avoidance strategy; A setting module, configured to convert the load differential avoidance strategy into a safety distance classification standard, and set exclusive safety intervals for vehicles with different load levels through a load classification distance algorithm to obtain a load classification safety distance; An adjustment module, configured to guide low-load vehicles to perform active avoidance operations according to the load classification safety distance, and adjust the vehicle speed and path through a load compensation control algorithm to obtain a collaborative avoidance control strategy based on load differences.

9. An unmanned container truck safety control device based on a port, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the port-based safety control method for driverless container trucks according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the port-based safety control method for driverless container trucks according to any one of claims 1 to 7.

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