Quay crane RTK positioning precision evaluation method and system and storage medium

Abstract

The invention relates to a quay crane RTK positioning precision evaluation method and system and a storage medium, and the method comprises the steps: L1, when an unmanned container truck carries out the container loading and unloading operation under a quay crane, the quay crane RTK feeds back the positioning value of a vehicle according to a fixed frequency, and obtains the data information of the positioning value of the vehicle within a fixed time; l2, based on the data information of the positioning value of the vehicle within the fixed time, predicting the positioning value of the vehicle by adopting an improved ELMAN dynamic recurrent neural network prediction algorithm optimized based on a particle swarm optimization algorithm to obtain the data information of the predicted positioning value of the vehicle; and L3, based on the predicted data information of the positioning value of the vehicle, optimizing the positioning value of the vehicle by adopting a zebra optimization algorithm of a self-adaptive dynamic learning factor based on global search to obtain the optimized data information of the positioning value of the vehicle. The alignment success rate of the unmanned container truck can be increased, and harbor area operation can be prevented from being affected.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned container truck port operations, and in particular to a method, system and storage medium for evaluating quay crane RTK positioning accuracy. Background Art

[0002] When unmanned container trucks are loading and unloading containers under quay cranes, they must maintain a "positioning rate"—the success rate at which they accurately dock directly under the crane's spreader center. This alignment relies on real-time feedback from the quay crane's RTK equipment, so RTK positioning accuracy directly impacts this rate. Because quay crane RTK equipment cannot accurately calculate the error range of positioning data, when RTK accuracy degrades significantly (such as during magnetic storms), the unmanned container trucks are unable to detect this, affecting their alignment and ultimately rendering operations impossible. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method, system and storage medium for evaluating the RTK positioning accuracy of quay cranes, which can not only improve the alignment success rate of unmanned container trucks, but also avoid affecting port operations.

[0004] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions:

[0005] A method for evaluating quay crane RTK positioning accuracy, the method comprising:

[0006] L1. When an unmanned container truck is loading and unloading containers under a quay crane, the quay crane's RTK system provides feedback on the vehicle's positioning at a fixed frequency, acquiring data on the vehicle's positioning within a fixed timeframe.

[0007] L2. Based on the data information of the vehicle positioning value within the fixed time, the vehicle positioning value is predicted using an improved ELMAN dynamic recursive neural network prediction algorithm based on particle swarm optimization to obtain the predicted vehicle positioning value data information;

[0008] L3 based on the predicted vehicle positioning value data information, the use of global search based on the adaptive dynamic learning factor zebra optimization algorithm to optimize the vehicle positioning value, to obtain the optimized vehicle positioning value data information;

[0009] L4. Based on the data information of the positioning value of the optimized vehicle, obtain the data information of the center position of the loading and unloading operation spreader, construct the positioning accuracy evaluation function Q of the unmanned container truck and the spreader, and calculate the positioning accuracy evaluation value of the unmanned container truck and the spreader to obtain the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader.

[0010] Furthermore, the method further comprises:

[0011] L5. Based on the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader, a preset threshold is set. If the positioning accuracy evaluation value of the unmanned container truck and the spreader is less than the preset threshold, the unmanned container truck needs to exit the operation task and re-position. If the positioning accuracy evaluation value of the unmanned container truck and the spreader is greater than the preset threshold, the positioning is completed and the operation task is carried out.

[0012] Furthermore, the positioning accuracy evaluation function Q of the unmanned container truck and the spreader is:

[0013]

[0014] Among them, x is the data information of the optimized vehicle positioning value, x0 is the data information of the center position of the loading and unloading spreader, ɑ1, ɑ2 and ɑ3 are the penalty factors of the positioning accuracy of the unmanned container truck and the spreader.

[0015] Furthermore, the penalty factors ɑ1, ɑ2 and ɑ3 of the positioning accuracy of the unmanned container truck and the spreader are:

[0016]

[0017] Among them, x is the data information of the optimized vehicle positioning value, and x0 is the data information of the center position of the loading and unloading operation spreader.

[0018] Furthermore, in step L2, the method of predicting the vehicle's positioning value using the improved ELMAN dynamic recursive neural network prediction algorithm optimized based on the particle swarm algorithm includes:

[0019] L21. Based on the data information of the vehicle positioning value within the fixed time, construct a normalized function W of the vehicle positioning value,

[0020]

[0021] Among them, y is the data information of the vehicle's positioning value within a fixed time, y max is the maximum value of the vehicle's positioning value within a fixed time, y min is the minimum value of the vehicle positioning value within a fixed time, β1 is the maximum value after normalization, and β2 is the minimum value after normalization. The data is normalized to obtain the data information of the normalized vehicle positioning value;

[0022] L22. The normalized vehicle positioning value data is input into the ELMAN neural network for training and learning, determining the topology of the ELMAN neural network, initializing the network weights and thresholds, and obtaining the initialized ELMAN neural network weights and threshold data;

[0023] L23. Based on the weights and threshold data of the initialized ELMAN neural network, the particle population is initialized to determine the maximum number of iterations T, and the data information of the initialized particle population is obtained;

[0024] L24. Based on the data information of the initialized particle population, establish the fitness value function R of the population individual,

[0025]

[0026] Among them, z is the data information of the initialized particle population, δ1, δ2 and δ3 are the adaptive adjustment factors of the particle population individuals, and the fitness values of the population individuals are calculated to obtain the data information of the fitness values of the population individuals;

[0027] L25. Based on the data information of the fitness values of the individuals in the population, establish an optimization function P,

[0028] Among them, r is the data information of the fitness value of the individual in the population, γ1, γ2 and γ3 are the optimization factors of the population, and the weights and thresholds of the ELMAN neural network are optimized to obtain the optimized ELMAN neural network.

[0029] Furthermore, the method of predicting the vehicle's positioning value using the improved ELMAN dynamic recursive neural network prediction algorithm optimized based on the particle swarm algorithm also includes:

[0030] L26. Based on the optimized ELMAN neural network, the data information of the vehicle positioning value within the fixed time is input, the vehicle positioning value is predicted, and the data information of the predicted vehicle positioning value is obtained.

[0031] Furthermore, the constraints of the adaptive adjustment factors δ1, δ2 and δ3 of the individual particle population are:

[0032]

[0033] The optimization factors γ1, γ2 and γ3 of the population are,

[0034]

[0035] Among them, r is the data information of the fitness value of the individual in the population.

[0036] Furthermore, in step L3, the optimization of the vehicle positioning value using the zebra optimization algorithm based on the adaptive dynamic learning factor of the global search includes:

[0037] L31. Based on the data information of the predicted vehicle positioning value, the zebra population is initialized, the parameters of the population are determined, and the data information of the initialized zebra population is obtained;

[0038] L32. Based on the data information of the initialized zebra population, establish the fitness value function S of the individual zebra population,

[0039]

[0040] Among them, h is the data information of the initialized zebra population, and the fitness of the zebra population individuals is calculated to obtain the data information of the fitness value of the zebra population individuals;

[0041] L33. Based on the data information of the fitness value of the zebra population individuals, establish the objective function F,

[0042]

[0043]

[0044] Among them, q is the data information of the fitness value of the zebra population individual, η1, η2 and η3 are adaptive dynamic learning factors, which optimize the positioning value of the vehicle to obtain the data information of the optimized vehicle positioning value.

[0045] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a system for evaluating the RTK positioning accuracy of a quay crane, including a computer device, which is programmed or configured to execute the steps of any one of the methods for evaluating the RTK positioning accuracy of a quay crane.

[0046] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the methods for evaluating the quay crane RTK positioning accuracy.

[0047] The present invention has the following positive effects:

[0048] 1. The present invention predicts the vehicle's positioning value by adopting an improved ELMAN dynamic recursive neural network prediction algorithm optimized by a particle swarm algorithm, and optimizes the vehicle's positioning value in combination with a zebra optimization algorithm with an adaptive dynamic learning factor based on global search. This not only enables the accurate positioning of unmanned container trucks, ensuring that the vehicle can determine the operating point in advance and ensure the safety of the operation, but also reduces the phenomenon of unmanned container trucks being unable to find the quay crane and "parking randomly", thereby improving the efficiency of port operations.

[0049] 2. The present invention constructs a positioning accuracy evaluation function Q for the unmanned container truck and the spreader, and calculates the positioning accuracy evaluation value of the unmanned container truck and the spreader, which can not only improve the alignment success rate of the unmanned container truck, but also avoid affecting port operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the operation of the present invention;

[0051] Figure 2 Schematic diagram of the method flow of the present invention;

[0052] Figure 3 Schematic diagram of the process of the improved ELMAN dynamic recurrent neural network prediction algorithm based on particle swarm optimization of the present invention;

[0053] Figure 4 The figure is a flow chart of the zebra optimization algorithm based on global search and adaptive dynamic learning factors of the present invention. DETAILED DESCRIPTION

[0054] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0055] Example 1: Figure 1 or Figure 2 As shown, a method for evaluating the RTK positioning accuracy of a quay crane includes:

[0056] L1. When an unmanned container truck is loading and unloading containers under a quay crane, the quay crane's RTK system provides feedback on the vehicle's positioning at a fixed frequency, acquiring data on the vehicle's positioning within a fixed timeframe.

[0057] L2. Based on the data information of the vehicle positioning value within the fixed time, the vehicle positioning value is predicted using an improved ELMAN dynamic recursive neural network prediction algorithm based on particle swarm optimization to obtain the predicted vehicle positioning value data information;

[0058] L3 based on the predicted vehicle positioning value data information, the use of global search based on the adaptive dynamic learning factor zebra optimization algorithm to optimize the vehicle positioning value, to obtain the optimized vehicle positioning value data information;

[0059] L4. Based on the data information of the positioning value of the optimized vehicle, obtain the data information of the center position of the loading and unloading operation spreader, construct the positioning accuracy evaluation function Q of the unmanned container truck and the spreader, and calculate the positioning accuracy evaluation value of the unmanned container truck and the spreader to obtain the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader.

[0060] In this embodiment, if Figure 3 As shown, in step L2, the method of using the improved ELMAN dynamic recursive neural network prediction algorithm based on particle swarm optimization to predict the vehicle positioning value includes:

[0061] L21. Based on the data information of the vehicle positioning value within the fixed time, construct a normalized function W of the vehicle positioning value,

[0062]

[0063] Among them, y is the data information of the vehicle's positioning value within a fixed time, y max is the maximum value of the vehicle's positioning value within a fixed time, y min is the minimum value of the vehicle positioning value within a fixed time, β1 is the maximum value after normalization, and β2 is the minimum value after normalization. The data is normalized to obtain the data information of the normalized vehicle positioning value;

[0064] L22. The normalized vehicle positioning value data is input into the ELMAN neural network for training and learning, determining the topology of the ELMAN neural network, initializing the network weights and thresholds, and obtaining the initialized ELMAN neural network weights and threshold data;

[0065] L23. Based on the weights and threshold data of the initialized ELMAN neural network, the particle population is initialized to determine the maximum number of iterations T, and the data information of the initialized particle population is obtained;

[0066] L24. Based on the data information of the initialized particle population, establish the fitness value function R of the population individual,

[0067]

[0068] Among them, z is the data information of the initialized particle population, δ1, δ2 and δ3 are the adaptive adjustment factors of the particle population individuals, and the fitness values of the population individuals are calculated to obtain the data information of the fitness values of the population individuals;

[0069] L25. Based on the data information of the fitness values of the individuals in the population, establish an optimization function P,

[0070] Among them, r is the data information of the fitness value of the individual in the population, γ1, γ2 and γ3 are the optimization factors of the population, and the weights and thresholds of the ELMAN neural network are optimized to obtain the optimized ELMAN neural network.

[0071] In this embodiment, the method of predicting the vehicle positioning value using the improved ELMAN dynamic recursive neural network prediction algorithm optimized based on the particle swarm algorithm further includes:

[0072] L26. Based on the optimized ELMAN neural network, the data information of the vehicle positioning value within the fixed time is input, the vehicle positioning value is predicted, and the data information of the predicted vehicle positioning value is obtained.

[0073] In this embodiment, the constraints of the adaptive adjustment factors δ1, δ2 and δ3 of the individual particle population are:

[0074]

[0075] The optimization factors γ1, γ2 and γ3 of the population are,

[0076]

[0077] Among them, r is the data information of the fitness value of the individual in the population.

[0078] In this embodiment, if Figure 4 As shown, in step L3, the optimization of the vehicle positioning value using the zebra optimization algorithm based on the adaptive dynamic learning factor of the global search includes:

[0079] L31. Based on the data information of the predicted vehicle positioning value, the zebra population is initialized, the parameters of the population are determined, and the data information of the initialized zebra population is obtained;

[0080] L32. Based on the data information of the initialized zebra population, establish the fitness value function S of the individual zebra population,

[0081]

[0082] Among them, h is the data information of the initialized zebra population, and the fitness of the zebra population individuals is calculated to obtain the data information of the fitness value of the zebra population individuals;

[0083] L33. Based on the data information of the fitness value of the zebra population individuals, establish the objective function F,

[0084]

[0085]

[0086] Among them, q is the data information of the fitness value of the zebra population individual, η1, η2 and η3 are adaptive dynamic learning factors, which optimize the positioning value of the vehicle to obtain the data information of the optimized vehicle positioning value.

[0087] Example 2: Based on the evaluation method of the quay crane RTK positioning accuracy in Example 1, the present invention is further illustrated and described below.

[0088] like Figure 1 or Figure 2 As shown, a method for evaluating the RTK positioning accuracy of a quay crane includes:

[0089] L1. When an unmanned container truck is loading and unloading containers under a quay crane, the quay crane's RTK system provides feedback on the vehicle's positioning at a fixed frequency, acquiring data on the vehicle's positioning within a fixed timeframe.

[0090] L2. Based on the data information of the vehicle positioning value within the fixed time, the vehicle positioning value is predicted using an improved ELMAN dynamic recursive neural network prediction algorithm based on particle swarm optimization to obtain the predicted vehicle positioning value data information;

[0091] L3 based on the predicted vehicle positioning value data information, the use of global search based on the adaptive dynamic learning factor zebra optimization algorithm to optimize the vehicle positioning value, to obtain the optimized vehicle positioning value data information;

[0092] L4. Based on the data information of the positioning value of the optimized vehicle, obtain the data information of the center position of the loading and unloading operation spreader, construct the positioning accuracy evaluation function Q of the unmanned container truck and the spreader, and calculate the positioning accuracy evaluation value of the unmanned container truck and the spreader to obtain the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader.

[0093] In this embodiment, the method further includes:

[0094] L5. Based on the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader, a preset threshold is set. If the positioning accuracy evaluation value of the unmanned container truck and the spreader is less than the preset threshold, the unmanned container truck needs to exit the operation task and re-position. If the positioning accuracy evaluation value of the unmanned container truck and the spreader is greater than the preset threshold, the positioning is completed and the operation task is carried out.

[0095] In this embodiment, the positioning accuracy evaluation function Q of the unmanned container truck and the spreader is:

[0096] Among them, x is the data information of the optimized vehicle positioning value, x0 is the data information of the center position of the loading and unloading spreader, ɑ1, ɑ2 and ɑ3 are the penalty factors of the positioning accuracy of the unmanned container truck and the spreader.

[0097] In this embodiment, the penalty factors ɑ1, ɑ2 and ɑ3 for the positioning accuracy of the unmanned container truck and the spreader are:

[0098]

[0099] Among them, x is the data information of the optimized vehicle positioning value, and x0 is the data information of the center position of the loading and unloading operation spreader.

[0100] In this embodiment, if Figure 2 As shown in the figure, the alignment principle is as follows: during loading and unloading operations, the spreader center position is (X0, Y0). Since the quay crane can only operate along a fixed track, X0 is a fixed value, while Y0 is a variable value. The positioning value fed back by the quay crane RTK is (Xi, Yi). During operation, the unmanned container truck drives along route x = X0 according to the map or lane markings. Therefore, no alignment is required in the X direction, while the Y direction is aligned according to Yi. The Y direction error △L = Y0 - Yi determines the alignment success rate.

[0101] To improve the success rate of alignment, traditional methods include auxiliary positioning methods such as "LiDAR scanning of quay crane beams" and "LiDAR scanning of spreaders". However, due to structural and environmental factors, the accuracy of radar scanning is still limited (for example, if the spreader is in a shaking state). During operation, the unmanned container truck first drives to (X0, Yi) and then performs secondary positioning through LiDAR scanning. The possible results are:

[0102] (1) RTK accuracy is typically centimeter-level, while successful alignment only requires approximately ±15 cm. Simply using secondary alignment may result in a decrease in final alignment accuracy, or even exceed the required accuracy.

[0103] (2) When the RTK accuracy decreases appropriately, such as exceeding ±15 cm but falling below the meter level (such as ±5 to 10 meters), secondary alignment can significantly improve the alignment accuracy.

[0104] (3) When the RTK accuracy drops seriously, such as exceeding the meter level (such as ±5 to 10 meters), the secondary positioning will also become invalid. At this time, if forced operation is continued, the unmanned container truck will not be able to find the quay crane and will "park randomly", seriously affecting port operations.

[0105] In this embodiment, the present invention provides a system for evaluating the RTK positioning accuracy of a quay crane, including a computer device programmed or configured to execute the steps of any one of the methods for evaluating the RTK positioning accuracy of a quay crane.

[0106] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the methods for evaluating the quay crane RTK positioning accuracy.

[0107] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0108] In summary, the present invention can not only improve the alignment success rate of unmanned container trucks, but also avoid affecting port operations.

[0109] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for evaluating the RTK positioning accuracy of a quay crane, characterized in that: The method comprises: L1. When an unmanned container truck is loading and unloading containers under a quay crane, the quay crane's RTK system provides feedback on the vehicle's positioning at a fixed frequency, acquiring data on the vehicle's positioning within a fixed timeframe. L2. Based on the data information of the vehicle positioning value within the fixed time, the vehicle positioning value is predicted using an improved ELMAN dynamic recursive neural network prediction algorithm based on particle swarm optimization to obtain the predicted vehicle positioning value data information; L3 based on the predicted vehicle positioning value data information, the use of global search based on the adaptive dynamic learning factor zebra optimization algorithm to optimize the vehicle positioning value, to obtain the optimized vehicle positioning value data information; L4. Based on the data information of the positioning value of the optimized vehicle, obtain the data information of the center position of the loading and unloading operation spreader, construct the positioning accuracy evaluation function Q of the unmanned container truck and the spreader, and calculate the positioning accuracy evaluation value of the unmanned container truck and the spreader to obtain the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader.

2. The method for evaluating the quay crane RTK positioning accuracy according to claim 1, characterized in that: The method further comprises: L5. Based on the data information of the positioning accuracy evaluation value of the unmanned container truck and the spreader, a preset threshold is set. If the positioning accuracy evaluation value of the unmanned container truck and the spreader is less than the preset threshold, the unmanned container truck needs to exit the operation task and re-position. If the positioning accuracy evaluation value of the unmanned container truck and the spreader is greater than the preset threshold, the positioning is completed and the operation task is carried out.

3. The method for evaluating the RTK positioning accuracy of a quay crane according to claim 1, wherein: The positioning accuracy evaluation function Q of the unmanned container truck and spreader is: Among them, x is the data information of the optimized vehicle positioning value, x0 is the data information of the center position of the loading and unloading spreader, ɑ1, ɑ2 and ɑ3 are the penalty factors of the positioning accuracy of the unmanned container truck and the spreader.

4. The method for evaluating the RTK positioning accuracy of a quay crane according to claim 3, wherein: The penalty factors ɑ1, ɑ2 and ɑ3 for the positioning accuracy of the unmanned container truck and the spreader are: Among them, x is the data information of the optimized vehicle positioning value, and x0 is the data information of the center position of the loading and unloading operation spreader.

5. The method for evaluating the RTK positioning accuracy of a quay crane according to claim 1, wherein: In step L2, the prediction of the vehicle's positioning value using the improved ELMAN dynamic recursive neural network prediction algorithm optimized based on the particle swarm algorithm includes: L21. Based on the data information of the vehicle positioning value within the fixed time, construct a normalized function W of the vehicle positioning value, Among them, y is the data information of the vehicle's positioning value within a fixed time, y max is the maximum value of the vehicle's positioning value within a fixed time, y min is the minimum value of the vehicle positioning value within a fixed time, β1 is the maximum value after normalization, and β2 is the minimum value after normalization. The data is normalized to obtain the data information of the normalized vehicle positioning value; L22. The normalized vehicle positioning value data is input into the ELMAN neural network for training and learning, determining the topology of the ELMAN neural network, initializing the network weights and thresholds, and obtaining the initialized ELMAN neural network weights and threshold data; L23. Based on the weights and threshold data of the initialized ELMAN neural network, the particle population is initialized to determine the maximum number of iterations T, and the data information of the initialized particle population is obtained; L24. Based on the data information of the initialized particle population, establish the fitness value function R of the population individual, Among them, z is the data information of the initialized particle population, δ1, δ2 and δ3 are the adaptive adjustment factors of the particle population individuals, and the fitness values of the population individuals are calculated to obtain the data information of the fitness values of the population individuals; L25. Based on the data information of the fitness values of the individuals in the population, establish an optimization function P, Among them, r is the data information of the fitness value of the individual in the population, γ1, γ2 and γ3 are the optimization factors of the population, and the weights and thresholds of the ELMAN neural network are optimized to obtain the optimized ELMAN neural network.

6. The method for evaluating the quay crane RTK positioning accuracy according to claim 5, characterized in that: The method of predicting the vehicle positioning value using the improved ELMAN dynamic recursive neural network prediction algorithm based on particle swarm optimization also includes: L26. Based on the optimized ELMAN neural network, the data information of the vehicle positioning value within the fixed time is input, the vehicle positioning value is predicted, and the data information of the predicted vehicle positioning value is obtained.

7. The method for evaluating the RTK positioning accuracy of a quay crane according to claim 5, wherein: The constraints of the adaptive adjustment factors δ1, δ2 and δ3 of the individual particle population are: The optimization factors γ1, γ2 and γ3 of the population are, Among them, r is the data information of the fitness value of the individual in the population.

8. The method for evaluating the quay crane RTK positioning accuracy according to claim 1, characterized in that: In step L3, the optimization of the vehicle positioning value using the zebra optimization algorithm based on the global search adaptive dynamic learning factor includes: L31. Based on the data information of the predicted vehicle positioning value, the zebra population is initialized, the parameters of the population are determined, and the data information of the initialized zebra population is obtained; L32. Based on the data information of the initialized zebra population, establish the fitness value function S of the individual zebra population, Among them, h is the data information of the initialized zebra population, and the fitness of the zebra population individuals is calculated to obtain the data information of the fitness value of the zebra population individuals; L33. Based on the data information of the fitness value of the zebra population individuals, establish the objective function F, Among them, q is the data information of the fitness value of the zebra population individual, η1, η2 and η3 are adaptive dynamic learning factors, which optimize the positioning value of the vehicle to obtain the data information of the optimized vehicle positioning value.

9. A quay crane RTK positioning accuracy evaluation system, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the method for evaluating the quay crane RTK positioning accuracy according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program programmed or configured to execute the method for evaluating the quay crane RTK positioning accuracy according to any one of claims 1 to 8.

Images